From c034285f6fbe9b2e50675981c5e4bb6c1e0e2173 Mon Sep 17 00:00:00 2001 From: "wangnan39@huawei.com" Date: Sat, 12 Sep 2020 11:43:28 +0800 Subject: [PATCH 001/156] delete DataWrapper --- docs/source_en/operator_list.md | 1 - docs/source_zh_cn/operator_list.md | 1 - 2 files changed, 2 deletions(-) diff --git a/docs/source_en/operator_list.md b/docs/source_en/operator_list.md index 55e915c973..aeab716759 100644 --- a/docs/source_en/operator_list.md +++ b/docs/source_en/operator_list.md @@ -83,7 +83,6 @@ | [mindspore.nn.WithLossCell](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.WithLossCell) | Supported | Supported | Doing |wrap/cell_wrapper | [mindspore.nn.WithGradCell](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.WithGradCell) | Supported | Supported | Doing |wrap/cell_wrapper | [mindspore.nn.TrainOneStepCell](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.TrainOneStepCell) | Supported | Supported | Doing |wrap/cell_wrapper -| [mindspore.nn.DataWrapper](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.DataWrapper) |Doing | Supported | Doing |wrap/cell_wrapper | [mindspore.nn.GetNextSingleOp](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.GetNextSingleOp) |Doing | Supported | Doing |wrap/cell_wrapper | [mindspore.nn.WithEvalCell](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.WithEvalCell) | Supported | Supported | Doing |wrap/cell_wrapper | [mindspore.nn.ParameterUpdate](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.ParameterUpdate) | Supported |Doing | Doing |wrap/cell_wrapper diff --git a/docs/source_zh_cn/operator_list.md b/docs/source_zh_cn/operator_list.md index 03fe448780..7e4cb09f27 100644 --- a/docs/source_zh_cn/operator_list.md +++ b/docs/source_zh_cn/operator_list.md @@ -83,7 +83,6 @@ | [mindspore.nn.WithLossCell](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.WithLossCell) | Supported | Supported | Doing |wrap/cell_wrapper | [mindspore.nn.WithGradCell](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.WithGradCell) | Supported | Supported | Doing |wrap/cell_wrapper | [mindspore.nn.TrainOneStepCell](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.TrainOneStepCell) | Supported | Supported | Doing |wrap/cell_wrapper -| [mindspore.nn.DataWrapper](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.DataWrapper) |Doing | Supported | Doing |wrap/cell_wrapper | [mindspore.nn.GetNextSingleOp](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.GetNextSingleOp) |Doing | Supported | Doing |wrap/cell_wrapper | [mindspore.nn.WithEvalCell](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.WithEvalCell) | Supported | Supported | Doing |wrap/cell_wrapper | [mindspore.nn.ParameterUpdate](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.ParameterUpdate) | Supported |Doing | Doing |wrap/cell_wrapper -- Gitee From 45d2c57537e12572f4b64b4ea380d276128c53bc Mon Sep 17 00:00:00 2001 From: lihongkang <[lihongkang1@huawei.com]> Date: Sat, 12 Sep 2020 15:03:26 +0800 Subject: [PATCH 002/156] update op list --- docs/source_en/operator_list.md | 2 +- docs/source_zh_cn/operator_list.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source_en/operator_list.md b/docs/source_en/operator_list.md index 672de46b5a..f4ff3cd3c2 100644 --- a/docs/source_en/operator_list.md +++ b/docs/source_en/operator_list.md @@ -37,7 +37,7 @@ | [mindspore.nn.Flatten](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.Flatten) |Supported | Supported | Supported |layer/basic | [mindspore.nn.Dense](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.Dense) |Supported | Supported | Supported |layer/basic | [mindspore.nn.ClipByNorm](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.ClipByNorm) |Supported | Supported | Doing |layer/basic -| [mindspore.nn.Norm](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.Norm) |Doing | Supported | Doing |layer/basic +| [mindspore.nn.Norm](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.Norm) |Supported | Supported | Doing |layer/basic | [mindspore.nn.OneHot](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.OneHot) | Supported | Supported | Supported |layer/basic | [mindspore.nn.Range](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.Range) | Supported | Doing | Doing |layer/basic | [mindspore.nn.SequentialCell](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.SequentialCell) |Supported | Supported | Doing |layer/container diff --git a/docs/source_zh_cn/operator_list.md b/docs/source_zh_cn/operator_list.md index 016d4b5f80..3c3b4833fb 100644 --- a/docs/source_zh_cn/operator_list.md +++ b/docs/source_zh_cn/operator_list.md @@ -37,7 +37,7 @@ | [mindspore.nn.Flatten](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.Flatten) |Supported | Supported | Supported |layer/basic | [mindspore.nn.Dense](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.Dense) |Supported | Supported | Supported |layer/basic | [mindspore.nn.ClipByNorm](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.ClipByNorm) |Supported | Supported | Doing |layer/basic -| [mindspore.nn.Norm](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.Norm) |Doing | Supported | Doing |layer/basic +| [mindspore.nn.Norm](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.Norm) |Supported | Supported | Doing |layer/basic | [mindspore.nn.OneHot](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.OneHot) | Supported | Supported | Supported |layer/basic | [mindspore.nn.Range](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.Range) | Supported | Doing | Doing |layer/basic | [mindspore.nn.SequentialCell](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.SequentialCell) |Supported | Supported | Doing |layer/container -- Gitee From 72b3f10640558ea6d514b047f2401bf1e8342c4a Mon Sep 17 00:00:00 2001 From: shenwei41 Date: Sat, 12 Sep 2020 16:21:28 +0800 Subject: [PATCH 003/156] Change function name --- lite/docs/source_en/apicc/dataset.md | 4 ++-- lite/docs/source_zh_cn/apicc/dataset.md | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/lite/docs/source_en/apicc/dataset.md b/lite/docs/source_en/apicc/dataset.md index 984ffc15ea..0ea531d17a 100644 --- a/lite/docs/source_en/apicc/dataset.md +++ b/lite/docs/source_en/apicc/dataset.md @@ -91,10 +91,10 @@ Normalize image, currently the supports data type is float. Return True or False. ``` -bool Padd(LiteMat &src, LiteMat &dst, const int top, const int bottom, const int left, const int right, const PaddBorderType pad_type, uint8_t fill_r, uint8_t fill_g, uint8_t fill_b) +bool Pad(LiteMat &src, LiteMat &dst, const int top, const int bottom, const int left, const int right, const PaddBorderType pad_type, uint8_t fill_r, uint8_t fill_g, uint8_t fill_b) ``` -Padd image, the channel supports is 3 and 1. +Pad image, the channel supports is 3 and 1. - Parameters diff --git a/lite/docs/source_zh_cn/apicc/dataset.md b/lite/docs/source_zh_cn/apicc/dataset.md index 379d3e1163..ef441d6ab7 100644 --- a/lite/docs/source_zh_cn/apicc/dataset.md +++ b/lite/docs/source_zh_cn/apicc/dataset.md @@ -91,7 +91,7 @@ bool SubStractMeanNormalize(LiteMat &src, LiteMat &dst, const float *mean, float 返回True或者False。 ``` -bool Padd(LiteMat &src, LiteMat &dst, const int top, const int bottom, const int left, const int right, const PaddBorderType pad_type, uint8_t fill_r, uint8_t fill_g, uint8_t fill_b) +bool Pad(LiteMat &src, LiteMat &dst, const int top, const int bottom, const int left, const int right, const PaddBorderType pad_type, uint8_t fill_r, uint8_t fill_g, uint8_t fill_b) ``` 填充图像，通道支持为3和1。 -- Gitee From c00ae3888fdcbad67042aa5108824b5c0f761926 Mon Sep 17 00:00:00 2001 From: eric Date: Fri, 11 Sep 2020 23:53:37 -0400 Subject: [PATCH 004/156] Fix API changes for v1.0 --- .../programming_guide/augmentation.md | 34 +++++++++---------- .../programming_guide/auto_augmentation.md | 2 +- .../programming_guide/dataset_conversion.md | 8 ++--- .../programming_guide/pipeline.md | 2 +- .../programming_guide/tokenizer.md | 2 +- resource/api_mapping.md | 18 +++++----- .../computer_vision_application.ipynb | 6 ++-- .../customized_debugging_information.ipynb | 14 ++++---- .../data_loading_enhancement.ipynb | 21 ++++++------ .../notebook/debugging_in_pynative_mode.ipynb | 14 ++++---- .../calculate_and_datagraphic.ipynb | 26 +++++++------- ...nsight_image_histogram_scalar_tensor.ipynb | 18 +++++----- ...sight_model_lineage_and_data_lineage.ipynb | 16 ++++----- tutorials/notebook/mixed_precision.ipynb | 8 ++--- tutorials/notebook/model_security.ipynb | 14 ++++---- ..._the_performance_of_data_preparation.ipynb | 6 ++-- tutorials/notebook/quick_start.ipynb | 10 +++--- ...chronization_training_and_evaluation.ipynb | 8 ++--- .../computer_vision_application.md | 4 +-- .../source_en/advanced_use/hub_tutorial.md | 5 +-- .../source_en/advanced_use/model_security.md | 2 +- .../data_processing_and_augmentation.md | 2 +- .../advanced_use/auto_augmentation.md | 10 +++--- tutorials/source_zh_cn/advanced_use/cache.md | 6 ++-- .../computer_vision_application.md | 4 +-- .../advanced_use/differential_privacy.md | 4 +-- .../source_zh_cn/advanced_use/hub_tutorial.md | 5 +-- .../advanced_use/model_security.md | 2 +- tutorials/source_zh_cn/use/image_loading.md | 6 ++-- tutorials/source_zh_cn/use/text_loading.md | 2 +- .../resnet50_distributed_training.py | 4 +-- tutorials/tutorial_code/lenet/lenet.py | 10 +++--- .../tutorial_code/resnet/cifar_resnet50.py | 4 +-- .../tutorial_code/sample_for_cloud/dataset.py | 4 +-- 34 files changed, 152 insertions(+), 149 deletions(-) diff --git a/api/source_zh_cn/programming_guide/augmentation.md b/api/source_zh_cn/programming_guide/augmentation.md index 74fd8b0ba8..35920d2dfb 100644 --- a/api/source_zh_cn/programming_guide/augmentation.md +++ b/api/source_zh_cn/programming_guide/augmentation.md @@ -10,7 +10,7 @@ - [Resize](#resize) - [Invert](#invert) - [py_transforms](#py_transforms) - - [ComposeOp](#composeop) + - [Compose](#compose) - [使用说明](#使用说明) @@ -40,7 +40,7 @@ MindSpore目前支持的常用数据增强算子如下表所示，更多数据 | py_transforms | RandomCrop | 在图像随机位置裁剪指定大小子图像。 | | | Resize | 将图像缩放到指定大小。 | | | Invert | 将图像进行反相。 | -| |ComposeOp | 将列表中的数据增强操作依次执行。 | +| |Compose | 将列表中的数据增强操作依次执行。 | ## c_transforms @@ -62,7 +62,7 @@ MindSpore目前支持的常用数据增强算子如下表所示，更多数据 import matplotlib.pyplot as plt import mindspore.dataset as ds -import mindspore.dataset.transforms.vision.c_transforms as c_trans +import mindspore.dataset.vision.c_transforms as c_trans # 下载Cifar10数据集，将其解压到Cifar10Data目录 DATA_DIR = "../data/dataset/testCifar10Data2" @@ -77,7 +77,7 @@ dataset1 = ds.Cifar10Dataset(DATA_DIR, sampler=sampler) random_crop = c_trans.RandomCrop([10, 10]) # 使用map算子将其作用到数据管道的数据集中 -dataset2 = dataset1.map(input_columns=["image"], operations=random_crop) +dataset2 = dataset1.map(operations=random_crop, input_columns=["image"]) # 启动数据管道，输出3个样本数据 image_list1, label_list1 = [], [] @@ -130,7 +130,7 @@ Cropped image Shape: (10, 10, 3) , Cropped label: 9 import matplotlib.pyplot as plt import mindspore.dataset as ds -import mindspore.dataset.transforms.vision.c_transforms as c_trans +import mindspore.dataset.vision.c_transforms as c_trans # 设置全局随机种子 ds.config.set_seed(6) @@ -148,7 +148,7 @@ dataset1 = ds.Cifar10Dataset(DATA_DIR, sampler=sampler) random_horizontal_flip = c_trans.RandomHorizontalFlip(prob=0.8) # 使用map算子将其作用到数据管道的数据集中 -dataset2 = dataset1.map(input_columns=["image"], operations=random_horizontal_flip) +dataset2 = dataset1.map(operations=random_horizontal_flip, input_columns=["image"]) # 启动数据管道，输出4个样本数据 image_list1, label_list1 = [], [] @@ -205,7 +205,7 @@ Flipped image Shape: (32, 32, 3) , Flipped label: 9 import matplotlib.pyplot as plt import mindspore.dataset as ds -import mindspore.dataset.transforms.vision.c_transforms as c_trans +import mindspore.dataset.vision.c_transforms as c_trans # 下载MNIST数据集，将其解压到MnistData目录 DATA_DIR = "../data/dataset/testMnistData2" @@ -217,7 +217,7 @@ dataset1 = ds.MnistDataset(DATA_DIR, num_samples=4, shuffle=False) resize = c_trans.Resize(size=[101, 101]) # 使用map算子将其作用到数据管道的数据集中 -dataset2 = dataset1.map(input_columns=["image"], operations=resize) +dataset2 = dataset1.map(operations=resize, input_columns=["image"]) # 启动数据管道 image_list1, label_list1 = [], [] @@ -270,7 +270,7 @@ Flipped image Shape: (101, 101, 1) , Flipped label: 1 import matplotlib.pyplot as plt import mindspore.dataset as ds -import mindspore.dataset.transforms.vision.c_transforms as c_trans +import mindspore.dataset.vision.c_transforms as c_trans # 设置全局随机种子 ds.config.set_seed(8) @@ -288,7 +288,7 @@ resize = c_trans.Resize(size=[101, 101]) invert = c_trans.Invert() # 使用map算子将其作用到数据管道的数据集中(两个算子按顺序起作用) -dataset2 = dataset1.map(input_columns=["image"], operations=[resize, invert]) +dataset2 = dataset1.map(operations=[resize, invert], input_columns=["image"]) # 启动数据管道 image_list1, label_list1 = [], [] @@ -336,23 +336,23 @@ Flipped image Shape: (32, 32, 3) , Flipped label: 5 下面将简要介绍几种常用的py_transforms模块数据增强算子的使用方法，更多的py_transforms模块数据增强算子参见[API文档](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.dataset.vision.html#module-mindspore.dataset.vision.py_transforms)。 -### ComposeOp +### Compose ```python # 对输入图像进行解码，缩放组合操作 import matplotlib.pyplot as plt import mindspore.dataset as ds -import mindspore.dataset.transforms.vision.py_transforms as py_trans - +import mindspore.dataset.vision.py_transforms as py_trans +from mindspore.dataset.transforms.py_transforms import Compose # 设置全局随机种子 ds.config.set_seed(8) # 图像数据集目录 DATA_DIR = "../data/dataset/testPK/data" -# 使用ImageFolderDatasetV2读取数据集，获取5个样本 -dataset1 = ds.ImageFolderDatasetV2(DATA_DIR, num_samples=5, shuffle=True) +# 使用ImageFolderDataset读取数据集，获取5个样本 +dataset1 = ds.ImageFolderDataset(DATA_DIR, num_samples=5, shuffle=True) # 创建一组数据增强算子的集合 transforms_list = [ @@ -360,10 +360,10 @@ transforms_list = [ py_trans.Resize(size=(200,200)), # 缩放图像到[200, 200]大小 py_trans.ToTensor() # 将PIL图像转换到Numpy ] -compose_trans = py_trans.ComposeOp(transforms_list) +compose_trans = Compose(transforms_list) # 使用map算子将其作用到数据管道的数据集中 -dataset2 = dataset1.map(input_columns=["image"], operations=compose_trans()) +dataset2 = dataset1.map(operations=compose_trans, input_columns=["image"]) # 启动数据管道，输出5个样本数据 image_list, label_list = [], [] diff --git a/api/source_zh_cn/programming_guide/auto_augmentation.md b/api/source_zh_cn/programming_guide/auto_augmentation.md index 57ec65060e..ef71980b51 100644 --- a/api/source_zh_cn/programming_guide/auto_augmentation.md +++ b/api/source_zh_cn/programming_guide/auto_augmentation.md @@ -67,7 +67,7 @@ Mindspore的`sync_wait`接口支持按batch或epoch粒度来调整数据增强 1. 用户预先定义class`Augment`，其中`preprocess`为`map`操作中的自定义数据增强函数，`update`为更新数据增强策略的回调函数。 ```python - import mindspore.dataset.transforms.vision.py_transforms as transforms + import mindspore.dataset.vision.py_transforms as transforms import mindspore.dataset as de import numpy as np diff --git a/api/source_zh_cn/programming_guide/dataset_conversion.md b/api/source_zh_cn/programming_guide/dataset_conversion.md index 59e68a061f..255bab0f4d 100644 --- a/api/source_zh_cn/programming_guide/dataset_conversion.md +++ b/api/source_zh_cn/programming_guide/dataset_conversion.md @@ -41,7 +41,7 @@ import os import mindspore.dataset as ds from mindspore.mindrecord import FileWriter - import mindspore.dataset.transforms.vision.c_transforms as vision + import mindspore.dataset.vision.c_transforms as vision from PIL import Image ################################ 生成MindRecord文件 ################################ @@ -91,7 +91,7 @@ data_set = ds.MindDataset(dataset_file=mindrecord_filename) # 创建读取对象，默认开启shuffle decode_op = vision.Decode() - data_set = data_set.map(input_columns=["data"], operations=decode_op, num_parallel_workers=2) # 解码data字段 + data_set = data_set.map(operations=decode_op, input_columns=["data"], num_parallel_workers=2) # 解码data字段 count = 0 for item in data_set.create_dict_iterator(): # 循环读取MindRecord中所有数据 print("sample: {}".format(item)) @@ -421,7 +421,7 @@ MindSpore提供转换常见数据集的工具类，能够将常见的经典数 import os import mindspore.dataset as ds from mindspore.mindrecord import TFRecordToMR - import mindspore.dataset.transforms.vision.c_transforms as vision + import mindspore.dataset.vision.c_transforms as vision from PIL import Image import tensorflow as tf # 需要tensorflow >= 2.1.0 @@ -512,7 +512,7 @@ MindSpore提供转换常见数据集的工具类，能够将常见的经典数 data_set = ds.MindDataset(dataset_file=MINDRECORD_FILE_NAME) # 创建读取对象，默认开启shuffle decode_op = vision.Decode() - data_set = data_set.map(input_columns=["image_bytes"], operations=decode_op, num_parallel_workers=2) # 解码图像字段 + data_set = data_set.map(operations=decode_op, input_columns=["image_bytes"], num_parallel_workers=2) # 解码图像字段 count = 0 for item in data_set.create_dict_iterator(): # 循环读取MindRecord中所有数据 print("sample: {}".format(item)) diff --git a/api/source_zh_cn/programming_guide/pipeline.md b/api/source_zh_cn/programming_guide/pipeline.md index bd8af3408a..bb87c70caf 100644 --- a/api/source_zh_cn/programming_guide/pipeline.md +++ b/api/source_zh_cn/programming_guide/pipeline.md @@ -111,7 +111,7 @@ print("") # 为数据集创建一个映射操作 # input_columns指定要处理的列，operation指定映射函数 -dataset = dataset.map(input_columns=["data"], operations=pyfunc) +dataset = dataset.map(operations=pyfunc, input_columns=["data"]) # 创建数据管道，输出映射后的数据 for data in dataset.create_dict_iterator(): diff --git a/api/source_zh_cn/programming_guide/tokenizer.md b/api/source_zh_cn/programming_guide/tokenizer.md index 8ac94e6a54..225ce08707 100644 --- a/api/source_zh_cn/programming_guide/tokenizer.md +++ b/api/source_zh_cn/programming_guide/tokenizer.md @@ -179,7 +179,7 @@ tokenizer_op = text.JiebaTokenizer(HMM_FILE, MP_FILE) #打印分词后的数据输出 print("------------------------after tokenize-----------------------------") -dataset = dataset.map(input_columns=["text"], operations=jieba_op, num_parallel_workers=1) +dataset = dataset.map(operations=jieba_op, input_columns=["text"], num_parallel_workers=1) for i in dataset.create_dict_iterator(num_epochs=1): token = text.to_str(i['text']) diff --git a/resource/api_mapping.md b/resource/api_mapping.md index 0eed65d611..3f4a30cd61 100644 --- a/resource/api_mapping.md +++ b/resource/api_mapping.md @@ -36,7 +36,7 @@ Mapping between PyTorch APIs and MindSpore APIs, which is provided by the commun | torch.expm1 | mindspore.ops.operations.Expm1 | | torch.eye | mindspore.ops.operations.Eye | | torch.flatten | mindspore.ops.operations.Flatten | -| torch.flip | mindspore.ops.operations.ReverseV2 +| torch.flip | mindspore.ops.operations.ReverseV2 | | torch.floor | mindspore.ops.operations.Floor | | torch.fmod | mindspore.ops.operations.Mod | | torch.linspace | mindspore.nn.LinSpace | @@ -167,13 +167,13 @@ Mapping between PyTorch APIs and MindSpore APIs, which is provided by the commun | torch.utils.data.distributed.DistributedSampler | mindspore.dataset.DistributedSampler | | torch.zeros | mindspore.ops.operations.ZerosLike | | torch.zeros_like | mindspore.ops.operations.ZerosLike | -| torchvision.datasets.ImageFolder | mindspore.dataset.ImageFolderDatasetV2 | +| torchvision.datasets.ImageFolder | mindspore.dataset.ImageFolderDataset | | torchvision.ops.nms | mindspore.ops.operations.NMSWithMask | | torchvision.ops.roi_align | mindspore.ops.operations.ROIAlign | -| torchvision.transforms.CenterCrop | mindspore.dataset.vision.py_transforms.CenterCrop | -| torchvision.transforms.ColorJitter | mindspore.dataset.vision.py_transforms.RandomColorAdjust | -| torchvision.transforms.Compose | mindspore.dataset.vision.py_transforms.Compose | -| torchvision.transforms.Normalize | mindspore.dataset.vision.py_transforms.Normalize | -| torchvision.transforms.RandomHorizontalFlip | mindspore.dataset.vision.py_transforms.RandomHorizontalFlip | -| torchvision.transforms.Resize | mindspore.dataset.vision.py_transforms.Resize | -| torchvision.transforms.ToTensor | mindspore.dataset.vision.py_transforms.ToTensor | +| torchvision.transforms.CenterCrop | mindspore.dataset.vision.py_transforms.CenterCrop | +| torchvision.transforms.ColorJitter | mindspore.dataset.vision.py_transforms.RandomColorAdjust | +| torchvision.transforms.Compose | mindspore.dataset.transforms.py_transforms.Compose | +| torchvision.transforms.Normalize | mindspore.dataset.vision.py_transforms.Normalize | +| torchvision.transforms.RandomHorizontalFlip | mindspore.dataset.vision.py_transforms.RandomHorizontalFlip | +| torchvision.transforms.Resize | mindspore.dataset.vision.py_transforms.Resize | +| torchvision.transforms.ToTensor | mindspore.dataset.vision.py_transforms.ToTensor | diff --git a/tutorials/notebook/computer_vision_application.ipynb b/tutorials/notebook/computer_vision_application.ipynb index 6d8dfd2d87..17065fcbb5 100644 --- a/tutorials/notebook/computer_vision_application.ipynb +++ b/tutorials/notebook/computer_vision_application.ipynb @@ -213,7 +213,7 @@ "import mindspore.common.dtype as mstype\n", "import mindspore.ops.functional as F\n", "import mindspore.dataset as ds\n", - "import mindspore.dataset.transforms.vision.c_transforms as C\n", + "import mindspore.dataset.vision.c_transforms as C\n", "import mindspore.dataset.transforms.c_transforms as C2\n", "\n", "\n", @@ -252,8 +252,8 @@ " changeswap_op]\n", "\n", " # Apply map operations on images\n", - " cifar_ds = cifar_ds.map(input_columns=\"label\", operations=type_cast_op)\n", - " cifar_ds = cifar_ds.map(input_columns=\"image\", operations=c_trans)\n", + " cifar_ds = cifar_ds.map(operations=type_cast_op, input_columns=\"label\")\n", + " cifar_ds = cifar_ds.map(operations=c_trans, input_columns=\"image\")\n", "\n", " # Apply shuffle operations\n", " cifar_ds = cifar_ds.shuffle(buffer_size=10)\n", diff --git a/tutorials/notebook/customized_debugging_information.ipynb b/tutorials/notebook/customized_debugging_information.ipynb index 44be7bd3a7..b05388f1b4 100644 --- a/tutorials/notebook/customized_debugging_information.ipynb +++ b/tutorials/notebook/customized_debugging_information.ipynb @@ -84,9 +84,9 @@ "outputs": [], "source": [ "import mindspore.dataset as ds\n", - "import mindspore.dataset.transforms.vision.c_transforms as CV\n", + "import mindspore.dataset.vision.c_transforms as CV\n", "import mindspore.dataset.transforms.c_transforms as C\n", - "from mindspore.dataset.transforms.vision import Inter\n", + "from mindspore.dataset.vision import Inter\n", "from mindspore.common import dtype as mstype\n", "\n", "def create_dataset(data_path, batch_size=32, repeat_size=1,\n", @@ -116,11 +116,11 @@ " type_cast_op = C.TypeCast(mstype.int32)\n", "\n", " # apply map operations on images\n", - " mnist_ds = mnist_ds.map(input_columns=\"label\", operations=type_cast_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=resize_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=rescale_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns=\"label\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=resize_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=rescale_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", "\n", " # apply DatasetOps\n", " buffer_size = 10000\n", diff --git a/tutorials/notebook/data_loading_enhance/data_loading_enhancement.ipynb b/tutorials/notebook/data_loading_enhance/data_loading_enhancement.ipynb index fb56262084..125d1cc4f1 100644 --- a/tutorials/notebook/data_loading_enhance/data_loading_enhancement.ipynb +++ b/tutorials/notebook/data_loading_enhance/data_loading_enhancement.ipynb @@ -343,7 +343,7 @@ " print(data[\"data\"])\n", "\n", "func = lambda x:x**2\n", - "ds11 = ds10.map(input_columns=\"data\",operations=func)\n", + "ds11 = ds10.map(operations=func, input_columns=\"data\")\n", "print(\"After map:\")\n", "for data in ds11.create_dict_iterator():\n", " print(data[\"data\"])" @@ -449,7 +449,7 @@ "outputs": [], "source": [ "DATA_DIR = \"./enhance_images\"\n", - "ds1 = ds.ImageFolderDatasetV2(DATA_DIR, decode=True)" + "ds1 = ds.ImageFolderDataset(DATA_DIR, decode=True)" ] }, { @@ -465,8 +465,8 @@ "metadata": {}, "outputs": [], "source": [ - "from mindspore.dataset.transforms.vision import Inter\n", - "import mindspore.dataset.transforms.vision.c_transforms as transforms" + "from mindspore.dataset.vision import Inter\n", + "import mindspore.dataset.vision.c_transforms as transforms" ] }, { @@ -476,7 +476,7 @@ "outputs": [], "source": [ "resize_op = transforms.Resize(size=(800,800), interpolation=Inter.LINEAR)\n", - "ds2 = ds1.map(input_columns=\"image\", operations=resize_op)" + "ds2 = ds1.map(operations=resize_op, input_columns=\"image\")" ] }, { @@ -537,14 +537,15 @@ "metadata": {}, "outputs": [], "source": [ - "import mindspore.dataset.transforms.vision.py_transforms as transforms" + "from mindspore.dataset.transforms.py_transforms import Compose" + "import mindspore.dataset.vision.py_transforms as transforms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "2. 定义数据增强算子，通过`ComposeOp`接口将多个数据增强组合使用。" + "2. 定义数据增强算子，通过`Compose`接口将多个数据增强组合使用。" ] }, { @@ -578,15 +579,15 @@ } ], "source": [ - "ds3 = ds.ImageFolderDatasetV2(DATA_DIR)\n", + "ds3 = ds.ImageFolderDataset(DATA_DIR)\n", "\n", "transforms_list = [\n", " transforms.Decode(), # Decode images to PIL format.\n", " transforms.RandomCrop(size=(800,800)),\n", " transforms.ToTensor() # Convert PIL images to Numpy ndarray.\n", "]\n", - "compose = transforms.ComposeOp(transforms_list)\n", - "ds4 = ds3.map(input_columns=\"image\", operations=compose())\n", + "compose = Compose(transforms_list)\n", + "ds4 = ds3.map(operations=compose, input_columns=\"image\")\n", "for data in ds4.create_dict_iterator():\n", " imgplot_resized = plt.imshow(data[\"image\"].transpose(1, 2, 0))\n", " plt.show()" diff --git a/tutorials/notebook/debugging_in_pynative_mode.ipynb b/tutorials/notebook/debugging_in_pynative_mode.ipynb index ce3d50557b..c5e9993c98 100644 --- a/tutorials/notebook/debugging_in_pynative_mode.ipynb +++ b/tutorials/notebook/debugging_in_pynative_mode.ipynb @@ -92,9 +92,9 @@ "metadata": {}, "outputs": [], "source": [ - "import mindspore.dataset.transforms.vision.c_transforms as CV\n", + "import mindspore.dataset.vision.c_transforms as CV\n", "import mindspore.dataset.transforms.c_transforms as C\n", - "from mindspore.dataset.transforms.vision import Inter\n", + "from mindspore.dataset.vision import Inter\n", "from mindspore.common import dtype as mstype\n", "import mindspore.dataset as ds\n", "import numpy as np\n", @@ -126,11 +126,11 @@ " type_cast_op = C.TypeCast(mstype.int32)\n", "\n", " # using map method to apply operations to a dataset\n", - " mnist_ds = mnist_ds.map(input_columns=\"label\", operations=type_cast_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=resize_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=rescale_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns=\"label\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=resize_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=rescale_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", " \n", " # process the generated dataset\n", " buffer_size = 10000\n", diff --git a/tutorials/notebook/mindinsight/calculate_and_datagraphic.ipynb b/tutorials/notebook/mindinsight/calculate_and_datagraphic.ipynb index 39bffb88e5..2e9bf71b08 100644 --- a/tutorials/notebook/mindinsight/calculate_and_datagraphic.ipynb +++ b/tutorials/notebook/mindinsight/calculate_and_datagraphic.ipynb @@ -142,9 +142,9 @@ "outputs": [], "source": [ "import mindspore.dataset as ds\n", - "import mindspore.dataset.transforms.vision.c_transforms as CV\n", + "import mindspore.dataset.vision.c_transforms as CV\n", "import mindspore.dataset.transforms.c_transforms as C\n", - "from mindspore.dataset.transforms.vision import Inter\n", + "from mindspore.dataset.vision import Inter\n", "from mindspore.common import dtype as mstype\n", "\n", "\n", @@ -177,11 +177,11 @@ " type_cast_op = C.TypeCast(mstype.int32)\n", "\n", " # using map method to apply operations to a dataset\n", - " mnist_ds = mnist_ds.map(input_columns=\"label\", operations=type_cast_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=resize_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=rescale_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns=\"label\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=resize_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=rescale_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", " \n", " # process the generated dataset\n", " buffer_size = 10000\n", @@ -368,11 +368,11 @@ "2. 下面代码为上面的 `create_dataset` 函数中作数据预处理与数据增强的相关操作。可以从数据图中清晰地看到数据处理的流程。通过查看数据图，可以帮助分析是否存在不恰当的数据处理流程。\n", "\n", "```\n", - "mnist_ds = mnist_ds.map(input_columns=\"label\", operations=type_cast_op, num_parallel_workers=num_parallel_workers)\n", - "mnist_ds = mnist_ds.map(input_columns=\"image\", operations=resize_op, num_parallel_workers=num_parallel_workers)\n", - "mnist_ds = mnist_ds.map(input_columns=\"image\", operations=rescale_op, num_parallel_workers=num_parallel_workers)\n", - "mnist_ds = mnist_ds.map(input_columns=\"image\", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)\n", - "mnist_ds = mnist_ds.map(input_columns=\"image\", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)\n", + "mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns=\"label\", num_parallel_workers=num_parallel_workers)\n", + "mnist_ds = mnist_ds.map(operations=resize_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", + "mnist_ds = mnist_ds.map(operations=rescale_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", + "mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", + "mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", "\n", "mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script\n", "mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)\n", @@ -418,4 +418,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/tutorials/notebook/mindinsight/mindinsight_image_histogram_scalar_tensor.ipynb b/tutorials/notebook/mindinsight/mindinsight_image_histogram_scalar_tensor.ipynb index 082a64b00e..3b0654081e 100644 --- a/tutorials/notebook/mindinsight/mindinsight_image_histogram_scalar_tensor.ipynb +++ b/tutorials/notebook/mindinsight/mindinsight_image_histogram_scalar_tensor.ipynb @@ -157,7 +157,7 @@ "source": [ "import mindspore.dataset as ds\n", "import mindspore.dataset.transforms.c_transforms as C\n", - "import mindspore.dataset.transforms.vision.c_transforms as CV\n", + "import mindspore.dataset.vision.c_transforms as CV\n", "from mindspore.common import dtype as mstype\n", "\n", "\n", @@ -177,14 +177,14 @@ " random_horizontal_op = CV.RandomHorizontalFlip()\n", " channel_swap_op = CV.HWC2CHW()\n", " typecast_op = C.TypeCast(mstype.int32)\n", - " cifar_ds = cifar_ds.map(input_columns=\"label\", operations=typecast_op)\n", + " cifar_ds = cifar_ds.map(operations=typecast_op, input_columns=\"label\")\n", " if status == \"train\":\n", - " cifar_ds = cifar_ds.map(input_columns=\"image\", operations=random_crop_op)\n", - " cifar_ds = cifar_ds.map(input_columns=\"image\", operations=random_horizontal_op)\n", - " cifar_ds = cifar_ds.map(input_columns=\"image\", operations=resize_op)\n", - " cifar_ds = cifar_ds.map(input_columns=\"image\", operations=rescale_op)\n", - " cifar_ds = cifar_ds.map(input_columns=\"image\", operations=normalize_op)\n", - " cifar_ds = cifar_ds.map(input_columns=\"image\", operations=channel_swap_op)\n", + " cifar_ds = cifar_ds.map(operations=random_crop_op, input_columns=\"image\")\n", + " cifar_ds = cifar_ds.map(operations=random_horizontal_op, input_columns=\"image\")\n", + " cifar_ds = cifar_ds.map(operations=resize_op, input_columns=\"image\")\n", + " cifar_ds = cifar_ds.map(operations=rescale_op, input_columns=\"image\")\n", + " cifar_ds = cifar_ds.map(operations=normalize_op, input_columns=\"image\")\n", + " cifar_ds = cifar_ds.map(operations=channel_swap_op, input_columns=\"image\")\n", "\n", " cifar_ds = cifar_ds.shuffle(buffer_size=1000)\n", " cifar_ds = cifar_ds.batch(batch_size, drop_remainder=True)\n", @@ -1282,4 +1282,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/tutorials/notebook/mindinsight/mindinsight_model_lineage_and_data_lineage.ipynb b/tutorials/notebook/mindinsight/mindinsight_model_lineage_and_data_lineage.ipynb index b76c1500a9..d8a27986e8 100644 --- a/tutorials/notebook/mindinsight/mindinsight_model_lineage_and_data_lineage.ipynb +++ b/tutorials/notebook/mindinsight/mindinsight_model_lineage_and_data_lineage.ipynb @@ -165,9 +165,9 @@ "metadata": {}, "outputs": [], "source": [ - "import mindspore.dataset.transforms.vision.c_transforms as CV\n", + "import mindspore.dataset.vision.c_transforms as CV\n", "import mindspore.dataset.transforms.c_transforms as C\n", - "from mindspore.dataset.transforms.vision import Inter\n", + "from mindspore.dataset.vision import Inter\n", "from mindspore.common import dtype as mstype\n", "import mindspore.dataset as ds\n", "\n", @@ -200,11 +200,11 @@ " type_cast_op = C.TypeCast(mstype.int32)\n", "\n", " # using map method to apply operations to a dataset\n", - " mnist_ds = mnist_ds.map(input_columns=\"label\", operations=type_cast_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=resize_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=rescale_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns=\"label\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=resize_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=rescale_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", " \n", " # process the generated dataset\n", " buffer_size = 10000\n", @@ -493,4 +493,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/tutorials/notebook/mixed_precision.ipynb b/tutorials/notebook/mixed_precision.ipynb index 149d1ebf8a..a595551727 100644 --- a/tutorials/notebook/mixed_precision.ipynb +++ b/tutorials/notebook/mixed_precision.ipynb @@ -196,7 +196,7 @@ "import os\n", "import mindspore.common.dtype as mstype\n", "import mindspore.dataset.engine as de\n", - "import mindspore.dataset.transforms.vision.c_transforms as C\n", + "import mindspore.dataset.vision.c_transforms as C\n", "import mindspore.dataset.transforms.c_transforms as C2\n", "\n", "def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target=\"GPU\"):\n", @@ -220,8 +220,8 @@ "\n", " type_cast_op = C2.TypeCast(mstype.int32)\n", "\n", - " ds = ds.map(input_columns=\"label\", num_parallel_workers=8, operations=type_cast_op)\n", - " ds = ds.map(input_columns=\"image\", num_parallel_workers=8, operations=trans)\n", + " ds = ds.map(operations=type_cast_op, input_columns=\"label\", num_parallel_workers=8)\n", + " ds = ds.map(operations=trans, input_columns=\"image\", num_parallel_workers=8)\n", "\n", " # apply batch operations\n", " ds = ds.batch(batch_size, drop_remainder=True)\n", @@ -991,4 +991,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/tutorials/notebook/model_security.ipynb b/tutorials/notebook/model_security.ipynb index d958155df7..53f79dfe40 100644 --- a/tutorials/notebook/model_security.ipynb +++ b/tutorials/notebook/model_security.ipynb @@ -184,9 +184,9 @@ "outputs": [], "source": [ "import mindspore.dataset as ds\n", - "import mindspore.dataset.transforms.vision.c_transforms as CV\n", + "import mindspore.dataset.vision.c_transforms as CV\n", "import mindspore.dataset.transforms.c_transforms as C\n", - "from mindspore.dataset.transforms.vision import Inter\n", + "from mindspore.dataset.vision import Inter\n", "from mindspore.common import dtype as mstype\n", "\n", "\n", @@ -214,16 +214,16 @@ " # apply map operations on images\n", " if not sparse:\n", " one_hot_enco = C.OneHot(10)\n", - " ds1 = ds1.map(input_columns=\"label\", operations=one_hot_enco,\n", + " ds1 = ds1.map(operations=one_hot_enco,\n", input_columns=\"label\", " num_parallel_workers=num_parallel_workers)\n", " type_cast_op = C.TypeCast(mstype.float32)\n", - " ds1 = ds1.map(input_columns=\"label\", operations=type_cast_op,\n", + " ds1 = ds1.map(operations=type_cast_op,\n", input_columns=\"label\", " num_parallel_workers=num_parallel_workers)\n", - " ds1 = ds1.map(input_columns=\"image\", operations=resize_op,\n", + " ds1 = ds1.map(operations=resize_op,\n", input_columns=\"image\", " num_parallel_workers=num_parallel_workers)\n", - " ds1 = ds1.map(input_columns=\"image\", operations=rescale_op,\n", + " ds1 = ds1.map(operations=rescale_op,\n", input_columns=\"image\", " num_parallel_workers=num_parallel_workers)\n", - " ds1 = ds1.map(input_columns=\"image\", operations=hwc2chw_op,\n", + " ds1 = ds1.map(operations=hwc2chw_op,\n", input_columns=\"image\", " num_parallel_workers=num_parallel_workers)\n", "\n", " # apply DatasetOps\n", diff --git a/tutorials/notebook/optimize_the_performance_of_data_preparation/optimize_the_performance_of_data_preparation.ipynb b/tutorials/notebook/optimize_the_performance_of_data_preparation/optimize_the_performance_of_data_preparation.ipynb index dd9aa3ad20..4e92276777 100644 --- a/tutorials/notebook/optimize_the_performance_of_data_preparation/optimize_the_performance_of_data_preparation.ipynb +++ b/tutorials/notebook/optimize_the_performance_of_data_preparation/optimize_the_performance_of_data_preparation.ipynb @@ -600,7 +600,7 @@ ], "source": [ "import mindspore.dataset.transforms.c_transforms as c_transforms\n", - "import mindspore.dataset.transforms.vision.c_transforms as C\n", + "import mindspore.dataset.vision.c_transforms as C\n", "import matplotlib.pyplot as plt\n", "cifar10_path = \"./dataset/Cifar10Data/cifar-10-batches-bin/\"\n", "\n", @@ -608,7 +608,7 @@ "cifar10_dataset = ds.Cifar10Dataset(cifar10_path,num_parallel_workers=4)\n", "transforms = C.RandomResizedCrop((800,800))\n", "# apply the transform to the dataset through dataset.map()\n", - "cifar10_dataset = cifar10_dataset.map(input_columns=\"image\",operations=transforms,num_parallel_workers=4)\n", + "cifar10_dataset = cifar10_dataset.map(operations=transforms,input_columns=\"image\",num_parallel_workers=4)\n", "\n", "data = next(cifar10_dataset.create_dict_iterator())\n", "plt.imshow(data[\"image\"])\n", @@ -657,7 +657,7 @@ " print(data[\"data\"])\n", "\n", "func = lambda x:x**2\n", - "ds4 = ds3.map(input_columns=\"data\",operations=func,python_multiprocessing=True,num_parallel_workers=4)\n", + "ds4 = ds3.map(operations=func,input_columns=\"data\",python_multiprocessing=True,num_parallel_workers=4)\n", "print(\"after map:\")\n", "for data in ds4.create_dict_iterator():\n", " print(data[\"data\"])" diff --git a/tutorials/notebook/quick_start.ipynb b/tutorials/notebook/quick_start.ipynb index 80dec971e2..18590d37b5 100644 --- a/tutorials/notebook/quick_start.ipynb +++ b/tutorials/notebook/quick_start.ipynb @@ -336,11 +336,11 @@ " type_cast_op = C.TypeCast(mstype.int32)\n", "\n", " # using map to apply operations to a dataset\n", - " mnist_ds = mnist_ds.map(input_columns=\"label\", operations=type_cast_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=resize_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=rescale_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns=\"label\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=resize_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=rescale_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", " \n", " # process the generated dataset\n", " buffer_size = 10000\n", diff --git a/tutorials/notebook/synchronization_training_and_evaluation.ipynb b/tutorials/notebook/synchronization_training_and_evaluation.ipynb index 80f8573919..d3670b2382 100644 --- a/tutorials/notebook/synchronization_training_and_evaluation.ipynb +++ b/tutorials/notebook/synchronization_training_and_evaluation.ipynb @@ -94,9 +94,9 @@ "source": [ "import os\n", "import mindspore.dataset as ds\n", - "import mindspore.dataset.transforms.vision.c_transforms as CV\n", + "import mindspore.dataset.vision.c_transforms as CV\n", "import mindspore.dataset.transforms.c_transforms as C\n", - "from mindspore.dataset.transforms.vision import Inter\n", + "from mindspore.dataset.vision import Inter\n", "from mindspore.common import dtype as mstype\n", "\n", "def create_dataset(data_path, batch_size=32, repeat_size=1,\n", @@ -112,8 +112,8 @@ " type_cast_op = C.TypeCast(mstype.int32) \n", "\n", " # apply map operations on images\n", - " mnist_ds = mnist_ds.map(input_columns=\"label\", operations=type_cast_op, num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(input_columns=\"image\", operations=[resize_op,rescale_op,rescale_nml_op,hwc2chw_op],\n", + " mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns=\"label\", num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=[resize_op,rescale_op,rescale_nml_op,hwc2chw_op],\n", input_columns=\"image\", " num_parallel_workers=num_parallel_workers)\n", "\n", " # apply DatasetOps\n", diff --git a/tutorials/source_en/advanced_use/computer_vision_application.md b/tutorials/source_en/advanced_use/computer_vision_application.md index 8ead2a76fd..ac9c06bd00 100644 --- a/tutorials/source_en/advanced_use/computer_vision_application.md +++ b/tutorials/source_en/advanced_use/computer_vision_application.md @@ -119,8 +119,8 @@ tar -zvxf cifar-10-binary.tar.gz c_trans += [resize_op, rescale_op, normalize_op, changeswap_op] # apply map operations on images - cifar_ds = cifar_ds.map(input_columns="label", operations=type_cast_op) - cifar_ds = cifar_ds.map(input_columns="image", operations=c_trans) + cifar_ds = cifar_ds.map(operations=type_cast_op, input_columns="label") + cifar_ds = cifar_ds.map(operations=c_trans, input_columns="image") ``` 3. Shuffle and batch process the data. diff --git a/tutorials/source_en/advanced_use/hub_tutorial.md b/tutorials/source_en/advanced_use/hub_tutorial.md index 13e98abd3f..f832f4b432 100644 --- a/tutorials/source_en/advanced_use/hub_tutorial.md +++ b/tutorials/source_en/advanced_use/hub_tutorial.md @@ -62,8 +62,9 @@ import mindspore from mindspore import context, Tensor, nn from mindspore.train.model import Model from mindspore.common import dtype as mstype -from mindspore.dataset.transforms import py_transforms +from mindspore.dataset.transforms import Compose from PIL import Image +import mindspore.dataset.vision.py_transforms as py_transforms import cv2 context.set_context(mode=context.GRAPH_MODE, @@ -73,7 +74,7 @@ context.set_context(mode=context.GRAPH_MODE, model = "mindspore/ascend/0.7/googlenet_v1_cifar10" image = Image.open('cifar10/a.jpg') -transforms = py_transforms.ComposeOp([py_transforms.ToTensor()]) +transforms = Compose([py_transforms.ToTensor()]) # Initialize the number of classes based on the pre-trained model. network = mshub.load(model, num_classes=10) diff --git a/tutorials/source_en/advanced_use/model_security.md b/tutorials/source_en/advanced_use/model_security.md index a13cf2a91d..ecfab36032 100644 --- a/tutorials/source_en/advanced_use/model_security.md +++ b/tutorials/source_en/advanced_use/model_security.md @@ -99,7 +99,7 @@ def generate_mnist_dataset(data_path, batch_size=32, repeat_size=1, # apply map operations on images if not sparse: one_hot_enco = C.OneHot(10) - ds1 = ds1.map(input_columns="label", operations=one_hot_enco, + ds1 = ds1.map(operations=one_hot_enco, input_columns="label", num_parallel_workers=num_parallel_workers) type_cast_op = C.TypeCast(mstype.float32) ds1 = ds1.map(operations=type_cast_op, input_columns="label", diff --git a/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md b/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md index 419b1edc55..74a9ad497e 100644 --- a/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md +++ b/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md @@ -286,7 +286,7 @@ Data augmentation requires the `map` function. For details about how to use the ```python # path to imagefolder directory. This directory needs to contain sub-directories which contain the images DATA_DIR = "/path/to/imagefolder_directory" - dataset = ds.ImageFolderDatasetV2(DATA_DIR, decode=True) # Decode images. + dataset = ds.ImageFolderDataset(DATA_DIR, decode=True) # Decode images. resize_op = transforms.Resize(size=(500,500), interpolation=Inter.LINEAR) dataset.map(operations=resize_op, input_columns="image") diff --git a/tutorials/source_zh_cn/advanced_use/auto_augmentation.md b/tutorials/source_zh_cn/advanced_use/auto_augmentation.md index c8d4fefc22..94250841f2 100644 --- a/tutorials/source_zh_cn/advanced_use/auto_augmentation.md +++ b/tutorials/source_zh_cn/advanced_use/auto_augmentation.md @@ -159,7 +159,7 @@ imagenet_policy = [ ```python def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, shuffle=True, num_samples=5, target="Ascend"): # create a train or eval imagenet2012 dataset for resnet50 - ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, + ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=shuffle, num_samples=num_samples) image_size = 224 @@ -185,12 +185,12 @@ imagenet_policy = [ c_vision.Normalize(mean=mean, std=std), c_vision.HWC2CHW() ] - ds = ds.map(input_columns="image", num_parallel_workers=8, operations=trans) + ds = ds.map(operations=trans, input_columns="image", num_parallel_workers=8) if do_train: - ds = ds.map(input_columns=["image"], operations=c_vision.RandomSelectSubpolicy(imagenet_policy)) - ds = ds.map(input_columns=["image"], num_parallel_workers=8, operations=post_trans) + ds = ds.map(operations=c_vision.RandomSelectSubpolicy(imagenet_policy), input_columns=["image"]) + ds = ds.map(operations=post_trans, input_columns=["image"], num_parallel_workers=8) type_cast_op = c_transforms.TypeCast(mstype.int32) - ds = ds.map(input_columns="label", num_parallel_workers=8, operations=type_cast_op) + ds = ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8) # apply batch operations ds = ds.batch(batch_size, drop_remainder=True) diff --git a/tutorials/source_zh_cn/advanced_use/cache.md b/tutorials/source_zh_cn/advanced_use/cache.md index 797cac1e85..2039720a55 100644 --- a/tutorials/source_zh_cn/advanced_use/cache.md +++ b/tutorials/source_zh_cn/advanced_use/cache.md @@ -124,7 +124,7 @@ ```python import mindspore.dataset as ds import mindspore.common.dtype as mstype - import mindspore.dataset.transforms.vision.c_transforms as c_vision + import mindspore.dataset.vision.c_transforms as c_vision test_cache = ds.DatasetCache(session_id=1, size=0, spilling=True) ``` @@ -142,7 +142,7 @@ # apply cache to map rescale_op = c_vision.Rescale(1.0 / 255.0, -1.0) - data = data.map(input_columns=["image"], operations=rescale_op, cache=test_cache) + data = data.map(operations=rescale_op, input_columns=["image"], cache=test_cache) num_iter = 0 for item in data.create_dict_iterator(num_epochs=1): # each data is a dictionary @@ -224,7 +224,7 @@ # use the session id passed in from the outside script when defining the cache test_cache = msds.DatasetCache(session_id = session_id, size = 0, spilling=False) - ds = de.ImageFolderDatasetV2(data_dir, num_samples=num_samples, cache = test_cache) + ds = de.ImageFolderDataset(data_dir, num_samples=num_samples, cache = test_cache) ``` - **Step 5:** diff --git a/tutorials/source_zh_cn/advanced_use/computer_vision_application.md b/tutorials/source_zh_cn/advanced_use/computer_vision_application.md index 4bed55f52a..4a54e92841 100644 --- a/tutorials/source_zh_cn/advanced_use/computer_vision_application.md +++ b/tutorials/source_zh_cn/advanced_use/computer_vision_application.md @@ -122,8 +122,8 @@ tar -zvxf cifar-10-binary.tar.gz c_trans += [resize_op, rescale_op, normalize_op, changeswap_op] # apply map operations on images - cifar_ds = cifar_ds.map(input_columns="label", operations=type_cast_op) - cifar_ds = cifar_ds.map(input_columns="image", operations=c_trans) + cifar_ds = cifar_ds.map(operations=type_cast_op, input_columns="label") + cifar_ds = cifar_ds.map(operations=c_trans, input_columns="image") ``` 3. 数据混洗和批处理 diff --git a/tutorials/source_zh_cn/advanced_use/differential_privacy.md b/tutorials/source_zh_cn/advanced_use/differential_privacy.md index 6813d7f001..1d13bd138b 100644 --- a/tutorials/source_zh_cn/advanced_use/differential_privacy.md +++ b/tutorials/source_zh_cn/advanced_use/differential_privacy.md @@ -67,7 +67,7 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net import mindspore.dataset as ds import mindspore.dataset.vision.c_transforms as CV import mindspore.dataset.transforms.c_transforms as C -from mindspore.dataset.vision.import Inter +from mindspore.dataset.vision import Inter import mindspore.common.dtype as mstype from mindarmour.diff_privacy import DPModel @@ -151,7 +151,7 @@ def generate_mnist_dataset(data_path, batch_size=32, repeat_size=1, # apply map operations on images if not sparse: one_hot_enco = C.OneHot(10) - ds1 = ds1.map(input_columns="label", operations=one_hot_enco, + ds1 = ds1.map(operations=one_hot_enco, input_columns="label", num_parallel_workers=num_parallel_workers) type_cast_op = C.TypeCast(mstype.float32) ds1 = ds1.map(operations=type_cast_op, input_columns="label", diff --git a/tutorials/source_zh_cn/advanced_use/hub_tutorial.md b/tutorials/source_zh_cn/advanced_use/hub_tutorial.md index 5969c90d63..ffe12cfd47 100644 --- a/tutorials/source_zh_cn/advanced_use/hub_tutorial.md +++ b/tutorials/source_zh_cn/advanced_use/hub_tutorial.md @@ -62,9 +62,10 @@ from mindspore import context, Tensor, nn from mindspore.train.model import Model from mindspore.common import dtype as mstype - from mindspore.dataset.transforms import py_transforms + from mindspore.dataset.transforms.py_transforms import Compose from PIL import Image import cv2 + import mindspore.dataset.vision.py_transforms as py_transforms context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", @@ -73,7 +74,7 @@ model = "mindspore/ascend/0.7/googlenet_v1_cifar10" image = Image.open('cifar10/a.jpg') - transforms = py_transforms.ComposeOp([py_transforms.ToTensor()]) + transforms = Compose([py_transforms.ToTensor()]) # Initialize the number of classes based on the pre-trained model. network = mshub.load(model, num_classes=10) diff --git a/tutorials/source_zh_cn/advanced_use/model_security.md b/tutorials/source_zh_cn/advanced_use/model_security.md index c5b97a4bae..52d4a2fd09 100644 --- a/tutorials/source_zh_cn/advanced_use/model_security.md +++ b/tutorials/source_zh_cn/advanced_use/model_security.md @@ -50,7 +50,7 @@ from mindspore import dataset as ds import mindspore.common.dtype as mstype import mindspore.dataset.vision.c_transforms as CV import mindspore.dataset.transforms.c_transforms as C -from mindspore.dataset.vision.import Inter +from mindspore.dataset.vision import Inter import mindspore.nn as nn from mindspore.nn import SoftmaxCrossEntropyWithLogits from mindspore.common.initializer import TruncatedNormal diff --git a/tutorials/source_zh_cn/use/image_loading.md b/tutorials/source_zh_cn/use/image_loading.md index 0c51bec4a3..be239acfd1 100644 --- a/tutorials/source_zh_cn/use/image_loading.md +++ b/tutorials/source_zh_cn/use/image_loading.md @@ -154,8 +154,8 @@ for data in mnist_dataset.create_dict_iterator(): 1. 导入相关模块，重新加载数据集。 ```python - from mindspore.dataset.transforms.vision import Inter - import mindspore.dataset.transforms.vision.c_transforms as transforms + from mindspore.dataset.vision import Inter + import mindspore.dataset.vision.c_transforms as transforms # 重新加载数据集 mnist_dataset = ds.MnistDataset(DATA_DIR, num_samples=6, shuffle=False) @@ -167,7 +167,7 @@ for data in mnist_dataset.create_dict_iterator(): resize_op = transforms.Resize(size=(200,200), interpolation=Inter.LINEAR) crop_op = transforms.RandomCrop(150) transforms_list = [resize_op, crop_op] - ds4 = mnist_dataset.map(input_columns="image", operations=transforms_list) + ds4 = mnist_dataset.map(operations=transforms_list, input_columns="image") ``` 3. 查看数据增强效果。 diff --git a/tutorials/source_zh_cn/use/text_loading.md b/tutorials/source_zh_cn/use/text_loading.md index 7150cfd67d..76f8109e0e 100644 --- a/tutorials/source_zh_cn/use/text_loading.md +++ b/tutorials/source_zh_cn/use/text_loading.md @@ -105,7 +105,7 @@ MindSpore目前支持的数据采样器及其详细使用方法，可参考编 3. 执行操作。 ```python - dataset = dataset.map(input_columns=["text"], operations=text.SlidingWindow(2, 0)) + dataset = dataset.map(operations=text.SlidingWindow(2, 0), input_columns=["text"]) ``` 4. 执行之后输出效果。 diff --git a/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py b/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py index f7bde7ec41..99ca5892f0 100644 --- a/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py +++ b/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py @@ -67,8 +67,8 @@ def create_dataset(data_path, repeat_num=1, batch_size=32, rank_id=0, rank_size= c_trans += [resize_op, rescale_op, normalize_op, changeswap_op] # apply map operations on images - data_set = data_set.map(input_columns="label", operations=type_cast_op) - data_set = data_set.map(input_columns="image", operations=c_trans) + data_set = data_set.map(operations=type_cast_op, input_columns="label") + data_set = data_set.map(operations=c_trans, input_columns="image") # apply shuffle operations data_set = data_set.shuffle(buffer_size=10) diff --git a/tutorials/tutorial_code/lenet/lenet.py b/tutorials/tutorial_code/lenet/lenet.py index 3a25515233..0bfca4e918 100644 --- a/tutorials/tutorial_code/lenet/lenet.py +++ b/tutorials/tutorial_code/lenet/lenet.py @@ -59,11 +59,11 @@ def create_dataset(data_path, batch_size=32, repeat_size=1, type_cast_op = C.TypeCast(mstype.int32) # change data type of label to int32 to fit network # apply map operations on images - mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers) - mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers) - mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers) - mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers) - mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers) # apply DatasetOps buffer_size = 10000 diff --git a/tutorials/tutorial_code/resnet/cifar_resnet50.py b/tutorials/tutorial_code/resnet/cifar_resnet50.py index edf4e34bd8..816b9f6218 100644 --- a/tutorials/tutorial_code/resnet/cifar_resnet50.py +++ b/tutorials/tutorial_code/resnet/cifar_resnet50.py @@ -89,8 +89,8 @@ def create_dataset(repeat_num=1, training=True): changeswap_op] # apply map operations on images - cifar_ds = cifar_ds.map(input_columns="label", operations=type_cast_op) - cifar_ds = cifar_ds.map(input_columns="image", operations=c_trans) + cifar_ds = cifar_ds.map(operations=type_cast_op, input_columns="label") + cifar_ds = cifar_ds.map(operations=c_trans, input_columns="image") # apply shuffle operations cifar_ds = cifar_ds.shuffle(buffer_size=10) diff --git a/tutorials/tutorial_code/sample_for_cloud/dataset.py b/tutorials/tutorial_code/sample_for_cloud/dataset.py index 4f0546724d..7d8dfe9337 100644 --- a/tutorials/tutorial_code/sample_for_cloud/dataset.py +++ b/tutorials/tutorial_code/sample_for_cloud/dataset.py @@ -74,8 +74,8 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32): type_cast_op = C2.TypeCast(mstype.int32) - ds = ds.map(input_columns="label", num_parallel_workers=8, operations=type_cast_op) - ds = ds.map(input_columns="image", num_parallel_workers=8, operations=trans) + ds = ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8) + ds = ds.map(operations=trans, input_columns="image", num_parallel_workers=8) # apply batch operations ds = ds.batch(batch_size, drop_remainder=True) -- Gitee From be64e9e9aa9274d4ec378448458c2fab97696b5c Mon Sep 17 00:00:00 2001 From: caozhou Date: Sun, 13 Sep 2020 16:11:51 +0800 Subject: [PATCH 005/156] The DepthwiseConv2d need to be deleted --- api/source_zh_cn/programming_guide/cell.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/source_zh_cn/programming_guide/cell.md b/api/source_zh_cn/programming_guide/cell.md index 8570e80c0a..698e7f5995 100644 --- a/api/source_zh_cn/programming_guide/cell.md +++ b/api/source_zh_cn/programming_guide/cell.md @@ -127,7 +127,7 @@ MindSpore框架在nn的layer层内置了丰富的接口，主要内容如下： 卷积层提供了一些卷积计算的功能，如普通卷积，深度卷积和卷积转置等。 - 主要接口有Conv2d，Conv1d，Conv2dTranspose，DepthwiseConv2d，Conv1dTranspose等。 + 主要接口有Conv2d，Conv1d，Conv2dTranspose，Conv1dTranspose等。 - 池化层： -- Gitee From 0fccd9c74b09db07dd34f323f5abb3750f7e9225 Mon Sep 17 00:00:00 2001 From: SebastianHan Date: Mon, 14 Sep 2020 09:25:44 +0800 Subject: [PATCH 006/156] fix gpu pic --- tutorials/source_en/quick_start/quick_video.md | 2 +- .../quick_start/quick_video/gpu_operator_development.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorials/source_en/quick_start/quick_video.md b/tutorials/source_en/quick_start/quick_video.md index a46f6ae41d..b11de9ebd0 100644 --- a/tutorials/source_en/quick_start/quick_video.md +++ b/tutorials/source_en/quick_start/quick_video.md @@ -269,7 +269,7 @@ Provides video tutorials from installation to try-on, helping you quickly use Mi class="video-item-wraper" style="width: 33.3%;display: flex;justify-content: center;align-items: center;padding: 10px;box-sizing: border-box;"> - (This document contains Hands-on Tutorial Series. Gitee does not support display. Please check tutorials on the official website) -
FP16 | CPU
FP32 | CPU
Int8 | CPU
UInt8 | GPU
FP16 | GPU
FP32 | Tensorflow
Lite op supported | Caffe
Lite op supported | Onnx
Lite op supported | |-----------------------|----------|----------|-----------|----------|----------|------------------|----------|----------|----------| -| Abs | | √ | √ | √ | | | Abs | | Abs | -| Add | √ | √ | √ | √ | | √ | Add | | Add | +| Abs | | √ | √ | √ | √ | √ | Abs | | Abs | +| Add | √ | √ | √ | √ | √ | √ | Add | | Add | | AddN | | √ | | | | | AddN | | | | Argmax | | √ | √ | √ | | | Argmax | ArgMax | ArgMax | | Argmin | | √ | √ | √ | | | Argmin | | | -| AvgPool | √ | √ | √ | √ | | √ | MeanPooling| Pooling | AveragePool | -| BatchNorm | √ | √ | √ | √ | | √ | | BatchNorm | BatchNormalization | +| AvgPool | √ | √ | √ | √ | √ | √ | MeanPooling| Pooling | AveragePool | +| BatchNorm | √ | √ | √ | √ | √ | √ | | BatchNorm | BatchNormalization | | BatchToSpace | | √ | √ | √ | | | BatchToSpace, BatchToSpaceND | | | -| BiasAdd | | √ | √ | √ | | √ | | | BiasAdd | +| BiasAdd | | √ | √ | √ | √ | √ | | | BiasAdd | | Broadcast | | √ | | | | | BroadcastTo | | Expand | -| Cast | √ | √ | | √ | | | Cast, DEQUANTIZE* | | Cast | -| Ceil | | √ | √ | √ | | | Ceil | | Ceil | +| Cast | √ | √ | | √ | √ | √ | Cast, DEQUANTIZE* | | Cast | +| Ceil | | √ | √ | √ | √ | √ | Ceil | | Ceil | | Concat | √ | √ | √ | √ | √ | √ | Concat | Concat | Concat | | Conv2d | √ | √ | √ | √ | √ | √ | Conv2D | Convolution | Conv | | Conv2dTranspose | √ | √ | √ | √ | √ | √ | DeConv2D | Deconvolution | ConvTranspose | -| Cos | | √ | √ | √ | | | Cos | | Cos | +| Cos | | √ | √ | √ | √ | √ | Cos | | Cos | | Crop | | √ | √ | √ | | | | Crop | | | DeDepthwiseConv2D | | √ | √ | √ | | | | Deconvolution| ConvTranspose | | DepthToSpace | | √ | √ | √ | | | DepthToSpace| | DepthToSpace | | DepthwiseConv2dNative | √ | √ | √ | √ | √ | √ | DepthwiseConv2D | Convolution | Convolution | -| Div | √ | √ | √ | √ | | √ | Div, RealDiv | | Div | +| Div | √ | √ | √ | √ | √ | √ | Div, RealDiv | | Div | | Eltwise | √ | √ | | | | | | Eltwise | | | Elu | | √ | | | | | Elu | | Elu | | Equal | √ | √ | √ | √ | | | Equal | | Equal | -| Exp | | √ | | | | | Exp | | Exp | +| Exp | | √ | | | √ | √ | Exp | | Exp | | ExpandDims | | √ | | | | | | | | | Fill | | √ | | | | | Fill | | | | Flatten | | √ | | | | | | Flatten | | -| Floor | | √ | √ | √ | | | flOOR | | Floor | +| Floor | | √ | √ | √ | √ | √ | flOOR | | Floor | | FloorDiv | √ | √ | | | | | FloorDiv | | | | FloorMod | √ | √ | | | | | FloorMod | | | -| FullConnection | | √ | √ | √ | | | FullyConnected | InnerProduct | | +| FullConnection | | √ | √ | √ | √ | √ | FullyConnected | InnerProduct | | | GatherNd | | √ | √ | √ | | | GatherND | | | | GatherV2 | | √ | √ | √ | | | Gather | | Gather | | Greater | √ | √ | √ | √ | | | Greater | | Greater | | GreaterEqual | √ | √ | √ | √ | | | GreaterEqual| | | | Hswish | √ | √ | √ | √ | | | HardSwish | | | -| LeakyReLU | √ | √ | | | | √ | LeakyRelu | | LeakyRelu | +| LeakyReLU | √ | √ | | | √ | √ | LeakyRelu | | LeakyRelu | | Less | √ | √ | √ | √ | | | Less | | Less | | LessEqual | √ | √ | √ | √ | | | LessEqual | | | | LRN | | √ | | | | | LocalResponseNorm | | Lrn | -| Log | | √ | √ | √ | | | Log | | Log | +| Log | | √ | √ | √ | √ | √ | Log | | Log | | LogicalAnd | √ | √ | | | | | LogicalAnd | | | -| LogicalNot | | √ | √ | √ | | | LogicalNot | | | +| LogicalNot | | √ | √ | √ | √ | √ | LogicalNot | | | | LogicalOr | √ | √ | | | | | LogicalOr | | | | LSTM | | √ | | | | | | | | | MatMul | | √ | √ | √ | √ | √ | | | MatMul | | Maximum | √ | √ | | | | | Maximum | | Max | -| MaxPool | √ | √ | √ | √ | | √ | MaxPooling | Pooling | MaxPool | +| MaxPool | √ | √ | √ | √ | √ | √ | MaxPooling | Pooling | MaxPool | | Minimum | √ | √ | | | | | Minimum | | Min | -| Mul | √ | √ | √ | √ | | √ | Mul | | Mul | +| Mul | √ | √ | √ | √ | √ | √ | Mul | | Mul | | NotEqual | √ | √ | √ | √ | | | NotEqual | | | | OneHot | | √ | | | | | OneHot | | | | Pad | | √ | √ | √ | | | Pad | | Pad | | Pow | | √ | √ | √ | | | Pow | Power | Power | -| PReLU | | √ | | | | √ | | PReLU | | +| PReLU | | √ | | | √ | √ | | PReLU | | | Range | | √ | | | | | Range | | | | Rank | | √ | | | | | Rank | | | | ReduceMax | √ | √ | √ | √ | | | ReduceMax | | ReduceMax | @@ -70,37 +70,37 @@ | ReduceProd | √ | √ | √ | √ | | | ReduceProd | | | | ReduceSum | √ | √ | √ | √ | | | Sum | | ReduceSum | | ReduceSumSquare | √ | √ | √ | √ | | | | | | -| ReLU | √ | √ | √ | √ | | √ | Relu | ReLU | Relu | -| ReLU6 | √ | √ | √ | √ | | √ | Relu6 | ReLU6 | Clip* | -| Reshape | √ | √ | √ | √ | | √ | Reshape | Reshape | Reshape,Flatten | +| ReLU | √ | √ | √ | √ | √ | √ | Relu | ReLU | Relu | +| ReLU6 | √ | √ | √ | √ | √ | √ | Relu6 | ReLU6 | Clip* | +| Reshape | √ | √ | √ | √ | √ | √ | Reshape | Reshape | Reshape,Flatten | | Resize | | √ | √ | √ | | | ResizeBilinear, NearestNeighbor | Interp | | | Reverse | | √ | | | | | reverse | | | | ReverseSequence | | √ | | | | | ReverseSequence | | | -| Round | | √ | √ | √ | | | Round | | | -| Rsqrt | | √ | √ | √ | | | Rsqrt | | | -| Scale | | √ | | | | | | Scale | | +| Round | | √ | √ | √ | √ | √ | Round | | | +| Rsqrt | | √ | √ | √ | √ | √ | Rsqrt | | | +| Scale | | √ | | | √ | √ | | Scale | | | ScatterNd | | √ | | | | | ScatterNd | | | | Shape | | √ | | | | | Shape | | Shape | -| Sigmoid | √ | √ | √ | √ | | √ | Logistic | Sigmoid | Sigmoid | -| Sin | | √ | √ | √ | | | Sin | | Sin | +| Sigmoid | √ | √ | √ | √ | √ | √ | Logistic | Sigmoid | Sigmoid | +| Sin | | √ | √ | √ | √ | √ | Sin | | Sin | | Slice | | √ | √ | √ | √ | √ | Slice | | Slice | -| Softmax | √ | √ | √ | √ | | √ | Softmax | Softmax | Softmax | +| Softmax | √ | √ | √ | √ | √ | √ | Softmax | Softmax | Softmax | | SpaceToBatch | | √ | | | | | | | | | SpaceToBatchND | | √ | | | | | SpaceToBatchND | | | | SpaceToDepth | | √ | | | | | SpaceToDepth | | SpaceToDepth | | SparseToDense | | √ | | | | | SpareToDense | | | | Split | √ | √ | √ | √ | | | Split, SplitV | | | -| Sqrt | | √ | √ | √ | | | Sqrt | | Sqrt | -| Square | | √ | √ | √ | | | Square | | | -| SquaredDifference | | √ | | | | | SquaredDifference | | | +| Sqrt | | √ | √ | √ | √ | √ | Sqrt | | Sqrt | +| Square | | √ | √ | √ | √ | √ | Square | | | +| SquaredDifference | | √ | | | | | SquaredDifference | | | | Squeeze | | √ | √ | √ | | | Squeeze | | Squeeze | | StridedSlice | | √ | √ | √ | | | StridedSlice| | | | Stack | | √ | | | | | Stack | | | -| Sub | √ | √ | √ | √ | | √ | Sub | | Sub | -| Tanh | √ | √ | | | | | Tanh | TanH | | +| Sub | √ | √ | √ | √ | √ | √ | Sub | | Sub | +| Tanh | √ | √ | | | √ | √ | Tanh | TanH | | | Tile | | √ | | | | | Tile | | Tile | | TopK | | √ | √ | √ | | | TopKV2 | | | -| Transpose | √ | √ | | | | √ | Transpose | Permute | Transpose | +| Transpose | √ | √ | | | √ | √ | Transpose | Permute | Transpose | | Unique | | √ | | | | | Unique | | | | Unsqueeze | | √ | √ | √ | | | | | Unsqueeze | | Unstack | | √ | | | | | Unstack | | | diff --git a/lite/docs/source_zh_cn/operator_list.md b/lite/docs/source_zh_cn/operator_list.md index 3384d8baf9..333e209f4e 100644 --- a/lite/docs/source_zh_cn/operator_list.md +++ b/lite/docs/source_zh_cn/operator_list.md @@ -6,62 +6,62 @@ | 操作名 | CPU
FP16 | CPU
FP32 | CPU
Int8 | CPU
UInt8 | GPU
FP16 | GPU
FP32 | 支持的Tensorflow
Lite op | 支持的Caffe
Lite op | 支持的Onnx
Lite op | |-----------------------|----------|----------|----------|-----------|----------|-------------------|----------|----------|---------| -| Abs | | √ | √ | √ | | | Abs | | Abs | -| Add | √ | √ | √ | √ | | √ | Add | | Add | +| Abs | | √ | √ | √ | √ | √ | Abs | | Abs | +| Add | √ | √ | √ | √ | √ | √ | Add | | Add | | AddN | | √ | | | | | AddN | | | | Argmax | | √ | √ | √ | | | Argmax | ArgMax | ArgMax | | Argmin | | √ | √ | √ | | | Argmin | | | -| AvgPool | √ | √ | √ | √ | | √ | MeanPooling| Pooling | AveragePool | -| BatchNorm | √ | √ | √ | √ | | √ | | BatchNorm | BatchNormalization | +| AvgPool | √ | √ | √ | √ | √ | √ | MeanPooling| Pooling | AveragePool | +| BatchNorm | √ | √ | √ | √ | √ | √ | | BatchNorm | BatchNormalization | | BatchToSpace | | √ | √ | √ | | | BatchToSpace, BatchToSpaceND | | | -| BiasAdd | | √ | √ | √ | | √ | | | BiasAdd | +| BiasAdd | | √ | √ | √ | √ | √ | | | BiasAdd | | Broadcast | | √ | | | | | BroadcastTo | | Expand | -| Cast | √ | √ | | √ | | | Cast, DEQUANTIZE* | | Cast | -| Ceil | | √ | √ | √ | | | Ceil | | Ceil | +| Cast | √ | √ | | √ | √ | √ | Cast, DEQUANTIZE* | | Cast | +| Ceil | | √ | √ | √ | √ | √ | Ceil | | Ceil | | Concat | √ | √ | √ | √ | √ | √ | Concat | Concat | Concat | | Conv2d | √ | √ | √ | √ | √ | √ | Conv2D | Convolution | Conv | | Conv2dTranspose | √ | √ | √ | √ | √ | √ | DeConv2D | Deconvolution | ConvTranspose | -| Cos | | √ | √ | √ | | | Cos | | Cos | +| Cos | | √ | √ | √ | √ | √ | Cos | | Cos | | Crop | | √ | √ | √ | | | | Crop | | | DeDepthwiseConv2D | | √ | √ | √ | | | | Deconvolution| ConvTranspose | | DepthToSpace | | √ | √ | √ | | | DepthToSpace| | DepthToSpace | | DepthwiseConv2dNative | √ | √ | √ | √ | √ | √ | DepthwiseConv2D | Convolution | Convolution | -| Div | √ | √ | √ | √ | | √ | Div, RealDiv | | Div | +| Div | √ | √ | √ | √ | √ | √ | Div, RealDiv | | Div | | Eltwise | √ | √ | | | | | | Eltwise | | | Elu | | √ | | | | | Elu | | Elu | | Equal | √ | √ | √ | √ | | | Equal | | Equal | -| Exp | | √ | | | | | Exp | | Exp | +| Exp | | √ | | | √ | √ | Exp | | Exp | | ExpandDims | | √ | | | | | | | | | Fill | | √ | | | | | Fill | | | | Flatten | | √ | | | | | | Flatten | | -| Floor | | √ | √ | √ | | | flOOR | | Floor | +| Floor | | √ | √ | √ | √ | √ | flOOR | | Floor | | FloorDiv | √ | √ | | | | | FloorDiv | | | | FloorMod | √ | √ | | | | | FloorMod | | | -| FullConnection | | √ | √ | √ | | | FullyConnected | InnerProduct | | +| FullConnection | | √ | √ | √ | √ | √ | FullyConnected | InnerProduct | | | GatherNd | | √ | √ | √ | | | GatherND | | | | GatherV2 | | √ | √ | √ | | | Gather | | Gather | | Greater | √ | √ | √ | √ | | | Greater | | Greater | | GreaterEqual | √ | √ | √ | √ | | | GreaterEqual| | | | Hswish | √ | √ | √ | √ | | | HardSwish | | | -| LeakyReLU | √ | √ | | | | √ | LeakyRelu | | LeakyRelu | +| LeakyReLU | √ | √ | | | √ | √ | LeakyRelu | | LeakyRelu | | Less | √ | √ | √ | √ | | | Less | | Less | | LessEqual | √ | √ | √ | √ | | | LessEqual | | | | LRN | | √ | | | | | LocalResponseNorm | | Lrn | -| Log | | √ | √ | √ | | | Log | | Log | +| Log | | √ | √ | √ | √ | √ | Log | | Log | | LogicalAnd | √ | √ | | | | | LogicalAnd | | | -| LogicalNot | | √ | √ | √ | | | LogicalNot | | | +| LogicalNot | | √ | √ | √ | √ | √ | LogicalNot | | | | LogicalOr | √ | √ | | | | | LogicalOr | | | | LSTM | | √ | | | | | | | | | MatMul | | √ | √ | √ | √ | √ | | | MatMul | | Maximum | √ | √ | | | | | Maximum | | Max | -| MaxPool | √ | √ | √ | √ | | √ | MaxPooling | Pooling | MaxPool | +| MaxPool | √ | √ | √ | √ | √ | √ | MaxPooling | Pooling | MaxPool | | Minimum | √ | √ | | | | | Minimum | | Min | -| Mul | √ | √ | √ | √ | | √ | Mul | | Mul | +| Mul | √ | √ | √ | √ | √ | √ | Mul | | Mul | | NotEqual | √ | √ | √ | √ | | | NotEqual | | | | OneHot | | √ | | | | | OneHot | | | | Pad | | √ | √ | √ | | | Pad | | Pad | | Pow | | √ | √ | √ | | | Pow | Power | Power | -| PReLU | | √ | | | | √ | | PReLU | | +| PReLU | | √ | | | √ | √ | | PReLU | | | Range | | √ | | | | | Range | | | | Rank | | √ | | | | | Rank | | | | ReduceMax | √ | √ | √ | √ | | | ReduceMax | | ReduceMax | @@ -70,37 +70,37 @@ | ReduceProd | √ | √ | √ | √ | | | ReduceProd | | | | ReduceSum | √ | √ | √ | √ | | | Sum | | ReduceSum | | ReduceSumSquare | √ | √ | √ | √ | | | | | | -| ReLU | √ | √ | √ | √ | | √ | Relu | ReLU | Relu | -| ReLU6 | √ | √ | √ | √ | | √ | Relu6 | ReLU6 | Clip* | -| Reshape | √ | √ | √ | √ | | √ | Reshape | Reshape | Reshape,Flatten | +| ReLU | √ | √ | √ | √ | √ | √ | Relu | ReLU | Relu | +| ReLU6 | √ | √ | √ | √ | √ | √ | Relu6 | ReLU6 | Clip* | +| Reshape | √ | √ | √ | √ | √ | √ | Reshape | Reshape | Reshape,Flatten | | Resize | | √ | √ | √ | | | ResizeBilinear, NearestNeighbor | Interp | | | Reverse | | √ | | | | | reverse | | | | ReverseSequence | | √ | | | | | ReverseSequence | | | -| Round | | √ | √ | √ | | | Round | | | -| Rsqrt | | √ | √ | √ | | | Rsqrt | | | -| Scale | | √ | | | | | | Scale | | +| Round | | √ | √ | √ | √ | √ | Round | | | +| Rsqrt | | √ | √ | √ | √ | √ | Rsqrt | | | +| Scale | | √ | | | √ | √ | | Scale | | | ScatterNd | | √ | | | | | ScatterNd | | | | Shape | | √ | | | | | Shape | | Shape | -| Sigmoid | √ | √ | √ | √ | | √ | Logistic | Sigmoid | Sigmoid | -| Sin | | √ | √ | √ | | | Sin | | Sin | +| Sigmoid | √ | √ | √ | √ | √ | √ | Logistic | Sigmoid | Sigmoid | +| Sin | | √ | √ | √ | √ | √ | Sin | | Sin | | Slice | | √ | √ | √ | √ | √ | Slice | | Slice | -| Softmax | √ | √ | √ | √ | | √ | Softmax | Softmax | Softmax | +| Softmax | √ | √ | √ | √ | √ | √ | Softmax | Softmax | Softmax | | SpaceToBatch | | √ | | | | | | | | | SpaceToBatchND | | √ | | | | | SpaceToBatchND | | | | SpaceToDepth | | √ | | | | | SpaceToDepth | | SpaceToDepth | | SparseToDense | | √ | | | | | SpareToDense | | | | Split | √ | √ | √ | √ | | | Split, SplitV | | | -| Sqrt | | √ | √ | √ | | | Sqrt | | Sqrt | -| Square | | √ | √ | √ | | | Square | | | -| SquaredDifference | | √ | | | | | SquaredDifference | | | +| Sqrt | | √ | √ | √ | √ | √ | Sqrt | | Sqrt | +| Square | | √ | √ | √ | √ | √ | Square | | | +| SquaredDifference | | √ | | | | | SquaredDifference | | | | Squeeze | | √ | √ | √ | | | Squeeze | | Squeeze | | StridedSlice | | √ | √ | √ | | | StridedSlice| | | | Stack | | √ | | | | | Stack | | | -| Sub | √ | √ | √ | √ | | √ | Sub | | Sub | -| Tanh | √ | √ | | | | | Tanh | TanH | | +| Sub | √ | √ | √ | √ | √ | √ | Sub | | Sub | +| Tanh | √ | √ | | | √ | √ | Tanh | TanH | | | Tile | | √ | | | | | Tile | | Tile | | TopK | | √ | √ | √ | | | TopKV2 | | | -| Transpose | √ | √ | | | | √ | Transpose | Permute | Transpose | +| Transpose | √ | √ | | | √ | √ | Transpose | Permute | Transpose | | Unique | | √ | | | | | Unique | | | | Unsqueeze | | √ | √ | √ | | | | | Unsqueeze | | Unstack | | √ | | | | | Unstack | | | -- Gitee From 4103550705dd74ade14561f649b749029378be67 Mon Sep 17 00:00:00 2001 From: yingchen Date: Mon, 14 Sep 2020 10:03:56 +0800 Subject: [PATCH 009/156] update_computervisionapplication --- .../advanced_use/computer_vision_application.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/tutorials/source_en/advanced_use/computer_vision_application.md b/tutorials/source_en/advanced_use/computer_vision_application.md index 8ead2a76fd..b15b0539b6 100644 --- a/tutorials/source_en/advanced_use/computer_vision_application.md +++ b/tutorials/source_en/advanced_use/computer_vision_application.md @@ -22,13 +22,13 @@ ## Overview -Computer vision is the most widely researched and mature technology field of deep learning, and is widely used in scenarios such as mobile phone photographing, intelligent security protection, and automated driving. Since AlexNet won the ImageNet competition in 2012, deep learning has greatly promoted the development of the computer vision field. Almost all the most advanced computer vision algorithms are related to deep learning. Deep neural network can extract image features layer by layer and retain local invariance. It is widely used in visual tasks such as classification, detection, segmentation, tracking, retrieval, recognition, promotion, and reconstruction. +Computer vision is one of the most widely researched and mature technology fields of deep learning, and is widely applied to scenarios such as mobile phone photographing, intelligent security protection, and automated driving. Since AlexNet won the ImageNet competition in 2012, deep learning has greatly promoted the development of the computer vision field. Almost all the most advanced computer vision algorithms are related to deep learning. Deep neural network can extract image features layer by layer and retain local invariance. It is widely used in visual tasks such as classification, detection, segmentation, tracking, retrieval, recognition, promotion, and reconstruction. This chapter describes how to apply MindSpore to computer vision scenarios based on image classification tasks. ## Image Classification -Image classification is the most basic computer vision application and belongs to the supervised learning category. For example, determine the class of a digital image, such as cat, dog, airplane, or car. The function is as follows: +Image classification is one of the most basic computer vision applications and belongs to the supervised learning category. For example, determine the class of a digital image, such as cat, dog, airplane, or car. The function is as follows: ```python def classify(image): @@ -49,9 +49,9 @@ MindSpore supports the following image classification networks: LeNet, AlexNet, Figure 1: CIFAR-10 dataset [1] -Figure 1 shows that the CIFAR-10 dataset contains 10 classes of 60,000 images. Each class contains 6000 images. 50,000 images are for training and 10,000 images are for testing. The size of each image is 32 x 32 pixels. +The CIFAR-10 dataset contains 10 classes of 60,000 images. Each class contains 6000 images. 50,000 images are for training and 10,000 images are for testing. The size of each image is 32 x 32 pixels. -Generally, a training indicator of image classification is accuracy, that is, a ratio of a quantity of accurately predicted examples to a total quantity of predicted examples. +Generally, a training indicator of image classification is accuracy, that is, a ratio of the quantity of accurately predicted examples to the total quantity of predicted examples. Next, let's use MindSpore to solve the image classification task. The overall process is as follows: 1. Download the CIFAR-10 dataset. @@ -61,12 +61,12 @@ Next, let's use MindSpore to solve the image classification task. The overall pr 5. Call the high-level `Model` API to train and save the model file. 6. Load the saved model for inference. -> This example is for the hardware platform of the Ascend 910 AI processor. You can find the complete executable sample code at： . +> This example uses the hardware platform of the Ascend 910 AI processor. You can find the complete executable sample code at： . The key parts of the task process code are explained below. ### Downloading the CIFAR-10 Dataset -CIFAR-10 dataset download address: [the website of Cifar-10 Dataset](https://www.cs.toronto.edu/~kriz/cifar.html) In this example, the data is in binary format. In the Linux environment, run the following command to download the dataset: +CIFAR-10 dataset download address: [the website of Cifar-10 Dataset](https://www.cs.toronto.edu/~kriz/cifar.html). In this example, the data is in binary format. In the Linux environment, run the following command to download the dataset: ```shell wget https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz -- Gitee From 7c8e40f00ef96a7b9ae48cf65a4f0fc4f8845aa1 Mon Sep 17 00:00:00 2001 From: liuchongming74 Date: Sun, 13 Sep 2020 11:08:16 +0800 Subject: [PATCH 010/156] Update description of mindconverter. --- .../model_scripts_transformation.md | 28 +++++++++++++------ 1 file changed, 19 insertions(+), 9 deletions(-) diff --git a/tutorials/source_zh_cn/advanced_use/model_scripts_transformation.md b/tutorials/source_zh_cn/advanced_use/model_scripts_transformation.md index 20fbfb3570..65811e84eb 100644 --- a/tutorials/source_zh_cn/advanced_use/model_scripts_transformation.md +++ b/tutorials/source_zh_cn/advanced_use/model_scripts_transformation.md @@ -59,10 +59,10 @@ optional arguments: `--project_path` when use graph based schema. ``` -MindConverter提供两种模型脚本迁移方案： +**MindConverter提供两种模型脚本迁移方案：** -1. 基于抽象语法树（Abstract syntax tree, AST）的脚本转换：指定`--in_file`的值，将使用基于AST的脚本转换方案； -2. 基于图结构的脚本生成：指定`--model_file`与`--shape`将使用基于图结构的脚本生成方案。 +1. **基于抽象语法树(Abstract syntax tree, AST)的脚本转换**：指定`--in_file`的值，将使用基于AST的脚本转换方案； +2. **基于图结构的脚本生成**：指定`--model_file`与`--shape`将使用基于图结构的脚本生成方案。 > 若同时指定了`--in_file`，`--model_file`将默认使用AST方案进行脚本迁移。 @@ -70,9 +70,11 @@ MindConverter提供两种模型脚本迁移方案： 其中，`--output`与`--report`参数可省略。若省略，MindConverter将在当前工作目录（Working directory）下自动创建`output`目录，将生成的脚本、转换报告输出至该目录。 -另外，当使用基于图结构的脚本生成方案时，请确保原PyTorch项目已在PYTHONPATH中，可通过CLI进入Python交互式命令行，通过import的方式判断是否已满足；若未加入，可通过`--project_path`命令手动将项目路径传入，以确保MindConverter可引用到原PyTorch脚本。 +另外，当使用基于图结构的脚本生成方案时，请确保原PyTorch项目已在Python包搜索路径中，可通过CLI进入Python交互式命令行，通过import的方式判断是否已满足；若未加入，可通过`--project_path`命令手动将项目路径传入，以确保MindConverter可引用到原PyTorch脚本。 +> 假设用户项目目录为`/home/user/project/model_training`，用户可通过如下命令手动项目添加至包搜索路径中：`export PYTHONPATH=/home/user/project/model_training:$PYTHONPATH` + > 此处MindConverter需要引用原PyTorch脚本，是因为PyTorch模型反向序列化过程中会引用原脚本。 @@ -86,6 +88,10 @@ MindConverter提供两种技术方案，以应对不同脚本迁移场景： 对于上述第二种场景，推荐用户使用基于图结构的脚本生成方案，计算图作为一种标准的模型描述语言，可以消除用户代码风格多样导致的脚本转换率不稳定的问题。在已支持算子的情况下，该方案可提供优于AST方案的转换率。 +目前已基于典型图像分类网络(Resnet, VGG)对图结构的脚本转换方案进行测试。 + +> 1. 基于图结构的脚本生成方案，目前仅支持单输入、单输出模型，对于多输入模型暂不支持； +> 2. 基于图结构的脚本生成方案，由于要基于推理模式加载PyTorch模型，会导致转换后网络中Dropout算子丢失，需要用户手动补齐； ## 使用示例 @@ -94,7 +100,9 @@ MindConverter提供两种技术方案，以应对不同脚本迁移场景： 若用户希望使用基于AST的方案进行脚本迁移，假设原PyTorch脚本路径为`/home/user/model.py`，希望将脚本输出至`/home/user/output`，转换报告输出至`/home/user/output/report`，则脚本转换命令为： ```bash -mindconverter --in_file /home/user/model.py --output /home/user/output --report /home/user/output/report +mindconverter --in_file /home/user/model.py \ + --output /home/user/output \ + --report /home/user/output/report ``` 转换报告中，对于未转换的代码行形式为如下，其中x, y指明的是原PyTorch脚本中代码的行、列号。对于未成功转换的算子，可参考[MindSporeAPI映射查询功能](https://www.mindspore.cn/docs/zh-CN/master/index.html#operator_api) 手动对代码进行迁移。对于工具无法迁移的算子，会保留原脚本中的代码。 @@ -114,7 +122,7 @@ line x:y: [UnConvert] 'operator' didn't convert. ... [Convert Over] ``` -对于部分未成功转换的算子，报告中会提供修改建议，如`line 157:23`，会建议将`torch.nn.AdaptiveAvgPool2d`替换为`mindspore.ops.operations.ReduceMean`。 +对于部分未成功转换的算子，报告中会提供修改建议，如`line 157:23`，MindConverter建议将`torch.nn.AdaptiveAvgPool2d`替换为`mindspore.ops.operations.ReduceMean`。 ### 基于图结构的脚本生成示例 @@ -122,7 +130,10 @@ line x:y: [UnConvert] 'operator' didn't convert. ... 若用户已将PyTorch模型保存为.pth格式，假设模型绝对路径为`/home/user/model.pth`，该模型期望的输入样本shape为(3, 224, 224)，原PyTorch脚本位于`/home/user/project/model_training`，希望将脚本输出至`/home/user/output`，转换报告输出至`/home/user/output/report`，则脚本生成命令为： ```bash -mindconverter --model_file /home/user/model.pth --shape 3,224,224 --output /home/user/output --report /home/user/output/report --project_path /home/user/project/model_training +mindconverter --model_file /home/user/model.pth --shape 3,224,224 \ + --output /home/user/output \ + --report /home/user/output/report \ + --project_path /home/user/project/model_training ``` 执行该命令，MindSpore代码文件、转换报告生成至相应目录。 @@ -139,5 +150,4 @@ mindconverter --model_file /home/user/model.pth --shape 3,224,224 --output /home ## 注意事项 1. PyTorch不作为MindInsight明确声明的依赖库，但若想使用基于图结构的脚本生成工具，需要用户手动安装与生成PyTorch模型版本一致的PyTorch库； -2. MindConverter目前仅支持单输入模型，对于多输入模型暂不支持； -3. 脚本转换工具本质上为算子驱动，对于MindConverter未维护的PyTorch或ONNX算子与MindSpore算子映射，将会出现相应的算子无法转换的问题，对于该类算子，用户可手动修改，或基于MindConverter实现映射关系，向MindInsight仓库贡献。 \ No newline at end of file +2. 脚本转换工具本质上为算子驱动，对于MindConverter未维护的PyTorch或ONNX算子与MindSpore算子映射，将会出现相应的算子无法转换的问题，对于该类算子，用户可手动修改，或基于MindConverter实现映射关系，向MindInsight仓库贡献。 -- Gitee From 65f1ba12c24cba7a2725b9505d5db3af68bddaec Mon Sep 17 00:00:00 2001 From: liuwenhao4 Date: Sat, 12 Sep 2020 03:48:32 +0800 Subject: [PATCH 011/156] Remove windows compilation chapter of converter tools --- lite/tutorials/source_en/build.md | 55 +----------------- .../tutorials/source_en/use/converter_tool.md | 11 +--- lite/tutorials/source_zh_cn/build.md | 56 +------------------ .../source_zh_cn/use/converter_tool.md | 11 +--- 4 files changed, 8 insertions(+), 125 deletions(-) diff --git a/lite/tutorials/source_en/build.md b/lite/tutorials/source_en/build.md index ef1282a257..c4e50d0298 100644 --- a/lite/tutorials/source_en/build.md +++ b/lite/tutorials/source_en/build.md @@ -9,12 +9,7 @@ - [Compilation Example](#compilation-example) - [Output Description](#output-description) - [Description of Converter's Directory Structure](#description-of-converters-directory-structure) - - [Description of Runtime and Other tools' Directory Structure](#description-of-runtime-and-other-tools-directory-structure) - - [Windows Environment Compilation](#windows-environment-compilation) - - [Environment Requirements](#environment-requirements-1) - - [Compilation Options](#compilation-options-1) - - [Compilation Example](#compilation-example-1) - - [Output Description](#output-description-1) + - [Description of Runtime and Other tools' Directory Structure](#description-of-runtime-and-other-tools-directory-structure) @@ -24,7 +19,7 @@ This chapter introduces how to quickly compile MindSpore Lite, which includes th | Module | Support Platform | Description | | --- | ---- | ---- | -| converter | Linux、Windows | Model Conversion Tool | +| converter | Linux | Model Conversion Tool | | runtime | Linux、Android | Model Inference Framework | | benchmark | Linux、Android | Benchmarking Tool | | time_profiler | Linux、Android | Performance Analysis Tool | @@ -183,49 +178,3 @@ The inference framework can be obtained under `-I x86_64`, `-I arm64` and `-I ar > 1. `liboptimize.so` only exists in the output package of runtime-arm64 and is only used on ARMv8.2 and CPUs that support fp16. > 2. Compile ARM64 to get the inference framework output of arm64-cpu by default, if you add `-e gpu`, you will get the inference framework output of arm64-gpu, and the package name is `mindspore-lite-{version}-runtime-arm64-gpu.tar.gz`, compiling ARM32 is in the same way. > 3. Before running the tools in the converter, benchmark or time_profile directory, you need to configure environment variables, and configure the path where the dynamic libraries of MindSpore Lite and Protobuf are located to the path where the system searches for dynamic libraries. Take the compiled under version 0.7.0-beta as an example: configure converter: `export LD_LIBRARY_PATH=./output/mindspore-lite-0.7.0-converter-ubuntu/third_party/protobuf/lib:./output/mindspore-lite-0.7.0-converter-ubuntu/third_party/flatbuffers/lib:${LD_LIBRARY_PATH}`; configure benchmark and timeprofiler: `export LD_LIBRARY_PATH= ./output/mindspore-lite-0.7.0-runtime-x86-cpu/lib:${LD_LIBRARY_PATH}`. - -## Windows Environment Compilation - -### Environment Requirements - -- The supported compilation environment is: Windows 10, 64-bit. - -- Compilation dependencies are: - - [CMake](https://cmake.org/download/) >= 3.14.1 - - [MinGW GCC](https://sourceforge.net/projects/mingw-w64/files/Toolchains%20targetting%20Win64/Personal%20Builds/mingw-builds/7.3.0/threads-posix/seh/x86_64-7.3.0-release-posix-seh-rt_v5-rev0.7z/download) = 7.3.0 - - [Python](https://www.python.org/) >= 3.7.5 - -> The compilation script will execute `git clone` to obtain the code of the third-party dependent libraries. Please make sure that the git network settings are correct and available in advance. - -### Compilation Options - -The compilation options of MindSpore Lite are as follows: - -| Parameter | Parameter Description | Mandatory or Not | -| -------- | ----- | ---- | -| **lite** | **Set this parameter to compile the Mindspore Lite project.** | **Yes** | -| [n] | Set the number of threads used during compilation, otherwise the default is set to 6 threads. | No | - -### Compilation Example - -First, use the git tool to download the source code from the MindSpore code repository. -```bash -git clone https://gitee.com/mindspore/mindspore.git -``` - -Then, use the cmd tool to compile MindSpore Lite in the root directory of the source code and execute the following commands. - -- Compile the Windows version with the default number of threads (6 threads). - ```bash - call build.bat lite - ``` -- Compile the Windows version with the specified number of threads 8. - ```bash - call build.bat lite 8 - ``` - -### Output Description - -After the compilation is complete, enter the `mindspore/output/` directory, unzip the output file `mindspore-lite-{version}-converter-win-cpu.zip`, which contains the conversion tool executable file. - -> version: version of the output, consistent with that of the MindSpore. diff --git a/lite/tutorials/source_en/use/converter_tool.md b/lite/tutorials/source_en/use/converter_tool.md index 38cd115fb1..e8dc13fa4b 100644 --- a/lite/tutorials/source_en/use/converter_tool.md +++ b/lite/tutorials/source_en/use/converter_tool.md @@ -119,9 +119,7 @@ The following describes the parameters in detail. To use the MindSpore Lite model conversion tool, the following environment preparations are required. -- Compile: The model conversion tool code is in the `mindspore/lite/tools/converter` directory of the MindSpore source code, refer to the [Environment Requirements](https://www.mindspore.cn/lite/tutorial/en/master/build.html#environment-requirements-1) and [Compilation Example](https://www.mindspore.cn/lite/tutorial/en/master/build.html#compilation-example-1) in the build document. - -- Run: Refer to [Output Description](https://www.mindspore.cn/lite/tutorial/en/master/build.html#output-description-1) in the deployment document to obtain the `converter` tool, and set the environment variable of MinGW(Add the bin directory of MinGW in the system variable Path). +- Get the toolkit: To obtain the 'Converter' tool, download the zip package of windows conversion tool and unzip it to the local directory. ### Parameter Description @@ -129,12 +127,7 @@ Reference description Linux environment model conversion tool [parameter descrip ### Example -First, use the cmd tool to enter the command to compile in the root directory of the source code, refer to `build.md`. -```bash -call build.bat lite -``` - -Then, set the log printing level to INFO. +At first, set the log printing level to INFO. ```bash set MSLOG=INFO ``` diff --git a/lite/tutorials/source_zh_cn/build.md b/lite/tutorials/source_zh_cn/build.md index a3e60383d3..6a26b28a90 100644 --- a/lite/tutorials/source_zh_cn/build.md +++ b/lite/tutorials/source_zh_cn/build.md @@ -9,12 +9,7 @@ - [编译示例](#编译示例) - [编译输出](#编译输出) - [模型转换工具converter目录结构说明](#模型转换工具converter目录结构说明) - - [模型推理框架runtime及其他工具目录结构说明](#模型推理框架runtime及其他工具目录结构说明) - - [Windows环境编译](#windows环境编译) - - [环境要求](#环境要求-1) - - [编译选项](#编译选项-1) - - [编译示例](#编译示例-1) - - [编译输出](#编译输出-1) + - [模型推理框架runtime及其他工具目录结构说明](#模型推理框架runtime及其他工具目录结构说明) @@ -24,7 +19,7 @@ | 模块 | 支持平台 | 说明 | | --- | ---- | ---- | -| converter | Linux、Windows | 模型转换工具 | +| converter | Linux | 模型转换工具 | | runtime | Linux、Android | 模型推理框架 | | benchmark | Linux、Android | 基准测试工具 | | time_profiler | Linux、Android | 性能分析工具 | @@ -184,50 +179,3 @@ tar -xvf mindspore-lite-{version}-runtime-{os}-{device}.tar.gz > 1. `liboptimize.so`仅在runtime-arm64的输出包中存在，仅在ARMv8.2和支持fp16特性的CPU上使用。 > 2. 编译ARM64默认可获得arm64-cpu的推理框架输出件，若添加`-e gpu`则获得arm64-gpu的推理框架输出件，此时包名为`mindspore-lite-{version}-runtime-arm64-gpu.tar.gz`，编译ARM32同理。 > 3. 运行converter、benchmark或time_profile目录下的工具前，都需配置环境变量，将MindSpore Lite和Protobuf的动态库所在的路径配置到系统搜索动态库的路径中。以0.7.0-beta版本下编译为例：配置converter：`export LD_LIBRARY_PATH=./output/mindspore-lite-0.7.0-converter-ubuntu/third_party/protobuf/lib:./output/mindspore-lite-0.7.0-converter-ubuntu/third_party/flatbuffers/lib:${LD_LIBRARY_PATH}`；配置benchmark和timeprofiler：`export LD_LIBRARY_PATH=./output/mindspore-lite-0.7.0-runtime-x86-cpu/lib:${LD_LIBRARY_PATH}`。 - -## Windows环境编译 - -### 环境要求 - -- 支持的编译环境为：Windows 10，64位。 - -- 编译依赖 - - [CMake](https://cmake.org/download/) >= 3.14.1 - - [MinGW GCC](https://sourceforge.net/projects/mingw-w64/files/Toolchains%20targetting%20Win64/Personal%20Builds/mingw-builds/7.3.0/threads-posix/seh/x86_64-7.3.0-release-posix-seh-rt_v5-rev0.7z/download) = 7.3.0 - - [Python](https://www.python.org/) >= 3.7.5 - -> 编译脚本中会执行`git clone`获取第三方依赖库的代码，请提前确保git的网络设置正确可用。 - -### 编译选项 - -MindSpore Lite的编译选项如下。 - -| 参数 | 参数说明 | 是否必选 | -| -------- | ----- | ---- | -| **lite** | **设置该参数，则对Mindspore Lite工程进行编译** | **是** | -| [n] | 设定编译时所用的线程数，否则默认设定为6线程 | 否 | - -### 编译示例 - -首先，使用git工具从MindSpore代码仓下载源码。 - -```bash -git clone https://gitee.com/mindspore/mindspore.git -``` - -然后，使用cmd工具在源码根目录下，执行如下命令即可编译MindSpore Lite。 - -- 以默认线程数（6线程）编译Windows版本。 - ```bash - call build.bat lite - ``` -- 以指定线程数8编译Windows版本。 - ```bash - call build.bat lite 8 - ``` - -### 编译输出 - -编译完成之后，进入`mindspore/output/`目录，解压后即可获取输出件`mindspore-lite-{version}-converter-win-cpu.zip`，其中含有转换工具可执行文件。 - -> version：输出件版本号，与所编译的分支代码对应的版本一致。 diff --git a/lite/tutorials/source_zh_cn/use/converter_tool.md b/lite/tutorials/source_zh_cn/use/converter_tool.md index 1b9ad944df..917d02cee7 100644 --- a/lite/tutorials/source_zh_cn/use/converter_tool.md +++ b/lite/tutorials/source_zh_cn/use/converter_tool.md @@ -120,9 +120,7 @@ MindSpore Lite模型转换工具提供了多种参数设置，用户可根据需 使用MindSpore Lite模型转换工具，需要进行如下环境准备工作。 -- 编译：模型转换工具代码在MindSpore源码的`mindspore/lite/tools/converter`目录中，参考部署文档中的[环境要求](https://www.mindspore.cn/lite/tutorial/zh-CN/master/build.html#id5)和[编译示例](https://www.mindspore.cn/lite/tutorial/zh-CN/master/build.html#id7)编译Windows版本。 - -- 运行：参考部署文档中的[编译输出](https://www.mindspore.cn/lite/tutorial/zh-CN/master/build.html#id8)，获得`converter`工具,，并配置MinGW环境变量（在系统变量Path里添加MinGW的bin目录）。 +- 获取工具包：下载Windows转换工具的Zip包并解压至本地目录，获得`converter`工具。 ### 参数说明 @@ -130,12 +128,7 @@ MindSpore Lite模型转换工具提供了多种参数设置，用户可根据需 ### 使用示例 -首先，使用cmd工具在源码根目录下，输入命令进行编译，可参考`build.md`。 -```bash -call build.bat lite -``` - -然后，设置日志打印级别为INFO。 +首先，设置日志打印级别为INFO。 ```bash set MSLOG=INFO ``` -- Gitee From f683b60d8d2af421364ec5f87167b18d379e2ae1 Mon Sep 17 00:00:00 2001 From: bingyaweng Date: Mon, 14 Sep 2020 14:51:01 +0800 Subject: [PATCH 012/156] add probability --- .../programming_guide/probability.md | 1419 +++++++++++++++++ 1 file changed, 1419 insertions(+) create mode 100644 api/source_zh_cn/programming_guide/probability.md diff --git a/api/source_zh_cn/programming_guide/probability.md b/api/source_zh_cn/programming_guide/probability.md new file mode 100644 index 0000000000..c8437fee82 --- /dev/null +++ b/api/source_zh_cn/programming_guide/probability.md @@ -0,0 +1,1419 @@ +# 深度概率编程库编程指南 + + + +- [深度概率编程库编程指南](#深度概率编程库编程指南) + - [概率分布： mindspore.nn.probability.distribution](#概率分布-mindsporennprobabilitydistribution) + - [概率分布类](#概率分布类) + - [Distribution基类](#distribution基类) + - [伯努利分布Bernoulli](#伯努利分布bernoulli) + - [指数分布Exponential](#指数分布exponential) + - [几何分布Geometric](#几何分布geometric) + - [正态分布Normal](#正态分布normal) + - [均匀分布Uniform](#均匀分布uniform) + - [概率分布类在PyNative模式下的应用](#概率分布类在pynative模式下的应用) + - [概率分布类在图模式下的应用](#概率分布类在图模式下的应用) + - [概率分布映射： mindspore.nn.probability.bijector](#概率分布映射-mindsporennprobabilitybijector) + - [Bijector类接口设计](#bijector类接口设计) + - [Bijector基类](#bijector基类) + - [幂函数变换映射PowerTransform](#幂函数变换映射powertransform) + - [指数变换映射Exp](#指数变换映射exp) + - [标量仿射变换映射ScalarAffine](#标量仿射变换映射scalaraffine) + - [Softplus变换映射Softplus](#softplus变换映射softplus) + - [PyNative模式下调用Bijector实例](#pynative模式下调用bijector实例) + - [图模式下调用Bijector实例](#图模式下调用bijector实例) + - [TransformedDistribution类接口设计](#transformeddistribution类接口设计) + - [PyNative模式下调用TransformedDistribution实例](#pynative模式下调用transformeddistribution实例) + - [图模式下调用TransformedDistribution实例](#图模式下调用transformeddistribution实例) + - [深度概率网络：mindspore.nn.probability.dpn](#深度概率网络mindsporennprobabilitydpn) + - [VAE](#vae) + - [ConditionalVAE](#conditionalvae) + - [概率推断算法：mindspore.nn.probability.infer](#概率推断算法mindsporennprobabilityinfer) + - [贝叶斯层：mindspore.nn.probability.bnn_layers](#贝叶斯层mindsporennprobabilitybnn_layers) + - [定义模型](#定义模型) + - [训练模型](#训练模型) + - [贝叶斯转换：mindspore.nn.probability.transform](#贝叶斯转换mindsporennprobabilitytransform) + - [定义DNN模型](#定义dnn模型) + - [定义损失函数和优化器](#定义损失函数和优化器) + - [实例化TransformToBNN](#实例化transformtobnn) + - [功能一：转换整个模型](#功能一转换整个模型) + - [功能二：转换指定类型的层](#功能二转换指定类型的层) + - [贝叶斯工具箱：mindspore.nn.probability.toolbox](#贝叶斯工具箱mindsporennprobabilitytoolbox) + + + + + +## 概率分布： mindspore.nn.probability.distribution + +概率分布是概率编程的基础。**Distribution** 类提供多样的概率统计接口, 例如概率密度函数 *pdf* 、累积密度函数*cdf* ，散度计算 *kl_loss* ，抽样 *sample* 等。现有的概率分布实例包括高斯分布，伯努利分布，指数型分布，几何分布和均匀分布。 + +### 概率分布类 + +- `Distribution`: 所有概率分布的基类。 + +- `Bernoulli`: 伯努利分布。参数为试验成功的概率。 + +- `Exponential`： 指数型分布。参数为率参数。 + +- `Geometric`: 几何分布。参数为一次伯努利试验成功的概率。 + +- `Normal`: 正态（高斯）分布。参数为均值和标准差。 + +- `Uniform`: 均匀分布。参数为数轴上的最小值和最大值。 + +#### Distribution基类 + +`Distribution` 继承`Cell`，是所有概率分布的基类。构造函数参数包括: + +- `seed`: 随机抽样种子。 +- `dtype`: 分布的数据类型。 +- `name`: 分布的名称。 +- `param`: 分布初始化时所用参数。 + +接口介绍：`Distribution`类支持的函数包括 `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, `log_survival`, `mean`, `sd`, `var`, `entropy`, `kl_loss`, `cross_entropy`,和 `sample`。分布不同，所需传入的参数也不同。只有在派生类中才能使用，由派生类的函数实现决定参数。 + +- `prob` : 概率密度函数（pdf）/ 概率质量函数（pmf） +- `log_prob` ：对数似然函数。 +- `cdf` : 累积分布函数（cdf）。 +- `log_cdf` : 对数累积分布函数（cdf）。 +- `survival_function` : 生存函数。 +- `log_survival` : 对数生存函数。 +- `mean` : 均值。 +- `sd` : 标准差。 +- `var` : 方差。 +- `entropy` : 熵。 +- `kl_loss` : Kullback-Leibler散度。 +- `cross_entropy` : 两个概率分布的交叉熵。 +- `sample` : 概率分布的随机抽样。 + +#### 伯努利分布Bernoulli +伯努利分布，继承自 `Distribution` 类。构造函数参数如下： +- `probs`: 伯努利试验成功的概率。 +- `seed`: 随机种子。 +- `dtype`: 分布类型。 +- `name`: 分布名称。 + +Property: +- `Bernoulli.probs`: 伯努利试验成功的概率。 + +`Distribution` 基类调用 `Bernoulli` 中私有接口以实现基类中的公有接口。`Bernoulli` 支持的公有接口为 + +- `mean`, `mode`, `var`: 可选择传入 *probs1* 指定试验成功的概率。 +- `entropy`：可选择传入 *probs1* 指定试验成功的概率. +- `cross_entropy`, `kl_loss`：必须传入 *dist* 和 *probs1_b* 。*dist*: 另一分布类型的名称，目前只支持此处为 *‘Bernoulli’* ， *probs1_b* 指定分布 *b* 的试验成功概率。可选择传入 *probs1_a* 分布 *a* 的参数。 +- `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, `log_survival`: 必须传入 *value* 。 可选择传入 *probs* 指定试验成功的概率。 +- `sample`: 可选择传入 *shape* 样本形状和 *probs1* 指定试验成功的概率。 + +#### 指数分布Exponential +指数分布，继承自`Distribution`类。其构造函数参数如下： +- `rate`: 分布的率参数。 +- `seed`: 随机种子。 +- `dtype`: 分布类型。 +- `name`: 分布名称。 + +Property: +- `Exponential.rate`: 率参数。 + +`Distribution` 基类调用 `Exponential` 私有接口以实现基类中的公有接口。`Exponential` 支持的公有接口为 + +- `mean`, `mode`, `var`: 可选择传入 *rate* 指定率参数 +- `entropy`：可选择传入 *rate* 指定率参数 +- `cross_entropy`, `kl_loss`：。必须传入 *dist* 和 *rate_b*. *dist*: 另一分布类型的名称， 目前只支持此处为 *‘Exponential’* ，*rate_b* 指定分布 *b* 的率参数。可选择传入 *rate_a* 分布 *a* 的参数. +- `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, `log_survival`: 必须传入 *value*，可选择传入 *rate* 指定率参数。 +- `sample`: 可选择传入 *shape* 样本形状，以及 *rate* 指定率参数。 + +#### 几何分布Geometric +几何分布，继承自`Distribution`类。其构造函数参数如下： +- `probs`: 伯努利试验成功的概率。 +- `seed`: 随机种子。 +- `dtype`: 分布类型。 +- `name`: 分布名称。 + +Property: +- `Geometric.probs`: 伯努利试验成功的概率。 + +`Distribution` 基类调用 `Geometric` 中私有接口以实现基类中的公有接口。`Geometric` 支持的公有接口为 + +- `mean`, `mode`, `var`: 可选择传入 *probs1* 指定试验成功的概率。 +- `entropy`：可选择传入 *probs1* 指定试验成功的概率. +- `cross_entropy`, `kl_loss`：必须传入 *dist* 和 *probs1_b* 。*dist*: 另一分布类型的名称，目前只支持此处为 *‘Geometric’* ， *probs1_b* 指定分布 *b* 的试验成功概率。可选择传入 *probs1_a* 分布 *a* 的参数。 +- `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, `log_survival`: 必须传入 *value*。 可选择传入 *probs1* 指定试验成功的概率。 +- `sample`: 可选择传入 *shape* 样本形状和 *probs1* 指定试验成功的概率。 + +#### 正态分布Normal +正态（高斯）分布，继承自 **Distribution** 类。其构造函数参数如下： +- `mean`: 分布均值。 +- `sd`: 分布方差。 +- `seed`: 随机种子。 +- `dtype`: 分布类型。 +- `name`: 分布名称。 + +**Distribution** 基类调用 **Normal** 中私有接口以实现基类中的公有接口。**Normal** 支持的公有接口为 +- `mean`, `mode`, `var`: 可选择传入 *mean* 和 *sd* 指定分布的参数。 +- `entropy`：可选择传入 *mean* 和 *sd* 指定分布的参数。 +- `cross_entropy`, `kl_loss`：必须传入 *dist* ，*mean_b* 和 *sd_b*。*dist*: 另一分布类型的名称，目前只支持此处为 *‘Normal’* ，*mean_b* 和 *sd_b* 指定分布b的参数。可选择传入 *mean_a* 和 *sd_a* 指定分布的参数。 +- `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, `log_survival`: 必须传入 *value*。可选择传入 *mean* 和 *sd* 指定分布的参数。 +- `sample`: 可选择传入 *shape* 样本形状。可选择传入 *mean_a* 和 *sd_a* 指定分布的参数。 + +#### 均匀分布Uniform +均匀分布，继承自`Distribution`类。其构造函数参数如下： +- `low`: 最小值。 +- `high`: 最大值。 +- `seed`: 随机种子。 +- `dtype`: 分布类型。 +- `name`: 分布名称。 + +Property: +- `Uniform.low`: 最小值。 +- `Uniform.high`: 最大值。 + +`Distribution` 基类调用 `Uniform` 以实现基类中的公有接口。`Uniform` 支持的公有接口为 + +- `mean`, `mode`, `var`: 可选择传入 *high* 和 *low* 指定分布的参数。 +- `entropy`：可选择传入 *high* 和 *low* 指定分布的参数。 +- `cross_entropy`, `kl_loss`：必须传入 *dist* ，*high_b* 和 *low_b* 。*dist* : 另一分布类型的名称，目前只支持此处为 *‘Uniform’* ，*high_b* 和 *low_b* 指定分布 *b* 的参数。可选择传入 *high_a* 和 *low_a* 指定分布的参数。 +- `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, `log_survival`: 必须传入 *value* 。可选择传入 *high* 和 *low* 指定分布的参数。 +- `sample`: 可选择传入 *shape* 。 可选择传入 *high* 和 *low* 指定分布的参数。 + +### 概率分布类在PyNative模式下的应用 + +`Distribution`子类可在 **PyNative** 模式下使用。 +导入相关模块： + +```python +from mindspore import Tensor +from mindspore import dtype as mstype +import mindspore.context as context +import mindspore.nn.probability.distribution as msd +context.set_context(mode=context.PYNATIVE_MODE) +``` +以 **Normal** 为例， 创建一个均值为0.0 标准差为1.0 的正态分布： +```python +my_normal = msd.Normal(0.0, 1.0, dtype=mstype.float32) +``` +计算均值： +```python +mean = my_normal.mean() +print(mean) +``` +输出为： +``` +0.0 +``` +计算方差： +```python +var = my_normal.var() +print(var) +``` +输出为： +``` +1.0 +``` +计算熵： +```python +entropy = my_normal.entropy() +print(entropy) +``` +``` +1.4189385 +``` +计算 **pdf**: +```python +value = Tensor([-0.5, 0.0, 0.5], dtype=mstype.float32) +prob = my_normal.prob(value) +print(prob) +``` +输出为： +``` +[0.35206532, 0.3989423, 0.35206532] +``` +计算 **cdf**: +```python +cdf = my_normal.cdf(value) +print(cdf) +``` +输出为： +``` +[0.30852754, 0.5, 0.69146246] +``` +计算 **kl_loss**: +```python +mean_b = Tensor(1.0, dtype=mstype.float32) +sd_b = Tensor(2.0, dtype=mstype.float32) +kl = my_normal.kl_loss('Normal', mean_b, sd_b) +print(kl) +``` +输出为： +``` +0.44314718 +``` + +### 概率分布类在图模式下的应用 + +在图模式下，**Distribution**子类可用在网络中。 +导入相关模块： +```python +import mindspore.nn as nn +from mindspore import Tensor +from mindspore import dtype as mstype +import mindspore.context as context +import mindspore.nn.probability.distribution as msd +context.set_context(mode=context.GRAPH_MODE) +``` +创建网络： +```python +# 网络继承nn.Cell +class Net(nn.Cell): + def __init__(self): + super(Net, self).__init__() + # 创建 Normal 实例 + self.normal = msd.Normal(0.0, 1.0, dtype=mstype.float32) + + #定义计算 + def construct(self, value, mean, sd): + pdf = self.normal.prob(value) + kl = self.normal.kl_loss("Normal", mean, sd) + return pdf, kl +``` +调用网络 +```python +net = Net() +value = Tensor([-0.5, 0.0, 0.5], dtype=mstype.float32) +mean = Tensor(1.0, dtype=mstype.float32) +sd = Tensor(1.0, dtype=mstype.float32) +pdf, kl = net(value, mean, sd) +print("pdf: ", pdf) +print("kl: ", kl) +``` +输出为： +``` +pdf: [0.3520653, 0.39894226, 0.3520653] +kl: 0.5 +``` + +## 概率分布映射： mindspore.nn.probability.bijector + +Bijector是概率编程的基本组成部分。Bijector描述了一种随机变量的变换方法，可以通过一个已有的随机变量X和一个映射函数f生成一个新的随机变量Y = f(x)。 +`Bijector`提供了映射相关的四种变换方法。它可以当做算子直接使用，也可以作用在某个随机变量`Distribution`类实例上生成新的随机变量的`Distribution`类实例。 + +### Bijector类接口设计 + +#### Bijector基类 + +`Bijector`类是所有Bejictor的基类，继承自`Cell`基类。 + +* 基类构造函数__init__ + + 输入参数： + `is_constant_jacobian`：映射函数的导数是否常数，只在创建衍生类的时候使用，默认false。 + `is_injective`：射函数是否是单设，只在创建衍生类的时候使用，默认true。 + `name`：Bijector的名字，创建派生类后由派生类的具体函数名决定，只在创建衍生类的时候使用，可以接受输入为str。默认None。 + `dtype`：Bijector对应的函数的类型（自变量和因变量的数据类型），创建派生类后由由派生类的具体函数决定，只在创建衍生类的时候使用，默认为None。 + `param`：Bijector内部的参数，创建派生类后由由派生类的具体函数参数决定，只在创建衍生类的时候使用，默认为None。 + +* 类特征函数property +`name`：无参函数，返回`name`的值。 +`is_dtype`：无参函数，返回`dtype`的值。 +`parameter`：无参函数，返回`parameter`的值。 +`is_constant_jacobian`：无参函数，返回`is_constant_jacobian`的值。 +`is_injective`：无参函数，返回`is_injective`的值。 + +* 映射函数 +`forward (args)`：正向映射，创建派生类后由派生类的`_forward`决定参数。 +`inverse (args)`：反向映射，创建派生类后由派生类的`_inverse`决定参数。 +`forward_log_jacobian (args)`：正向映射的(log)导数，创建派生类后由派生类的`_forward_log_jacobian`决定参数。 +`inverse_log_jacobian (args)`：反向映射的(log)导数，创建派生类后由派生类的`_inverse_log_jacobian`决定参数。 + +* `Bijector` 作为函数调用： +输入是一个`Distribution`类：生成一个`TransformedDistribution`**（不可在图内调用）** +其他输入：内部使用，不对外开放。 + +#### 幂函数变换映射PowerTransform +`PowerTransform`做如下变量替换：Y = g(X) = (1 + X * c)^(1 / c)。 + +* `PowerTransform`构造函数`__init__` +输入参数： +`power`：`PowerTransform`双射类对象的指数，可以接受输入为scalar(int or float), 默认0。 +`name`：`PowerTransform`双射类对象的名字，可以接受输入为str。默认为“PowerTransform”。 +`param`：`PowerTransform`内部的参数，创建派生类后由由派生类(exp)的具体函数参数决定，只在创建衍生类的时候使用。默认为None。 + +* 类特征函数property +`power`：无参函数，返回power的值。 + +* 映射函数 +`forward (args)`：正向映射，输入为tensor。 +`inverse (args)`：反向映射，输入为tensor。 +`forward_log_jacobian (args)`：正向映射的(log)导数，输入为tensor。 +`inverse_log_jacobian (args)`：反向映射的(log)导数，输入为tensor。 + +#### 指数变换映射Exp +`Exp`做如下变量替换：Y = g(X)= exp(X)。 + +* `Exp`构造函数`__init__` +输入参数： +`name`：Exp双射类对象的名字，可以接受输入为str。默认为“Exp”。 + +* 映射函数 +`forward (args)`：正向映射，输入为tensor。继承自父类`PowerTransform`双射。 +`inverse (args)`：反向映射，输入为tensor。继承自父类`PowerTransform`双射。 +`forward_log_jacobian (args)`：正向映射的(log)导数，输入为tensor。继承自父类`PowerTransform`双射。 +`inverse_log_jacobian (args)`：反向映射的(log)导数，输入为tensor。继承自父类`PowerTransform`双射。 + +#### 标量仿射变换映射ScalarAffine +`ScalarAffine`做如下变量替换：Y = g(X) = a * X + b。 + +* `ScalarAffine`构造函数`__init__` +输入参数： +`scale`：ScalarAffine的斜率，可以接受输入为float, 默认1.0。 +`shift`：ScalarAffine的截距，可以接受输入为float, 默认0.0。 +`name`：ScalarAffine的名字，可以接受输入为str。默认“ScalarAffine”。 + +* 类特征函数property +`scale`：无参函数，返回scale的值。 +`shift`：无参函数，返回shift的值。 + +* 映射函数 +`forward (args)`：正向映射，输入为tensor。 +`inverse (args)`：反向映射，输入为tensor。 +`forward_log_jacobian (args)`：正向映射的(log)导数，输入为tensor。 +`inverse_log_jacobian (args)`：反向映射的(log)导数，输入为tensor。 + +#### Softplus变换映射Softplus +`Softplus`做如下变量替换：Y = g(X) = \frac{\log(1 + e ^ {kX})}{k}。 + +* `Softplus`构造函数`__init__` +输入参数： +`sharpness`：Softplus的斜率，可以接受输入为float, 默认1.0。 +`name`：Softplus的名字，可以接受输入为str。默认“Softplus”。 +* 类特征函数property +`sharpness`：无参函数，返回sharpness的值。 +* 映射函数 + `forward (args)`：正向映射，输入为tensor。 + `inverse (args)`：反向映射，输入为tensor。 + `forward_log_jacobian (args)`：正向映射的(log)导数，输入为tensor。 + `inverse_log_jacobian (args)`：反向映射的(log)导数，输入为tensor。 + +### PyNative模式下调用Bijector实例 + +PyNative模式类似于命令行调用，可以即时输入函数及其参数，完成函数调用。PyNative模式为异步执行，仅在访问函数结果时函数会被调用。 + +在执行之前，我们需要导入需要的库文件包。双射类最主要的库是`mindspore.nn.probability.bijector`，导入后我们使用`msb`作为库的缩写并进行调用。 + +导入相关模块： +```python +import numpy as np +import mindspore.nn as nn +import mindspore.nn.probability.bijector as msb +import mindspore.context as context +from mindspore import Tensor +from mindspore import dtype +context.set_context(mode=context.PYNATIVE_MODE) +``` + +通常我们并不直接构造基类`Bijector`，而是直接构造派生的子类，并调用子类对应的函数接口。下面我们以`PowerTransform`为例。 + +创建一个`powertransform`对象，初始化参数中power=2。 + +构造`PowerTransform`: +```python +powertransform = msb.PowerTransform(power=2) +powertransform +``` + +输出: +```python +PowerTransform +``` + +接下来可以使用映射函数进行运算。 + +调用`forward`方法，计算正向映射: +```python +x = np.array([2.0, 3.0, 4.0, 5.0], dtype=np.float32) +tx = Tensor(x, dtype=dtype.float32) +forward = powertransform.forward(tx) +foward +``` + +输出: +```python +Tensor(shape=[4], dtype=Float32, [ 2.23606801e+00 2.64575124e+00 3.00000000e+00 3.31662488e+00]) +``` + +输入`inverse`方法，计算反向映射: +```python +inverse = powertransform.inverse(tx) +inverse +``` + +输出: +```python +Tensor(shape=[4], dtype=Float32, [ 1.50000000e+00 4.00000048e+00 7.50000000e+00 1.20000010e+01]) +``` + +输入`forward_log_jacobian`方法，计算正向映射导数: +```python +forward_log_jaco = powertransform.forward_log_jacobian(tx) +forward_log_jaco +``` + +输出: +```python +Tensor(shape=[4], dtype=Float32, [-8.04718971e-01 -9.72955048e-01 -1.09861231e+00 -1.19894767e+00]) +``` + +输入`inverse_log_jacobian`方法，计算正向映射导数: +```python +inverse_log_jaco = powertransform.inverse_log_jacobian(tx) +inverse_log_jaco +``` + +输出: +```python +Tensor(shape=[4], dtype=Float32, [ 6.93147182e-01 1.09861231e+00 1.38629436e+00 1.60943794e+00]) +``` + +### 图模式下调用Bijector实例 + +在图模式下，`Bijector`子类可用在网络中。 + +导入相关模块： +```python +import mindspore.nn as nn +from mindspore import Tensor +from mindspore import dtype as mstype +import mindspore.context as context +import mindspore.nn.probability.Bijector as msb +context.set_context(mode=context.GRAPH_MODE) +``` + +创建网络： +```python +# 网络继承nn.Cell +class Net(nn.Cell): + def __init__(self): + super(Net, self).__init__() + # 创建PowerTransform实例 + self.powertransform = msb.PowerTransform(power=2) + + #定义计算 + def construct(self, value): + forward = self.s1.forward(value) + inverse = self.s1.inverse(value) + forward_log_jaco = self.s1.forward_log_jacobian(value) + inverse_log_jaco = self.s1.inverse_log_jacobian(value) + return forward, inverse, forward_log_jaco, inverse_log_jaco +``` +调用网络 +```python +net = Net() +x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32) +tx = Tensor(x, dtype=dtype.float32) +forward, inverse, forward_log_jaco, inverse_log_jaco = net(tx) +print("forward: ", forward) +print("inverse: ", inverse) +print("forward_log_jaco: ", forward_log_jaco) +print("inverse_log_jaco: ", inverse_log_jaco) +``` +输出为： +```python +forward: [2.236068 2.6457512 3. 3.3166249] +inverse: [ 1.5 4.0000005 7.5 12.000001 ] +forward_log_jaco: [-0.804719 -0.97295505 -1.0986123 -1.1989477 ] +inverse_log_jaco: [0.6931472 1.0986123 1.3862944 1.609438 ] +``` + +### TransformedDistribution类接口设计 + +`TransformedDistribution`继承自`Distribution`，是可通过映射f(x)变化得到的数学分布的基类。 + +* 构造函数`__init__` + + 输入参数： + `bijector`：分布的变换方法。可以接受输入为bijector的派生类，作为映射变换函数f。 + `distribution`：原始分布。可以接受输入为distribution的派生类，作为映射需要变换的分布类*X*。 + `seed`: 随机种子。 + `dtype`: 分布类型。 + `name`：分布名称。可以接受输入为str。默认为“transformed_distribution”。 + +* 类特征函数property +`bijector`：无参函数，返回分布的变换方法。 +`distribution`：无参函数，返回原始分布。 +`is_linear_transformation`：无参函数，返回线性变换标志。 + +* 接口函数 +`cdf`：累积分布函数（cdf）。 +`log_cdf`：对数累积分布函数（cdf）。 +`survival_function`：生存函数。与 cdf 互补。 +`log_survival`：对数生存函数。 +`prob`：概率密度函数（pdf）/ 概率质量函数（pmf）。 +`log_prob`：对数似然函数。 +`sample`：随机取样。必须传入shape，类型tuple。 +`mean`：无参数。只有当`Bijector.is_constant_jacobian=true`时可调用。 + +### PyNative模式下调用TransformedDistribution实例 + +`TransformedDistribution` 子类可在 **PyNative** 模式下使用。 +在执行之前，我们需要导入需要的库文件包。 + +导入相关模块： +```python +import numpy as np +import mindspore.nn as nn +import mindspore.nn.probability.bijector as msb +import mindspore.nn.probability.distribution as msd +import mindspore.context as context +from mindspore import Tensor +from mindspore import dtype +context.set_context(mode=context.PYNATIVE_MODE) +``` + +构造一个`TransformedDistribution`实例，使用Normal分布作为需要变换的分布类，使用Exp作为映射变换，可以生成`LogNormal`分布。 +```python +normal = msd.Normal(0.0, 1.0, dtype=dtype.float32) +exp = msb.Exp() +LogNormal = msd.TransformedDistribution(exp, normal, dtype=dtype.float32, seed=0, name="LogNormal") +LogNormal +``` + +输出： +```python +TransformedDistribution< + (_bijector): Exp + (_distribution): Normal + > +``` + +可以对`LogNormal`进行概率分布计算。例如： + +计算 **cdf** ： +```python +x = np.array([2.0, 5.0, 10.0], dtype=np.float32) +tx = Tensor(x, dtype=dtype.float32) +cdf = LogNormal.cdf(tx) +cdf +``` + +输出： +```python +Tensor(shape=[3], dtype=Float32, [ 7.55891383e-01 9.46239710e-01 9.89348888e-01]) +``` + +计算 **log_cdf** ： +```python +x = np.array([2.0, 5.0, 10.0], dtype=np.float32) +tx = Tensor(x, dtype=dtype.float32) +log_cdf = LogNormal.log_cdf(tx) +log_cdf +``` + +输出： +```python +Tensor(shape=[3], dtype=Float32, [-2.79857576e-01 -5.52593507e-02 -1.07082408e-02]) +``` + +计算 **survival_function** ： +```python +x = np.array([2.0, 5.0, 10.0], dtype=np.float32) +tx = Tensor(x, dtype=dtype.float32) +survival_function = LogNormal.survival_function(tx) +survival_function +``` + +输出： +```python +Tensor(shape=[3], dtype=Float32, [ 2.44108617e-01 5.37602901e-02 1.06511116e-02]) +``` + +计算 **log_survival** ： +```python +x = np.array([2.0, 5.0, 10.0], dtype=np.float32) +tx = Tensor(x, dtype=dtype.float32) +log_survival = LogNormal.log_survival(tx) +log_survival +``` + +输出： +```python +Tensor(shape=[3], dtype=Float32, [-1.41014194e+00 -2.92322016e+00 -4.54209089e+00]) +``` + +计算 **prob** ： +```python +x = np.array([2.0, 5.0, 10.0], dtype=np.float32) +tx = Tensor(x, dtype=dtype.float32) +prob = LogNormal.prob(tx) +prob +``` + +输出： +```python +Tensor(shape=[3], dtype=Float32, [ 1.56874031e-01 2.18507163e-02 2.81590177e-03]) +``` + +计算 **log_prob** ： +```python +x = np.array([2.0, 5.0, 10.0], dtype=np.float32) +tx = Tensor(x, dtype=dtype.float32) +log_prob = LogNormal.log_prob(tx) +log_prob +``` + +输出： +```python +Tensor(shape=[3], dtype=Float32, [-1.85231221e+00 -3.82352161e+00 -5.87247276e+00]) +``` + +调用取样函数 **sample** ： +```python +shape = ((3, 2)) +sample = LogNormal.sample(shape) +sample +``` + +输出： +```python +Tensor(shape=[3, 2], dtype=Float32, +[[ 7.64315844e-01 3.01435232e-01] + [ 1.17166102e+00 2.60277224e+00] + [ 7.02699006e-01 3.91564220e-01]]) +``` + +### 图模式下调用TransformedDistribution实例 + +在图模式下，**TransformedDistribution**子类可用在网络中。 + +导入相关模块： +```python +import mindspore.nn as nn +from mindspore import Tensor +from mindspore import dtype +import mindspore.context as context +import mindspore.nn.probability.Bijector as msb +import mindspore.nn.probability.Distribution as msd +context.set_context(mode=self.GRAPH_MODE) +``` + +创建网络： +```python +# 网络继承nn.Cell +class Net(nn.Cell): + def __init__(self, shape, dtype=dtype.float32, seed=0, name='transformed_distribution'): + super(Net, self).__init__() + # 创建TransformedDistribution实例 + self.exp = msb.Exp() + self.normal = msd.Normal(0.0, 1.0, dtype=dtype) + self.lognormal = msd.TransformedDistribution(self.exp, self.normal, dtype=dtype, seed=seed, name=name) + self.shape = shape + + #定义计算cdf，以及随机取样 + def construct(self, value): + cdf = self.lognormal.cdf(value) + sample = self.lognormal.sample(self.shape) + return cdf, sample +``` + +调用网络 +```python +shape = (2, 3) +net = Net(shape=shape, name="LogNormal") +x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32) +tx = Tensor(x, dtype=dtype.float32) +cdf, sample = net(tx) +print("cdf: ", cdf) +print("sample: ", sample) +``` +输出为： +```python +cdf: [0.7558914 0.8640314 0.9171715 0.9462397] +sample: [[0.21036398 0.44932044 0.5669641 ] + [1.4103683 6.724116 0.97894996]] +``` + +## 深度概率网络：mindspore.nn.probability.dpn + +使用MindSpore深度概率编程库来构造变分自编码器（VAE）进行推理尤为简单。我们只需要自定义编码器和解码器（DNN模型），调用VAE或CVAE接口形成其派生网络，然后调用ELBO接口进行优化，最后使用SVI接口进行变分推理。这样做的好处是，不熟悉变分推理的用户可以像构建DNN模型一样来构建概率模型，而熟悉的用户可以调用这些接口来构建更为复杂的概率模型。VAE的接口在`mindspore.nn.probability.dpn`下面，dpn代表的是Deep probabilistic network，这里提供了一些基本的深度概率网络的接口，例如VAE。 + +### VAE + +首先，我们需要先自定义encoder和decoder，调用`mindspore.nn.probability.dpn.VAE`接口来构建VAE网络，我们除了传入encoder和decoder之外，还需要传入encoder输出变量的维度hidden size，以及VAE网络存储潜在变量的维度latent size，一般latent size会小于hidden size。 + +```python +import mindspore.nn as nn +from mindspore.ops import operations as P +from mindspore.nn.probability.dpn import VAE + +IMAGE_SHAPE = (-1, 1, 32, 32) + + +class Encoder(nn.Cell): + def __init__(self): + super(Encoder, self).__init__() + self.fc1 = nn.Dense(1024, 800) + self.fc2 = nn.Dense(800, 400) + self.relu = nn.ReLU() + self.flatten = nn.Flatten() + + def construct(self, x): + x = self.flatten(x) + x = self.fc1(x) + x = self.relu(x) + x = self.fc2(x) + x = self.relu(x) + return x + + +class Decoder(nn.Cell): + def __init__(self): + super(Decoder, self).__init__() + self.fc1 = nn.Dense(400, 1024) + self.sigmoid = nn.Sigmoid() + self.reshape = P.Reshape() + + def construct(self, z): + z = self.fc1(z) + z = self.reshape(z, IMAGE_SHAPE) + z = self.sigmoid(z) + return z + + +encoder = Encoder() +decoder = Decoder() +vae = VAE(encoder, decoder, hidden_size=400, latent_size=20) +``` +### ConditionalVAE + +类似地，ConditionalVAE与VAE的使用方法比较相近，不同的是，ConditionalVAE利用了数据集的标签信息，属于有监督学习算法，其生成效果一般会比VAE好。 + +首先，先自定义encoder和decoder，并调用`mindspore.nn.probability.dpn.ConditionalVAE`接口来构建ConditionalVAE网络，这里的encoder和VAE的不同，因为需要传入数据集的标签信息；decoder和上述的一样。ConditionalVAE接口的传入则还需要传入数据集的标签类别个数，其余和VAE接口一样。 + +``` +import mindspore.nn as nn +from mindspore.ops import operations as P +from mindspore.nn.probability.dpn import ConditionalVAE + +IMAGE_SHAPE = (-1, 1, 32, 32) + + +class Encoder(nn.Cell): + def __init__(self, num_classes): + super(Encoder, self).__init__() + self.fc1 = nn.Dense(1024 + num_classes, 400) + self.relu = nn.ReLU() + self.flatten = nn.Flatten() + self.concat = P.Concat(axis=1) + self.one_hot = nn.OneHot(depth=num_classes) + + def construct(self, x, y): + x = self.flatten(x) + y = self.one_hot(y) + input_x = self.concat((x, y)) + input_x = self.fc1(input_x) + input_x = self.relu(input_x) + return input_x + + +class Decoder(nn.Cell): + def __init__(self): + super(Decoder, self).__init__() + self.fc1 = nn.Dense(400, 1024) + self.sigmoid = nn.Sigmoid() + self.reshape = P.Reshape() + + def construct(self, z): + z = self.fc1(z) + z = self.reshape(z, IMAGE_SHAPE) + z = self.sigmoid(z) + return z + + +encoder = Encoder(num_classes=10) +decoder = Decoder() +cvae = ConditionalVAE(encoder, decoder, hidden_size=400, latent_size=20, num_classes=10) +``` + +加载数据集，我们可以使用Mnist数据集，具体的数据加载和预处理过程可以参考这里[Implementing an Image Classification Application](https://www.mindspore.cn/tutorial/en/master/quick_start/quick_start.html)，这里会用到create_dataset函数创建数据迭代器。 + +```python +ds_train = create_dataset(image_path, 128, 1) +``` +接下来，需要用到infer接口进行VAE网络的变分推断。 + +## 概率推断算法：mindspore.nn.probability.infer + +调用ELBO接口(`mindspore.nn.probability.infer.ELBO`)来定义VAE网络的损失函数，调用`WithLossCell`封装VAE网络和损失函数，并定义优化器，之后传入SVI接口(`mindspore.nn.probability.infer.SVI`)。SVI的`run`函数可理解为VAE网络的训练，可以指定训练的`epochs`，返回结果为训练好的网络；`get_train_loss`函数可以返回训练好后模型的loss。 + +```python +from mindspore.nn.probability.infer import ELBO, SVI + +net_loss = ELBO(latent_prior='Normal', output_prior='Normal') +net_with_loss = nn.WithLossCell(vae, net_loss) +optimizer = nn.Adam(params=vae.trainable_params(), learning_rate=0.001) + +vi = SVI(net_with_loss=net_with_loss, optimizer=optimizer) +vae = vi.run(train_dataset=ds_train, epochs=10) +trained_loss = vi.get_train_loss() +``` +最后，得到训练好的VAE网络后，我们可以使用`vae.generate_sample`生成新样本，需要传入待生成样本的个数，及生成样本的shape，shape需要保持和原数据集中的样本shape一样；当然，我们也可以使用`vae.reconstruct_sample`重构原来数据集中的样本，来测试VAE网络的重建能力。 +```python +generated_sample = vae.generate_sample(64, IMAGE_SHAPE) +for sample in ds_train.create_dict_iterator(): + sample_x = Tensor(sample['image'], dtype=mstype.float32) + reconstructed_sample = vae.reconstruct_sample(sample_x) +print('The shape of the generated sample is ', generated_sample.shape) +``` +我们可以看一下新生成样本的shape： +``` +The shape of the generated sample is (64, 1, 32, 32) +``` +ConditionalVAE训练过程和vae的过程类似，但需要注意的是使用训练好的ConditionalVAE网络生成新样本和重建新样本时，需要输入标签信息，例如下面生成的新样本就是64个0-7的数字。 + +``` +sample_label = Tensor([i for i in range(0, 8)] * 8, dtype=mstype.int32) +generated_sample = cvae.generate_sample(sample_label, 64, IMAGE_SHAPE) +for sample in ds_train.create_dict_iterator(): + sample_x = Tensor(sample['image'], dtype=mstype.float32) + sample_y = Tensor(sample['label'], dtype=mstype.int32) + reconstructed_sample = cvae.reconstruct_sample(sample_x, sample_y) +print('The shape of the generated sample is ', generated_sample.shape) +``` +查看一下新生成的样本的shape： +``` +The shape of the generated sample is (64, 1, 32, 32) +``` + +如果希望新生成的样本更好，更清晰，用户可以自己定义更复杂的encoder和decoder，这里的示例只用了两层全连接层，仅供示例的指导。 + +## 贝叶斯层：mindspore.nn.probability.bnn_layers + +下面的范例使用MindSpore的`nn.probability.bnn_layers`中的API实现BNN图片分类模型。MindSpore的`nn.probability.bnn_layers`中的API包括`NormalPrior`，`NormalPosterior`，`ConvReparam`，`DenseReparam`和`WithBNNLossCell`。 + +- ### 处理数据 + +``` +import mindspore.dataset as ds +import mindspore.dataset.transforms.vision.c_transforms as CV +import mindspore.dataset.transforms.c_transforms as C +from mindspore.dataset.transforms.vision import Inter +from mindspore.common import dtype as mstype + + +def create_dataset(data_path, batch_size=32, repeat_size=1, + num_parallel_workers=1): + """ + create dataset for train or test + """ + # 定义数据集 + mnist_ds = ds.MnistDataset(data_path) + + resize_height, resize_width = 32, 32 + rescale = 1.0 / 255.0 + shift = 0.0 + rescale_nml = 1 / 0.3081 + shift_nml = -1 * 0.1307 / 0.3081 + + # 定义映射操作 + resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) + rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) + rescale_op = CV.Rescale(rescale, shift) + hwc2chw_op = CV.HWC2CHW() + type_cast_op = C.TypeCast(mstype.int32) + + # 对图像应用映射操作 + mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers) + + # 应用DatasetOps + buffer_size = 10000 + mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script + mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True) + mnist_ds = mnist_ds.repeat(repeat_size) + + return mnist_ds + +``` +### 定义模型 + +利用bnn_layers构建贝叶斯神经网络的方法与构建普通的神经网络相同。值得注意的是，bnn_layers和普通的神经网络层可以互相组合。当网络中包含bnn_layers时，需要将`WithLossCel`l替换为适用于BNN的`WithBNNLossCell`。除了`backbone`和`loss_fn`两个参数之外，`WithBNNLossCell`增加了`dnn_factor`和`bnn_factor`两个参数。`dnn_factor`是由损失函数计算得到的网络整体损失的系数，`bnn_factor`是每个贝叶斯层的KL散度的系数，这两个参数是用来平衡网络整体损失和贝叶斯层的KL散度的，防止KL散度的值过大掩盖了网络整体损失。 + +``` +import mindspore.nn as nn +from mindspore.nn.probability import bnn_layers +from mindspore.ops import operations as P + +class BNNLeNet5(nn.Cell): + def __init__(self, num_class=10): + super(BNNLeNet5, self).__init__() + self.num_class = num_class + self.conv1 = bnn_layers.ConvReparam(1, 6, 5, stride=1, padding=0, has_bias=False, pad_mode="valid") + self.conv2 = bnn_layers.ConvReparam(6, 16, 5, stride=1, padding=0, has_bias=False, pad_mode="valid") + self.fc1 = bnn_layers.DenseReparam(16 * 5 * 5, 120, activation=nn.ReLU()) + self.fc2 = bnn_layers.DenseReparam(120, 84) + self.fc3 = bnn_layers.DenseReparam(84, self.num_class) + self.relu = nn.ReLU() + self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) + self.flatten = nn.Flatten() + + def construct(self, x): + x = self.conv1(x) + x = self.relu(x) + x = self.max_pool2d(x) + x = self.conv2(x) + x = self.relu(x) + x = self.max_pool2d(x) + x = self.flatten(x) + x = self.fc1(x) + x = self.relu(x) + x = self.fc2(x) + x = self.relu(x) + x = self.fc3(x) + return x + +network = BNNLeNet5() +# 定义损失函数 +criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") +# 定义优化器 +optimizer = nn.AdamWeightDecay(params=network.trainable_params(), learning_rate=0.001) +net_with_loss = bnn_layers.WithBNNLossCell(network, criterion, dnn_factor=60000, bnn_factor=0.00001) +train_bnn_network = TrainOneStepCell(net_with_loss, optimizer) +train_bnn_network.set_train() +``` +### 训练模型 + +``` +def train_model(train_net, net, dataset): + accs = [] + loss_sum = 0 + for i, data in enumerate(dataset.create_dict_iterator()): + train_x = Tensor(data['image'].astype(np.float32)) + label = Tensor(data['label'].astype(np.int32)) + loss = train_net(train_x, label) + output = net(train_x) + log_output = P.LogSoftmax(axis=1)(output) + acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy()) + accs.append(acc) + loss_sum += loss.asnumpy() + + loss_sum = loss_sum / len(accs) + acc_mean = np.mean(accs) + return loss_sum, acc_mean + + +def validate_model(net, dataset): + accs = [] + for _, data in enumerate(dataset.create_dict_iterator()): + train_x = Tensor(data['image'].astype(np.float32)) + label = Tensor(data['label'].astype(np.int32)) + output = net(train_x) + log_output = P.LogSoftmax(axis=1)(output) + acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy()) + accs.append(acc) + + acc_mean = np.mean(accs) + return acc_mean + +train_set = create_dataset('./mnist_data/train', 64, 1) +test_set = create_dataset('./mnist_data/test', 64, 1) + +epoch = 10 + +for i in range(epoch): + train_loss, train_acc = train_model(train_bnn_network, network, train_set) + + valid_acc = validate_model(network, test_set) + + print('Epoch: {} \tTraining Loss: {:.4f} \tTraining Accuracy: {:.4f} \tvalidation Accuracy: {:.4f}'.format( + i, train_loss, train_acc, valid_acc)) +``` + + +``` +Epoch 0 Training Loss 12925.5249 Training Accuracy 0.9413 validation Accuracy 0.9279 +Epoch 1 Training Loss 5287.0440 Training Accuracy 0.9771 validation Accuracy 0.9592 +Epoch 2 Training Loss 4186.9598 Training Accuracy 0.9821 validation Accuracy 0.9786 +Epoch 3 Training Loss 3538.2411 Training Accuracy 0.9857 validation Accuracy 0.9696 +Epoch 4 Training Loss 3255.8431 Training Accuracy 0.9869 validation Accuracy 0.9756 +Epoch 5 Training Loss 2880.1353 Training Accuracy 0.9880 validation Accuracy 0.9792 +Epoch 6 Training Loss 2569.0690 Training Accuracy 0.9897 validation Accuracy 0.9802 +Epoch 7 Training Loss 2391.5061 Training Accuracy 0.9906 validation Accuracy 0.9825 +Epoch 8 Training Loss 2165.1392 Training Accuracy 0.9912 validation Accuracy 0.9794 +Epoch 9 Training Loss 1910.2262 Training Accuracy 0.9931 validation Accuracy 0.9833 +``` + +## 贝叶斯转换：mindspore.nn.probability.transform + +对于不熟悉贝叶斯模型的DNN研究人员，MDP提供了高级API`TransformToBNN`，支持DNN模型一键转换成BNN模型。 + +### 定义DNN模型 + +本例使用的DNN模型是LeNet。 + +``` +from mindspore.common.initializer import TruncatedNormal +import mindspore.nn as nn +import mindspore.ops.operations as P + +def conv(in_channels, out_channels, kernel_size, stride=1, padding=0): + """weight initial for conv layer""" + weight = weight_variable() + return nn.Conv2d(in_channels, out_channels, + kernel_size=kernel_size, stride=stride, padding=padding, + weight_init=weight, has_bias=False, pad_mode="valid") + + +def fc_with_initialize(input_channels, out_channels): + """weight initial for fc layer""" + weight = weight_variable() + bias = weight_variable() + return nn.Dense(input_channels, out_channels, weight, bias) + + +def weight_variable(): + """weight initial""" + return TruncatedNormal(0.02) + + +class LeNet5(nn.Cell): + """ + Lenet network + + Args: + num_class (int): Num classes. Default: 10. + + Returns: + Tensor, output tensor + Examples: + >>> LeNet5(num_class=10) + + """ + def __init__(self, num_class=10): + super(LeNet5, self).__init__() + self.num_class = num_class + self.conv1 = conv(1, 6, 5) + self.conv2 = conv(6, 16, 5) + self.fc1 = fc_with_initialize(16 * 5 * 5, 120) + self.fc2 = fc_with_initialize(120, 84) + self.fc3 = fc_with_initialize(84, self.num_class) + self.relu = nn.ReLU() + self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) + self.flatten = nn.Flatten() + self.reshape = P.Reshape() + + def construct(self, x): + x = self.conv1(x) + x = self.relu(x) + x = self.max_pool2d(x) + x = self.conv2(x) + x = self.relu(x) + x = self.max_pool2d(x) + x = self.flatten(x) + x = self.fc1(x) + x = self.relu(x) + x = self.fc2(x) + x = self.relu(x) + x = self.fc3(x) + return x +``` +LeNet网络结构如下： + +``` +LeNet5 + (conv1) Conv2dinput_channels=1, output_channels=6, kernel_size=(5, 5),stride=(1, 1), pad_mode=valid, padding=0, dilation=(1, 1), group=1, has_bias=False + (conv2) Conv2dinput_channels=6, output_channels=16, kernel_size=(5, 5),stride=(1, 1), pad_mode=valid, padding=0, dilation=(1, 1), group=1, has_bias=False + (fc1) Densein_channels=400, out_channels=120, weight=Parameter (name=fc1.weight), has_bias=True, bias=Parameter (name=fc1.bias) + (fc2) Densein_channels=120, out_channels=84, weight=Parameter (name=fc2.weight), has_bias=True, bias=Parameter (name=fc2.bias) + (fc3) Densein_channels=84, out_channels=10, weight=Parameter (name=fc3.weight), has_bias=True, bias=Parameter (name=fc3.bias) + (relu) ReLU + (max_pool2d) MaxPool2dkernel_size=2, stride=2, pad_mode=VALID + (flatten) Flatten + +``` + +### 定义损失函数和优化器 + +接下来需要定义损失函数（Loss）和优化器（Optimizer）。本例使用交叉熵损失作为损失函数，Adam作为优化器。 + +``` +network = LeNet5() + +# loss function definition +criterion = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") + +# optimization definition +optimizer = AdamWeightDecay(params=network.trainable_params(), learning_rate=0.0001) + +net_with_loss = WithLossCell(network, criterion) +train_network = TrainOneStepCell(net_with_loss, optimizer) +``` +### 实例化TransformToBNN + +`TransformToBNN`的`__init__`函数定义如下： + +``` +class TransformToBNN: + def __init__(self, trainable_dnn, dnn_factor=1, bnn_factor=1): + net_with_loss = trainable_dnn.network + self.optimizer = trainable_dnn.optimizer + self.backbone = net_with_loss.backbone_network + self.loss_fn = getattr(net_with_loss, "_loss_fn") + self.dnn_factor = dnn_factor + self.bnn_factor = bnn_factor + self.bnn_loss_file = None +``` +参数`trainable_bnn`是经过`TrainOneStepCell`包装的可训练DNN模型，`dnn_factor`和`bnn_factor`分别为由损失函数计算得到的网络整体损失的系数和每个贝叶斯层的KL散度的系数。 +MindSpore中实例化`TransformToBNN`的代码如下： + +``` +from mindspore.nn.probability import transforms + +bnn_transformer = transforms.TransformToBNN(train_network, 60000, 0.000001) +``` +#### 功能一：转换整个模型 +`transform_to_bnn_model`方法可以将整个DNN模型转换为BNN模型。其定义如下 + +``` + def transform_to_bnn_model(self, + get_dense_args=lambda dp: {"in_channels": dp.in_channels, "has_bias": dp.has_bias, + "out_channels": dp.out_channels, "activation": dp.activation}, + get_conv_args=lambda dp: {"in_channels": dp.in_channels, "out_channels": dp.out_channels, + "pad_mode": dp.pad_mode, "kernel_size": dp.kernel_size, + "stride": dp.stride, "has_bias": dp.has_bias, + "padding": dp.padding, "dilation": dp.dilation, + "group": dp.group}, + add_dense_args=None, + add_conv_args=None): + r""" + Transform the whole DNN model to BNN model, and wrap BNN model by TrainOneStepCell. + + Args: + get_dense_args (function): The arguments gotten from the DNN full connection layer. Default: lambda dp: + {"in_channels": dp.in_channels, "out_channels": dp.out_channels, "has_bias": dp.has_bias}. + get_conv_args (function): The arguments gotten from the DNN convolutional layer. Default: lambda dp: + {"in_channels": dp.in_channels, "out_channels": dp.out_channels, "pad_mode": dp.pad_mode, + "kernel_size": dp.kernel_size, "stride": dp.stride, "has_bias": dp.has_bias}. + add_dense_args (dict): The new arguments added to BNN full connection layer. Default: {}. + add_conv_args (dict): The new arguments added to BNN convolutional layer. Default: {}. + + Returns: + Cell, a trainable BNN model wrapped by TrainOneStepCell. + """ + +``` +参数`get_dense_args`指定从DNN模型的全连接层中获取哪些参数，`get_conv_args`指定从DNN模型的卷积层中获取哪些参数，参数`add_dense_args`和`add_conv_args`分别指定了要为BNN层指定哪些新的参数值。需要注意的是，`add_dense_args`中的参数不能与`get_dense_args`重复，`add_conv_args`和`get_conv_args`也是如此。 +在MindSpore中将整个DNN模型转换成BNN模型的代码如下： + +``` +train_bnn_network = bnn_transformer.transform_to_bnn_model() +``` +整个模型转换后的结构如下： + +``` +LeNet5 + (conv1) ConvReparam + in_channels=1, out_channels=6, kernel_size=(5, 5), stride=(1, 1), pad_mode=valid, padding=0, dilation=(1, 1), group=1, weight_mean=Parameter (name=conv1.weight_posterior.mean), weight_std=Parameter (name=conv1.weight_posterior.untransformed_std), has_bias=False + (weight_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (weight_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + + (conv2) ConvReparam + in_channels=6, out_channels=16, kernel_size=(5, 5), stride=(1, 1), pad_mode=valid, padding=0, dilation=(1, 1), group=1, weight_mean=Parameter (name=conv2.weight_posterior.mean), weight_std=Parameter (name=conv2.weight_posterior.untransformed_std), has_bias=False + (weight_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (weight_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + + (fc1) DenseReparam + in_channels=400, out_channels=120, weight_mean=Parameter (name=fc1.weight_posterior.mean), weight_std=Parameter (name=fc1.weight_posterior.untransformed_std), has_bias=True, bias_mean=Parameter (name=fc1.bias_posterior.mean), bias_std=Parameter (name=fc1.bias_posterior.untransformed_std) + (weight_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (weight_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + (bias_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (bias_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + + (fc2) DenseReparam + in_channels=120, out_channels=84, weight_mean=Parameter (name=fc2.weight_posterior.mean), weight_std=Parameter (name=fc2.weight_posterior.untransformed_std), has_bias=True, bias_mean=Parameter (name=fc2.bias_posterior.mean), bias_std=Parameter (name=fc2.bias_posterior.untransformed_std) + (weight_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (weight_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + (bias_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (bias_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + + (fc3) DenseReparam + in_channels=84, out_channels=10, weight_mean=Parameter (name=fc3.weight_posterior.mean), weight_std=Parameter (name=fc3.weight_posterior.untransformed_std), has_bias=True, bias_mean=Parameter (name=fc3.bias_posterior.mean), bias_std=Parameter (name=fc3.bias_posterior.untransformed_std) + (weight_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (weight_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + (bias_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (bias_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + + (relu) ReLU + (max_pool2d) MaxPool2dkernel_size=2, stride=2, pad_mode=VALID + (flatten) Flatten + +``` + +#### 功能二：转换指定类型的层 +`transform_to_bnn_layer`方法可以将DNN模型中指定类型的层（`nn.Dense`或者`nn.Conv2d`）转换为对应的贝叶斯层。其定义如下 + +``` + def transform_to_bnn_layer(self, dnn_layer, bnn_layer, get_args=None, add_args=None): + r""" + Transform a specific type of layers in DNN model to corresponding BNN layer. + + Args: + dnn_layer_type (Cell): The type of DNN layer to be transformed to BNN layer. The optional values are + nn.Dense, nn.Conv2d. + bnn_layer_type (Cell): The type of BNN layer to be transformed to. The optional values are + DenseReparameterization, ConvReparameterization. + get_args (dict): The arguments gotten from the DNN layer. Default: None. + add_args (dict): The new arguments added to BNN layer. Default: None. + + Returns: + Cell, a trainable model wrapped by TrainOneStepCell, whose sprcific type of layer is transformed to the corresponding bayesian layer. + """ +``` +参数`dnn_layer`指定将哪个类型的DNN层转换成BNN层，`bnn_layer`指定DNN层将转换成哪个类型的BNN层，`get_args`和`add_args`分别指定从DNN层中获取哪些参数和要为BNN层的哪些参数重新赋值。 +在MindSpore中将DNN模型中的Dense层转换成相应贝叶斯层`DenseReparam`的代码如下： + +``` +train_bnn_network = bnn_transformer.transform_to_bnn_layer(nn.Dense, bnn_layers.DenseReparam) +``` +转换后的结构如下： + +``` +LeNet5 + (conv1) Conv2dinput_channels=1, output_channels=6, kernel_size=(5, 5),stride=(1, 1), pad_mode=valid, padding=0, dilation=(1, 1), group=1, has_bias=False + (conv2) Conv2dinput_channels=6, output_channels=16, kernel_size=(5, 5),stride=(1, 1), pad_mode=valid, padding=0, dilation=(1, 1), group=1, has_bias=False + (fc1) DenseReparam + in_channels=400, out_channels=120, weight_mean=Parameter (name=fc1.weight_posterior.mean), weight_std=Parameter (name=fc1.weight_posterior.untransformed_std), has_bias=True, bias_mean=Parameter (name=fc1.bias_posterior.mean), bias_std=Parameter (name=fc1.bias_posterior.untransformed_std) + (weight_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (weight_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + (bias_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (bias_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + + (fc2) DenseReparam + in_channels=120, out_channels=84, weight_mean=Parameter (name=fc2.weight_posterior.mean), weight_std=Parameter (name=fc2.weight_posterior.untransformed_std), has_bias=True, bias_mean=Parameter (name=fc2.bias_posterior.mean), bias_std=Parameter (name=fc2.bias_posterior.untransformed_std) + (weight_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (weight_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + (bias_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (bias_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + + (fc3) DenseReparam + in_channels=84, out_channels=10, weight_mean=Parameter (name=fc3.weight_posterior.mean), weight_std=Parameter (name=fc3.weight_posterior.untransformed_std), has_bias=True, bias_mean=Parameter (name=fc3.bias_posterior.mean), bias_std=Parameter (name=fc3.bias_posterior.untransformed_std) + (weight_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (weight_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + (bias_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (bias_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + + (relu) ReLU + (max_pool2d) MaxPool2dkernel_size=2, stride=2, pad_mode=VALID + (flatten) Flatten + +``` + +## 贝叶斯工具箱：mindspore.nn.probability.toolbox + +贝叶斯神经网络的优势之一就是可以获取不确定性，MDP在上层提供了不确定性估计的工具箱，用户可以很方便地使用该工具箱计算不确定性。不确定性意味着深度学习模型不知道什么。目前，大多数深度学习算法只能给出高置信度的预测结果，而不能判断预测结果的确定性，不确定性主要有两种类型：偶然不确定性和认知不确定性。 + +- 偶然不确定性（Aleatoric Uncertainty）：描述数据中的内在噪声，即无法避免的误差，这个现象不能通过增加采样数据来削弱。 +- 认知不确定性（Epistemic Uncertainty）：模型自身对输入数据的估计可能因为训练不佳、训练数据不够等原因而不准确，可以通过增加训练数据等方式来缓解。 + +不确定性评估工具箱的接口如下： +- `model`: 待评估不确定性的已训练好的模型。 +- `train_dataset`: 用于训练的数据集，迭代器类型。 +- `task_type`: 模型的类型，字符串，输入“regression”或者“classification”。 +- `num_classes`: 如果是分类模型，需要指定类别的标签数量。 +- `epochs`: 用于训练不确定模型的迭代数。 +- `epi_uncer_model_path`: 用于存储或加载计算认知不确定性的模型的路径。 +- `ale_uncer_model_path`: 用于存储或加载计算偶然不确定性的模型的路径。 +- `save_model`: 布尔类型，是否需要存储模型。 + +在使用前，需要先训练好模型，以LeNet5为例，使用方式如下： +``` +from mindspore.nn.probability.toolbox.uncertainty_evaluation import UncertaintyEvaluation +from mindspore.train.serialization import load_checkpoint, load_param_into_net + +if __name__ == '__main__': + # get trained model + network = LeNet5() + param_dict = load_checkpoint('checkpoint_lenet.ckpt') + load_param_into_net(network, param_dict) + # get train and eval dataset + ds_train = create_dataset('workspace/mnist/train') + ds_eval = create_dataset('workspace/mnist/test') + evaluation = UncertaintyEvaluation(model=network, + train_dataset=ds_train, + task_type='classification', + num_classes=10, + epochs=1, + epi_uncer_model_path=None, + ale_uncer_model_path=None, + save_model=False) + for eval_data in ds_eval.create_dict_iterator(): + eval_data = Tensor(eval_data['image'], mstype.float32) + epistemic_uncertainty = evaluation.eval_epistemic_uncertainty(eval_data) + aleatoric_uncertainty = evaluation.eval_aleatoric_uncertainty(eval_data) + print('The shape of epistemic uncertainty is ', epistemic_uncertainty.shape) + print('The shape of epistemic uncertainty is ', aleatoric_uncertainty.shape) +``` +`eval_epistemic_uncertainty`计算的是认知不确定性，也叫模型不确定性，对于每一个样本的每个预测标签都会有一个不确定值；`eval_aleatoric_uncertainty`计算的是偶然不确定性，也叫数据不确定性，对于每一个样本都会有一个不确定值。 +所以输出为： + +``` +The shape of epistemic uncertainty is (32, 10) +The shape of epistemic uncertainty is (32,) +``` \ No newline at end of file -- Gitee From 3be8eefb0b7a32e6354fef07006aacbe1127a593 Mon Sep 17 00:00:00 2001 From: huzhifeng Date: Mon, 14 Sep 2020 15:53:54 +0800 Subject: [PATCH 013/156] remove mindspore.hub interface docs --- .../api/python/mindspore/mindspore.hub.rst | 4 ---- api/source_en/index.rst | 1 - .../api/python/mindspore/mindspore.hub.rst | 4 ---- api/source_zh_cn/index.rst | 1 - tutorials/source_en/use/multi_platform_inference.md | 11 +++++------ .../source_zh_cn/use/multi_platform_inference.md | 13 ++++++------- 6 files changed, 11 insertions(+), 23 deletions(-) delete mode 100644 api/source_en/api/python/mindspore/mindspore.hub.rst delete mode 100644 api/source_zh_cn/api/python/mindspore/mindspore.hub.rst diff --git a/api/source_en/api/python/mindspore/mindspore.hub.rst b/api/source_en/api/python/mindspore/mindspore.hub.rst deleted file mode 100644 index 458c704fc3..0000000000 --- a/api/source_en/api/python/mindspore/mindspore.hub.rst +++ /dev/null @@ -1,4 +0,0 @@ -mindspore.hub -============= - -.. autofunction:: mindspore.hub.load_weights diff --git a/api/source_en/index.rst b/api/source_en/index.rst index 12f19eaff2..ebaf340a02 100644 --- a/api/source_en/index.rst +++ b/api/source_en/index.rst @@ -15,7 +15,6 @@ MindSpore API api/python/mindspore/mindspore.common.initializer api/python/mindspore/mindspore.communication api/python/mindspore/mindspore.context - api/python/mindspore/mindspore.hub api/python/mindspore/mindspore.nn api/python/mindspore/mindspore.nn.dynamic_lr api/python/mindspore/mindspore.nn.learning_rate_schedule diff --git a/api/source_zh_cn/api/python/mindspore/mindspore.hub.rst b/api/source_zh_cn/api/python/mindspore/mindspore.hub.rst deleted file mode 100644 index 458c704fc3..0000000000 --- a/api/source_zh_cn/api/python/mindspore/mindspore.hub.rst +++ /dev/null @@ -1,4 +0,0 @@ -mindspore.hub -============= - -.. autofunction:: mindspore.hub.load_weights diff --git a/api/source_zh_cn/index.rst b/api/source_zh_cn/index.rst index 502e8495e2..0c669503f6 100644 --- a/api/source_zh_cn/index.rst +++ b/api/source_zh_cn/index.rst @@ -21,7 +21,6 @@ MindSpore API api/python/mindspore/mindspore.common.initializer api/python/mindspore/mindspore.communication api/python/mindspore/mindspore.context - api/python/mindspore/mindspore.hub api/python/mindspore/mindspore.nn api/python/mindspore/mindspore.nn.dynamic_lr api/python/mindspore/mindspore.nn.learning_rate_schedule diff --git a/tutorials/source_en/use/multi_platform_inference.md b/tutorials/source_en/use/multi_platform_inference.md index 15b2ce276e..28a9c4f887 100644 --- a/tutorials/source_en/use/multi_platform_inference.md +++ b/tutorials/source_en/use/multi_platform_inference.md @@ -80,19 +80,18 @@ MindSpore supports the following inference scenarios based on the hardware platf `model.eval` is an API for model validation. For details about the API, see . > Inference sample code: . - 1.2 Remote Storage + 1.2 Load from MindSpore Hub - When the pre-trained models are saved remotely, the steps of performing inference on validation dataset are as follows: firstly creating a model, then loading model and parameters using `hub.load_weights`, and finally performing inference on validation dataset once created. The processing method of the validation dataset is the same as that of the training dataset. + When the models are saved in MindSpore Hub, the steps of performing inference on validation dataset are as follows: firstly determine which model to be used, then loading model and parameters using `mindspore_hub.load`, and finally performing inference on validation dataset once created. The processing method of the validation dataset is the same as that of the training dataset. ```python - network = LeNet5(cfg.num_classes) + model_uid = "mindspore/ascend/0.7/googlenet_v1_cifar10" # using GoogleNet as an example. + network = mindspore_hub.load(model_uid, num_classes=10) net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) print("============== Starting Testing ==============") - hub.load_weights(network, network_name="lenet", **{"device_target": - "ascend", "dataset":"mnist", "version": "0.5.0"}) dataset = create_dataset(os.path.join(args.data_path, "test"), cfg.batch_size, 1) @@ -101,7 +100,7 @@ MindSpore supports the following inference scenarios based on the hardware platf ``` In the preceding information: - `hub.load_weights` is an API for loading model parameters. PLease check the details in . + `mindspore_hub.load` is an API for loading model parameters. PLease check the details in . 2. Use the `model.predict` API to perform inference. ```python diff --git a/tutorials/source_zh_cn/use/multi_platform_inference.md b/tutorials/source_zh_cn/use/multi_platform_inference.md index 5628bdc5c1..7aaac69064 100644 --- a/tutorials/source_zh_cn/use/multi_platform_inference.md +++ b/tutorials/source_zh_cn/use/multi_platform_inference.md @@ -79,18 +79,17 @@ CPU | ONNX格式 | 支持ONNX推理的runtime/SDK，如TensorRT。 `model.eval`为模型验证接口，对应接口说明：。 > 推理样例代码：。 - 1.2 模型保存在华为云 + 1.2 使用MindSpore Hub从华为云加载模型 - 首先构建模型，然后使用`hub.load_weights`从云端加载模型参数，传入验证数据集后即可进行推理，验证数据集的处理方式与训练数据集相同。 + 首先构建模型，然后使用`mindspore_hub.load`从云端加载模型参数，传入验证数据集后即可进行推理，验证数据集的处理方式与训练数据集相同。 ```python - network = LeNet5(cfg.num_classes) + model_uid = "mindspore/ascend/0.7/googlenet_v1_cifar10" # using GoogleNet as an example. + network = mindspore_hub.load(model_uid, num_classes=10) net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) print("============== Starting Testing ==============") - hub.load_weights(network, network_name="lenet", **{"device_target": - "ascend", "dataset":"mnist", "version": "0.5.0"}) dataset = create_dataset(os.path.join(args.data_path, "test"), cfg.batch_size, 1) @@ -98,8 +97,8 @@ CPU | ONNX格式 | 支持ONNX推理的runtime/SDK，如TensorRT。 print("============== {} ==============".format(acc)) ``` 其中， - `hub.load_weights`为加载模型参数接口，对应接口说明：。 - + `mindspore_hub.load`为加载模型参数接口，对应接口说明：。 + 2. 使用`model.predict`接口来进行推理操作。 ```python model.predict(input_data) -- Gitee From c9b252bd1e1e69d779837a6dee5121215ad0292a Mon Sep 17 00:00:00 2001 From: Ziyan Date: Fri, 11 Sep 2020 09:37:56 +0800 Subject: [PATCH 014/156] optimizer distributed training design --- .../mindspore/distributed_training_design.md | 144 ++++++++++++++++++ .../design/mindspore/images/auto_parallel.png | Bin 0 -> 56566 bytes .../design/mindspore/images/data_parallel.png | Bin 0 -> 57944 bytes .../mindspore/images/operator_split.png | Bin 0 -> 31956 bytes .../images/tensor_redistribution.png | Bin .../images/tensor_redistribution1.png | Bin 0 -> 54984 bytes .../images/tensor_redistribution2.png | Bin 0 -> 59677 bytes .../images/tensor_redistribution3.png | Bin 0 -> 54617 bytes .../mindspore/distributed_training_design.md | 8 +- .../design/mindspore/images/data_parallel.png | Bin 14300 -> 57944 bytes .../images/tensor_redistribution1.png | Bin 62482 -> 54984 bytes .../images/tensor_redistribution2.png | Bin 65607 -> 59677 bytes .../images/tensor_redistribution3.png | Bin 53765 -> 54617 bytes 13 files changed, 148 insertions(+), 4 deletions(-) create mode 100644 docs/source_en/design/mindspore/distributed_training_design.md create mode 100644 docs/source_en/design/mindspore/images/auto_parallel.png create mode 100644 docs/source_en/design/mindspore/images/data_parallel.png create mode 100644 docs/source_en/design/mindspore/images/operator_split.png rename docs/{source_zh_cn => source_en}/design/mindspore/images/tensor_redistribution.png (100%) create mode 100644 docs/source_en/design/mindspore/images/tensor_redistribution1.png create mode 100644 docs/source_en/design/mindspore/images/tensor_redistribution2.png create mode 100644 docs/source_en/design/mindspore/images/tensor_redistribution3.png diff --git a/docs/source_en/design/mindspore/distributed_training_design.md b/docs/source_en/design/mindspore/distributed_training_design.md new file mode 100644 index 0000000000..d0d6b536a1 --- /dev/null +++ b/docs/source_en/design/mindspore/distributed_training_design.md @@ -0,0 +1,144 @@ +# Distributed Training Design + +`Ascend` `GPU` `Model Development` `Model Optimization` `Framework Development` `Intermediate` `Expert` `Contributor` + + + +- [Distributed Training Design](#distributed-training-design) + - [Background](#background) + - [Concepts](#concepts) + - [Collective Communication](#collective-communication) + - [Synchronization Mode](#synchronization-mode) + - [Data Parallelism](#data-parallelism) + - [Principle of Data Parallelism](#principle-of-data-parallelism) + - [Data Parallel Code](#data-parallel-code) + - [Automatic Parallelism](#automatic-parallelism) + - [Principle of Automatic Parallelism](#principle-of-automatic-parallelism) + - [Automatic Parallel Code](#automatic-parallel-code) + + + + + +## Background + +With the rapid development of deep learning, the number of datasets and parameters are growing exponentially to improve the accuracy and generalization capability of neural networks. Parallel distributed training has become a development trend to resolve the performance bottleneck of ultra-large scale networks. MindSpore supports the mainstream distributed training paradigm and develops an automatic hybrid parallel solution. The following describes the design principles of several parallel training modes and provides guidance for users to perform custom development. + + +## Concepts + +### Collective Communication + +Collective communication is defined as communication that involves a group of processes. All processes in the group send and receive data after meeting certain conditions. MindSpore implements data transmission during parallel training through collective communication. On Ascend chips, MindSpore depends on the Huawei Collective Communication Library (`HCCL`) to implement the task. On GPU, MindSpore depends on the NVIDIA Collective Communication Library (`NCCL`) to implement the task. + +### Synchronization Mode + +In synchronous mode, all devices strart training at the same time and update parameter values synchronously after the backward propagation algorithm is executed. Currently, MindSpore uses the synchronous training mode. + +## Data Parallelism + +This section describes how the data parallel mode `ParallelMode.DATA_PARALLEL` works in MindSpore. + +### Principle of Data Parallelism + +![Data Parallel Description](./images/data_parallel.png) + +1. Environment dependencies + + Each time before parallel training starts, the `mindspore.communication.init` API is called to initialize communication resources and the global communication group `WORLD_COMM_GROUP` is automatically created. + +2. Data distribution + + The key of data parallelism is to split datasets based on the sample dimension and deliver the split datasets to different devices. Each dataset loading API provided by the `mindspore.dataset` module has the `num_shards` and `shard_id` parameters. The parameters are used to split a dataset into multiple datasets, perform cyclic sampling, and collect data of the `batch` size to each device. When the data volume is insufficient, the sampling restarts from the beginning. + +3. Network structure + + The scripting method of data parallel network is the same as that of standalone network. This is because, although models of each device are executed independently during the forward and backward propagation processes, the same network structure is maintained. To ensure the synchronous training between devices, the initial values of corresponding network parameters must be the same. You are advised to set the same random number seed on each device by using `numpy.random.seed` to broadcast models. + +4. Gradient aggregation + + Theoretically, the training effect of data parallel network should be the same as that of the standalone network. To ensure the consistency of the calculation logic, the `AllReduce` operator is inserted after gradient calculation to implement the gradient aggregation operation between devices. You can enable `mean` to average the sum of gradient values, or regard `mean` as a hyperparameter. Enabling `mean` is equivalent to reducing the learning rate by multiple times. + +5. Parameter update + + Because the gradient aggregation operation is introduced, the models of each device perform parameter update with the same gradient value. Therefore, MindSpore implements a synchronous data parallel training mode. Theoretically, models trained by each device are the same. If the reduce operation on samples is involved on the network, the network output may be different. This is determined by the sharding attribute of data parallelism. + +### Data Parallel Code + +1. Collective communication + + - [management.py](https://gitee.com/mindspore/mindspore/blob/master/mindspore/communication/management.py): This file covers the `helper` function APIs commonly used during the collective communication process, for example, the APIs for obtaining the number of clusters and device ID. When collective communication is executed on the Ascend chip, the framework loads the `libhccl.so` library file in the environment and uses it to call the communication APIs from the Python layer to the underlying layer. + - [comm_ops.py](https://gitee.com/mindspore/mindspore/blob/master/mindspore/ops/operations/comm_ops.py): MindSpore encapsulates supported collective communication operations as operators and stores the operators in this file. The operators include `AllReduce`, `AllGather`, `ReduceScatter`, and `Broadcast`. `PrimitiveWithInfer` defines the attributes required by the operators, as well as the `shape` and `dtype` inference methods from the input to the output during graph composition. + +2. Gradient aggregation + + - [grad_reducer.py](https://gitee.com/mindspore/mindspore/blob/master/mindspore/nn/wrap/grad_reducer.py): This file implements the gradient aggregation process. After the input parameter `grads` is expanded by using `HyperMap`, the `AllReduce` operator is inserted. The global communication group is used. You can also perform custom development by referring to this section based on your network requirements. In MindSpore, standalone and distributed execution shares a set of network encapsulation APIs. In the `Cell`, `ParallelMode` is used to determine whether to perform gradient aggregation. For details about the network encapsulation APIs, see the `TrainOneStepCell` code implementation. + + +## Automatic Parallelism + +As a key feature of MindSpore, automatic parallelism is used to implement hybrid parallel training that combines automatic data parallelism and model parallelism. It aims to help users express the parallel algorithm logic using standalone scripts, reduce the difficulty of distributed training, improve the algorithm R&D efficiency, and maintain the high performance of training. This section describes how the automatic parallel mode `ParallelMode.AUTO_PARALLEL` and semi-automatic parallel mode `ParallelMode.SEMI_AUTO_PARALLEL` work in MindSpore. + +### Principle of Automatic Parallelism + +![Automatic Parallel Description](./images/auto_parallel.png) + +1. Distributed operator and tensor layout + + As shown in the preceding figure, the automatic parallel process traverses the standalone forward ANF graphs and performs shard modeling on tensors in the unit of distributed operator, indicating how the input and output tensors of an operator are distributed to each device of the cluster, that is, the tensor layout. Users do not need to know which device runs which slice of a model. The framework automatically schedules and allocates model slices. + + To obtain the tensor layout model, each operator has a shard strategy, which indicates the shard status of each input of the operator in the corresponding dimension. Generally, tensors can be sharded in any dimension as long as the value is a multiple of 2, and the even distribution principle is met. The following figure shows an example of the three-dimensional `BatchMatmul` operation. The parallel strategy consists of two tuples, indicating the sharding of `input` and `weight`, respectively. Elements in a tuple correspond to tensor dimensions one by one. `2^N` indicates the shard unit, and `1` indicates that the tuple is not sharded. If you want to express a parallel data shard strategy, that is, only data in the `batch` dimension of `input` is sharded, and data in other dimensions are not sharded, you can use `strategy=((2^N, 1, 1),(1, 1, 1))`. If you want to express a parallel model shard strategy, that is, only model in the non-`batch` dimension of `weight` is sharded, for example, only the `channel` dimension is sharded, you can use `strategy=((1, 1, 1),(1, 1, 2^N))`. If you want to express a hybrid parallel shard strategy, one of which is `strategy=((2^N, 1, 1),(1, 1, 2^N))`. + + ![Operator Sharding Definition](./images/operator_split.png) + + Based on the shard strategy of an operator, the framework automatically derives the distribution model of input tensors and output tensors of the operator. This distribution model consists of `device_matrix`, `tensor_shape`, and `tensor map`, which indicate the device matrix shape, tensor shape, and mapping between devices and tensor dimensions, respectively. Based on the tensor layout model, distributed operator determines whether to insert extra computation and communication operations in the graph to ensure that the operator computing logic is correct. + +2. Tensor Redistribution + + When the output tensor model of an operator is inconsistent with the input tensor model of the next operator, computation and communication operations need to be introduced to implement the change between tensor layouts. The automatic parallel process introduces the tensor redistribution algorithm, which can be used to derive the communication conversion operations between random tensor layouts. The following three examples represent a parallel computing process of the formula `Z=(X×W)×V`, that is, a `MatMul` operation of two two-dimensional matrices, and show how to perform conversion between different parallel modes. + + In example 1, the output of the first data parallel matrix multiplication is sharded in the row rection, and the input of the second model parallel matrix multiplication requires full tensors. The framework automatically inserts the `AllGather` operator to implement redistribution. + + ![Tensor Redistribution](./images/tensor_redistribution1.png) + + In example 2, the output of parallel matrix multiplication of the first model is sharded in the column direction, and the input of parallel matrix multiplication of the second model is sharded in the row direction. The framework automatically inserts a communication operator equivalent to the `AlltoAll` operation in collective communication to implement redistribution. + + ![Tensor Redistribution](./images/tensor_redistribution2.png) + + In example 3, an output shard mode of the first hybrid parallel matrix multiplication is the same as an input shard mode of the second hybrid parallel matrix multiplication. Therefore, redistribution does not need to be introduced. In the second matrix multiplication operation, the related dimensions of the two inputs are sharded. Therefore, the `AllReduce` operator needs to be inserted to ensure the operation correctness. + + ![Tensor Redistribution](./images/tensor_redistribution3.png) + + In general, this distributed representation breaks the boundary between data parallelism and model parallelism, making it easy to implement hybrid parallelism. From the perspective of scripts, users only need to construct a standalone network to express the parallel algorithm logic. Framework automatically shards the entire graph. + +3. Efficient parallel strategy search algorithm + + The `SEMI_AUTO_PARALLEL` semi-automatic parallel mode indicates that you manually configure the parallel strategy for operators when you are familiar with the operator sharding representation. This mode is helpful for manual optimization, with certain commissioning difficulty. You need to master the parallel principle and obtain a high-performance parallel solution based on the network structure and cluster topology. To further help users accelerate the parallel network training process, the automatic parallel mode `AUTO_PARALLEL` introduces the automatic search feature of the parallel strategy on the basis of the semi-automatic parallel mode. Automatic parallelism builds cost models based on the hardware platform, and calculates the computation cost, memory cost, and communication cost of a certain amount of data and specific operators based on different parallel strategies Then, by using the dynamic programming algorithm or recursive programming algorithm and taking the memory upper limit of a single device as a constraint condition, a parallel strategy with optimal performance is efficiently searched out. + + Strategy search replaces manual model sharding and provides a high-performance sharding solution within a short period of time, greatly reducing the threshold for parallel training. + + +4. Convenient distributed automatic differentiation + + In addition to forward network communication, the traditional manual model sharding needs to consider backward parallel computing. MindSpore encapsulates communication operations into operators and automatically generates backward propagation of communication operators based on the original automatic differentiation operations of the framework. Therefore, even during distributed training, users only need to pay attention to the forward propagation of the network to implement actual automatic parallel training. + +### Automatic Parallel Code + +1. Tensor layout model + - [tensor_layout](https://gitee.com/mindspore/mindspore/tree/master/mindspore/ccsrc/frontend/parallel/tensor_layout): This directory contains the definitions and implementation of functions related to the tensor distribution model. `tensor_layout.h` declares the member variables `tensor_map_origin_`, `tensor_shape_`, and `device_arrangement_` required by a tensor distribution model. In `tensor_redistribution.h`, the related methods for implementing the `from_origin_` and `to_origin_` transformation between tensor distributions are declared. The deduced redistribution operation is stored in `operator_list_` and returned, in addition, the communication cost `comm_cost_`,, memory cost `memory_cost_`, and calculation cost `computation_cost_` required for redistribution are calculated. + +2. Distributed operators + - [ops_info](https://gitee.com/mindspore/mindspore/tree/master/mindspore/ccsrc/frontend/parallel/ops_info): This directory contains the implementation of distributed operators. In `operator_info.h`, the base class `OperatorInfo` of distributed operator implementation is defined. A distributed operator to be developed shall inherit the base class and explicitly implement related imaginary functions. The `InferTensorInfo`, `InferTensorMap`, and `InferDevMatrixShape` functions define the algorithms for deriving the input and output tensor distribution model of the operator. The `InferForwardCommunication` and `InferMirrorOps` functions define the extra calculation and communication operations to be inserted for operator sharding. The `CheckStrategy` and `GenerateStrategies` functions define the parallel strategy validation and generation for the operator. According to the parallel strategy `SetCostUnderStrategy`, the parallel cost `operator_cost_` of the distributed operator is generated. + +3. Strategy search algorithm + - [auto_parallel](https://gitee.com/mindspore/mindspore/tree/master/mindspore/ccsrc/frontend/parallel/auto_parallel): The shard strategy search algorithm is implemented in this directory. `graph_costmodel.h` defines the graph composition information. Each point indicates an operator `OperatorInfo`. The directed edge `edge_costmodel.h` indicates the input and output relationship of operators and the redistribution cost. `operator_costmodel.h` defines the cost model of each operator, including the calculation cost, communication cost, and memory cost. `dp_algorithm_costmodel.h` describes the main process of the dynamic planning algorithm, which consists of a series of graph operations. `costmodel.h` defines the data structures of cost and graph operations. + +4. Device management + - [device_manager.h](https://gitee.com/mindspore/mindspore/blob/master/mindspore/ccsrc/frontend/parallel/device_manager.h): This file is used to create and manage cluster device communication groups. The device matrix model is defined by `device_matrix.h`, and the communication domain is managed by `group_manager.h`. + +5. Entire graph sharding + - [step_auto_parallel.h](https://gitee.com/mindspore/mindspore/blob/master/mindspore/ccsrc/frontend/parallel/step_auto_parallel.h), and [step_parallel.h](https://gitee.com/mindspore/mindspore/blob/master/mindspore/ccsrc/frontend/parallel/step_parallel.h): The two files contain the core implementation of the automatic parallel process. `step_auto_parallel.h` calls the strategy search process and generates the `OperatorInfo` of the distributed operator. Then in `step_parallel.h`, processes such as operator sharding and tensor redistribution are processed to reconstruct the standalone computing graph in distributed mode. + + +6. Backward propagation of communication operators + - [grad_comm_ops.py](https://gitee.com/mindspore/mindspore/blob/master/mindspore/ops/_grad/grad_comm_ops.py): This file defines the backward propagation of communication operators, such as `AllReduce` and `AllGather`. diff --git a/docs/source_en/design/mindspore/images/auto_parallel.png b/docs/source_en/design/mindspore/images/auto_parallel.png new file mode 100644 index 0000000000000000000000000000000000000000..800b3b2536c739dcc48a1e46b5f65fc327e4ce8d GIT binary patch literal 56566 zcmbrmWmp{R+WkpzcY?dSySuvwcZc8(0fM``ySrO(Z`>`oOK@jM_TKL~=l`C$X0Dn3 z!d1o7UD91o-D|DiU7-qc;&9Md&_F;yaFP-tN zWt?WZYN!mY{O~nHPokp8`zEL;pwb^js}`YSUEAyyiN(6-gN4ExDp{pF8^K8CA!{hZ z;u61hFA?qU=QRtOF44|ddkdj9y9)AHlIjzG%ZXRI0wf6kAb77YFqLMtShSsF=eg!4L)t8>8pmS3Qjc3 zy1H|Z@#5Ck^oWo-9b6bs66c+FRQJ6N1eCEEmimh}IJWf7Lv0MJTvRc&Z zfN#uan0_qasdgRDm=@$UI{awzy311%3+AE)$xOt(Yeg#;&MXo%Fbi%pet7qHNcD(G zSXf#v9i8`-+gelg8s|*Sd(DP5VFh+~Z)4vR_N#M2IZbc;QTy(cm9CH-;T=46cw>I& 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For the following figure, the output of the object detection model is shown in the following table. The rectangular box is used to identify the position of the object in the graph and the probability of the object category is marked. The four numbers in the coordinates are Xmin, Ymin, Xmax, Ymax; the probability represents the probility of the detected object. + +![object_detectiontion](images/object_detection.png) + +| Category | Probability | Coordinate | +| -------- | ----------- | ---------------- | +| mouse | 0.78 | [10, 25, 35, 43] | + +Using MindSpore Lite to implement object detection [example](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/lite/object_detection). + +## Object detection model list + +The following table shows the data of some object detection models using MindSpore Lite inference. + +> The performance of the table below is tested on the mate30. + +| model name | link | size | precision | CPU 4 thread delay | +|-----------------------|----------|----------|----------|-----------| +| SSD | | | | | +| Faster_RCNN | | | | | +| Yolov3_Darknet | | | | | +| Mask_RCNN | | | | | + diff --git a/lite/docs/source_zh_cn/image_classification.md b/lite/docs/source_zh_cn/image_classification.md new file mode 100644 index 0000000000..147ce0f851 --- /dev/null +++ b/lite/docs/source_zh_cn/image_classification.md @@ -0,0 +1,33 @@ +# 图像分类 + + + +## 图像分类介绍 + +图像分类模型可以预测图片中出现哪些物体，识别出图片中出现物体列表及其概率。 比如下图经过模型推理的分类结果为下表： + +![image_classification](images/image_classification_result.png) + +| 类别 | 概率 | +| ---------- | ------ | +| plant | 0.9359 | +| flower | 0.8641 | +| tree | 0.8584 | +| houseplant | 0.7867 | + +使用MindSpore Lite实现图像分类的[示例代码](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/lite/image_classification)。 + +## 图像分类模型列表 + +下表是使用MindSpore Lite推理的部分图像分类模型的数据。 + +> 下表的性能是在mate30手机上测试的。 + +| 模型名称 | 模型链接 | 大小 | 精度 | CPU 4线程时延 | +|-----------------------|----------|----------|----------|-----------| +| MobileNetV2 | | | | | +| LeNet | | | | | +| AlexNet | | | | | +| GoogleNet | | | | | +| ResNext50 | | | | | + diff --git a/lite/docs/source_zh_cn/images/image_classification_result.png 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+![image_classification](images/object_detection.png) + +| 类别 | 概率 | 坐标 | +| ----- | ---- | ---------------- | +| mouse | 0.78 | [10, 25, 35, 43] | + +使用MindSpore Lite实现对象检测的[示例代码](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/lite/object_detection)。 + +## 对象检测模型列表 + +下表是使用MindSpore Lite推理的部分对象检测模型的数据。 + +> 下表的性能是在mate30手机上测试的。 + +| 模型名称 | 模型链接 | 大小 | 精度 | CPU 4线程时延 | +|-----------------------|----------|----------|----------|-----------| +| SSD | | | | | +| Faster_RCNN | | | | | +| YoloV3_Darknet53 | | | | | +| Mask_RCNN | | | | | + -- Gitee From 11dc2c204beba69d156ab6682f81d5a9fa69b632 Mon Sep 17 00:00:00 2001 From: Ting Wang Date: Mon, 14 Sep 2020 20:25:33 +0800 Subject: [PATCH 016/156] optimize programming guide Signed-off-by: Ting Wang --- api/source_zh_cn/index.rst | 2 + api/source_zh_cn/programming_guide/cell.md | 154 +++++----- .../programming_guide/compute_component.rst | 8 + .../programming_guide/data_type.rst | 8 + .../programming_guide/{type.md => dtype.md} | 29 +- .../{component.md => network_component.md} | 29 +- .../programming_guide/operator.md | 278 +++++++++++------- .../programming_guide/parameter.md | 23 +- api/source_zh_cn/programming_guide/tensor.md | 20 +- 9 files changed, 316 insertions(+), 235 deletions(-) create mode 100644 api/source_zh_cn/programming_guide/compute_component.rst create mode 100644 api/source_zh_cn/programming_guide/data_type.rst rename api/source_zh_cn/programming_guide/{type.md => dtype.md} (32%) rename api/source_zh_cn/programming_guide/{component.md => network_component.md} (80%) diff --git a/api/source_zh_cn/index.rst b/api/source_zh_cn/index.rst index 0c669503f6..871c4d0f6a 100644 --- a/api/source_zh_cn/index.rst +++ b/api/source_zh_cn/index.rst @@ -11,6 +11,8 @@ MindSpore API :caption: 编程指南 programming_guide/api_structure + programming_guide/data_type + programming_guide/compute_component .. toctree:: :maxdepth: 1 diff --git a/api/source_zh_cn/programming_guide/cell.md b/api/source_zh_cn/programming_guide/cell.md index 698e7f5995..47d0aaebb9 100644 --- a/api/source_zh_cn/programming_guide/cell.md +++ b/api/source_zh_cn/programming_guide/cell.md @@ -1,33 +1,41 @@ -# cell模块概述 +# Cell -- [cell模块概述](#cell模块概述) +- [Cell](#cell) - [概念用途](#概念用途) - [关键成员函数](#关键成员函数) + - [construct方法](#construct方法) + - [parameters_dict](#parameters_dict) + - [cells_and_names](#cells_and_names) - [模型层](#模型层) + - [内置模型层](#内置模型层) + - [应用实例](#应用实例) - [损失函数](#损失函数) - - [网络构造](#Cell构造自定义网络) + - [内置损失函数](#内置损失函数) + - [应用实例](#应用实例-1) + - [构建自定义网络](#构建自定义网络) + + ## 概念用途 -MindSpose的Cell类是构建所有网络的基类，也是网络的基本单元。当用户需要自定义网络时，需要继承Cell类，并重写__init_方法和contruct方法。 +MindSpore的`Cell`类是构建所有网络的基类，也是网络的基本单元。当用户需要自定义网络时，需要继承Cell类，并重写`__init_`方法和`contruct`方法。 -损失函数，优化器和模型层等本质上也属于网络结构，也需要继承Cell类才能实现功能，同样用户也可以根据业务需求自定义这部分内容。 +损失函数、优化器和模型层等本质上也属于网络结构，也需要继承`Cell`类才能实现功能，同样用户也可以根据业务需求自定义这部分内容。 -本节内容首先将会介绍Cell类的关键成员函数，然后介绍基于Cell实现的MindSpore内置损失函数，优化器和模型层及使用方法，最后通过实例介绍 -如何利用Cell类构建自定义网络。 +本节内容首先将会介绍`Cell`类的关键成员函数，然后介绍基于`Cell`实现的MindSpore内置损失函数、优化器和模型层及使用方法，最后通过实例介绍如何利用`Cell`类构建自定义网络。 ## 关键成员函数 ### construct方法 -Cell类重写了__call__方法，在cell类的实例被调用时，会执行contruct方法。网络结构在contruct方法里面定义。 +`Cell`类重写了`__call__`方法，在`Cell`类的实例被调用时，会执行`contruct`方法。网络结构在`contruct`方法里面定义。 下面的样例中，我们构建了一个简单的网络。用例的网络结构为Conv2d->BatchNorm2d->ReLU->Flatten->Dense。 -在construct方法中，x为输入数据， out是经过网络的每层计算后得到的计算结果。 +在`construct`方法中，`x`为输入数据，`out`是经过网络的每层计算后得到的计算结果。 ``` class Net(nn.Cell): @@ -50,9 +58,9 @@ class Net(nn.Cell): ### parameters_dict -parameters_dict方法识别出网络结构中所有的参数，返回一个以key为参数名，value为参数值的OrderedDict()。 +`parameters_dict`方法识别出网络结构中所有的参数，返回一个以key为参数名，value为参数值的`OrderedDict`。 -Cell类中返回参数的方法还有许多，例如get_parameters()，trainable_params()等， 具体使用方法可以参见MindSpore API手册。 +`Cell`类中返回参数的方法还有许多，例如`get_parameters`、`trainable_params`等，具体使用方法可以参见[API文档](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.Cell)。 代码样例如下： @@ -63,9 +71,9 @@ print(result.keys()) print(result['conv.weight']) ``` -样例中的Net()采用上文构造网络的用例，打印了网络中是所有参数的名字和conv.weight参数的结果。 +样例中的`Net`采用上文构造网络的用例，打印了网络中所有参数的名字和`conv.weight`参数的结果。 -运行结果如下： +输出如下： ``` odict_keys(['conv.weight', 'bn.moving_mean', 'bn.moving_variance', 'bn.gamma', 'bn.beta', 'fc.weight', 'fc.bias']) Parameter (name=conv.weight, value=[[[[ 1.07402597e-02 7.70052336e-03 5.55867562e-03] @@ -76,9 +84,9 @@ Parameter (name=conv.weight, value=[[[[ 1.07402597e-02 7.70052336e-03 5.558675 ### cells_and_names -cells_and_names方法是一个迭代器，返回网络中每个cell的名字和它的内容本身。 +`cells_and_names`方法是一个迭代器，返回网络中每个`Cell`的名字和它的内容本身。 -用例简单实现了网络的cell获取与打印每个cell名字的功能，其中根据上文网络结构可知，存在五个cell分别是'conv'，'bn'，'relu'，'flatten'，'fc'。 +用例简单实现了获取与打印每个`Cell`名字的功能，其中根据上文网络结构可知，存在五个`Cell`分别是'conv'、'bn'、'relu'、'flatten'和'fc'。 代码样例如下： ``` @@ -89,88 +97,88 @@ for m in net.cells_and_names(): print(names) ``` -运行结果： +输出如下： ``` ['conv', 'bn', 'relu', 'flatten', 'fc'] ``` + ## 模型层 -在讲述了Cell的使用方法后可知，MindSpore能够以Cell为基类构造网络结构。 +在讲述了`Cell`的使用方法后可知，MindSpore能够以`Cell`为基类构造网络结构。 -为了方便业界需求及用户使用方便，MindSpore框架内置了大量的模型层，用户可以通过接口直接调用。 +为了方便用户的使用，MindSpore框架内置了大量的模型层，用户可以通过接口直接调用。 -同样，用户也可以自定义模型，此内容在cell自定义构建中介绍。 +同样，用户也可以自定义模型，此内容在“构建自定义网络”中介绍。 ### 内置模型层 MindSpore框架在nn的layer层内置了丰富的接口，主要内容如下： -- 激活层： +- 激活层 激活层内置了大量的激活函数，在定义网络结构中经常使用。激活函数为网络加入了非线性运算，使得网络能够拟合效果更好。 - 主要接口有Softmax，Relu，Elu，Tanh，Sigmoid等。 + 主要接口有`Softmax`、`Relu`、`Elu`、`Tanh`、`Sigmoid`等。 -- 基础层： +- 基础层 - 基础层实现了网络中一些常用的基础结构，例如全连接层，Onehot编码，Dropout，平铺层等都在此部分实现。 + 基础层实现了网络中一些常用的基础结构，例如全连接层、Onehot编码、Dropout、平铺层等都在此部分实现。 - 主要接口有Dense，Flatten，Dropout，Norm，OneHot等。 + 主要接口有`Dense`、`Flatten`、`Dropout`、`Norm`、`OneHot`等。 -- 容器层： +- 容器层 - 容器层主要功能是实现一些存储多个cell的数据结构。 + 容器层主要功能是实现一些存储多个Cell的数据结构。 - 主要接口有SequentialCell，CellList等。 + 主要接口有`SequentialCell`、`CellList`等。 -- 卷积层： +- 卷积层 - 卷积层提供了一些卷积计算的功能，如普通卷积，深度卷积和卷积转置等。 + 卷积层提供了一些卷积计算的功能，如普通卷积、深度卷积和卷积转置等。 - 主要接口有Conv2d，Conv1d，Conv2dTranspose，Conv1dTranspose等。 + 主要接口有`Conv2d`、`Conv1d`、`Conv2dTranspose`、`Conv1dTranspose`等。 -- 池化层： +- 池化层 池化层提供了平均池化和最大池化等计算的功能。 - 主要接口有AvgPool2d，MaxPool2d，AvgPool1d。 + 主要接口有`AvgPool2d`、`MaxPool2d`和`AvgPool1d`。 -- 嵌入层： +- 嵌入层 嵌入层提供word embedding的计算功能，将输入的单词映射为稠密向量。 - 主要接口有：Embedding，EmbeddingLookup，EmbeddingLookUpSplitMode等。 + 主要接口有`Embedding`、`EmbeddingLookup`、`EmbeddingLookUpSplitMode`等。 -- 长短记忆循环层： +- 长短记忆循环层 - 长短记忆循环层提供LSTM计算功能。其中LSTM内部会调用LSTMCell接口， LSTMCell是一个LSTM单元， - 对一个LSTM层做运算，当涉及多LSTM网络层运算时，使用LSTM接口。 + 长短记忆循环层提供LSTM计算功能。其中`LSTM`内部会调用`LSTMCell`接口，`LSTMCell`是一个LSTM单元，对一个LSTM层做运算，当涉及多LSTM网络层运算时，使用`LSTM`接口。 - 主要接口有：LSTM，LSTMCell。 + 主要接口有`LSTM`和`LSTMCell`。 -- 标准化层： +- 标准化层 标准化层提供了一些标准化的方法，即通过线性变换等方式将数据转换成均值和标准差。 - 主要接口有：BatchNorm1d，BatchNorm2d，LayerNorm，GroupNorm，GlobalBatchNorm等。 + 主要接口有`BatchNorm1d`、`BatchNorm2d`、`LayerNorm`、`GroupNorm`、`GlobalBatchNorm`等。 -- 数学计算层： +- 数学计算层 数据计算层提供一些算子拼接而成的计算功能，例如数据生成和一些数学计算等。 - 主要接口有ReduceLogSumExp，Range，LinSpace，LGamma等。 + 主要接口有`ReduceLogSumExp`、`Range`、`LinSpace`、`LGamma`等。 -- 图片层： +- 图片层 图片计算层提供了一些矩阵计算相关的功能，将图片数据进行一些变换与计算。 - 主要接口有ImageGradients，SSIM，MSSSIM，PSNR，CentralCrop等。 + 主要接口有`ImageGradients`、`SSIM`、`MSSSIM`、`PSNR`、`CentralCrop`等。 -- 量化层： +- 量化层 量化是指将数据从float的形式转换成一段数据范围的int类型，所以量化层提供了一些数据量化的方法和模型层结构封装。 - 主要接口有Conv2dBnAct，DenseBnAct，Conv2dBnFoldQuant，LeakyReLUQuant等。 + 主要接口有`Conv2dBnAct`、`DenseBnAct`、`Conv2dBnFoldQuant`、`LeakyReLUQuant`等。 ### 应用实例 @@ -195,44 +203,41 @@ class Net(nn.Cell): return out ``` -依然是上述网络构造的用例，从这个用例中可以看出，程序调用了Conv2d，BatchNorm2d，ReLU，Flatten和Dense模型层的接口。 -在Net初始化方法里面被定义，然后在construct方法里面真正运行，这些模型层接口有序的连接，形成一个可执行的网络。 +依然是上述网络构造的用例，从这个用例中可以看出，程序调用了`Conv2d`、`BatchNorm2d`、`ReLU`、`Flatten`和`Dense`模型层的接口。 + +在`Net`初始化方法里被定义，然后在`construct`方法里真正运行，这些模型层接口有序的连接，形成一个可执行的网络。 ## 损失函数 -目前MindSpore主要支持的损失函数有L1Loss，MSELoss，SmoothL1Loss，SoftmaxCrossEntropyWithLogits，SoftmaxCrossEntropyExpand -和CosineEmbeddingLoss。 +目前MindSpore主要支持的损失函数有`L1Loss`、`MSELoss`、`SmoothL1Loss`、`SoftmaxCrossEntropyWithLogits`、`SoftmaxCrossEntropyExpand`和`CosineEmbeddingLoss`。 -MindSpore的损失函数全部是Cell的子类实现，所以也支持用户自定义损失函数，其构造方法在cell自定义构建中进行介绍。 +MindSpore的损失函数全部是`Cell`的子类实现，所以也支持用户自定义损失函数，其构造方法在“构建自定义网络”中进行介绍。 ### 内置损失函数 -- L1Loss： - - 计算两个输入数据的绝对值误差，用于回归模型。reduction参数默认值为mean，返回loss平均值结果， -若reduction值为sum，返回loss累加结果，若reduction值为none，返回每个loss的结果。 +- L1Loss -- MSELoss: + 计算两个输入数据的绝对值误差，用于回归模型。`reduction`参数默认值为mean，返回loss平均值结果，若`reduction`值为sum，返回loss累加结果，若`reduction`值为none，返回每个loss的结果。 - 计算两个输入数据的平方误差，用于回归模型。reduction参数默认值为mean，返回loss平均值结果， -若reduction值为sum，返回loss累加结果，若reduction值为none，返回每个loss的结果。 +- MSELoss -- SmoothL1Loss： + 计算两个输入数据的平方误差，用于回归模型。`reduction`参数同`L1Loss`。 - SmoothL1Loss为平滑L1损失函数，用于回归模型，阈值sigma默认参数为1。 +- SmoothL1Loss -- SoftmaxCrossEntropyWithLogits： + `SmoothL1Loss`为平滑L1损失函数，用于回归模型，阈值`sigma`默认参数为1。 +` +- SoftmaxCrossEntropyWithLogits - 交叉熵损失函数，用于分类模型。当标签数据不是one-hot编码形式时，需要输入参数sparse为True。reduction参数 - 与L1Loss一致。 + 交叉熵损失函数，用于分类模型。当标签数据不是one-hot编码形式时，需要输入参数`sparse`为True。`reduction`参数同`L1Loss`。 -- SoftmaxCrossEntropyExpand： +- SoftmaxCrossEntropyExpand - 交叉熵扩展损失函数，用于分类模型。当标签数据不是one-hot编码形式时，需要输入参数sparse为True。 + 交叉熵扩展损失函数，用于分类模型。当标签数据不是one-hot编码形式时，需要输入参数`sparse`为True。 -- CosineEmbeddingLoss： +- CosineEmbeddingLoss - CosineEmbeddingLoss用于衡量两个输入相似程度，用于分类模型。margin默认为0.0，reduction参数与L1Loss一致 + CosineEmbeddingLoss用于衡量两个输入相似程度，用于分类模型。`margin`默认为0.0，`reduction`参数同`L1Loss`。 ### 应用实例 @@ -249,21 +254,18 @@ target_data = Tensor(np.array([[0, 2, 5], [3, 1, 1]]).astype(np.float32)) print(loss(input_data, target_data)) ``` -此用例构造了两个Tensor数据，利用nn.L1Loss()接口定义了L1Loss，将input_data和target_data传入loss, -执行L1Loss的计算，结果为1.5。若loss = nn.L1Loss(reduction='sum')，则结果为9.0。 -若loss = nn.L1Loss(reduction='none')，结果为[[1. 0. 2.] [1. 2. 3.]] +此用例构造了两个Tensor数据，利用`nn.L1Loss`接口定义了L1Loss，将`input_data`和`target_data`传入loss，执行L1Loss的计算，结果为1.5。若`loss = nn.L1Loss(reduction='sum')`，则结果为9.0。若`loss = nn.L1Loss(reduction='none')`，结果为[[1. 0. 2.] [1. 2. 3.]]。 -## Cell构造自定义网络 +## 构建自定义网络 -无论是网络结构，还是前文提到的模型层，损失函数和优化器等，本质上都是一个Cell，因此都可以自定义实现。 +无论是网络结构，还是前文提到的模型层、损失函数和优化器等，本质上都是一个Cell，因此都可以自定义实现。 -首先构造一个继承cell的子类，然后在__init__方法里面定义算子和模型层等，然后在construct方法里面构造网络结构。 +首先构造一个继承`Cell`的子类，然后在`__init__`方法里面定义算子和模型层等，在`construct`方法里面构造网络结构。 -以lenet5网络为例，在__init__方法中定义了卷积层，池化层和全连接层等结构单元，然后在construct方法将定义的内容连接在一起， -形成一个完整lenet5的网络结构。 +以LeNet网络为例，在`__init__`方法中定义了卷积层，池化层和全连接层等结构单元，然后在`construct`方法将定义的内容连接在一起，形成一个完整LeNet的网络结构。 -lenet5网络实现方式如下所示： +LeNet网络实现方式如下所示： ``` import mindspore.nn as nn diff --git a/api/source_zh_cn/programming_guide/compute_component.rst b/api/source_zh_cn/programming_guide/compute_component.rst new file mode 100644 index 0000000000..de586587e0 --- /dev/null +++ b/api/source_zh_cn/programming_guide/compute_component.rst @@ -0,0 +1,8 @@ +计算组件 +=========== + +.. toctree:: + :maxdepth: 1 + + cell + network_component \ No newline at end of file diff --git a/api/source_zh_cn/programming_guide/data_type.rst b/api/source_zh_cn/programming_guide/data_type.rst new file mode 100644 index 0000000000..ee35ad4275 --- /dev/null +++ b/api/source_zh_cn/programming_guide/data_type.rst @@ -0,0 +1,8 @@ +数据类型 +=========== + +.. toctree:: + :maxdepth: 1 + + dtype + tensor \ No newline at end of file diff --git a/api/source_zh_cn/programming_guide/type.md b/api/source_zh_cn/programming_guide/dtype.md similarity index 32% rename from api/source_zh_cn/programming_guide/type.md rename to api/source_zh_cn/programming_guide/dtype.md index 3ccdb56038..a8359c161c 100644 --- a/api/source_zh_cn/programming_guide/type.md +++ b/api/source_zh_cn/programming_guide/dtype.md @@ -1,11 +1,10 @@ -# 数据类型 +# dtype -- [数据类型](#数据类型) +- [dtype](#dtype) - [概述](#概述) - - [操作接口](#操作接口) - + - [数据类型转换接口](#数据类型转换接口) @@ -13,25 +12,21 @@ ## 概述 -MindSpore张量支持不同的数据类型，有`int8`、`int16`、`int32`、`int64`、`uint8`、`uint16`、`uint32`、`uint64`、 -`float16`、`float32`、`float64`、`bool_`， 与NumPy的数据类型一一对应，Python里的`int`数会被转换为定义的int64进行运算， -Python里的`float`数会被转换为定义的`float32`进行运算。 +MindSpore张量支持不同的数据类型，包含`int8`、`int16`、`int32`、`int64`、`uint8`、`uint16`、`uint32`、`uint64`、`float16`、`float32`、`float64`、`bool_`，与NumPy的数据类型一一对应。 -## 操作接口 -- `dtype_to_nptype` +在MindSpore的运算处理流程中，Python中的`int`数会被转换为定义的int64类型，`float`数会被转换为定义的`float32`类型。 - 可通过该接口将MindSpore的数据类型转换为NumPy对应的数据类型。 +详细的类型支持情况请参考。 -- `dtype_to_pytype` +## 数据类型转换接口 - 可通过该接口将MindSpore的数据类型转换为Python对应的内置数据类型。 +MindSpore提供了以下几个接口，实现与NumPy数据类型和Python内置的数据类型间的转换。 +- `dtype_to_nptype`：将MindSpore的数据类型转换为NumPy对应的数据类型。 +- `dtype_to_pytype`：将MindSpore的数据类型转换为Python对应的内置数据类型。 +- `pytype_to_dtype`：将Python内置的数据类型转换为MindSpore对应的数据类型。 -- `pytype_to_dtype` - - 可通过该接口将Python内置的数据类型转换为MindSpore对应的数据类型。 - -示例如下： +以下代码实现了不同数据类型间的转换，并打印转换后的类型。 ``` from mindspore import dtype as mstype diff --git a/api/source_zh_cn/programming_guide/component.md b/api/source_zh_cn/programming_guide/network_component.md similarity index 80% rename from api/source_zh_cn/programming_guide/component.md rename to api/source_zh_cn/programming_guide/network_component.md index dd92ce5515..9c8098cd75 100644 --- a/api/source_zh_cn/programming_guide/component.md +++ b/api/source_zh_cn/programming_guide/network_component.md @@ -4,24 +4,23 @@ - [常用网络组件](#常用网络组件) - [概述](#概述) - - [GradOperation](#GradOperation) - - [WithLossCell](#WithLossCell) - - [TrainOneStepCell](#TrainOneStepCell) + - [GradOperation](#gradoperation) + - [WithLossCell](#withlosscell) + - [TrainOneStepCell](#trainonestepcell) ## 概述 -MindSpore封装一些常用的网络组件，用于网络的训练，推理，求梯度和数据处理等。 +MindSpore封装了一些常用的网络组件，用于网络的训练、推理、求梯度和数据处理等操作。 这些网络组件可以直接被用户使用，同样也会在`model.train`和`model.eval`等更高级的封装接口内部进行使用。 -本节内容将会介绍三个网络组件，分别是`GradOperation`，`WithLossCell`和`TrainOneStepCell`，将会从功能，用户使用和内部使用三个方面来进行介绍。 +本节内容将会介绍三个网络组件，分别是`GradOperation`、`WithLossCell`和`TrainOneStepCell`，将会从功能、用户使用和内部使用三个方面来进行介绍。 ## GradOperation -GradOperation组件用于生成输入函数的梯度，利用`get_all`，`get_by_list`和`sens_param`参数 -控制梯度的计算方式，细节内容详见API文档。 +GradOperation组件用于生成输入函数的梯度，利用`get_all`、`get_by_list`和`sens_param`参数控制梯度的计算方式，细节内容详见[API文档](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.html#mindspore.ops.GradOperation)。 GradOperation的使用实例如下： @@ -59,9 +58,7 @@ y = Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.fl GradNetWrtX(Net())(x, y) ``` -上面的例子是计算`Net`相对与x的梯度值，首先需要定义网络`Net`作为`GradOperation`的输入， -实例创建了包含梯度运算的`GradNetWrtX`。调用`GradNetWrtX`是将网络传入`GradOperation`生成梯度函数， -将输入数据传入梯度函数中返回最终结果。 +上面的例子是计算`Net`相对与x的梯度值，首先需要定义网络`Net`作为`GradOperation`的输入，实例创建了包含梯度运算的`GradNetWrtX`。调用`GradNetWrtX`是将网络传入`GradOperation`生成梯度函数，将输入数据传入梯度函数中返回最终结果。 输出如下： @@ -76,7 +73,7 @@ MindSpore涉及梯度计算的其他组件，例如`WithGradCell`和`TrainOneSte ## WithLossCell -`WithLossCell`本质上是一个包含损失函数的`Cell`, 构造`WithLossCell`需要事先定义好网络和损失函数。 +`WithLossCell`本质上是一个包含损失函数的`Cell`，构造`WithLossCell`需要事先定义好网络和损失函数。 下面通过一个实例来介绍其具体的使用， 首先需要构造一个网络，内容如下： @@ -124,8 +121,7 @@ class LeNet(nn.Cell): return output ``` -下面是`WithLossCell`的使用实例，分别定义好网络和损失函数，然后创建一个`WithLossCell`， -然后传入输入数据和标签数据，`WithLossCell`内部根据网络和损失函数返回计算结果 +下面是`WithLossCell`的使用实例，分别定义好网络和损失函数，然后创建一个`WithLossCell`，传入输入数据和标签数据，`WithLossCell`内部根据网络和损失函数返回计算结果。 ``` data = Tensor(np.ones([32, 1, 32, 32]).astype(np.float32) * 0.01) label = Tensor(np.ones([32]).astype(np.int32)) @@ -137,7 +133,7 @@ print("+++++++++Loss+++++++++++++") print(loss) ``` -输出结果如下： +输出如下： ``` +++++++++Loss+++++++++++++ 2.302585 @@ -167,10 +163,9 @@ for i in range(5): print(res) ``` -用例中构造了优化器和一个`WithLossCell`的实例，然后传入`TrainOneStepCell` 中初始化一个训练网络，用例循环五次，相当于网络训练了五次， -并输出每次的loss结果，由结果可以看出每次训练后loss值在逐渐减小。 +用例中构造了优化器和一个`WithLossCell`的实例，然后传入`TrainOneStepCell`中初始化一个训练网络，用例循环五次，相当于网络训练了五次，并输出每次的loss结果，由结果可以看出每次训练后loss值在逐渐减小。 -输出结果如下： +输出如下： ``` +++++++++result:0++++++++++++ 2.302585 diff --git a/api/source_zh_cn/programming_guide/operator.md b/api/source_zh_cn/programming_guide/operator.md index a62e225025..8bb948a9e1 100644 --- a/api/source_zh_cn/programming_guide/operator.md +++ b/api/source_zh_cn/programming_guide/operator.md @@ -1,23 +1,19 @@ # 算子组件 - -算子组件指常用的算子及其操作，按功能大致可分为张量操作，网络操作，数组操作，图像操作，编码操作，调试操作，量化操作等七个模块。所有的算子在Ascend芯片或者CPU, GPU的支持情况，参见[这里](https://www.mindspore.cn/docs/zh-CN/master/operator_list.html "list") - - -这七类算子操作的相互关系见下: - [算子组件](#算子组件) + - [概述](#概述) - [张量操作](#张量操作) - [标量运算](#标量运算) - [加法](#加法) - - [Element-wise 除法](#element-wise-除法) - - [Element-wise 乘](#element-wise-乘) - - [三角函数](#求三角函数) + - [Element-wise除法](#element-wise除法) + - [Element-wise乘法](#element-wise乘法) + - [求三角函数](#求三角函数) - [向量运算](#向量运算) - - [Concat](#concat-算子) + - [Concat](#concat) - [Squeeze](#squeeze) - - [Sparse2Dense](#求sparse2dense改变tensor维度使其变稠密) + - [求Sparse2Dense(改变tensor维度使其变稠密)](#求sparse2dense改变tensor维度使其变稠密) - [ScalarCast](#scalarcast) - [矩阵运算](#矩阵运算) - [矩阵乘法](#矩阵乘法) @@ -26,10 +22,10 @@ - [网络操作](#网络操作) - [特征提取](#特征提取) - [卷积操作](#卷积操作) - - [卷积的反向传播操作](#卷积的反向传播算子操作) + - [卷积的反向传播算子操作](#卷积的反向传播算子操作) - [激活函数](#激活函数) - [LossFunction](#lossfunction) - - [L1 Loss](#l1loss) + - [L1Loss](#l1loss) - [优化算法](#优化算法) - [SGD](#sgd) - [数组操作](#数组操作) @@ -40,7 +36,7 @@ - [编码运算](#编码运算) - [BoundingBoxEncode](#boundingboxencode) - [BoundingBoxDecode](#boundingboxdecode) - - [IOU](#iou-计算) + - [IOU计算](#iou计算) - [调试操作](#调试操作) - [Debug](#debug) - [HookBackward](#hookbackward) @@ -49,62 +45,75 @@ + + +## 概述 +算子组件包含了常用的算子及其操作，按功能大致可分为张量操作、网络操作、数组操作、图像操作、编码操作、调试操作和量化操作七个模块。所有的算子在Ascend AI处理器、GPU和CPU的支持情况，参见[算子支持列表](https://www.mindspore.cn/docs/zh-CN/master/operator_list.html)。 ## 张量操作 - -主要包括张量的结构操作和张量的数学运算。 -张量结构操作诸如：张量创建，索引切片，维度变换，合并分割。 -张量数学运算主要有：标量运算，向量运算，矩阵运算。另外我们会介绍张量运算的广播机制。 -本篇我们介绍张量的数学运算。 - - +张量操作包括张量的结构操作和张量的数学运算。 + +张量结构操作有：张量创建、索引切片、维度变换和合并分割。 + +张量数学运算有：标量运算、向量运算和矩阵运算。 + +这里以张量的数学运算和运算的广播机制为例，介绍使用方法。 ### 标量运算 -张量的数学运算符可以分为标量运算符、向量运算符、以及矩阵运算符。 -加减乘除乘方，以及三角函数，指数，对数等常见函数，逻辑比较运算符等都是标量运算符。 + +张量的数学运算符可以分为标量运算符、向量运算符以及矩阵运算符。 + +加减乘除乘方，以及三角函数、指数、对数等常见函数，逻辑比较运算符等都是标量运算符。 + 标量运算符的特点是对张量实施逐元素运算。 -有些标量运算符对常用的数学运算符进行了重载。并且支持类似numpy的广播特性。 -举例说明: +有些标量运算符对常用的数学运算符进行了重载。并且支持类似NumPy的广播特性。 + +以下代码实现了XXX（TODO）： ```python import numpy as np -import mindspore # 导入mindspore包 -from mindspore import Tensor # 导入mindspore下的Tensor包 +import mindspore +from mindspore import Tensor import mindspore.ops.operations as P input_x = mindspore.Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32) input_y = 3.0 -input_x**input_y +print(input_x**input_y) ``` -真实输入为： -```python -print(input_x) -[ 1. 8. 64.] +输出如下： ``` - -真实输出为： -```python -print(input_x**input_y) [ 1. 8. 64.] ``` #### 加法 + +上述代码中`input_x`和`input_y`的相加实现方式如下： ```python -input_x + input_y +print(input_x + input_y) +``` + +输出如下： +``` [4.0 5.0 7.0] ``` -除普通加外，还有element-wise加法: +除了普通加外，还有element-wise加法： ```python +import TODO + net = NetAddN() input_x = Tensor(np.array([1, 2, 3]), mindspore.float32) input_y = Tensor(np.array([4, 5, 6]), mindspore.float32) net(input_x, input_y, input_x, input_y)[10.0, 14.0, 18.0] ``` -#### Element-wise 除法 +输出如下：（TODO） + +#### Element-wise除法 + +以下代码实现了XXX（TODO）： ```python input_x = Tensor(np.array([-4.0, 5.0, 6.0]), mindspore.float32) input_y = Tensor(np.array([3.0, 2.0, 3.0]), mindspore.float32) @@ -112,7 +121,9 @@ div = P.Div() div(input_x, input_y) ``` -求FloorDiv: +输出如下：（TODO） + +以下代码实现了XXX（TODO）： ```python input_x = Tensor(np.array([2, 4, -1]), mindspore.int32)) input_y = Tensor(np.array([3, 3, 3]), mindspore.int32) @@ -120,7 +131,11 @@ floor_div = P.FloorDiv() floor_div(input_x, input_y)[0, 1, -1] ``` -#### Element-wise 乘 +输出如下：（TODO） + +#### Element-wise乘法 + +以下代码实现了XXX（TODO）： ```python input_x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32) input_y = Tensor(np.array([4.0, 5.0, 6.0]), mindspore.float32) @@ -128,22 +143,29 @@ mul = P.Mul() mul(input_x, input_y) ``` -真实输出: -```python +输出如下： +``` [4, 10, 18] ``` -#### 求三角函数: +#### 求三角函数 + +以下代码实现了XXX（TODO）： ```python acos = P.ACos() input_x = Tensor(np.array([0.74, 0.04, 0.30, 0.56]), mindspore.float32) output = acos(input_x) ``` +输出如下：（TODO） + ### 向量运算 + 向量运算符只在一个特定轴上运算，将一个向量映射到一个标量或者另外一个向量。 -#### Concat 算子: +#### Concat + +以下代码实现了XXX（TODO）： ```python data1 = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32)) data2 = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32)) @@ -151,14 +173,22 @@ op = P.Concat() output = op((data1, data2)) ``` +输出如下：（TODO） + #### Squeeze + +以下代码实现了XXX（TODO）： ```python input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32) squeeze = P.Squeeze(2) output = squeeze(input_tensor) ``` -#### 求Sparse2Dense(改变tensor维度使其变稠密): +输出如下：（TODO） + +#### 求Sparse2Dense(改变tensor维度使其变稠密) + +以下代码实现了XXX（TODO）： ```python indices = Tensor([[0, 1], [1, 2]]) values = Tensor([1, 2], dtype=ms.float32) @@ -166,16 +196,25 @@ dense_shape = (3, 4) out = P.SparseToDense()(indices, values, dense_shape) ``` -#### ScalarCast: +输出如下：（TODO） + +#### ScalarCast + +以下代码实现了XXX（TODO）： ```python scalar_cast = P.ScalarCast() output = scalar_cast(255.0, mindspore.int32) ``` +输出如下：（TODO） + ### 矩阵运算 -矩阵运算包括: 矩阵乘法，矩阵范数，矩阵行列式，矩阵求特征值，矩阵分解等运算。 -#### 矩阵乘法: +矩阵运算包括矩阵乘法、矩阵范数、矩阵行列式、矩阵求特征值、矩阵分解等运算。 + +#### 矩阵乘法 + +以下代码实现了XXX（TODO）： ```python input_x = Tensor(np.ones(shape=[1, 3]), mindspore.float32) input_y = Tensor(np.ones(shape=[3, 4]), mindspore.float32) @@ -183,8 +222,11 @@ matmul = P.MatMul() output = matmul(input_x, input_y) ``` -#### 常见范数: +输出如下：（TODO） + +#### 常见范数 +以下代码实现了XXX（TODO）： ```python input_x = Tensor(np.ones([128, 64, 32, 64]), mindspore.float32) scale = Tensor(np.ones([64]), mindspore.float32) @@ -195,10 +237,13 @@ batch_norm = P.BatchNorm() output = batch_norm(input_x, scale, bias, mean, variance) ``` +输出如下：（TODO） + #### 广播机制 -Broadcast 广播一个tensor到整个group -举例说明: +广播表示XXX（TODO）。 + +以下代码实现了XXX（TODO）： ```python from mindspore import Tensor from mindspore.communication import init @@ -218,15 +263,19 @@ net = Net() output = net(input_) ``` +输出如下：（TODO） + ## 网络操作 - -网络操作包括特征提取, 激活函数， LossFunction, 优化算法等： +网络操作包括特征提取、激活函数、LossFunction、优化算法等。 ### 特征提取 +特征提取是XXX（TODO）。 + #### 卷积操作 -举例说明: + +以下代码实现了XXX（TODO）： ```python input = Tensor(np.ones([10, 32, 32, 32]), mindspore.float32) weight = Tensor(np.ones([32, 32, 3, 3]), mindspore.float32)) @@ -234,8 +283,9 @@ conv2d = P.Conv2D(out_channel=32, kernel_size=3) conv2d(input, weight) ``` -#### 卷积的反向传播算子操作： -输出结果: +#### 卷积的反向传播算子操作 + +以下代码实现了XXX（TODO）： ```python dout = Tensor(np.ones([10, 32, 30, 30]), mindspore.float32) weight = Tensor(np.ones([32, 32, 3, 3]), mindspore.float32) @@ -245,22 +295,24 @@ conv2d_backprop_input(dout, weight, F.shape(x)) ``` ### 激活函数 -举例说明: + +以下代码实现了XXX（TODO）： ```python input_x = Tensor(np.array([1, 2, 3, 4, 5]), mindspore.float32) softmax = P.Softmax() softmax(input_x) ``` -输出结果: -```python +输出如下： +``` [0.01165623, 0.03168492, 0.08612854, 0.23412167, 0.6364086] ``` ### LossFunction -#### L1Loss: -举例说明： +#### L1Loss + +以下代码实现了XXX（TODO）： ```python loss = P.SmoothL1Loss() input_data = Tensor(np.array([1, 2, 3]), mindspore.float32) @@ -268,13 +320,16 @@ target_data = Tensor(np.array([1, 2, 2]), mindspore.float32) loss(input_data, target_data) ``` -输出结果: -```python +输出如下： +``` [0, 0, 0.5] ``` ### 优化算法 -#### SGD: + +#### SGD + +以下代码实现了XXX（TODO）： ```python sgd = P.SGD() parameters = Tensor(np.array([2, -0.5, 1.7, 4]), mindspore.float32) @@ -286,23 +341,29 @@ stat = Tensor(np.array([1.5, -0.3, 0.2, -0.7]), mindspore.float32) result = sgd(parameters, gradient, learning_rate, accum, momentum, stat) ``` -## 数组操作 +输出如下：（TODO） - +## 数组操作 数组操作指操作对象是一些数组的操作。 -### DType -返回跟输入的数据类型一致的并且适配Mindspore的tensor变量， 常用于Mindspore 工程内。 -举例说明: +### DType + +返回跟输入的数据类型一致的并且适配Mindspore的Tensor变量，常用于Mindspore工程内。 + +以下代码实现了XXX（TODO）： ```python input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) type = P.DType()(input_tensor) ``` +输出如下：（TODO） + ### Cast -转换输入的数据类型并且输出与目标数据类型相同的变量 -举例说明: + +转换输入的数据类型并且输出与目标数据类型相同的变量。 + +以下代码实现了XXX（TODO）： ```python input_np = np.random.randn(2, 3, 4, 5).astype(np.float32) input_x = Tensor(input_np) @@ -311,21 +372,26 @@ cast = P.Cast() result = cast(input_x, type_dst) ``` -### Shape -返回输入数据的形状 -举例说明: +输出如下：（TODO） + +### Shape + +返回输入数据的形状。 + +以下代码实现了XXX（TODO）： ```python input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32) shape = P.Shape() output = shape(input_tensor) ``` +输出如下：（TODO） + ## 图像操作 - -图像操作包括图像预处理操作， 如图像剪切（Crop，便于得到大量训练样本）和大小变化（Reise,用于构建图像金子塔等）： +图像操作包括图像预处理操作，如图像剪切（Crop，便于得到大量训练样本）和大小变化（Reise，用于构建图像金子塔等）。 -举例说明: +以下代码实现了XXX（TODO）： ```python class CropAndResizeNet(nn.Cell): def __init__(self, crop_size): @@ -350,47 +416,55 @@ output = crop_and_resize(Tensor(image), Tensor(boxes), Tensor(box_index)) print(output.asnumpy()) ``` +输出如下：（TODO） + ## 编码运算 - -编码运算包括 BoundingBox Encoding和 BoundingBox Decoding， IOU计算等。 +编码运算包括BoundingBox Encoding、BoundingBox Decoding、IOU计算等。 ### BoundingBoxEncode + 对物体所在区域方框进行编码，得到类似PCA的更精简信息，以便做后续类似特征提取，物体检测，图像恢复等任务。 -举例说明: +以下代码实现了XXX（TODO）： ```python anchor_box = Tensor([[4,1,2,1],[2,2,2,3]],mindspore.float32) groundtruth_box = Tensor([[3,1,2,2],[1,2,1,4]],mindspore.float32) boundingbox_encode = P.BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0)) boundingbox_encode(anchor_box, groundtruth_box) ``` -输出结果为: -```python + +输出如下: + +``` [[5.0000000e-01 5.0000000e-01 -6.5504000e+04 6.9335938e-01] [-1.0000000e+00 2.5000000e-01 0.0000000e+00 4.0551758e-01]] ``` -### BoundingBoxDecode +### BoundingBoxDecode + 编码器对区域位置信息解码之后，用此算子进行解码。 -举例说明: +以下代码实现了XXX（TODO）： ```python anchor_box = Tensor([[4,1,2,1],[2,2,2,3]],mindspore.float32) deltas = Tensor([[3,1,2,2],[1,s2,1,4]],mindspore.float32) boundingbox_decode = P.BoundingBoxDecode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0), max_shape=(768, 1280), wh_ratio_clip=0.016) boundingbox_decode(anchor_box, deltas) ``` -输出结果: -```python + +输出如下 + +``` [[4.1953125 0. 0. 5.1953125] [2.140625 0. 3.859375 60.59375]] ``` -### IOU 计算： -计算预测的物体所在方框和真实物体所在方框的交集区域与并集区域的占比大小。其常作为一种损失函数，用以优化模型。 +### IOU计算 + +计算预测的物体所在方框和真实物体所在方框的交集区域与并集区域的占比大小，常作为一种损失函数，用以优化模型。 -举例说明: +以下代码实现了XXX（TODO）： ```python iou = P.IOU() anchor_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16) @@ -398,12 +472,14 @@ gt_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16) ``` ## 调试操作 -调试操作指的是用于调试网络的一些常用算子及其操作， 例如Debug等 + +调试操作指的是用于调试网络的一些常用算子及其操作，例如Debug等。 ### Debug -输出tensor变量的数值， 方便用户随时随地打印想了解或者debug必需的某变量数值。 -参考示例: +输出Tensor变量的数值，方便用户随时随地打印想了解或者debug必需的某变量数值。 + +以下代码实现了XXX（TODO）： ```python class DebugNN(nn.Cell): def __init__(self,): @@ -416,8 +492,10 @@ class DebugNN(nn.Cell): ``` ### HookBackward -打印中间变量的梯度，这一算子特别常用，遂举例在此，虽目前仅支持Pynative 形式 -参考示例: + +打印中间变量的梯度，是比较常用的算子，目前仅支持Pynative模式。 + +以下代码实现了XXX（TODO）： ```python def hook_fn(grad_out): print(grad_out) @@ -439,13 +517,13 @@ backward(1, 2) ## 量化操作 - -量化操作指对tensor做量化或者反量化操作。 量化操作指将浮点数用整数的加和表示，利用整数加和并行加速时速度快的优点， 实 -现在可接受精度损失下的性能提升。反量化指其反过程，其在精度要求高的地方常被用到。 +量化操作指对Tensor做量化或者反量化操作。量化操作指将浮点数用整数的加和表示，利用整数加和并行加速时速度快的优点，实现在可接受精度损失下的性能提升。反量化指其反过程，其在精度要求高的地方常被用到。 + +### MinMaxUpdatePerLayer + +完成在训练时的量化和反量化操作。 -### MinMaxUpdatePerLayer -完成在训练时的量化和反量化操作 -举例说明: +以下代码实现了XXX（TODO）： ```python input_tensor = Tensor(np.random.rand(3, 16, 5, 5), mstype.float32) min_tensor = Tensor(np.array([-6]), mstype.float32) diff --git a/api/source_zh_cn/programming_guide/parameter.md b/api/source_zh_cn/programming_guide/parameter.md index 4e6bf70213..9f8ff76a2c 100644 --- a/api/source_zh_cn/programming_guide/parameter.md +++ b/api/source_zh_cn/programming_guide/parameter.md @@ -6,36 +6,30 @@ - [概述](#概述) - [初始化](#初始化) - [属性](#属性) - - [接口](#方法) + - [方法](#方法) - [ParameterTuple](#parametertuple) - - - + ## 概述 -Parameter是变量张量，代表在训练网络时，需要被更新的参数，是MetaTensor的一个子类。 - +`Parameter`是变量张量，代表在训练网络时，需要被更新的参数，是`MetaTensor`的一个子类。 +` ## 初始化 ``` def __init__(self, default_input, name, requires_grad=True, layerwise_parallel=False) ``` 初始化一个`Parameter`对象，传入的数据支持`Tensor`、`Initializer`、`int`和`float`四种类型。 -`Initializer`是初始化器，保存了shape和dtype信息，可调用`to_tensor`方法生成存有数据的Tensor。 - -当网络采用半自动或者全自动并行策略，并且使用`Initializer`初始化`Parameter`时， -`Parameter`里保存的不是`Tensor`，而是`MetaTensor`。 +- `Initializer`是初始化器，保存了shape和dtype信息，可调用`to_tensor`方法生成存有数据的Tensor。 -`MetaTensor`与`Tensor`不同，`MetaTensor`仅保存张量的形状和类型，而不保存实际数据， -所以不会占用任何内存，可调用`init_data`接口将`Parameter`里保存的`MetaTensor`转化为`Tensor`。 +- 当网络采用半自动或者全自动并行策略，并且使用`Initializer`初始化`Parameter`时，`Parameter`里保存的不是`Tensor`，而是`MetaTensor`。`MetaTensor`与`Tensor`不同，`MetaTensor`仅保存张量的形状和类型，而不保存实际数据，所以不会占用任何内存，可调用`init_data`接口将`Parameter`里保存的`MetaTensor`转化为`Tensor`。 -可为每个`Parameter`指定一个名称，便于后续操作和更新。 +- 可为每个`Parameter`指定一个名称，便于后续操作和更新。 -当`layerwise_parallel`为`True`时，参数广播和参数梯度聚合时会过滤掉该参数。 +- 当`layerwise_parallel`为`True`时，参数广播和参数梯度聚合时会过滤掉该参数。 下例通过三种不同的数据类型构造了`Parameter`，三个`Parameter`都需要更新，都不采用layerwise并行。如下： ``` @@ -64,6 +58,7 @@ Parameter (name=z, value=2.0) ``` ## 属性 + - `inited_param`：返回保存了实际数据的`Parameter`，如果`Parameter`原本保存的是`MetaTensor`，会将其转换为`Tensor`。 - `name`：实例化`Parameter`时，为其指定的名字。 diff --git a/api/source_zh_cn/programming_guide/tensor.md b/api/source_zh_cn/programming_guide/tensor.md index 3959362bb8..711f87a9b5 100644 --- a/api/source_zh_cn/programming_guide/tensor.md +++ b/api/source_zh_cn/programming_guide/tensor.md @@ -1,8 +1,8 @@ -# 张量 +# Tensor -- [张量](#张量) +- [Tensor](#tensor) - [概述](#概述) - [张量构造](#张量构造) - [张量的属性和方法](#张量的属性和方法) @@ -15,20 +15,17 @@ ## 概述 -张量是MindSpore网络运算中的基本数据结构，即为多维数组。张量里的数据分为不同的类型， -支持的类型有`int8`、`int16`、`int32`、`int64`、`uint8`、`uint16`、`uint32`、`uint64`、`float16`、`float32`、`float64`、`bool_`， -与NumPy里的数据类型一一对应。 +张量（Tensor）是MindSpore网络运算中的基本数据结构。张量中的数据类型可参考[dtype](https://www.mindspore.cn/api/zh-CN/master/programming_guide/dtype.html)。 不同维度的张量分别表示不同的数据，0维张量表示标量，1维张量表示向量，2维张量表示矩阵，3维张量可以表示彩色图像的RGB三通道等等。 -> 本文档中的所有示例，都是在PyNative模式下运行的，暂不支持CPU。 +> 本文中的所有示例，支持在PyNative模式下运行，暂不支持CPU。 ## 张量构造 -构造张量时支持传入`Tensor`、`float`、`int`、`bool`、`tuple`、`list`和`NumPy.array`。 +构造张量时，支持传入`Tensor`、`float`、`int`、`bool`、`tuple`、`list`和`NumPy.array`类型。 -`Tensor`作为初始值可指定dtype，如果没有指定dtype，`int`、`float`、`bool`分别对应`int32`、`float32`、`bool_`， -`tuple`和`list`生成的1维`Tensor`数据类型与`tuple`和`list`里存放数据的类型相对应。 +`Tensor`作为初始值时，可指定dtype，如果没有指定dtype，`int`、`float`、`bool`分别对应`int32`、`float32`、`bool_`，`tuple`和`list`生成的1维`Tensor`数据类型与`tuple`和`list`里存放数据的类型相对应。 代码样例如下： @@ -65,6 +62,7 @@ True ``` ## 张量的属性和方法 + ### 属性 张量的属性包括形状（shape）和数据类型（dtype）。 @@ -94,8 +92,8 @@ print(x_shape, x_dtype) ### 方法 张量的方法包括`all`、`any`和`asnumpy`。 -- `all(axis, keep_dims)`：在指定维度上通过`and`操作进行归约，axis代表归约维度，keep_dims表示是否保留归约后的维度。 -- `any(axis, keep_dims)`：在指定维度上通过`or`操作进行归约，axis代表归约维度，keep_dims表示是否保留归约后的维度。 +- `all(axis, keep_dims)`：在指定维度上通过`and`操作进行归约，`axis`代表归约维度，`keep_dims`表示是否保留归约后的维度。 +- `any(axis, keep_dims)`：在指定维度上通过`or`操作进行归约，参数含义同`all`。 - `asnumpy()`：将`Tensor`转换为NumPy的array。 代码样例如下： -- Gitee From 95b7b0ccb810f7fc08c469a99d8ce928e3acd190 Mon Sep 17 00:00:00 2001 From: chenjianping Date: Mon, 14 Sep 2020 21:07:25 +0800 Subject: [PATCH 017/156] refresh resize api --- lite/tutorials/source_en/use/runtime.md | 10 ++++++---- lite/tutorials/source_zh_cn/use/runtime.md | 10 ++++++---- 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/lite/tutorials/source_en/use/runtime.md b/lite/tutorials/source_en/use/runtime.md index 748ef39812..e5e987fb16 100644 --- a/lite/tutorials/source_en/use/runtime.md +++ b/lite/tutorials/source_en/use/runtime.md @@ -187,10 +187,11 @@ virtual std::vector GetInputs() const = 0; /// \brief Resize inputs shape. /// -/// \param[in] inputs Define the new inputs shape. +/// \param[in] inputs Define Model inputs. +/// \param[in] dims Define all inputs new shape. /// /// \return STATUS as an error code of resize inputs, STATUS is defined in errorcode.h. -virtual int Resize(const std::vector &inputs) = 0; +virtual int Resize(const std::vector &inputs, const std::vector> &dims) = 0; ``` ### Example @@ -201,9 +202,10 @@ The following code demonstrates how to resize the input of MindSpore Lite: // Assume we have created a LiteSession instance named session. auto inputs = session->GetInputs(); std::vector resize_shape = {1, 128, 128, 3}; +std::vector> new_shapes; +new_shapes.push_back(resize_shape); // Assume the model has only one input,resize input shape to [1, 128, 128, 3] -inputs[0]->set_shape(resize_shape); -session->Resize(inputs); +session->Resize(inputs, new_shapes); ``` ### Compiling Graphs diff --git a/lite/tutorials/source_zh_cn/use/runtime.md b/lite/tutorials/source_zh_cn/use/runtime.md index 2ba5ab7bad..3e4bca6b62 100644 --- a/lite/tutorials/source_zh_cn/use/runtime.md +++ b/lite/tutorials/source_zh_cn/use/runtime.md @@ -186,10 +186,11 @@ virtual std::vector GetInputs() const = 0; /// \brief Resize inputs shape. /// -/// \param[in] inputs Define the new inputs shape. +/// \param[in] inputs Define Model inputs. +/// \param[in] dims Define all inputs new shape. /// /// \return STATUS as an error code of resize inputs, STATUS is defined in errorcode.h. -virtual int Resize(const std::vector &inputs) = 0; +virtual int Resize(const std::vector &inputs, const std::vector> &dims) = 0; ``` ### 使用示例 @@ -199,9 +200,10 @@ virtual int Resize(const std::vector &inputs) = 0; // Assume we have created a LiteSession instance named session. auto inputs = session->GetInputs(); std::vector resize_shape = {1, 128, 128, 3}; +std::vector> new_shapes; +new_shapes.push_back(resize_shape); // Assume the model has only one input,resize input shape to [1, 128, 128, 3] -inputs[0]->set_shape(resize_shape); -session->Resize(inputs); +session->Resize(inputs, new_shapes); ``` ### 图编译 -- Gitee From fb89d6963b77af03a655d04adc3a75f3034eb10f Mon Sep 17 00:00:00 2001 From: yingchen Date: Tue, 15 Sep 2020 09:59:30 +0800 Subject: [PATCH 018/156] update SMTandV --- .../advanced_use/synchronization_training_and_evaluation.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tutorials/source_en/advanced_use/synchronization_training_and_evaluation.md b/tutorials/source_en/advanced_use/synchronization_training_and_evaluation.md index d42c89c840..193c6f9f11 100644 --- a/tutorials/source_en/advanced_use/synchronization_training_and_evaluation.md +++ b/tutorials/source_en/advanced_use/synchronization_training_and_evaluation.md @@ -32,7 +32,7 @@ Implementation idea: The model accuracy is validated every n epochs. The model a Core implementation: Validation points are set in `epoch_end` of the callback function as follows: -`cur_epoch % eval_per_epoch == 0`: indicates that the model accuracy is validated every `eval_per_epoch` epochs. +`cur_epoch % eval_per_epoch == 0`: indicates that the model accuracy is validated every `eval_per_epoch` epoch. - `cur_epoch`: indicates epoch value in the current training process. - `eval_per_epoch`: indicates user-defined value, that is, the validation frequency. @@ -40,7 +40,7 @@ Core implementation: Validation points are set in `epoch_end` of the callback fu Other parameters are described as follows: - `model`: indicates `Model` function in MindSpore. -- `eval_dataset`: indicates validation dataset. +- `eval_dataset`: indicates the validation dataset. - `epoch_per_eval`: records the accuracy of the validation model and the corresponding number of epochs. The data format is `{"epoch": [], "acc": []}`. ```python @@ -75,7 +75,7 @@ The parameters are described as follows: - `keep_checkpoint_max`: indicates the maximum number of models that can be saved. - `ckpoint_cb`: defines the name and path for saving the model. - `model`: defines a model. -- `model.train`: indicates model training function. +- `model.train`: indicates the model training function. - `epoch_per_eval`: defines the number for collecting `epoch` and the dictionary of corresponding model accuracy information. ```python -- Gitee From fd21dd23e6b011412cf9189e94fafe9321ef1ef0 Mon Sep 17 00:00:00 2001 From: SebastianHan Date: Tue, 15 Sep 2020 11:15:52 +0800 Subject: [PATCH 019/156] add 910 video --- .../source_zh_cn/quick_start/quick_video.md | 24 +++++++++++++++++++ .../ascend910_operator_development.md | 7 ++++++ 2 files changed, 31 insertions(+) create mode 100644 tutorials/source_zh_cn/quick_start/quick_video/ascend910_operator_development.md diff --git a/tutorials/source_zh_cn/quick_start/quick_video.md b/tutorials/source_zh_cn/quick_start/quick_video.md index 115ef7d67e..cd6af62eb4 100644 --- a/tutorials/source_zh_cn/quick_start/quick_video.md +++ b/tutorials/source_zh_cn/quick_start/quick_video.md @@ -337,6 +337,30 @@ +

    + + + + play + + + + + + + +
    diff --git a/tutorials/source_zh_cn/quick_start/quick_video/ascend910_operator_development.md b/tutorials/source_zh_cn/quick_start/quick_video/ascend910_operator_development.md new file mode 100644 index 0000000000..1e7d45170d --- /dev/null +++ b/tutorials/source_zh_cn/quick_start/quick_video/ascend910_operator_development.md @@ -0,0 +1,7 @@ +# Ascend 910算子开发 + +[comment]: <> (本文档中包含手把手系列视频，码云Gitee不支持展示，请于官方网站对应教程中查看) + + -- Gitee From 61ccea86250766f32345060de3137961d6146a2b Mon Sep 17 00:00:00 2001 From: AGroupofProbiotocs Date: Tue, 15 Sep 2020 11:18:19 +0800 Subject: [PATCH 020/156] Update the MindSpore Lite docs, remove some attributes and methods not supported anymore --- lite/docs/source_en/apicc/lite.md | 31 +------------ lite/docs/source_en/apicc/tensor.md | 56 ----------------------- lite/docs/source_zh_cn/apicc/lite.md | 42 +++--------------- lite/docs/source_zh_cn/apicc/session.md | 6 +-- lite/docs/source_zh_cn/apicc/tensor.md | 59 ++----------------------- 5 files changed, 14 insertions(+), 180 deletions(-) diff --git a/lite/docs/source_en/apicc/lite.md b/lite/docs/source_en/apicc/lite.md index 93bc93edf0..1557ed9206 100644 --- a/lite/docs/source_en/apicc/lite.md +++ b/lite/docs/source_en/apicc/lite.md @@ -23,23 +23,6 @@ Context() Constructor of MindSpore Lite Context using default value for parameters. -``` -Context(int thread_num, std::shared_ptr allocator, DeviceContext device_ctx) -``` -Constructor of MindSpore Lite Context using input value for parameters. - -- Parameters - - - `thread_num`: Define the work thread number during the runtime. - - - `allocator`: Define the allocator for malloc. - - - `device_ctx`: Define device information during the runtime. - -- Returns - - The instance of MindSpore Lite Context. - ``` ~Context() ``` @@ -53,9 +36,9 @@ float16_priority A **bool** value. Defaults to **false**. Prior enable float16 inference. ``` -device_ctx_{DT_CPU} +device_type ``` -A [**DeviceContext**](https://www.mindspore.cn/lite/docs/en/master/apicc/lite.html#devicecontext) struct defined at the bottom of the text. Using to specify the device. +A [**DeviceType**](https://www.mindspore.cn/lite/docs/en/master/apicc/lite.html#devicetype) **enum** type. Defaults to **DT_CPU**. Using to specify the device. ``` thread_num_ @@ -153,16 +136,6 @@ GPU device type. DT_NPU = 0 ``` NPU device type, not supported yet. -## DeviceContext - -A **struct**. DeviceContext defined for holding DeviceType. - -**Attributes** -``` -type -``` -A [**DeviceType**](https://www.mindspore.cn/lite/docs/en/master/apicc/lite.html#devicetype) variable. The device type. - ## Version ``` diff --git a/lite/docs/source_en/apicc/tensor.md b/lite/docs/source_en/apicc/tensor.md index 014929ba12..c721fd22d5 100644 --- a/lite/docs/source_en/apicc/tensor.md +++ b/lite/docs/source_en/apicc/tensor.md @@ -36,19 +36,6 @@ Get data type of the MindSpore Lite MSTensor. MindSpore Lite TypeId of the MindSpore Lite MSTensor. -``` -virtual TypeId set_data_type(TypeId data_type) -``` -Set data type for the MindSpore Lite MSTensor. - -- Parameters - - - `data_type`: Define MindSpore Lite TypeId to be set in the MindSpore Lite MSTensor. - -- Returns - - MindSpore Lite TypeId of the MindSpore Lite MSTensor after set. - ``` virtual std::vector shape() const ``` @@ -59,19 +46,6 @@ Get shape of the MindSpore Lite MSTensor. A vector of int as the shape of the MindSpore Lite MSTensor. -``` -virtual size_t set_shape(const std::vector &shape) -``` -Set shape for the MindSpore Lite MSTensor. - -- Parameters - - - `shape`: Define a vector of int as shape to be set into the MindSpore Lite MSTensor. - -- Returns - - Size of shape of the MindSpore Lite MSTensor after set. - ``` virtual int DimensionSize(size_t index) const ``` @@ -96,16 +70,6 @@ Get number of element in MSTensor. Number of element in MSTensor. -``` -virtual std::size_t hash() const -``` - -Get hash of the MindSpore Lite MSTensor. - -- Returns - - Hash of the MindSpore Lite MSTensor. - ``` virtual size_t Size() const ``` @@ -129,23 +93,3 @@ Get the pointer of data in MSTensor. - Returns The pointer points to data in MSTensor. - -**Static Public Member Functions** - -``` -static MSTensor *CreateTensor(TypeId data_type, const std::vector &shape) -``` - -Static method to create a MSTensor pointer. - -> Note: TypeId is defined in [mindspore/mindspore/core/ir/dtype/type_id.h](https://gitee.com/mindspore/mindspore/blob/master/mindspore/core/ir/dtype/type_id.h). Only number types in TypeId enum are suitable for MSTensor. - -- Parameters - - - `data_type`: Define the data type of tensor to be created. - - - `shape`: Define the shape of tensor to be created. - -- Returns - - The pointer of MSTensor. \ No newline at end of file diff --git a/lite/docs/source_zh_cn/apicc/lite.md b/lite/docs/source_zh_cn/apicc/lite.md index 2673487a86..eafcd8ec9c 100644 --- a/lite/docs/source_zh_cn/apicc/lite.md +++ b/lite/docs/source_zh_cn/apicc/lite.md @@ -21,31 +21,13 @@ Context类用于保存执行中的环境变量。 Context() ``` -用默认参数构造MindSpore Lite Context 对象。 - -``` -Context(int thread_num, std::shared_ptr allocator, DeviceContext device_ctx) -``` - -根据输入参数构造MindSpore Lite Context 对象。 - -- 参数 - - - `thread_num`: 定义了执行线程数。 - - - `allocator`: 定义了内存分配器。 - - - `device_ctx`: 定义了设备信息。 - -- 返回值 - - MindSpore Lite Context 指针。 +用默认参数构造MindSpore Lite Context对象。 ``` ~Context() ``` -MindSpore Lite Context 的析构函数。 +MindSpore Lite Context的析构函数。 **公有属性** @@ -53,19 +35,19 @@ MindSpore Lite Context 的析构函数。 float16_priority ``` -**bool** 值，默认为**false**，用于使能float16 推理。 +**bool**值，默认为**false**，用于使能float16推理。 ``` -device_ctx_{DT_CPU} +device_type ``` -[**DeviceContext**](https://www.mindspore.cn/lite/docs/zh-CN/master/apicc/lite.html#devicecontext)结构体。用于设置设备信息。 +[**DeviceType**](https://www.mindspore.cn/lite/docs/zh-CN/master/apicc/lite.html#devicetype)枚举类型。默认为**DT_CPU**，用于设置设备信息。 ``` thread_num_ ``` -**int** 值，默认为**2**，设置线程数。 +**int**值，默认为**2**，设置线程数。 ``` allocator @@ -173,18 +155,6 @@ DT_NPU = 0 设备为NPU，暂不支持。 -## DeviceContext - -定义设备类型的结构体。 - -**属性** - -``` -type -``` - -[**DeviceType**](https://www.mindspore.cn/lite/docs/zh-CN/master/apicc/lite.html#devicetype) 变量。设备类型。 - ## Version ``` diff --git a/lite/docs/source_zh_cn/apicc/session.md b/lite/docs/source_zh_cn/apicc/session.md index 86556e1351..604ec5ff87 100644 --- a/lite/docs/source_zh_cn/apicc/session.md +++ b/lite/docs/source_zh_cn/apicc/session.md @@ -73,9 +73,9 @@ virtual int RunGraph(const KernelCallBack &before = nullptr, const KernelCallBac - 参数 - - `before`: 一个[**KernelCallBack**](https://www.mindspore.cn/lite/docs/zh-CN/master/apicc/session.html#kernelcallback) 结构体。定义了运行每个节点之前调用的回调函数。 + - `before`: 一个[**KernelCallBack**](https://www.mindspore.cn/lite/docs/zh-CN/master/apicc/session.html#kernelcallback)结构体。定义了运行每个节点之前调用的回调函数。 - - `after`: 一个[**KernelCallBack**](https://www.mindspore.cn/lite/docs/zh-CN/master/apicc/session.html#kernelcallback) 结构体。定义了运行每个节点之后调用的回调函数。 + - `after`: 一个[**KernelCallBack**](https://www.mindspore.cn/lite/docs/zh-CN/master/apicc/session.html#kernelcallback)结构体。定义了运行每个节点之后调用的回调函数。 - 返回值 @@ -159,7 +159,7 @@ static LiteSession *CreateSession(lite::Context *context) using KernelCallBack = std::function inputs, std::vector outputs, const CallBackParam &opInfo)> ``` -一个函数包装器。KernelCallBack 定义了指向回调函数的指针。 +一个函数包装器。KernelCallBack定义了指向回调函数的指针。 ## CallBackParam diff --git a/lite/docs/source_zh_cn/apicc/tensor.md b/lite/docs/source_zh_cn/apicc/tensor.md index e9eae1f0fd..32d918604f 100644 --- a/lite/docs/source_zh_cn/apicc/tensor.md +++ b/lite/docs/source_zh_cn/apicc/tensor.md @@ -8,6 +8,7 @@ MSTensor定义了MindSpore Lite中的张量。 **构造函数和析构函数** + ``` MSTensor() ``` @@ -15,7 +16,7 @@ MindSpore Lite MSTensor的构造函数。 - 返回值 - MindSpore Lite MSTensor 的实例。 + MindSpore Lite MSTensor的实例。 ``` virtual ~MSTensor() @@ -35,19 +36,6 @@ virtual TypeId data_type() const MindSpore Lite MSTensor类的MindSpore Lite TypeId。 -``` -virtual TypeId set_data_type(TypeId data_type) -``` -设置MindSpore Lite MSTensor的数据类型。 - -- 参数 - - - `data_type`: 定义了MindSpore Lite MSTensor所需设置的MindSpore Lite TypeId。 - -- 返回值 - - 设置后的MindSpore Lite MSTensor的MindSpore Lite TypeI。 - ``` virtual std::vector shape() const ``` @@ -57,23 +45,10 @@ virtual std::vector shape() const 一个包含MindSpore Lite MSTensor形状数值的整型向量。 -``` -virtual size_t set_shape(const std::vector &shape) -``` -设置MindSpore Lite MSTensor的形状. - -- 参数 - - - `shape`: 定义了一个整型向量，包含了所需设置的MindSpore Lite MSTensor形状数值。 - -- 返回值 - - 设置形状后的MindSpore Lite MSTensor的大小。 - ``` virtual int DimensionSize(size_t index) const ``` -Get size of the dimension of the MindSpore Lite MSTensor index by the parameter index. +通过参数索引获取MindSpore Lite MSTensor的维度的大小。 - 参数 @@ -92,15 +67,6 @@ virtual int ElementsNum() const MSTensor中的元素个数 -``` -virtual std::size_t hash() const -``` -获取MindSpore Lite MSTensor的哈希码。 - -- 返回值 - - MindSpore Lite MSTensor的哈希码。 - ``` virtual size_t Size() const ``` @@ -121,22 +87,3 @@ virtual void *MutableData() const - 返回值 指向MSTensor中的数据的指针。 - -**静态公有成员函数** - -``` -static MSTensor *CreateTensor(TypeId data_type, const std::vector &shape) -``` -创建MSTensor指针的静态方法。 - -> 注意：TypeId在[mindspore/mindspore/core/ir/dtype/type_id\.h](https://gitee.com/mindspore/mindspore/blob/master/mindspore/core/ir/dtype/type_id.h)中定义。只有TypeId枚举中的数字类型可用于MSTensor。 - -- 参数 - - - `data_type`: 定义了所要创建的张量的数据类型。 - - - `shape`: 定义了所要创建的张量的形状。 - -- 返回值 - - 指向MSTensor的指针。 \ No newline at end of file -- Gitee From 668dcb9bd10fd10ae985da9a1c6cbd39da5ee515 Mon Sep 17 00:00:00 2001 From: JunYuLiu Date: Tue, 15 Sep 2020 10:03:16 +0800 Subject: [PATCH 021/156] Modify the format of markdown --- .../mindspore/distributed_training_design.md | 2 +- docs/source_en/design/mindspore/ir.md | 2 +- docs/source_zh_cn/design/mindspore/ir.md | 2 +- .../source_en/advanced_use/hub_tutorial.md | 2 +- .../images/cifar10_c_transforms.png | Bin 0 -> 46693 bytes .../source_en/advanced_use/images/compose.png | Bin 0 -> 18941 bytes .../data_enhancement_performance_scheme.png | Bin 0 -> 54529 bytes .../data_loading_performance_scheme.png | Bin 0 -> 48509 bytes .../advanced_use/images/operator_fusion.png | Bin 0 -> 13533 bytes .../advanced_use/images/pipeline.png | Bin 0 -> 5954 bytes .../images/shuffle_performance_scheme.png | Bin 0 -> 22805 bytes ...ize_the_performance_of_data_preparation.md | 387 ++++++++++++++++++ tutorials/source_en/index.rst | 2 +- .../advanced_use/auto_augmentation.md | 2 +- tutorials/source_zh_cn/advanced_use/cache.md | 2 +- .../checkpoint_for_hybrid_parallel.md | 2 - .../computer_vision_application.md | 10 +- .../data_processing_acceleration.md | 2 +- .../advanced_use/dataset_conversion.md | 2 +- .../advanced_use/deep_probability_program.md | 12 +- .../source_zh_cn/advanced_use/hub_tutorial.md | 2 +- .../advanced_use/membership_inference.md | 2 + .../mobilenetv2_incremental_learning.md | 2 +- .../model_scripts_transformation.md | 2 +- tutorials/source_zh_cn/use/image_loading.md | 2 +- tutorials/source_zh_cn/use/text_loading.md | 2 +- 26 files changed, 414 insertions(+), 27 deletions(-) create mode 100644 tutorials/source_en/advanced_use/images/cifar10_c_transforms.png create mode 100644 tutorials/source_en/advanced_use/images/compose.png create mode 100644 tutorials/source_en/advanced_use/images/data_enhancement_performance_scheme.png create mode 100644 tutorials/source_en/advanced_use/images/data_loading_performance_scheme.png create mode 100644 tutorials/source_en/advanced_use/images/operator_fusion.png create mode 100644 tutorials/source_en/advanced_use/images/pipeline.png create mode 100644 tutorials/source_en/advanced_use/images/shuffle_performance_scheme.png create mode 100644 tutorials/source_en/advanced_use/optimize_the_performance_of_data_preparation.md diff --git a/docs/source_en/design/mindspore/distributed_training_design.md b/docs/source_en/design/mindspore/distributed_training_design.md index d0d6b536a1..14c13e4f3e 100644 --- a/docs/source_en/design/mindspore/distributed_training_design.md +++ b/docs/source_en/design/mindspore/distributed_training_design.md @@ -1,6 +1,6 @@ # Distributed Training Design -`Ascend` `GPU` `Model Development` `Model Optimization` `Framework Development` `Intermediate` `Expert` `Contributor` +`Linux` `Ascend` `GPU` `Model Development` `Model Optimization` `Framework Development` `Intermediate` `Expert` `Contributor` diff --git a/docs/source_en/design/mindspore/ir.md b/docs/source_en/design/mindspore/ir.md index 4837ba94ba..9874351845 100644 --- a/docs/source_en/design/mindspore/ir.md +++ b/docs/source_en/design/mindspore/ir.md @@ -1,7 +1,7 @@ # MindSpore IR (MindIR) -`Framework Development` `Intermediate` `Expert` `Contributor` +`Linux` `Windows` `Framework Development` `Intermediate` `Expert` `Contributor` diff --git a/docs/source_zh_cn/design/mindspore/ir.md b/docs/source_zh_cn/design/mindspore/ir.md index 77bc45014d..362544c7f1 100644 --- a/docs/source_zh_cn/design/mindspore/ir.md +++ b/docs/source_zh_cn/design/mindspore/ir.md @@ -1,6 +1,6 @@ # MindSpore IR（MindIR） -`Linux` `框架开发` `中级` `高级` `贡献者` +`Linux` `Windows` `框架开发` `中级` `高级` `贡献者` diff --git a/tutorials/source_en/advanced_use/hub_tutorial.md b/tutorials/source_en/advanced_use/hub_tutorial.md index f832f4b432..d056ae8108 100644 --- a/tutorials/source_en/advanced_use/hub_tutorial.md +++ b/tutorials/source_en/advanced_use/hub_tutorial.md @@ -1,6 +1,6 @@ ## Submitting, Loading and 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b/tutorials/source_en/advanced_use/optimize_the_performance_of_data_preparation.md @@ -0,0 +1,387 @@ +# Optimizing the Data Preparation Performance + +`Linux` `Ascend` `GPU` `CPU` `Data Preparation` `Beginner` `Intermediate` `Expert` + + + +- [Optimizing the Data Preparation Performance](#optimizing-the-data-preparation-performance) + - [Overview](#overview) + - [Overall Process](#overall-process) + - [Preparations](#preparations) + - [Importing Modules](#importing-modules) + - [Downloading the Required Dataset](#downloading-the-required-dataset) + - [Optimizing the Data Loading Performance](#optimizing-the-data-loading-performance) + - [Performance Optimization Solution](#performance-optimization-solution) + - [Code Example](#code-example) + - [Optimizing the Shuffle Performance](#optimizing-the-shuffle-performance) + - [Performance Optimization Solution](#performance-optimization-solution-1) + - [Code Example](#code-example-1) + - [Optimizing the Data Augmentation Performance](#optimizing-the-data-augmentation-performance) + - [Performance Optimization Solution](#performance-optimization-solution-2) + - [Code Example](#code-example-2) + - [Performance Optimization Solution Summary](#performance-optimization-solution-summary) + - [Multi-thread Optimization Solution](#multi-thread-optimization-solution) + - [Multi-process Optimization Solution](#multi-process-optimization-solution) + - [Compose Optimization Solution](#compose-optimization-solution) + - [Operator Fusion Optimization Solution](#operator-fusion-optimization-solution) + + + +   + +## Overview + +Data is the most important factor of deep learning. Data quality determines the upper limit of deep learning result, whereas model quality enables the result to approach the upper limit.Therefore, high-quality data input is beneficial to the entire deep neural network. During the entire data processing and data augmentation process, data continuously flows through a "pipeline" to the training system, as shown in the following figure: + +![title](./images/pipeline.png) + +MindSpore provides data processing and data augmentation functions for users. In the pipeline process, if each step can be properly used, the data performance will be greatly improved. This section describes how to optimize performance during data loading, data processing, and data augmentation based on the CIFAR-10 dataset. + +## Overall Process +- Prepare data. +- Optimize the data loading performance. +- Optimize the shuffle performance. +- Optimize the data augmentation performance. +- Summarize the performance optimization solution. + +## Preparations + +### Importing Modules + +The `dataset` module provides APIs for loading and processing datasets. + + +```python +import mindspore.dataset as ds +``` + +The `numpy` module is used to generate ndarrays. + + +```python +import numpy as np +``` + +### Downloading the Required Dataset + +1. Create the `./dataset/Cifar10Data` directory in the current working directory. The dataset used for this practice is stored in this directory. +2. Create the `./transform` directory in the current working directory. The dataset generated during the practice is stored in this directory. +3. Download [the CIFAR-10 dataset in binary format](https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz) and decompress the dataset file to the `./dataset/Cifar10Data/cifar-10-batches-bin` directory. The dataset will be used during data loading. +4. Download [the CIFAR-10 Python dataset in file-format](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz) and decompress the dataset file to the `./dataset/Cifar10Data/cifar-10-batches-py` directory. The dataset will be used for data conversion. + +The directory structure is as follows: + + + dataset/Cifar10Data + ├── cifar-10-batches-bin + │   ├── batches.meta.txt + │   ├── data_batch_1.bin + │   ├── data_batch_2.bin + │   ├── data_batch_3.bin + │   ├── data_batch_4.bin + │   ├── data_batch_5.bin + │   ├── readme.html + │   └── test_batch.bin + └── cifar-10-batches-py + ├── batches.meta + ├── data_batch_1 + ├── data_batch_2 + ├── data_batch_3 + ├── data_batch_4 + ├── data_batch_5 + ├── readme.html + └── test_batch + +In the preceding information: +- The `cifar-10-batches-bin` directory is the directory for storing the CIFAR-10 dataset in binary format. +- The `cifar-10-batches-py` directory is the directory for storing the CIFAR-10 dataset in Python file format. + +## Optimizing the Data Loading Performance + +MindSpore provides multiple data loading methods, including common dataset loading, user-defined dataset loading, and MindSpore data format loading. For details, see [Loading Datasets](https://www.mindspore.cn/tutorial/en/master/use/data_preparation/loading_the_datasets.html). The dataset loading performance varies depending on the underlying implementation method. + +| | Common Dataset | User-defined Dataset | MindRecord Dataset | +| :----: | :----: | :----: | :----: | +| Underlying implementation | C++ | Python | C++ | +| Performance | High | Medium | High | + +### Performance Optimization Solution + +![title](./images/data_loading_performance_scheme.png) + +Suggestions on data loading performance optimization are as follows: +- Built-in loading operators are preferred for supported dataset formats. For details, see [Built-in Loading Operators](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.dataset.html). If the performance cannot meet the requirements, use the multi-thread concurrency solution. For details, see [Multi-thread Optimization Solution](#multi-thread-optimization-solution). +- For a dataset format that is not supported, convert the format to MindSpore data format and then use the `MindDataset` class to load the dataset. For details, see [Converting Datasets into MindSpore Data Format](https://www.mindspore.cn/tutorial/en/master/use/data_preparation/converting_datasets.html). If the performance cannot meet the requirements, use the multi-thread concurrency solution, for details, see [Multi-thread Optimization Solution](#multi-thread-optimization-solution). +- For dataset formats that are not supported, the user-defined `GeneratorDataset` class is preferred for implementing fast algorithm verification. If the performance cannot meet the requirements, the multi-process concurrency solution can be used. For details, see [Multi-process Optimization Solution](#multi-process-optimization-solution). + +### Code Example + +Based on the preceding suggestions of data loading performance optimization, the `Cifar10Dataset` class of built-in loading operators, the `MindDataset` class after data conversion, and the `GeneratorDataset` class are used to load data. The sample code is displayed as follows: + +1. Use the `Cifar10Dataset` class of built-in operators to load the CIFAR-10 dataset in binary format. The multi-thread optimization solution is used for data loading. Four threads are enabled to concurrently complete the task. Finally, a dictionary iterator is created for the data and a data record is read through the iterator. + + + ```python + cifar10_path = "./dataset/Cifar10Data/cifar-10-batches-bin/" + + # create Cifar10Dataset for reading data + cifar10_dataset = ds.Cifar10Dataset(cifar10_path, num_parallel_workers=4) + # create a dictionary iterator and read a data record through the iterator + print(next(cifar10_dataset.create_dict_iterator())) + ``` + + The output is as follows: + ``` + {'image': array([[[235, 235, 235], + [230, 230, 230], + [234, 234, 234], + ..., + [248, 248, 248], + [248, 248, 248], + [249, 249, 249]], + ..., + [120, 120, 119], + [146, 146, 146], + [177, 174, 190]]], dtype=uint8), 'label': array(9, dtype=uint32)} + ``` + +2. Use the `Cifar10ToMR` class to convert the CIFAR-10 dataset into MindSpore data format. In this example, the CIFAR-10 dataset in Python file format is used. Then use the `MindDataset` class to load the dataset in MindSpore data format. The multi-thread optimization solution is used for data loading. Four threads are enabled to concurrently complete the task. Finally, a dictionary iterator is created for data and a data record is read through the iterator. + + + ```python + from mindspore.mindrecord import Cifar10ToMR + + cifar10_path = './dataset/Cifar10Data/cifar-10-batches-py/' + cifar10_mindrecord_path = './transform/cifar10.record' + + cifar10_transformer = Cifar10ToMR(cifar10_path, cifar10_mindrecord_path) + # executes transformation from Cifar10 to MindRecord + cifar10_transformer.transform(['label']) + + # create MindDataset for reading data + cifar10_mind_dataset = ds.MindDataset(dataset_file=cifar10_mindrecord_path, num_parallel_workers=4) + # create a dictionary iterator and read a data record through the iterator + print(next(cifar10_mind_dataset.create_dict_iterator())) + ``` + + The output is as follows: + ``` + {'data': array([255, 216, 255, ..., 63, 255, 217], dtype=uint8), 'id': array(30474, dtype=int64), 'label': array(2, dtype=int64)} + ``` + +3. The `GeneratorDataset` class is used to load the user-defined dataset, and the multi-process optimization solution is used. Four processes are enabled to concurrently complete the task. Finally, a dictionary iterator is created for the data, and a data record is read through the iterator. + + + ```python + def generator_func(num): + for i in range(num): + yield (np.array([i]),) + + # create GeneratorDataset for reading data + dataset = ds.GeneratorDataset(source=generator_func(5), column_names=["data"], num_parallel_workers=4) + # create a dictionary iterator and read a data record through the iterator + print(next(dataset.create_dict_iterator())) + ``` + + The output is as follows: + ``` + {'data': array([0], dtype=int64)} + ``` + +## Optimizing the Shuffle Performance + +The shuffle operation is used to shuffle ordered datasets or repeated datasets. MindSpore provides the `shuffle` function for users. A larger value of `buffer_size` indicates a higher shuffling degree, consuming more time and computing resources. This API allows users to shuffle the data at any time during the entire pipeline process. For details, see [Shuffle Processing](https://www.mindspore.cn/tutorial/en/master/use/data_preparation/data_processing_and_augmentation.html#shuffle). However, because the underlying implementation methods are different, the performance of this method is not as good as that of setting the `shuffle` parameter to directly shuffle data by referring to the [Built-in Loading Operators](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.dataset.html). + +### Performance Optimization Solution + +![title](./images/shuffle_performance_scheme.png) + +Suggestions on shuffle performance optimization are as follows: +- Use the `shuffle` parameter of built-in loading operators to shuffle data. +- If the `shuffle` function is used and the performance still cannot meet the requirements, increase the value of the `buffer_size` parameter to improve the performance. + +### Code Example + +Based on the preceding shuffle performance optimization suggestions, the `shuffle` parameter of the `Cifar10Dataset` class of built-in loading operators and the `Shuffle` function are used to shuffle data. The sample code is displayed as follows: + +1. Use the built-in operator in `Cifar10Dataset` class to load the CIFAR-10 dataset. In this example, the CIFAR-10 dataset in binary format is used, and the `shuffle` parameter is set to True to perform data shuffle. Finally, a dictionary iterator is created for the data and a data record is read through the iterator. + + + ```python + cifar10_path = "./dataset/Cifar10Data/cifar-10-batches-bin/" + + # create Cifar10Dataset for reading data + cifar10_dataset = ds.Cifar10Dataset(cifar10_path, shuffle=True) + # create a dictionary iterator and read a data record through the iterator + print(next(cifar10_dataset.create_dict_iterator())) + ``` + + The output is as follows: + ``` + {'image': array([[[254, 254, 254], + [255, 255, 254], + [255, 255, 254], + ..., + [232, 234, 244], + [226, 230, 242], + [228, 232, 243]], + ..., + [ 64, 61, 63], + [ 63, 58, 60], + [ 61, 56, 58]]], dtype=uint8), 'label': array(9, dtype=uint32)} + ``` + +2. Use the `shuffle` function to shuffle data. Set `buffer_size` to 3 and use the `GeneratorDataset` class to generate data. + + + ```python + def generator_func(): + for i in range(5): + yield (np.array([i, i+1, i+2, i+3, i+4]),) + + ds1 = ds.GeneratorDataset(source=generator_func, column_names=["data"]) + print("before shuffle:") + for data in ds1.create_dict_iterator(): + print(data["data"]) + + ds2 = ds1.shuffle(buffer_size=3) + print("after shuffle:") + for data in ds2.create_dict_iterator(): + print(data["data"]) + ``` + ``` + The output is as follows: + + before shuffle: + [0 1 2 3 4] + [1 2 3 4 5] + [2 3 4 5 6] + [3 4 5 6 7] + [4 5 6 7 8] + after shuffle: + [2 3 4 5 6] + [0 1 2 3 4] + [4 5 6 7 8] + [1 2 3 4 5] + [3 4 5 6 7] + ``` + +## Optimizing the Data Augmentation Performance + +During image classification training, especially when the dataset is small, users can use data augmentation to preprocess images to enrich the dataset. MindSpore provides multiple data augmentation methods, including: +- Use the built-in C operator (`c_transforms` module) to perform data augmentation. +- Use the built-in Python operator (`py_transforms` module) to perform data augmentation. +- Users can define Python functions as needed to perform data augmentation. + +For details, see [Data Augmentation](https://www.mindspore.cn/tutorial/en/master/use/data_preparation/data_processing_and_augmentation.html#id3). The performance varies according to the underlying implementation methods. + +| Module | Underlying API | Description | +| :----: | :----: | :----: | +| c_transforms | C++ (based on OpenCV) | High performance | +| py_transforms | Python (based on PIL) | This module provides multiple image augmentation functions and the method for converting PIL images into NumPy arrays. | + + +### Performance Optimization Solution + +![title](./images/data_enhancement_performance_scheme.png) + + +Suggestions on data augmentation performance optimization are as follows: +- The `c_transforms` module is preferentially used to perform data augmentation for its highest performance. If the performance cannot meet the requirements, refer to [Multi-thread Optimization Solution](#multi-thread-optimization-solution), [Compose Optimization Solution](#compose-optimization-solution), or [Operator Fusion Optimization Solution](#operator-fusion-optimization-solution). +- If the `py_transforms` module is used to perform data augmentation and the performance still cannot meet the requirements, refer to [Multi-thread Optimization Solution](#multi-thread-optimization-solution), [Multi-process Optimization Solution](#multi-process-optimization-solution), [Compose Optimization Solution](#compose-optimization-solution), or [Operator Fusion Optimization Solution](#operator-fusion-optimization-solution). +- The `c_transforms` module maintains buffer management in C++, and the `py_transforms` module maintains buffer management in Python. Because of the performance cost of switching between Python and C++, it is advised not to use different operator types together. +- If the user-defined Python functions are used to perform data augmentation and the performance still cannot meet the requirements, use the [Multi-thread Optimization Solution](#multi-thread-optimization-solution) or [Multi-process Optimization Solution](#multi-process-optimization-solution). If the performance still cannot be improved, in this case, optimize the user-defined Python code. + +### Code Example + +Based on the preceding suggestions of data augmentation performance optimization, the `c_transforms` module and user-defined Python function are used to perform data augmentation. The code is displayed as follows: + +1. The `c_transforms` module is used to perform data augmentation. During data augmentation, the multi-thread optimization solution is used. Four threads are enabled to concurrently complete the task. The operator fusion optimization solution is used and the `RandomResizedCrop` fusion class is used to replace the `RandomResize` and `RandomCrop` classes. + + + ```python + import mindspore.dataset.transforms.c_transforms as c_transforms + import mindspore.dataset.vision.c_transforms as C + import matplotlib.pyplot as plt + cifar10_path = "./dataset/Cifar10Data/cifar-10-batches-bin/" + + # create Cifar10Dataset for reading data + cifar10_dataset = ds.Cifar10Dataset(cifar10_path, num_parallel_workers=4) + transforms = C.RandomResizedCrop((800, 800)) + # apply the transform to the dataset through dataset.map() + cifar10_dataset = cifar10_dataset.map(operations=transforms, input_columns="image", num_parallel_workers=4) + + data = next(cifar10_dataset.create_dict_iterator()) + plt.imshow(data["image"]) + plt.show() + ``` + + The output is as follows: + + ![png](./images/cifar10_c_transforms.png) + + +2. A user-defined Python function is used to perform data augmentation. During data augmentation, the multi-process optimization solution is used, and four processes are enabled to concurrently complete the task. + + + ```python + def generator_func(): + for i in range(5): + yield (np.array([i, i+1, i+2, i+3, i+4]),) + + ds3 = ds.GeneratorDataset(source=generator_func, column_names=["data"]) + print("before map:") + for data in ds3.create_dict_iterator(): + print(data["data"]) + + func = lambda x:x**2 + ds4 = ds3.map(operations=func, input_columns="data", python_multiprocessing=True, num_parallel_workers=4) + print("after map:") + for data in ds4.create_dict_iterator(): + print(data["data"]) + ``` + + The output is as follows: + ``` + before map: + [0 1 2 3 4] + [1 2 3 4 5] + [2 3 4 5 6] + [3 4 5 6 7] + [4 5 6 7 8] + after map: + [ 0 1 4 9 16] + [ 1 4 9 16 25] + [ 4 9 16 25 36] + [ 9 16 25 36 49] + [16 25 36 49 64] + ``` + +## Performance Optimization Solution Summary + +### Multi-thread Optimization Solution + +During the data pipeline process, the number of threads for related operators can be set to improve the concurrency and performance. For example: +- During data loading, the `num_parallel_workers` parameter in the built-in data loading class is used to set the number of threads. +- During data augmentation, the `num_parallel_workers` parameter in the `map` function is used to set the number of threads. +- During batch processing, the `num_parallel_workers` parameter in the `batch` function is used to set the number of threads. + +For details, see [Built-in Loading Operators](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.dataset.html). + +### Multi-process Optimization Solution + +During data processing, operators implemented by Python support the multi-process mode. For example: +- By default, the `GeneratorDataset` class is in multi-process mode. The `num_parallel_workers` parameter indicates the number of enabled processes. The default value is 1. For details, see [Generator Dataset](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.dataset.html#mindspore.dataset.GeneratorDataset) +- If the user-defined Python function or the `py_transforms` module is used to perform data augmentation and the `python_multiprocessing` parameter of the `map` function is set to True, the `num_parallel_workers` parameter indicates the number of processes and the default value of the `python_multiprocessing` parameter is False. In this case, the `num_parallel_workers` parameter indicates the number of threads. For details, see [Built-in Loading Operators](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.dataset.html). + +### Compose Optimization Solution + +Map operators can receive the Tensor operator list and apply all these operators based on a specific sequence. Compared with the Map operator used by each Tensor operator, such Fat Map operators can achieve better performance, as shown in the following figure: + +![title](./images/compose.png) + +### Operator Fusion Optimization Solution + +Some fusion operators are provided to aggregate the functions of two or more operators into one operator. For details, see [Data Augmentation Operators](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.dataset.vision.html). Compared with the pipelines of their components, such fusion operators provide better performance. As shown in the figure: + +![title](./images/operator_fusion.png) diff --git a/tutorials/source_en/index.rst b/tutorials/source_en/index.rst index 0c0e217dec..370059fdab 100644 --- a/tutorials/source_en/index.rst +++ b/tutorials/source_en/index.rst @@ -32,7 +32,7 @@ MindSpore Tutorials advanced_use/computer_vision_application advanced_use/nlp_application advanced_use/synchronization_training_and_evaluation.md - + advanced_use/optimize_the_performance_of_data_preparation.md .. toctree:: :glob: :maxdepth: 1 diff --git a/tutorials/source_zh_cn/advanced_use/auto_augmentation.md b/tutorials/source_zh_cn/advanced_use/auto_augmentation.md index 94250841f2..9a159b0529 100644 --- a/tutorials/source_zh_cn/advanced_use/auto_augmentation.md +++ b/tutorials/source_zh_cn/advanced_use/auto_augmentation.md @@ -1,6 +1,6 @@ # 自动数据增强 -`Ascend` `GPU` `CPU` `中级` `高级` +`Linux` `Ascend` `GPU` `CPU` `中级` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/cache.md b/tutorials/source_zh_cn/advanced_use/cache.md index 2039720a55..9268187593 100644 --- a/tutorials/source_zh_cn/advanced_use/cache.md +++ b/tutorials/source_zh_cn/advanced_use/cache.md @@ -1,6 +1,6 @@ # 单节点缓存 -`Ascend` `GPU` `CPU` `中级` `高级` +`Linux` `Ascend` `GPU` `CPU` `中级` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md b/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md index 36f8ef9995..bb1307dc06 100644 --- a/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md +++ b/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md @@ -1,5 +1,3 @@ - - # 手动设置并行场景模型参数的保存和加载 `Linux` `Ascend` `GPU` `模型训练` `中级` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/computer_vision_application.md b/tutorials/source_zh_cn/advanced_use/computer_vision_application.md index 4a54e92841..c00a1d9efd 100644 --- a/tutorials/source_zh_cn/advanced_use/computer_vision_application.md +++ b/tutorials/source_zh_cn/advanced_use/computer_vision_application.md @@ -9,11 +9,11 @@ - [图像分类](#图像分类) - [任务描述及准备](#任务描述及准备) - [下载CIFAR-10数据集](#下载cifar-10数据集) - - [数据预加载和预处理](#数据预加载和预处理) - - [定义卷积神经网络](#定义卷积神经网络) - - [定义损失函数和优化器](#定义损失函数和优化器) - - [调用`Model`高阶API进行训练和保存模型文件](#调用model高阶api进行训练和保存模型文件) - - [加载保存的模型，并进行验证](#加载保存的模型并进行验证) + - [数据预加载和预处理](#数据预加载和预处理) + - [定义卷积神经网络](#定义卷积神经网络) + - [定义损失函数和优化器](#定义损失函数和优化器) + - [调用`Model`高阶API进行训练和保存模型文件](#调用model高阶api进行训练和保存模型文件) + - [加载保存的模型，并进行验证](#加载保存的模型并进行验证) - [参考文献](#参考文献) diff --git a/tutorials/source_zh_cn/advanced_use/data_processing_acceleration.md b/tutorials/source_zh_cn/advanced_use/data_processing_acceleration.md index 141b82df3c..4f3edad004 100644 --- a/tutorials/source_zh_cn/advanced_use/data_processing_acceleration.md +++ b/tutorials/source_zh_cn/advanced_use/data_processing_acceleration.md @@ -1,6 +1,6 @@ # 数据处理性能调试 -`Ascend` `GPU` `CPU` `中级` `高级` +`Linux` `Ascend` `GPU` `CPU` `中级` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/dataset_conversion.md b/tutorials/source_zh_cn/advanced_use/dataset_conversion.md index f4f4e73e2f..1a7f640e45 100644 --- a/tutorials/source_zh_cn/advanced_use/dataset_conversion.md +++ b/tutorials/source_zh_cn/advanced_use/dataset_conversion.md @@ -1,6 +1,6 @@ # MindSpore数据格式转换 -`Ascend` `GPU` `CPU` `初级` `中级` `高级` + `Linux` `Ascend` `GPU` `CPU` `初级` `中级` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/deep_probability_program.md b/tutorials/source_zh_cn/advanced_use/deep_probability_program.md index 13e92b1452..b1294d3633 100644 --- a/tutorials/source_zh_cn/advanced_use/deep_probability_program.md +++ b/tutorials/source_zh_cn/advanced_use/deep_probability_program.md @@ -39,7 +39,7 @@ MindSpore深度概率编程（MindSpore Deep Probabilistic Programming, MDP） 本例子使用的是MNIST数据集，数据处理过程与教程中的[实现一个图片分类应用](https://www.mindspore.cn/tutorial/zh-CN/master/quick_start/quick_start.html)一致。 #### 定义贝叶斯神经网络 -本例子使用的是贝叶斯LeNet。利用bnn_layers构建贝叶斯神经网络的方法与构建普通的神经网络相同。值得注意的是，bnn_layers和普通的神经网络层可以互相组合。 +本例子使用的是贝叶斯LeNet。利用`bnn_layers`构建贝叶斯神经网络的方法与构建普通的神经网络相同。值得注意的是，`bnn_layers`和普通的神经网络层可以互相组合。 ``` import mindspore.nn as nn @@ -89,8 +89,8 @@ class BNNLeNet5(nn.Cell): ``` #### 定义损失函数和优化器 接下来需要定义损失函数（Loss）和优化器（Optimizer）。损失函数是深度学习的训练目标，也叫目标函数，可以理解为神经网络的输出（Logits）和标签(Labels)之间的距离，是一个标量数据。 -常见的损失函数包括均方误差、L2损失、Hinge损失、交叉熵等等。图像分类应用通常采用交叉熵损失（CrossEntropy）。 -优化器用于神经网络求解（训练）。由于神经网络参数规模庞大，无法直接求解，因而深度学习中采用随机梯度下降算法（SGD）及其改进算法进行求解。MindSpore封装了常见的优化器，如SGD、Adam、Momemtum等等。本例采用Adam优化器，通常需要设定两个参数，学习率（learnin _rate）和权重衰减项（weight decay）。 +常见的损失函数包括均方误差、L2损失、Hinge损失、交叉熵等等。图像分类应用通常采用交叉熵损失（`CrossEntropy`）。 +优化器用于神经网络求解（训练）。由于神经网络参数规模庞大，无法直接求解，因而深度学习中采用随机梯度下降算法（SGD）及其改进算法进行求解。MindSpore封装了常见的优化器，如`SGD`、`Adam`、`Momemtum`等等。本例采用Adam优化器，通常需要设定两个参数，学习率（`learnin _rate`）和权重衰减项（`weight decay`）。 MindSpore中定义损失函数和优化器的代码样例如下： ``` @@ -198,7 +198,7 @@ decoder = Decoder() vae = VAE(encoder, decoder, hidden_size=400, latent_size=20) ``` ### 定义损失函数和优化器 -接下来需要定义损失函数（Loss）和优化器（Optimizer）。本例使用的损失函数是ELBO，ELBO是变分推断专用的损失函数；本例使用的优化器是Adam。 +接下来需要定义损失函数（Loss）和优化器（Optimizer）。本例使用的损失函数是`ELBO`，`ELBO`是变分推断专用的损失函数；本例使用的优化器是`Adam`。 MindSpore中定义损失函数和优化器的代码样例如下： ``` @@ -307,7 +307,7 @@ class LeNet5(nn.Cell): return x ``` #### 定义损失函数和优化器 -接下来需要定义损失函数（Loss）和优化器（Optimizer）。本例使用交叉熵损失作为损失函数，Adam作为优化器。 +接下来需要定义损失函数（Loss）和优化器（Optimizer）。本例使用交叉熵损失作为损失函数，`Adam`作为优化器。 ``` network = LeNet5() @@ -403,7 +403,7 @@ train_bnn_network = bnn_transformer.transform_to_bnn_model() ### 不确定性估计 不确定性估计工具箱基于MindSpore Deep probability Programming (MDP)，适用于主流的深度学习模型，如回归、分类、目标检测等。在推理阶段，利用不确定性估计工具箱，开发人员只需通过训练模型和训练数据集，指定需要估计的任务和样本，即可得到任意不确定性（aleatoric uncertainty）和认知不确定性（epistemic uncertainty）。基于不确定性信息，开发人员可以更好地理解模型和数据集。 -以分类任务为例，本例中使用的模型是LeNet，数据集为MNist，数据处理过程与教程中的[实现一个图片分类应用](https://www.mindspore.cn/tutorial/zh-CN/master/quick_start/quick_start.html)一致。为了评估测试示例的不确定性，使用工具箱的方法如下: +以分类任务为例，本例中使用的模型是LeNet，数据集为MNIST，数据处理过程与教程中的[实现一个图片分类应用](https://www.mindspore.cn/tutorial/zh-CN/master/quick_start/quick_start.html)一致。为了评估测试示例的不确定性，使用工具箱的方法如下: ``` from mindspore.nn.probability.toolbox.uncertainty_evaluation import UncertaintyEvaluation diff --git a/tutorials/source_zh_cn/advanced_use/hub_tutorial.md b/tutorials/source_zh_cn/advanced_use/hub_tutorial.md index ffe12cfd47..607eed984a 100644 --- a/tutorials/source_zh_cn/advanced_use/hub_tutorial.md +++ b/tutorials/source_zh_cn/advanced_use/hub_tutorial.md @@ -1,6 +1,6 @@ ## 使用MindSpore Hub提交、加载和微调模型 -`Ascend` `GPU` `MindSpore Hub` `模型上传` `模型加载` `模型微调` `初级` `中级` `高级` +`Linux` `Ascend` `GPU` `MindSpore Hub` `模型上传` `模型加载` `模型微调` `初级` `中级` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/membership_inference.md b/tutorials/source_zh_cn/advanced_use/membership_inference.md index c54935a650..4f887583fb 100644 --- a/tutorials/source_zh_cn/advanced_use/membership_inference.md +++ b/tutorials/source_zh_cn/advanced_use/membership_inference.md @@ -1,5 +1,7 @@ # 成员推理攻击 +`Linux` `Ascend` `全流程` `初级` `中级` `高级` + - [成员推理攻击](#成员推理攻击) diff --git a/tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learning.md b/tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learning.md index 3822551830..107f666b76 100644 --- a/tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learning.md +++ b/tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learning.md @@ -1,5 +1,5 @@ # MobileNetV2 增量学习 -`Linux` `CPU` `Ascend` `GPU` `模型开发` `中级` `高级` +`Linux` `Windows` `CPU` `Ascend` `GPU` `模型开发` `中级` `高级` diff --git a/tutorials/source_zh_cn/advanced_use/model_scripts_transformation.md b/tutorials/source_zh_cn/advanced_use/model_scripts_transformation.md index 65811e84eb..ce721759cd 100644 --- a/tutorials/source_zh_cn/advanced_use/model_scripts_transformation.md +++ b/tutorials/source_zh_cn/advanced_use/model_scripts_transformation.md @@ -1,6 +1,6 @@ # 模型脚本迁移 -`Ascend` `模型开发` `初级` `Linux` +`Linux` `Ascend` `模型开发` `初级` `Linux` diff --git a/tutorials/source_zh_cn/use/image_loading.md b/tutorials/source_zh_cn/use/image_loading.md index be239acfd1..db5545aeb2 100644 --- a/tutorials/source_zh_cn/use/image_loading.md +++ b/tutorials/source_zh_cn/use/image_loading.md @@ -1,6 +1,6 @@ # 加载图像 -`Ascend` `GPU` `CPU` `数据准备` `初级` `中级` `高级` +`Linux` `Ascend` `GPU` `CPU` `数据准备` `初级` `中级` `高级` diff --git a/tutorials/source_zh_cn/use/text_loading.md b/tutorials/source_zh_cn/use/text_loading.md index 76f8109e0e..340d2c0828 100644 --- a/tutorials/source_zh_cn/use/text_loading.md +++ b/tutorials/source_zh_cn/use/text_loading.md @@ -1,6 +1,6 @@ # 加载文本 -`Ascend` `GPU` `CPU` `数据准备` `初级` `中级` `高级` +`Linux` `Ascend` `GPU` `CPU` `数据准备` `初级` `中级` `高级` -- Gitee From 9245296d6e80626d34137dbba2c86ebd493cbfa3 Mon Sep 17 00:00:00 2001 From: liuwenhao4 Date: Tue, 15 Sep 2020 14:38:42 +0800 Subject: [PATCH 022/156] Add Convertor tool docs for windows enviroment --- lite/tutorials/source_en/use/converter_tool.md | 2 +- lite/tutorials/source_zh_cn/use/converter_tool.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/lite/tutorials/source_en/use/converter_tool.md b/lite/tutorials/source_en/use/converter_tool.md index e8dc13fa4b..fbdb7f42b3 100644 --- a/lite/tutorials/source_en/use/converter_tool.md +++ b/lite/tutorials/source_en/use/converter_tool.md @@ -127,7 +127,7 @@ Reference description Linux environment model conversion tool [parameter descrip ### Example -At first, set the log printing level to INFO. +Set the log printing level to INFO. ```bash set MSLOG=INFO ``` diff --git a/lite/tutorials/source_zh_cn/use/converter_tool.md b/lite/tutorials/source_zh_cn/use/converter_tool.md index 917d02cee7..618ac57a3a 100644 --- a/lite/tutorials/source_zh_cn/use/converter_tool.md +++ b/lite/tutorials/source_zh_cn/use/converter_tool.md @@ -35,7 +35,7 @@ MindSpore Lite提供离线转换模型功能的工具，支持多种类型的模 ### 使用示例 -首先，在源码根目录下，输入命令进行编译，可参考`build.md`。 +在源码根目录下，输入命令进行编译，可参考`build.md`。 ```bash bash build.sh -I x86_64 ``` -- Gitee From 3b01213ca1ee1d7d54446a9346a0d64eaa21eef9 Mon Sep 17 00:00:00 2001 From: meng_chunyang Date: Tue, 15 Sep 2020 11:38:55 +0800 Subject: [PATCH 023/156] update docs of benchmark & timeprofiler tools --- lite/tutorials/source_en/build.md | 10 +++++----- lite/tutorials/source_en/use/benchmark_tool.md | 9 ++++----- lite/tutorials/source_en/use/timeprofiler_tool.md | 8 ++++---- lite/tutorials/source_zh_cn/build.md | 14 +++++++------- lite/tutorials/source_zh_cn/use/benchmark_tool.md | 9 ++++----- .../source_zh_cn/use/timeprofiler_tool.md | 8 ++++---- 6 files changed, 28 insertions(+), 30 deletions(-) diff --git a/lite/tutorials/source_en/build.md b/lite/tutorials/source_en/build.md index c4e50d0298..b59964d357 100644 --- a/lite/tutorials/source_en/build.md +++ b/lite/tutorials/source_en/build.md @@ -22,7 +22,7 @@ This chapter introduces how to quickly compile MindSpore Lite, which includes th | converter | Linux | Model Conversion Tool | | runtime | Linux、Android | Model Inference Framework | | benchmark | Linux、Android | Benchmarking Tool | -| time_profiler | Linux、Android | Performance Analysis Tool | +| timeprofiler | Linux、Android | Performance Analysis Tool | ## Linux Environment Compilation @@ -142,7 +142,7 @@ The inference framework can be obtained under `-I x86_64`, `-I arm64` and `-I ar │ └── third_party # Header files and libraries of third party libraries │ ├── flatbuffers # Header files of FlatBuffers │ └── include # Header files of inference framework - │ └── time_profile # Model network layer time-consuming analysis tool + │ └── time_profiler # Model network layer time-consuming analysis tool ``` @@ -157,7 +157,7 @@ The inference framework can be obtained under `-I x86_64`, `-I arm64` and `-I ar │ └── third_party # Header files and libraries of third party libraries │ ├── flatbuffers # Header files of FlatBuffers │ └── include # Header files of inference framework - │ └── time_profile # Model network layer time-consuming analysis tool + │ └── time_profiler # Model network layer time-consuming analysis tool ``` @@ -171,10 +171,10 @@ The inference framework can be obtained under `-I x86_64`, `-I arm64` and `-I ar │ └── third_party # Header files and libraries of third party libraries │ ├── flatbuffers # Header files of FlatBuffers │ └── include # Header files of inference framework - │ └── time_profile # Model network layer time-consuming analysis tool + │ └── time_profiler # Model network layer time-consuming analysis tool ``` > 1. `liboptimize.so` only exists in the output package of runtime-arm64 and is only used on ARMv8.2 and CPUs that support fp16. > 2. Compile ARM64 to get the inference framework output of arm64-cpu by default, if you add `-e gpu`, you will get the inference framework output of arm64-gpu, and the package name is `mindspore-lite-{version}-runtime-arm64-gpu.tar.gz`, compiling ARM32 is in the same way. -> 3. Before running the tools in the converter, benchmark or time_profile directory, you need to configure environment variables, and configure the path where the dynamic libraries of MindSpore Lite and Protobuf are located to the path where the system searches for dynamic libraries. Take the compiled under version 0.7.0-beta as an example: configure converter: `export LD_LIBRARY_PATH=./output/mindspore-lite-0.7.0-converter-ubuntu/third_party/protobuf/lib:./output/mindspore-lite-0.7.0-converter-ubuntu/third_party/flatbuffers/lib:${LD_LIBRARY_PATH}`; configure benchmark and timeprofiler: `export LD_LIBRARY_PATH= ./output/mindspore-lite-0.7.0-runtime-x86-cpu/lib:${LD_LIBRARY_PATH}`. +> 3. Before running the tools in the converter, benchmark or time_profiler directory, you need to configure environment variables, and configure the path where the dynamic libraries of MindSpore Lite and Protobuf are located to the path where the system searches for dynamic libraries. Take the compiled under version 0.7.0-beta as an example: configure converter: `export LD_LIBRARY_PATH=./output/mindspore-lite-0.7.0-converter-ubuntu/third_party/protobuf/lib:./output/mindspore-lite-0.7.0-converter-ubuntu/third_party/flatbuffers/lib:${LD_LIBRARY_PATH}`; configure benchmark and timeprofiler: `export LD_LIBRARY_PATH= ./output/mindspore-lite-0.7.0-runtime-x86-cpu/lib:${LD_LIBRARY_PATH}`. diff --git a/lite/tutorials/source_en/use/benchmark_tool.md b/lite/tutorials/source_en/use/benchmark_tool.md index d6b3a09ae8..000e270c60 100644 --- a/lite/tutorials/source_en/use/benchmark_tool.md +++ b/lite/tutorials/source_en/use/benchmark_tool.md @@ -76,11 +76,10 @@ The command used for benchmark testing based on the compiled Benchmark tool is a ```bash ./benchmark [--modelPath=] [--accuracyThreshold=] - [--calibDataPath=] [--cpuBindMode=] - [--device=] [--help] [--inDataPath=] - [--inDataType=] [--loopCount=] - [--numThreads=] [--omModelPath=] - [--resizeDims=] [--warmUpLoopCount=] + [--calibDataPath=] [--calibDataType=] + [--cpuBindMode=] [--device=] [--help] + [--inDataPath=] [--loopCount=] + [--numThreads=] [--warmUpLoopCount=] [--fp16Priority=] ``` diff --git a/lite/tutorials/source_en/use/timeprofiler_tool.md b/lite/tutorials/source_en/use/timeprofiler_tool.md index b0e3d35860..1442ecc46d 100644 --- a/lite/tutorials/source_en/use/timeprofiler_tool.md +++ b/lite/tutorials/source_en/use/timeprofiler_tool.md @@ -20,16 +20,16 @@ After model conversion and before inference, you can use the TimeProfiler tool t To use the TimeProfiler tool, you need to prepare the environment as follows: -- Compilation: Install build dependencies and perform build. The code of the TimeProfiler tool is stored in the `mindspore/lite/tools/time_profile` directory of the MindSpore source code. For details about the build operations, see the [Environment Requirements](https://www.mindspore.cn/lite/tutorial/en/master/build.html#environment-requirements) and [Compilation Example](https://www.mindspore.cn/lite/tutorial/en/master/build.html#compilation-example) in the build document. +- Compilation: Install build dependencies and perform build. The code of the TimeProfiler tool is stored in the `mindspore/lite/tools/time_profiler` directory of the MindSpore source code. For details about the build operations, see the [Environment Requirements](https://www.mindspore.cn/lite/tutorial/en/master/build.html#environment-requirements) and [Compilation Example](https://www.mindspore.cn/lite/tutorial/en/master/build.html#compilation-example) in the build document. -- Run: Obtain the `timeprofile` tool and configure environment variables by referring to [Output Description](https://www.mindspore.cn/lite/tutorial/en/master/build.html#output-description) in the build document. +- Run: Obtain the `timeprofiler` tool and configure environment variables by referring to [Output Description](https://www.mindspore.cn/lite/tutorial/en/master/build.html#output-description) in the build document. ## Parameter Description The command used for analyzing the time consumption of forward inference at the network layer based on the compiled TimeProfiler tool is as follows: ```bash -./timeprofile --modelPath= [--help] [--loopCount=] [--numThreads=] [--cpuBindMode=] [--inDataPath=] [--fp16Priority=] +./timeprofiler --modelPath= [--help] [--loopCount=] [--numThreads=] [--cpuBindMode=] [--inDataPath=] [--fp16Priority=] ``` The following describes the parameters in detail. @@ -49,7 +49,7 @@ The following describes the parameters in detail. Take the `test_timeprofiler.ms` model as an example and set the number of model inference cycles to 10. The command for using TimeProfiler to analyze the time consumption at the network layer is as follows: ```bash -./timeprofile --modelPath=./models/test_timeprofiler.ms --loopCount=10 +./timeprofiler --modelPath=./models/test_timeprofiler.ms --loopCount=10 ``` After this command is executed, the TimeProfiler tool outputs the statistics on the running time of the model at the network layer. In this example, the command output is as follows: The statistics are displayed by`opName` and `optype`. `opName` indicates the operator name, `optype` indicates the operator type, and `avg` indicates the average running time of the operator per single run, `percent` indicates the ratio of the operator running time to the total operator running time, `calledTimess` indicates the number of times that the operator is run, and `opTotalTime` indicates the total time that the operator is run for a specified number of times. Finally, `total time` and `kernel cost` show the average time consumed by a single inference operation of the model and the sum of the average time consumed by all operators in the model inference, respectively. diff --git a/lite/tutorials/source_zh_cn/build.md b/lite/tutorials/source_zh_cn/build.md index 6a26b28a90..e3bf825521 100644 --- a/lite/tutorials/source_zh_cn/build.md +++ b/lite/tutorials/source_zh_cn/build.md @@ -22,7 +22,7 @@ | converter | Linux | 模型转换工具 | | runtime | Linux、Android | 模型推理框架 | | benchmark | Linux、Android | 基准测试工具 | -| time_profiler | Linux、Android | 性能分析工具 | +| timeprofiler | Linux、Android | 性能分析工具 | ## Linux环境编译 @@ -30,7 +30,7 @@ - 系统环境：Linux x86_64，推荐使用Ubuntu 18.04.02LTS -- runtime、benchmark、time_profiler编译依赖 +- runtime、benchmark、timeprofiler编译依赖 - [CMake](https://cmake.org/download/) >= 3.14.1 - [GCC](https://gcc.gnu.org/releases.html) >= 7.3.0 - [Android_NDK](https://dl.google.com/android/repository/android-ndk-r20b-linux-x86_64.zip) >= r20 @@ -101,7 +101,7 @@ git clone https://gitee.com/mindspore/mindspore.git 编译完成后，进入`mindspore/output/`目录，可查看编译后生成的文件。文件分为两部分： - `mindspore-lite-{version}-converter-{os}.tar.gz`：包含模型转换工具converter。 -- `mindspore-lite-{version}-runtime-{os}-{device}.tar.gz`：包含模型推理框架runtime、基准测试工具benchmark和性能分析工具time_profiler。 +- `mindspore-lite-{version}-runtime-{os}-{device}.tar.gz`：包含模型推理框架runtime、基准测试工具benchmark和性能分析工具timeprofiler。 > version：输出件版本号，与所编译的分支代码对应的版本一致。 > @@ -143,7 +143,7 @@ tar -xvf mindspore-lite-{version}-runtime-{os}-{device}.tar.gz │ └── third_party # 第三方库头文件和库 │ ├── flatbuffers # FlatBuffers头文件 │ └── include # 推理框架头文件 - │ └── time_profile # 模型网络层耗时分析工具 + │ └── time_profiler # 模型网络层耗时分析工具 ``` @@ -158,7 +158,7 @@ tar -xvf mindspore-lite-{version}-runtime-{os}-{device}.tar.gz │ └── third_party # 第三方库头文件和库 │ ├── flatbuffers # FlatBuffers头文件 │ └── include # 推理框架头文件 - │ └── time_profile # 模型网络层耗时分析工具 + │ └── time_profiler # 模型网络层耗时分析工具 ``` @@ -172,10 +172,10 @@ tar -xvf mindspore-lite-{version}-runtime-{os}-{device}.tar.gz │ └── third_party # 第三方库头文件和库 │ ├── flatbuffers # FlatBuffers头文件 │ └── include # 推理框架头文件 - │ └── time_profile # 模型网络层耗时分析工具 + │ └── time_profiler # 模型网络层耗时分析工具 ``` > 1. `liboptimize.so`仅在runtime-arm64的输出包中存在，仅在ARMv8.2和支持fp16特性的CPU上使用。 > 2. 编译ARM64默认可获得arm64-cpu的推理框架输出件，若添加`-e gpu`则获得arm64-gpu的推理框架输出件，此时包名为`mindspore-lite-{version}-runtime-arm64-gpu.tar.gz`，编译ARM32同理。 -> 3. 运行converter、benchmark或time_profile目录下的工具前，都需配置环境变量，将MindSpore Lite和Protobuf的动态库所在的路径配置到系统搜索动态库的路径中。以0.7.0-beta版本下编译为例：配置converter：`export LD_LIBRARY_PATH=./output/mindspore-lite-0.7.0-converter-ubuntu/third_party/protobuf/lib:./output/mindspore-lite-0.7.0-converter-ubuntu/third_party/flatbuffers/lib:${LD_LIBRARY_PATH}`；配置benchmark和timeprofiler：`export LD_LIBRARY_PATH=./output/mindspore-lite-0.7.0-runtime-x86-cpu/lib:${LD_LIBRARY_PATH}`。 +> 3. 运行converter、benchmark或time_profiler目录下的工具前，都需配置环境变量，将MindSpore Lite和Protobuf的动态库所在的路径配置到系统搜索动态库的路径中。以0.7.0-beta版本下编译为例：配置converter：`export LD_LIBRARY_PATH=./output/mindspore-lite-0.7.0-converter-ubuntu/third_party/protobuf/lib:./output/mindspore-lite-0.7.0-converter-ubuntu/third_party/flatbuffers/lib:${LD_LIBRARY_PATH}`；配置benchmark和timeprofiler：`export LD_LIBRARY_PATH=./output/mindspore-lite-0.7.0-runtime-x86-cpu/lib:${LD_LIBRARY_PATH}`。 diff --git a/lite/tutorials/source_zh_cn/use/benchmark_tool.md b/lite/tutorials/source_zh_cn/use/benchmark_tool.md index 83c6aadc63..7caaad2e63 100644 --- a/lite/tutorials/source_zh_cn/use/benchmark_tool.md +++ b/lite/tutorials/source_zh_cn/use/benchmark_tool.md @@ -76,11 +76,10 @@ Mean bias of all nodes: 0% ```bash ./benchmark [--modelPath=] [--accuracyThreshold=] - [--calibDataPath=] [--cpuBindMode=] - [--device=] [--help] [--inDataPath=] - [--inDataType=] [--loopCount=] - [--numThreads=] [--omModelPath=] - [--resizeDims=] [--warmUpLoopCount=] + [--calibDataPath=] [--calibDataType=] + [--cpuBindMode=] [--device=] [--help] + [--inDataPath=] [--loopCount=] + [--numThreads=] [--warmUpLoopCount=] [--fp16Priority=] ``` diff --git a/lite/tutorials/source_zh_cn/use/timeprofiler_tool.md b/lite/tutorials/source_zh_cn/use/timeprofiler_tool.md index fbe404c178..7c7a60576b 100644 --- a/lite/tutorials/source_zh_cn/use/timeprofiler_tool.md +++ b/lite/tutorials/source_zh_cn/use/timeprofiler_tool.md @@ -20,16 +20,16 @@ 使用TimeProfiler工具，需要进行如下环境准备工作。 -- 编译：TimeProfiler工具代码在MindSpore源码的`mindspore/lite/tools/time_profile`目录中，参考构建文档中的[环境要求](https://www.mindspore.cn/lite/tutorial/zh-CN/master/build.html#id1)和[编译示例](https://www.mindspore.cn/lite/tutorial/zh-CN/master/build.html#id3)执行编译。 +- 编译：TimeProfiler工具代码在MindSpore源码的`mindspore/lite/tools/time_profiler`目录中，参考构建文档中的[环境要求](https://www.mindspore.cn/lite/tutorial/zh-CN/master/build.html#id1)和[编译示例](https://www.mindspore.cn/lite/tutorial/zh-CN/master/build.html#id3)执行编译。 -- 运行：参考部署文档中的[编译输出](https://www.mindspore.cn/lite/tutorial/zh-CN/master/build.html#id4)，获得`timeprofile`工具，并配置环境变量。 +- 运行：参考部署文档中的[编译输出](https://www.mindspore.cn/lite/tutorial/zh-CN/master/build.html#id4)，获得`timeprofiler`工具，并配置环境变量。 ## 使用示例 使用TimeProfiler对`test_timeprofiler.ms`模型的网络层进行耗时分析，并且设置模型推理循环运行次数为10，则其命令代码如下： ```bash -./timeprofile --modelPath=./models/test_timeprofiler.ms --loopCount=10 +./timeprofiler --modelPath=./models/test_timeprofiler.ms --loopCount=10 ``` 该条命令执行后，TimeProfiler工具会输出模型网络层运行耗时的相关统计信息。对于本例命令，输出的统计信息如下。其中统计信息按照`opName`和`optype`两种划分方式分别显示，`opName`表示算子名，`optype`表示算子类别，`avg`表示该算子的平均单次运行时间，`percent`表示该算子运行耗时占所有算子运行总耗时的比例，`calledTimess`表示该算子的运行次数，`opTotalTime`表示该算子运行指定次数的总耗时。最后，`total time`和`kernel cost`分别显示了该模型单次推理的平均耗时和模型推理中所有算子的平均耗时之和。 @@ -77,7 +77,7 @@ total time : 2.90800 ms, kernel cost : 2.74851 ms 使用编译好的TimeProfiler工具进行模型网络层耗时分析时，其命令格式如下所示。 ```bash -./timeprofile --modelPath= [--help] [--loopCount=] [--numThreads=] [--cpuBindMode=] [--inDataPath=] [--fp16Priority=] +./timeprofiler --modelPath= [--help] [--loopCount=] [--numThreads=] [--cpuBindMode=] [--inDataPath=] [--fp16Priority=] ``` 下面提供详细的参数说明。 -- Gitee From 035e105082e895e741208607db7c1e12ae09a432 Mon Sep 17 00:00:00 2001 From: buxue Date: Tue, 15 Sep 2020 10:01:11 +0800 Subject: [PATCH 024/156] add restrict modify class member that is not parameter --- .../constraints_on_network_construction.md | 73 +++++++++++-------- .../constraints_on_network_construction.md | 73 +++++++++++-------- 2 files changed, 88 insertions(+), 58 deletions(-) diff --git a/docs/source_en/constraints_on_network_construction.md b/docs/source_en/constraints_on_network_construction.md index 2da31582ec..ccd96972dc 100644 --- a/docs/source_en/constraints_on_network_construction.md +++ b/docs/source_en/constraints_on_network_construction.md @@ -232,34 +232,49 @@ Currently, the following syntax is not supported in network constructors: ### Other Constraints -Input parameters of the construct function on the entire network and parameters of functions modified by the ms_function decorator are generalized during the graph compilation. Therefore, they cannot be transferred to operators as constant input. Therefore, in graph mode, the parameter passed to the entry network can only be Tensor. As shown in the following example: -* The following is an example of incorrect input: - ```python - class ExpandDimsTest(Cell): - def __init__(self): - super(ExpandDimsTest, self).__init__() - self.expandDims = P.ExpandDims() - - def construct(self, input_x, input_axis): - return self.expandDims(input_x, input_axis) - expand_dim = ExpandDimsTest() - input_x = Tensor(np.random.randn(2,2,2,2).astype(np.float32)) - expand_dim(input_x, 0) - ``` - In the example, ExpandDimsTest is a single-operator network with two inputs: input_x and input_axis. The second input of the ExpandDims operator must be a constant. This is because input_axis is required when the output dimension of the ExpandDims operator is deduced during graph compilation. As the network parameter input, the value of input_axis is generalized into a variable and cannot be determined. As a result, the output dimension of the operator cannot be deduced, causing the graph compilation failure. Therefore, the input required by deduction in the graph compilation phase must be a constant. In APIs, the "constant input is needed" is marked for parameters that require constant input of these operators. +1. Input parameters of the `construct` function on the entire network and parameters of functions modified by the `ms_function` decorator are generalized during the graph compilation and cannot be passed to operators as constant input. Therefore, in graph mode, the parameter passed to the entry network can only be `Tensor`. As shown in the following example: + + * The following is an example of incorrect input: + ```python + class ExpandDimsTest(Cell): + def __init__(self): + super(ExpandDimsTest, self).__init__() + self.expandDims = P.ExpandDims() + + def construct(self, input_x, input_axis): + return self.expandDims(input_x, input_axis) + expand_dim = ExpandDimsTest() + input_x = Tensor(np.random.randn(2,2,2,2).astype(np.float32)) + expand_dim(input_x, 0) + ``` + In the example, `ExpandDimsTest` is a single-operator network with two inputs: `input_x` and `input_axis`. The second input of the `ExpandDims` operator must be a constant. This is because `input_axis` is required when the output dimension of the `ExpandDims` operator is deduced during graph compilation. As the network parameter input, the value of `input_axis` is generalized into a variable and cannot be determined. As a result, the output dimension of the operator cannot be deduced, causing the graph compilation failure. Therefore, the input required by deduction in the graph compilation phase must be a constant. In the API, the parameters of this type of operator that require constant input will be explained, marked `const input is needed`. + + * Directly enter the needed value or a member variable in a class for the constant input of the operator in the construct function. The following is an example of correct input: + ```python + class ExpandDimsTest(Cell): + def __init__(self, axis): + super(ExpandDimsTest, self).__init__() + self.expandDims = P.ExpandDims() + self.axis = axis + + def construct(self, input_x): + return self.expandDims(input_x, self.axis) + axis = 0 + expand_dim = ExpandDimsTest(axis) + input_x = Tensor(np.random.randn(2,2,2,2).astype(np.float32)) + expand_dim(input_x) + ``` + +2. It is not allowed to modify `non-Parameter` type data members of the network. Examples are as follows: -* Directly enter the needed value or a member variable in a class for the constant input of the operator in the construct function. The following is an example of correct input: - ```python - class ExpandDimsTest(Cell): - def __init__(self, axis): - super(ExpandDimsTest, self).__init__() - self.expandDims = P.ExpandDims() - self.axis = axis - - def construct(self, input_x): - return self.expandDims(input_x, self.axis) - axis = 0 - expand_dim = ExpandDimsTest(axis) - input_x = Tensor(np.random.randn(2,2,2,2).astype(np.float32)) - expand_dim(input_x) ``` + class Net(Cell): + def __init__(self): + super(Net, self).__init__() + self.num = 2 + self.par = Parameter(Tensor(np.ones((2, 3, 4))), name="par") + + def construct(self, x, y): + return x + y + ``` +In the network defined above, `self.num` is not a `Parameter` and cannot be modified, but `self.par` is a `Parameter` and can be modified. diff --git a/docs/source_zh_cn/constraints_on_network_construction.md b/docs/source_zh_cn/constraints_on_network_construction.md index 8b352b2625..6b3b2204a0 100644 --- a/docs/source_zh_cn/constraints_on_network_construction.md +++ b/docs/source_zh_cn/constraints_on_network_construction.md @@ -231,34 +231,49 @@ tuple也支持切片取值操作, 但不支持切片类型为Tensor类型，支 ### 其他约束 -整网construct函数输入的参数以及使用ms_function装饰器修饰的函数的参数在图编译过程中会进行泛化，不能作为常量输入传给算子使用。所以，在图模式下，限制入口网络的参数只能是Tensor，如下例所示： -* 错误的写法如下： - ```python - class ExpandDimsTest(Cell): - def __init__(self): - super(ExpandDimsTest, self).__init__() - self.expandDims = P.ExpandDims() - - def construct(self, input_x, input_axis): - return self.expandDims(input_x, input_axis) - expand_dim = ExpandDimsTest() - input_x = Tensor(np.random.randn(2,2,2,2).astype(np.float32)) - expand_dim(input_x, 0) - ``` - 在示例中，ExpandDimsTest是一个只有单算子的网络，网络的输入有input_x和input_axis两个。因为ExpandDims算子的第二个输入需要是常量，这是因为在图编译过程中推导ExpandDims算子输出维度的时候需要用到，而input_axis作为网络参数输入会泛化成变量，无法确定其值，从而无法推导算子的输出维度导致图编译失败。所以在图编译阶段需要值推导的输入都应该是常量输入。在API中，这类算子需要常量输入的参数会进行说明，标注"constant input is needed"。 +1. 整网`construct`函数输入的参数以及使用`ms_function`装饰器修饰的函数的参数在图编译过程中会进行泛化，不能作为常量输入传给算子使用。所以，在图模式下，限制入口网络的参数只能是`Tensor`，如下例所示： + + * 错误的写法如下： + ```python + class ExpandDimsTest(Cell): + def __init__(self): + super(ExpandDimsTest, self).__init__() + self.expandDims = P.ExpandDims() + + def construct(self, input_x, input_axis): + return self.expandDims(input_x, input_axis) + expand_dim = ExpandDimsTest() + input_x = Tensor(np.random.randn(2,2,2,2).astype(np.float32)) + expand_dim(input_x, 0) + ``` + 在示例中，`ExpandDimsTest`是一个只有单算子的网络，网络的输入有`input_x`和`input_axis`两个。因为`ExpandDims`算子的第二个输入需要是常量，这是因为在图编译过程中推导`ExpandDims`算子输出维度的时候需要用到，而`input_axis`作为网络参数输入会泛化成变量，无法确定其值，从而无法推导算子的输出维度导致图编译失败。所以在图编译阶段需要值推导的输入都应该是常量输入。在API中，这类算子需要常量输入的参数会进行说明，标注"constant input is needed"。 + + * 正确的写法是在construct函数里面对算子的常量输入直接填入需要的值或者是一个类的成员变量，如下： + ```python + class ExpandDimsTest(Cell): + def __init__(self, axis): + super(ExpandDimsTest, self).__init__() + self.expandDims = P.ExpandDims() + self.axis = axis + + def construct(self, input_x): + return self.expandDims(input_x, self.axis) + axis = 0 + expand_dim = ExpandDimsTest(axis) + input_x = Tensor(np.random.randn(2,2,2,2).astype(np.float32)) + expand_dim(input_x) + ``` + +2. 不允许修改网络的非`Parameter`类型数据成员。示例如下： -* 正确的写法是在construct函数里面对算子的常量输入直接填入需要的值或者是一个类的成员变量，如下： - ```python - class ExpandDimsTest(Cell): - def __init__(self, axis): - super(ExpandDimsTest, self).__init__() - self.expandDims = P.ExpandDims() - self.axis = axis - - def construct(self, input_x): - return self.expandDims(input_x, self.axis) - axis = 0 - expand_dim = ExpandDimsTest(axis) - input_x = Tensor(np.random.randn(2,2,2,2).astype(np.float32)) - expand_dim(input_x) ``` + class Net(Cell): + def __init__(self): + super(Net, self).__init__() + self.num = 2 + self.par = Parameter(Tensor(np.ones((2, 3, 4))), name="par") + + def construct(self, x, y): + return x + y + ``` + 上面所定义的网络里，`self.num`不是一个`Parameter`，不允许被修改，而`self.par`是一个`Parameter`，可以被修改。 -- Gitee From 164c94e2484165d9c2c920f302209043dedf98a4 Mon Sep 17 00:00:00 2001 From: Payne Date: Tue, 15 Sep 2020 01:58:29 +0800 Subject: [PATCH 025/156] modify some confusion expression in mobilenetv2_incremental_learning --- .../mobilenetv2_incremental_learning.md | 133 +++++++++--------- 1 file changed, 70 insertions(+), 63 deletions(-) diff --git a/tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learning.md b/tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learning.md index 3822551830..e621d61df5 100644 --- a/tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learning.md +++ b/tutorials/source_zh_cn/advanced_use/mobilenetv2_incremental_learning.md @@ -1,27 +1,27 @@ # MobileNetV2 增量学习 -`Linux` `CPU` `Ascend` `GPU` `模型开发` `中级` `高级` +`Linux` `Windows` `CPU` `Ascend` `GPU` `模型开发` `中级` `高级` - [MobileNetV2 增量学习](#mobilenetv2-增量学习) - - [概述](#概述) - - [任务描述及准备](#任务描述及准备) - - [环境配置](#环境配置) - - [下载代码](#下载代码) - - [准备预训练模型](#准备预训练模型) - - [准备数据](#准备数据) - - [预训练模型加载代码详解](#预训练模型加载代码详解) - - [参数简介](#参数简介) - - [运行Python文件](#运行python文件) - - [运行Shell脚本](#运行shell脚本) - - [加载增量学习训练](#加载增量学习训练) - - [CPU加载训练](#cpu加载训练) - - [GPU加载训练](#gpu加载训练) - - [Ascend加载训练](#ascend加载训练) - - [增量学习训练结果](#增量学习训练结果) - - [验证增量学习训练模型](#验证增量学习训练模型) - - [验证模型](#验证模型) - - [验证结果](#验证结果) + - [概述](#概述) + - [任务描述及准备](#任务描述及准备) + - [环境配置](#环境配置) + - [下载代码](#下载代码) + - [准备预训练模型](#准备预训练模型) + - [准备数据](#准备数据) + - [预训练模型加载代码详解](#预训练模型加载代码详解) + - [参数简介](#参数简介) + - [运行Python文件](#运行python文件) + - [运行Shell脚本](#运行shell脚本) + - [加载增量学习训练](#加载增量学习训练) + - [CPU加载训练](#cpu加载训练) + - [GPU加载训练](#gpu加载训练) + - [Ascend加载训练](#ascend加载训练) + - [增量学习训练结果](#增量学习训练结果) + - [验证增量学习训练模型](#验证增量学习训练模型) + - [验证模型](#验证模型) + - [验证结果](#验证结果) @@ -33,7 +33,7 @@ MindSpore是一个多元化的机器学习框架。既可以在手机等端侧和PC等设备上运行，也可以在云上的服务器集群上运行。目前MobileNetV2支持在Windows系统中使用单核CPU做增量学习，在EulerOS、Ubuntu系统中使用单个或者多个Ascend AI处理器或GPU中做增量学习，本教程将会介绍如何在不同系统与处理器下的MindSpore框架中做增量学习的训练与验证。 -目前，Window上暂只支持支持CPU，Ubuntu与EulerOS上支持CPU、GPU与Ascend AI处理器三种处理器。 +目前，Window上暂只支持支持CPU，Ubuntu与EulerOS上支持CPU、GPU与Ascend AI处理器三种处理器。 >你可以在这里找到完整可运行的样例代码： @@ -43,6 +43,8 @@ MindSpore是一个多元化的机器学习框架。既可以在手机等端侧 若在本地环境运行，需要安装MindSpore框架，配置CPU、GPU或Ascend AI处理器。若在华为云环境上运行，不需要安装MindSpore框架，不需要配置Ascend AI处理器、CPU与GPU，可以跳过本小节。 +Windows操作系统中使用`\`，Linux操作系统中使用`/`分割路径地址中不同层级目录，下文中默认使用`/`，若用户使用Windows操作系统，路径地址中`/`需自行更改为`\`。 + 1. 安装MindSpore框架 在EulerOS、Ubuntu或者Windows等系统上需要根据系统和处理器架构[安装对应版本MindSpore框架](https://www.mindspore.cn/install)。 @@ -120,7 +122,7 @@ cd ./mindspore/model_zoo/official/cv/mobilenetv2 代码结构如下： -``` +```bash ├─MobileNetV2 ├─README.md # descriptions about MobileNetV2 ├─scripts @@ -132,6 +134,7 @@ cd ./mindspore/model_zoo/official/cv/mobilenetv2 │ launch.py # start Python script │ lr_generator.py # learning rate config │ mobilenetV2.py # MobileNetV2 architecture + │ mobilenetV2_fusion.py # MobileNetV2 fusion architecture │ models.py # net utils to load ckpt_file, define_net... │ utils.py # net utils to switch precision, set_context and so on ├─train.py # training script @@ -145,9 +148,9 @@ cd ./mindspore/model_zoo/official/cv/mobilenetv2 ### 准备预训练模型 [下载预训练模型](https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetv2.ckpt)到以下目录： -`./pretrain_checkpoint/[pretrain_checkpoint_file]` +`./pretrain_checkpoint/` -```Python +```bash mkdir pretrain_checkpoint wget -P ./pretrain_checkpoint https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetv2.ckpt ``` @@ -158,7 +161,7 @@ wget -P ./pretrain_checkpoint https://download.mindspore.cn/model_zoo/official/l 数据集结构如下： -``` +```bash └─ImageFolder ├─train │ class1Folder @@ -203,17 +206,20 @@ wget -P ./pretrain_checkpoint https://download.mindspore.cn/model_zoo/official/l 24: param.requires_grad = False ``` -## 参数简介 +## 参数简介 + +每个参数需要用户根据自己本地的处理器类型、数据地址与预训练模型地址等修改为相应的值。 ### 运行Python文件 + 在Windows与Linux系统上训练时，运行`train.py`时需要传入`dataset_path`、`platform`、`train_method`与`pretrain_ckpt`四个参数。验证时，运行`eval.py`并且传入`dataset_path`、`platform`、`pretrain_ckpt`与`head_ckpt`四个参数。 -```Shell +```bash # Windows/Linux train with Python file -python train.py --dataset_path [dataset_path] --platform [platform] --pretrain_ckpt [pretrain_checkpoint_path] --train_method[("train", "fine_tune", "incremental_learn")] +python train.py --platform [PLATFORM] --dataset_path [DATASET_PATH] --train_method[("train", "fine_tune", "incremental_learn")] --pretrain_ckpt [PRETRAIN_CHECKPOINT_PATH] # Windows/Linux eval with Python file -python eval.py --dataset_path [dataset_path] --platform [platform] --pretrain_ckpt [pretrain_checkpoint_path] --head_ckpt [head_ckpt_path] +python eval.py --platform [PLATFORM] --dataset_path [DATASET_PATH] --pretrain_ckpt [PRETRAIN_CHECKPOINT_PATH] --head_ckpt [HEAD_CHECKPOINT_PATH] ``` - `--dataset_path`：训练与验证数据集地址，无默认值，用户训练/验证时必须输入。 @@ -222,13 +228,13 @@ python eval.py --dataset_path [dataset_path] --platform [platform] --pretrain_ck - `--pretrain_ckpt`：增量训练或调优时，需要传入pretrain_checkpoint文件路径以加载预训练好的模型参数权重。 - `--head_ckpt`：增量训练模型验证时，需要传入head_net预训练模型路径以加载预训练好的模型参数权重。 - ### 运行Shell脚本 + 在Linux系统上时，可以选择运行Shell脚本文件`./scripts/run_train.sh`与`./scripts/run_eval.sh`。运行时需要在交互界面中同时传入参数。 -```Shell +```bash # Windows doesn't support Shell -# Linux train with Shell script +# Linux train with Shell script sh run_train.sh [PLATFORM] [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [RANK_TABLE_FILE] [DATASET_PATH] [TRAIN_METHOD] [CKPT_PATH] # Linux eval with Shell script for incremental learn @@ -249,7 +255,7 @@ sh run_eval.sh [PLATFORM] [DATASET_PATH] [PRETRAIN_CKPT_PATH] [HEAD_CKPT_PATH] Windows系统上，MobileNetV2做增量学习训练时，只能运行`train.py`。Linux系统上，使用MobileNetV2做增量学习训练时，可以选择运行`run_train.sh`， 并在运行Shell脚本文件时传入[参数](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/mobilenetv2_incremental_learning.html#id8)。 -Windows系统输出信息到交互式命令行，Linux系统环境下运行`run_train.sh`时，命令行结尾使用`&> [log_file_path]`将标准输出与错误输出写入log文件。 增量学习成功开始训练，`./train/device*/log*.log`中会持续写入每一个epoch的训练时间与Loss等信息。若未成功，上述log文件会写入失败报错信息。 +Windows系统输出信息到交互式命令行，Linux系统环境下运行`run_train.sh`时，命令行结尾使用`&> [log_file_path]`将标准输出与错误输出写入log文件。 增量学习成功开始训练，`./train/rank*/log*.log`中会持续写入每一个epoch的训练时间与Loss等信息。若未成功，上述log文件会写入失败报错信息。 ### CPU加载训练 @@ -261,16 +267,16 @@ Windows系统输出信息到交互式命令行，Linux系统环境下运行`run_ 使用样例1：通过Python文件调用1个CPU处理器。 - ```Shell + ```bash # Windows or Linux with Python - python train.py --platform CPU --dataset_path /store/dataset/OpenImage/train/ -- train_method incremental_learn --pretrain_ckpt ./pretrain_checkpoint/mobilenetV2.ckpt + python train.py --platform CPU --dataset_path [TRAIN_DATASET_PATH] -- train_method incremental_learn --pretrain_ckpt ./pretrain_checkpoint/mobilenetv2.ckpt ``` 使用样例2：通过Shell文件调用1个CPU处理器。 - ```Shell + ```bash # Linux with Shell - sh run_train.sh CPU /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt + sh run_train.sh CPU [TRAIN_DATASET_PATH] incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt ``` ### GPU加载训练 @@ -283,23 +289,23 @@ Windows系统输出信息到交互式命令行，Linux系统环境下运行`run_ - 使用样例1：通过Python文件调用1个GPU处理器。 - ```Shell + ```bash # Windows or Linux with Python - python train.py --platform GPU --dataset_path /store/dataset/OpenImage/train/ --pretrain_ckpt ./pretrain_checkpoint/mobilenetV2.ckpt --train_method incremental_learn + python train.py --platform GPU --dataset_path [TRAIN_DATASET_PATH] --train_method incremental_learn --pretrain_ckpt ./pretrain_checkpoint/mobilenetV2.ckpt ``` - 使用样例2：通过Shell脚本调用1个GPU处理器，设备ID为`“0”`。 - ```Shell + ```bash # Linux with Shell - sh run_train.sh GPU 1 0 /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt + sh run_train.sh GPU 1 0 [TRAIN_DATASET_PATH] incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt ``` - 使用样例3：通过Shell脚本调用8个GPU处理器，设备ID为`“0,1,2,3,4,5,6,7”`。 - ```Shell + ```bash # Linux with Shell - sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt + sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 [TRAIN_DATASET_PATH] incremental_learn ../pretrain_checkpoint/mobilenetv2.ckpt ``` ### Ascend加载训练 @@ -312,23 +318,23 @@ Windows系统输出信息到交互式命令行，Linux系统环境下运行`run_ - 使用样例1：通过Python文件调用1个Ascend处理器。 - ```Shell + ```bash # Windows or Linux with Python - python train.py --platform Ascend --dataset_path /store/dataset/OpenImage/train/ --train_method incremental_learn --pretrain_ckpt ./pretrain_checkpoint/mobilenetV2.ckpt + python train.py --platform Ascend --dataset_path [TRAIN_DATASET_PATH] --train_method incremental_learn --pretrain_ckpt ./pretrain_checkpoint mobilenetv2.ckpt ``` - 使用样例2：通过Shell脚本调用1个Ascend AI处理器，设备ID为“0”。 - ```Shell + ```bash # Linux with Shell - sh run_train.sh Ascend 1 0 ~/rank_table.json /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt + sh run_train.sh Ascend 1 0 ~/rank_table.json [TRAIN_DATASET_PATH] incremental_learn ../pretrain_checkpoint/mobilenetv2.ckpt ``` - 使用样例3：通过Shell脚本调用8个Ascend AI处理器，设备ID为”0,1,2,3,4,5,6,7“。 - ```Shell + ```bash # Linux with Shell - sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 ~/rank_table.json /store/dataset/OpenImage/train/ incremental_learn ../pretrain_checkpoint/mobilenetV2.ckpt + sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 ~/rank_table.json [TRAIN_DATASET_PATH] incremental_learn ../pretrain_checkpoint/mobilenetv2.ckpt ``` ### 增量学习训练结果 @@ -337,23 +343,23 @@ Windows系统输出信息到交互式命令行，Linux系统环境下运行`run_ - 运行Python文件时在交互式命令行中查看打印信息，`Linux`上运行Shell脚本运行后使用`cat ./train/device0/log0.log`中查看打印信息，输出结果如下： - ``` - train args: Namespace(dataset_path='.\\dataset\\train', platform='CPU', \ - pretrain_ckpt='.\\pretrain_checkpoint\\mobilenetV2.ckpt', train_method='incremental_learn') + ```bash + train args: Namespace(dataset_path='./dataset/train', platform='CPU', \ + pretrain_ckpt='./pretrain_checkpoint/mobilenetv2.ckpt', train_method='incremental_learn') cfg: {'num_classes': 26, 'image_height': 224, 'image_width': 224, 'batch_size': 150, \ - 'epoch_size': 15, 'warmup_epochs': 0, 'lr_max': 0.03, 'lr_end': 0.03, 'momentum': 0.9, \ + 'epoch_size': 200, 'warmup_epochs': 0, 'lr_max': 0.03, 'lr_end': 0.03, 'momentum': 0.9, \ 'weight_decay': 4e-05, 'label_smooth': 0.1, 'loss_scale': 1024, 'save_checkpoint': True, \ 'save_checkpoint_epochs': 1, 'keep_checkpoint_max': 20, 'save_checkpoint_path': './checkpoint', \ 'platform': 'CPU'} Processing batch: 16: 100%|███████████████████████████████████████████ █████████████████████| 16/16 [00:00 Date: Mon, 14 Sep 2020 17:29:54 +0800 Subject: [PATCH 026/156] add post training quantization docs --- .../use/post_training_quantization.md | 111 ++++++++++++++---- 1 file changed, 85 insertions(+), 26 deletions(-) diff --git a/lite/tutorials/source_zh_cn/use/post_training_quantization.md b/lite/tutorials/source_zh_cn/use/post_training_quantization.md index 839a7347ac..a138e6de95 100644 --- a/lite/tutorials/source_zh_cn/use/post_training_quantization.md +++ b/lite/tutorials/source_zh_cn/use/post_training_quantization.md @@ -4,9 +4,15 @@ - [训练后量化](#训练后量化) - [概述](#概述) - - [使用示例](#使用示例) - - [部分模型精度结果](#部分模型精度结果) - - [参数说明](#参数说明) + - [权重量化](#权重量化) + - [参数说明](#参数说明) + - [使用步骤](#使用步骤) + - [部分模型精度结果](#部分模型精度结果) + - [全量化](#全量化) + - [参数说明](#参数说明-1) + - [使用步骤](#使用步骤-1) + - [部分模型精度结果](#部分模型精度结果-1) + @@ -14,14 +20,84 @@ ## 概述 -对于已经训练好的`float32`模型，通过训练后量化将模型转为`int8`模型，不仅能减小模型大小，而且能显著提高推理性能。在MindSpore端侧框架中，这部分功能集成在模型转换工具`conveter_lite`中，通过增加命令行参数，便能够转换得到量化后模型。 +对于已经训练好的`float32`模型，通过训练后量化将其转为`int8`，不仅能减小模型大小，而且能显著提高推理性能。在MindSpore Lite中，这部分功能集成在模型转换工具`conveter_lite`内，通过增加命令行参数，便能够转换得到量化后模型。 目前训练后量化属于alpha阶段（支持部分网络，不支持多输入模型），正在持续完善中。 +MindSpore Lite训练后量化分为两类： +1. 权重量化：单独对模型的权值进行量化； +2. 全量化：对模型的权值、激活值、bias值统一进行量化。 + +训练后量化在两种情况下所需的数据类型和参数设定不同，但均可通过转换工具设定。有关转换工具`converter_lite`的使用方法可参考[转换为MindSpore Lite模型](https://www.mindspore.cn/lite/tutorial/zh-CN/master/use/converter_tool.html)。在此基础之上进行配置，启用训练后量化。 + +## 权重量化 + +下面对权重量化的使用方式和效果进行阐述。 + +### 参数说明 + +权重量化转换命令的一般形式为： +``` +./converter_lite --fmk=ModelType --modelFile=ModelFilePath --outputFile=ConvertedModelPath --quantType=WeightQuant --bitNum=BitNumValue --quantSize=QuantizationSizeThresholdValue --convWeightQuantChannelThreshold=ConvWeightQuantChannelThresholdValue +``` +下面对此命令的量化相关参数进行说明： + +| 参数 | 属性 | 功能描述 | 参数类型 | 默认值 | 取值范围 | +| -------- | ------- | ----- | ----- |----- | ----- | +| `--quantType=` | 必选 | 设置为WeightQuant，启用权重量化 | String | - | 必须设置为WeightQuant | +| `--bitNum=` | 可选 | 设定权重量化的比特数，目前仅支持8bit量化 | Integer | 8 | 8 | +| `--quantSize=` | 可选 | 设定参与权重量化的卷积核尺寸阈值，若卷积核尺寸大于该值，则对此权重进行量化；建议设置为500 | Integer | 0 | （0，+∞） | +| `--convWeightQuantChannelThreshold=` | 可选 | 设定参与权重量化的卷积通道数阈值，若卷积通道数大于该值，则对此权重进行量化；建议设置为16 | Integer | 16 | （0，+∞） | + +用户可根据模型及自身需要对权重量化的参数作出调整。 + + +### 使用步骤 + +1. 正确编译出`converter_lite`可执行文件。该部分可参考构建文档[编译MindSpore Lite](https://www.mindspore.cn/lite/tutorial/zh-CN/master/build.html)，获得`converter_lite`工具，并配置环境变量。 +2. 以TensorFlow Lite模型为例，执行权重量化模型转换命令: + ``` + ./converter_lite --fmk=TFLITE --modelFile=Inception_v3.tflite --outputFile=Inception_v3.tflite --quantType=WeightQuant --bitNum=8 --quantSize=0 --convWeightQuantChannelThreshold=0 + ``` +3. 上述命令执行成功后，便可得到量化后的模型`Inception_v3.tflite.ms`，量化后的模型大小通常会下降到FP32模型的1/4。 + +### 部分模型精度结果 + + | 模型 | 测试数据集 | FP32模型精度 | 权重量化精度 | + | -------- | ------- | ----- | ----- | + | [Inception_V3](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz) | [ImageNet](http://image-net.org/) | 77.92% | - | + | [Mobilenet_V1_1.0_224](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz) | [ImageNet](http://image-net.org/) | 70.96% | - | + +> 以上所有结果均在x86环境上测得。 + +## 全量化 + +下面对全量化的使用方式和效果进行阐述。 + +### 参数说明 + +全量化转换命令的一般形式为： ``` ./converter_lite --fmk=ModelType --modelFile=ModelFilePath --outputFile=ConvertedModelPath --quantType=PostTraining --config_file=config.cfg ``` +下面对此命令的量化相关参数进行说明： -## 使用示例 +| 参数 | 属性 | 功能描述 | 参数类型 | 默认值 | 取值范围 | +| -------- | ------- | ----- | ----- |----- | ----- | +| `--quantType=` | 必选 | 设置为PostTraining，启用全量化 | String | - | 必须设置为PostTraining | +| `--config_file=` | 必选 | 校准数据集配置文件路径 | String | - | - | + +为了计算激活值的量化参数，用户需要提供校准数据集。校准数据集最好来自真实推理场景，能表征模型的实际输入情况，数量在100个左右。 +校准数据集配置文件采用`key=value`的方式定义相关参数，需要配置的`key`如下: + +| 参数名 | 属性 | 功能描述 | 参数类型 | 默认值 | 取值范围 | +| -------- | ------- | ----- | ----- | ----- | ----- | +| image_path | 必选 | 存放校准数据集的目录 | String | - | 该目录存放可直接用于执行推理的输入数据。由于目前框架还不支持数据预处理，所有数据必须事先完成所需的转换，使得它们满足推理的输入要求。 | +| batch_count | 可选 | 使用的输入数目 | Integer | 100 | （0，+∞） | +| method_x | 可选 | 网络层输入输出数据量化算法 | String | KL | KL，MAX_MIN。 KL: 基于[KL散度](http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf)对数据范围作量化校准； MAX_MIN：基于最大值、最小值计算数据的量化参数。 在模型以及数据集比较较简单的情况下，推荐使用MAX_MIN | +| thread_num | 可选 | 使用校准数据集执行推理流程时的线程数 | Integer | 1 | （0，+∞） | + + +### 使用步骤 1. 正确编译出`converter_lite`可执行文件。 2. 准备校准数据集，假设存放在`/dir/images`目录，编写配置文件`config.cfg`，内容如下： @@ -32,34 +108,17 @@ thread_num=1 ``` 校准数据集可以选择测试数据集的子集，要求`/dir/images`目录下存放的每个文件均是预处理好的输入数据，每个文件都可以直接用于推理的输入。 -3. 以MindSpore模型为例，执行带训练后量化的模型转换命令: +3. 以MindSpore模型为例，执行全量化的模型转换命令: ``` ./converter_lite --fmk=MS --modelFile=lenet.ms --outputFile=lenet_quant --quantType=PostTraining --config_file=config.cfg ``` -4. 上述命令执行成功后，便可得到量化后的模型lenet_quant.ms，通常量化后的模型大小会下降到FP32模型的1/4。 +4. 上述命令执行成功后，便可得到量化后的模型`lenet_quant.ms`，通常量化后的模型大小会下降到FP32模型的1/4。 -## 部分模型精度结果 +### 部分模型精度结果 - | 模型 | 测试数据集 | method_x | FP32模型精度 | 训练后量化精度 | 说明 | + | 模型 | 测试数据集 | method_x | FP32模型精度 | 全量化精度 | 说明 | | -------- | ------- | ----- | ----- | ----- | ----- | | [Inception_V3](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz) | [ImageNet](http://image-net.org/) | KL | 77.92% | 77.95% | 校准数据集随机选择ImageNet Validation数据集中的100张 | | [Mobilenet_V1_1.0_224](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz) | [ImageNet](http://image-net.org/) | KL | 70.96% | 70.69% | 校准数据集随机选择ImageNet Validation数据集中的100张 | > 以上所有结果均在x86环境上测得。 - -## 参数说明 - -| 参数 | 属性 | 功能描述 | 参数类型 | 默认值 | 取值范围 | -| -------- | ------- | ----- | ----- |----- | ----- | -| --quantType | 必选 | 设置为PostTraining，启用训练后量化 | String | - | 必须设置为PostTraining | -| --config_file | 必选 | 校准数据集配置文件路径 | String | - | - | - -为了计算激活值的量化参数，用户需要提供校准数据集。校准数据集最好来自真实推理场景，能表征模型的实际输入情况，数量在100个左右。 -校准数据集配置文件采用`key=value`的方式定义相关参数，需要配置的`key`如下: - -| 参数名 | 属性 | 功能描述 | 参数类型 | 默认值 | 取值范围 | -| -------- | ------- | ----- | ----- | ----- | ----- | -| image_path | 必选 | 存放校准数据集的目录 | String | - | 该目录存放可直接用于执行推理的输入数据。由于目前框架还不支持数据预处理，所有数据必须事先完成所需的转换，使得它们满足推理的输入要求。 | -| batch_count | 可选 | 使用的输入数目 | Integer | 100 | 大于0 | -| method_x | 可选 | 网络层输入输出数据量化算法 | String | KL | KL，MAX_MIN。 KL: 基于[KL散度](http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf)对数据范围作量化校准； MAX_MIN：基于最大值、最小值计算数据的量化参数。 在模型以及数据集比较较简单的情况下，推荐使用MAX_MIN | -| thread_num | 可选 | 使用校准数据集执行推理流程时的线程数 | Integer | 1 | 大于0 | \ No newline at end of file -- Gitee From ae67bfb71d2c22d1c75eb6c28b9dd56ee881b404 Mon Sep 17 00:00:00 2001 From: caifubi Date: Mon, 14 Sep 2020 16:28:28 +0800 Subject: [PATCH 027/156] Combine E2E Dump and Async Dump --- .../customized_debugging_information.md | 108 ++++++++++++++---- .../customized_debugging_information.md | 107 +++++++++++++---- 2 files changed, 166 insertions(+), 49 deletions(-) diff --git a/tutorials/source_en/advanced_use/customized_debugging_information.md b/tutorials/source_en/advanced_use/customized_debugging_information.md index f903dc9467..257b85410a 100644 --- a/tutorials/source_en/advanced_use/customized_debugging_information.md +++ b/tutorials/source_en/advanced_use/customized_debugging_information.md @@ -10,7 +10,9 @@ - [Custom Callback](#custom-callback) - [MindSpore Metrics](#mindspore-metrics) - [MindSpore Print Operator](#mindspore-print-operator) - - [Asynchronous Data Dump](#asynchronous-data-dump) + - [Data Dump Introduction](#data-dump-introduction) + - [Synchronous Dump](#synchronous-dump) + - [Asynchronous Dump](#asynchronous-dump) - [Log-related Environment Variables and Configurations](#log-related-environment-variables-and-configurations) @@ -223,50 +225,106 @@ val:[[1 1] [1 1]] ``` -## Asynchronous Data Dump +## Data Dump Introduction -When the training result deviates from the expectation on Ascend, the input and output of the operator can be dumped for debugging through Asynchronous Data Dump. +The input and output of the operator can be saved for debugging through the data dump when the training result deviates from the expectation. Data dump includes Synchronous Dump and Asynchronous Dump. -> `comm_ops` operators are not supported by Asynchronous Data Dump. `comm_ops` can be found in [Operator List](https://www.mindspore.cn/docs/en/master/operator_list.html). +### Synchronous Dump -1. Turn on the switch to save graph IR: `context.set_context(save_graphs=True)`. -2. Execute training script. -3. Open `hwopt_d_end_graph_{graph id}.ir` in the directory you execute the script and find the name of the operators you want to Dump. -4. Configure json file: `data_dump.json`. +1. Create dump json file:`data_dump.json`. + + The name and location of the JSON file can be customized. ```json { - "DumpSettings": { + "common_dump_settings": { + "dump_mode": 0, + "path": "/tmp/net/", "net_name": "ResNet50", + "iteration": 0, + "input_output": 0, + "kernels": ["Default/Conv-op12"], + "support_device": [0,1,2,3,4,5,6,7] + }, + "e2e_dump_settings": { + "enable": false, + "trans_flag": false + } + } + ``` + + - `dump_mode`：0:dump all kernels in graph, 1: dump kernels in kernels list. + - `path`：The absolute path where dump saves data. + - `net_name`：net name eg:ResNet50. + - `iteration`：Specify the iterations to dump. All kernels in graph will be dumped. + - `input_output`：0:dump input and output of kernel, 1:dump input of kernel, 2:dump output of kernel. + - `kernels`：full name of kernel. Enable `context.set_context(save_graphs=True)` and get full name of kernel from `hwopt_d_end_graph_{graph_id}.ir`. + - `support_device`：support devices, default setting is `[0,1,2,3,4,5,6,7]`. You can specify specific device ids to dump specific device data. + - `enable`：enable synchronous dump. + - `trans_flag`：enable trans flag. Transform the device data format into NCHW. + +2. Specify the location of the JSON file. + + ```bash + export MINDSPORE_DUMP_CONFIG={Absolute path of data_dump.json} + ``` + + - Set the environment variables before executing the training script. Settings will not take effect during training. + - Dump environment variables need to be configured before calling `mindspore.communication.management.init`. + +3. Execute the training script to dump data. + +4. Parse the Dump file + + Call `numpy.fromfile` to parse dump data file. + +### Asynchronous Dump + +1. Create dump json file:`data_dump.json`. + + The name and location of the JSON file can be customized. + ```json + { + "common_dump_settings": { "dump_mode": 0, - "op_debug_mode": 0, + "path": "/relative_path", + "net_name": "ResNet50", "iteration": 0, - "kernels": ["Default/Conv2D-op2", "Default/TensorAdd-op10"] + "input_output": 0, + "kernels": ["Default/Conv-op12"], + "support_device": [0,1,2,3,4,5,6,7] + }, + "async_dump_settings": { + "enable": false, + "op_debug_mode": 0 } } ``` - > - `net_name`: net name eg:ResNet50. - > - `dump_mode`: 0: dump all kernels, 1: dump kernels in kernels list. - > - `op_debug_mode`: please set to 0. - > - `iteration`: specified iteration to dump. `iteration` should be set to 0 when `dataset_sink_mode` is False and data of every iteration will be dumped. - > - `kernels`: `fullname_with_scope` of kernel which need to dump. + - `dump_mode`：0:dump all kernels in graph, 1: dump kernels in kernels list. + - `path`：Relative path where dump data saves. eg:data will be saved in `/var/log/npu/ide_deam/dump/relative_path`. + - `net_name`：net name eg:ResNet50. + - `iteration`：Specify the iterations to dump. Iteration should be set to 0 when dataset_sink_mode is False and data of every iteration will be dumped. + - `input_output`：0:dump input and output of kernel, 1:dump input of kernel, 2:dump output of kernel. + - `kernels`：Full name of kernel. Enable `context.set_context(save_graphs=True)` and get full name of kernel from `hwopt_d_end_graph_{graph_id}.ir`. `kernels` only support TBE operator, AiCPU operator and communication operator. Data of communication operation input operator will be dumped if `kernels` is set to the name of communication operator. + - `support_device`：support devices, default setting is `[0,1,2,3,4,5,6,7]`. You can specify specific device ids to dump specific device data. + - `enable`：enable Asynchronous Dump. + - `op_debug_mode`：please set to 0. -5. Set environment variables. +2. Specify the json configuration file of Dump. ```bash - export ENABLE_DATA_DUMP=1 - export DATA_DUMP_PATH=/test - export DATA_DUMP_CONFIG_PATH=data_dump.json + export MINDSPORE_DUMP_CONFIG={Absolute path of data_dump.json} ``` - > - Set the environment variables before executing the training script. Setting environment variables during training will not take effect. - > - Dump environment variables need to be configured before calling `mindspore.communication.management.init`. + - Set the environment variables before executing the training script. Setting environment variables during training will not take effect. + - Dump environment variables need to be configured before calling `mindspore.communication.management.init`. + +3. Execute the training script to dump data. -6. Execute the training script again. -7. Parse the Dump file. +4. Parse the Dump file - Change directory to `/var/log/npu/ide_daemon/dump/` after training and execute the following commands to parse Dump data file: + Change directory to /var/log/npu/ide_daemon/dump/ after training, execute the following commands to parse Dump data file: ```bash python /usr/local/Ascend/toolkit/tools/operator_cmp/compare/dump_data_conversion.pyc -type offline -target numpy -i ./{Dump file path}} -o ./{output file path} diff --git a/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md b/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md index a940c9db36..1a4eff3ebe 100644 --- a/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md +++ b/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md @@ -11,7 +11,9 @@ - [自定义Callback](#自定义callback) - [MindSpore metrics功能介绍](#mindspore-metrics功能介绍) - [print算子功能介绍](#print算子功能介绍) - - [异步数据Dump功能介绍](#异步数据dump功能介绍) + - [数据Dump功能介绍](#数据dump功能介绍) + - [同步Dump功能介绍](#同步dump功能介绍) + - [异步Dump功能介绍](#异步dump功能介绍) - [日志相关的环境变量和配置](#日志相关的环境变量和配置) @@ -228,48 +230,105 @@ val:[[1 1] [1 1]] ``` -## 异步数据Dump功能介绍 +## 数据Dump功能介绍 -在Ascend环境上执行训练，当训练结果和预期有偏差时，可以通过异步数据Dump功能保存算子的输入输出进行调试。 +训练网络时，当训练结果和预期有偏差时，可以通过数据Dump功能保存算子的输入输出进行调试。Dump功能分为同步Dump和异步Dump，同步Dump同时支持GPU和Ascend，而异步Dump只支持Ascend。 -> 异步数据Dump不支持`comm_ops`类别的算子，算子类别详见[算子支持列表](https://www.mindspore.cn/docs/zh-CN/master/operator_list.html)。 +### 同步Dump功能介绍 -1. 开启IR保存开关： `context.set_context(save_graphs=True)`。 -2. 执行网络脚本。 -3. 查看执行目录下的`hwopt_d_end_graph_{graph id}.ir`，找到需要Dump的算子名称。 -4. 配置Dump的json配置文件`data_dump.json`。 +1. 创建配置文件`data_dump.json`。 + + JSON文件的名称和位置可以自定义设置。 ```json { - "DumpSettings": { + "common_dump_settings": { + "dump_mode": 0, + "path": "/tmp/net/", "net_name": "ResNet50", + "iteration": 0, + "input_output": 0, + "kernels": ["Default/Conv-op12"], + "support_device": [0,1,2,3,4,5,6,7] + }, + "e2e_dump_settings": { + "enable": false, + "trans_flag": false + } + } + ``` + + - `dump_mode`：设置成0，表示Dump出改网络中的所有算子；设置成1，表示Dump`"kernels"`里面制定的算子。 + - `path`：Dump保存数据的绝对路径。 + - `net_name`：自定义的网络名称，例如："ResNet50"。 + - `iteration`：指定需要Dump的迭代，若设置成0，表示Dump所有的迭代。 + - `input_output`：设置成0，表示Dump出算子的输入和算子的输出；设置成1，表示Dump出算子的输入；设置成2，表示Dump出算子的输出。 + - `kernels`：算子的全称，可以通过开启IR保持开关`context.set_context(save_graphs=True)`执行用例，从生成的`hwopt_d_end_graph_{graph_id}.ir`文件获取。 + - `support_device`：支持的设备，默认设置成0到7即可；在分布式训练场景下，需要dump个别设备上的数据，可以只在`support_device`中指定需要Dump的设备Id。 + - `enable`：开启E2E Dump。 + - `trans_flag`：开启格式转换。将设备上的数据格式转换成NCHW格式。 + +2. 指定Dump的json配置文件。 + + ```bash + export MINDSPORE_DUMP_CONFIG={Absolute path of data_dump.json} + ``` + + - 在网络脚本执行前，设置好环境变量；网络脚本执行过程中设置将会不生效。 + - 在分布式场景下，Dump环境变量需要调用`mindspore.communication.management.init`之前配置。 + +3. 执行用例Dump数据。 + +4. 解析Dump数据。 + + 通过`numpy.fromfile`读取Dump数据文件即可解析。 + +### 异步Dump功能介绍 + +1. 创建配置文件`data_dump.json`。 + + JSON文件的名称和位置可以自定义设置。 + + ```json + { + "common_dump_settings": { "dump_mode": 0, - "op_debug_mode": 0, + "path": "/test", + "net_name": "ResNet50", "iteration": 0, - "kernels": ["Default/Conv2D-op2", "Default/TensorAdd-op10"] + "input_output": 0, + "kernels": ["Default/Conv-op12"], + "support_device": [0,1,2,3,4,5,6,7] + }, + "async_dump_settings": { + "enable": false, + "op_debug_mode": 0 } } ``` - > - `net_name`：自定义的网络名称，例如："Resnet50"。 - > - `dump_mode`：设置成0，表示Dump所有的算子；设置成1，表示Dump`"kernel"`里面制定的算子。 - > - `op_debug_mode`：该属性用于算子溢出调试，在使用Dump功能的时候，请设置成0。 - > - `iteration`：指定需要Dump的迭代。非数据下沉模式下，`iteration`需要设置成0，并且会Dump出每个迭代的数据。 - > - `kernels`：指定需要Dump的算子名称(`fullname_with_scope`)。 + - `dump_mode`：设置成0，表示Dump出改网络中的所有算子；设置成1，表示Dump`"kernels"`里面指定的算子。 + - `path`：Dump保存数据的相对路径，异步Dump生成的数据都会保存在`/var/log/npu/ide_deam/dump/`目录下。 + - `net_name`：自定义的网络名称，例如："ResNet50"。 + - `iteration`：指定需要Dump的迭代。非数据下沉模式下，`iteration`需要设置成0，并且会Dump出每个迭代的数据。 + - `input_output`：设置成0，表示Dump出算子的输入和算子的输出；设置成1，表示Dump出算子的输入；设置成2，表示Dump出算子的输出。 + - `kernels`：算子的全称。开启IR保持开关`context.set_context(save_graphs=True)`并执行用例，从生成的`hwopt_d_end_graph_{graph_id}.ir`文件获取。`kernels`仅支持TBE算子、AiCPU算子、通信算子，若设置成通信算子的名称，将会Dump出通信算子的输入算子的数据。 + - `support_device`：支持的设备，默认设置成0到7即可；在分布式训练场景下，需要dump个别设备上的数据，可以只在`support_device`中指定需要Dump的设备Id。 + - `enable`：开启异步Dump。 + - `op_debug_mode`：该属性用于算子溢出调试，在使用Dump功能的时候，请设置成0。 -5. 设置数据Dump的环境变量。 +2. 设置数据Dump的环境变量。 ```bash - export ENABLE_DATA_DUMP=1 - export DATA_DUMP_PATH=/test - export DATA_DUMP_CONFIG_PATH=data_dump.json + export MINDSPORE_DUMP_CONFIG={Absolute path of data_dump.json} ``` - > - 在网络脚本执行前，设置好环境变量；网络脚本执行过程中设置将会不生效。 - > - 在分布式场景下，Dump环境变量需要调用`mindspore.communication.management.init`之前配置。 + - 在网络脚本执行前，设置好环境变量；网络脚本执行过程中设置将会不生效。 + - 在分布式场景下，Dump环境变量需要调用`mindspore.communication.management.init`之前配置。 + +3. 执行用例Dump数据。 -6. 再次执行用例进行异步数据Dump。 -7. 解析文件。 +4. 解析文件。 执行完用例后去`/var/log/npu/ide_daemon/dump/`目录下，运行如下命令解析Dump数据： -- Gitee From 5ca39d865a9a2af162f5f607c3bb44c8ef2d4611 Mon Sep 17 00:00:00 2001 From: lihongkang <[lihongkang1@huawei.com]> Date: Tue, 15 Sep 2020 17:10:14 +0800 Subject: [PATCH 028/156] update op list --- docs/source_en/operator_list.md | 2 +- docs/source_zh_cn/operator_list.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source_en/operator_list.md b/docs/source_en/operator_list.md index 5f8f57179a..680dc94188 100644 --- a/docs/source_en/operator_list.md +++ b/docs/source_en/operator_list.md @@ -99,7 +99,7 @@ | :----------- |:------ |:------ |:-----|:--- | [mindspore.ops.operations.Flatten](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Flatten) | Supported | Supported |Supported | nn_ops | [mindspore.ops.operations.Softmax](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Softmax) | Supported | Supported | Supported | nn_ops -| [mindspore.ops.operations.Acosh](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Acosh) | Doing | Doing | Doing | nn_ops +| [mindspore.ops.operations.Acosh](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Acosh) | Supported | Doing | Doing | nn_ops | [mindspore.ops.operations.FloorMod](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.FloorMod) | Supported | Doing | Doing | nn_ops | [mindspore.ops.operations.Elu](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Elu) | Supported | Doing | Doing | nn_ops | [mindspore.ops.operations.MirrorPad](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.MirrorPad) | Supported | Supported | Doing | nn_ops diff --git a/docs/source_zh_cn/operator_list.md b/docs/source_zh_cn/operator_list.md index f29cc3fd7e..49fe2ae19f 100644 --- a/docs/source_zh_cn/operator_list.md +++ b/docs/source_zh_cn/operator_list.md @@ -99,7 +99,7 @@ | :----------- |:------ |:------ |:-----|:--- | [mindspore.ops.operations.Flatten](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Flatten) | Supported | Supported |Supported | nn_ops | [mindspore.ops.operations.Softmax](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Softmax) | Supported | Supported | Supported | nn_ops -| [mindspore.ops.operations.Acosh](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Acosh) | Doing | Doing | Doing | nn_ops +| [mindspore.ops.operations.Acosh](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Acosh) | Supported | Doing | Doing | nn_ops | [mindspore.ops.operations.FloorMod](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.FloorMod) | Supported | Doing | Doing | nn_ops | [mindspore.ops.operations.Elu](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.Elu) | Supported | Doing | Doing | nn_ops | [mindspore.ops.operations.MirrorPad](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.MirrorPad) | Supported | Supported | Doing | nn_ops -- Gitee From 34cc3feb4e6d7c8ef250cdf7d08b3da8a8aa2f8e Mon Sep 17 00:00:00 2001 From: cjh9368 Date: Tue, 15 Sep 2020 17:20:47 +0800 Subject: [PATCH 029/156] bug fix --- lite/tutorials/source_en/use/converter_tool.md | 7 +++---- lite/tutorials/source_zh_cn/use/converter_tool.md | 7 +++---- 2 files changed, 6 insertions(+), 8 deletions(-) diff --git a/lite/tutorials/source_en/use/converter_tool.md b/lite/tutorials/source_en/use/converter_tool.md index 70acf426a6..a9d28ccc49 100644 --- a/lite/tutorials/source_en/use/converter_tool.md +++ b/lite/tutorials/source_en/use/converter_tool.md @@ -75,9 +75,9 @@ The following describes how to use the conversion command by using several commo ./converter_lite --fmk=TFLITE --modelFile=model.tflite --outputFile=model --quantType=AwareTraining ``` - - TensorFlow Lite aware quantization model `model_quant.tflite` set the input and output data type to be int8 + - TensorFlow Lite aware quantization model `model_quant.tflite` set the input and output data type to be float ```bash - ./converter_lite --fmk=TFLITE --modelFile=model.tflite --outputFile=model --quantType=AwareTraining --inputInferenceType=INT8 --inferenceType=INT8 + ./converter_lite --fmk=TFLITE --modelFile=model.tflite --outputFile=model --quantType=AwareTraining --inferenceType=FLOAT ``` In the preceding scenarios, the following information is displayed, indicating that the conversion is successful. In addition, the target file `model.ms` is obtained. @@ -101,8 +101,7 @@ The following describes the parameters in detail. | `--outputFile=` | Yes | Path of the output model. (If the path does not exist, a directory will be automatically created.) The suffix `.ms` can be automatically generated. | - | - | | `--weightFile=` | Yes (for Caffe models only) | Path of the weight file of the input model. | - | - | | `--quantType=` | No | Sets the quant type of the model. | PostTraining: quantization after training
    AwareTraining: perceptual quantization | - | -|`--inputInferenceType=` | No(supported by aware quant models only) | Sets the input data type of the converted model. If the type is different from the origin model, the convert tool will insert data type convert op before the model to make sure the input data type is same as the input of origin model. | FLOAT or INT8 | FLOAT | -|`--inferenceType= `| No(supported by aware quant models only) | Sets the output data type of the converted model. If the type is different from the origin model, the convert tool will insert data type convert op before the model to make sure the output data type is same as the input of origin model. | FLOAT or INT8 | FLOAT | +|`--inferenceType= `| No(supported by aware quant models only) | Sets the input and output data type of the converted model. If the type is different from the origin model, the convert tool will insert data type convert op before the model to make sure the output data type is same as the input of origin model. | SAME FLOAT or INT8 | SAME | |`--stdDev=`| No(supported by aware quant models only) | Sets the standard deviation of the input data. | （0，+∞） | 128 | |`--mean=`| No(supported by aware quant models only) | Sets the mean value of the input data. | [-128, 127] | -0.5 | diff --git a/lite/tutorials/source_zh_cn/use/converter_tool.md b/lite/tutorials/source_zh_cn/use/converter_tool.md index bc3a79f85b..e414f7eb34 100644 --- a/lite/tutorials/source_zh_cn/use/converter_tool.md +++ b/lite/tutorials/source_zh_cn/use/converter_tool.md @@ -75,10 +75,10 @@ bash build.sh -I x86_64 ./converter_lite --fmk=TFLITE --modelFile=model_quant.tflite --outputFile=model --quantType=AwareTraining ``` - - 感知量化模型输入设置为int8，输出设置为int8 + - 感知量化模型输入输出类型设置为float ```bash - ./converter_lite --fmk=TFLITE --modelFile=model_quant.tflite --outputFile=model --quantType=AwareTraining --inputInferenceType=INT8 --inferenceType=INT8 + ./converter_lite --fmk=TFLITE --modelFile=model_quant.tflite --outputFile=model --quantType=AwareTraining --inferenceType=FLOAT ``` 以上几种情况下，均显示如下转换成功提示，且同时获得`model.ms`目标文件。 @@ -102,8 +102,7 @@ MindSpore Lite模型转换工具提供了多种参数设置，用户可根据需 | `--outputFile=` | 是 | 输出模型的路径（不存在时将自动创建目录），不需加后缀，可自动生成`.ms`后缀。 | - | - | | `--weightFile=` | 转换Caffe模型时必选 | 输入模型weight文件的路径。 | - | - | | `--quantType=` | 否 | 设置模型的量化类型。 | PostTraining：训练后量化
    AwareTraining：感知量化。 | - | -|` --inputInferenceType=` | 否 | 设置感知量化模型输入数据类型，如果和原模型不一致则转换工具会在模型前插转换算子，使得转换后的模型输入类型和inputInferenceType保持一致。 | FLOAT、INT8 | FLOAT | -| `--inferenceType=` | 否 | 设置感知量化模型输出数据类型，如果和原模型不一致则转换工具会在模型前插转换算子，使得转换后的模型输出类型和inferenceType保持一致。 | FLOAT、INT8 | FLOAT | +|` --inferenceType=` | 否 | 设置感知量化模型输入输出数据类型，如果和原模型不一致则转换工具会在模型前插转换算子，使得转换后的模型输入类型和inferenceType保持一致。 |SAME、 FLOAT、INT8 | SAME | | `--stdDev= `| 否 | 感知量化模型转换时用于设置输入数据的标准差。 | （0，+∞） | 128 | | `--mean=` | 否 | 感知量化模型转换时用于设置输入数据的均值。 | [-128, 127] | -0.5 | -- Gitee From 8bae5651a312f4f27033b6740a50fbb25f572aa7 Mon Sep 17 00:00:00 2001 From: Jiaqi Date: Tue, 15 Sep 2020 14:52:09 +0800 Subject: [PATCH 030/156] modify optim api --- api/source_zh_cn/programming_guide/optim.md | 108 +++++++++++--------- 1 file changed, 57 insertions(+), 51 deletions(-) diff --git a/api/source_zh_cn/programming_guide/optim.md b/api/source_zh_cn/programming_guide/optim.md index 7a8ebb7096..28b3a65289 100644 --- a/api/source_zh_cn/programming_guide/optim.md +++ b/api/source_zh_cn/programming_guide/optim.md @@ -1,8 +1,8 @@ -# optim模块 +# Optim -- [优化器](#优化器) +- [Optim](#Optim) - [概述](#概述) - [学习率](#学习率) - [dynamic_lr](#dynamic_lr) @@ -17,36 +17,41 @@ ## 概述 -mindSpore.nn.optim是Mindspore框架中实现各种优化算法的模块，包含常用的优化器，学习率等，并且接口具备足够的通用性，可以将以后更新、更复杂的方法集成到模块里。 +`mindspore.nn.optim`是MindSpore框架中实现各种优化算法的模块，包含常用的优化器、学习率等，并且接口具备足够的通用性，可以将以后更新、更复杂的方法集成到模块里。 -mindspore.nn.optim为模型提供常用的优化器，如SGD、ADAM、Momentum。优化器用于计算和更新梯度，模型优化算法的选择直接关系到最终模型的性能，如果有时候效果不好，未必是特征或者模型设计的问题，很有可能是优化算法的问题； -同时还有mindspore.nn提供的学习率的模块，学习率learing_rate分为dynamic_lr和learning_rate_schedule，都是动态学习率，但是实现方式不同，学习率最为监督学习以及深度学习中重要的参数，其决定着目标函数是否能收敛到局部最小值以及何时能收敛到最小值。 -合适的学习率能够使目标函数在合适的的时间内收敛到局部最小值。 +`mindspore.nn.optim`为模型提供常用的优化器，如`SGD`、`ADAM`、`Momentum`。优化器用于计算和更新梯度，模型优化算法的选择直接关系到最终模型的性能，如果有时候效果不好，未必是特征或者模型设计的问题，很有可能是优化算法的问题；同时还有`mindspore.nn`提供的学习率的模块，学习率分为`dynamic_lr`和`learning_rate_schedule`，都是动态学习率，但是实现方式不同，学习率最为监督学习以及深度学习中重要的参数，其决定着目标函数是否能收敛到局部最小值以及何时能收敛到最小值。合适的学习率能够使目标函数在合适的的时间内收敛到局部最小值。 > 本文档中的所有示例，支持CPU，GPU，Ascend环境。 ## 学习率 ### dynamic_lr -mindspore.nn.dynamic_lr模块有以下几个类，piecewise_constant_lr类是得到分段不变的学习速率，exponential_decay_lr类是基于指数衰减函数计算学习率，natural_exp_decay_lr类是基于自然指数衰减函数计算学习率，inverse_decay_lr类是基于反时间衰减函数计算学习速率，cosine_decay_lr类是基于余弦衰减函数计算学习率，polynomial_decay_lr类是基于多项式衰减函数计算学习率，warmup_lr类是提高学习率，它们是属于dynamic_lr的不同实现方式。 +`mindspore.nn.dynamic_lr`模块有以下几个类： -例如piecewise_constant_lr类代码样例如下： +- `piecewise_constant_lr`类：基于得到分段不变的学习速率。 +- `exponential_decay_lr`类：基于指数衰减函数计算学习率。 +- `natural_exp_decay_lr`类：基于自然指数衰减函数计算学习率。 +- `inverse_decay_lr`类：基于反时间衰减函数计算学习速率。 +- `cosine_decay_lr`类：基于余弦衰减函数计算学习率。 +- `polynomial_decay_lr`类：基于多项式衰减函数计算学习率。 +- `warmup_lr`类：提高学习率。 -``` -class mindspore.nn.dynamic_lr.piecewise_constant_lr(milestone, learning_rates) +它们是属于`dynamic_lr`的不同实现方式。 -Parameters: - milestone (Union[list[int], tuple[int]]) – A list of milestone. This list is a monotone increasing list. Every element is a milestone step, and must be greater than 0. - learning_rates (Union[list[float], tuple[float]]) – A list of learning rates. +例如`piecewise_constant_lr`类代码样例如下： -Returns: - list[float]. The size of list ``` +from mindspore.nn.dynamic_lr import piecewise_constant_lr -``` -milestone = [2, 5, 10] -learning_rates = [0.1, 0.05, 0.01] -piecewise_constant_lr(milestone, learning_rates) +def test_dynamic_lr(): + milestone = [2, 5, 10] + learning_rates = [0.1, 0.05, 0.01] + lr = piecewise_constant_lr(milestone, learning_rates) + print(lr) + + +if __name__ == '__main__': + test_dynamic_lr() ``` 返回结果如下： @@ -56,49 +61,50 @@ piecewise_constant_lr(milestone, learning_rates) ### learning_rate_schedule -mindspore.nn.learning_rate_schedule模块下有以下几个类。ExponentialDecayLR类，NaturalExpDecayLR类，InverseDecayLR类，CosineDecayLR类，PolynomialDecayLR类，WarmUpLR类。它们都属于learning_rate_schedule，只是实现方式不同。 +`mindspore.nn.learning_rate_schedule`模块下有以下几个类：`ExponentialDecayLR`类、`NaturalExpDecayLR`类、`InverseDecayLR`类、`CosineDecayLR`类、`PolynomialDecayLR`类和`WarmUpLR`类。它们都属于`learning_rate_schedule`，只是实现方式不同，各自含义如下： -ExponentialDecayLR类是基于指数衰减函数计算学习率，NaturalExpDecayLR类是基于自然指数衰减函数巨酸学习率,InverseDecayLR类是基于反时间衰减函数计算学习速率，CosineDecayLR类是基于余弦衰减函数计算学习率，PolynomialDecayLR类是基于多项式衰减函数计算学习率，WarmUpLR类是提高学习率，它们是属于learning_rate_schedule的不同实现方式。 +- `ExponentialDecayLR`类：基于指数衰减函数计算学习率。 +- `NaturalExpDecayLR`类：基于自然指数衰减函数计算学习率。 +- `InverseDecayLR`类：基于反时间衰减函数计算学习速率。 +- `CosineDecayLR`类：基于余弦衰减函数计算学习率。 +- `PolynomialDecayLR`类：基于多项式衰减函数计算学习率。 +- `WarmUpLR`类：提高学习率。 + +它们是属于`learning_rate_schedule`的不同实现方式。 例如ExponentialDecayLR类代码样例如下： ``` -class ExponentialDecayLR(learning_rate, decay_rate, decay_steps, is_stair=False) +from mindspore.common import dtype as mstype +from mindspore import Tensor +from mindspore.nn.learning_rate_schedule import ExponentialDecayLR -Parameters: - learning_rate(float) - The initial value of learning rate. - decay_rate(float) - The decay rate. - decay_steps(int) - A value used to calculate decayed learning rate. - is_stair(bool) - if true,learning rate decay once every decay_steps times. Default: False. +def test_learning_rate_schedule(): + learning_rate = 0.1 # learning_rate(float) - The initial value of learning rate. + decay_rate = 0.9 # decay_rate(float) - The decay rate. + decay_steps = 4 # decay_steps(int) - A value used to calculate decayed learning rate. + global_step = Tensor(2, mystype.int32) + exponential_decay_lr = ExponentialDecayLR(learning_rate, decay_rate, decay_steps) + res = exponential_decay_lr(global_step) + print(res) -inputs: - Tensor.The current step number. -Returns: - Tensor. The learning rate value for the current step. +if __name__ == '__main__': + test_learning_rate_schedule() ``` +返回结果如下： ``` -from mindspore.common import dtype as mstype -from mindspore import Tensor - - -learning_rate = 0.1 # learning_rate(float) - The initial value of learning rate. -decay_rate = 0.9 # decay_rate(float) - The decay rate. -decay_steps = 4 # decay_steps(int) - A value used to calculate decayed learning rate. -global_step = Tensor(2, mystype.int32) -exponential_decay_lr = ExponentialDecayLR(learning_rate, decay_rate, decay_steps) -exponential_decay_lr(global_step) - +0.094868325 ``` ## optimzer ### 如何使用 -为了使用mindspore.nn.optim，我们需要构建一个optimizer对象。这个对象能够保持当前参数状态并基于计算得到的梯度进行参数更新。 +为了使用`mindspore.nn.optim`，我们需要构建一个`Optimizer`对象。这个对象能够保持当前参数状态并基于计算得到的梯度进行参数更新。 - 构建 -为了构建一个Optimizer，我们需要给它一个包含可需要优化的参数（必须是Variable对象）的iterable。然后，你可以设置optimizer的参数选项，比如学习率，权重衰减等等。 +为了构建一个`Optimizer`，我们需要给它一个包含可需要优化的参数（必须是Variable对象）的iterable。然后，你可以设置Optimizer的参数选项，比如学习率，权重衰减等等。 代码样例如下： @@ -117,7 +123,7 @@ optim = nn.Adam(group_params, learning_rate=0.1, weight_decay=0.0) 优化器也支持为没个参数单独设置选项。若想这么做，不要直接传入变量Variable，而是传入一个字典的iterable。每一个字典都分别定义了一组参数，并且包含一个key键，这个key键对应相应的参数value值。其他的key键应该是优化器所接受的其他参数，并且会被用于对这组参数的优化。 我们仍然能够传递选项作为关键字参数，在未重写这些选项的组中，它们会被用作默认值。当你只想改动一个参数组的选项，但其他参数组的选项不变时，这是非常有用的。 -例如，当我们想制定每一层的学习率时，以SGD为例： +例如，当我们想制定每一层的学习率时，以`SGD`为例： ``` from mindspore import nn @@ -132,16 +138,16 @@ optim = nn.SGD([{'params': conv_params, 'weight_decay': 0.01}, ### 内置优化器 -深度学习优化算法大概常用的有SGD、Adam、Ftrl、lazyadam、Momentum、RMSprop、Lars、Proximal_ada_grad和lamb这几种。 -在mindspore.nn.optim模块中，他们都有对应的类实现。例如： +深度学习优化算法大概常用的有`SGD`、`Adam`、`Ftrl`、`lazyadam`、`Momentum`、`RMSprop`、`Lars`、`Proximal_ada_grad`和`lamb`这几种。 +在`mindspore.nn.optim`模块中，他们都有对应的类实现。例如： -- SGD，默认参数为纯SGD，设置momentum参数不为0，考虑了一阶动量，设置nesterov为True后变成NAG，即Nesterov Accelerated Gradient，在计算梯度时计算的是向前走一步所在位置的梯度。 +- `SGD`，默认参数为纯SGD，设置`momentum`参数不为0，考虑了一阶动量，设置`nesterov`为True后变成`NAG`，即`Nesterov Accelerated Gradient`，在计算梯度时计算的是向前走一步所在位置的梯度。 -- RMSprop，考虑了二阶动量，对于不同的参数有不同的学习率，即自适应学习率，对Adagrad进行了优化，通过指数平滑只考虑一定窗口内的二阶动量。 +- `RMSprop`，考虑了二阶动量，对于不同的参数有不同的学习率，即自适应学习率，对`Adagrad`进行了优化，通过指数平滑只考虑一定窗口内的二阶动量。 -- Adam，同时考虑了一阶动量和二阶动量，可以看成RMSprop上进一步考虑了一阶动量。 +- `Adam`，同时考虑了一阶动量和二阶动量，可以看成`RMSprop`上进一步考虑了一阶动量。 -例如SGD的代码样例如下： +例如`SGD`的代码样例如下： ``` from mindspore import nn -- Gitee From a90ac0c53d3b8798558178a5f4664df4948c6744 Mon Sep 17 00:00:00 2001 From: guohongzilong <2713219276@qq.com> Date: Tue, 15 Sep 2020 11:41:40 +0800 Subject: [PATCH 031/156] fix cell and component --- api/source_zh_cn/programming_guide/cell.md | 124 ++++++++++++------ .../programming_guide/network_component.md | 4 +- 2 files changed, 88 insertions(+), 40 deletions(-) diff --git a/api/source_zh_cn/programming_guide/cell.md b/api/source_zh_cn/programming_guide/cell.md index 47d0aaebb9..76b78ef77c 100644 --- a/api/source_zh_cn/programming_guide/cell.md +++ b/api/source_zh_cn/programming_guide/cell.md @@ -3,11 +3,12 @@ - [Cell](#cell) - - [概念用途](#概念用途) + - [概述](#概述) - [关键成员函数](#关键成员函数) - [construct方法](#construct方法) - [parameters_dict](#parameters_dict) - [cells_and_names](#cells_and_names) + - [set_grad](#set_grad) - [模型层](#模型层) - [内置模型层](#内置模型层) - [应用实例](#应用实例) @@ -20,9 +21,9 @@ -## 概念用途 +## 概述 -MindSpore的`Cell`类是构建所有网络的基类，也是网络的基本单元。当用户需要自定义网络时，需要继承Cell类，并重写`__init_`方法和`contruct`方法。 +MindSpore的`Cell`类是构建所有网络的基类，也是网络的基本单元。当用户需要自定义网络时，需要继承`Cell`类，并重写`__init__`方法和`contruct`方法。 损失函数、优化器和模型层等本质上也属于网络结构，也需要继承`Cell`类才能实现功能，同样用户也可以根据业务需求自定义这部分内容。 @@ -34,26 +35,29 @@ MindSpore的`Cell`类是构建所有网络的基类，也是网络的基本单 `Cell`类重写了`__call__`方法，在`Cell`类的实例被调用时，会执行`contruct`方法。网络结构在`contruct`方法里面定义。 -下面的样例中，我们构建了一个简单的网络。用例的网络结构为Conv2d->BatchNorm2d->ReLU->Flatten->Dense。 -在`construct`方法中，`x`为输入数据，`out`是经过网络的每层计算后得到的计算结果。 +下面的样例中，我们构建了一个简单的网络实现卷积计算功能。构成网络的算子在`__init__`中定义，在`contruct`方法里面使用，用例的网络结构为`Conv2d`->`BiasAdd`。 + +在`construct`方法中，`x`为输入数据，`output`是经过网络结构计算后得到的计算结果。 ``` +import mindspore.nn as nn +from mindspore.ops import operations as P +from mindspore.common.parameter import Parameter +from mindspore.common.initializer import initializer + class Net(nn.Cell): - def __init__(self): + def __init__(self, in_channels=10, out_channels=20, kernel_size=3): super(Net, self).__init__() - self.conv = nn.Conv2d(3, 64, 3, has_bias=False, weight_init='normal') - self.bn = nn.BatchNorm2d(64) - self.relu = nn.ReLU() - self.flatten = nn.Flatten() - self.fc = nn.Dense(64 * 222 * 222, 3) + self.conv2d = P.Conv2D(out_channels, kernel_size) + self.bias_add = P.BiasAdd() + self.weight = Parameter( + initializer('normal', [out_channels, in_channels, kernel_size, kernel_size]), + name='conv.weight') def construct(self, x): - x = self.conv(x) - x = self.bn(x) - x = self.relu(x) - x = self.flatten(x) - out = self.fc(x) - return out + output = self.conv2d(x, self.weight) + output = self.bias_add(output, self.bias) + return output ``` ### parameters_dict @@ -75,33 +79,74 @@ print(result['conv.weight']) 输出如下： ``` -odict_keys(['conv.weight', 'bn.moving_mean', 'bn.moving_variance', 'bn.gamma', 'bn.beta', 'fc.weight', 'fc.bias']) -Parameter (name=conv.weight, value=[[[[ 1.07402597e-02 7.70052336e-03 5.55867562e-03] - [-3.21971579e-03 -3.75304517e-04 -8.73021083e-04] -... -[-1.81201510e-02 -1.31190736e-02 -4.27651079e-03]]]]) +odict_keys(['conv.weight']) +Parameter (name=conv.weight, value=[[[[-3.95042636e-03 1.08830128e-02 -6.51786150e-03] + [ 8.66129529e-03 7.36288540e-03 -4.32638079e-03] + [-1.47628486e-02 8.24100431e-03 -2.71035335e-03]] + ...... + [ 1.58852488e-02 -1.03505487e-02 1.72988791e-02]]]]) ``` ### cells_and_names `cells_and_names`方法是一个迭代器，返回网络中每个`Cell`的名字和它的内容本身。 -用例简单实现了获取与打印每个`Cell`名字的功能，其中根据上文网络结构可知，存在五个`Cell`分别是'conv'、'bn'、'relu'、'flatten'和'fc'。 +用例简单实现了获取与打印每个`Cell`名字的功能，其中根据网络结构可知，存在1个`Cell`为`nn.Conv2d`。 + +其中`nn.Conv2d`是MindSpore以`Cell`为基类封装好的一个卷积层，其具体内容将在“模型层”中进行介绍。 代码样例如下： ``` -net = Net() +import mindspore.nn as nn + +class Net1(nn.Cell): + def __init__(self): + super(Net1, self).__init__() + self.conv = nn.Conv2d(3, 64, 3, has_bias=False, weight_init='normal') + + def construct(self, x): + out = self.conv(x) + return out + +net = Net1() names = [] for m in net.cells_and_names(): + print(m) names.append(m[0]) if m[0] else None +print('-------names-------') print(names) ``` 输出如下： ``` -['conv', 'bn', 'relu', 'flatten', 'fc'] +('', Net1< + (conv): Conv2d + >) +('conv', Conv2d) +-------names------- +['conv'] ``` +### set_grad + +`set_grad`接口功能是使用户构建反向网络，在不传入参数调用时，默认设置`requires_grad`为True，需要在计算网络反向的场景中使用。 + +以`TrainOneStepCell`为例，其接口功能是使网络进行单步训练，需要计算网络反向，因此初始化方法里需要使用`set_grad`。 + +`TrainOneStepCell`部分代码如下： +``` +class TrainOneStepCell(Cell): + def __init__(self, network, optimizer, sens=1.0): + super(TrainOneStepCell, self).__init__(auto_prefix=False) + self.network = network + self.network.set_grad() + ...... +``` + +如果用户使用`TrainOneStepCell`等类似接口无需使用`set_grad`， 内部已封装实现。 + +若用户需要自定义此类训练功能的接口，需要在其内部调用，或者在外部设置`network.set_grad`。 + ## 模型层 在讲述了`Cell`的使用方法后可知，MindSpore能够以`Cell`为基类构造网络结构。 @@ -112,7 +157,7 @@ print(names) ### 内置模型层 -MindSpore框架在nn的layer层内置了丰富的接口，主要内容如下： +MindSpore框架在`mindspore.nn`的layer层内置了丰富的接口，主要内容如下： - 激活层 @@ -177,14 +222,16 @@ MindSpore框架在nn的layer层内置了丰富的接口，主要内容如下： - 量化层 量化是指将数据从float的形式转换成一段数据范围的int类型，所以量化层提供了一些数据量化的方法和模型层结构封装。 - + 主要接口有`Conv2dBnAct`、`DenseBnAct`、`Conv2dBnFoldQuant`、`LeakyReLUQuant`等。 ### 应用实例 -MindSpore的模型层在mindspore.nn下，使用方法如下所示： +MindSpore的模型层在`mindspore.nn`下，使用方法如下所示： ``` +import mindspore.nn as nn + class Net(nn.Cell): def __init__(self): super(Net, self).__init__() @@ -209,7 +256,7 @@ class Net(nn.Cell): ## 损失函数 -目前MindSpore主要支持的损失函数有`L1Loss`、`MSELoss`、`SmoothL1Loss`、`SoftmaxCrossEntropyWithLogits`、`SoftmaxCrossEntropyExpand`和`CosineEmbeddingLoss`。 +目前MindSpore主要支持的损失函数有`L1Loss`、`MSELoss`、`SmoothL1Loss`、`SoftmaxCrossEntropyWithLogits`和`CosineEmbeddingLoss`。 MindSpore的损失函数全部是`Cell`的子类实现，所以也支持用户自定义损失函数，其构造方法在“构建自定义网络”中进行介绍。 @@ -229,15 +276,11 @@ MindSpore的损失函数全部是`Cell`的子类实现，所以也支持用户 ` - SoftmaxCrossEntropyWithLogits - 交叉熵损失函数，用于分类模型。当标签数据不是one-hot编码形式时，需要输入参数`sparse`为True。`reduction`参数同`L1Loss`。 + 交叉熵损失函数，用于分类模型。当标签数据不是one-hot编码形式时，需要输入参数`sparse`为True。`reduction`参数默认值为none，其参数含义同`L1Loss`。 -- SoftmaxCrossEntropyExpand - - 交叉熵扩展损失函数，用于分类模型。当标签数据不是one-hot编码形式时，需要输入参数`sparse`为True。 - - CosineEmbeddingLoss - CosineEmbeddingLoss用于衡量两个输入相似程度，用于分类模型。`margin`默认为0.0，`reduction`参数同`L1Loss`。 + `CosineEmbeddingLoss`用于衡量两个输入相似程度，用于分类模型。`margin`默认为0.0，`reduction`参数同`L1Loss`。 ### 应用实例 @@ -254,12 +297,17 @@ target_data = Tensor(np.array([[0, 2, 5], [3, 1, 1]]).astype(np.float32)) print(loss(input_data, target_data)) ``` -此用例构造了两个Tensor数据，利用`nn.L1Loss`接口定义了L1Loss，将`input_data`和`target_data`传入loss，执行L1Loss的计算，结果为1.5。若`loss = nn.L1Loss(reduction='sum')`，则结果为9.0。若`loss = nn.L1Loss(reduction='none')`，结果为[[1. 0. 2.] [1. 2. 3.]]。 +输出结果： +``` +1.5 +``` + +此用例构造了两个Tensor数据，利用`nn.L1Loss()`接口定义了loss，将`input_data`和`target_data`传入loss，执行L1Loss的计算，结果为1.5。若loss = nn.L1Loss(reduction='sum')，则结果为9.0。若loss = nn.L1Loss(reduction='none')，结果为[[1. 0. 2.] [1. 2. 3.]]。 ## 构建自定义网络 -无论是网络结构，还是前文提到的模型层、损失函数和优化器等，本质上都是一个Cell，因此都可以自定义实现。 +无论是网络结构，还是前文提到的模型层、损失函数和优化器等，本质上都是一个`Cell`，因此都可以自定义实现。 首先构造一个继承`Cell`的子类，然后在`__init__`方法里面定义算子和模型层等，在`construct`方法里面构造网络结构。 @@ -272,7 +320,7 @@ import mindspore.nn as nn class LeNet5(nn.Cell): def __init__(self): super(LeNet5, self).__init__() - self.conv1 = nn.Conv2d(3, 6, 5, pad_mode="valid") + self.conv1 = nn.Conv2d(1, 6, 5, pad_mode="valid") self.conv2 = nn.Conv2d(6, 16, 5, pad_mode="valid") self.fc1 = nn.Dense(16 * 5 * 5, 120) self.fc2 = nn.Dense(120, 84) diff --git a/api/source_zh_cn/programming_guide/network_component.md b/api/source_zh_cn/programming_guide/network_component.md index 9c8098cd75..8ccdd76102 100644 --- a/api/source_zh_cn/programming_guide/network_component.md +++ b/api/source_zh_cn/programming_guide/network_component.md @@ -126,7 +126,7 @@ class LeNet(nn.Cell): data = Tensor(np.ones([32, 1, 32, 32]).astype(np.float32) * 0.01) label = Tensor(np.ones([32]).astype(np.int32)) net = LeNet() -criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True) +criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') net_with_criterion = WithLossCell(net, criterion) loss = net_with_criterion(data, label) print("+++++++++Loss+++++++++++++") @@ -153,7 +153,7 @@ learning_rate = 0.01 momentum = 0.9 optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate, momentum) -criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True) +criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') net_with_criterion = WithLossCell(net, criterion) train_network = TrainOneStepCell(net_with_criterion, optimizer) # optimizer for i in range(5): -- Gitee From 1fc049914ca07af199b589d3fc86a046313531bf Mon Sep 17 00:00:00 2001 From: bingyaweng Date: Tue, 15 Sep 2020 14:03:12 +0800 Subject: [PATCH 032/156] modify tutorial of mdp --- .../advanced_use/deep_probability_program.md | 267 +++++++++++++++--- 1 file changed, 224 insertions(+), 43 deletions(-) diff --git a/tutorials/source_zh_cn/advanced_use/deep_probability_program.md b/tutorials/source_zh_cn/advanced_use/deep_probability_program.md index b1294d3633..14079f1b17 100644 --- a/tutorials/source_zh_cn/advanced_use/deep_probability_program.md +++ b/tutorials/source_zh_cn/advanced_use/deep_probability_program.md @@ -5,41 +5,52 @@ - [深度概率编程](#深度概率编程) - [概述](#概述) - - [贝叶斯神经网络](#贝叶斯神经网络) + - [使用贝叶斯神经网络](#使用贝叶斯神经网络) - [处理数据集](#处理数据集) + - [定义贝叶斯神经网络](#定义贝叶斯神经网络) - [定义损失函数和优化器](#定义损失函数和优化器) - [训练网络](#训练网络) - - [变分推断](#变分推断) + - [使用变分自编码器](#使用变分自编码器) - [定义变分自编码器](#定义变分自编码器) - - [定义损失函数和优化器](#定义损失函数和优化器) + - [定义损失函数和优化器](#定义损失函数和优化器-1) - [处理数据](#处理数据) - - [训练网络](#训练网络) - - [DNN一键转换成BNN](#DNN一键转换成BNN) - - [定义DNN模型](#定义DNN模型) - - [定义损失函数和优化器](#定义损失函数和优化器) - - [功能一：转换整个模型](#功能一：转换整个模型) - - [功能二：转换指定类型的层](#功能二：转换指定类型的层) - - [不确定性估计](#不确定性估计) + - [训练网络](#训练网络-1) + - [生成新样本或重构输入样本](#生成新样本或重构输入样本) + - [DNN一键转换成BNN](#dnn一键转换成bnn) + - [定义DNN模型](#定义dnn模型) + - [定义损失函数和优化器](#定义损失函数和优化器-2) + - [实例化TransformToBNN](#实例化transformtobnn) + - [实现功能一：转换整个模型](#实现功能一转换整个模型) + - [实现功能二：转换指定类型的层](#实现功能二转换指定类型的层) + - [使用不确定性估计工具箱](#使用不确定性估计工具箱) -## 概述 -MindSpore深度概率编程（MindSpore Deep Probabilistic Programming, MDP）的目标是将深度学习和贝叶斯学习结合，并能面向不同的开发者。具体来说，对于专业的贝叶斯学习用户，提供概率采样、推理算法和模型构建库；另一方面，为不熟悉贝叶斯深度学习的用户提供了高级的API，从而不用更改深度学习编程逻辑，即可利用贝叶斯模型。 - -本章将详细介绍深度概率编程在MindSpore上的应用。 + -### 贝叶斯神经网络 -本例子利用贝叶斯神经网络实现一个简单的图片分类功能，整体流程如下： -1. 处理MNIST数据集。 -2. 定义贝叶斯LeNet网络。 -3. 定义损失函数和优化器。 +## 概述 +深度学习模型具有强大的拟合能力，而贝叶斯理论具有很好的可解释能力。MindSpore深度概率编程（MindSpore Deep Probabilistic Programming, MDP）将深度学习和贝叶斯学习结合，通过设置网络权重为分布、引入隐空间分布等，可以对分布进行采样前向传播，由此引入了不确定性，从而增强了模型的鲁棒性和可解释性。MDP不仅包含通用、专业的概率学习编程语言，适用于“专业”用户，而且支持使用开发深度学习模型的逻辑进行概率编程，让初学者轻松上手；此外，还提供深度概率学习的工具箱，拓展贝叶斯应用功能。 + +本章将详细介绍深度概率编程在MindSpore上的应用。在动手进行实践之前，确保，你已经正确安装了MindSpore 0.7.0-beta及其以上版本。本章的具体内容如下： +1. 介绍如何使用[bnn_layers模块](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/bnn_layers)实现贝叶斯神经网（Bayesian Neural Network, BNN）； +2. 介绍如何使用[variational模块](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/infer/variational)和[dpn模块](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/dpn)实现变分自编码器（Variational AutoEncoder, VAE）； +3. 介绍如何使用[transforms模块](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/transforms)实现DNN（Deep Neural Network, DNN）一键转BNN； +4. 介绍如何使用[toolbox模块](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/toolbox/uncertainty_evaluation.py)实现不确定性估计。 + +## 使用贝叶斯神经网络 +贝叶斯神经网络是由概率模型和神经网络组成的基本模型，它的权重不再是一个确定的值，而是一个分布。本例介绍了如何使用MDP中的bnn_layers模块实现贝叶斯神经网络，并利用贝叶斯神经网络实现一个简单的图片分类功能，整体流程如下： +1. 处理MNIST数据集； +2. 定义贝叶斯LeNet网络； +3. 定义损失函数和优化器； 4. 加载数据集并进行训练。 -#### 处理数据集 +> 本例面向GPU或Ascend 910 AI处理器平台，你可以在这里下载完整的样例代码： + +### 处理数据集 本例子使用的是MNIST数据集，数据处理过程与教程中的[实现一个图片分类应用](https://www.mindspore.cn/tutorial/zh-CN/master/quick_start/quick_start.html)一致。 -#### 定义贝叶斯神经网络 -本例子使用的是贝叶斯LeNet。利用`bnn_layers`构建贝叶斯神经网络的方法与构建普通的神经网络相同。值得注意的是，`bnn_layers`和普通的神经网络层可以互相组合。 +### 定义贝叶斯神经网络 +本例使用的是Bayesian LeNet。利用bnn_layers模块构建贝叶斯神经网络的方法与构建普通的神经网络相同。值得注意的是，`bnn_layers`和普通的神经网络层可以互相组合。 ``` import mindspore.nn as nn @@ -87,10 +98,13 @@ class BNNLeNet5(nn.Cell): x = self.fc3(x) return x ``` -#### 定义损失函数和优化器 +### 定义损失函数和优化器 接下来需要定义损失函数（Loss）和优化器（Optimizer）。损失函数是深度学习的训练目标，也叫目标函数，可以理解为神经网络的输出（Logits）和标签(Labels)之间的距离，是一个标量数据。 -常见的损失函数包括均方误差、L2损失、Hinge损失、交叉熵等等。图像分类应用通常采用交叉熵损失（`CrossEntropy`）。 -优化器用于神经网络求解（训练）。由于神经网络参数规模庞大，无法直接求解，因而深度学习中采用随机梯度下降算法（SGD）及其改进算法进行求解。MindSpore封装了常见的优化器，如`SGD`、`Adam`、`Momemtum`等等。本例采用Adam优化器，通常需要设定两个参数，学习率（`learnin _rate`）和权重衰减项（`weight decay`）。 + +常见的损失函数包括均方误差、L2损失、Hinge损失、交叉熵等等。图像分类应用通常采用交叉熵损失（CrossEntropy）。 + +优化器用于神经网络求解（训练）。由于神经网络参数规模庞大，无法直接求解，因而深度学习中采用随机梯度下降算法（SGD）及其改进算法进行求解。MindSpore封装了常见的优化器，如`SGD`、`Adam`、`Momemtum`等等。本例采用`Adam`优化器，通常需要设定两个参数，学习率（`learning_rate`）和权重衰减项（`weight_decay`）。 + MindSpore中定义损失函数和优化器的代码样例如下： ``` @@ -101,7 +115,7 @@ criterion = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") optimizer = AdamWeightDecay(params=network.trainable_params(), learning_rate=0.0001) ``` -#### 训练网络 +### 训练网络 贝叶斯神经网络的训练过程与DNN基本相同，唯一不同的是将`WithLossCell`替换为适用于BNN的`WithBNNLossCell`。除了`backbone`和`loss_fn`两个参数之外，`WithBNNLossCell`增加了`dnn_factor`和`bnn_factor`两个参数。`dnn_factor`是由损失函数计算得到的网络整体损失的系数，`bnn_factor`是每个贝叶斯层的KL散度的系数，这两个参数是用来平衡网络整体损失和贝叶斯层的KL散度的，防止KL散度的值过大掩盖了网络整体损失。 ``` @@ -157,9 +171,16 @@ def validate_model(net, dataset): return acc_mean ``` -### 变分推断 -#### 定义变分自编码器 -我们只需要自定义编码器和解码器，编码器和解码器都是神经网络。 +## 使用变分自编码器 +接下来介绍如何使用MDP中的variational模块和dpn模块实现变分自编码器。变分自编码器是经典的应用了变分推断的深度概率模型，用来学习潜在变量的表示，通过该模型，不仅可以压缩输入数据，还可以生成该类型的新图像。本例的整体流程如下： +1. 定义变分自编码器； +2. 定义损失函数和优化器； +3. 处理数据； +4. 训练网络； +5. 生成新样本或重构输入样本。 +> 本例面向GPU或Ascend 910 AI处理器平台，你可以在这里下载完整的样例代码： +### 定义变分自编码器 +使用dpn模块来构造变分自编码器尤为简单，你只需要自定义编码器和解码器（DNN模型），调用`VAE`接口即可。 ``` class Encoder(nn.Cell): @@ -210,11 +231,11 @@ optimizer = nn.Adam(params=vae.trainable_params(), learning_rate=0.001) net_with_loss = nn.WithLossCell(vae, net_loss) ``` -#### 处理数据 +### 处理数据 本例使用的是MNIST数据集，数据处理过程与教程中的[实现一个图片分类应用](https://www.mindspore.cn/tutorial/zh-CN/master/quick_start/quick_start.html)一致。 -#### 训练网络 -使用`SVI`接口对VAE网络进行训练。 +### 训练网络 +使用variational模块中的`SVI`接口对VAE网络进行训练。 ``` from mindspore.nn.probability.infer import SVI @@ -224,7 +245,7 @@ vae = vi.run(train_dataset=ds_train, epochs=10) trained_loss = vi.get_train_loss() ``` 通过`vi.run`可以得到训练好的网络，使用`vi.get_train_loss`可以得到训练之后的损失。 -#### 生成新样本或重构输入样本 +### 生成新样本或重构输入样本 利用训练好的VAE网络，我们可以生成新的样本或重构输入样本。 ``` @@ -235,9 +256,14 @@ for sample in ds_train.create_dict_iterator(): reconstructed_sample = vae.reconstruct_sample(sample_x) ``` -### DNN一键转换成BNN -对于不熟悉贝叶斯模型的DNN研究人员，MDP提供了高级API`TransformToBNN`，支持DNN模型一键转换成BNN模型。 -#### 定义DNN模型 +## DNN一键转换成BNN +对于不熟悉贝叶斯模型的DNN研究人员，MDP提供了高级API`TransformToBNN`，支持DNN模型一键转换成BNN模型。本例将会介绍如何使用transforms模块中的`TransformToBNN`API实现DNN一键转换成BNN，整体流程如下： +1. 定义DNN模型； +2. 定义损失函数和优化器； +3. 实现功能一：转换整个模型； +4. 实现功能二：转换指定类型的层。 +> 本例面向GPU或Ascend 910 AI处理器平台，你可以在这里下载完整的样例代码： +### 定义DNN模型 本例使用的DNN模型是LeNet。 ``` @@ -306,7 +332,21 @@ class LeNet5(nn.Cell): x = self.fc3(x) return x ``` -#### 定义损失函数和优化器 +LeNet的网络结构如下： + +``` +LeNet5 + (conv1) Conv2dinput_channels=1, output_channels=6, kernel_size=(5, 5),stride=(1, 1), pad_mode=valid, padding=0, dilation=(1, 1), group=1, has_bias=False + (conv2) Conv2dinput_channels=6, output_channels=16, kernel_size=(5, 5),stride=(1, 1), pad_mode=valid, padding=0, dilation=(1, 1), group=1, has_bias=False + (fc1) Densein_channels=400, out_channels=120, weight=Parameter (name=fc1.weight), has_bias=True, bias=Parameter (name=fc1.bias) + (fc2) Densein_channels=120, out_channels=84, weight=Parameter (name=fc2.weight), has_bias=True, bias=Parameter (name=fc2.bias) + (fc3) Densein_channels=84, out_channels=10, weight=Parameter (name=fc3.weight), has_bias=True, bias=Parameter (name=fc3.bias) + (relu) ReLU + (max_pool2d) MaxPool2dkernel_size=2, stride=2, pad_mode=VALID + (flatten) Flatten +``` + +### 定义损失函数和优化器 接下来需要定义损失函数（Loss）和优化器（Optimizer）。本例使用交叉熵损失作为损失函数，`Adam`作为优化器。 ``` @@ -321,7 +361,7 @@ optimizer = AdamWeightDecay(params=network.trainable_params(), learning_rate=0.0 net_with_loss = WithLossCell(network, criterion) train_network = TrainOneStepCell(net_with_loss, optimizer) ``` -#### 实例化TransformToBNN +### 实例化TransformToBNN `TransformToBNN`的`__init__`函数定义如下： ``` @@ -343,7 +383,7 @@ from mindspore.nn.probability import transforms bnn_transformer = transforms.TransformToBNN(train_network, 60000, 0.000001) ``` -#### 功能一：转换整个模型 +### 实现功能一：转换整个模型 `transform_to_bnn_model`方法可以将整个DNN模型转换为BNN模型。其定义如下: ``` @@ -374,12 +414,86 @@ bnn_transformer = transforms.TransformToBNN(train_network, 60000, 0.000001) """ ``` 参数`get_dense_args`指定从DNN模型的全连接层中获取哪些参数，`get_conv_args`指定从DNN模型的卷积层中获取哪些参数，参数`add_dense_args`和`add_conv_args`分别指定了要为BNN层指定哪些新的参数值。需要注意的是，`add_dense_args`中的参数不能与`get_dense_args`重复，`add_conv_args`和`get_conv_args`也是如此。 + 在MindSpore中将整个DNN模型转换成BNN模型的代码如下： ``` train_bnn_network = bnn_transformer.transform_to_bnn_model() ``` -#### 功能二：转换指定类型的层 +整个模型转换后的结构如下： + +``` +LeNet5 + (conv1) ConvReparam + in_channels=1, out_channels=6, kernel_size=(5, 5), stride=(1, 1), pad_mode=valid, padding=0, dilation=(1, 1), group=1, weight_mean=Parameter (name=conv1.weight_posterior.mean), weight_std=Parameter (name=conv1.weight_posterior.untransformed_std), has_bias=False + (weight_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (weight_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + + (conv2) ConvReparam + in_channels=6, out_channels=16, kernel_size=(5, 5), stride=(1, 1), pad_mode=valid, padding=0, dilation=(1, 1), group=1, weight_mean=Parameter (name=conv2.weight_posterior.mean), weight_std=Parameter (name=conv2.weight_posterior.untransformed_std), has_bias=False + (weight_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (weight_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + + (fc1) DenseReparam + in_channels=400, out_channels=120, weight_mean=Parameter (name=fc1.weight_posterior.mean), weight_std=Parameter (name=fc1.weight_posterior.untransformed_std), has_bias=True, bias_mean=Parameter (name=fc1.bias_posterior.mean), bias_std=Parameter (name=fc1.bias_posterior.untransformed_std) + (weight_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (weight_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + (bias_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (bias_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + + (fc2) DenseReparam + in_channels=120, out_channels=84, weight_mean=Parameter (name=fc2.weight_posterior.mean), weight_std=Parameter (name=fc2.weight_posterior.untransformed_std), has_bias=True, bias_mean=Parameter (name=fc2.bias_posterior.mean), bias_std=Parameter (name=fc2.bias_posterior.untransformed_std) + (weight_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (weight_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + (bias_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (bias_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + + (fc3) DenseReparam + in_channels=84, out_channels=10, weight_mean=Parameter (name=fc3.weight_posterior.mean), weight_std=Parameter (name=fc3.weight_posterior.untransformed_std), has_bias=True, bias_mean=Parameter (name=fc3.bias_posterior.mean), bias_std=Parameter (name=fc3.bias_posterior.untransformed_std) + (weight_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (weight_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + (bias_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (bias_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + + (relu) ReLU + (max_pool2d) MaxPool2dkernel_size=2, stride=2, pad_mode=VALID + (flatten) Flatten +``` +可以看到，整个LeNet网络中的卷积层和全连接层都转变成了相应的贝叶斯层。 + +### 实现功能二：转换指定类型的层 `transform_to_bnn_layer`方法可以将DNN模型中指定类型的层（nn.Dense或者nn.Conv2d）转换为对应的贝叶斯层。其定义如下: ``` @@ -401,8 +515,76 @@ train_bnn_network = bnn_transformer.transform_to_bnn_model() ``` 参数`dnn_layer`指定将哪个类型的DNN层转换成BNN层，`bnn_layer`指定DNN层将转换成哪个类型的BNN层，`get_args`和`add_args`分别指定从DNN层中获取哪些参数和要为BNN层的哪些参数重新赋值。 -### 不确定性估计 -不确定性估计工具箱基于MindSpore Deep probability Programming (MDP)，适用于主流的深度学习模型，如回归、分类、目标检测等。在推理阶段，利用不确定性估计工具箱，开发人员只需通过训练模型和训练数据集，指定需要估计的任务和样本，即可得到任意不确定性（aleatoric uncertainty）和认知不确定性（epistemic uncertainty）。基于不确定性信息，开发人员可以更好地理解模型和数据集。 +在MindSpore中将DNN模型中的Dense层转换成相应贝叶斯层`DenseReparam`的代码如下： + +``` +train_bnn_network = bnn_transformer.transform_to_bnn_layer(nn.Dense, bnn_layers.DenseReparam) +``` +转换后网络的结构如下： + +``` +LeNet5 + (conv1) Conv2dinput_channels=1, output_channels=6, kernel_size=(5, 5),stride=(1, 1), pad_mode=valid, padding=0, dilation=(1, 1), group=1, has_bias=False + (conv2) Conv2dinput_channels=6, output_channels=16, kernel_size=(5, 5),stride=(1, 1), pad_mode=valid, padding=0, dilation=(1, 1), group=1, has_bias=False + (fc1) DenseReparam + in_channels=400, out_channels=120, weight_mean=Parameter (name=fc1.weight_posterior.mean), weight_std=Parameter (name=fc1.weight_posterior.untransformed_std), has_bias=True, bias_mean=Parameter (name=fc1.bias_posterior.mean), bias_std=Parameter (name=fc1.bias_posterior.untransformed_std) + (weight_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (weight_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + (bias_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (bias_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + + (fc2) DenseReparam + in_channels=120, out_channels=84, weight_mean=Parameter (name=fc2.weight_posterior.mean), weight_std=Parameter (name=fc2.weight_posterior.untransformed_std), has_bias=True, bias_mean=Parameter (name=fc2.bias_posterior.mean), bias_std=Parameter (name=fc2.bias_posterior.untransformed_std) + (weight_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (weight_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + (bias_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (bias_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + + (fc3) DenseReparam + in_channels=84, out_channels=10, weight_mean=Parameter (name=fc3.weight_posterior.mean), weight_std=Parameter (name=fc3.weight_posterior.untransformed_std), has_bias=True, bias_mean=Parameter (name=fc3.bias_posterior.mean), bias_std=Parameter (name=fc3.bias_posterior.untransformed_std) + (weight_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (weight_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + (bias_prior) NormalPrior + (normal) Normalmean = 0.0, standard deviation = 0.1 + + (bias_posterior) NormalPosterior + (normal) Normalbatch_shape = None + + + (relu) ReLU + (max_pool2d) MaxPool2dkernel_size=2, stride=2, pad_mode=VALID + (flatten) Flatten +``` +可以看到，LeNet网络中的卷积层保持不变，全连接层变成了对应的贝叶斯层`DenseReparam`。 + +## 使用不确定性估计工具箱 +贝叶斯神经网络的优势之一就是可以获取不确定性，MDP在上层提供了不确定性估计的工具箱，用户可以很方便地使用该工具箱计算不确定性。不确定性意味着深度学习模型对预测结果的不确定程度。目前，大多数深度学习算法只能给出预测结果，而不能判断预测结果的可靠性。不确定性主要有两种类型：偶然不确定性和认知不确定性。 +- 偶然不确定性（Aleatoric Uncertainty）：描述数据中的内在噪声，即无法避免的误差，这个现象不能通过增加采样数据来削弱。 +- 认知不确定性（Epistemic Uncertainty）：模型自身对输入数据的估计可能因为训练不佳、训练数据不够等原因而不准确，可以通过增加训练数据等方式来缓解。 + +不确定性估计工具箱，适用于主流的深度学习模型，如回归、分类等。在推理阶段，利用不确定性估计工具箱，开发人员只需通过训练模型和训练数据集，指定需要估计的任务和样本，即可得到任意不确定性和认知不确定性。基于不确定性信息，开发人员可以更好地理解模型和数据集。 +> 本例面向GPU或Ascend 910 AI处理器平台，你可以在这里下载完整的样例代码： + 以分类任务为例，本例中使用的模型是LeNet，数据集为MNIST，数据处理过程与教程中的[实现一个图片分类应用](https://www.mindspore.cn/tutorial/zh-CN/master/quick_start/quick_start.html)一致。为了评估测试示例的不确定性，使用工具箱的方法如下: ``` @@ -429,4 +611,3 @@ for eval_data in ds_eval.create_dict_iterator(): aleatoric_uncertainty = evaluation.eval_aleatoric_uncertainty(eval_data) ``` - -- Gitee From 3a1c541227667e610ec106b5455c1d1bf715e5e0 Mon Sep 17 00:00:00 2001 From: zhangyi Date: Tue, 15 Sep 2020 16:11:43 +0800 Subject: [PATCH 033/156] Adjust the structure of API's contents in web. --- .../mindspore/mindspore.dataset.config.rst | 5 - .../python/mindspore/mindspore.dataset.rst | 7 ++ .../api/python/mindspore/mindspore.dtype.rst | 111 ----------------- .../mindspore/mindspore.ops.composite.rst | 5 - .../mindspore/mindspore.ops.operations.rst | 5 - .../api/python/mindspore/mindspore.rst | 115 +++++++++++++++++- .../api/python/mindspore/mindspore.train.rst | 12 ++ api/source_en/index.rst | 16 +-- .../mindspore/mindspore.dataset.config.rst | 5 - .../api/python/mindspore/mindspore.dtype.rst | 112 ----------------- .../mindspore/mindspore.ops.composite.rst | 5 - .../mindspore/mindspore.ops.operations.rst | 5 - .../api/python/mindspore/mindspore.rst | 115 +++++++++++++++++- .../api/python/mindspore/mindspore.train.rst | 12 ++ api/source_zh_cn/index.rst | 15 +-- .../_static/logo_online_experience.png | Bin 0 -> 1991 bytes .../source_zh_cn/quick_start/quick_start.md | 3 + 17 files changed, 273 insertions(+), 275 deletions(-) delete mode 100644 api/source_en/api/python/mindspore/mindspore.dataset.config.rst delete mode 100644 api/source_en/api/python/mindspore/mindspore.dtype.rst delete mode 100644 api/source_en/api/python/mindspore/mindspore.ops.composite.rst delete mode 100644 api/source_en/api/python/mindspore/mindspore.ops.operations.rst delete mode 100644 api/source_zh_cn/api/python/mindspore/mindspore.dataset.config.rst delete mode 100644 api/source_zh_cn/api/python/mindspore/mindspore.dtype.rst delete mode 100644 api/source_zh_cn/api/python/mindspore/mindspore.ops.composite.rst delete mode 100644 api/source_zh_cn/api/python/mindspore/mindspore.ops.operations.rst create mode 100644 tutorials/source_zh_cn/_static/logo_online_experience.png diff --git a/api/source_en/api/python/mindspore/mindspore.dataset.config.rst b/api/source_en/api/python/mindspore/mindspore.dataset.config.rst deleted file mode 100644 index 55cf3631f4..0000000000 --- a/api/source_en/api/python/mindspore/mindspore.dataset.config.rst +++ /dev/null @@ -1,5 +0,0 @@ -mindspore.dataset.config -======================== - -.. automodule:: mindspore.dataset.config - :members: diff --git a/api/source_en/api/python/mindspore/mindspore.dataset.rst b/api/source_en/api/python/mindspore/mindspore.dataset.rst index c5e1cdf7d3..1437a22b8e 100644 --- a/api/source_en/api/python/mindspore/mindspore.dataset.rst +++ b/api/source_en/api/python/mindspore/mindspore.dataset.rst @@ -5,3 +5,10 @@ mindspore.dataset :members: :inherited-members: :exclude-members: get_args, read_dir + +mindspore.dataset.config +------------------------ + +.. automodule:: mindspore.dataset.config + :members: + \ No newline at end of file diff --git a/api/source_en/api/python/mindspore/mindspore.dtype.rst b/api/source_en/api/python/mindspore/mindspore.dtype.rst deleted file mode 100644 index ecedea9718..0000000000 --- a/api/source_en/api/python/mindspore/mindspore.dtype.rst +++ /dev/null @@ -1,111 +0,0 @@ -mindspore.dtype -=============== - -Data Type ----------- - -.. class:: mindspore.dtype - -Create a data type object of MindSpore. - -The actual path of ``dtype`` is ``/mindspore/common/dtype.py``. -Run the following command to import the package: - -.. code-block:: - - import mindspore.common.dtype as mstype - -or - -.. code-block:: - - from mindspore import dtype as mstype - -Numeric Type -~~~~~~~~~~~~ - -Currently, MindSpore supports ``Int`` type, ``Uint`` type and ``Float`` type. -The following table lists the details. - -============================================== ============================= -Definition Description -============================================== ============================= -``mindspore.int8`` , ``mindspore.byte`` 8-bit integer -``mindspore.int16`` , ``mindspore.short`` 16-bit integer -``mindspore.int32`` , ``mindspore.intc`` 32-bit integer -``mindspore.int64`` , ``mindspore.intp`` 64-bit integer -``mindspore.uint8`` , ``mindspore.ubyte`` unsigned 8-bit integer -``mindspore.uint16`` , ``mindspore.ushort`` unsigned 16-bit integer -``mindspore.uint32`` , ``mindspore.uintc`` unsigned 32-bit integer -``mindspore.uint64`` , ``mindspore.uintp`` unsigned 64-bit integer -``mindspore.float16`` , ``mindspore.half`` 16-bit floating-point number -``mindspore.float32`` , ``mindspore.single`` 32-bit floating-point number -``mindspore.float64`` , ``mindspore.double`` 64-bit floating-point number -============================================== ============================= - -Other Type -~~~~~~~~~~ - -For other defined types, see the following table. - -============================ ================= -Type Description -============================ ================= -``tensor`` MindSpore's ``tensor`` type. Data format uses NCHW. For details, see [tensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/tensor.py). -``MetaTensor`` A tensor only has data type and shape. For details, see [MetaTensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/parameter.py). -``bool_`` Boolean ``True`` or ``False``. -``int_`` Integer scalar. -``uint`` Unsigned integer scalar. -``float_`` Floating-point scalar. -``number`` Number, including ``int_`` , ``uint`` , ``float_`` and ``bool_`` . -``list_`` List constructed by ``tensor`` , such as ``List[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types. -``tuple_`` Tuple constructed by ``tensor`` , such as ``Tuple[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types. -``function`` Function. Return in two ways, when function is not None, returns Func directly, the other returns Func(args: List[T0,T1,...,Tn], retval: T) when function is None. -``type_type`` Type definition of type. -``type_none`` No matching return type, corresponding to the ``type(None)`` in Python. -``symbolic_key`` The value of a variable is used as a key of the variable in ``env_type`` . -``env_type`` Used to store the gradient of the free variable of a function, where the key is the ``symbolic_key`` of the free variable's node and the value is the gradient. -============================ ================= - -Tree Topology -~~~~~~~~~~~~~~ - -The relationships of the above types are as follows: - -.. code-block:: - - - └─────── number - │ ├─── bool_ - │ ├─── int_ - │ │ ├─── int8, byte - │ │ ├─── int16, short - │ │ ├─── int32, intc - │ │ └─── int64, intp - │ ├─── uint - │ │ ├─── uint8, ubyte - │ │ ├─── uint16, ushort - │ │ ├─── uint32, uintc - │ │ └─── uint64, uintp - │ └─── float_ - │ ├─── float16 - │ ├─── float32 - │ └─── float64 - ├─── tensor - │ ├─── Array[Float32] - │ └─── ... - ├─── list_ - │ ├─── List[Int32,Float32] - │ └─── ... - ├─── tuple_ - │ ├─── Tuple[Int32,Float32] - │ └─── ... - ├─── function - │ ├─── Func - │ ├─── Func[(Int32, Float32), Int32] - │ └─── ... - ├─── MetaTensor - ├─── type_type - ├─── type_none - ├─── symbolic_key - └─── env_type \ No newline at end of file diff --git a/api/source_en/api/python/mindspore/mindspore.ops.composite.rst b/api/source_en/api/python/mindspore/mindspore.ops.composite.rst deleted file mode 100644 index 4dc22f1dcf..0000000000 --- a/api/source_en/api/python/mindspore/mindspore.ops.composite.rst +++ /dev/null @@ -1,5 +0,0 @@ -mindspore.ops.composite -======================= - -.. automodule:: mindspore.ops.composite - :members: diff --git a/api/source_en/api/python/mindspore/mindspore.ops.operations.rst b/api/source_en/api/python/mindspore/mindspore.ops.operations.rst deleted file mode 100644 index 29bf49176b..0000000000 --- a/api/source_en/api/python/mindspore/mindspore.ops.operations.rst +++ /dev/null @@ -1,5 +0,0 @@ -mindspore.ops.operations -======================== - -.. automodule:: mindspore.ops.operations - :members: diff --git a/api/source_en/api/python/mindspore/mindspore.rst b/api/source_en/api/python/mindspore/mindspore.rst index 44c49e3df3..118c3a9c04 100644 --- a/api/source_en/api/python/mindspore/mindspore.rst +++ b/api/source_en/api/python/mindspore/mindspore.rst @@ -1,5 +1,118 @@ mindspore ========= +mindspore.dtype +--------------- + +Data Type +~~~~~~~~~ + +.. class:: mindspore.dtype + +Create a data type object of MindSpore. + +The actual path of ``dtype`` is ``/mindspore/common/dtype.py``. +Run the following command to import the package: + +.. code-block:: + + import mindspore.common.dtype as mstype + +or + +.. code-block:: + + from mindspore import dtype as mstype + +Numeric Type +++++++++++++ + +Currently, MindSpore supports ``Int`` type, ``Uint`` type and ``Float`` type. +The following table lists the details. + +============================================== ============================= +Definition Description +============================================== ============================= +``mindspore.int8`` , ``mindspore.byte`` 8-bit integer +``mindspore.int16`` , ``mindspore.short`` 16-bit integer +``mindspore.int32`` , ``mindspore.intc`` 32-bit integer +``mindspore.int64`` , ``mindspore.intp`` 64-bit integer +``mindspore.uint8`` , ``mindspore.ubyte`` unsigned 8-bit integer +``mindspore.uint16`` , ``mindspore.ushort`` unsigned 16-bit integer +``mindspore.uint32`` , ``mindspore.uintc`` unsigned 32-bit integer +``mindspore.uint64`` , ``mindspore.uintp`` unsigned 64-bit integer +``mindspore.float16`` , ``mindspore.half`` 16-bit floating-point number +``mindspore.float32`` , ``mindspore.single`` 32-bit floating-point number +``mindspore.float64`` , ``mindspore.double`` 64-bit floating-point number +============================================== ============================= + +Other Type +++++++++++ + +For other defined types, see the following table. + +============================ ================= +Type Description +============================ ================= +``tensor`` MindSpore's ``tensor`` type. Data format uses NCHW. For details, see [tensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/tensor.py). +``MetaTensor`` A tensor only has data type and shape. For details, see [MetaTensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/parameter.py). +``bool_`` Boolean ``True`` or ``False``. +``int_`` Integer scalar. +``uint`` Unsigned integer scalar. +``float_`` Floating-point scalar. +``number`` Number, including ``int_`` , ``uint`` , ``float_`` and ``bool_`` . +``list_`` List constructed by ``tensor`` , such as ``List[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types. +``tuple_`` Tuple constructed by ``tensor`` , such as ``Tuple[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types. +``function`` Function. Return in two ways, when function is not None, returns Func directly, the other returns Func(args: List[T0,T1,...,Tn], retval: T) when function is None. +``type_type`` Type definition of type. +``type_none`` No matching return type, corresponding to the ``type(None)`` in Python. +``symbolic_key`` The value of a variable is used as a key of the variable in ``env_type`` . +``env_type`` Used to store the gradient of the free variable of a function, where the key is the ``symbolic_key`` of the free variable's node and the value is the gradient. +============================ ================= + +Tree Topology ++++++++++++++ + +The relationships of the above types are as follows: + +.. code-block:: + + + └─────── number + │ ├─── bool_ + │ ├─── int_ + │ │ ├─── int8, byte + │ │ ├─── int16, short + │ │ ├─── int32, intc + │ │ └─── int64, intp + │ ├─── uint + │ │ ├─── uint8, ubyte + │ │ ├─── uint16, ushort + │ │ ├─── uint32, uintc + │ │ └─── uint64, uintp + │ └─── float_ + │ ├─── float16 + │ ├─── float32 + │ └─── float64 + ├─── tensor + │ ├─── Array[Float32] + │ └─── ... + ├─── list_ + │ ├─── List[Int32,Float32] + │ └─── ... + ├─── tuple_ + │ ├─── Tuple[Int32,Float32] + │ └─── ... + ├─── function + │ ├─── Func + │ ├─── Func[(Int32, Float32), Int32] + │ └─── ... + ├─── MetaTensor + ├─── type_type + ├─── type_none + ├─── symbolic_key + └─── env_type + .. automodule:: mindspore - :members: \ No newline at end of file + :members: + :exclude-members: Model, dataset_helper, \ No newline at end of file diff --git a/api/source_en/api/python/mindspore/mindspore.train.rst b/api/source_en/api/python/mindspore/mindspore.train.rst index eb6753e672..3d24633055 100644 --- a/api/source_en/api/python/mindspore/mindspore.train.rst +++ b/api/source_en/api/python/mindspore/mindspore.train.rst @@ -1,6 +1,18 @@ mindspore.train =============== +mindspore.train.model +--------------------- + +.. automodule:: mindspore.train.model + :members: + +mindspore.train.dataset_helper +------------------------------ + +.. automodule:: mindspore.train.dataset_helper + :members: + mindspore.train.summary ----------------------- diff --git a/api/source_en/index.rst b/api/source_en/index.rst index ebaf340a02..e74b234d3d 100644 --- a/api/source_en/index.rst +++ b/api/source_en/index.rst @@ -11,25 +11,21 @@ MindSpore API :caption: MindSpore Python API api/python/mindspore/mindspore - api/python/mindspore/mindspore.dtype api/python/mindspore/mindspore.common.initializer api/python/mindspore/mindspore.communication api/python/mindspore/mindspore.context - api/python/mindspore/mindspore.nn - api/python/mindspore/mindspore.nn.dynamic_lr - api/python/mindspore/mindspore.nn.learning_rate_schedule - api/python/mindspore/mindspore.nn.probability - api/python/mindspore/mindspore.ops - api/python/mindspore/mindspore.ops.composite - api/python/mindspore/mindspore.ops.operations - api/python/mindspore/mindspore.train api/python/mindspore/mindspore.dataset - api/python/mindspore/mindspore.dataset.config api/python/mindspore/mindspore.dataset.text api/python/mindspore/mindspore.dataset.transforms api/python/mindspore/mindspore.dataset.vision api/python/mindspore/mindspore.mindrecord + api/python/mindspore/mindspore.nn + api/python/mindspore/mindspore.nn.dynamic_lr + api/python/mindspore/mindspore.nn.probability + api/python/mindspore/mindspore.ops api/python/mindspore/mindspore.profiler + api/python/mindspore/mindspore.train + .. toctree:: :maxdepth: 1 diff --git a/api/source_zh_cn/api/python/mindspore/mindspore.dataset.config.rst b/api/source_zh_cn/api/python/mindspore/mindspore.dataset.config.rst deleted file mode 100644 index 55cf3631f4..0000000000 --- a/api/source_zh_cn/api/python/mindspore/mindspore.dataset.config.rst +++ /dev/null @@ -1,5 +0,0 @@ -mindspore.dataset.config -======================== - -.. automodule:: mindspore.dataset.config - :members: diff --git a/api/source_zh_cn/api/python/mindspore/mindspore.dtype.rst b/api/source_zh_cn/api/python/mindspore/mindspore.dtype.rst deleted file mode 100644 index 633cd1e23e..0000000000 --- a/api/source_zh_cn/api/python/mindspore/mindspore.dtype.rst +++ /dev/null @@ -1,112 +0,0 @@ -mindspore.dtype -=============== - -Data Type ----------- - -.. class:: mindspore.dtype - -Create a data type object of MindSpore. - -The actual path of ``dtype`` is ``/mindspore/common/dtype.py``. -Run the following command to import the package: - -.. code-block:: - - import mindspore.common.dtype as mstype - -or - -.. code-block:: - - from mindspore import dtype as mstype - -Numeric Type -~~~~~~~~~~~~ - -Currently, MindSpore supports ``Int`` type, ``Uint`` type and ``Float`` type. -The following table lists the details. - -============================================== ============================= -Definition Description -============================================== ============================= -``mindspore.int8`` , ``mindspore.byte`` 8-bit integer -``mindspore.int16`` , ``mindspore.short`` 16-bit integer -``mindspore.int32`` , ``mindspore.intc`` 32-bit integer -``mindspore.int64`` , ``mindspore.intp`` 64-bit integer -``mindspore.uint8`` , ``mindspore.ubyte`` unsigned 8-bit integer -``mindspore.uint16`` , ``mindspore.ushort`` unsigned 16-bit integer -``mindspore.uint32`` , ``mindspore.uintc`` unsigned 32-bit integer -``mindspore.uint64`` , ``mindspore.uintp`` unsigned 64-bit integer -``mindspore.float16`` , ``mindspore.half`` 16-bit floating-point number -``mindspore.float32`` , ``mindspore.single`` 32-bit floating-point number -``mindspore.float64`` , ``mindspore.double`` 64-bit floating-point number -============================================== ============================= - -Other Type -~~~~~~~~~~ - -For other defined types, see the following table. - -============================ ================= -Type Description -============================ ================= -``tensor`` MindSpore's ``tensor`` type. Data format uses NCHW. For details, see [tensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/tensor.py). -``MetaTensor`` A tensor only has data type and shape. For details, see [MetaTensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/parameter.py). -``bool_`` Boolean ``True`` or ``False``. -``int_`` Integer scalar. -``uint`` Unsigned integer scalar. -``float_`` Floating-point scalar. -``number`` Number, including ``int_`` , ``uint`` , ``float_`` and ``bool_`` . -``list_`` List constructed by ``tensor`` , such as ``List[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types. -``tuple_`` Tuple constructed by ``tensor`` , such as ``Tuple[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types. -``function`` Function. Return in two ways, when function is not None, returns Func directly, the other returns Func(args: List[T0,T1,...,Tn], retval: T) when function is None. -``type_type`` Type definition of type. -``type_none`` No matching return type, corresponding to the ``type(None)`` in Python. -``symbolic_key`` The value of a variable is used as a key of the variable in ``env_type`` . -``env_type`` Used to store the gradient of the free variable of a function, where the key is the ``symbolic_key`` of the free variable's node and the value is the gradient. -============================ ================= - -Tree Topology -~~~~~~~~~~~~~~ - -The relationships of the above types are as follows: - -.. code-block:: - - - └─── mindspore.dtype - ├─── number - │ ├─── bool_ - │ ├─── int_ - │ │ ├─── int8, byte - │ │ ├─── int16, short - │ │ ├─── int32, intc - │ │ └─── int64, intp - │ ├─── uint - │ │ ├─── uint8, ubyte - │ │ ├─── uint16, ushort - │ │ ├─── uint32, uintc - │ │ └─── uint64, uintp - │ └─── float_ - │ ├─── float16 - │ ├─── float32 - │ └─── float64 - ├─── tensor - │ ├─── Array[float32] - │ └─── ... - ├─── list_ - │ ├─── List[int32,float32] - │ └─── ... - ├─── tuple_ - │ ├─── Tuple[int32,float32] - │ └─── ... - ├─── function - │ ├─── Func - │ ├─── Func[(int32, float32), int32] - │ └─── ... - ├─── MetaTensor - ├─── type_type - ├─── type_none - ├─── symbolic_key - └─── env_type \ No newline at end of file diff --git a/api/source_zh_cn/api/python/mindspore/mindspore.ops.composite.rst b/api/source_zh_cn/api/python/mindspore/mindspore.ops.composite.rst deleted file mode 100644 index 4dc22f1dcf..0000000000 --- a/api/source_zh_cn/api/python/mindspore/mindspore.ops.composite.rst +++ /dev/null @@ -1,5 +0,0 @@ -mindspore.ops.composite -======================= - -.. automodule:: mindspore.ops.composite - :members: diff --git a/api/source_zh_cn/api/python/mindspore/mindspore.ops.operations.rst b/api/source_zh_cn/api/python/mindspore/mindspore.ops.operations.rst deleted file mode 100644 index 29bf49176b..0000000000 --- a/api/source_zh_cn/api/python/mindspore/mindspore.ops.operations.rst +++ /dev/null @@ -1,5 +0,0 @@ -mindspore.ops.operations -======================== - -.. automodule:: mindspore.ops.operations - :members: diff --git a/api/source_zh_cn/api/python/mindspore/mindspore.rst b/api/source_zh_cn/api/python/mindspore/mindspore.rst index 44c49e3df3..118c3a9c04 100644 --- a/api/source_zh_cn/api/python/mindspore/mindspore.rst +++ b/api/source_zh_cn/api/python/mindspore/mindspore.rst @@ -1,5 +1,118 @@ mindspore ========= +mindspore.dtype +--------------- + +Data Type +~~~~~~~~~ + +.. class:: mindspore.dtype + +Create a data type object of MindSpore. + +The actual path of ``dtype`` is ``/mindspore/common/dtype.py``. +Run the following command to import the package: + +.. code-block:: + + import mindspore.common.dtype as mstype + +or + +.. code-block:: + + from mindspore import dtype as mstype + +Numeric Type +++++++++++++ + +Currently, MindSpore supports ``Int`` type, ``Uint`` type and ``Float`` type. +The following table lists the details. + +============================================== ============================= +Definition Description +============================================== ============================= +``mindspore.int8`` , ``mindspore.byte`` 8-bit integer +``mindspore.int16`` , ``mindspore.short`` 16-bit integer +``mindspore.int32`` , ``mindspore.intc`` 32-bit integer +``mindspore.int64`` , ``mindspore.intp`` 64-bit integer +``mindspore.uint8`` , ``mindspore.ubyte`` unsigned 8-bit integer +``mindspore.uint16`` , ``mindspore.ushort`` unsigned 16-bit integer +``mindspore.uint32`` , ``mindspore.uintc`` unsigned 32-bit integer +``mindspore.uint64`` , ``mindspore.uintp`` unsigned 64-bit integer +``mindspore.float16`` , ``mindspore.half`` 16-bit floating-point number +``mindspore.float32`` , ``mindspore.single`` 32-bit floating-point number +``mindspore.float64`` , ``mindspore.double`` 64-bit floating-point number +============================================== ============================= + +Other Type +++++++++++ + +For other defined types, see the following table. + +============================ ================= +Type Description +============================ ================= +``tensor`` MindSpore's ``tensor`` type. Data format uses NCHW. For details, see [tensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/tensor.py). +``MetaTensor`` A tensor only has data type and shape. For details, see [MetaTensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/parameter.py). +``bool_`` Boolean ``True`` or ``False``. +``int_`` Integer scalar. +``uint`` Unsigned integer scalar. +``float_`` Floating-point scalar. +``number`` Number, including ``int_`` , ``uint`` , ``float_`` and ``bool_`` . +``list_`` List constructed by ``tensor`` , such as ``List[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types. +``tuple_`` Tuple constructed by ``tensor`` , such as ``Tuple[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types. +``function`` Function. Return in two ways, when function is not None, returns Func directly, the other returns Func(args: List[T0,T1,...,Tn], retval: T) when function is None. +``type_type`` Type definition of type. +``type_none`` No matching return type, corresponding to the ``type(None)`` in Python. +``symbolic_key`` The value of a variable is used as a key of the variable in ``env_type`` . +``env_type`` Used to store the gradient of the free variable of a function, where the key is the ``symbolic_key`` of the free variable's node and the value is the gradient. +============================ ================= + +Tree Topology ++++++++++++++ + +The relationships of the above types are as follows: + +.. code-block:: + + + └─────── number + │ ├─── bool_ + │ ├─── int_ + │ │ ├─── int8, byte + │ │ ├─── int16, short + │ │ ├─── int32, intc + │ │ └─── int64, intp + │ ├─── uint + │ │ ├─── uint8, ubyte + │ │ ├─── uint16, ushort + │ │ ├─── uint32, uintc + │ │ └─── uint64, uintp + │ └─── float_ + │ ├─── float16 + │ ├─── float32 + │ └─── float64 + ├─── tensor + │ ├─── Array[Float32] + │ └─── ... + ├─── list_ + │ ├─── List[Int32,Float32] + │ └─── ... + ├─── tuple_ + │ ├─── Tuple[Int32,Float32] + │ └─── ... + ├─── function + │ ├─── Func + │ ├─── Func[(Int32, Float32), Int32] + │ └─── ... + ├─── MetaTensor + ├─── type_type + ├─── type_none + ├─── symbolic_key + └─── env_type + .. automodule:: mindspore - :members: \ No newline at end of file + :members: + :exclude-members: Model, dataset_helper, \ No newline at end of file diff --git a/api/source_zh_cn/api/python/mindspore/mindspore.train.rst b/api/source_zh_cn/api/python/mindspore/mindspore.train.rst index eb6753e672..3d24633055 100644 --- a/api/source_zh_cn/api/python/mindspore/mindspore.train.rst +++ b/api/source_zh_cn/api/python/mindspore/mindspore.train.rst @@ -1,6 +1,18 @@ mindspore.train =============== +mindspore.train.model +--------------------- + +.. automodule:: mindspore.train.model + :members: + +mindspore.train.dataset_helper +------------------------------ + +.. automodule:: mindspore.train.dataset_helper + :members: + mindspore.train.summary ----------------------- diff --git a/api/source_zh_cn/index.rst b/api/source_zh_cn/index.rst index 871c4d0f6a..8763f86224 100644 --- a/api/source_zh_cn/index.rst +++ b/api/source_zh_cn/index.rst @@ -19,25 +19,20 @@ MindSpore API :caption: MindSpore Python API api/python/mindspore/mindspore - api/python/mindspore/mindspore.dtype api/python/mindspore/mindspore.common.initializer api/python/mindspore/mindspore.communication api/python/mindspore/mindspore.context - api/python/mindspore/mindspore.nn - api/python/mindspore/mindspore.nn.dynamic_lr - api/python/mindspore/mindspore.nn.learning_rate_schedule - api/python/mindspore/mindspore.nn.probability - api/python/mindspore/mindspore.ops - api/python/mindspore/mindspore.ops.composite - api/python/mindspore/mindspore.ops.operations - api/python/mindspore/mindspore.train api/python/mindspore/mindspore.dataset - api/python/mindspore/mindspore.dataset.config api/python/mindspore/mindspore.dataset.text api/python/mindspore/mindspore.dataset.transforms api/python/mindspore/mindspore.dataset.vision api/python/mindspore/mindspore.mindrecord + api/python/mindspore/mindspore.nn + api/python/mindspore/mindspore.nn.dynamic_lr + api/python/mindspore/mindspore.nn.probability + api/python/mindspore/mindspore.ops api/python/mindspore/mindspore.profiler + api/python/mindspore/mindspore.train .. toctree:: :maxdepth: 1 diff --git a/tutorials/source_zh_cn/_static/logo_online_experience.png b/tutorials/source_zh_cn/_static/logo_online_experience.png new file mode 100644 index 0000000000000000000000000000000000000000..c630af6e0c580998357c4865f25f459e9508333c GIT binary patch literal 1991 zcmV;&2RQhNP)H00009a7bBm000XT z000XT0n*)m`~Uz4c}YY;RA_#sFF z>i_GF9;?;x(Gd<6e$Z%)i19zG)zH~9hIU@Nz zOb90AnV9idG|rV%l$cbmvnrg){^!huGg-s?25nS@JlNZvCIvNMx4mGs4&RJZ6m`*N zoAhRVXV%vBcU&|*?Qzd~`wr^DzRv91Z@Fmt0Fps5Nm10o;J|?mcya{AWK1e& z-+l{%Ik4XWtmT%slF-V^TRNCVec+bM}L| z!E^S7!GXud^)O)CD1d zw|Z#nXh-``+S?Q5yUyk7i@Chn$bj9J;72C9yCy#Vq+*ij7=&Cj4ZmrRn?Wd2C{PqN zmr4t=tck%TCS+!9J*Oyhsr1a+`dlhqrwtPVVn9$%QG#M}W^LUpBbgzb$?AvbFJbDL zwe_Hwgj5+6lbWgFOjffBtc9$rjfxbC@Klg73=e0rn2?3R0Uyg!6y;-CNE@1@o&-=5 zlgfHp>R8eX&!y5$O3Z#}18Ra5-yB`MaQ=f=hi(jC+#VnM_1QD;-ntzvm735rf9~a0 zgy!#j`GmB>o(pHPf{gX#3NproXldL<(~T)zkTEbN$QXQsr4tmBPZeE3mi3frms2aN zkwVc$(>|8XDazE!r|?wcTE7UwnQTldGYCzsti~h>mTyo@LhJ=u2FXaFIO*tSbT0!K zgf>PmPkY>YlQbou5R@3Q1j6A=)bll+ z*J46qQhCudf#GCb?;n{;&RiyY5!F-eL^WuCM1oL!JHWMi#$SQs3DAB0dJr%8uHkgt)sAeh07F#fQfZ#EPkY?j%Zh1_JDka? zG9EY}%zFD^TQ{=Ab9NugsApMDt#pzZH!Y!1=tn0{&fob`tW-wZ+fTH#7|QjXJjtCn z@wZ*+7cai}-f1t0ZC9&5UHz1^ocO)Tdw{XdPRk>O9Ttm^WmmuY%HP(;bKL!}zfRq| zw_B@$8~(N*c33Q!5d9GKew(3A{iEI5eNf(HcD)!hqAx3xjr_2JIHEG;cpoX+3& z{}{%q{&u)%`iiq>w|4(e`TTCJcE#!ZW$#Nc(2%9E&d$`mf5XZE$vRH~`|prNj03TF zD?)~^Db$x!t*rTFAE5*Sv!4%Lj{8`KTYudh4V*oeJ(&z Z)HjqDSD~6lCIkQg002ovPDHLkV1f|c?x+9& literal 0 HcmV?d00001 diff --git a/tutorials/source_zh_cn/quick_start/quick_start.md b/tutorials/source_zh_cn/quick_start/quick_start.md index 0a2b025632..e8e52f37dc 100644 --- a/tutorials/source_zh_cn/quick_start/quick_start.md +++ b/tutorials/source_zh_cn/quick_start/quick_start.md @@ -28,6 +28,9 @@    +   + + ## 概述 -- Gitee From aeed0d055eaaa64bac48871c69919f2f52b9f6c1 Mon Sep 17 00:00:00 2001 From: xuanyue Date: Tue, 15 Sep 2020 16:31:22 +0800 Subject: [PATCH 034/156] add errorcode category --- lite/docs/source_en/apicc/errorcode_and_metatype.md | 3 +++ lite/docs/source_zh_cn/apicc/errorcode_and_metatype.md | 3 +++ lite/tutorials/source_en/build.md | 2 +- lite/tutorials/source_en/use/converter_tool.md | 3 +++ lite/tutorials/source_zh_cn/use/converter_tool.md | 2 ++ 5 files changed, 12 insertions(+), 1 deletion(-) diff --git a/lite/docs/source_en/apicc/errorcode_and_metatype.md b/lite/docs/source_en/apicc/errorcode_and_metatype.md index df56621340..45b4877a85 100644 --- a/lite/docs/source_en/apicc/errorcode_and_metatype.md +++ b/lite/docs/source_en/apicc/errorcode_and_metatype.md @@ -13,6 +13,7 @@ Description of error code and meta type supported in MindSpore Lite. | RET_NO_CHANGE | -4 | No change. | | RET_SUCCESS_EXIT | -5 | No error but exit. | | RET_MEMORY_FAILED | -6 | Fail to create memory. | +| RET_NOT_SUPPORT | -7 | Fail to support. | | RET_OUT_OF_TENSOR_RANGE | -101 | Failed to check range. | | RET_INPUT_TENSOR_ERROR | -102 | Failed to check input tensor. | | RET_REENTRANT_ERROR | -103 | Exist executor running. | @@ -24,6 +25,8 @@ Description of error code and meta type supported in MindSpore Lite. | RET_FORMAT_ERR | -401 | Failed to check the tensor format. | | RET_INFER_ERR | -501 | Failed to infer shape. | | RET_INFER_INVALID | -502 | Invalid infer shape before runtime. | +| RET_INPUT_PARAM_INVALID | -601 | Invalid input param by user. | +| RET_INPUT_PARAM_LACK | -602 | Lack input param by user. | ## MetaType An **enum** type. diff --git a/lite/docs/source_zh_cn/apicc/errorcode_and_metatype.md b/lite/docs/source_zh_cn/apicc/errorcode_and_metatype.md index 4195eaedcf..59f0d81ea4 100644 --- a/lite/docs/source_zh_cn/apicc/errorcode_and_metatype.md +++ b/lite/docs/source_zh_cn/apicc/errorcode_and_metatype.md @@ -13,6 +13,7 @@ | RET_NO_CHANGE | -4 | 无改变。 | | RET_SUCCESS_EXIT | -5 | 无错误退出。 | | RET_MEMORY_FAILED | -6 | 创建内存失败。 | +| RET_NOT_SUPPORT | -7 | 尚未支持。 | | RET_OUT_OF_TENSOR_RANGE | -101 | 输出检查越界。 | | RET_INPUT_TENSOR_ERROR | -102 | 输入检查越界。 | | RET_REENTRANT_ERROR | -103 | 存在运行中的执行器。 | @@ -24,6 +25,8 @@ | RET_FORMAT_ERR | -401 | 张量格式检查失败。 | | RET_INFER_ERR | -501 | 维度推理失败。 | | RET_INFER_INVALID | -502 | 无效的维度推理。 | +| RET_INPUT_PARAM_INVALID | -601 | 无效的用户输入参数。 | +| RET_INPUT_PARAM_LACK | -602 | 缺少必要的输入参数。 | ## MetaType diff --git a/lite/tutorials/source_en/build.md b/lite/tutorials/source_en/build.md index b59964d357..64a1035d9a 100644 --- a/lite/tutorials/source_en/build.md +++ b/lite/tutorials/source_en/build.md @@ -30,7 +30,7 @@ This chapter introduces how to quickly compile MindSpore Lite, which includes th - The compilation environment supports Linux x86_64 only. Ubuntu 18.04.02 LTS is recommended. -- Compilation dependencies of runtime、benchmark and time_profiler: +- Compilation dependencies of runtime、benchmark and timeprofiler: - [CMake](https://cmake.org/download/) >= 3.14.1 - [GCC](https://gcc.gnu.org/releases.html) >= 7.3.0 - [Android_NDK r20b](https://dl.google.com/android/repository/android-ndk-r20b-linux-x86_64.zip) diff --git a/lite/tutorials/source_en/use/converter_tool.md b/lite/tutorials/source_en/use/converter_tool.md index fbdb7f42b3..810e087cc1 100644 --- a/lite/tutorials/source_en/use/converter_tool.md +++ b/lite/tutorials/source_en/use/converter_tool.md @@ -89,6 +89,8 @@ The following describes how to use the conversion command by using several commo INFO [converter/converter.cc:190] Runconverter] CONVERTER RESULT: SUCCESS! ``` +- If fail to run the conversion command, an [errorcode](https://www.mindspore.cn/lite/docs/en/master/apicc/errorcode_and_metatype.html) will be output. + ### Parameter Description MindSpore Lite model conversion tool provides multiple parameters. @@ -174,3 +176,4 @@ Several common examples are selected below to illustrate the use of conversion c ``` INFO [converter/converter.cc:190] Runconverter] CONVERTER RESULT: SUCCESS! ``` +- If fail to run the conversion command, an [errorcode](https://www.mindspore.cn/lite/docs/en/master/apicc/errorcode_and_metatype.html) will be output. diff --git a/lite/tutorials/source_zh_cn/use/converter_tool.md b/lite/tutorials/source_zh_cn/use/converter_tool.md index 618ac57a3a..a7890385af 100644 --- a/lite/tutorials/source_zh_cn/use/converter_tool.md +++ b/lite/tutorials/source_zh_cn/use/converter_tool.md @@ -89,6 +89,7 @@ bash build.sh -I x86_64 ``` INFO [converter/converter.cc:190] Runconverter] CONVERTER RESULT: SUCCESS! ``` +- 如果转换命令执行失败，程序会返回一个[错误码](https://www.mindspore.cn/lite/docs/zh-CN/master/apicc/errorcode_and_metatype.html)。 > 训练后量化示例请参考。 @@ -175,3 +176,4 @@ set MSLOG=INFO ``` INFO [converter/converter.cc:190] Runconverter] CONVERTER RESULT: SUCCESS! ``` +- 如果转换命令执行失败，程序会返回一个[错误码](https://www.mindspore.cn/lite/docs/zh-CN/master/apicc/errorcode_and_metatype.html)。 -- Gitee From 552e0e3a31dffe90f182f9fb5b357a0f6b39ea40 Mon Sep 17 00:00:00 2001 From: buxue Date: Tue, 15 Sep 2020 16:41:16 +0800 Subject: [PATCH 035/156] improve parameter describe --- api/source_zh_cn/programming_guide/dtype.md | 15 ++++++++++++ .../programming_guide/parameter.md | 23 ++++++++++++------- 2 files changed, 30 insertions(+), 8 deletions(-) diff --git a/api/source_zh_cn/programming_guide/dtype.md b/api/source_zh_cn/programming_guide/dtype.md index a8359c161c..64debf4168 100644 --- a/api/source_zh_cn/programming_guide/dtype.md +++ b/api/source_zh_cn/programming_guide/dtype.md @@ -18,6 +18,21 @@ MindSpore张量支持不同的数据类型，包含`int8`、`int16`、`int32`、 详细的类型支持情况请参考。 +以下代码，打印MindSpore的数据类型int32。 +``` +from mindspore import dtype as mstype + +data_type = mstype.int32 +print(data_type) +``` + +输出如下： + +``` +Int32 +``` + + ## 数据类型转换接口 MindSpore提供了以下几个接口，实现与NumPy数据类型和Python内置的数据类型间的转换。 diff --git a/api/source_zh_cn/programming_guide/parameter.md b/api/source_zh_cn/programming_guide/parameter.md index 9f8ff76a2c..03b7d35e50 100644 --- a/api/source_zh_cn/programming_guide/parameter.md +++ b/api/source_zh_cn/programming_guide/parameter.md @@ -15,21 +15,27 @@ ## 概述 -`Parameter`是变量张量，代表在训练网络时，需要被更新的参数，是`MetaTensor`的一个子类。 -` +`Parameter`是变量张量，代表在训练网络时，需要被更新的参数。本章主要介绍了`Parameter`的初始化以及属性和方法的使用，同时介绍了`ParameterTuple`。 + ## 初始化 ``` def __init__(self, default_input, name, requires_grad=True, layerwise_parallel=False) ``` 初始化一个`Parameter`对象，传入的数据支持`Tensor`、`Initializer`、`int`和`float`四种类型。 -- `Initializer`是初始化器，保存了shape和dtype信息，可调用`to_tensor`方法生成存有数据的Tensor。 +`Initializer`是初始化器，保存了shape和dtype信息，提供`to_tensor`方法生成存有数据的`Tensor`，可调用`initializer`接口生成`Initializer`对象。 + +当网络采用半自动或者全自动并行策略，并且使用`Initializer`初始化`Parameter`时，`Parameter`里保存的不是`Tensor`，而是`MetaTensor`。 + +`MetaTensor`与`Tensor`不同，`MetaTensor`仅保存张量的形状和类型，而不保存实际数据，所以不会占用任何内存，可调用`init_data`接口将`Parameter`里保存的`MetaTensor`转化为`Tensor`。 + +可为每个`Parameter`指定一个名称，便于后续操作和更新。 -- 当网络采用半自动或者全自动并行策略，并且使用`Initializer`初始化`Parameter`时，`Parameter`里保存的不是`Tensor`，而是`MetaTensor`。`MetaTensor`与`Tensor`不同，`MetaTensor`仅保存张量的形状和类型，而不保存实际数据，所以不会占用任何内存，可调用`init_data`接口将`Parameter`里保存的`MetaTensor`转化为`Tensor`。 +当参数需要被更新时，需要将`requires_grad`设置为`True`。 -- 可为每个`Parameter`指定一个名称，便于后续操作和更新。 +当`layerwise_parallel`（混合并行）配置为True时，参数广播和参数梯度聚合时会过滤掉该参数。 -- 当`layerwise_parallel`为`True`时，参数广播和参数梯度聚合时会过滤掉该参数。 +有关分布式并行的相关配置，可以参考文档：。 下例通过三种不同的数据类型构造了`Parameter`，三个`Parameter`都需要更新，都不采用layerwise并行。如下： ``` @@ -70,7 +76,8 @@ Parameter (name=z, value=2.0) 如果是，就不再对其进行切分，如果不是，需要根据网络并行策略确认是否对其进行切分。 -- `is_init`：`Parameter`的初始化状态。 +- `is_init`：`Parameter`的初始化状态。在GE后端，Parameter需要一个`init graph`来从主机同步数据到设备侧，该标志表示数据是否已同步到设备。 + 此标志仅在GE后端起作用，其他后端将被设置为False。 - `layerwise_parallel`：`Parameter`是否支持layerwise并行。如果支持，参数就不会进行广播和梯度聚合，反之则需要。 @@ -119,7 +126,7 @@ data: Parameter (name=x, value=[[0 1 2] - `set_data`：设置`Parameter`保存的数据，支持传入`Tensor`、`Initializer`、`int`和`float`进行设置， 将slice_shape设置为True时，可改变`Parameter`的shape，反之，设置的数据shape必须与`Parameter`原来的shape保持一致。 -- `set_param_ps`：控制训练参数是否通过[Parameter Server](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/advanced_use/parameter_server_training.md)进行训练。 +- `set_param_ps`：控制训练参数是否通过[Parameter Server](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/parameter_server_training.html)进行训练。 - `clone`：克隆`Parameter`，需要指定克隆之后的参数名称。 -- Gitee From 001cfa144b22fcdc6f8fba3da4ea5866efe5e74b Mon Sep 17 00:00:00 2001 From: jinyaohui Date: Tue, 15 Sep 2020 16:24:36 +0800 Subject: [PATCH 036/156] add run, train and context doc --- api/source_zh_cn/programming_guide/context.md | 135 ++++++ api/source_zh_cn/programming_guide/run.md | 372 +++++++++++++++ api/source_zh_cn/programming_guide/train.md | 443 ++++++++++++++++++ 3 files changed, 950 insertions(+) create mode 100644 api/source_zh_cn/programming_guide/context.md create mode 100644 api/source_zh_cn/programming_guide/run.md create mode 100644 api/source_zh_cn/programming_guide/train.md diff --git a/api/source_zh_cn/programming_guide/context.md b/api/source_zh_cn/programming_guide/context.md new file mode 100644 index 0000000000..12f3d3571c --- /dev/null +++ b/api/source_zh_cn/programming_guide/context.md @@ -0,0 +1,135 @@ +# 运行管理context + + + +- [运行管理context](#运行管理context) + - [概述](#概述) + - [执行模式管理](#执行模式管理) + - [模式选择](#模式选择) + - [模式切换](#模式切换) + - [硬件管理](#硬件管理) + - [分布式管理](#分布式管理) + - [维测管理](#维测管理) + - [采集profiling数据](#采集profiling数据) + - [异步数据dump功能](#异步数据dump功能) + - [print算子落盘](#print算子落盘) + + + + + +## 概述 +初始化网络之前要配置context参数，用于控制程序执行的策略。比如选择执行模式、选择执行后端、配置分布式相关参数等。按照context参数设置实现的不同功能，可以将其分为执行模式管理、硬件管理、分布式管理和维测管理等。 + +## 执行模式管理 +MindSpore支持PyNative和Graph这两种运行模式： + +- `PYNATIVE_MODE`：动态图模式，将神经网络中的各个算子逐一下发执行，方便用户编写和调试神经网络模型。 + +- `GRAPH_MODE`：静态图模式或者图模式，将神经网络模型编译成一整张图，然后下发执行。该模式利用图优化等技术提高运行性能，同时有助于规模部署和跨平台运行。 + +### 模式选择 +通过设置可以控制程序运行的模式，默认情况下，MindSpore处于PyNative模式。 + +代码样例如下： +```python +from mindspore import context +context.set_context(mode=context.GRAPH_MODE) +``` + +### 模式切换 +实现两种模式之间的切换，可以通过`context.set_context(mode=context.GRAPH_MODE)`切换为Graph模式；同样地，MindSpore处于Graph模式时，可以通过 `context.set_context(mode=context.PYNATIVE_MODE)`切换为PyNative模式。 + +代码样例如下： +```python +import numpy as np +import mindspore.nn as nn +from mindspore import context, Tensor + +context.set_context(mode=context.GRAPH_MODE, device_target="GPU") + +conv = nn.Conv2d(3, 4, 3, bias_init='zeros') +input_data = Tensor(np.ones([1, 3, 5, 5]).astype(np.float32)) +conv(input_data) +context.set_context(mode=context.PYNATIVE_MODE) +conv(input_data) +``` + +上面的例子先将运行模式设置为`GRAPH_MODE`模式，然后将模式切换为`PYNATIVE_MODE`模式，实现了模式的切换。 + +## 硬件管理 +硬件管理部分主要包括`device_target`和`device_id`两个参数。 + +- `device_target`： 用于设置目标设备，支持Ascend、GPU和CPU，默认设置是Ascend。 + +- `device_id`： 表示卡物理序号，即卡所在机器中的实际序号。如果目标设备为Ascend，且规格为N*Ascend（其中N>1，如8*Ascend），在非分布式模式执行的情况下，为了避免设备的使用冲突，可以通过设置`device_id`决定程序执行的device编号，该编号范围为：0 ~ 服务器总设备数量-1，服务器总设备数量不能超过4096，默认为设备0。 + +> 在GPU和CPU上，设置`device_id`参数无效。 + +代码样例如下： +```python +from mindspore import context +context.set_context(device_target="Ascend", device_id=6) +``` + +## 分布式管理 +context中有专门用于配置并行训练参数的接口：context.set_auto_parallel_context，该接口必须在初始化网络之前调用。 + +- `parallel_mode`：分布式并行模式，默认为单机模式`ParallelMode.STAND_ALONE`。可选数据并行`ParallelMode.DATA_PARALLEL`及自动并行`ParallelMode.AUTO_PARALLEL`。 + +- `gradients_mean`：反向计算时，框架内部会将数据并行参数分散在多台机器的梯度值进行收集，得到全局梯度值后再传入优化器中更新。默认值为`False`，设置为True对应`allreduce_mean`操作，False对应`allreduce_sum`操作。 + +- `enable_parallel_optimizer`：开发中特性。打开优化器模型并行开关，通过拆分权重到各卡分别进行更新再同步的方式以提升性能。该参数目前只在数据并行模式和参数量大于机器数时有效，支持`Lamb`和`Adam`优化器。 + +> `device_num`和`global_rank`建议采用默认值，框架内会调用HCCL接口获取。 + +代码样例如下： +```python +from mindspore import context +from mindspore.context import ParallelMode +context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, gradients_mean=True) +``` + +> 分布式并行训练详细介绍可以查看[分布式并行训练](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/distributed_training_tutorials.html)。 + +## 维测管理 +为了方便维护和定位问题，context提供了大量维测相关的参数配置，如采集profiling数据、异步数据dump功能和print算子落盘等。 + +### 采集profiling数据 +系统支持在训练过程中采集profiling数据，然后通过profiling工具进行性能分析。当前支持采集的profiling数据包括： + +- `enable_profiling`：是否开启profiling功能。设置为True，表示开启profiling功能，从enable_options读取profiling的采集选项；设置为False，表示关闭profiling功能，仅采集training_trace。 + +- `enable_options`：profiling采集选项，取值如下，支持采集多项数据。training_trace：采集迭代轨迹数据，即训练任务及AI软件栈的软件信息，实现对训练任务的性能分析，重点关注数据增强、前后向计算、梯度聚合更新等相关数据；task_trace：采集任务轨迹数据，即昇腾910处理器HWTS/AICore的硬件信息，分析任务开始、结束等信息；op_trace：采集单算子性能数据。格式：['op_trace','task_trace','training_trace'] + +代码样例如下： +```python +from mindspore import context +context.set_context(enable_profiling=True, profiling_options="training_trace") +``` + +### 异步数据dump功能 +在Ascend环境上执行训练，当训练结果和预期有偏差时，可以通过异步数据dump功能保存算子的输入输出进行调试。 + +代码样例如下： +```python +from mindspore import context +context.set_context(save_graphs=True) +``` + +> 详细的调试方法可以查看[异步数据Dump功能介绍](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/customized_debugging_information.html#dump)。 + +### print算子落盘 +默认情况下，MindSpore的自研print算子可以将用户输入的Tensor或字符串信息打印出来，支持多字符串输入，多Tensor输入和字符串与Tensor的混合输入，输入参数以逗号隔开。 + +> Print打印功能可以查看[Print算子功能介绍](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/customized_debugging_information.html#print)。 + +- `print_file_path`：可以将print算子数据保存到文件，同时关闭屏幕打印功能。如果保存的文件已经存在，则会给文件添加时间戳后缀。数据保存到文件可以解决数据量较大时屏幕打印数据丢失的问题。 + +代码样例如下： +```python +from mindspore import context +context.set_context(print_file_path="print.pb") +``` + +> context接口详细介绍可以查看[mindspore.context](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.context.html)。 \ No newline at end of file diff --git a/api/source_zh_cn/programming_guide/run.md b/api/source_zh_cn/programming_guide/run.md new file mode 100644 index 0000000000..822ab57b33 --- /dev/null +++ b/api/source_zh_cn/programming_guide/run.md @@ -0,0 +1,372 @@ +# 运行方式 + + + +- [运行方式](#运行方式) + - [概述](#概述) + - [执行单算子](#执行单算子) + - [执行普通函数](#执行普通函数) + - [执行网络模型](#执行网络模型) + - [执行训练模型](#执行训练模型) + - [执行推理模型](#执行推理模型) + + + + + +## 概述 +执行主要有三种方式：单算子、普通函数和网络训练模型。 + + +## 执行单算子 + +执行单个算子，并打印相关结果。 + +代码样例如下： +```python +import numpy as np +import mindspore.nn as nn +from mindspore import context, Tensor + +context.set_context(mode=context.GRAPH_MODE, device_target="GPU") + +conv = nn.Conv2d(3, 4, 3, bias_init='zeros') +input_data = Tensor(np.ones([1, 3, 5, 5]).astype(np.float32)) +output = conv(input_data) +print(output.asnumpy()) +``` + +输出如下： +```python +[[[[ 0.06022915 0.06149777 0.06149777 0.06149777 0.01145121] + [ 0.06402162 0.05889071 0.05889071 0.05889071 -0.00933781] + [ 0.06402162 0.05889071 0.05889071 0.05889071 -0.00933781] + [ 0.06402162 0.05889071 0.05889071 0.05889071 -0.00933781] + [ 0.02712326 0.02096302 0.02096302 0.02096302 -0.01119636]] + + [[-0.0258286 -0.03362969 -0.03362969 -0.03362969 -0.00799183] + [-0.0513729 -0.06778982 -0.06778982 -0.06778982 -0.03168458] + [-0.0513729 -0.06778982 -0.06778982 -0.06778982 -0.03168458] + [-0.0513729 -0.06778982 -0.06778982 -0.06778982 -0.03168458] + [-0.04186669 -0.07266843 -0.07266843 -0.07266843 -0.04836193]] + + [[-0.00840744 -0.03043237 -0.03043237 -0.03043237 0.00172079] + [ 0.00401019 -0.03755453 -0.03755453 -0.03755453 -0.00851137] + [ 0.00401019 -0.03755453 -0.03755453 -0.03755453 -0.00851137] + [ 0.00401019 -0.03755453 -0.03755453 -0.03755453 -0.00851137] + [ 0.00270888 -0.03718876 -0.03718876 -0.03718876 -0.03043662]] + + [[-0.00982172 0.02009856 0.02009856 0.02009856 0.03327979] + [ 0.02529106 0.04035065 0.04035065 0.04035065 0.01782833] + [ 0.02529106 0.04035065 0.04035065 0.04035065 0.01782833] + [ 0.02529106 0.04035065 0.04035065 0.04035065 0.01782833] + [ 0.01015155 0.00781826 0.00781826 0.00781826 -0.02884173]]]] +``` + + +## 执行普通函数 + +将若干算子组合成一个函数，然后直接通过函数调用的方式执行这些算子，并打印相关结果，如下例所示。 + +代码样例如下： +```python +import numpy as np +from mindspore import context, Tensor +from mindspore.ops import functional as F + +context.set_context(mode=context.GRAPH_MODE, device_target="GPU") + +def tensor_add_func(x, y): + z = F.tensor_add(x, y) + z = F.tensor_add(z, x) + return z + +x = Tensor(np.ones([3, 3], dtype=np.float32)) +y = Tensor(np.ones([3, 3], dtype=np.float32)) +output = tensor_add_func(x, y) +print(output.asnumpy()) +``` + +输出如下： +```python +[[3. 3. 3.] + [3. 3. 3.] + [3. 3. 3.]] +``` + +## 执行网络模型 +MindSpore的Model接口是用于训练和验证的高级接口。可以将有训练或推理功能的layers组合成一个对象，通过调用train、eval、predict接口可以分别实现训练、推理和预测功能。 + +用户可以根据实际需要传入网络、损失函数和优化器等初始化Model接口，还可以通过配置amp_level实现混合精度，配置metrics实现模型评估。 + +### 执行训练模型 +通过调用Model的train接口可以实现训练。 + +代码样例如下： +```python +import os + +import mindspore.dataset.vision.c_transforms as CV +from mindspore.dataset.vision import Inter + +import mindspore.dataset as ds +import mindspore.dataset.transforms.c_transforms as CT +import mindspore.dataset.vision.c_transforms as CV +import mindspore.nn as nn +from mindspore import context +from mindspore.common import dtype as mstype +from mindspore.common.initializer import Normal +from mindspore.common.initializer import TruncatedNormal +from mindspore.dataset.vision import Inter +from mindspore.train import Model +from mindspore.train.callback import LossMonitor + + +def create_dataset(data_path, batch_size=32, repeat_size=1, + num_parallel_workers=1): + """ + create dataset for train or test + """ + # define dataset + mnist_ds = ds.MnistDataset(data_path) + + resize_height, resize_width = 32, 32 + rescale = 1.0 / 255.0 + shift = 0.0 + rescale_nml = 1 / 0.3081 + shift_nml = -1 * 0.1307 / 0.3081 + + # define map operations + resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode + rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) + rescale_op = CV.Rescale(rescale, shift) + hwc2chw_op = CV.HWC2CHW() + type_cast_op = CT.TypeCast(mstype.int32) + + # apply map operations on images + mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers) + + # apply DatasetOps + buffer_size = 10000 + mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script + mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True) + mnist_ds = mnist_ds.repeat(repeat_size) + + return mnist_ds + + +def conv(in_channels, out_channels, kernel_size, stride=1, padding=0): + """weight initial for conv layer""" + weight = weight_variable() + return nn.Conv2d(in_channels, out_channels, + kernel_size=kernel_size, stride=stride, padding=padding, + weight_init=weight, has_bias=False, pad_mode="valid") + + +def fc_with_initialize(input_channels, out_channels): + """weight initial for fc layer""" + weight = weight_variable() + bias = weight_variable() + return nn.Dense(input_channels, out_channels, weight, bias) + + +def weight_variable(): + """weight initial""" + return TruncatedNormal(0.02) + + +class LeNet5(nn.Cell): + """ + Lenet network + + Args: + num_class (int): Num classes. Default: 10. + num_channel (int): Num channels. Default: 1. + + Returns: + Tensor, output tensor + Examples: + >>> LeNet(num_class=10) + + """ + + def __init__(self, num_class=10, num_channel=1): + super(LeNet5, self).__init__() + self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid') + self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid') + self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02)) + self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02)) + self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02)) + self.relu = nn.ReLU() + self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) + self.flatten = nn.Flatten() + + def construct(self, x): + x = self.max_pool2d(self.relu(self.conv1(x))) + x = self.max_pool2d(self.relu(self.conv2(x))) + x = self.flatten(x) + x = self.relu(self.fc1(x)) + x = self.relu(self.fc2(x)) + x = self.fc3(x) + return x + + +if __name__ == "__main__": + context.set_context(mode=context.GRAPH_MODE, device_target="GPU") + ds_train = create_dataset(os.path.join("/home/workspace/mindspore_dataset/MNIST_Data/", "train"), 32) + + network = LeNet5(10) + net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") + net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9) + model = Model(network, net_loss, net_opt) + + print("============== Starting Training ==============") + model.train(1, ds_train, callbacks=[LossMonitor()], dataset_sink_mode=True) +``` + +输出如下： +```python +epoch: 1 step: 1, loss is 2.300784 +epoch: 1 step: 2, loss is 2.3076947 +epoch: 1 step: 3, loss is 2.2993166 +... +epoch: 1 step: 1873, loss is 0.13014838 +epoch: 1 step: 1874, loss is 0.0346688 +epoch: 1 step: 1875, loss is 0.017264696 +``` + +> 使用PyNative模式调试， 请参考[使用PyNative模式调试](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/debugging_in_pynative_mode.html)， 包括单算子、普通函数和网络训练模型的执行。 + +### 执行推理模型 +通过调用Model的train接口可以实现推理。为了方便评估模型的好坏，可以在Model接口初始化的时候设置评估指标Metric。 + +Metric是用于评估模型好坏的指标。常见的主要有Accuracy、Fbeta、Precision、Recall和TopKCategoricalAccuracy等，通常情况下，一种模型指标无法全面的评估模型的好坏，一般会结合多个指标共同作用对模型进行评估。 + +常用的内置评估指标： +- `Accuracy`（准确率）：是一个用于评估分类模型的指标。通俗来说，准确率是指我们的模型预测正确的结果所占的比例。 公式：$$Accuracy = （TP+TN）/（TP+TN+FP+FN）$$ + +- `Precision`（精确率）：在被识别为正类别的样本中，确实为正类别的比例。公式：$$Precision = TP/(TP+FP)$$ + +- `Recall`（召回率）：在所有正类别样本中，被正确识别为正类别的比例。 公式：$$Recall = TP/(TP+FN)$$ + +- `Fbeta`（调和均值）：综合考虑precision和recall的调和均值。 +公式：$$F_\beta = (1 + \beta^2) \cdot \frac{precisiont \cdot recall}{(\beta^2 \cdot precision) + recall}$$ + +- `TopKCategoricalAccuracy`（多分类TopK准确率）：计算TopK分类准确率。 + +代码样例如下： +```python +import os + +import mindspore.dataset as ds +import mindspore.dataset.transforms.c_transforms as CT +import mindspore.dataset.vision.c_transforms as CV +import mindspore.nn as nn +from mindspore import context +from mindspore.common import dtype as mstype +from mindspore.common.initializer import Normal +from mindspore.dataset.vision import Inter +from mindspore.nn.metrics import Accuracy, Precision +from mindspore.train import Model +from mindspore.train.serialization import load_checkpoint, load_param_into_net + + +class LeNet5(nn.Cell): + """ + Lenet network + + Args: + num_class (int): Num classes. Default: 10. + num_channel (int): Num channels. Default: 1. + + Returns: + Tensor, output tensor + Examples: + >>> LeNet(num_class=10) + + """ + + def __init__(self, num_class=10, num_channel=1): + super(LeNet5, self).__init__() + self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid') + self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid') + self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02)) + self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02)) + self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02)) + self.relu = nn.ReLU() + self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) + self.flatten = nn.Flatten() + + def construct(self, x): + x = self.max_pool2d(self.relu(self.conv1(x))) + x = self.max_pool2d(self.relu(self.conv2(x))) + x = self.flatten(x) + x = self.relu(self.fc1(x)) + x = self.relu(self.fc2(x)) + x = self.fc3(x) + return x + + +def create_dataset(data_path, batch_size=32, repeat_size=1, + num_parallel_workers=1): + """ + create dataset for train or test + """ + # define dataset + mnist_ds = ds.MnistDataset(data_path) + + resize_height, resize_width = 32, 32 + rescale = 1.0 / 255.0 + shift = 0.0 + rescale_nml = 1 / 0.3081 + shift_nml = -1 * 0.1307 / 0.3081 + + # define map operations + resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode + rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) + rescale_op = CV.Rescale(rescale, shift) + hwc2chw_op = CV.HWC2CHW() + type_cast_op = CT.TypeCast(mstype.int32) + + # apply map operations on images + mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers) + + # apply DatasetOps + buffer_size = 10000 + mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script + mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True) + mnist_ds = mnist_ds.repeat(repeat_size) + + return mnist_ds + + +if __name__ == "__main__": + context.set_context(mode=context.GRAPH_MODE, device_target="GPU") + + network = LeNet5(10) + net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") + repeat_size = 10 + net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9) + model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy(), "Precision": Precision()}) + + print("============== Starting Testing ==============") + param_dict = load_checkpoint("./ckpt/checkpoint_lenet-1_1875.ckpt") + load_param_into_net(network, param_dict) + ds_eval = create_dataset(os.path.join("/home/workspace/mindspore_dataset/MNIST_Data", "test"), 32, 1) + acc = model.eval(ds_eval, dataset_sink_mode=True) + print("============== {} ==============".format(acc)) +``` + +输出如下： +```python +============== {'Accuracy': 0.96875, 'Precision': array([0.97782258, 0.99451052, 0.98031496, 0.92723881, 0.98352214, + 0.97165533, 0.98726115, 0.9472196 , 0.9394551 , 0.98236515])} ============== +``` \ No newline at end of file diff --git a/api/source_zh_cn/programming_guide/train.md b/api/source_zh_cn/programming_guide/train.md new file mode 100644 index 0000000000..e9c7db1821 --- /dev/null +++ b/api/source_zh_cn/programming_guide/train.md @@ -0,0 +1,443 @@ +# 训练 + + + +- [训练](#训练) + - [概述](#概述) + - [自定义训练网络](#自定义训练网络) + - [自定义训练循环](#自定义训练循环) + - [梯度累积](#梯度累积) + - [边训练边推理](#边训练边推理) + - [on-device执行](#on-device执行) + - [计算图下沉](#计算图下沉) + - [数据下沉](#数据下沉) + + + + + +## 概述 +MindSpore在Model_zoo也已经提供了大量的目标检测、自然语言处理等多种网络模型，供用户直接使用，但是对于某些高级用户而言可能想要自行设计网络或者自定义训练循环，下面就对自定义训练网络、自定义训练循环和边训练边推理三种场景进行介绍，另外对On device执行方式进行详细介绍。 + +## 自定义训练网络 +在自定义训练网络前，需要先了解下MindSpore的网络支持、Python源码构造网络约束和算子支持情况。 + +- 网络支持：当前MindSpore已经支持多种网络，按类型分为计算机视觉、自然语言处理、推荐和图神经网络，可以通过[网络支持](https://www.mindspore.cn/docs/zh-CN/master/network_list.html)查看具体支持的网络情况。如果现有网络无法满足用户需求，用户可以根据实际需要定义自己的网络。 + +- Python源码构造网络约束：MindSpore暂不支持将任意Python源码转换成计算图，所以对于用户源码支持的写法有所限制，主要包括语法约束和网络定义约束两方面。详细情况可以查看[Python源码构造网络约束](https://www.mindspore.cn/docs/zh-CN/master/constraints_on_network_construction.html)了解。随着MindSpore的演进，这些约束可能会发生变化。 + +- 算子支持：顾名思义，网络的基础是算子，所以用户自定义训练网络前要对MindSpore当前支持的算子有所了解，可以通过查看[算子支持](https://www.mindspore.cn/docs/zh-CN/master/operator_list.html)了解不同的后端（Ascend、GPU和CPU）的算子实现情况。 + +> 当开发网络遇到内置算子不足以满足需求时，用户也可以参考[自定义算子](https://www.mindspore.cn/tutorial/zh-CN/master/use/custom_operator.html)，方便快捷地扩展昇腾AI处理器的自定义算子。 + +代码样例如下： +```python +import numpy as np + +from mindspore.common.tensor import Tensor +from mindspore.nn import Cell, Dense, SoftmaxCrossEntropyWithLogits, Momentum, TrainOneStepCell, WithLossCell +from mindspore.ops import operations as P + + +class ReLUReduceMeanDense(Cell): + def __init__(self, kernel, bias, in_channel, num_class): + super().__init__() + self.relu = P.ReLU() + self.mean = P.ReduceMean(keep_dims=False) + self.dense = Dense(in_channel, num_class, kernel, bias) + + def construct(self, x): + x = self.relu(x) + x = self.mean(x, (2, 3)) + x = self.dense(x) + return x + + +if __name__ == "__main__": + weight_np = np.ones((1000, 2048)).astype(np.float32) + weight = Tensor(weight_np.copy()) + bias_np = np.ones((1000,)).astype(np.float32) + bias = Tensor(bias_np.copy()) + net = ReLUReduceMeanDense(weight, bias, 2048, 1000) + criterion = SoftmaxCrossEntropyWithLogits(sparse=False) + optimizer = Momentum(learning_rate=0.1, momentum=0.1, + params=filter(lambda x: x.requires_grad, net.get_parameters())) + net_with_criterion = WithLossCell(net, criterion) + train_network = TrainOneStepCell(net_with_criterion, optimizer) + train_network.set_train() + input_np = np.random.randn(32, 2048, 7, 7).astype(np.float32) + input = Tensor(input_np.copy()) + label_np_onehot = np.zeros(shape=(32, 1000)).astype(np.float32) + label = Tensor(label_np_onehot.copy()) + for i in range(1): + loss = train_network(input, label) + print("-------loss------", loss) +``` + +输出如下： +```python +-------loss------ [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. + 0. 0. 0. 0. 0. 0. 0. 0.] +``` + +## 自定义训练循环 +用户如果不想使用MindSpore提供的Model接口，可以将模仿Model的train接口自由控制循环的迭代次数和每个epoch的step数量。 + +代码样例如下： +```python +import os + +import mindspore.dataset as ds +import mindspore.dataset.transforms.c_transforms as CT +import mindspore.dataset.vision.c_transforms as CV +import mindspore.nn as nn +from mindspore import context +from mindspore.common import dtype as mstype +from mindspore.common.initializer import TruncatedNormal +from mindspore.common.parameter import ParameterTuple +from mindspore.dataset.vision import Inter +from mindspore.nn.wrap.cell_wrapper import WithLossCell +from mindspore.ops import composite as C +from mindspore.ops import functional as F +from mindspore.ops import operations as P +from mindspore.train.dataset_helper import DatasetHelper, connect_network_with_dataset + + +def create_dataset(data_path, batch_size=32, repeat_size=1, + num_parallel_workers=1): + """ + create dataset for train or test + """ + # define dataset + mnist_ds = ds.MnistDataset(data_path) + + resize_height, resize_width = 32, 32 + rescale = 1.0 / 255.0 + shift = 0.0 + rescale_nml = 1 / 0.3081 + shift_nml = -1 * 0.1307 / 0.3081 + + # define map operations + resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode + rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) + rescale_op = CV.Rescale(rescale, shift) + hwc2chw_op = CV.HWC2CHW() + type_cast_op = CT.TypeCast(mstype.int32) + + # apply map operations on images + mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers) + + # apply DatasetOps + buffer_size = 10000 + mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script + mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True) + mnist_ds = mnist_ds.repeat(repeat_size) + + return mnist_ds + + +def conv(in_channels, out_channels, kernel_size, stride=1, padding=0): + """weight initial for conv layer""" + weight = weight_variable() + return nn.Conv2d(in_channels, out_channels, + kernel_size=kernel_size, stride=stride, padding=padding, + weight_init=weight, has_bias=False, pad_mode="valid") + + +def fc_with_initialize(input_channels, out_channels): + """weight initial for fc layer""" + weight = weight_variable() + bias = weight_variable() + return nn.Dense(input_channels, out_channels, weight, bias) + + +def weight_variable(): + """weight initial""" + return TruncatedNormal(0.02) + + +class LeNet5(nn.Cell): + """ + Lenet network + Args: + num_class (int): Num classes. Default: 10. + + Returns: + Tensor, output tensor + + Examples: + >>> LeNet(num_class=10) + """ + + def __init__(self, num_class=10): + super(LeNet5, self).__init__() + self.num_class = num_class + self.batch_size = 32 + self.conv1 = conv(1, 6, 5) + self.conv2 = conv(6, 16, 5) + self.fc1 = fc_with_initialize(16 * 5 * 5, 120) + self.fc2 = fc_with_initialize(120, 84) + self.fc3 = fc_with_initialize(84, self.num_class) + self.relu = nn.ReLU() + self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) + self.reshape = P.Reshape() + + def construct(self, x): + x = self.conv1(x) + x = self.relu(x) + x = self.max_pool2d(x) + x = self.conv2(x) + x = self.relu(x) + x = self.max_pool2d(x) + x = self.reshape(x, (self.batch_size, -1)) + x = self.fc1(x) + x = self.relu(x) + x = self.fc2(x) + x = self.relu(x) + x = self.fc3(x) + return x + + +class TrainOneStepCell(nn.Cell): + def __init__(self, network, optimizer, sens=1.0): + super(TrainOneStepCell, self).__init__(auto_prefix=False) + self.network = network + self.weights = ParameterTuple(network.trainable_params()) + self.optimizer = optimizer + self.grad = C.GradOperation(get_by_list=True, sens_param=True) + self.sens = sens + + def set_sens(self, value): + self.sens = value + + def construct(self, data, label): + weights = self.weights + loss = self.network(data, label) + sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens) + grads = self.grad(self.network, weights)(data, label, sens) + return F.depend(loss, self.optimizer(grads)) + + +if __name__ == "__main__": + context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") + ds_train = create_dataset(os.path.join("/home/workspace/mindspore_dataset/MNIST_Data/", "train"), 32) + + network = LeNet5(10) + net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") + net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9) + net = WithLossCell(network, net_loss) + net = TrainOneStepCell(net, net_opt) + dataset_helper = DatasetHelper(ds_train, dataset_sink_mode=True, sink_size=100, epoch_num=10) + net = connect_network_with_dataset(net, dataset_helper) + network.set_train() + print("============== Starting Training ==============") + epoch = 10 + for step in range(epoch): + for inputs in dataset_helper: + output = net(*inputs) + print("epoch: {0}/{1}, losses: {2}".format(step + 1, epoch, output.asnumpy(), flush=True)) +``` + +输出如下： +```python +epoch: 1/10, losses: 2.294034719467163 +epoch: 2/10, losses: 2.3150298595428467 +epoch: 3/10, losses: 2.3107073307037354 +epoch: 4/10, losses: 2.3155436515808105 +epoch: 5/10, losses: 2.28973388671875 +epoch: 6/10, losses: 2.3108928203582764 +epoch: 7/10, losses: 2.293713092803955 +epoch: 8/10, losses: 2.29837703704834 +epoch: 9/10, losses: 2.305952548980713 +epoch: 10/10, losses: 1.4282708168029785 +``` + +> 典型的使用场景是梯度累积，详细查看[梯度累积](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/gradient_accumulation.html)。 + +## 边训练边推理 +对于某些数据量较大、训练时间较长的复杂网络，为了能掌握训练的不同阶段模型精度的指标变化情况，可以通过边训练边推理的方式跟踪精度的变化情况。具体可以参考[同步训练和验证模型](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/synchronization_training_and_evaluation.html)。 + +## on-device执行 +当前MindSpore支持的后端包括Ascend、GPU、CPU，所谓On Device中的Device通常指Ascend（昇腾）AI处理器。 + +昇腾芯片上集成了AICORE、AICPU和CPU。其中，AICORE负责大型Tensor Vector运算，AICPU负责标量运算，CPU负责逻辑控制和任务分发。 + +Host侧CPU负责将图或算子下发到昇腾芯片。昇腾芯片由于具备了运算、逻辑控制和任务分发的功能，所以不需要与Host侧的CPU进行频繁的交互，只需要将计算完的最终结果返回给Host侧，实现整图下沉到Device执行，避免Host-Device频繁交互，减小了开销。 + +以下是Device的主要组成结构： +- 片上32G内存：5G(parameter) + 26G(feature map) + 1G(HCCL) +- 多流水线并行：6条流水线 +- AICORE&带宽：32Cores、读写带宽128GBps +- 通信协议：HCCS、PCIe4.0、RoCEv2 + +### 计算图下沉 +计算图整图下沉到Device上执行，减少Host-Device交互开销。可以结合循环下沉实现多个Step下沉，进一步减少Host和Device的交互次数。 + +循环下沉是在On Device执行的基础上的优化，目的是进一步减少Host侧和Device侧之间的交互次数。通常情况下，每个step都返回一个结果，循环下沉是控制每隔多少个step返回一次结果。 + +默认配置下是每一个epoch返回一次结果，这样每个epoch里，Host侧和Device侧只需要进行一次数据交互。 + +也可以结合`train`接口的`dataset_sink_mode`和`sink_size`控制每个epoch的下沉数据量。 + +### 数据下沉 +`Model`的`train`接口参数`dataset_sink_mode`可以控制数据是否下沉。`dataset_sink_mode`为True表示数据下沉，否则为非下沉。所谓下沉即数据通过通道直接传送到Device上。 + +dataset_sink_mode参数可以配合`sink_size`控制每个`epoch`下沉的数据量大小。当`dataset_sink_mode`设置为True，即数据下沉模式时： + +如果`sink_size`为默认值-1，则每一个`epoch`下沉的数据量为原始的整个数据集大小； + +如果`sink_size`>0，此时原始数据集可以被无限次遍历，每个`epoch`下沉`sink_size`大小的数据量，下一个`epoch`继续从上次遍历的结束位置继续遍历。 + +下沉的总数据量由`epoch`和`sink_size`两个变量共同控制，即总数据量=`epoch`*`sink_size`。 + +代码样例如下： +```python +import os + +import mindspore.dataset as ds +import mindspore.dataset.transforms.c_transforms as CT +import mindspore.dataset.vision.c_transforms as CV +import mindspore.nn as nn +from mindspore import context +from mindspore.common import dtype as mstype +from mindspore.common.initializer import TruncatedNormal +from mindspore.dataset.vision import Inter +from mindspore.nn.metrics import Accuracy +from mindspore.ops import operations as P +from mindspore.train import Model +from mindspore.train.callback import LossMonitor + + +def create_dataset(data_path, batch_size=32, repeat_size=1, + num_parallel_workers=1): + """ + create dataset for train or test + """ + # define dataset + mnist_ds = ds.MnistDataset(data_path) + + resize_height, resize_width = 32, 32 + rescale = 1.0 / 255.0 + shift = 0.0 + rescale_nml = 1 / 0.3081 + shift_nml = -1 * 0.1307 / 0.3081 + + # define map operations + resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode + rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) + rescale_op = CV.Rescale(rescale, shift) + hwc2chw_op = CV.HWC2CHW() + type_cast_op = CT.TypeCast(mstype.int32) + + # apply map operations on images + mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers) + + # apply DatasetOps + buffer_size = 10000 + mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script + mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True) + mnist_ds = mnist_ds.repeat(repeat_size) + + return mnist_ds + + +def conv(in_channels, out_channels, kernel_size, stride=1, padding=0): + """weight initial for conv layer""" + weight = weight_variable() + return nn.Conv2d(in_channels, out_channels, + kernel_size=kernel_size, stride=stride, padding=padding, + weight_init=weight, has_bias=False, pad_mode="valid") + + +def fc_with_initialize(input_channels, out_channels): + """weight initial for fc layer""" + weight = weight_variable() + bias = weight_variable() + return nn.Dense(input_channels, out_channels, weight, bias) + + +def weight_variable(): + """weight initial""" + return TruncatedNormal(0.02) + + +class LeNet5(nn.Cell): + """ + Lenet network + Args: + num_class (int): Num classes. Default: 10. + + Returns: + Tensor, output tensor + + Examples: + >>> LeNet(num_class=10) + """ + + def __init__(self, num_class=10): + super(LeNet5, self).__init__() + self.num_class = num_class + self.batch_size = 32 + self.conv1 = conv(1, 6, 5) + self.conv2 = conv(6, 16, 5) + self.fc1 = fc_with_initialize(16 * 5 * 5, 120) + self.fc2 = fc_with_initialize(120, 84) + self.fc3 = fc_with_initialize(84, self.num_class) + self.relu = nn.ReLU() + self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) + self.reshape = P.Reshape() + + def construct(self, x): + x = self.conv1(x) + x = self.relu(x) + x = self.max_pool2d(x) + x = self.conv2(x) + x = self.relu(x) + x = self.max_pool2d(x) + x = self.reshape(x, (self.batch_size, -1)) + x = self.fc1(x) + x = self.relu(x) + x = self.fc2(x) + x = self.relu(x) + x = self.fc3(x) + return x + + +if __name__ == "__main__": + context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") + ds_train = create_dataset(os.path.join("/home/workspace/mindspore_dataset/MNIST_Data/", "train"), 32) + + network = LeNet5(10) + net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") + net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9) + model = Model(network, net_loss, net_opt) + + print("============== Starting Training ==============") + model.train(epoch=10, train_dataset=ds_train, callbacks=[LossMonitor()], dataset_sink_mode=True, sink_size=1000) +``` + +`batch_size`为32的情况下，数据集的大小为1875，当`sink_size`设置为1000时，表示每个`epoch`下沉1000个batch的数据，下沉次数为`epoch`=10，下沉的总数据量为：`epoch`*`sink_size`=10000。 + +输出如下： +```python +epoch: 1 step: 1000, loss is 0.5399815 +epoch: 2 step: 1000, loss is 0.033433747 +epoch: 3 step: 1000, loss is 0.054761313 +epoch: 4 step: 1000, loss is 0.007882872 +epoch: 5 step: 1000, loss is 0.00658499 +epoch: 6 step: 1000, loss is 0.0413095 +epoch: 7 step: 1000, loss is 0.13373856 +epoch: 8 step: 1000, loss is 0.015793817 +epoch: 9 step: 1000, loss is 0.00017951085 +epoch: 10 step: 1000, loss is 0.01490275 +``` + +> `dataset_sink_mode`为False时，`sink_size`参数设置无效。 \ No newline at end of file -- Gitee From 50734c49d9099f4d2b0ab543113462b4d0caf0af Mon Sep 17 00:00:00 2001 From: lichenever Date: Tue, 15 Sep 2020 20:48:42 +0800 Subject: [PATCH 037/156] fix_auto_parallel_context_guide_bug --- .../programming_guide/auto_parallel_context.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/api/source_zh_cn/programming_guide/auto_parallel_context.md b/api/source_zh_cn/programming_guide/auto_parallel_context.md index 14ce9bdab9..3a1cad9cee 100644 --- a/api/source_zh_cn/programming_guide/auto_parallel_context.md +++ b/api/source_zh_cn/programming_guide/auto_parallel_context.md @@ -105,7 +105,7 @@ context.get_auto_parallel_context("gradients_mean") ```python from mindspore import context -context.set_auto_parallel_context(parallel_mode=“auto_parallel”) +context.set_auto_parallel_context(parallel_mode="auto_parallel") context.get_auto_parallel_context("parallel_mode") ``` @@ -162,7 +162,7 @@ MindSpore提供了`dynamic_programming`和`recursive_programming`两种搜索策 ```python from mindspore import context -context.set_auto_parallel_context(auto_parallel_search_mode=“dynamic_programming”) +context.set_auto_parallel_context(auto_parallel_search_mode="dynamic_programming") context.get_auto_parallel_context("auto_parallel_search_mode") ``` @@ -175,7 +175,7 @@ context.get_auto_parallel_context("auto_parallel_search_mode") ```python from mindspore import context -context.set_auto_parallel_context(strategy_ckpt_load_file=“./”) +context.set_auto_parallel_context(strategy_ckpt_load_file="./") context.get_auto_parallel_context("strategy_ckpt_load_file") ``` @@ -188,7 +188,7 @@ context.get_auto_parallel_context("strategy_ckpt_load_file") ```python from mindspore import context -context.set_auto_parallel_context(strategy_ckpt_save_file=“./”) +context.set_auto_parallel_context(strategy_ckpt_save_file="./") context.get_auto_parallel_context("strategy_ckpt_save_file") ``` @@ -299,4 +299,4 @@ rank_id = get_rank() 具体用例请参考MindSpore分布式并行训练教程： -。 \ No newline at end of file +。 -- Gitee From b8f7517e623ff71ec6efb383442bb361bb4a5ffc Mon Sep 17 00:00:00 2001 From: Jolin Zhang46 Date: Tue, 15 Sep 2020 21:00:13 +0800 Subject: [PATCH 038/156] update lite quick start and update docs/index --- lite/docs/source_en/index.rst | 2 + lite/docs/source_zh_cn/index.rst | 4 +- .../source_en/quick_start/quick_start.md | 167 +++++++++-------- .../source_zh_cn/quick_start/quick_start.md | 170 ++++++++++-------- 4 files changed, 196 insertions(+), 147 deletions(-) diff --git a/lite/docs/source_en/index.rst b/lite/docs/source_en/index.rst index abecfe957e..10e8c04337 100644 --- a/lite/docs/source_en/index.rst +++ b/lite/docs/source_en/index.rst @@ -12,5 +12,7 @@ MindSpore Lite Documentation architecture apicc/apicc + image_classification + object_detection operator_list glossary diff --git a/lite/docs/source_zh_cn/index.rst b/lite/docs/source_zh_cn/index.rst index 20ecdbb72c..53e1d51b28 100644 --- a/lite/docs/source_zh_cn/index.rst +++ b/lite/docs/source_zh_cn/index.rst @@ -1,4 +1,4 @@ -.. MindSpore documentation master file, created by +.. MindSpore documentation master file, created by sphinx-quickstart on Thu Aug 17 10:00:00 2020. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. @@ -12,5 +12,7 @@ MindSpore端侧文档 architecture apicc/apicc + image_classification + object_detection operator_list glossary diff --git a/lite/tutorials/source_en/quick_start/quick_start.md b/lite/tutorials/source_en/quick_start/quick_start.md index b0712f03d6..f76a8f21a0 100644 --- a/lite/tutorials/source_en/quick_start/quick_start.md +++ b/lite/tutorials/source_en/quick_start/quick_start.md @@ -54,9 +54,9 @@ The following section describes how to build and execute an on-device image clas - Android Studio 3.2 or later (Android 4.0 or later is recommended.) - Native development kit (NDK) 21.3 -- CMake 3.10.2 +- [CMake](https://cmake.org/download) 3.10.2 - Android software development kit (SDK) 26 or later -- OpenCV 4.0.0 or later (included in the sample code) +- [JDK]( https://www.oracle.com/downloads/otn-pub/java/JDK/) 1.8 or later ### Building and Running @@ -80,6 +80,8 @@ The following section describes how to build and execute an on-device image clas For details about how to connect the Android Studio to a device for debugging, see . + The mobile phone needs to be turn on "USB debugging mode" before Android Studio can recognize the mobile phone. Huawei mobile phones generally turn on "USB debugging model" in Settings > system and update > developer Options > USB debugging. + 3. Continue the installation on the Android device. After the installation is complete, you can view the content captured by a camera and the inference result. ![result](../images/lite_quick_start_app_result.png) @@ -95,23 +97,14 @@ This image classification sample program on the Android device includes a Java l ``` app -| -├── libs # library files that store MindSpore Lite dependencies -│ └── arm64-v8a -│ ├── libopencv_java4.so -│ └── libmindspore-lite.so │ -├── opencv # dependency files related to OpenCV -│ └── ... -| ├── src/main │ ├── assets # resource files -| | └── model.ms # model file +| | └── mobilenetv2.ms # model file │ | │ ├── cpp # main logic encapsulation classes for model loading and prediction -| | ├── include # header files related to MindSpore calling -| | | └── ... -│ | | +| | |── ... +| | ├── mindspore_lite_x.x.x-minddata-arm64-cpu` #MindSpore Lite version | | ├── MindSporeNetnative.cpp # JNI methods related to MindSpore calling │ | └── MindSporeNetnative.h # header file │ | @@ -119,7 +112,7 @@ app │ │ └── com.huawei.himindsporedemo │ │ ├── gallery.classify # implementation related to image processing and MindSpore JNI calling │ │ │ └── ... -│ │ └── obejctdetect # implementation related to camera enabling and drawing +│ │ └── widget # implementation related to camera enabling and drawing │ │ └── ... │ │ │ ├── res # resource files related to Android @@ -128,6 +121,7 @@ app ├── CMakeList.txt # CMake compilation entry file │ ├── build.gradle # Other Android configuration file +├── download.gradle # MindSpore version download └── ... ``` @@ -156,42 +150,40 @@ android{ Create a link to the `.so` library file in the `app/CMakeLists.txt` file: ``` -# Set MindSpore Lite Dependencies. -include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/include/MindSpore) +# ============== Set MindSpore Dependencies. ============= +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/third_party/flatbuffers/include) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/include) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/include/ir/dtype) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/include/schema) + add_library(mindspore-lite SHARED IMPORTED ) -set_target_properties(mindspore-lite PROPERTIES - IMPORTED_LOCATION "${CMAKE_SOURCE_DIR}/libs/libmindspore-lite.so") +add_library(minddata-lite SHARED IMPORTED ) -# Set OpenCV Dependecies. -include_directories(${CMAKE_SOURCE_DIR}/opencv/sdk/native/jni/include) -add_library(lib-opencv SHARED IMPORTED ) -set_target_properties(lib-opencv PROPERTIES - IMPORTED_LOCATION "${CMAKE_SOURCE_DIR}/libs/libopencv_java4.so") +set_target_properties(mindspore-lite PROPERTIES IMPORTED_LOCATION + ${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/lib/libmindspore-lite.so) +set_target_properties(minddata-lite PROPERTIES IMPORTED_LOCATION + ${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/lib/libminddata-lite.so) +# --------------- MindSpore Lite set End. -------------------- # Link target library. target_link_libraries( ... - mindspore-lite - lib-opencv + # --- mindspore --- + minddata-lite + mindspore-lite ... ) ``` -In this example, the download.gradle File configuration auto download ` libmindspot-lite.so `and `libopencv_ Java4.so` library file, placed in the 'app / libs / arm64-v8a' directory. +In this example, the download.gradle File configuration auto download MindSpore Lite version, placed in the `app/src/main/cpp/mindspore_lite_x.x.x-minddata-arm64-cpu` directory. Note: if the automatic download fails, please manually download the relevant library files and put them in the corresponding location. -libmindspore-lite.so [libmindspore-lite.so]( https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%200.7/libmindspore-lite.so) - -libmindspore-lite include [libmindspore-lite include]( https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%200.7/include.zip) - -libopencv_java4.so [libopencv_java4.so](https://download.mindspore.cn/model_zoo/official/lite/lib/opencv%204.4.0/libopencv_java4.so) - -libopencv include [libopencv include]( https://download.mindspore.cn/model_zoo/official/lite/lib/opencv%204.4.0/include.zip) - - +MindSpore Lite version [MindSpore Lite version]( https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%200.7/libmindspore-lite.so) ### Downloading and Deploying a Model File @@ -201,8 +193,6 @@ Note: if the automatic download fails, please manually download the relevant lib mobilenetv2.ms [mobilenetv2.ms]( https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetv2.ms) - - ### Compiling On-Device Inference Code Call MindSpore Lite C++ APIs at the JNI layer to implement on-device inference. @@ -225,10 +215,8 @@ The inference code process is as follows. For details about the complete code, s *labelEnv = labelNet; // Create context. - lite::Context *context = new lite::Context; - - context->device_ctx_.type = lite::DT_CPU; - context->thread_num_ = numThread; //Specify the number of threads to run inference + mindspore::lite::Context *context = new mindspore::lite::Context; + context->thread_num_ = num_thread; // Create the mindspore session. labelNet->CreateSessionMS(modelBuffer, bufferLen, "device label", context); @@ -253,7 +241,7 @@ The inference code process is as follows. For details about the complete code, s ```cpp // Convert the Bitmap image passed in from the JAVA layer to Mat for OpenCV processing - BitmapToMat(env, srcBitmap, matImageSrc); + BitmapToMat(env, srcBitmap, matImageSrc); // Processing such as zooming the picture size. matImgPreprocessed = PreProcessImageData(matImageSrc); @@ -278,7 +266,38 @@ The inference code process is as follows. For details about the complete code, s delete[] (dataHWC); ``` -3. Perform inference on the input tensor based on the model, obtain the output tensor, and perform post-processing. +3. Pretreat the input data. + + ```cpp + bool PreProcessImageData(const LiteMat &lite_mat_bgr, LiteMat *lite_norm_mat_ptr) { + bool ret = false; + LiteMat lite_mat_resize; + LiteMat &lite_norm_mat_cut = *lite_norm_mat_ptr; + ret = ResizeBilinear(lite_mat_bgr, lite_mat_resize, 256, 256); + if (!ret) { + MS_PRINT("ResizeBilinear error"); + return false; + } + LiteMat lite_mat_convert_float; + ret = ConvertTo(lite_mat_resize, lite_mat_convert_float, 1.0 / 255.0); + if (!ret) { + MS_PRINT("ConvertTo error"); + return false; + } + LiteMat lite_mat_cut; + ret = Crop(lite_mat_convert_float, lite_mat_cut, 16, 16, 224, 224); + if (!ret) { + MS_PRINT("Crop error"); + return false; + } + float means[3] = {0.485, 0.456, 0.406}; + float vars[3] = {1.0 / 0.229, 1.0 / 0.224, 1.0 / 0.225}; + SubStractMeanNormalize(lite_mat_cut, lite_norm_mat_cut, means, vars); + return true; + } + ``` + +4. Perform inference on the input tensor based on the model, obtain the output tensor, and perform post-processing. - Perform graph execution and on-device inference. @@ -289,7 +308,12 @@ The inference code process is as follows. For details about the complete code, s - Obtain the output data. ```cpp - auto msOutputs = mSession->GetOutputs(); + auto names = mSession->GetOutputTensorNames(); + std::unordered_map msOutputs; + for (const auto &name : names) { + auto temp_dat =mSession->GetOutputByTensorName(name); + msOutputs.insert(std::pair {name, temp_dat}); + } std::string retStr = ProcessRunnetResult(msOutputs, ret); ``` @@ -298,39 +322,34 @@ The inference code process is as follows. For details about the complete code, s std::string ProcessRunnetResult(std::unordered_map msOutputs, int runnetRet) { - // Get model output results. - std::unordered_map::iterator iter; - iter = msOutputs.begin(); - auto brach1_string = iter->first; - auto branch1_tensor = iter->second; + std::unordered_map::iterator iter; + iter = msOutputs.begin(); - int OUTPUTS_LEN = branch1_tensor->ElementsNum(); + // The mobilenetv2.ms model output just one branch. + auto outputTensor = iter->second; + int tensorNum = outputTensor->ElementsNum(); - float *temp_scores = static_cast(branch1_tensor->MutableData()); + // Get a pointer to the first score. + float *temp_scores = static_cast(outputTensor->MutableData()); - float scores[RET_CATEGORY_SUM]; - for (int i = 0; i < RET_CATEGORY_SUM; ++i) { - scores[i] = temp_scores[i]; + float scores[RET_CATEGORY_SUM]; + for (int i = 0; i < RET_CATEGORY_SUM; ++i) { + if (temp_scores[i] > 0.5) { + MS_PRINT("MindSpore scores[%d] : [%f]", i, temp_scores[i]); } + scores[i] = temp_scores[i]; + } - // Converted to text information that needs to be displayed in the APP. - std::string retStr = ""; - if (runnetRet == 0) { - for (int i = 0; i < RET_CATEGORY_SUM; ++i) { - if (scores[i] > 0.3){ - retStr += g_labels_name_map[i]; - retStr += ":"; - std::string score_str = std::to_string(scores[i]); - retStr += score_str; - retStr += ";"; - } - } - else { - MS_PRINT("MindSpore run net failed!"); - for (int i = 0; i < RET_CATEGORY_SUM; ++i) { - retStr += " :0.0;"; - } - } - return retStr; + // Score for each category. + // Converted to text information that needs to be displayed in the APP. + std::string categoryScore = ""; + for (int i = 0; i < RET_CATEGORY_SUM; ++i) { + categoryScore += labels_name_map[i]; + categoryScore += ":"; + std::string score_str = std::to_string(scores[i]); + categoryScore += score_str; + categoryScore += ";"; + } + return categoryScore; } ``` \ No newline at end of file diff --git a/lite/tutorials/source_zh_cn/quick_start/quick_start.md b/lite/tutorials/source_zh_cn/quick_start/quick_start.md index ef76d900d3..974d38de10 100644 --- a/lite/tutorials/source_zh_cn/quick_start/quick_start.md +++ b/lite/tutorials/source_zh_cn/quick_start/quick_start.md @@ -53,9 +53,9 @@ MindSpore Model Zoo中图像分类模型可[在此下载](https://download.minds - Android Studio >= 3.2 (推荐4.0以上版本) - NDK 21.3 -- CMake 3.10.2 +- [CMake](https://cmake.org/download) 3.10.2 - Android SDK >= 26 -- OpenCV >= 4.0.0 （本示例代码已包含） +- [JDK]( https://www.oracle.com/downloads/otn-pub/java/JDK/) >= 1.8 ### 构建与运行 @@ -79,10 +79,14 @@ MindSpore Model Zoo中图像分类模型可[在此下载](https://download.minds Android Studio连接设备调试操作，可参考。 + 手机需开启“USB调试模式”，Android Studio才能识别到手机。 华为手机一般在`设置->系统和更新->开发人员选项->USB调试`中打开“USB调试模式”。 + 3. 在Android设备上，点击“继续安装”，安装完即可查看到设备摄像头捕获的内容和推理结果。 ![install](../images/lite_quick_start_install.png) + + 识别结果如下图所示。 ![result](../images/lite_quick_start_app_result.png) @@ -98,29 +102,22 @@ MindSpore Model Zoo中图像分类模型可[在此下载](https://download.minds ``` app -| -├── libs # 存放MindSpore Lite依赖的库文件 -│ └── arm64-v8a -│ ├── libopencv_java4.so -│ └── libmindspore-lite.so -│ -├── opencv # opencv 相关依赖文件 -│ └── ... -| ├── src/main │ ├── assets # 资源文件 -| | └── model.ms # 存放模型文件 +| | └── mobilenetv2.ms # 存放模型文件 │ | │ ├── cpp # 模型加载和预测主要逻辑封装类 | | ├── .. +| | ├── mindspore_lite_x.x.x-minddata-arm64-cpu # MindSpore Lite版本 | | ├── MindSporeNetnative.cpp # MindSpore调用相关的JNI方法 │ | └── MindSporeNetnative.h # 头文件 +| | └── MsNetWork.cpp # MindSpore接口封装 │ | │ ├── java # java层应用代码 │ │ └── com.huawei.himindsporedemo │ │ ├── gallery.classify # 图像处理及MindSpore JNI调用相关实现 │ │ │ └── ... -│ │ └── obejctdetect # 开启摄像头及绘制相关实现 +│ │ └── widget # 开启摄像头及绘制相关实现 │ │ └── ... │ │ │ ├── res # 存放Android相关的资源文件 @@ -129,6 +126,7 @@ app ├── CMakeList.txt # cmake编译入口文件 │ ├── build.gradle # 其他Android配置文件 +├── download.gradle # 工程依赖文件下载 └── ... ``` @@ -136,19 +134,11 @@ app Android JNI层调用MindSpore C++ API时，需要相关库文件支持。可通过MindSpore Lite[源码编译](https://www.mindspore.cn/lite/tutorial/zh-CN/master/build.html)生成`libmindspore-lite.so`库文件。 -本示例中，bulid过程由download.gradle文件配置自动下载`libmindspore-lite.so`以及OpenCV的`libopencv_java4.so`库文件，并放置在`app/libs/arm64-v8a`目录下。 +本示例中，build过程由download.gradle文件自动从华为服务器下载MindSpore Lite版本文件，并放置在`app/src/ main/cpp/mindspore_lite_x.x.x-minddata-arm64-cpu`目录下。 注： 若自动下载失败，请手动下载相关库文件并将其放在对应位置： -libmindspore-lite.so [下载链接](https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%200.7/libmindspore-lite.so) - -libmindspore-lite include文件 [下载链接](https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%200.7/include.zip) - -libopencv_java4.so [下载链接](https://download.mindspore.cn/model_zoo/official/lite/lib/opencv%204.4.0/libopencv_java4.so) - -libopencv include文件 [下载链接](https://download.mindspore.cn/model_zoo/official/lite/lib/opencv%204.4.0/include.zip) - - +MindSpore Lite版本 [下载链接](https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%200.7/libmindspore-lite.so) ``` android{ @@ -169,23 +159,29 @@ android{ 在`app/CMakeLists.txt`文件中建立`.so`库文件链接，如下所示。 ``` -# Set MindSpore Lite Dependencies. -include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/include/MindSpore) +# ============== Set MindSpore Dependencies. ============= +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/third_party/flatbuffers/include) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/include) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/include/ir/dtype) +include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/include/schema) + add_library(mindspore-lite SHARED IMPORTED ) -set_target_properties(mindspore-lite PROPERTIES - IMPORTED_LOCATION "${CMAKE_SOURCE_DIR}/libs/libmindspore-lite.so") +add_library(minddata-lite SHARED IMPORTED ) -# Set OpenCV Dependecies. -include_directories(${CMAKE_SOURCE_DIR}/opencv/sdk/native/jni/include) -add_library(lib-opencv SHARED IMPORTED ) -set_target_properties(lib-opencv PROPERTIES - IMPORTED_LOCATION "${CMAKE_SOURCE_DIR}/libs/libopencv_java4.so") +set_target_properties(mindspore-lite PROPERTIES IMPORTED_LOCATION + ${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/lib/libmindspore-lite.so) +set_target_properties(minddata-lite PROPERTIES IMPORTED_LOCATION + ${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/lib/libminddata-lite.so) +# --------------- MindSpore Lite set End. -------------------- # Link target library. target_link_libraries( ... - mindspore-lite - lib-opencv + # --- mindspore --- + minddata-lite + mindspore-lite ... ) ``` @@ -218,13 +214,12 @@ target_link_libraries( *labelEnv = labelNet; // Create context. - lite::Context *context = new lite::Context; - context->device_ctx_.type = lite::DT_CPU; - context->thread_num_ = numThread; //Specify the number of threads to run inference + mindspore::lite::Context *context = new mindspore::lite::Context; + context->thread_num_ = num_thread; // Create the mindspore session. - labelNet->CreateSessionMS(modelBuffer, bufferLen, "device label", context); - delete(context); + labelNet->CreateSessionMS(modelBuffer, bufferLen, context); + delete (context); ``` @@ -245,7 +240,7 @@ target_link_libraries( ```cpp // Convert the Bitmap image passed in from the JAVA layer to Mat for OpenCV processing - BitmapToMat(env, srcBitmap, matImageSrc); + BitmapToMat(env, srcBitmap, matImageSrc); // Processing such as zooming the picture size. matImgPreprocessed = PreProcessImageData(matImageSrc); @@ -270,7 +265,38 @@ target_link_libraries( delete[] (dataHWC); ``` -3. 对输入Tensor按照模型进行推理，获取输出Tensor，并进行后处理。 +3. 对输入数据进行处理。 + + ```cpp + bool PreProcessImageData(const LiteMat &lite_mat_bgr, LiteMat *lite_norm_mat_ptr) { + bool ret = false; + LiteMat lite_mat_resize; + LiteMat &lite_norm_mat_cut = *lite_norm_mat_ptr; + ret = ResizeBilinear(lite_mat_bgr, lite_mat_resize, 256, 256); + if (!ret) { + MS_PRINT("ResizeBilinear error"); + return false; + } + LiteMat lite_mat_convert_float; + ret = ConvertTo(lite_mat_resize, lite_mat_convert_float, 1.0 / 255.0); + if (!ret) { + MS_PRINT("ConvertTo error"); + return false; + } + LiteMat lite_mat_cut; + ret = Crop(lite_mat_convert_float, lite_mat_cut, 16, 16, 224, 224); + if (!ret) { + MS_PRINT("Crop error"); + return false; + } + float means[3] = {0.485, 0.456, 0.406}; + float vars[3] = {1.0 / 0.229, 1.0 / 0.224, 1.0 / 0.225}; + SubStractMeanNormalize(lite_mat_cut, lite_norm_mat_cut, means, vars); + return true; + } + ``` + +4. 对输入Tensor按照模型进行推理，获取输出Tensor，并进行后处理。 - 图执行，端测推理。 @@ -281,7 +307,12 @@ target_link_libraries( - 获取输出数据。 ```cpp - auto msOutputs = mSession->GetOutputs(); + auto names = mSession->GetOutputTensorNames(); + std::unordered_map msOutputs; + for (const auto &name : names) { + auto temp_dat =mSession->GetOutputByTensorName(name); + msOutputs.insert(std::pair {name, temp_dat}); + } std::string retStr = ProcessRunnetResult(msOutputs, ret); ``` @@ -290,39 +321,34 @@ target_link_libraries( std::string ProcessRunnetResult(std::unordered_map msOutputs, int runnetRet) { - // Get model output results. - std::unordered_map::iterator iter; - iter = msOutputs.begin(); - auto brach1_string = iter->first; - auto branch1_tensor = iter->second; + std::unordered_map::iterator iter; + iter = msOutputs.begin(); - int OUTPUTS_LEN = branch1_tensor->ElementsNum(); + // The mobilenetv2.ms model output just one branch. + auto outputTensor = iter->second; + int tensorNum = outputTensor->ElementsNum(); - float *temp_scores = static_cast(branch1_tensor->MutableData()); - float scores[RET_CATEGORY_SUM]; - for (int i = 0; i < RET_CATEGORY_SUM; ++i) { - scores[i] = temp_scores[i]; - } + // Get a pointer to the first score. + float *temp_scores = static_cast(outputTensor->MutableData()); - // Converted to text information that needs to be displayed in the APP. - std::string retStr = ""; - if (runnetRet == 0) { - for (int i = 0; i < RET_CATEGORY_SUM; ++i) { - if (scores[i] > 0.3){ - retStr += g_labels_name_map[i]; - retStr += ":"; - std::string score_str = std::to_string(scores[i]); - retStr += score_str; - retStr += ";"; - } - } - else { - MS_PRINT("MindSpore run net failed!"); - for (int i = 0; i < RET_CATEGORY_SUM; ++i) { - retStr += " :0.0;"; - } - } + float scores[RET_CATEGORY_SUM]; + for (int i = 0; i < RET_CATEGORY_SUM; ++i) { + if (temp_scores[i] > 0.5) { + MS_PRINT("MindSpore scores[%d] : [%f]", i, temp_scores[i]); + } + scores[i] = temp_scores[i]; + } - return retStr; + // Score for each category. + // Converted to text information that needs to be displayed in the APP. + std::string categoryScore = ""; + for (int i = 0; i < RET_CATEGORY_SUM; ++i) { + categoryScore += labels_name_map[i]; + categoryScore += ":"; + std::string score_str = std::to_string(scores[i]); + categoryScore += score_str; + categoryScore += ";"; + } + return categoryScore; } ``` -- Gitee From 2c18995d2f1c3af57c528e610e40ac67614bad5c Mon Sep 17 00:00:00 2001 From: lvmingfu <630944715@qq.com> Date: Mon, 14 Sep 2020 14:55:25 +0800 Subject: [PATCH 039/156] modify linear regression files in tutorias --- tutorials/notebook/linear_regression.ipynb | 713 ++++++++---------- .../quick_start/images/linear_regression.gif | Bin 73509 -> 1181553 bytes .../linear_regression_eval_datasets.png | Bin 6278 -> 9694 bytes .../images/model_net_and_eval_datasets.png | Bin 6903 -> 9138 bytes .../quick_start/linear_regression.md | 409 +++++----- tutorials/tutorial_code/linear_regression.py | 115 ++- 6 files changed, 529 insertions(+), 708 deletions(-) diff --git a/tutorials/notebook/linear_regression.ipynb b/tutorials/notebook/linear_regression.ipynb index 4e3665dcf1..6e2549f7c3 100644 --- a/tutorials/notebook/linear_regression.ipynb +++ b/tutorials/notebook/linear_regression.ipynb @@ -4,33 +4,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "##
    使用MindSpore实现简单线性函数拟合" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 概述" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "回归问题算法通常是利用一系列属性来预测一个值，预测的值是连续的。例如给出一套房子的一些特征数据，如面积、卧室数等等来预测房价，利用最近一周的气温变化和卫星云图来预测未来的气温情况等。如果一套房子实际价格为500万元，通过回归分析的预测值为499万元，则认为这是一个比较好的回归分析。在机器学习问题中，常见的回归分析有线性回归、多项式回归、逻辑回归等。本例子介绍线性回归算法，并通过MindSpore进行线性回归AI训练体验。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "主要流程如下：\n", + "## 概述\n", + "\n", + "回归问题算法通常是利用一系列属性来预测一个值，预测的值是连续的。例如给出一套房子的一些特征数据，如面积、卧室数等等来预测房价，利用最近一周的气温变化和卫星云图来预测未来的气温情况等。如果一套房子实际价格为500万元，通过回归分析的预测值为499万元，则认为这是一个比较好的回归分析。在机器学习问题中，常见的回归分析有线性回归、多项式回归、逻辑回归等。本例子介绍线性回归算法，并通过MindSpore进行线性回归AI训练体验。\n", + "\n", + "整体流程如下：\n", "\n", "1. 生成数据集\n", - "2. 定义前向传播网络\n", - "3. 定义反向传播网络\n", - "4. 定义线性拟合过程的可视化函数\n", + "2. 定义训练网络\n", + "3. 定义前向传播网络与反向传播网络并关联\n", + "4. 拟合过程可视化准备\n", "5. 执行训练" ] }, @@ -38,16 +21,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 环境准备" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "系统：Ubuntu18.04\n", + "## 环境准备\n", + "\n", + "系统：Windows\n", "\n", - "MindSpore版本：GPU\n", + "MindSpore版本：CPU\n", "\n", "设置MindSpore运行配置\n", "\n", @@ -57,95 +35,80 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2020-09-14T10:38:40.002170Z", + "start_time": "2020-09-14T10:38:39.441746Z" + } + }, "outputs": [], "source": [ "from mindspore import context\n", "\n", - "context.set_context(mode=context.PYNATIVE_MODE, device_target=\"GPU\")" + "context.set_context(mode=context.GRAPH_MODE, device_target=\"CPU\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "`PYNATIVE_MODE`：自定义调试模式。\n", + "`GRAPH_MODE`：自定义调试模式。\n", "\n", - "`device_target`：设置MindSpore的训练硬件为GPU。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 生成数据集" + "`device_target`：设置MindSpore的训练硬件为CPU。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "## 生成数据集\n", + "\n", "### 定义数据集生成函数\n", "\n", - "`get_data`用于生成训练数据集和测试数据集。由于拟合的是线性数据，假定要拟合的目标函数为：$y=2x+3$，那么我们需要的训练数据集应随机分布于函数周边，这里采用了$y=2x+3+noise$的方式生成，其中`noise`为遵循标准正态分布规律的随机数值。" + "`get_data`用于生成训练数据集和测试数据集。由于拟合的是线性数据，假定要拟合的目标函数为：$f(x)=2x+3$，那么我们需要的训练数据集应随机分布于函数周边，这里采用了$f(x)=2x+3+noise$的方式生成，其中`noise`为遵循标准正态分布规律的随机数值。" ] }, { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2020-09-14T10:38:40.007850Z", + "start_time": "2020-09-14T10:38:40.003169Z" + } + }, "outputs": [], "source": [ "import numpy as np\n", - "import mindspore as ms\n", - "from mindspore import Tensor\n", - " \n", - "def get_data(num,w=2.0, b=3.0):\n", - " np_x = np.ones([num, 1])\n", - " np_y = np.ones([num, 1])\n", + "\n", + "def get_data(num, w=2.0, b=3.0):\n", " for i in range(num):\n", " x = np.random.uniform(-10.0, 10.0)\n", - " np_x[i] = x\n", " noise = np.random.normal(0, 1)\n", " y = x * w + b + noise\n", - " np_y[i] = y\n", - " return Tensor(np_x,ms.float32), Tensor(np_y,ms.float32)" + " yield np.array([x]).astype(np.float32), np.array([y]).astype(np.float32)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "对于数据生成函数我们将有以下两个作用。\n", - "\n", - "1. 生成训练数据，对模型函数进行训练。\n", - "2. 生成验证数据，在训练结束后，对模型函数进行精度验证。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 生成测试数据" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "使用数据生成函数`get_data`随机生成50组验证数据，并可视化展示。" + "使用`get_data`生成50组测试数据，并可视化。" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { - "scrolled": true + "ExecuteTime": { + "end_time": "2020-09-14T10:38:40.355635Z", + "start_time": "2020-09-14T10:38:40.009930Z" + } }, "outputs": [ { "data": { - "image/png": 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Lwkxkdbm02w3TuOrk2rWMmgFKislQwyxropLU/uQlAKXQl8lQGABZXS50wwCVwjj5YdZqohKTl4DKoCVfVe2OYc9aSoDlBYDKIOSriDHsAFKEfBXtbW12ZqkCQ4WQr6JWF09p4QNDhwuvVdTqgiqzVIGhQ0u+qrIunjI8Ehg6tOSHCWu7A0OHkB82rO0ODBW6awCgwgh5AKgwQh4AKoyQB4AKI+T7idmnAApGyPcLs08B9AAh3y97W18GAHLSVcjbPtP2I7anbNea9n3W9mbbj9l+e3fVrCBmnwLogW4nQ22U9C5J32rcaHuJpJWSXivpYEm32T4iInZ1WV51MPsUQA901ZKPiE0R8VjGrhWSroyI5yPi55I2S1raTVmVxM05ABSsqD75QyQ93fB6LN32ArZX2R61PTpBvzQA5Gra7hrbt0k6KGPX+RFxfatvy9iWOU4wIlZLWi1JtVqNsYQAkKNpQz4iTp7B+45JWtjweoGkLTN4HwBAF4rqrlkjaaXt/WwfKulwST8tqKzqYHIUgJx1O4TyDNtjko6V9CPbN0tSRDwi6WpJ/yPpx5I+xsiaaTA5CkABHCVqNdZqtRgdHe13NfpjfDwJ+MnJZOz82BjrvgNoi+31EVHL2seM17JgchSAAnBnqLJgchSAAhDyZcKt+QDkjO6aojFiBkAfEfJ5ag50RswA6DNCPi9Zgc5ywgD6jJDPS1agM2IGQJ8R8nnJCvT6iJmxMWntWkbMAOg5RtfkpdUQSEbMAOgjQj5PBDqAkqG7plMMiQQwQAj5TkxOSm96E0MiAQwMumvaNTUlLVsm3Xtv8vruu5MW/axZLEMAoLRoybdrYkK6//7dr2s16b3vpVUPoNQI+XbNnSsdf7w0e7Z0zDHStddK99zDRCcApUZ3Tbuah0hKyXj4u+9mohOA0iLkO9E8RJKlgQGUXLe3/zvT9iO2p2zXGrYvtv0H2xvSxyXdVzUHeQ9/rIc+AQ+gpLrtk98o6V2S7srY90REHJU+zumynO6xIiSAIdRVyEfEpoh4LK/K5K6x5c6KkACGUJGjaw61/TPbd9pe1uog26tsj9oencgzeJtb7nPmsCIkgKEz7YVX27dJOihj1/kRcX2Lb9sqaVFE7LD9Bkk/tP3aiHiu+cCIWC1ptSTVarWZd5bX12+vXwRtbrlv386FUgBDZ9qWfEScHBGvy3i0CnhFxPMRsSN9vl7SE5KOyK/aTbL627OW/uVCKYAhU8gQStsjkp6NiF22D5N0uKQniyhLUnZ/+7x5tNwBDL1uh1CeYXtM0rGSfmT75nTXmyU9ZPtBST+QdE5EPNtdVfei1R2YaLkDGHKOEi2ZW6vVYnR0dGbf3NwnDwBDwvb6iKhl7avO2jX1VnsE670DQKo6IS8x4QkAmlQr5JnwBAB7qFbIt7oACwBDqlqrUDYvB8wFWABDrlohL71wOWAAGGLV6q4BAOyBkAeACiPkAaDCCHkAqDBCHgAqjJAHgAor1QJltick/WKaw+ZI2t6D6nSDOuaj7HUse/0k6piXstfxVRExkrWjVCHfDtujrVZbKwvqmI+y17Hs9ZOoY14GoY6t0F0DABVGyANAhQ1iyK/udwXaQB3zUfY6lr1+EnXMyyDUMdPA9ckDANo3iC15AECbCHkAqLBShrztM20/YnvKdq1p32dtb7b9mO23t/j+Q23fZ/tx21fZ3rfg+l5le0P6eMr2hhbHPWX74fS4Gd6xfMZ1vMD2Mw31PK3Fcaek53az7fN6WL9/sf2o7YdsX2f75S2O6/k5nO6c2N4v/QxsTj93i3tRr4byF9q+w/am9PfmExnHLLf9m4af/+d7Wce0Dnv92Tnxr+l5fMj263tcvyMbzs8G28/Z/mTTMX0/jx2LiNI9JL1G0pGS1kqqNWxfIulBSftJOlTSE5JmZ3z/1ZJWps8vkfSRHtb9q5I+32LfU5Lm9OmcXiDpM9McMzs9p4dJ2jc910t6VL+3Sdonff4VSV8pwzls55xI+qikS9LnKyVd1eOf7XxJr0+fHyDpfzPquFzSDf347LX7s5N0mqSbJFnSMZLu62NdZ0v6pZJJRqU6j50+StmSj4hNEfFYxq4Vkq6MiOcj4ueSNkta2niAbUt6i6QfpJsul3R6kfVtKvs9kq7oRXkFWCppc0Q8GRF/lHSlknNeuIi4JSIm05f3SlrQi3Lb0M45WaHkcyYln7uT0s9CT0TE1oh4IH2+U9ImSYf0qvwcrZD03UjcK+nltuf3qS4nSXoiIqabgV96pQz5vThE0tMNr8f0wg/zKyX9uiEwso4pyjJJ4xHxeIv9IekW2+ttr+pRnRqdm/4bfJntAzP2t3N+e+EDSlp0WXp9Dts5J386Jv3c/UbJ57Dn0q6ioyXdl7H7WNsP2r7J9mt7WrHEdD+7snz+pOQ/slaNtX6fx4707fZ/tm+TdFDGrvMj4vpW35axrXkMaDvHdKzN+p6lvbfij4+ILbbnSrrV9qMRcVe3dWunjpIulvQlJefiS0q6lT7Q/BYZ35vbGNt2zqHt8yVNSvpei7cp9Bxm6NtnrlO2XyrpGkmfjIjnmnY/oKTr4bfp9ZgfSjq8x1Wc7mdXlvO4r6R3Svpsxu4ynMeO9C3kI+LkGXzbmKSFDa8XSNrSdMx2Jf/m7ZO2qrKO6dh09bW9j6R3SXrDXt5jS/p1m+3rlHQF5BZQ7Z5T25dKuiFjVzvnd8baOIdnS3qHpJMi7QDNeI9Cz2GGds5J/Zix9HPwMknPFlinF7D9IiUB/72IuLZ5f2PoR8SNtv/N9pyI6NmiW2387Ar9/HXgVEkPRMR4844ynMdODVp3zRpJK9PRDIcq+Qv608YD0nC4Q9K7001nS2r1n0GeTpb0aESMZe20/RLbB9SfK7nQuLEH9aqX39i3eUaLsu+XdLiT0Un7KvmXdU2P6neKpH+Q9M6I+H2LY/pxDts5J2uUfM6k5HP3k1Z/pIqQ9v9/W9KmiPhai2MOql8nsL1Uye/+jh7WsZ2f3RpJf5eOsjlG0m8iYmuv6tig5X/k/T6PM9LvK79ZDyUhNCbpeUnjkm5u2He+ktEOj0k6tWH7jZIOTp8fpiT8N0v6T0n79aDO35F0TtO2gyXd2FCnB9PHI0q6KHp5Tv9D0sOSHlLyyzS/uY7p69OUjM54opd1TH9WT0vakD4uaa5fv85h1jmR9EUlf5Ak6c/Sz9nm9HN3WI9/tm9S0q3xUMP5O03SOfXPpKRz03P2oJIL28f1uI6ZP7umOlrSN9Pz/LAaRtb1sJ77KwntlzVsK815nMmDZQ0AoMIGrbsGANABQh4AKoyQB4AKI+QBoMIIeQCoMEIeACqMkAeACvt/V7N/5YFcCdgAAAAASUVORK5CYII=\n", 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\n", 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    " ] @@ -159,10 +122,14 @@ "source": [ "import matplotlib.pyplot as plt\n", "\n", - "eval_x, eval_label = get_data(50)\n", - "x1, y1 = eval_x.asnumpy(), eval_label.asnumpy()\n", - "plt.scatter(x1, y1, color=\"red\", s=5)\n", - "plt.title(\"Eval_data\")\n", + "eval_data = list(get_data(50))\n", + "x_target_label = np.array([-10, 10, 0.1])\n", + "y_target_label = x_target_label * 2 + 3\n", + "x_eval_label,y_eval_label = zip(*eval_data)\n", + "\n", + "plt.scatter(x_eval_label, y_eval_label, color=\"red\", s=5)\n", + "plt.plot(x_target_label, y_target_label, color=\"green\")\n", + "plt.title(\"Eval data\")\n", "plt.show()" ] }, @@ -170,500 +137,426 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 定义前向传播网络" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 初始化网络模型" + "上图中绿色线条部分为目标函数，红点部分为验证数据`eval_data`。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "使用`nn.Dense`定义了网络模型，即为线性模型，\n", + "### 定义数据增强函数\n", "\n", - "$$y=wx+b\\tag{1}$$\n", + "使用MindSpore的数据增强函数，将数据进行增强操作，操作解释如下：\n", "\n", - "其中，权重值$w$对应`weight`，$b$对应`bias`，并将其打印出来。" + "- `ds.GeneratorDataset`：将生成的数据转换为MindSpore的数据集，并且将生成的数据的x，y值存入到`data`和`label`的数组中。\n", + "- `batch`：将`batch_size`个数据组合成一个batch。\n", + "- `repeat`：将数据集数量倍增。" ] }, { "cell_type": "code", "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "weight: -0.00034249047 bias: -0.019308656\n" - ] + "metadata": { + "ExecuteTime": { + "end_time": "2020-09-14T10:38:40.390782Z", + "start_time": "2020-09-14T10:38:40.356644Z" } - ], + }, + "outputs": [], "source": [ - "from mindspore.common.initializer import TruncatedNormal\n", - "from mindspore import nn\n", + "from mindspore import dataset as ds\n", "\n", - "net = nn.Dense(1,1,TruncatedNormal(0.02),TruncatedNormal(0.02))\n", - "print(\"weight:\", net.weight.set_data([0][0]), \"bias:\", net.bias.set_data([0]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 查看初始化的网络模型" + "def create_dataset(num_data, batch_size=16, repeat_size=1):\n", + " input_data = ds.GeneratorDataset(list(get_data(num_data)), column_names=['data','label'])\n", + " input_data = input_data.batch(batch_size)\n", + " input_data = input_data.repeat(repeat_size)\n", + " return input_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "我们将验证数据集和初始化的模型函数可视化。" + "使用数据集增强函数生成训练数据，并查看训练数据的格式。" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { - "scrolled": true + "ExecuteTime": { + "end_time": "2020-09-14T10:38:40.435708Z", + "start_time": "2020-09-14T10:38:40.391790Z" + } }, "outputs": [ { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataset size of ds_train: 100\n", + "dict_keys(['data', 'label'])\n", + "The x label value shape: (16, 1)\n", + "The y label value shape: (16, 1)\n" + ] } ], "source": [ - "x = np.arange(-10, 10, 0.1)\n", - "y = x * (net.weight.set_data([0][0]).asnumpy()) + (net.bias.set_data([0]).asnumpy())\n", - "plt.scatter(x1, y1, color=\"red\", s=5)\n", - "plt.plot(x, y, \"blue\")\n", - "plt.title(\"Eval data and net\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "红色的点：为之前生成的50组验证数据集。\n", + "num_data = 1600\n", + "batch_size = 16\n", + "repeat_size = 1\n", "\n", - "蓝色的线：初始化的模型网络。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 定义损失函数" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "我们的网络模型表达式为：\n", + "ds_train = create_dataset(num_data, batch_size=batch_size, repeat_size=repeat_size) \n", + "print(\"The dataset size of ds_train:\", ds_train.get_dataset_size())\n", + "dict_datasets = ds_train.create_dict_iterator().get_next()\n", "\n", - "$$h(x)=wx+b\\tag{2}$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "一般地，数学上对线性回归模型采用均方差的方式来判断模型是否拟合得很好，即均方差的值$J(w)$值越小，函数模型便拟合得越好，验证数据代入后，预测得到的y值就越准确。公式2对应m个数据的均方差公式为：" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$J(w)=\\frac{1}{m}\\sum_{i=1}^m(h(x_i)-y^{(i)})^2\\tag{3}$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "为了方便后续的计算，我们采用0.5倍的均方差的表达式来进行计算，均方差值整体缩小至0.5倍的计算方式对判断模型拟合的好坏没有影响。" + "print(dict_datasets.keys())\n", + "print(\"The x label value shape:\", dict_datasets[\"data\"].shape)\n", + "print(\"The y label value shape:\", dict_datasets[\"label\"].shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "$$J(w)=\\frac{1}{2m}\\sum_{i=1}^m(h(x_i)-y^{(i)})^2\\tag{4}$$" + "通过定义的`create_dataset`将生成的1600个数据增强为了100组shape为16x1的数据集。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "公式4即为网络训练中的损失函数，其中参数：\n", + "## 定义训练网络\n", "\n", - "- $J(w)$为均方差。\n", + "在MindSpore中使用`nn.Dense`生成单个数据输入，单个数据暑促和的线性模型为：\n", "\n", - "- $m$为样本数据的数量。\n", + "$$f(x)=wx+b\\tag{1}$$\n", "\n", - "- $h(x_i)$为第$i$个数据的$x_i$值代入模型网络（公式2）后的预测值。\n", - "\n", - "- $y^{(i)}$为第$i$个数据中的$y$值（label值）。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "在MindSpore中定义损失函数的方法如下。" + "并使用Normal算子随机初始化权重$w$和$b$。" ] }, { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2020-09-14T10:38:40.441532Z", + "start_time": "2020-09-14T10:38:40.436780Z" + } + }, "outputs": [], "source": [ - "from mindspore.ops import operations as P\n", - "\n", - "class MyLoss(nn.loss.loss._Loss):\n", - " def __init__(self,reduction='mean'):\n", - " super().__init__(reduction)\n", - " self.square = P.Square()\n", - " def construct(self, data, label):\n", - " x = self.square(data-label) * 0.5\n", - " return self.get_loss(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "其中:\n", - "\n", - "- `nn.loss.loss._Loss`：是MindSpore自定义loss算子的一个基类。\n", + "from mindspore.common.initializer import Normal\n", + "from mindspore import nn\n", "\n", - "- `P.Square`：MindSpore训练的框架中的平方算子，算子需要注册过才能在框架的计算图中使用。" + "class LinearNet(nn.Cell):\n", + " def __init__(self):\n", + " super(LinearNet, self).__init__()\n", + " self.fc = nn.Dense(1, 1, Normal(0.02), Normal(0.02))\n", + " \n", + " def construct(self, x):\n", + " x = self.fc(x)\n", + " return x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 损失函数与网络结合\n", - "\n", - "接下来我们需要将loss函数的表达式和网络net关联在一起，在MindSpore中需要`nn.WithLossCell`，实现方法如下：" + "调用网络查看初始化的模型参数。" ] }, { "cell_type": "code", "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "criterion = MyLoss()\n", - "loss_opeartion = nn.WithLossCell(net, criterion) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2020-09-14T10:38:40.456400Z", + "start_time": "2020-09-14T10:38:40.442544Z" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Parameter (name=fc.weight, value=Tensor(shape=[1, 1], dtype=Float32,\n", + "[[3.68014202e-002]])), Parameter (name=fc.bias, value=Tensor(shape=[1], dtype=Float32, [3.68014202e-002]))]\n" + ] + } + ], "source": [ - "其中：\n", - "\n", - "- `net`：网络模型。\n", - "\n", - "- `criterion`：即为实例化的loss函数。" + "net = LinearNet()\n", + "model_params = net.trainable_params()\n", + "print(model_params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "上述从数据代入到计算出loss值的过程为AI训练中的前向传播过程。" + "初始化网络模型后，接下来将初始化的网络函数和训练数据集进行可视化，了解拟合前的模型函数情况。" ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2020-09-14T10:38:40.607733Z", + "start_time": "2020-09-14T10:38:40.457985Z" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "## 定义反向传播网络" + "from mindspore import Tensor\n", + "\n", + "x_model_label = np.array([-10, 10, 0.1])\n", + "y_model_label = (x_model_label * Tensor(model_params[0]).asnumpy()[0][0] + \n", + " Tensor(model_params[1]).asnumpy()[0])\n", + "\n", + "plt.axis([-10, 10, -20, 25])\n", + "plt.scatter(x_eval_label, y_eval_label, color=\"red\", s=5)\n", + "plt.plot(x_model_label, y_model_label, color=\"blue\")\n", + "plt.plot(x_target_label, y_target_label, color=\"green\")\n", + "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "有了损失函数后，我们如何使得损失函数最小呢？我们可以将公式1代入到损失函数公式4中展开：\n", - "\n", - "$$J(w,b)=\\frac{1}{2m}\\sum_{i=1}^m(wx_i+b-y^{(i)})^2\\tag{5}$$" + "从上图中可以看出，蓝色线条的初始化模型函数与绿色线条的目标函数还是有较大的差别的。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "公式5可以将$J(w)$看作为凹函数，对权重值$w$微分可求得：\n", - "\n", - "$$\\frac{\\partial{J(w)}}{\\partial{w}}=\\frac{1}{m}\\sum_{i=1}^mx_i(wx_i+b-y^{(i)})\\tag{6}$$\n" + "## 定义前向传播网络与反向传播网络并关联" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "由凹函数的特性可以知道，当公式6等于0时，损失函数有最小值：\n", - "\n", - "$$\\sum_{i=1}^mx_i(wx_i+b-y^{(i)})=0\\tag{7}$$ \n", + "接下来需要定义模型的损失函数，这里采用均方差的方法用于判断拟合的效果如何，即均方差值越小，拟合的效果越好，其损失损失函数公式为：\n", "\n", - "假设有一个$w_{min}$使得公式7成立。我们如何将初始的权重$w_{s}$逐步的变成$w_{min}$，在这里采取迭代法，也就是梯度下降方法\n", + "$$J(w)=\\frac{1}{2m}\\sum_{i=1}^m(h(x_i)-y^{(i)})^2\\tag{2}$$\n", "\n", - "当权重$w_{s}w_{min}$，权重值需要左移即权重值变小接近$w_{min}$，才能使得损失函数逐步的变小，由凹函数的性质可知，在$w_{s}$处的导数为正（损失函数在$w_{min}$右边单调上升），公式8的值为正。其权重的更新公式为：\n", - "\n", - "$$w_{ud}=w_{s}-\\alpha\\frac{\\partial{J(w_{s})}}{\\partial{w}}\\tag{10}$$\n" + "net = LinearNet()\n", + "net_loss = nn.loss.MSELoss()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "当$w_{s}=w_{min}$时，到$\\frac{\\partial{J(w_{s})}}{\\partial{w}}$=0，即梯度消失，其表达式也可写为公式9的样式。\n", + "### 定义反向传播网络\n", "\n", - "在考虑了全区间的情况后，可以得出权重$w$的更新公式即为：\n", + "反向传播网络的目标是不断变换权重值，使得loss值取得最小值，一般的在线性网络中采用权重更新公式：\n", "\n", - "$$w_{ud}=w_{s}-\\alpha\\frac{\\partial{J(w_{s})}}{\\partial{w}}\\tag{11}$$\n", + "$$w_{t}=w_{t-1}-\\alpha\\frac{\\partial{J(w_{t-1})}}{\\partial{w}}\\tag{3}$$\n", "\n", - "当权重$w$在更新的过程中假如临近$w_{min}$在增加或者减少一个$\\Delta{w}$，从左边或者右边越过了$w_{min}$，公式11都会使权重往反的方向移动，那么最终$w_{s}$的值会在$w_{min}$附近来回迭代，在实际训练中我们也是这样采用迭代的方式取得最优权重$w$，使得损失函数无限逼近局部最小值。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "同理：对于公式5中的另一个权重$b$容易得出其更新公式为：\n", + "公式3参数解释：\n", "\n", - "$$b_{ud}=b_{s}-\\alpha\\frac{\\partial{J(b_{s})}}{\\partial{b}}\\tag{12}$$\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "当所有的权重更新完成后，将新的权重赋值给初始权重：即$w_{s}$=$w_{ud}$，$b_{s}$=$b_{ud}$。将新的初始权重传递回到模型函数中，这样就完成了反向传播的过程。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> 当遇到多项式的回归模型时，上述梯度方法也适用，由于权重数量的增加，需要将权重的名称更新为$w_0,w_1,w_2,...,w_n$，引入矩阵的表达方式，公式将会更加简洁，这里就不多介绍了。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 实现梯度函数" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "在MindSpore中的所有要编入计算图的类都需要继承`nn.Cell`算子，MindSpore的梯度计算函数采用如下方式。" + "- $w_{t}$为迭代后的权重值。\n", + "- $w_{t-1}$为迭代前的权重值。\n", + "- $\\alpha$为学习率。\n", + "- $\\frac{\\partial{J(w_{t-1})}}{\\partial{w}}$为损失函数对权重$w_{t-1}$的微分。\n", + "\n", + "函数中所有的权重值更新完成后，将值传入到模型函数中，这个过程就是反向传播过程，实现此过程需要使用MindSpore中的优化器函数，如下：" ] }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2020-09-14T10:38:40.629217Z", + "start_time": "2020-09-14T10:38:40.616392Z" + } + }, "outputs": [], "source": [ - "from mindspore.ops import composite as C\n", - "\n", - "class GradWrap(nn.Cell):\n", - " \"\"\" GradWrap definition \"\"\"\n", - " def __init__(self, network):\n", - " super().__init__(auto_prefix=False)\n", - " self.network = network\n", - " self.weights = ms.ParameterTuple(filter(lambda x: x.requires_grad,\n", - " network.get_parameters()))\n", - "\n", - " def construct(self, data, label):\n", - " weights = self.weights\n", - " return C.GradOperation(get_by_list=True) \\\n", - " (self.network, weights)(data, label)\n" + "opt = nn.Momentum(net.trainable_params(), learning_rate=0.005, momentum=0.9)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "上述代码中`GradWrap`实现的是对各个权重的微分$\\frac{\\partial{J(w)}}{\\partial{w}}$，其展开式子参考公式6。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 反向传播更新权重" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`nn.RMSProp`为完成权重更新的函数，更新方式大致为公式11，但是考虑的因素更多，具体信息请参考[官网说明](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html?highlight=rmsprop#mindspore.nn.RMSProp)。" + "### 关联前向和反向传播网络\n", + "\n", + "定义完成前向传播和反向传播后，在MindSpore中需要调用`Model`函数，将前面定义的网络，损失函数，优化器函数关联起来，使之变成完整的计算网络。" ] }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2020-09-14T10:38:40.645718Z", + "start_time": "2020-09-14T10:38:40.630789Z" + } + }, "outputs": [], "source": [ - "train_network = GradWrap(loss_opeartion) \n", - "train_network.set_train()\n", - "optim = nn.RMSProp(params=net.trainable_params(),learning_rate=0.02)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "通过以上操作，我们就完成了前向传播网络和反向传播网络的定义，接下来可以加载训练数据进行线性拟合了。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 定义模型拟合过程可视化函数" + "from mindspore.train import Model\n", + "\n", + "model = Model(net, net_loss, opt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "定义一个可视化函数`plot_model_and_datasets`，将模型函数和验证数据集打印出来，观察其变化。" + "## 拟合过程可视化准备\n", + "\n", + "### 定义绘图函数\n", + "\n", + "为了使得整个训练过程更容易理解，需要将训练过程的测试数据、目标函数和模型网络进行可视化，这里定义了可视化函数，将在每个step训练结束后调用，展示模型网络的拟合过程。" ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2020-09-14T10:38:40.680586Z", + "start_time": "2020-09-14T10:38:40.646738Z" + } + }, "outputs": [], "source": [ - "import time \n", + "import matplotlib.pyplot as plt\n", + "import time\n", "\n", - "def plot_model_and_datasets(weight, bias, data_x, data_y):\n", + "def plot_model_and_datasets(net, eval_data):\n", + " weight = net.trainable_params()[0]\n", + " bias = net.trainable_params()[1]\n", " x = np.arange(-10, 10, 0.1)\n", - " y = x * ((weight[0][0]).asnumpy()) + ((bias[0]).asnumpy())\n", - " plt.scatter(x1,y1,color=\"red\",s=5)\n", - " plt.scatter(data_x.asnumpy(), data_y.asnumpy(), color=\"black\", s=5)\n", - " plt.plot(x, y, \"blue\")\n", + " y = x * Tensor(weight).asnumpy()[0][0] + Tensor(bias).asnumpy()[0]\n", + " x1, y1 = zip(*eval_data)\n", + " x_target = x\n", + " y_target = x_target * 2 + 3\n", + " \n", " plt.axis([-11, 11, -20, 25])\n", + " plt.scatter(x1, y1, color=\"red\", s=5)\n", + " plt.plot(x, y, color=\"blue\")\n", + " plt.plot(x_target, y_target, color=\"green\")\n", " plt.show()\n", - " time.sleep(0.02)" + " time.sleep(0.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "上述函数的参数：\n", - "\n", - "- `weight`：模型函数的权重，即$w$。\n", + "### 定义回调函数\n", "\n", - "- `bias`：模型函数的权重，即$b$。\n", + "MindSpore提供的工具，可对模型训练过程进行自定义控制，这里在`step_end`中调用可视化函数，展示拟合过程。更多的使用可参考[官网说明](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/customized_debugging_information.html#callback)\n", "\n", - "- `data_x`：训练数据的x值。\n", - "\n", - "- `data_y`：训练数据的y值。" + "- `display.clear_output`：清除打印内容，实现动态拟合效果。" ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> 可视化过程中，红色的点是验证数据集，黑色的点是单个batch的训练数据，蓝色的线条是正在训练的回归模型。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2020-09-14T10:38:40.706063Z", + "start_time": "2020-09-14T10:38:40.681635Z" + } + }, + "outputs": [], "source": [ - "## 执行训练" + "from IPython import display\n", + "from mindspore.train.callback import Callback\n", + "\n", + "class ImageShowCallback(Callback):\n", + " def __init__(self, net, eval_data):\n", + " self.net = net\n", + " self.eval_data = eval_data\n", + " \n", + " def step_end(self, run_context):\n", + " plot_model_and_datasets(self.net, self.eval_data)\n", + " display.clear_output(wait=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "其训练过程如下：\n", + "## 执行训练\n", "\n", - "1. 设置训练的迭代次数`step_size`。\n", - "2. 设置单次迭代的训练数据量`batch_size`。\n", - "3. 正向传播训练`grads`。\n", - "4. 反向传播训练`optim`。\n", - "5. 图形展示模型函数和数据集。\n", - "6. 清除本轮迭代的输出`display.clear_output`，起到动态可视化效果。\n", + "完成以上过程后，可以使用训练数`ds_train`对模型训练，这里调用`model.train`进行，其中参数解释：\n", "\n", - "迭代完成后，输出网络模型的权重值$w和b$。" + "- `epoch`：训练迭代的整个数据集的次数，int类型。\n", + "- `ds_train`：训练数据集。\n", + "- `callbacks`：训练过程中需要调用的回调函数。\n", + "- `dataset_sink_model`：数据集下沉模式，bool类型，支持Ascend，GPU计算平台，本例为CPU计算平台设置为False。" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": { - "scrolled": true + "ExecuteTime": { + "end_time": "2020-09-14T10:47:22.917679Z", + "start_time": "2020-09-14T10:38:40.707096Z" + } }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss_value: 0.42879593\n" - ] - }, { "data": { - "image/png": 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\n", 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    " ] @@ -677,49 +570,41 @@ "name": "stdout", "output_type": "stream", "text": [ - "weight: 1.9990227 bias: 2.9115517\n" + "Parameter (name=fc.weight, value=[[2.0405223]]) \n", + "Parameter (name=fc.bias, value=[2.9574146])\n" ] } ], "source": [ - "from IPython import display\n", "\n", - "step_size = 200\n", - "batch_size = 16\n", + "from mindspore.train.callback import LossMonitor\n", "\n", - "for i in range(step_size):\n", - " data_x,data_y = get_data(batch_size)\n", - " grads = train_network(data_x,data_y) \n", - " optim(grads)\n", - " plot_model_and_datasets(net.weight.data, \n", - " net.bias.data, data_x, data_y)\n", - " display.clear_output(wait=True)\n", + "epoch = 1\n", + "imageshow_cb = ImageShowCallback(net, eval_data)\n", + "model.train(epoch, ds_train, callbacks=[imageshow_cb], dataset_sink_mode=False)\n", "\n", - "output = net(eval_x)\n", - "loss_output = criterion(output, eval_label)\n", - "print(\"loss_value:\", loss_output.asnumpy())\n", - "plot_model_and_datasets(net.weight.data, net.bias.data, data_x,data_y)\n", - "print(\"weight:\", net.weight.set_data([0][0]), \"bias:\", net.bias.set_data([0]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "可以看到最终得到的线性拟合的权重值非常接近目标函数权重weight=2、bias=3。" + "plot_model_and_datasets(net,eval_data)\n", + "print(net.trainable_params()[0], \"\\n%s\" % net.trainable_params()[1])" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2020-09-14T04:00:18.787349Z", + "start_time": "2020-09-14T04:00:18.784236Z" + } + }, "source": [ - "## 总结" + "训练完成后打印出最终模型的权重参数，其中weight接近于2.0，bias接近于3.0，模型训练完成，符合预期。。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "## 总结\n", + "\n", "本次体验我们了解了线性拟合的算法原理，并在MindSpore框架下实现了相应的算法定义，了解了线性拟合这类的线性回归模型在MindSpore中的训练过程，并最终拟合出了一条接近目标函数的模型函数。另外有兴趣的可以调整数据集的生成区间从（-10,10）扩展到（-100,100），看看权重值是否更接近目标函数；调整学习率大小，看看拟合的效率是否有变化；当然也可以探索如何使用MindSpore拟合$f(x)=ax^2+bx+c$这类的二次函数或者更高次的函数。" ] } @@ -745,4 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    Figure 1 Overall design of differential privacy
    + +Figure 1 shows an overall design of differential privacy training, and mainly including differential privacy noise mechanisms (DP mechanisms), a differential privacy optimizer (DP optimizer), and a privacy monitor. + + +### DP Optimizer + +DP optimizer inherits capabilities of the MindSpore optimizer and uses the DP mechanisms to scramble and protect gradients. Currently, MindArmour provides three types of DP optimizers: constant Gaussian optimizer, adaptive Gaussian optimizer, and adaptive clipping optimizer. Each type of DP optimizer adds differential privacy protection capabilities to common optimizers such as SGD and Momentum from different perspectives. + +* Constant Gaussian optimizer is a DP optimizer for non-adaptive Gaussian noise. The advantage is that the differential privacy budget ϵ can be strictly controlled. The disadvantage is that in the model training process, the noise amount added in each step is fixed. If the number of training steps is too large, the noise in the later phase of training makes the model convergence difficult, or even causes the performance to deteriorate greatly and the model availability to be poor. +* Adaptive Gaussian optimizer adaptively adjusts the standard deviation to adjust the Gaussian distribution noise. In the initial phase of model training, a large amount of noise is added. As the model gradually converges, the noise amount gradually decreases, and the impact of the noise on the model availability is reduced. A disadvantage of the adaptive Gaussian noise is that a differential privacy budget cannot be strictly controlled. +* Adaptive clipping optimizer is a DP optimizer that adaptively adjusts a clipping granularity. Gradient clipping is an important operation in differential privacy training. The adaptive clipping optimizer can control a ratio of gradient clipping to fluctuate within a given range and control the gradient clipping granularity during training steps. + +### DP Mechanisms + +The noise mechanism is a basis for building a differential privacy training capability. Different noise mechanisms meet requirements of different DP optimizers, including multiple mechanisms such as constant Gaussian distribution noise, adaptive Gaussian distribution noise, adaptive clipping Gaussian distribution noise, and Laplace distribution noise. + +### Monitor + +Monitor provides callback functions such as Rényi differential privacy (RDP) and zero-concentrated differential privacy (ZCDP) to monitor the differential privacy budget of the model. + +* ZCDP[2] + + ZCDP is a loose differential privacy definition. It uses the Rényi divergence to measure the distribution difference of random functions on adjacent datasets. + +* RDP[3] + + RDP is a more general differential privacy definition based on the Rényi divergence. It uses the Rényi divergence to measure the distribution difference between two adjacent datasets. + + +Compared with traditional differential privacy, ZCDP and RDP provide stricter privacy budget upper bound guarantee. + + +## Code Implementation + +* [mechanisms.py](https://gitee.com/mindspore/mindarmour/blob/master/mindarmour/privacy/diff_privacy/mechanisms/mechanisms.py): implements the noise generation mechanism required by differential privacy training, including simple Gaussian noise, adaptive Gaussian noise, and adaptive clipping Gaussian noise. +* [optimizer.py](https://gitee.com/mindspore/mindarmour/blob/master/mindarmour/privacy/diff_privacy/optimizer/optimizer.py): implements the fundamental logic of using the noise generation mechanism to add noise during backward propagation. +* [monitor.py](https://gitee.com/mindspore/mindarmour/blob/master/mindarmour/privacy/diff_privacy/monitor/monitor.py): implements the callback function for computing the differential privacy budget. During model training, the current differential privacy budget is returned. +* [model.py](https://gitee.com/mindspore/mindarmour/blob/master/mindarmour/privacy/diff_privacy/train/model.py): implements the logic of computing the loss and gradient as well as the gradient truncation logic of differential privacy training, which is the entry for users to use the differential privacy training capability. + +## References + +[1] Dwork, Cynthia, and Jing Lei. "Differential privacy and robust statistics." *Proceedings of the forty-first annual ACM symposium on Theory of computing*. 2009. + +[2] Lee, Jaewoo, and Daniel Kifer. "Concentrated differentially private gradient descent with adaptive per-iteration privacy budget." *Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining*. 2018. + +[3] Mironov, Ilya. "Rényi differential privacy." *2017 IEEE 30th Computer Security Foundations Symposium (CSF)*. 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zU!m_pOBfW456FV6=TWnBA(8WT_K_ontCl$uJ#96NZdhV?Hqp`@Y;Z`jvk)LijVbRa zFvcpB{`{$cS%BC+T7B@6sGiVhrcLGvzUf~QwnYK@nf|SUe z-caa553Zh;^;*r@yyNXNPU|k1U0OXH8mDu>; zib5vs+T4XdM!HmLFm|U$7h&aF8~iH))B4x3`9C&7YkyO%+KXq0{yNDIi)PO>o^e^< Ha^L>~&!+}) literal 0 HcmV?d00001 -- Gitee From 78939ae27c171257304dce7077124de5ec2b1cc0 Mon Sep 17 00:00:00 2001 From: yingchen Date: Wed, 16 Sep 2020 16:02:50 +0800 Subject: [PATCH 052/156] update debugging in pynative mode --- .../source_en/advanced_use/debugging_in_pynative_mode.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md b/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md index 98c55656a9..bbb9ad3688 100644 --- a/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md +++ b/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md @@ -17,7 +17,7 @@ ## Overview -MindSpore supports the following running modes which are optimized in terms of debugging or running: +MindSpore supports the following running modes which are optimized for debugging or running: - PyNative mode: dynamic graph mode. In this mode, operators in the neural network are delivered and executed one by one, facilitating the compilation and debugging of the neural network model. - Graph mode: static graph mode. In this mode, the neural network model is compiled into an entire graph and then delivered for execution. This mode uses technologies such as graph optimization to improve the running performance and facilitates large-scale deployment and cross-platform running. @@ -105,12 +105,12 @@ print(output.asnumpy()) [3. 3. 3.]] ``` -> Parallel execution and summary is not supported in PyNative mode, so parallel and summary related operators can not be used. +> Parallel execution and summary are not supported in PyNative mode, so parallel and summary related operators cannot be used. ### Improving PyNative Performance -MindSpore provides the staging function to improve the execution speed of inference tasks in PyNative mode. This function compiles Python functions or Python class methods into computational graphs in PyNative mode and improves the execution speed by using graph optimization technologies, as shown in the following example: +MindSpore provides the Staging function to improve the execution speed of inference tasks in PyNative mode. This function compiles Python functions or Python class methods into computational graphs in PyNative mode and improves the execution speed by using graph optimization technologies, as shown in the following example: ```python import numpy as np -- Gitee From afdcbf7863c456e0dc9ccc19d1ea5cb7bb2f45ee Mon Sep 17 00:00:00 2001 From: liuyang_655 Date: Wed, 16 Sep 2020 16:04:13 +0800 Subject: [PATCH 053/156] modify checkpoint description --- api/source_zh_cn/programming_guide/callback.md | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/api/source_zh_cn/programming_guide/callback.md b/api/source_zh_cn/programming_guide/callback.md index 5717a72a47..0695b39b01 100644 --- a/api/source_zh_cn/programming_guide/callback.md +++ b/api/source_zh_cn/programming_guide/callback.md @@ -13,17 +13,13 @@ ## 概述 Callback回调函数在MindSpore中被实现为一个类，Callback机制类似于一种监控模式，可以帮助用户观察网络训练过程中各种参数的变化情况和网络内部的状态，还可以根据用户的指定，在达到特定条件后执行相应的操作，在训练过程中，Callback列表会按照定义的顺序执行Callback函数。Callback机制让用户可以及时有效地掌握网络模型的训练状态，并根据需要随时作出调整，可以极大地提升用户的开发效率。 -在MindSpore中，Callback机制一般用在网络训练过程`model.train`中，用户可以通过配置不同的内置回调函数传入不同的参数，从而实现各种功能。例如，可以通过`LossMonitor`监控每一个epoch的loss变化情况，通过checkpoint保存网络参数和模型进行再训练或推理，通过`TimeMonitor`监控每一个epoch，每一个step的训练时间，以及提前终止训练，动态调整参数等。 +在MindSpore中，Callback机制一般用在网络训练过程`model.train`中，用户可以通过配置不同的内置回调函数传入不同的参数，从而实现各种功能。例如，可以通过`LossMonitor`监控每一个epoch的loss变化情况，通过`ModelCheckpoint`保存网络参数和模型进行再训练或推理，通过`TimeMonitor`监控每一个epoch，每一个step的训练时间，以及提前终止训练，动态调整参数等。 ## MindSpore内置回调函数 - ModelCheckpoint - 与模型训练过程相结合，保存训练后的模型和网络参数，方便进行再推理或再训练。 - -- CheckpointConfig - - 一般与`ModelCheckpoint`配合使用，可自定义配置checkpoint的保存策略。 + 与模型训练过程相结合，保存训练后的模型和网络参数，方便进行再推理或再训练。`ModelCheckpoint`一般与`CheckpointConfig`配合使用，`CheckpointConfig`是一个参数配置类，可自定义配置checkpoint的保存策略。 详细内容，请参考[Checkpoint官网教程](https://www.mindspore.cn/tutorial/zh-CN/master/use/saving_and_loading_model_parameters.html)。 -- Gitee From e06e640f93bbc335e82fcf4264d522116ba66940 Mon Sep 17 00:00:00 2001 From: zhangyi Date: Wed, 16 Sep 2020 15:15:21 +0800 Subject: [PATCH 054/156] Adjust API structure in web. --- .../mindspore/mindspore.dataset.config.rst | 6 + .../python/mindspore/mindspore.dataset.rst | 7 - .../api/python/mindspore/mindspore.rst | 211 +++++++++--------- api/source_en/index.rst | 1 + .../mindspore/mindspore.dataset.config.rst | 6 + .../python/mindspore/mindspore.dataset.rst | 7 - .../api/python/mindspore/mindspore.rst | 211 +++++++++--------- api/source_zh_cn/index.rst | 1 + 8 files changed, 216 insertions(+), 234 deletions(-) create mode 100644 api/source_en/api/python/mindspore/mindspore.dataset.config.rst create mode 100644 api/source_zh_cn/api/python/mindspore/mindspore.dataset.config.rst diff --git a/api/source_en/api/python/mindspore/mindspore.dataset.config.rst b/api/source_en/api/python/mindspore/mindspore.dataset.config.rst new file mode 100644 index 0000000000..459497b6a0 --- /dev/null +++ b/api/source_en/api/python/mindspore/mindspore.dataset.config.rst @@ -0,0 +1,6 @@ +mindspore.dataset.config +======================== + +.. automodule:: mindspore.dataset.config + :members: + \ No newline at end of file diff --git a/api/source_en/api/python/mindspore/mindspore.dataset.rst b/api/source_en/api/python/mindspore/mindspore.dataset.rst index 1437a22b8e..c5e1cdf7d3 100644 --- a/api/source_en/api/python/mindspore/mindspore.dataset.rst +++ b/api/source_en/api/python/mindspore/mindspore.dataset.rst @@ -5,10 +5,3 @@ mindspore.dataset :members: :inherited-members: :exclude-members: get_args, read_dir - -mindspore.dataset.config ------------------------- - -.. automodule:: mindspore.dataset.config - :members: - \ No newline at end of file diff --git a/api/source_en/api/python/mindspore/mindspore.rst b/api/source_en/api/python/mindspore/mindspore.rst index 118c3a9c04..f510c8b5fc 100644 --- a/api/source_en/api/python/mindspore/mindspore.rst +++ b/api/source_en/api/python/mindspore/mindspore.rst @@ -1,118 +1,109 @@ mindspore ========= -mindspore.dtype ---------------- - -Data Type -~~~~~~~~~ - .. class:: mindspore.dtype - -Create a data type object of MindSpore. - -The actual path of ``dtype`` is ``/mindspore/common/dtype.py``. -Run the following command to import the package: - -.. code-block:: - - import mindspore.common.dtype as mstype + + Create a data type object of MindSpore. + + The actual path of ``dtype`` is ``/mindspore/common/dtype.py``. + Run the following command to import the package: + + .. code-block:: + + import mindspore.common.dtype as mstype + + or + + .. code-block:: + + from mindspore import dtype as mstype + + * **Numeric Type** + + Currently, MindSpore supports ``Int`` type, ``Uint`` type and ``Float`` type. + The following table lists the details. + + ============================================== ============================= + Definition Description + ============================================== ============================= + ``mindspore.int8`` , ``mindspore.byte`` 8-bit integer + ``mindspore.int16`` , ``mindspore.short`` 16-bit integer + ``mindspore.int32`` , ``mindspore.intc`` 32-bit integer + ``mindspore.int64`` , ``mindspore.intp`` 64-bit integer + ``mindspore.uint8`` , ``mindspore.ubyte`` unsigned 8-bit integer + ``mindspore.uint16`` , ``mindspore.ushort`` unsigned 16-bit integer + ``mindspore.uint32`` , ``mindspore.uintc`` unsigned 32-bit integer + ``mindspore.uint64`` , ``mindspore.uintp`` unsigned 64-bit integer + ``mindspore.float16`` , ``mindspore.half`` 16-bit floating-point number + ``mindspore.float32`` , ``mindspore.single`` 32-bit floating-point number + ``mindspore.float64`` , ``mindspore.double`` 64-bit floating-point number + ============================================== ============================= + + * **Other Type** + + For other defined types, see the following table. + + ============================ ================= + Type Description + ============================ ================= + ``tensor`` MindSpore's ``tensor`` type. Data format uses NCHW. For details, see [tensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/tensor.py). + ``MetaTensor`` A tensor only has data type and shape. For details, see [MetaTensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/parameter.py). + ``bool_`` Boolean ``True`` or ``False``. + ``int_`` Integer scalar. + ``uint`` Unsigned integer scalar. + ``float_`` Floating-point scalar. + ``number`` Number, including ``int_`` , ``uint`` , ``float_`` and ``bool_`` . + ``list_`` List constructed by ``tensor`` , such as ``List[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types. + ``tuple_`` Tuple constructed by ``tensor`` , such as ``Tuple[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types. + ``function`` Function. Return in two ways, when function is not None, returns Func directly, the other returns Func(args: List[T0,T1,...,Tn], retval: T) when function is None. + ``type_type`` Type definition of type. + ``type_none`` No matching return type, corresponding to the ``type(None)`` in Python. + ``symbolic_key`` The value of a variable is used as a key of the variable in ``env_type`` . + ``env_type`` Used to store the gradient of the free variable of a function, where the key is the ``symbolic_key`` of the free variable's node and the value is the gradient. + ============================ ================= + + * **Tree Topology** + + The relationships of the above types are as follows: + + .. code-block:: -or - -.. code-block:: - - from mindspore import dtype as mstype - -Numeric Type -++++++++++++ - -Currently, MindSpore supports ``Int`` type, ``Uint`` type and ``Float`` type. -The following table lists the details. - -============================================== ============================= -Definition Description -============================================== ============================= -``mindspore.int8`` , ``mindspore.byte`` 8-bit integer -``mindspore.int16`` , ``mindspore.short`` 16-bit integer -``mindspore.int32`` , ``mindspore.intc`` 32-bit integer -``mindspore.int64`` , ``mindspore.intp`` 64-bit integer -``mindspore.uint8`` , ``mindspore.ubyte`` unsigned 8-bit integer -``mindspore.uint16`` , ``mindspore.ushort`` unsigned 16-bit integer -``mindspore.uint32`` , ``mindspore.uintc`` unsigned 32-bit integer -``mindspore.uint64`` , ``mindspore.uintp`` unsigned 64-bit integer -``mindspore.float16`` , ``mindspore.half`` 16-bit floating-point number -``mindspore.float32`` , ``mindspore.single`` 32-bit floating-point number -``mindspore.float64`` , ``mindspore.double`` 64-bit floating-point number -============================================== ============================= - -Other Type -++++++++++ - -For other defined types, see the following table. - -============================ ================= -Type Description -============================ ================= -``tensor`` MindSpore's ``tensor`` type. Data format uses NCHW. For details, see [tensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/tensor.py). -``MetaTensor`` A tensor only has data type and shape. For details, see [MetaTensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/parameter.py). -``bool_`` Boolean ``True`` or ``False``. -``int_`` Integer scalar. -``uint`` Unsigned integer scalar. -``float_`` Floating-point scalar. -``number`` Number, including ``int_`` , ``uint`` , ``float_`` and ``bool_`` . -``list_`` List constructed by ``tensor`` , such as ``List[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types. -``tuple_`` Tuple constructed by ``tensor`` , such as ``Tuple[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types. -``function`` Function. Return in two ways, when function is not None, returns Func directly, the other returns Func(args: List[T0,T1,...,Tn], retval: T) when function is None. -``type_type`` Type definition of type. -``type_none`` No matching return type, corresponding to the ``type(None)`` in Python. -``symbolic_key`` The value of a variable is used as a key of the variable in ``env_type`` . -``env_type`` Used to store the gradient of the free variable of a function, where the key is the ``symbolic_key`` of the free variable's node and the value is the gradient. -============================ ================= - -Tree Topology -+++++++++++++ - -The relationships of the above types are as follows: - -.. code-block:: - - - └─────── number - │ ├─── bool_ - │ ├─── int_ - │ │ ├─── int8, byte - │ │ ├─── int16, short - │ │ ├─── int32, intc - │ │ └─── int64, intp - │ ├─── uint - │ │ ├─── uint8, ubyte - │ │ ├─── uint16, ushort - │ │ ├─── uint32, uintc - │ │ └─── uint64, uintp - │ └─── float_ - │ ├─── float16 - │ ├─── float32 - │ └─── float64 - ├─── tensor - │ ├─── Array[Float32] - │ └─── ... - ├─── list_ - │ ├─── List[Int32,Float32] - │ └─── ... - ├─── tuple_ - │ ├─── Tuple[Int32,Float32] - │ └─── ... - ├─── function - │ ├─── Func - │ ├─── Func[(Int32, Float32), Int32] - │ └─── ... - ├─── MetaTensor - ├─── type_type - ├─── type_none - ├─── symbolic_key - └─── env_type - + + └─────── number + │ ├─── bool_ + │ ├─── int_ + │ │ ├─── int8, byte + │ │ ├─── int16, short + │ │ ├─── int32, intc + │ │ └─── int64, intp + │ ├─── uint + │ │ ├─── uint8, ubyte + │ │ ├─── uint16, ushort + │ │ ├─── uint32, uintc + │ │ └─── uint64, uintp + │ └─── float_ + │ ├─── float16 + │ ├─── float32 + │ └─── float64 + ├─── tensor + │ ├─── Array[Float32] + │ └─── ... + ├─── list_ + │ ├─── List[Int32,Float32] + │ └─── ... + ├─── tuple_ + │ ├─── Tuple[Int32,Float32] + │ └─── ... + ├─── function + │ ├─── Func + │ ├─── Func[(Int32, Float32), Int32] + │ └─── ... + ├─── MetaTensor + ├─── type_type + ├─── type_none + ├─── symbolic_key + └─── env_type + .. automodule:: mindspore :members: :exclude-members: Model, dataset_helper, \ No newline at end of file diff --git a/api/source_en/index.rst b/api/source_en/index.rst index e74b234d3d..906c163483 100644 --- a/api/source_en/index.rst +++ b/api/source_en/index.rst @@ -15,6 +15,7 @@ MindSpore API api/python/mindspore/mindspore.communication api/python/mindspore/mindspore.context api/python/mindspore/mindspore.dataset + api/python/mindspore/mindspore.dataset.config api/python/mindspore/mindspore.dataset.text api/python/mindspore/mindspore.dataset.transforms api/python/mindspore/mindspore.dataset.vision diff --git a/api/source_zh_cn/api/python/mindspore/mindspore.dataset.config.rst b/api/source_zh_cn/api/python/mindspore/mindspore.dataset.config.rst new file mode 100644 index 0000000000..459497b6a0 --- /dev/null +++ b/api/source_zh_cn/api/python/mindspore/mindspore.dataset.config.rst @@ -0,0 +1,6 @@ +mindspore.dataset.config +======================== + +.. automodule:: mindspore.dataset.config + :members: + \ No newline at end of file diff --git a/api/source_zh_cn/api/python/mindspore/mindspore.dataset.rst b/api/source_zh_cn/api/python/mindspore/mindspore.dataset.rst index 1437a22b8e..c5e1cdf7d3 100644 --- a/api/source_zh_cn/api/python/mindspore/mindspore.dataset.rst +++ b/api/source_zh_cn/api/python/mindspore/mindspore.dataset.rst @@ -5,10 +5,3 @@ mindspore.dataset :members: :inherited-members: :exclude-members: get_args, read_dir - -mindspore.dataset.config ------------------------- - -.. automodule:: mindspore.dataset.config - :members: - \ No newline at end of file diff --git a/api/source_zh_cn/api/python/mindspore/mindspore.rst b/api/source_zh_cn/api/python/mindspore/mindspore.rst index 118c3a9c04..f510c8b5fc 100644 --- a/api/source_zh_cn/api/python/mindspore/mindspore.rst +++ b/api/source_zh_cn/api/python/mindspore/mindspore.rst @@ -1,118 +1,109 @@ mindspore ========= -mindspore.dtype ---------------- - -Data Type -~~~~~~~~~ - .. class:: mindspore.dtype - -Create a data type object of MindSpore. - -The actual path of ``dtype`` is ``/mindspore/common/dtype.py``. -Run the following command to import the package: - -.. code-block:: - - import mindspore.common.dtype as mstype + + Create a data type object of MindSpore. + + The actual path of ``dtype`` is ``/mindspore/common/dtype.py``. + Run the following command to import the package: + + .. code-block:: + + import mindspore.common.dtype as mstype + + or + + .. code-block:: + + from mindspore import dtype as mstype + + * **Numeric Type** + + Currently, MindSpore supports ``Int`` type, ``Uint`` type and ``Float`` type. + The following table lists the details. + + ============================================== ============================= + Definition Description + ============================================== ============================= + ``mindspore.int8`` , ``mindspore.byte`` 8-bit integer + ``mindspore.int16`` , ``mindspore.short`` 16-bit integer + ``mindspore.int32`` , ``mindspore.intc`` 32-bit integer + ``mindspore.int64`` , ``mindspore.intp`` 64-bit integer + ``mindspore.uint8`` , ``mindspore.ubyte`` unsigned 8-bit integer + ``mindspore.uint16`` , ``mindspore.ushort`` unsigned 16-bit integer + ``mindspore.uint32`` , ``mindspore.uintc`` unsigned 32-bit integer + ``mindspore.uint64`` , ``mindspore.uintp`` unsigned 64-bit integer + ``mindspore.float16`` , ``mindspore.half`` 16-bit floating-point number + ``mindspore.float32`` , ``mindspore.single`` 32-bit floating-point number + ``mindspore.float64`` , ``mindspore.double`` 64-bit floating-point number + ============================================== ============================= + + * **Other Type** + + For other defined types, see the following table. + + ============================ ================= + Type Description + ============================ ================= + ``tensor`` MindSpore's ``tensor`` type. Data format uses NCHW. For details, see [tensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/tensor.py). + ``MetaTensor`` A tensor only has data type and shape. For details, see [MetaTensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/parameter.py). + ``bool_`` Boolean ``True`` or ``False``. + ``int_`` Integer scalar. + ``uint`` Unsigned integer scalar. + ``float_`` Floating-point scalar. + ``number`` Number, including ``int_`` , ``uint`` , ``float_`` and ``bool_`` . + ``list_`` List constructed by ``tensor`` , such as ``List[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types. + ``tuple_`` Tuple constructed by ``tensor`` , such as ``Tuple[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types. + ``function`` Function. Return in two ways, when function is not None, returns Func directly, the other returns Func(args: List[T0,T1,...,Tn], retval: T) when function is None. + ``type_type`` Type definition of type. + ``type_none`` No matching return type, corresponding to the ``type(None)`` in Python. + ``symbolic_key`` The value of a variable is used as a key of the variable in ``env_type`` . + ``env_type`` Used to store the gradient of the free variable of a function, where the key is the ``symbolic_key`` of the free variable's node and the value is the gradient. + ============================ ================= + + * **Tree Topology** + + The relationships of the above types are as follows: + + .. code-block:: -or - -.. code-block:: - - from mindspore import dtype as mstype - -Numeric Type -++++++++++++ - -Currently, MindSpore supports ``Int`` type, ``Uint`` type and ``Float`` type. -The following table lists the details. - -============================================== ============================= -Definition Description -============================================== ============================= -``mindspore.int8`` , ``mindspore.byte`` 8-bit integer -``mindspore.int16`` , ``mindspore.short`` 16-bit integer -``mindspore.int32`` , ``mindspore.intc`` 32-bit integer -``mindspore.int64`` , ``mindspore.intp`` 64-bit integer -``mindspore.uint8`` , ``mindspore.ubyte`` unsigned 8-bit integer -``mindspore.uint16`` , ``mindspore.ushort`` unsigned 16-bit integer -``mindspore.uint32`` , ``mindspore.uintc`` unsigned 32-bit integer -``mindspore.uint64`` , ``mindspore.uintp`` unsigned 64-bit integer -``mindspore.float16`` , ``mindspore.half`` 16-bit floating-point number -``mindspore.float32`` , ``mindspore.single`` 32-bit floating-point number -``mindspore.float64`` , ``mindspore.double`` 64-bit floating-point number -============================================== ============================= - -Other Type -++++++++++ - -For other defined types, see the following table. - -============================ ================= -Type Description -============================ ================= -``tensor`` MindSpore's ``tensor`` type. Data format uses NCHW. For details, see [tensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/tensor.py). -``MetaTensor`` A tensor only has data type and shape. For details, see [MetaTensor](https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/parameter.py). -``bool_`` Boolean ``True`` or ``False``. -``int_`` Integer scalar. -``uint`` Unsigned integer scalar. -``float_`` Floating-point scalar. -``number`` Number, including ``int_`` , ``uint`` , ``float_`` and ``bool_`` . -``list_`` List constructed by ``tensor`` , such as ``List[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types. -``tuple_`` Tuple constructed by ``tensor`` , such as ``Tuple[T0,T1,...,Tn]`` , where the element ``Ti`` can be of different types. -``function`` Function. Return in two ways, when function is not None, returns Func directly, the other returns Func(args: List[T0,T1,...,Tn], retval: T) when function is None. -``type_type`` Type definition of type. -``type_none`` No matching return type, corresponding to the ``type(None)`` in Python. -``symbolic_key`` The value of a variable is used as a key of the variable in ``env_type`` . -``env_type`` Used to store the gradient of the free variable of a function, where the key is the ``symbolic_key`` of the free variable's node and the value is the gradient. -============================ ================= - -Tree Topology -+++++++++++++ - -The relationships of the above types are as follows: - -.. code-block:: - - - └─────── number - │ ├─── bool_ - │ ├─── int_ - │ │ ├─── int8, byte - │ │ ├─── int16, short - │ │ ├─── int32, intc - │ │ └─── int64, intp - │ ├─── uint - │ │ ├─── uint8, ubyte - │ │ ├─── uint16, ushort - │ │ ├─── uint32, uintc - │ │ └─── uint64, uintp - │ └─── float_ - │ ├─── float16 - │ ├─── float32 - │ └─── float64 - ├─── tensor - │ ├─── Array[Float32] - │ └─── ... - ├─── list_ - │ ├─── List[Int32,Float32] - │ └─── ... - ├─── tuple_ - │ ├─── Tuple[Int32,Float32] - │ └─── ... - ├─── function - │ ├─── Func - │ ├─── Func[(Int32, Float32), Int32] - │ └─── ... - ├─── MetaTensor - ├─── type_type - ├─── type_none - ├─── symbolic_key - └─── env_type - + + └─────── number + │ ├─── bool_ + │ ├─── int_ + │ │ ├─── int8, byte + │ │ ├─── int16, short + │ │ ├─── int32, intc + │ │ └─── int64, intp + │ ├─── uint + │ │ ├─── uint8, ubyte + │ │ ├─── uint16, ushort + │ │ ├─── uint32, uintc + │ │ └─── uint64, uintp + │ └─── float_ + │ ├─── float16 + │ ├─── float32 + │ └─── float64 + ├─── tensor + │ ├─── Array[Float32] + │ └─── ... + ├─── list_ + │ ├─── List[Int32,Float32] + │ └─── ... + ├─── tuple_ + │ ├─── Tuple[Int32,Float32] + │ └─── ... + ├─── function + │ ├─── Func + │ ├─── Func[(Int32, Float32), Int32] + │ └─── ... + ├─── MetaTensor + ├─── type_type + ├─── type_none + ├─── symbolic_key + └─── env_type + .. automodule:: mindspore :members: :exclude-members: Model, dataset_helper, \ No newline at end of file diff --git a/api/source_zh_cn/index.rst b/api/source_zh_cn/index.rst index 4e58d830f0..45c73bb95d 100644 --- a/api/source_zh_cn/index.rst +++ b/api/source_zh_cn/index.rst @@ -26,6 +26,7 @@ MindSpore API api/python/mindspore/mindspore.communication api/python/mindspore/mindspore.context api/python/mindspore/mindspore.dataset + api/python/mindspore/mindspore.dataset.config api/python/mindspore/mindspore.dataset.text api/python/mindspore/mindspore.dataset.transforms api/python/mindspore/mindspore.dataset.vision -- Gitee From 4b1a22cd4af3ad71749fd387c8b4726c4a1feedc Mon Sep 17 00:00:00 2001 From: xuxinyu Date: Wed, 16 Sep 2020 16:22:17 +0800 Subject: [PATCH 055/156] update glossary --- docs/source_en/glossary.md | 5 +++-- docs/source_zh_cn/glossary.md | 5 +++-- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/docs/source_en/glossary.md b/docs/source_en/glossary.md index ae1fb21e91..3f08ac2a41 100644 --- a/docs/source_en/glossary.md +++ b/docs/source_en/glossary.md @@ -32,9 +32,10 @@ | LSTM | Long short-term memory, an artificial recurrent neural network (RNN) architecture used for processing and predicting an important event with a long interval and delay in a time sequence. | | Manifest | A data format file. Huawei ModelArt adopts this format. For details, see . | | ME | Mind Expression, MindSpore frontend, which is used to compile tasks from user source code to computational graphs, control execution during training, maintain contexts (in non-sink mode), and dynamically generate graphs (in PyNative mode). | -| MindArmour | MindSpore security component, which is used for AI adversarial example management, AI model attack defense and enhancement, and AI model robustness evaluation. | +| MindArmour | The security module of MindSpore, which improves the confidentiality, integrity and usability of the model through technical means such as differential privacy and adversarial attack and defense. MindArmour prevents attackers from maliciously modifying the model or cracking the internal components of the model to steal the parameters of the model. | | MindData | MindSpore data framework, which provides data loading, enhancement, dataset management, and visualization. | | MindInsight | MindSpore visualization component, which visualizes information such as scalars, images, computational graphs, and model hyperparameters. | +| MindRecord | It is a data format defined by MindSpore, it is a module for reading, writing, searching and converting data sets in MindSpore format. | | MindSpore | Huawei-leaded open-source deep learning framework. | | MindSpore Lite | A lightweight deep neural network inference engine that provides the inference function for models trained by MindSpore on the device side. | | MNIST database | Modified National Handwriting of Images and Technology database, a large handwritten digit database, which is usually used to train various image processing systems. | @@ -43,5 +44,5 @@ | ResNet-50 | Residual Neural Network 50, a residual neural network proposed by four Chinese people, including Kaiming He from Microsoft Research Institute. | | Schema | Data set structure definition file, which defines the fields contained in a dataset and the field types. | | Summary | An operator that monitors the values of tensors on the network. It is a peripheral operation in the figure and does not affect the data flow. | -| TBE | Tensor Boost Engine, an operator development tool that is extended based on the Tensor Virtual Machine (TVM) framework. | +| TBE | Tensor Boost Engine, it is a self-developed NPU operator development tool developed by Huawei, which is expanded on the basis of the TVM (Tensor Virtual Machine) framework. It provides a set of Python API to implement development activities and develop custom operators. | | TFRecord | Data format defined by TensorFlow. | diff --git a/docs/source_zh_cn/glossary.md b/docs/source_zh_cn/glossary.md index 647c9076f9..c5721652b0 100644 --- a/docs/source_zh_cn/glossary.md +++ b/docs/source_zh_cn/glossary.md @@ -32,9 +32,10 @@ | LSTM | Long short-term memory，长短期记忆，对应的网络是一种时间循环神经网络，适合于处理和预测时间序列中间隔和延迟非常长的重要事件。 | | Manifest | 一种数据格式文件，华为ModelArts采用了该格式，详细说明请参见。 | | ME | Mind Expression，MindSpore前端，主要完成从用户源码到计算图的编译任务、训练中控制执行及上下文维护（非下沉模式配置下）、动态图（PyNative模式）等。 | -| MindArmour | MindSpore安全组件，用于AI对抗样本管理，AI模型防攻击和增强，AI模型健壮性评测。 | +| MindArmour | MindSpore安全模块，通过差分隐私、对抗性攻防等技术手段，提升模型的保密性、完整性和可用性，阻止攻击者对模型进行恶意修改或是破解模型的内部构件，窃取模型的参数。 | | MindData | MindSpore数据框架，提供数据加载、增强、数据集管理以及可视化。 | | MindInsight | MindSpore可视化组件，可视化标量、图像、计算图以及模型超参等信息。 | +| MindRecord | MindSpore定义的一种数据格式，是一个执行读取、写入、搜索和转换MindSpore格式数据集的模块。 | | MindSpore | 华为主导开源的深度学习框架。 | | MindSpore Lite | 一个轻量级的深度神经网络推理引擎，提供了将MindSpore训练出的模型在端侧进行推理的功能。 | | MNIST database | Modified National Institute of Standards and Technology database，一个大型手写数字数据库，通常用于训练各种图像处理系统。 | @@ -43,5 +44,5 @@ | ResNet-50 | Residual Neural Network 50，由微软研究院的Kaiming He等四名华人提出的残差神经网络。 | | Schema | 数据集结构定义文件，用于定义数据集包含哪些字段以及字段的类型。 | | Summary | 是对网络中Tensor取值进行监测的一种算子，在图中是“外围”操作，不影响数据流本身。 | -| TBE | Tensor Boost Engine，在TVM（ Tensor Virtual Machine ）框架基础上扩展的算子开发工具。 | +| TBE | Tensor Boost Engine，华为自研的NPU算子开发工具，在TVM（ Tensor Virtual Machine ）框架基础上扩展，提供了一套Python API来实施开发活动，进行自定义算子开发。 | | TFRecord | Tensorflow定义的数据格式。 | -- Gitee From dd520d1a5ae919c254e280143fbb214e3b56d108 Mon Sep 17 00:00:00 2001 From: wangyue01 Date: Sun, 13 Sep 2020 22:19:48 +0800 Subject: [PATCH 056/156] add docs for mindspore debugger --- tutorials/source_en/advanced_use/debugger.md | 187 ++++++++++++++++++ .../images/debugger_before_train.png | Bin 0 -> 76792 bytes .../advanced_use/images/debugger_graph.png | Bin 0 -> 36902 bytes 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a/tutorials/source_en/advanced_use/debugger.md b/tutorials/source_en/advanced_use/debugger.md new file mode 100644 index 0000000000..8afd9ac05c --- /dev/null +++ b/tutorials/source_en/advanced_use/debugger.md @@ -0,0 +1,187 @@ +# Debugger + +`Linux` `Ascend` `GPU` `Graph Mode` `Debug Training` `Intermediate` `Expert` + + + +- [Debugger](#debugger) + - [Overview](#overview) + - [Operation Process](#operation-process) + - [Debugger Environment Preparation](#debugger-environment-preparation) + - [Debugger UI Introduction](#debugger-UI-introduction) + - [Computational Graph](#computational-graph) + - [Runtime Node List](#runtime-node-list) + - [Graph Node Details](#graph-node-details) + - [Conditional Breakpoint](#conditional-breakpoint) + - [Training Control](#training-control) + - [Debugger Usage Example](#debugger-usage-example) + - [Notices](#notices) + + + + + +## Overview + +MindSpore Debugger is a debugging tool for training in `Graph Mode`. It can be applied to visualize and analyze the intermediate computation results of the computational graph. + +In `Graph Mode` training, the computation results of intermediate nodes in the computational graph can not be acquired from python layer, which makes it difficult for users to do the debugging. By applying MindSpore Debugger, users can: + +- Visualize the computational graph on the UI and analyze the output of the graph node; +- Set conditional breakpoint to monitor training exceptions (such as Nan/Inf), if the condition (Nan/Inf etc.) is met, users can track the cause of the bug when an exception occurs; +- Visualize and analyze the change of parameters, such as weights. + +## Operation Process + +- Launch MindInsight in debugger mode, and set Debugger environment variables for the training; +- At the beginning of the training, set conditional breakpoints; +- Analyze the training progress on MindInsight Debugger UI. + +## Debugger Environment Preparation + +At first, install MindInsight and launch it in debugger mode. MindSpore will send training information to MindInsight Debugger Server in debugger mode, users can analyze the information on MindInsight UI. + +The command to launch MindInsight in debugger mode is as follows: + +```shell script +mindinsight start --port {PORT} --enable-debugger True --debugger-port {DEBUGGER_PORT} +``` + +The Debugger related parameters: + +|Name|Argument|Description|Type|Default|Scope| +|---|---|---|---|---|---| +|`--port {PORT}`|Optional|Specifies the port number of the web visualization service.|Integer|8080|1~65535| +|`--enable-debugger {ENABLE_DEBUGGER}`|Required|Should be set to `True`, this will launch the MindInsight debugger server.|Boolean|False|True/False| +|`--debugger-port {DEBUGGER_PORT}`|Optional|Specifies the port number of the debugger server.|Integer|50051|1~65535| + +For more launch parameters, please refer to [MindInsight Commands](https://www.mindspore.cn/tutorial/en/master/advanced_use/mindinsight_commands.html). + +Then，specify the environment variable `export ENABLE_MS_DEBUGGER=1` to set the train in debugger mode. +Set environment variable `export MS_DEBUGGER_PORT=50051` to specify the MindInsight debugger-port, which the training will communicate with. + +Besides, do not use dataset sink mode (Set the parameter `dataset_sink_mode` in `model.train` to `False`) to ensure the Debugger can acquire information for all steps. + +## Debugger UI Introduction + +After the Debugger environment preparation, users can run the training script. +Before the execution of the computational graph in the first step, the MindInsight Debugger UI will show the information of the computational graph after optimization. +The following are the Debugger UI components. + +### Computational Graph + +![debugger_graph.png](./images/debugger_graph.png) + +Figure 1： The Computational Graph Area + +![debugger_graph_expand.png](./images/debugger_graph_expand.png) + +Figure 2： The Expanded Computational Graph + +Debugger will display the optimized computational graph in the upper middle area of the page. +Users can click the box (stand for one `scope`) to expand the graph, and analyze the nodes contained in that `scope`. + +By clicking the buttons in the upper right corner, users can easily traverse the computational graph. + +The area on the top shows the training metadata, such as the `device` being used and the current training `step`. + +### Runtime Node List + +![debugger_node_list](./images/debugger_node_list.png) + +Figure 3： The Computational Graph `node list` + +As shown in Figure 3，the Computational Graph `node list` will be displayed on the left of the page. +The `node list` can be expanded according to the `scope` of the nodes. +When clicking one node in the list, the computational graph on the right will also be expanded and choose the corresponding node automatically. + +The search bar on the top can be used to search for nodes in the graph by node name. + +### Graph Node Details + +![debugger_tensor_overview](./images/debugger_tensor_overview.png) + +Figure 4： The Graph Node Details + +When choosing one node on the graph, the details of this node will be displayed at the bottom. +The `Tensor Value Overview` area will show the input nodes and the outputs of this node. The `Type`, `Shape` and `Value` of the `Tensor` can also be viewed. + +![debugger_tensor_value](./images/debugger_tensor_value.png) + +Figure 5： `Tensor` Value Visualization + +Some outputs of the node contain too many dimensions. +For these `Tensors`, users can click the `view` link and visualize the `Tensor` in the new panel, which is shown in Figure 5. + +### Conditional Breakpoint + +![debugger_watchpoint](./images/debugger_watchpoint.png) + +Figure 6： Set Conditional Breakpoint (Watch Point) + +In order to monitor the training and find out the bugs, users can set conditional breakpoints (called `Watch Points` on UI) to analyze the outputs of the +specified nodes automatically. Figure 6 displays how to set a `Watch Point`: +- At first, click the `+` button on the upper right corner, and then choose a watch condition; +- Select the nodes to be watched in the `Runtime Node List`, tick the boxes at the front of the chosen nodes; +- Click the `confirm` button to add this `Watch Point`. + +The outputs of the watched nodes will be checked by the corresponding conditions. Once the condition is satisfied, the training will pause, and users can analyze +the triggered `Watch Points` on the Debugger UI. + +![debugger_watchpoint_hit](./images/debugger_watchpoint_hit.png) + +Figure 7： The Triggered `Watch Points` + +Figure 7 displays the triggered `Watch Points`, the displayed area is the same as the `Runtime Node List`. +The triggered nodes and corresponding conditions are displayed in the execution order. Click one line in the list, the node will be shown in the computational graph automatically. +Users can further trace the reason of the bug by analyzing the node details. + +### Training Control + +![debugger_train_control](./images/debugger_train_control.png) + +Figure 8： The Training Control + +Figure 8 displays the Debugger training control, which contains `STEP`, `CONTINUE` and `TERMINATE`: + +- `STEP` stands for executing the training for several steps, the number of the `step` can be specified in the above bar; +- `CONTINUE` stands for executing the training until the `Watch Points` is triggered; +- `TERMINATE` stands for terminating the training. + +## Debugger Usage Example + +1. Prepare the debugger environment, and open the MindInsight Debugger UI. + + ![debugger_waiting](./images/debugger_waiting.png) + + Figure 9： Debugger Start and Waiting for the Training + + The Debugger server is launched and waiting for the training to connect. + +2. Run the training script, after a while, the computational graph will be displayed on Debugger UI. + + ![debugger_before_train](./images/debugger_before_train.png) + + Figure 10： Computational Graph is displayed on Debugger UI + +3. Set conditional breakpoints for the training. + + ![debugger_set_watchpoint](./images/debugger_set_watchpoint.png) + + Figure 11： Set Conditional Breakpoint + + In Figure 11, the condition `NAN` is selected, and will watch all the nodes in `Default scope`. All the outputs in forward computation will be checked to see if `NAN` exists. + After setting the conditional breakpoints, users can choose `STEP` or `CONTINUE` to continue the training. + +4. 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+30,12 @@ mindinsight --version ## Start the Service ```shell -mindinsight start [-h] [--config ] [--workspace ] - [--port ] [--url-path-prefix ] - [--reload-interval ] - [--summary-base-dir ] +mindinsight start [-h] [--config {CONFIG}] [--workspace {WORKSPACE}] + [--port {PORT}] [--url-path-prefix {URL_PATH_PREFIX}] + [--reload-interval {RELOAD_INTERVAL}] + [--summary-base-dir {SUMMARY_BASE_DIR}] + [--enable-debugger {ENABLE_DEBUGGER}] + [--debugger-port {DEBUGGER_PORT}] ``` Optional parameters as follows: @@ -41,12 +43,14 @@ Optional parameters as follows: |Name|Argument|Description|Type|Default|Scope|Specifications| |---|---|---|---|---|---|---| |`-h, --help`|Optional|Displays the help information about the start command.|-|-|-|-| -|`--config `|Optional|Specifies the configuration file or module.|String|Empty string|-|Physical file path (file:/path/to/config.py) or a module path (python:path.to.config.module) that can be identified by Python.| -|`--workspace `|Optional|Specifies the working directory.|String|$HOME/mindinsight|-|-| -|`--port `|Optional|Specifies the port number of the web visualization service.|Integer|8080|1~65535|-| -|`--url-path-prefix `|Optional|Specifies the URL path prefix of the web visualization service.|String|Empty string|-|URL path prefix consists of segments separated by slashes. Each segment supports alphabets / digits / underscores / dashes / dots, but not single dot or double dots.| -|`--reload-interval `|Optional|Specifies the interval (unit: second) for loading data.|Integer|3|-|The value 0 indicates that data is loaded only once.| -|`--summary-base-dir `|Optional|Specifies the root directory for loading training log data.|String|./|-|MindInsight traverses the direct subdirectories in this directory and searches for log files. If a direct subdirectory contains log files, it is identified as the log file directory. If a root directory contains log files, it is identified as the log file directory.| +|`--config {CONFIG}`|Optional|Specifies the configuration file or module.|String|Empty string|-|Physical file path (file:/path/to/config.py) or a module path (python:path.to.config.module) that can be identified by Python.| +|`--workspace {WORKSPACE}`|Optional|Specifies the working directory.|String|$HOME/mindinsight|-|-| +|`--port {PORT}`|Optional|Specifies the port number of the web visualization service.|Integer|8080|1~65535|-| +|`--url-path-prefix {URL_PATH_PREFIX}`|Optional|Specifies the URL path prefix of the web visualization service.|String|Empty string|-|URL path prefix consists of segments separated by slashes. Each segment supports alphabets / digits / underscores / dashes / dots, but not single dot or double dots.| +|`--reload-interval {RELOAD_INTERVAL}`|Optional|Specifies the interval (unit: second) for loading data.|Integer|3|-|The value 0 indicates that data is loaded only once.| +|`--summary-base-dir {SUMMARY_BASE_DIR}`|Optional|Specifies the root directory for loading training log data.|String|./|-|MindInsight traverses the direct subdirectories in this directory and searches for log files. If a direct subdirectory contains log files, it is identified as the log file directory. If a root directory contains log files, it is identified as the log file directory.| +|`--enable-debugger {ENABLE_DEBUGGER}`|Optional|Whether to launch the MindInsight Debugger.|Boolean|False|True/False|-| +|`--debugger-port {DEBUGGER_PORT}`|Optional|Specifies the port number of the debugger server.|Integer|50051|1~65535|-| > When the service is started, the parameter values of the command line are saved as the environment variables of the process and start with `MINDINSIGHT_`, for example, `MINDINSIGHT_CONFIG`, `MINDINSIGHT_WORKSPACE`, and `MINDINSIGHT_PORT`. diff --git a/tutorials/source_en/advanced_use/visualization_tutorials.rst b/tutorials/source_en/advanced_use/visualization_tutorials.rst index 17b1532bba..e291298793 100644 --- a/tutorials/source_en/advanced_use/visualization_tutorials.rst +++ b/tutorials/source_en/advanced_use/visualization_tutorials.rst @@ -9,4 +9,5 @@ Training Process Visualization lineage_and_scalars_comparision performance_profiling performance_profiling_gpu + debugger mindinsight_commands diff --git a/tutorials/source_zh_cn/advanced_use/debugger.md b/tutorials/source_zh_cn/advanced_use/debugger.md new file mode 100644 index 0000000000..e73d567742 --- /dev/null +++ b/tutorials/source_zh_cn/advanced_use/debugger.md @@ -0,0 +1,168 @@ +# 调试器 + +`Linux` `Ascend` `GPU` `静态图` `模型调试` `中级` `高级` + + + +- [Debugger](#debugger) + - [概述](#概述) + - [操作流程](#操作流程) + - [Debugger环境准备](#debugger环境准备) + - [Debugger页面介绍](#debugger页面介绍) + - [计算图](#计算图) + - [Runtime Node List](#runtime-node-list) + - [节点信息](#节点信息) + - [条件断点](#条件断点) + - [训练控制](#训练控制) + - [使用Debugger进行调试示例](#使用debugger进行调试示例) + - [注意事项](#注意事项) + + + + + +## 概述 +MindSpore Debugger是为图模式训练提供的调试工具，可以用来查看并分析计算图节点的中间结果。 + +在MindSpore图模式的训练过程中，用户无法从Python层获取到计算图中间节点的结果，使得训练调试变得很困难。使用MindSpore Debugger，用户可以： + +- 在MindInsight UI结合计算图，查看图节点的输出结果； +- 设置条件断点，监测训练异常情况（比如Nan/Inf），在异常发生时追踪错误原因； +- 查看权重等参数的变化情况。 + +## 操作流程 + +- 以Debugger模式启动MindInsight，配置相关环境变量; +- 训练开始，在MindInsight Debugger UI设置条件断点； +- 在MindInsight Debugger UI分析训练执行情况。 + +## Debugger环境准备 +开始训练前，请先安装MindInsight，并以Debugger模式启动。Debugger模式下，MindSpore会将训练信息发送给MindInsight Debugger Server，用户可在MindInsight UI进行查看和分析。 + +MindInsight Debugger服务启动命令： + +```shell script +mindinsight start --port {PORT} --enable-debugger True --debugger-port {DEBUGGER_PORT} +``` + +参数含义如下: + +|参数名|属性|功能描述|参数类型|默认值|取值范围| +|---|---|---|---|---|---| +|`--port {PORT}`|可选|指定Web可视化服务端口。|Integer|8080|1~65535| +|`--enable-debugger {ENABLE_DEBUGGER}`|必选|取值为True, 开启MindInsight侧Debugger|Boolean|False|True/False| +|`--debugger-port {DEBUGGER_PORT}`|可选|指定Debugger Server服务端口。|Integer|50051|1~65535| + +更多启动参数请参考[MindInsight相关命令](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/mindinsight_commands.html)。 + +然后，设置`export ENABLE_MS_DEBUGGER=1`将训练指定为Debugger模式，并设置训练要连接的Debugger Server `Port`: `export MS_DEBUGGER_PORT=50051`(该port需与MindInsight debugger-port一致)。 + +此外，训练时不要使用数据下沉模式（需设置`model.train`中的`dataset_sink_mode`为`False`），以保证Debugger可以获取每个step的训练信息。 + +## Debugger页面介绍 + +Debugger环境准备完成后，开始训练。在第一个step图执行前，可以在MindInsight Debugger UI接收到训练的计算图等元信息，Debugger页面布局由如下部分组成。 + +### 计算图 + +![debugger_graph.png](./images/debugger_graph.png) + +图1： 计算图面板 + +![debugger_graph_expand.png](./images/debugger_graph_expand.png) + +图2： 展开后的计算图 + +Debugger将图优化后的最终执行图展示在UI的中上位置，用户可以点击图上的方框 (代表一个`scope`) 将计算图进一步展开，查看`scope`中的节点信息。 + +通过右上角的两个按钮，用户可以方便地对计算图进行遍历。 + +面板的最上方也展示了训练所占用的`device`, 当前训练的`step`数目等元信息。 + +### Runtime Node List + +![debugger_node_list](./images/debugger_node_list.png) + +图3： 计算图`node list` + +如图3所示，在UI的左侧会展示计算图`node list`，可以将计算图中的节点按`scope`分层展开。点击`node list`中的节点，计算图也会联动展开到选中节点的位置。 +用户也可以使用`node list`上方的搜索框按名称进行节点的搜索。 + +### 节点信息 + +![debugger_tensor_overview](./images/debugger_tensor_overview.png) + +图4： 计算图节点信息查看 + +点击计算图上的节点后，可以在UI下方查看该节点的详细信息，如图4所示。该部分展示了节点的输出和输入，当前训练的`step`数目，`Tensor`的`Type`、`Shape`和`Value`等信息。 + + +![debugger_tensor_value](./images/debugger_tensor_value.png) + +图5： 查看`Tensor`值 + +一些`Tensor`的维度过多，无法直接在主页进行展示。用户可以点击对应的查看按钮，在弹出的TAB页中查看`Tensor`值的详细信息，如图5所示。 + +### 条件断点 + +![debugger_watchpoint](./images/debugger_watchpoint.png) + +图6： 条件断点设置 + +为了方便地对节点的计算结果进行监测分析，用户可以给计算图中的节点设置条件断点。图6展示了条件断点的设置方法，用户首先点击Watch Point List右上角的 `+` 按钮新增条件断点并监控条件，比如INF/NAN， +然后在Runtime Node List选择要监控的节点（勾选节点前的方框）。训练时，Debugger会对这些监控节点的输出进行实时分析，一旦监控条件触发，训练暂停，用户可在UI上查看触发的条件断点信息。 + +![debugger_watchpoint_hit](./images/debugger_watchpoint_hit.png) + +图7： 查看触发的条件断点 + +图7展示了条件断点触发后的展示页面，该页面和`Runtime Node List`所在位置相同。触发的节点以及监控条件会按照节点的执行序排列，用户点击某一行，会在计算图中跳转到对应节点，可以进一步查看节点信息分析INF/NAN等异常结果出现的原因。 + +### 训练控制 + +![debugger_train_control](./images/debugger_train_control.png) + +图8： 训练控制 + +图8展示了Debugger的训练控制功能，一共有`STEP`、`CONTINUE`、`TERMINATE`三个按钮。 +- `STEP`代表训练向前执行若干个`step`，可以在上方的输入框内指定执行的`step`数目； +- `CONTINUE`代表训练一直执行，直到条件断点触发后暂停； +- `TERMINATE`代表终止训练。 + +## 使用Debugger进行调试示例 + +1. 在Debugger环境准备完成后，打开Debugger UI，如下图所示： + + ![debugger_waiting](./images/debugger_waiting.png) + + 图9： Debugger等待训练连接 + + 此时，Debugger出于waiting状态，等待训练启动和连接。 + +2. 运行训练脚本，片刻后可以看到计算图显示在Debugger UI，如下图所示： + + ![debugger_before_train](./images/debugger_before_train.png) + + 图10： 计算图在Debugger UI展示 + +3. 设置条件断点，如下图所示： + + ![debugger_set_watchpoint](./images/debugger_set_watchpoint.png) + + 图11： 设置条件断点 + + 上图中，选择了`NAN`条件，并勾选了`Default scope`，代表监控前向计算过程是否存在`NAN`输出。设置完条件断点后，可以选择`STEP`或者`CONTINUE`继续训练。 + +4. 条件断点触发，如下图所示： + + ![debugger_watchpoint_trigger](./images/debugger_watchpoint_trigger.png) + + 图12： 条件断点触发 + + 条件断点触发后，用户查看对应的节点信息，找出异常原因后修改脚本，解掉bug。 + +## 注意事项 + +- 使用Debugger时，会对训练性能产生一定影响。 +- 一个Debugger Server目前只能够连接一个训练进程。 + diff --git a/tutorials/source_zh_cn/advanced_use/images/debugger_before_train.png b/tutorials/source_zh_cn/advanced_use/images/debugger_before_train.png new file mode 100644 index 0000000000000000000000000000000000000000..fdfc8f08218038c5058da747696b11eda032b061 GIT binary patch literal 76792 zcmdqIcT`i`*6^*zt{|dPl%^meUAlCYrc?pxB}(rRkQ$O$=|z+df`arCDIv5ZDufmw 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a/tutorials/source_zh_cn/advanced_use/mindinsight_commands.md +++ b/tutorials/source_zh_cn/advanced_use/mindinsight_commands.md @@ -30,10 +30,12 @@ mindinsight --version ## 启动服务 ```shell -mindinsight start [-h] [--config ] [--workspace ] - [--port ] [--url-path-prefix ] - [--reload-interval ] - [--summary-base-dir ] +mindinsight start [-h] [--config {CONFIG}] [--workspace {WORKSPACE}] + [--port {PORT}] [--url-path-prefix {URL_PATH_PREFIX}] + [--reload-interval {RELOAD_INTERVAL}] + [--summary-base-dir {SUMMARY_BASE_DIR}] + [--enable-debugger {ENABLE_DEBUGGER}] + [--debugger-port {DEBUGGER_PORT}] ``` 参数含义如下: @@ -41,12 +43,14 @@ mindinsight start [-h] [--config ] [--workspace ] |参数名|属性|功能描述|参数类型|默认值|取值范围|规则限制| |---|---|---|---|---|---|---| |`-h, --help`|可选|显示启动命令的帮助信息。|-|-|-|-| -|`--config `|可选|指定配置文件或配置模块。|String|空|-|物理文件路径（file:/path/to/config.py）或Python可识别的模块路径（python:path.to.config.module）。| -|`--workspace `|可选|指定工作目录路径。|String|$HOME/mindinsight|-|-| -|`--port `|可选|指定Web可视化服务端口。|Integer|8080|1~65535|-| -|`--url-path-prefix `|可选|指定Web服务URL地址前缀。|String|空|-|URL地址前缀由斜杠(/)分隔成多个部分，各部分支持由字母/数字/下划线/连字符/点号组成的字符串，但不能是单点号(.)或双点号(..)。| -|`--reload-interval `|可选|指定加载数据的时间间隔（单位：秒）。|Integer|3|-|设置为0时表示只加载一次数据。| -|`--summary-base-dir `|可选|指定加载训练日志数据的根目录路径。|String|./|-|MindInsight将遍历此路径下的直属子目录。若某个直属子目录包含日志文件，则该子目录被识别为日志文件目录，若根目录包含日志文件，则根目录被识别为日志文件目录。| +|`--config {CONFIG}`|可选|指定配置文件或配置模块。|String|空|-|物理文件路径（file:/path/to/config.py）或Python可识别的模块路径（python:path.to.config.module）。| +|`--workspace {WORKSPACE}`|可选|指定工作目录路径。|String|$HOME/mindinsight|-|-| +|`--port {PORT}`|可选|指定Web可视化服务端口。|Integer|8080|1~65535|-| +|`--url-path-prefix {URL_PATH_PREFIX}`|可选|指定Web服务URL地址前缀。|String|空|-|URL地址前缀由斜杠(/)分隔成多个部分，各部分支持由字母/数字/下划线/连字符/点号组成的字符串，但不能是单点号(.)或双点号(..)。| +|`--reload-interval {RELOAD_INTERVAL}`|可选|指定加载数据的时间间隔（单位：秒）。|Integer|3|-|设置为0时表示只加载一次数据。| +|`--summary-base-dir {SUMMARY_BASE_DIR}`|可选|指定加载训练日志数据的根目录路径。|String|./|-|MindInsight将遍历此路径下的直属子目录。若某个直属子目录包含日志文件，则该子目录被识别为日志文件目录，若根目录包含日志文件，则根目录被识别为日志文件目录。| +|`--enable-debugger {ENABLE_DEBUGGER}`|可选|是否开启Debugger功能|Boolean|False|True/False|-| +|`--debugger-port {DEBUGGER_PORT}`|可选|指定Debugger Server服务端口。|Integer|50051|1~65535|-| > 服务启动时，命令行参数值将被保存为进程的环境变量，并以 `MINDINSIGHT_` 开头作为标识，如 `MINDINSIGHT_CONFIG`，`MINDINSIGHT_WORKSPACE`，`MINDINSIGHT_PORT` 等。 diff --git a/tutorials/source_zh_cn/advanced_use/visualization_tutorials.rst b/tutorials/source_zh_cn/advanced_use/visualization_tutorials.rst index aeebef6943..65f7cb553c 100644 --- a/tutorials/source_zh_cn/advanced_use/visualization_tutorials.rst +++ b/tutorials/source_zh_cn/advanced_use/visualization_tutorials.rst @@ -9,4 +9,5 @@ lineage_and_scalars_comparision performance_profiling performance_profiling_gpu + debugger mindinsight_commands -- Gitee From c0664b74ef66d60f395ee0fbe7140e5ad6951801 Mon Sep 17 00:00:00 2001 From: liuchongming Date: Wed, 16 Sep 2020 17:03:29 +0800 Subject: [PATCH 057/156] Fix mindconverter cli. --- .../model_scripts_transformation.md | 50 +++++++++++-------- 1 file changed, 28 insertions(+), 22 deletions(-) diff --git a/tutorials/source_zh_cn/advanced_use/model_scripts_transformation.md b/tutorials/source_zh_cn/advanced_use/model_scripts_transformation.md index ce721759cd..41a160bd75 100644 --- a/tutorials/source_zh_cn/advanced_use/model_scripts_transformation.md +++ b/tutorials/source_zh_cn/advanced_use/model_scripts_transformation.md @@ -34,29 +34,35 @@ MindConverter是一款将PyTorch模型脚本转换至MindSpore的脚本迁移工 MindConverter提供命令行（Command-line interface, CLI）的使用方式，命令如下。 ```bash -mindconverter [-h] [--version] --in_file IN_FILE [--output OUTPUT] - [--report REPORT] +usage: mindconverter [-h] [--version] [--in_file IN_FILE] + [--model_file MODEL_FILE] [--shape SHAPE] + [--output OUTPUT] [--report REPORT] + [--project_path PROJECT_PATH] optional arguments: - -h, --help Show this help message and exit. - --version Show program's version number and exit. - --in_file IN_FILE Specify path for script file to use AST schema to - do script conversation. - --model_file MODEL_FILE PyTorch .pth model file path to use graph - based schema to do script generation. When - `--in_file` and `--model_path` are both provided, - use AST schema as default. - --shape SHAPE Optional, excepted input tensor shape of - `--model_file`. It's required when use graph based - schema. - --output OUTPUT Optional, specify path for converted script file - directory. Default is output directory in the - current working directory. - --report REPORT Optional, specify report directory. Default is - the current working directorys. - --project_path PROJECT Optional, pytorch scripts project path. If pytorch - project is not in PYTHONPATH, please assign - `--project_path` when use graph based schema. + -h, --help show this help message and exit + --version show program version number and exit + --in_file IN_FILE Specify path for script file to use AST schema to do + script conversation. + --model_file MODEL_FILE + PyTorch .pth model file path to use graph based schema + to do script generation. When `--in_file` and + `--model_file` are both provided, use AST schema as + default. + --shape SHAPE Optional, excepted input tensor shape of + `--model_file`. It is required when use graph based + schema. Usage: --shape 3,244,244 + --output OUTPUT Optional, specify path for converted script file + directory. Default output directory is `output` folder + in the current working directory. + --report REPORT Optional, specify report directory. Default is + converted script directory. + --project_path PROJECT_PATH + Optional, PyTorch scripts project path. If PyTorch + project is not in PYTHONPATH, please assign + `--project_path` when use graph based schema. Usage: + --project_path ~/script_file/ + ``` **MindConverter提供两种模型脚本迁移方案：** @@ -149,5 +155,5 @@ mindconverter --model_file /home/user/model.pth --shape 3,224,224 \ ## 注意事项 -1. PyTorch不作为MindInsight明确声明的依赖库，但若想使用基于图结构的脚本生成工具，需要用户手动安装与生成PyTorch模型版本一致的PyTorch库； +1. PyTorch不作为MindInsight明确声明的依赖库。若想使用基于图结构的脚本生成工具，需要用户手动安装与生成PyTorch模型版本一致的PyTorch库（MindConverter推荐使用PyTorch 1.4.0或PyTorch 1.6.0进行脚本生成）； 2. 脚本转换工具本质上为算子驱动，对于MindConverter未维护的PyTorch或ONNX算子与MindSpore算子映射，将会出现相应的算子无法转换的问题，对于该类算子，用户可手动修改，或基于MindConverter实现映射关系，向MindInsight仓库贡献。 -- Gitee From cce1c2ac0e478e9ee4bfb2c4bc567736ab22f9c0 Mon Sep 17 00:00:00 2001 From: lvmingfu <630944715@qq.com> Date: Wed, 16 Sep 2020 16:51:47 +0800 Subject: [PATCH 058/156] modify network_list.md and pictures of linear_regression.md --- docs/source_en/network_list.md | 12 ++++----- docs/source_zh_cn/network_list.md | 12 ++++----- tutorials/notebook/linear_regression.ipynb | 2 +- tutorials/source_zh_cn/index.rst | 2 +- .../quick_start/images/linear_regression.gif | Bin 1181553 -> 163898 bytes .../linear_regression_eval_datasets.png | Bin 9694 -> 10314 bytes .../images/model_net_and_eval_datasets.png | Bin 9138 -> 10854 bytes .../quick_start/linear_regression.md | 24 ++++++++---------- 8 files changed, 24 insertions(+), 28 deletions(-) diff --git a/docs/source_en/network_list.md b/docs/source_en/network_list.md index 897111be50..e38373c26c 100644 --- a/docs/source_en/network_list.md +++ b/docs/source_en/network_list.md @@ -27,12 +27,12 @@ | Computer Vision (CV) | Image Classification | [InceptionV3](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/inceptionv3/src/inception_v3.py) | Supported | Doing | Doing | Computer Vision (CV) | Mobile Image Classification
    Image Classification
    Semantic Tegmentation | [MobileNetV2](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/mobilenetv2/src/mobilenetV2.py) | Supported | Supported | Doing | Computer Vision (CV) | Mobile Image Classification
    Image Classification
    Semantic Tegmentation | [MobileNetV3](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/mobilenetv3/src/mobilenetV3.py) | Doing | Supported | Doing -|Computer Vision (CV) | Targets Detection | [SSD](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/ssd/src/ssd.py) | Supported |Doing | Doing -| Computer Vision (CV) | Targets Detection | [YoloV3-ResNet18](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/yolov3_resnet18/src/yolov3.py) | Supported | Doing | Doing -| Computer Vision (CV) | Targets Detection | [YoloV3-DarkNet53](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/yolov3_darknet53/src/yolo.py) | Supported | Doing | Doing -| Computer Vision (CV) | Targets Detection | [FasterRCNN](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/faster_rcnn/src/FasterRcnn/faster_rcnn_r50.py) | Supported | Doing | Doing +|Computer Vision (CV) | Object Detection | [SSD](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/ssd/src/ssd.py) | Supported |Doing | Doing +| Computer Vision (CV) | Object Detection | [YoloV3-ResNet18](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/yolov3_resnet18/src/yolov3.py) | Supported | Doing | Doing +| Computer Vision (CV) | Object Detection | [YoloV3-DarkNet53](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/yolov3_darknet53/src/yolo.py) | Supported | Doing | Doing +| Computer Vision (CV) | Object Detection | [FasterRCNN](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/faster_rcnn/src/FasterRcnn/faster_rcnn_r50.py) | Supported | Doing | Doing +| Computer Vision（CV） | Object Detection | [WarpCTC](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/warpctc/src/warpctc.py) | Doing | Supported | Doing | Computer Vision (CV) | Semantic Segmentation | [DeeplabV3](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/deeplabv3/src/deeplabv3.py) | Supported | Doing | Doing -| Computer Vision（CV） | Targets Detection | [WarpCTC](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/warpctc/src/warpctc.py) | Doing | Supported | Doing | Natural Language Processing (NLP) | Natural Language Understanding | [BERT](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/bert/src/bert_model.py) | Supported | Doing | Doing | Natural Language Processing (NLP) | Natural Language Understanding | [Transformer](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/transformer/src/transformer_model.py) | Supported | Doing | Doing | Natural Language Processing (NLP) | Natural Language Understanding | [SentimentNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/lstm/src/lstm.py) | Doing | Supported | Supported @@ -54,7 +54,7 @@ |Computer Vision (CV) | Image Classification| [LeNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/lenet/src/lenet.py)| MNIST | | | ✓ | [Download](http://download.mindspore.cn/model_zoo/official/cv/lenet/lenet_ascend_0.5.0_mnist_official_classification_20200716.tar.gz) |Computer Vision (CV) | Image Classification| [VGG16](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/vgg16/src/vgg.py)| CIFAR-10 | | | ✓ | [Download](http://download.mindspore.cn/model_zoo/official/cv/vgg/vgg16_ascend_0.5.0_cifar10_official_classification_20200715.tar.gz) |Computer Vision (CV) | Image Classification| [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py) | CIFAR-10| | | ✓ |[Download](http://download.mindspore.cn/model_zoo/official/cv/resnet/resnet50_v1.5_ascend_0.3.0_cifar10_official_classification_20200718.tar.gz) -|Computer Vision (CV) | Targets Detection| [YoloV3-DarkNet53](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/yolov3_darknet53/src/yolo.py) | COCO 2014| | | ✓ | [Download](http://download.mindspore.cn/model_zoo/official/cv/yolo/yolov3_darknet53_ascend_0.5.0_coco2014_official_object_detection_20200717.tar.gz) +|Computer Vision (CV) | Object Detection| [YoloV3-DarkNet53](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/yolov3_darknet53/src/yolo.py) | COCO 2014| | | ✓ | [Download](http://download.mindspore.cn/model_zoo/official/cv/yolo/yolov3_darknet53_ascend_0.5.0_coco2014_official_object_detection_20200717.tar.gz) | Natural Language Processing (NLP) | Natural Language Understanding| [BERT_Base](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/bert/src/bert_model.py) | zhwiki | | | ✓ | [Download](http://download.mindspore.cn/model_zoo/official/nlp/bert/bert_base_ascend_0.5.0_cn-wiki_official_nlp_20200720.tar.gz) | Natural Language Processing (NLP) | Natural Language Understanding| [BERT_NEZHA](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/bert/src/bert_model.py)| zhwiki| | | ✓ | [Download](http://download.mindspore.cn/model_zoo/official/nlp/bert/bert_nezha_ascend_0.5.0_cn-wiki_official_nlp_20200720.tar.gz) | Natural Language Processing (NLP) | Natural Language Understanding| [Transformer](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/transformer/src/transformer_model.py)| WMT English-German| | | ✓ | [Download](http://download.mindspore.cn/model_zoo/official/nlp/transformer/transformer_ascend_0.5.0_wmtende_official_machine_translation_20200713.tar.gz) diff --git a/docs/source_zh_cn/network_list.md b/docs/source_zh_cn/network_list.md index 351a5223c2..36880bbda1 100644 --- a/docs/source_zh_cn/network_list.md +++ b/docs/source_zh_cn/network_list.md @@ -27,12 +27,12 @@ | 计算机视觉（CV） | 图像分类（Image Classification） | [InceptionV3](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/inceptionv3/src/inception_v3.py) | Supported | Doing | Doing | 计算机视觉（CV） | 移动端图像分类（Mobile Image Classification）
    目标检测（Image Classification）
    语义分割（Semantic Tegmentation） | [MobileNetV2](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/mobilenetv2/src/mobilenetV2.py) | Supported | Supported | Doing | 计算机视觉（CV） | 移动端图像分类（Mobile Image Classification）
    目标检测（Image Classification）
    语义分割（Semantic Tegmentation） | [MobileNetV3](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/mobilenetv3/src/mobilenetV3.py) | Doing | Supported | Doing -|计算机视觉（CV） | 目标检测（Targets Detection） | [SSD](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/ssd/src/ssd.py) | Supported |Doing | Doing -| 计算机视觉（CV） | 目标检测（Targets Detection） | [YoloV3-ResNet18](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/yolov3_resnet18/src/yolov3.py) | Supported | Doing | Doing -| 计算机视觉（CV） | 目标检测（Targets Detection） | [YoloV3-DarkNet53](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/yolov3_darknet53/src/yolo.py) | Supported | Doing | Doing -| 计算机视觉（CV） | 目标检测（Targets Detection） | [FasterRCNN](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/faster_rcnn/src/FasterRcnn/faster_rcnn_r50.py) | Supported | Doing | Doing +|计算机视觉（CV） | 目标检测（Object Detection） | [SSD](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/ssd/src/ssd.py) | Supported |Doing | Doing +| 计算机视觉（CV） | 目标检测（Object Detection） | [YoloV3-ResNet18](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/yolov3_resnet18/src/yolov3.py) | Supported | Doing | Doing +| 计算机视觉（CV） | 目标检测（Object Detection） | [YoloV3-DarkNet53](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/yolov3_darknet53/src/yolo.py) | Supported | Doing | Doing +| 计算机视觉（CV） | 目标检测（Object Detection） | [FasterRCNN](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/faster_rcnn/src/FasterRcnn/faster_rcnn_r50.py) | Supported | Doing | Doing +| 计算机视觉（CV） | 目标检测（Object Detection） | [WarpCTC](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/warpctc/src/warpctc.py) | Doing | Supported | Doing | 计算机视觉（CV） | 语义分割（Semantic Segmentation） | [DeeplabV3](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/deeplabv3/src/deeplabv3.py) | Supported | Doing | Doing -| 计算机视觉（CV） | 目标检测（Targets Detection） | [WarpCTC](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/warpctc/src/warpctc.py) | Doing | Supported | Doing | 自然语言处理（NLP） | 自然语言理解（Natural Language Understanding） | [BERT](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/bert/src/bert_model.py) | Supported | Doing | Doing | 自然语言处理（NLP） | 自然语言理解（Natural Language Understanding） | [Transformer](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/transformer/src/transformer_model.py) | Supported | Doing | Doing | 自然语言处理（NLP） | 自然语言理解（Natural Language Understanding） | [SentimentNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/lstm/src/lstm.py) | Doing | Supported | Supported @@ -54,7 +54,7 @@ |计算机视觉（CV） | 图像分类（Image Classification）| [LeNet](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/lenet/src/lenet.py)| MNIST | | | ✓ | [下载](http://download.mindspore.cn/model_zoo/official/cv/lenet/lenet_ascend_0.5.0_mnist_official_classification_20200716.tar.gz) |计算机视觉（CV） | 图像分类（Image Classification）| [VGG16](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/vgg16/src/vgg.py)|CIFAR-10 | | | ✓ | [下载](http://download.mindspore.cn/model_zoo/official/cv/vgg/vgg16_ascend_0.5.0_cifar10_official_classification_20200715.tar.gz) |计算机视觉（CV） | 图像分类（Image Classification）| [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/cv/resnet/src/resnet.py) |CIFAR-10 | | | ✓ |[下载](http://download.mindspore.cn/model_zoo/official/cv/resnet/resnet50_v1.5_ascend_0.3.0_cifar10_official_classification_20200718.tar.gz) -|计算机视觉（CV） | 目标检测（Targets Detection）| [YoloV3-DarkNet53](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/yolov3_darknet53) |COCO 2014 | | | ✓ | [下载](http://download.mindspore.cn/model_zoo/official/cv/yolo/yolov3_darknet53_ascend_0.5.0_coco2014_official_object_detection_20200717.tar.gz) +|计算机视觉（CV） | 目标检测（Object Detection）| [YoloV3-DarkNet53](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/yolov3_darknet53) |COCO 2014 | | | ✓ | [下载](http://download.mindspore.cn/model_zoo/official/cv/yolo/yolov3_darknet53_ascend_0.5.0_coco2014_official_object_detection_20200717.tar.gz) | 自然语言处理（NLP） | 自然语言理解（Natural Language Understanding）| [BERT_Base](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/bert/src/bert_model.py) | zhwiki | | | ✓ | [下载](http://download.mindspore.cn/model_zoo/official/nlp/bert/bert_base_ascend_0.5.0_cn-wiki_official_nlp_20200720.tar.gz) | 自然语言处理（NLP） | 自然语言理解（Natural Language Understanding）| [BERT_NEZHA](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/bert/src/bert_model.py)| zhwiki | | | ✓ | [下载](http://download.mindspore.cn/model_zoo/official/nlp/bert/bert_nezha_ascend_0.5.0_cn-wiki_official_nlp_20200720.tar.gz) | 自然语言处理（NLP） | 自然语言理解（Natural Language Understanding）| [Transformer](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/official/nlp/transformer/src/transformer_model.py)|WMT English-German | | | ✓ | [下载](http://download.mindspore.cn/model_zoo/official/nlp/transformer/transformer_ascend_0.5.0_wmtende_official_machine_translation_20200713.tar.gz) diff --git a/tutorials/notebook/linear_regression.ipynb b/tutorials/notebook/linear_regression.ipynb index 6e2549f7c3..4676c3656f 100644 --- a/tutorials/notebook/linear_regression.ipynb +++ b/tutorials/notebook/linear_regression.ipynb @@ -228,7 +228,7 @@ "source": [ "## 定义训练网络\n", "\n", - "在MindSpore中使用`nn.Dense`生成单个数据输入，单个数据暑促和的线性模型为：\n", + "在MindSpore中使用`nn.Dense`生成单个数据输入，单个数据输出的线性函数模型：\n", "\n", "$$f(x)=wx+b\\tag{1}$$\n", "\n", diff --git a/tutorials/source_zh_cn/index.rst b/tutorials/source_zh_cn/index.rst 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-MindSpore版本：CPU-Windows，CPU-Linux +## 环境准备 设置MindSpore运行配置 -第三方支持包：`matplotlib`，未安装此包的，可使用命令`pip install matplotlib`预先安装。 - ```python from mindspore import context @@ -60,6 +56,8 @@ context.set_context(mode=context.GRAPH_MODE, device_target="CPU") `device_target`：设置MindSpore的训练硬件为CPU。 +> 本教程代码依赖`matplotlib`第三方支持包，可使用命令`pip install matplotlib`安装。 + ## 生成数据集 ### 定义数据集生成函数 @@ -151,7 +149,7 @@ print("The y label value shape:", dict_datasets["label"].shape) ## 定义训练网络 -在MindSpore中使用`nn.Dense`生成单个数据输入，单个数据暑促和的线性模型为： +在MindSpore中使用`nn.Dense`生成单个数据输入，单个数据输出的线性函数模型： $$f(x)=wx+b\tag{1}$$ @@ -253,7 +251,7 @@ $$w_{t}=w_{t-1}-\alpha\frac{\partial{J(w_{t-1})}}{\partial{w}}\tag{3}$$ - $w_{t}$为迭代后的权重值。 - $w_{t-1}$为迭代前的权重值。 - $\alpha$为学习率。 -- $\frac{\partial{J(w_{t-1})}}{\partial{w}}$为损失函数对权重$w_{t-1}$的微分。 +- $\frac{\partial{J(w_{t-1}\ )}}{\partial{w}}$为损失函数对权重$w_{t-1}$的微分。 函数中所有的权重值更新完成后，将值传入到模型函数中，这个过程就是反向传播过程，实现此过程需要使用MindSpore中的优化器函数，如下： @@ -303,9 +301,7 @@ def plot_model_and_datasets(net, eval_data): ### 定义回调函数 -MindSpore提供的工具，可对模型训练过程进行自定义控制，这里在`step_end`中调用可视化函数，展示拟合过程。更多的使用可参考[官网说明](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/customized_debugging_information.html#callback) - -- `display.clear_output`：清除打印内容，实现动态拟合效果。 +MindSpore提供的工具，可对模型训练过程进行自定义控制，这里在`step_end`中调用可视化函数，展示拟合过程。更多的使用可参考[官网说明]()。 ```python @@ -326,10 +322,10 @@ class ImageShowCallback(Callback): 完成以上过程后，可以使用训练数`ds_train`对模型训练，这里调用`model.train`进行，其中参数解释： -- `epoch`：训练迭代的整个数据集的次数，int类型。 +- `epoch`：训练迭代的整个数据集的次数。 - `ds_train`：训练数据集。 - `callbacks`：训练过程中需要调用的回调函数。 -- `dataset_sink_model`：数据集下沉模式，bool类型，支持Ascend，GPU计算平台，本例为CPU计算平台设置为False。 +- `dataset_sink_model`：数据集下沉模式，支持Ascend、GPU计算平台，本例为CPU计算平台设置为False。 ```python -- Gitee From c806bc9415342445f10f6c711770e3d488f6fd20 Mon Sep 17 00:00:00 2001 From: Xiao Tianci Date: Sat, 12 Sep 2020 19:34:37 +0800 Subject: [PATCH 059/156] modify content and comment of docs --- api/source_zh_cn/index.rst | 1 + .../programming_guide/augmentation.md | 162 ++--- .../programming_guide/auto_augmentation.md | 157 +++-- .../programming_guide/data_pipeline.rst | 13 + .../programming_guide/dataset_conversion.md | 663 +++++++----------- .../programming_guide/dataset_loading.md | 251 +++---- .../programming_guide/images/batch.png | Bin 37732 -> 12224 bytes .../programming_guide/images/concat.png | Bin 24282 -> 13809 bytes .../images/ctrans_invert.png | Bin 146960 -> 128890 bytes .../images/ctrans_resize.png | Bin 43654 -> 19400 bytes .../programming_guide/images/map.png | Bin 21621 -> 14962 bytes .../images/pytrans_compose.png | Bin 64995 -> 42922 bytes .../programming_guide/images/randomcrop.png | Bin 51488 -> 92040 bytes .../images/randomhorizontalflip.png | Bin 73862 -> 130799 bytes .../programming_guide/images/repeat.png | Bin 38083 -> 11920 bytes .../programming_guide/images/shuffle.png | Bin 37331 -> 12301 bytes .../programming_guide/images/tranform_bad.png | Bin 27679 -> 10375 bytes .../images/tranform_good_1.png | Bin 24757 -> 9345 bytes .../images/tranform_good_2.png | Bin 24291 -> 9300 bytes .../images/tranform_good_3.png | Bin 23743 -> 9246 bytes .../images/tranform_pipeline.png | Bin 62195 -> 14272 bytes .../programming_guide/images/zip.png | Bin 25299 -> 11011 bytes .../programming_guide/pipeline.md | 125 +--- api/source_zh_cn/programming_guide/sampler.md | 163 +---- .../programming_guide/tokenizer.md | 230 ++---- tutorials/notebook/loading_dataset.ipynb | 8 +- ..._the_performance_of_data_preparation.ipynb | 2 +- .../data_processing_and_augmentation.md | 14 +- .../advanced_use/auto_augmentation.md | 4 +- .../advanced_use/auto_data_acceleration.rst | 1 - tutorials/source_zh_cn/advanced_use/cache.md | 237 ------- .../advanced_use/dataset_conversion.md | 24 +- .../distributed_training_ascend.md | 8 +- .../images/data_conversion_concept.png | Bin 68261 -> 16993 bytes .../advanced_use/images/mindrecord.png | Bin 76130 -> 16840 bytes tutorials/source_zh_cn/use/image_loading.md | 22 +- tutorials/source_zh_cn/use/text_loading.md | 21 +- 37 files changed, 681 insertions(+), 1425 deletions(-) create mode 100644 api/source_zh_cn/programming_guide/data_pipeline.rst delete mode 100644 tutorials/source_zh_cn/advanced_use/cache.md diff --git a/api/source_zh_cn/index.rst b/api/source_zh_cn/index.rst index 45c73bb95d..b713fb3dcf 100644 --- a/api/source_zh_cn/index.rst +++ b/api/source_zh_cn/index.rst @@ -13,6 +13,7 @@ MindSpore API programming_guide/api_structure programming_guide/data_type programming_guide/compute_component + programming_guide/data_pipeline programming_guide/execution_management programming_guide/auto_parallel programming_guide/advanced_use diff --git a/api/source_zh_cn/programming_guide/augmentation.md b/api/source_zh_cn/programming_guide/augmentation.md index 6d20f22c24..7d5392c8c5 100644 --- a/api/source_zh_cn/programming_guide/augmentation.md +++ b/api/source_zh_cn/programming_guide/augmentation.md @@ -21,15 +21,14 @@ 在计算机视觉任务中，数据量过小或是样本场景单一等问题都会影响模型的训练效果，用户可以通过数据增强操作对图像进行预处理，从而提升模型的泛化性。 -MindSpore提供了c_transforms模块和py_transforms模块供用户进行数据增强操作，用户也可以自定义函数或者算子进行数据增强。 - -MindSpore目前支持的常用数据增强算子如下表所示，更多数据增强算子参见[API文档](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.dataset.vision.html)。 +MindSpore提供了`c_transforms`模块和`py_transforms`模块供用户进行数据增强操作，用户也可以自定义函数或者算子进行数据增强。 | 模块 | 实现 | 说明 | | ---- | ---- | ---- | | c_transforms | 基于C++的OpenCV实现 | 具有较高的性能。 | -| py_transforms | 基于Python的PIL实现 | 该模块提供了多种图像增强功能，并提供了PIL Image和numpy数组之间的传输方法。| +| py_transforms | 基于Python的PIL实现 | 该模块提供了多种图像增强功能，并提供了PIL Image和NumPy数组之间的传输方法。| +MindSpore目前支持的常用数据增强算子如下表所示，更多数据增强算子参见[API文档](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.dataset.vision.html)。 | 模块 | 算子 | 说明 | | ---- | ---- | ---- | @@ -44,42 +43,34 @@ MindSpore目前支持的常用数据增强算子如下表所示，更多数据 ## c_transforms -下面将简要介绍几种常用的c_transforms模块数据增强算子的使用方法，更多的c_transforms模块数据增强算子参见[API文档](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.dataset.vision.html#module-mindspore.dataset.vision.c_transforms)。 +下面将简要介绍几种常用的`c_transforms`模块数据增强算子的使用方法。 ### RandomCrop 对输入图像进行在随机位置的裁剪。 **参数说明：** -- *size: 裁剪图像的尺寸。* -- *padding: 填充的像素数量。* -- *pad_if_needed: 原图小于裁剪尺寸时，是否需要填充。* -- *fill_value: 在常量填充模式时使用的填充值。* -- *padding_mode: 填充模式。* +- `size`：裁剪图像的尺寸。 +- `padding`：填充的像素数量。 +- `pad_if_needed`：原图小于裁剪尺寸时，是否需要填充。 +- `fill_value`：在常量填充模式时使用的填充值。 +- `padding_mode`：填充模式。 -```python -# 对输入图像进行在随机位置的裁剪 +下面的样例首先使用顺序采样器加载CIFAR-10数据集，然后对已加载的图片进行长宽均为10的随机裁剪，最后输出裁剪前后的图片形状及对应标签，并对图片进行了展示。 +```python import matplotlib.pyplot as plt import mindspore.dataset as ds import mindspore.dataset.vision.c_transforms as c_trans -# 下载Cifar10数据集，将其解压到Cifar10Data目录 DATA_DIR = "../data/dataset/testCifar10Data2" -# 指定一个顺序采样器SequentialSampler，按照读取顺序获取3个样本数据 sampler = ds.SequentialSampler(num_samples=3) - -# 使用Cifar10Dataset读取数据集，指定sampler为上述采样器 dataset1 = ds.Cifar10Dataset(DATA_DIR, sampler=sampler) -# 创建一个随机裁剪算子，裁剪后的长宽分为10个像素 random_crop = c_trans.RandomCrop([10, 10]) - -# 使用map算子将其作用到数据管道的数据集中 dataset2 = dataset1.map(operations=random_crop, input_columns=["image"]) -# 启动数据管道，输出3个样本数据 image_list1, label_list1 = [], [] image_list2, label_list2 = [], [] for data1, data2 in zip(dataset1.create_dict_iterator(), dataset2.create_dict_iterator()): @@ -89,9 +80,8 @@ for data1, data2 in zip(dataset1.create_dict_iterator(), dataset2.create_dict_it image_list2.append(data2['image']) label_list2.append(data2['label']) print("Cropped image Shape:", data2['image'].shape, ", Cropped label:", data2['label']) - print("") + print("------") -# 将原图与裁剪后的图可视化 num_samples = len(image_list1) + len(image_list2) for i in range(num_samples): if i < len(image_list1): @@ -105,17 +95,22 @@ for i in range(num_samples): plt.show() ``` +输出结果如下： + ``` Source image Shape : (32, 32, 3) , Source label : 6 Cropped image Shape: (10, 10, 3) , Cropped label: 6 - +------ Source image Shape : (32, 32, 3) , Source label : 9 Cropped image Shape: (10, 10, 3) , Cropped label: 9 - +------ Source image Shape : (32, 32, 3) , Source label : 9 Cropped image Shape: (10, 10, 3) , Cropped label: 9 +------ ``` +图片展示如下： + ![randomcrop](./images/randomcrop.png) ### RandomHorizontalFlip @@ -123,34 +118,25 @@ Cropped image Shape: (10, 10, 3) , Cropped label: 9 对输入图像进行随机水平翻转。 **参数说明：** -- *prob: 单张图片发生翻转的概率。* +- `prob`: 单张图片发生翻转的概率。 -```python -# 对输入图像进行随机水平翻转 +下面的样例首先使用随机采样器加载CIFAR-10数据集，然后对已加载的图片进行概率为0.8的随机水平翻转，最后输出翻转前后的图片形状及对应标签，并对图片进行了展示。 +```python import matplotlib.pyplot as plt import mindspore.dataset as ds import mindspore.dataset.vision.c_transforms as c_trans -# 设置全局随机种子 ds.config.set_seed(6) -# 下载Cifar10数据集，将其解压到Cifar10Data目录 DATA_DIR = "../data/dataset/testCifar10Data2" -# 指定一个随机采样器RandomSampler，按照读取顺序获取4个样本数据 sampler = ds.RandomSampler(num_samples=4) - -# 使用Cifar10Dataset读取数据集，指定sampler为上述采样器 dataset1 = ds.Cifar10Dataset(DATA_DIR, sampler=sampler) -# 创建一个随机翻转算子，设置翻转概率为0.8 random_horizontal_flip = c_trans.RandomHorizontalFlip(prob=0.8) - -# 使用map算子将其作用到数据管道的数据集中 dataset2 = dataset1.map(operations=random_horizontal_flip, input_columns=["image"]) -# 启动数据管道，输出4个样本数据 image_list1, label_list1 = [], [] image_list2, label_list2 = [], [] for data1, data2 in zip(dataset1.create_dict_iterator(), dataset2.create_dict_iterator()): @@ -160,9 +146,8 @@ for data1, data2 in zip(dataset1.create_dict_iterator(), dataset2.create_dict_it image_list2.append(data2['image']) label_list2.append(data2['label']) print("Flipped image Shape:", data2['image'].shape, ", Flipped label:", data2['label']) - print("") + print("------") -# 将原图与裁剪后的图可视化 num_samples = len(image_list1) + len(image_list2) for i in range(num_samples): if i < len(image_list1): @@ -176,20 +161,25 @@ for i in range(num_samples): plt.show() ``` +输出结果如下： + ``` Source image Shape : (32, 32, 3) , Source label : 3 Flipped image Shape: (32, 32, 3) , Flipped label: 3 - +------ Source image Shape : (32, 32, 3) , Source label : 6 Flipped image Shape: (32, 32, 3) , Flipped label: 6 - +------ Source image Shape : (32, 32, 3) , Source label : 6 Flipped image Shape: (32, 32, 3) , Flipped label: 6 - +------ Source image Shape : (32, 32, 3) , Source label : 9 Flipped image Shape: (32, 32, 3) , Flipped label: 9 +------ ``` +图片展示如下： + ![randomhorizontalflip](./images/randomhorizontalflip.png) ### Resize @@ -197,29 +187,23 @@ Flipped image Shape: (32, 32, 3) , Flipped label: 9 对输入图像进行缩放。 **参数说明：** -- *self: 缩放的目标大小。* -- *interpolation: 缩放时采用的插值方式。* +- `self`：缩放的目标大小。 +- `interpolation`：缩放时采用的插值方式。 -```python -# 对输入图像进行指定大小缩放 +下面的样例首先加载MNIST数据集，然后将已加载的图片缩放至(101, 101)大小，最后输出缩放前后的图片形状及对应标签，并对图片进行了展示。 +```python import matplotlib.pyplot as plt import mindspore.dataset as ds import mindspore.dataset.vision.c_transforms as c_trans -# 下载MNIST数据集，将其解压到MnistData目录 DATA_DIR = "../data/dataset/testMnistData2" -# 使用MnistDataset读取数据集 dataset1 = ds.MnistDataset(DATA_DIR, num_samples=4, shuffle=False) -# 创建一个缩放算子，将MNIST的图片从(28, 28)缩放到(101, 101) resize = c_trans.Resize(size=[101, 101]) - -# 使用map算子将其作用到数据管道的数据集中 dataset2 = dataset1.map(operations=resize, input_columns=["image"]) -# 启动数据管道 image_list1, label_list1 = [], [] image_list2, label_list2 = [], [] for data1, data2 in zip(dataset1.create_dict_iterator(), dataset2.create_dict_iterator()): @@ -229,9 +213,8 @@ for data1, data2 in zip(dataset1.create_dict_iterator(), dataset2.create_dict_it image_list2.append(data2['image']) label_list2.append(data2['label']) print("Flipped image Shape:", data2['image'].shape, ", Flipped label:", data2['label']) - print("") + print("------") -# 将原图与裁剪后的图可视化 num_samples = len(image_list1) + len(image_list2) for i in range(num_samples): if i < len(image_list1): @@ -245,52 +228,48 @@ for i in range(num_samples): plt.show() ``` +输出结果如下： + ``` Source image Shape : (28, 28, 1) , Source label : 5 Flipped image Shape: (101, 101, 1) , Flipped label: 5 - +------ Source image Shape : (28, 28, 1) , Source label : 0 Flipped image Shape: (101, 101, 1) , Flipped label: 0 - +------ Source image Shape : (28, 28, 1) , Source label : 4 Flipped image Shape: (101, 101, 1) , Flipped label: 4 - +------ Source image Shape : (28, 28, 1) , Source label : 1 Flipped image Shape: (101, 101, 1) , Flipped label: 1 +------ ``` +图片展示如下： + ![ctrans_resize](./images/ctrans_resize.png) ### Invert 对输入图像进行反相处理。 -```python -# 对输入图像进行反相处理 +下面的样例首先加载CIFAR-10数据集，然后同时定义缩放和反相操作并作用于已加载的图片，最后输出缩放与反相前后的图片形状及对应标签，并对图片进行了展示。 +```python import matplotlib.pyplot as plt import mindspore.dataset as ds import mindspore.dataset.vision.c_transforms as c_trans -# 设置全局随机种子 ds.config.set_seed(8) -# 下载Cifar10数据集，将其解压到Cifar10Data目录 DATA_DIR = "../data/dataset/testCifar10Data2" -# 使用Cifar10Dataset读取数据集 dataset1 = ds.Cifar10Dataset(DATA_DIR, num_samples=4, shuffle=True) -# 创建一个缩放算子，将图片缩放到(101, 101) resize = c_trans.Resize(size=[101, 101]) - -# 创建一个反相算子 invert = c_trans.Invert() - -# 使用map算子将其作用到数据管道的数据集中(两个算子按顺序起作用) dataset2 = dataset1.map(operations=[resize, invert], input_columns=["image"]) -# 启动数据管道 image_list1, label_list1 = [], [] image_list2, label_list2 = [], [] for data1, data2 in zip(dataset1.create_dict_iterator(), dataset2.create_dict_iterator()): @@ -300,9 +279,8 @@ for data1, data2 in zip(dataset1.create_dict_iterator(), dataset2.create_dict_it image_list2.append(data2['image']) label_list2.append(data2['label']) print("Flipped image Shape:", data2['image'].shape, ", Flipped label:", data2['label']) - print("") + print("------") -# 将原图与裁剪后的图可视化 num_samples = len(image_list1) + len(image_list2) for i in range(num_samples): if i < len(image_list1): @@ -316,63 +294,63 @@ for i in range(num_samples): plt.show() ``` +输出结果如下： + ``` Source image Shape : (32, 32, 3) , Source label : 4 Flipped image Shape: (32, 32, 3) , Flipped label: 4 - +------ Source image Shape : (32, 32, 3) , Source label : 9 Flipped image Shape: (32, 32, 3) , Flipped label: 9 - +------ Source image Shape : (32, 32, 3) , Source label : 6 Flipped image Shape: (32, 32, 3) , Flipped label: 6 - +------ Source image Shape : (32, 32, 3) , Source label : 5 Flipped image Shape: (32, 32, 3) , Flipped label: 5 +------ ``` +图片展示如下： + ![ctrans_invert](./images/ctrans_invert.png) ## py_transforms -下面将简要介绍几种常用的py_transforms模块数据增强算子的使用方法，更多的py_transforms模块数据增强算子参见[API文档](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.dataset.vision.html#module-mindspore.dataset.vision.py_transforms)。 +下面将简要介绍几种常用的`py_transforms`模块数据增强算子的使用方法。 ### Compose -```python -# 对输入图像进行解码，缩放组合操作 +接收一个`transforms`列表，将列表中的数据增强操作依次作用于数据集图片。 + +下面的样例首先加载一个图片数据集，然后同时定义解码、缩放和数据类型转换操作，并作用于已加载的图片，最后输出处理后的图片形状及对应标签，并对图片进行了展示。 +```python import matplotlib.pyplot as plt import mindspore.dataset as ds import mindspore.dataset.vision.py_transforms as py_trans from mindspore.dataset.transforms.py_transforms import Compose -# 设置全局随机种子 + ds.config.set_seed(8) -# 图像数据集目录 DATA_DIR = "../data/dataset/testPK/data" -# 使用ImageFolderDataset读取数据集，获取5个样本 dataset1 = ds.ImageFolderDataset(DATA_DIR, num_samples=5, shuffle=True) -# 创建一组数据增强算子的集合 transforms_list = [ - py_trans.Decode(), # 解码图像到PIL格式 - py_trans.Resize(size=(200,200)), # 缩放图像到[200, 200]大小 - py_trans.ToTensor() # 将PIL图像转换到Numpy + py_trans.Decode(), + py_trans.Resize(size=(200,200)), + py_trans.ToTensor() ] compose_trans = Compose(transforms_list) - -# 使用map算子将其作用到数据管道的数据集中 dataset2 = dataset1.map(operations=compose_trans, input_columns=["image"]) -# 启动数据管道，输出5个样本数据 image_list, label_list = [], [] for data in dataset2.create_dict_iterator(): image_list.append(data['image']) label_list.append(data['label']) print("Transformed image Shape:", data['image'].shape, ", Transformed label:", data['label']) -# 将原图与裁剪后的图可视化 num_samples = len(image_list) for i in range(num_samples): plt.subplot(1, len(image_list), i + 1) @@ -381,6 +359,8 @@ for i in range(num_samples): plt.show() ``` +输出结果如下： + ``` Transformed image Shape: (3, 200, 200) , Transformed label: 3 Transformed image Shape: (3, 200, 200) , Transformed label: 0 @@ -389,11 +369,13 @@ Transformed image Shape: (3, 200, 200) , Transformed label: 0 Transformed image Shape: (3, 200, 200) , Transformed label: 3 ``` +图片展示如下： + ![pytrans_compose](./images/pytrans_compose.png) ## 使用说明 -请勿混用c_transforms与py_transforms，因为c_transforms是在C++内维护buffer管理，py_transforms是在Python内维护buffer管理，两者混用会降低性能。 +请勿混用`c_transforms`与`py_transforms`，因为`c_transforms`是在C++内维护buffer管理，`py_transforms`是在Python内维护buffer管理，两者混用会降低性能。 ![tranform_pipeline](./images/tranform_pipeline.png) @@ -401,15 +383,15 @@ Transformed image Shape: (3, 200, 200) , Transformed label: 3 **推荐的使用方式：** -- 单独使用py_transform或c_transform +- 单独使用`py_transform`或`c_transform` ![tranform_c_py](./images/tranform_good_1.png) -- 先使用py_transform，再使用c_transform +- 先使用`py_transform`，再使用`c_transform` ![tranform_c_py](./images/tranform_good_2.png) -- 先使用c_transform，再使用py_transform +- 先使用`c_transform`，再使用`py_transform` ![tranform_c_py](./images/tranform_good_3.png) diff --git a/api/source_zh_cn/programming_guide/auto_augmentation.md b/api/source_zh_cn/programming_guide/auto_augmentation.md index ef71980b51..a202c36877 100644 --- a/api/source_zh_cn/programming_guide/auto_augmentation.md +++ b/api/source_zh_cn/programming_guide/auto_augmentation.md @@ -3,110 +3,129 @@ - [自动数据增强](#自动数据增强) - - [基于概率动态调整数据增强策略](#基于概率动态调整数据增强策略) - - [基于训练结果信息动态调整数据增强策略](#基于训练结果信息动态调整数据增强策略) + - [概述](#概述) + - [基于概率的自动数据增强](#基于概率的自动数据增强) + - [RandomApply](#RandomApply) + - [RandomChoice](#RandomChoice) + - [RandomSelectSubpolicy](#RandomSelectSubpolicy) + - [基于回调参数的自动数据增强](#基于回调参数的自动数据增强) -## 基于概率动态调整数据增强策略 +## 概述 -MindSpore提供一系列基于概率的数据增强的API，用户可以对各种图像操作进行随机选择、组合，使数据增强更灵活。 +MindSpore除了可以让用户自定义数据增强的使用，还提供了一种自动数据增强方式，可以基于特定策略自动对图像进行数据增强处理。 -- [`RandomApply(transforms, prob=0.5)`](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.dataset.transforms.html?highlight=randomapply#mindspore.dataset.transforms.c_transforms.RandomApply) -以一定的概率指定`transforms`操作，即可能执行，也可以能不执行；`transform`可以是一个，也可以是一系列。 +自动数据增强主要分为基于概率的自动数据增强和基于回调参数的自动数据增强。 - ```python +## 基于概率的自动数据增强 - rand_apply_list = RandomApply([c_vision.RandomCrop(), c_vision.RandomColorAdjust()]) - ds = ds.map(operations=rand_apply_list) +MindSpore提供了一系列基于概率的自动数据增强API，用户可以对各种数据增强操作进行随机选择与组合，使数据增强更加灵活。 - ``` +关于API的详细说明，可以参见[API文档](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.dataset.transforms.html)。 - 按50%的概率来顺序执行`RandomCrop`和`RandomColorAdjust`操作,否则都不执行。 +### RandomApply -- [`RandomChoice(transforms)`](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.dataset.transforms.html?highlight=randomchoice#mindspore.dataset.transforms.c_transforms.RandomChoice) -从`transfrom`操作中随机选择一个执行。 +API接收一个数据增强操作列表`transforms`，以一定的概率顺序执行列表中各数据增强操作，默认概率为0.5，否则都不执行。 - ```python +在下面的代码示例中，以0.5的概率来顺序执行`RandomCrop`和`RandomColorAdjust`操作，否则都不执行。 - rand_choice = RandomChoice([c_vision.CenterCrop(), c_vision.RandomCrop()]) - ds = ds.map(operations=rand_choice) +```python +import mindspore.dataset.vision.c_transforms as c_vision +from mindspore.dataset.transforms.c_transforms import RandomApply - ``` +rand_apply_list = RandomApply([c_vision.RandomCrop(512), c_vision.RandomColorAdjust()]) +``` - 分别以50%概率来执行`CenterCrop`和`RandomCrop`操作。 +### RandomChoice -- [`RandomSelectSubpolicy(policy)`](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.dataset.transforms.vision.html?highlight=randomselectsubpolicy#mindspore.dataset.transforms.vision.c_transforms.RandomSelectSubpolicy) -用户可以预置策略（Policy），每次随机选择一个子策略（SubPolicy）组合，同一子策略中由若干个顺序执行的图像增强操作组成，每个操作与两个参数关联：1） 执行操作的概率 2）执行操作的幅度； -对于一个batch中的每张图像，随机选择子策略来变换图像。 +API接收一个数据增强操作列表`transforms`，从中随机选择一个数据增强操作执行。 - ```python +在下面的代码示例中，等概率地在`CenterCrop`和`RandomCrop`中选择一个操作执行。 - policy = [ - [(c_vision.RandomRotation((45, 45)), 0.5), (c_vision.RandomVerticalFlip(), 1.0), (c_vision.RandomColorAdjust(), 0.8)], - [(c_vision.RandomRotation((90, 90)), 1), (c_vision.RandomColorAdjust(), 0.2)] - ] - ds = ds.map(operations=c_vision.RandomSelectSubpolicy(policy), input_columns=["image"]) +```python +import mindspore.dataset.vision.c_transforms as c_vision +from mindspore.dataset.transforms.c_transforms import RandomChoice - ``` +rand_choice = RandomChoice([c_vision.CenterCrop(512), c_vision.RandomCrop(512)]) +``` - 示例中包括2条子策略，其中子策略1中包含`RandomRotation`、`RandomVerticalFlip`、`RandomColorAdjust`3个操作，概率分别为0.5、1.0、0.8；子策略2中包含`RandomRotation`和`RandomColorAdjust`，概率分别为1.0、2.0。 +### RandomSelectSubpolicy -## 基于训练结果信息动态调整数据增强策略 +API接收一个预置策略列表，包含一系列子策略组合，每一子策略由若干个顺序执行的数据增强操作及其执行概率组成。 -Mindspore的`sync_wait`接口支持按batch或epoch粒度来调整数据增强策略，实现训练过程中动态调整数据增强策略。 -`sync_wait`必须和`sync_update`配合使用实现数据pipeline上的同步回调。 +对各图像先等概率随机选择一种子策略，再依照子策略中的概率顺序执行各个操作。 -- [`sync_wait(condition_name, num_batch=1, callback=None)`](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.dataset.html?highlight=sync_wait#mindspore.dataset.ImageFolderDatasetV2.sync_wait) -- [`sync_update(condition_name, num_batch=None, data=None)`](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.dataset.html?highlight=sync_update#mindspore.dataset.ImageFolderDatasetV2.sync_update) +在下面的代码示例中，预置了两条子策略，子策略1中包含`RandomRotation`、`RandomVerticalFlip`和`RandomColorAdjust`三个操作，概率分别为0.5、1.0和0.8；子策略2中包含`RandomRotation`和`RandomColorAdjust`两个操作，概率分别为1.0和0.2。 -`sync_wait`将阻塞整个数据处理pipeline直到`sync_update`触发用户预先定义的`callback`函数。 +```python +import mindspore.dataset.vision.c_transforms as c_vision +from mindspore.dataset.vision.c_transforms import RandomSelectSubpolicy -1. 用户预先定义class`Augment`，其中`preprocess`为`map`操作中的自定义数据增强函数，`update`为更新数据增强策略的回调函数。 +policy_list = [ + [(c_vision.RandomRotation((45, 45)), 0.5), (c_vision.RandomVerticalFlip(), 1.0), (c_vision.RandomColorAdjust(), 0.8)], + [(c_vision.RandomRotation((90, 90)), 1.0), (c_vision.RandomColorAdjust(), 0.2)] + ] +policy = RandomSelectSubpolicy(policy_list) +``` - ```python - import mindspore.dataset.vision.py_transforms as transforms - import mindspore.dataset as de - import numpy as np +## 基于回调参数的自动数据增强 - class Augment: - def __init__(self): - self.ep_num = 0 - self.step_num = 0 +MindSpore的`sync_wait`接口支持按batch或epoch粒度在训练过程中动态调整数据增强策略，用户可以设定阻塞条件触发特定的数据增强操作。 - def preprocess(self, input_): - return (np.array((input_ + self.step_num ** self.ep_num - 1), )) +`sync_wait`将阻塞整个数据处理pipeline直到`sync_update`触发用户预先定义的`callback`函数，两者需配合使用，对应说明如下： - def update(self, data): - self.ep_num = data['ep_num'] - self.step_num = data['step_num'] +- sync_wait(condition_name, num_batch=1, callback=None) - ``` + 该API为数据集添加一个阻塞条件`condition_name`，当`sync_update`调用时执行指定的`callback`函数。 -2. 数据处理pipeline先回调自定义的增强策略更新函数`auto_aug.update`，然后在`map`操作中按更新后的策略来执行`auto_aug.preprocess`中定义的数据增强。 +- sync_update(condition_name, num_batch=None, data=None) - ```python + 该API用于释放对应`condition_name`的阻塞，并对`data`触发指定的`callback`函数。 - arr = list(range(1, 4)) - ds = de.NumpySlicesDataset(arr, shuffle=False) - aug = Augment() - ds= ds.sync_wait(condition_name="policy", callback=aug.update) - ds = ds.map(operations=[aug.preprocess]) +下面将演示基于回调参数的自动数据增强的用法。 - ``` +1. 用户预先定义`Augment`类，其中`preprocess`为自定义的数据增强函数，`update`为更新数据增强策略的回调函数。 -3. 在每个step调用`sync_update`进行数据增强策略的更新。 + ```python + import mindspore.dataset.vision.py_transforms as transforms + import mindspore.dataset as ds + import numpy as np - ```python - epochs = 5 - itr = ds.create_tuple_iterator(num_epochs=epochs) - step_num = 0 - for ep_num in range(epochs): - for data in itr: - print("epcoh: {}, step:{}, data :{}".format(ep_num, step_num, data)) - step_num += 1 - ds.sync_update(condition_name="policy", data={'ep_num': ep_num, 'step_num': step_num}) + class Augment: + def __init__(self): + self.ep_num = 0 + self.step_num = 0 - ``` + def preprocess(self, input_): + return (np.array((input_ + self.step_num ** self.ep_num - 1), )) + + def update(self, data): + self.ep_num = data['ep_num'] + self.step_num = data['step_num'] + ``` + +2. 数据处理pipeline先回调自定义的增强策略更新函数`update`，然后在`map`操作中按更新后的策略来执行`preprocess`中定义的数据增强操作。 + + ```python + arr = list(range(1, 4)) + dataset = ds.NumpySlicesDataset(arr, shuffle=False) + aug = Augment() + dataset = dataset.sync_wait(condition_name="policy", callback=aug.update) + dataset = dataset.map(operations=[aug.preprocess]) + ``` + +3. 在每个step中调用`sync_update`进行数据增强策略的更新。 + + ```python + epochs = 5 + itr = dataset.create_tuple_iterator(num_epochs=epochs) + step_num = 0 + for ep_num in range(epochs): + for data in itr: + print("epcoh: {}, step:{}, data :{}".format(ep_num, step_num, data)) + step_num += 1 + dataset.sync_update(condition_name="policy", data={'ep_num': ep_num, 'step_num': step_num}) + ``` diff --git a/api/source_zh_cn/programming_guide/data_pipeline.rst b/api/source_zh_cn/programming_guide/data_pipeline.rst new file mode 100644 index 0000000000..62f55a9e10 --- /dev/null +++ b/api/source_zh_cn/programming_guide/data_pipeline.rst @@ -0,0 +1,13 @@ +数据管道 +=========== + +.. toctree:: + :maxdepth: 1 + + dataset_loading + sampler + pipeline + augmentation + tokenizer + dataset_conversion + auto_augmentation diff --git a/api/source_zh_cn/programming_guide/dataset_conversion.md b/api/source_zh_cn/programming_guide/dataset_conversion.md index 29f7557b12..d221a26de1 100644 --- a/api/source_zh_cn/programming_guide/dataset_conversion.md +++ b/api/source_zh_cn/programming_guide/dataset_conversion.md @@ -4,16 +4,14 @@ - [MindSpore数据格式转换](#mindspore数据格式转换) - [概述](#概述) - - [非标准数据集转换MindRecord](#非标准数据集转换mindrecord) - - [CV类数据集](#cv类数据集) - - [NLP类数据集](#nlp类数据集) - - [常用数据集转换MindRecord](#常用数据集转换mindrecord) - - [转换CIFAR-10数据集](#转换cifar-10数据集) - - [转换CIFAR-100数据集](#转换cifar-100数据集) - - [转换ImageNet数据集](#转换imagenet数据集) - - [转换MNIST数据集](#转换mnist数据集) - - [转换CSV数据集](#转换csv数据集) - - [转换TFRecord数据集](#转换tfrecord数据集) + - [非标准数据集转换MindRecord](#非标准数据集转换mindrecord) + - [转换CV类数据集](#转换cv类数据集) + - [转换NLP类数据集](#转换nlp类数据集) + - [常用数据集转换MindRecord](#常用数据集转换mindrecord) + - [转换CIFAR-10数据集](#转换cifar-10数据集) + - [转换ImageNet数据集](#转换imagenet数据集) + - [转换CSV数据集](#转换csv数据集) + - [转换TFRecord数据集](#转换tfrecord数据集) @@ -21,162 +19,138 @@ ## 概述 -用户可以将非标准的数据集和常见的经典数据集转换为MindSpore数据格式即MindRecord，从而方便地加载到MindSpore中进行训练。同时，MindSpore在部分场景做了性能优化，使用MindSpore数据格式可以获得更好的性能体验。 +用户可以将非标准的数据集和常用的数据集转换为MindSpore数据格式，即MindRecord，从而方便地加载到MindSpore中进行训练。同时，MindSpore在部分场景做了性能优化，使用MindRecord可以获得更好的性能体验。 ## 非标准数据集转换MindRecord -主要介绍如何将CV类数据和NLP类数据转换为MindRecord格式，并通过MindDataset实现MindRecoed格式文件的读取。 +下面主要介绍如何将CV类数据和NLP类数据转换为MindRecord，并通过`MindDataset`实现MindRecoed文件的读取。 -### CV类数据集 +### 转换CV类数据集 - ```python - """ - 示例说明：本示例主要介绍用户如何将自己的CV类数据集转换成MindRecoed格式，并使用MindDataset读取。 - 详细步骤： - 1. 创建一个包含100条记录的MindRecord文件，其样本包含file_name（字符串）, label（整形）, data（二进制）三个字段； - 2. 使用MindDataset读取MindRecord文件。 - """ +本示例主要介绍用户如何将自己的CV类数据集转换成MindRecord，并使用`MindDataset`读取。 - from io import BytesIO - import os - import mindspore.dataset as ds - from mindspore.mindrecord import FileWriter - import mindspore.dataset.vision.c_transforms as vision - from PIL import Image +本示例首先创建一个包含100条记录的MindRecord文件，其样本包含`file_name`（字符串）、 +`label`（整形）、 `data`（二进制）三个字段，然后使用`MindDataset`读取该MindRecord文件。 - ################################ 生成MindRecord文件 ################################ +```python +from io import BytesIO +import os +import mindspore.dataset as ds +from mindspore.mindrecord import FileWriter +import mindspore.dataset.vision.c_transforms as vision +from PIL import Image - mindrecord_filename = "test.mindrecord" +mindrecord_filename = "test.mindrecord" - # 如果存在MindRecord文件，则需要先删除 - if os.path.exists(mindrecord_filename): - os.remove(mindrecord_filename) - os.remove(mindrecord_filename + ".db") +if os.path.exists(mindrecord_filename): + os.remove(mindrecord_filename) + os.remove(mindrecord_filename + ".db") - # 创建写对象，将会生成 mindrecord_filename 和 mindrecord_filename.db 两个文件 - writer = FileWriter(file_name=mindrecord_filename, shard_num=1) +writer = FileWriter(file_name=mindrecord_filename, shard_num=1) - # 定义数据集Schema - cv_schema = {"file_name": {"type": "string"}, "label": {"type": "int32"}, "data": {"type": "bytes"}} - writer.add_schema(cv_schema, "it is a cv dataset") +cv_schema = {"file_name": {"type": "string"}, "label": {"type": "int32"}, "data": {"type": "bytes"}} +writer.add_schema(cv_schema, "it is a cv dataset") - # [可选]定义索引字段，只能是标量字段 - writer.add_index(["file_name", "label"]) +writer.add_index(["file_name", "label"]) - # 按Schema方式组织训练数据，并将其写入MindRecord文件 - # 此处使用Image.new(...)模拟图片数据，真实场景可以使用io接口读取磁盘上的图像数据 - data = [] - for i in range(100): # 模拟数据集有100个样本 - i += 1 +data = [] +for i in range(100): + i += 1 - sample = {} - white_io = BytesIO() - Image.new('RGB', (i*10, i*10), (255, 255, 255)).save(white_io, 'JPEG') # 图片大小可以不同 - image_bytes = white_io.getvalue() - sample['file_name'] = str(i) + ".jpg" # 对应file_name字段 - sample['label'] = i # 对应label字段 - sample['data'] = white_io.getvalue() # 对应data字段 + sample = {} + white_io = BytesIO() + Image.new('RGB', (i*10, i*10), (255, 255, 255)).save(white_io, 'JPEG') + image_bytes = white_io.getvalue() + sample['file_name'] = str(i) + ".jpg" + sample['label'] = i + sample['data'] = white_io.getvalue() - data.append(sample) - if i % 10 == 0: # 每10条样本做一次写操作 - writer.write_raw_data(data) - data = [] + data.append(sample) + if i % 10 == 0: + writer.write_raw_data(data) + data = [] - if data: # 写入可能剩余的数据 - writer.write_raw_data(data) +if data: + writer.write_raw_data(data) + +writer.commit() + +data_set = ds.MindDataset(dataset_file=mindrecord_filename) +decode_op = vision.Decode() +data_set = data_set.map(operations=decode_op, input_columns=["data"], num_parallel_workers=2) +count = 0 +for item in data_set.create_dict_iterator(): + print("sample: {}".format(item)) + count += 1 +print("Got {} samples".format(count)) +``` + +### 转换NLP类数据集 + +> NLP类数据一般会先经过预处理转换为字典序，本示例只演示转换后的字典序数据如何写入MindRecord文件，而不包括预处理过程。 + +本示例主要介绍用户如何将自己的NLP类数据集转换成MindRecord，并使用`MindDataset`读取。 + +本示例首先创建一个包含100条记录的MindRecord文件，其样本包含八个字段，均为整形数组，然后使用`MindDataset`读取该MindRecord文件。 + +```python +import os +import numpy as np +import mindspore.dataset as ds +from mindspore.mindrecord import FileWriter + +mindrecord_filename = "test.mindrecord" + +if os.path.exists(mindrecord_filename): + os.remove(mindrecord_filename) + os.remove(mindrecord_filename + ".db") - writer.commit() # 关闭写入操作 - - ################################ 读取MindRecord文件 ################################ - - data_set = ds.MindDataset(dataset_file=mindrecord_filename) # 创建读取对象，默认开启shuffle - decode_op = vision.Decode() - data_set = data_set.map(operations=decode_op, input_columns=["data"], num_parallel_workers=2) # 解码data字段 - count = 0 - for item in data_set.create_dict_iterator(output_numpy=True): # 循环读取MindRecord中所有数据 - print("sample: {}".format(item)) - count += 1 - print("Got {} samples".format(count)) - ``` - -### NLP类数据集 - -> 因为NLP类数据一般会经过预处理转换为字典序，此预处理过程不在本示例范围，该示例只演示转换后的字典序数据如何写入MindRecord。 - - ```python - """ - 示例说明：本示例主要介绍用户如何将自己的NLP类数据集转换成MindRecoed格式，并使用MindDataset读取。 - 详细步骤： - 1. 创建一个包含100条记录的MindRecord文件，其样本包含八个字段，均为整形数组； - 2. 使用MindDataset读取MindRecord文件。 - """ - - import os - import numpy as np - import mindspore.dataset as ds - from mindspore.mindrecord import FileWriter - - ################################ 生成MindRecord文件 ################################ - - mindrecord_filename = "test.mindrecord" - - # 如果存在MindRecord文件，则需要先删除 - if os.path.exists(mindrecord_filename): - os.remove(mindrecord_filename) - os.remove(mindrecord_filename + ".db") - - # 创建写对象，将会生成 mindrecord_filename 和 mindrecord_filename.db 两个文件 - writer = FileWriter(file_name=mindrecord_filename, shard_num=1) - - # 定义数据集Schema，此处认为文本已经转为字典序 - nlp_schema = {"source_sos_ids": {"type": "int64", "shape": [-1]}, - "source_sos_mask": {"type": "int64", "shape": [-1]}, - "source_eos_ids": {"type": "int64", "shape": [-1]}, - "source_eos_mask": {"type": "int64", "shape": [-1]}, - "target_sos_ids": {"type": "int64", "shape": [-1]}, - "target_sos_mask": {"type": "int64", "shape": [-1]}, - "target_eos_ids": {"type": "int64", "shape": [-1]}, - "target_eos_mask": {"type": "int64", "shape": [-1]}} - writer.add_schema(nlp_schema, "it is a preprocessed nlp dataset") - - # 按Schema方式组织训练数据，并将其写入MindRecord文件 - data = [] - for i in range(100): # 模拟数据集有100个样本 - i += 1 - - # 组织训练数据 - sample = {"source_sos_ids": np.array([i, i+1, i+2, i+3, i+4], dtype=np.int64), - "source_sos_mask": np.array([i*1, i*2, i*3, i*4, i*5, i*6, i*7], dtype=np.int64), - "source_eos_ids": np.array([i+5, i+6, i+7, i+8, i+9, i+10], dtype=np.int64), - "source_eos_mask": np.array([19, 20, 21, 22, 23, 24, 25, 26, 27], dtype=np.int64), - "target_sos_ids": np.array([28, 29, 30, 31, 32], dtype=np.int64), - "target_sos_mask": np.array([33, 34, 35, 36, 37, 38], dtype=np.int64), - "target_eos_ids": np.array([39, 40, 41, 42, 43, 44, 45, 46, 47], dtype=np.int64), - "target_eos_mask": np.array([48, 49, 50, 51], dtype=np.int64)} - - data.append(sample) - if i % 10 == 0: # 每10条样本做一次写操作 - writer.write_raw_data(data) - data = [] - - if data: # 写入可能剩余的数据 +writer = FileWriter(file_name=mindrecord_filename, shard_num=1) + +nlp_schema = {"source_sos_ids": {"type": "int64", "shape": [-1]}, + "source_sos_mask": {"type": "int64", "shape": [-1]}, + "source_eos_ids": {"type": "int64", "shape": [-1]}, + "source_eos_mask": {"type": "int64", "shape": [-1]}, + "target_sos_ids": {"type": "int64", "shape": [-1]}, + "target_sos_mask": {"type": "int64", "shape": [-1]}, + "target_eos_ids": {"type": "int64", "shape": [-1]}, + "target_eos_mask": {"type": "int64", "shape": [-1]}} +writer.add_schema(nlp_schema, "it is a preprocessed nlp dataset") + +data = [] +for i in range(100): + i += 1 + + sample = {"source_sos_ids": np.array([i, i+1, i+2, i+3, i+4], dtype=np.int64), + "source_sos_mask": np.array([i*1, i*2, i*3, i*4, i*5, i*6, i*7], dtype=np.int64), + "source_eos_ids": np.array([i+5, i+6, i+7, i+8, i+9, i+10], dtype=np.int64), + "source_eos_mask": np.array([19, 20, 21, 22, 23, 24, 25, 26, 27], dtype=np.int64), + "target_sos_ids": np.array([28, 29, 30, 31, 32], dtype=np.int64), + "target_sos_mask": np.array([33, 34, 35, 36, 37, 38], dtype=np.int64), + "target_eos_ids": np.array([39, 40, 41, 42, 43, 44, 45, 46, 47], dtype=np.int64), + "target_eos_mask": np.array([48, 49, 50, 51], dtype=np.int64)} + + data.append(sample) + if i % 10 == 0: writer.write_raw_data(data) + data = [] - writer.commit() # 关闭写入操作 +if data: + writer.write_raw_data(data) - ################################ 读取MindRecord文件 ################################ +writer.commit() - data_set = ds.MindDataset(dataset_file=mindrecord_filename) # 创建读取对象，默认开启shuffle - count = 0 - for item in data_set.create_dict_iterator(): # 循环读取MindRecord中所有数据 - print("sample: {}".format(item)) - count += 1 - print("Got {} samples".format(count)) - ``` +data_set = ds.MindDataset(dataset_file=mindrecord_filename) +count = 0 +for item in data_set.create_dict_iterator(): + print("sample: {}".format(item)) + count += 1 +print("Got {} samples".format(count)) +``` ## 常用数据集转换MindRecord -MindSpore提供转换常见数据集的工具类，能够将常见的经典数据集转换为MindRecord格式。常见数据集及其对应的工具类列表如下。 +MindSpore提供转换常用数据集的工具类，能够将常用的数据集转换为MindRecord。常用数据集及其对应的工具类列表如下。 | 数据集 | 格式转换工具类 | | -------- | ------------ | @@ -191,9 +165,9 @@ MindSpore提供转换常见数据集的工具类，能够将常见的经典数 ### 转换CIFAR-10数据集 -用户可以通过`Cifar10ToMR`类，将CIFAR-10原始数据转换为MindRecord格式。 +用户可以通过`Cifar10ToMR`类，将CIFAR-10原始数据转换为MindRecord。 -1. 下载[CIFAR-10数据集](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)并解压，目录结构如下所示。 +1. 下载[CIFAR-10数据集](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)并解压，其目录结构如下所示。 ``` └─cifar-10-batches-py @@ -213,10 +187,10 @@ MindSpore提供转换常见数据集的工具类，能够将常见的经典数 from mindspore.mindrecord import Cifar10ToMR ``` -3. 创建`Cifar10ToMR`对象，调用`transform`接口，将CIFAR-10数据集转换为MindRecord格式。 +3. 创建`Cifar10ToMR`对象，调用`transform`接口，将CIFAR-10数据集转换为MindRecord。 ```python - CIFAR10_DIR = "./cifar10/cifar-10-batches-py" + CIFAR10_DIR = "./cifar-10-batches-py" MINDRECORD_FILE = "./cifar10.mindrecord" cifar10_transformer = Cifar10ToMR(CIFAR10_DIR, MINDRECORD_FILE) cifar10_transformer.transform(['label']) @@ -224,52 +198,14 @@ MindSpore提供转换常见数据集的工具类，能够将常见的经典数 **参数说明：** - `CIFAR10_DIR`：CIFAR-10数据集的文件夹路径。 - - `MINDRECORD_FILE`：输出的MindSpore数据格式文件路径。 - -### 转换CIFAR-100数据集 - -用户可以通过`Cifar100ToMR`类，将CIFAR-100原始数据转换为MindRecord格式。 - -1. 准备好CIFAR-100数据集，将文件解压至指定的目录（示例中将数据集保存到`cifar100`目录），如下所示。 - - ``` - % ll cifar100/cifar-100-python/ - meta - test - train - ``` - > CIFAR-100数据集下载地址： - -2. 导入转换数据集的工具类`Cifar100ToMR`。 - - ```python - from mindspore.mindrecord import Cifar100ToMR - ``` - -3. 实例化`Cifar100ToMR`对象，调用`transform`接口，将CIFAR-100数据集转换为MindSpore数据格式。 - - ```python - CIFAR100_DIR = "./cifar100/cifar-100-python" - MINDRECORD_FILE = "./cifar100.mindrecord" - cifar100_transformer = Cifar100ToMR(CIFAR100_DIR, MINDRECORD_FILE) - cifar100_transformer.transform(['fine_label', 'coarse_label']) - ``` - - **参数说明：** - - `CIFAR100_DIR`：CIFAR-100数据集的文件夹路径。 - - `MINDRECORD_FILE`：输出的MindSpore数据格式文件路径。 + - `MINDRECORD_FILE`：输出的MindRecord文件路径。 ### 转换ImageNet数据集 -用户可以通过`ImageNetToMR`类，将ImageNet原始数据（图片、标注）转换为MindSpore数据格式。 - -1. 下载并按照要求准备好ImageNet数据集。 +用户可以通过`ImageNetToMR`类，将ImageNet原始数据（图片、标注）转换为MindRecord。 - > ImageNet数据集下载地址： +1. 下载[ImageNet数据集](http://image-net.org/download)，将所有图片存放在同一文件夹，用一个映射文件记录图片和标签的对应关系。映射文件包含2列，分别为各类别图片目录和标签ID，用空格隔开，映射文件示例如下： - 对下载后的ImageNet数据集，整理数据集组织形式为一个包含所有图片的文件夹，以及一个记录图片对应标签的映射文件。 - - 标签映射文件包含2列，分别为各类别图片目录、标签ID，用空格隔开，映射文件示例如下： ``` n01440760 0 n01443537 1 @@ -279,13 +215,14 @@ MindSpore提供转换常见数据集的工具类，能够将常见的经典数 n01496331 5 ``` -2. 导入转换数据集的工具类`ImageNetToMR`。 +2. 导入数据集转换工具类`ImageNetToMR`。 ```python from mindspore.mindrecord import ImageNetToMR ``` -3. 实例化`ImageNetToMR`对象，调用`transform`接口，将数据集转换为MindSpore数据格式。 +3. 创建`ImageNetToMR`对象，调用`transform`接口，将数据集转换为MindRecord。 + ```python IMAGENET_MAP_FILE = "./testImageNetDataWhole/labels_map.txt" IMAGENET_IMAGE_DIR = "./testImageNetDataWhole/images" @@ -294,228 +231,156 @@ MindSpore提供转换常见数据集的工具类，能够将常见的经典数 imagenet_transformer = ImageNetToMR(IMAGENET_MAP_FILE, IMAGENET_IMAGE_DIR, MINDRECORD_FILE, PARTITION_NUMBER) imagenet_transformer.transform() ``` - 其中， - `IMAGENET_MAP_FILE`：ImageNetToMR数据集的标签映射文件路径。 - `IMAGENET_IMAGE_DIR`：包含ImageNet所有图片的文件夹路径。 - `MINDRECORD_FILE`：输出的MindSpore数据格式文件路径。 -### 转换MNIST数据集 + **参数说明：** + - `IMAGENET_MAP_FILE`：ImageNet数据集标签映射文件的路径。 + - `IMAGENET_IMAGE_DIR`：包含ImageNet所有图片的文件夹路径。 + - `MINDRECORD_FILE`：输出的MindRecord文件路径。 -用户可以通过`MnistToMR`类，将MNIST原始数据转换为MindSpore数据格式。 +### 转换CSV数据集 -1. 准备MNIST数据集，将下载好的文件放至指定的目录，如下所示： +本示例首先创建一个包含5条记录的CSV文件，然后通过`CsvToMR`工具类将CSV文件转换为MindRecord，并最终通过`MindDataset`将其读取出来。 - ``` - % ll mnist_data/ - train-images-idx3-ubyte.gz - train-labels-idx1-ubyte.gz - t10k-images-idx3-ubyte.gz - t10k-labels-idx1-ubyte.gz - ``` +```python +import csv +import os +import mindspore.dataset as ds +from mindspore.mindrecord import CsvToMR - > MNIST数据集下载地址： +CSV_FILE_NAME = "test.csv" +MINDRECORD_FILE_NAME = "test.mindrecord" +PARTITION_NUM = 1 -2. 导入转换数据集的工具类`MnistToMR`。 +def generate_csv(): + headers = ["id", "name", "math", "english"] + rows = [(1, "Lily", 78.5, 90), + (2, "Lucy", 99, 85.2), + (3, "Mike", 65, 71), + (4, "Tom", 95, 99), + (5, "Jeff", 85, 78.5)] + with open(CSV_FILE_NAME, 'w', encoding='utf-8') as f: + writer = csv.writer(f) + writer.writerow(headers) + writer.writerows(rows) - ```python - from mindspore.mindrecord import MnistToMR - ``` +generate_csv() -3. 实例化`MnistToMR`对象，调用`transform`接口，将MNIST数据集转换为MindSpore数据格式。 +if os.path.exists(MINDRECORD_FILE_NAME): + os.remove(MINDRECORD_FILE_NAME) + os.remove(MINDRECORD_FILE_NAME + ".db") - ```python - MNIST_DIR = "./mnist_data" - MINDRECORD_FILE = "./mnist.mindrecord" - mnist_transformer = MnistToMR(MNIST_DIR, MINDRECORD_FILE) - mnist_transformer.transform() - ``` +csv_transformer = CsvToMR(CSV_FILE_NAME, MINDRECORD_FILE_NAME, partition_number=PARTITION_NUM) - ***参数说明：*** - - `MNIST_DIR`：MNIST数据集的文件夹路径。 - - `MINDRECORD_FILE`：输出的MindSpore数据格式文件路径。 +csv_transformer.transform() +assert os.path.exists(MINDRECORD_FILE_NAME) +assert os.path.exists(MINDRECORD_FILE_NAME + ".db") -### 转换CSV数据集 - - ```python - """ - 示例说明：本示例首先创建一个CSV文件，然后通过MindSpore中CsvToMR工具， - 将Csv文件转换为MindRecord文件，并最终通过MindDataset将其读取出来。 - 详细步骤： - 1. 创建一个包含5条记录的CSV文件； - 2. 使用CsvToMR工具将CSV转换为MindRecord； - 3. 使用MindDataset读取MindRecord文件。 - """ - - import csv - import os - import mindspore.dataset as ds - from mindspore.mindrecord import CsvToMR - - CSV_FILE_NAME = "test.csv" # 创建的CSV文件 - MINDRECORD_FILE_NAME = "test.mindrecord" # 转换后的MindRecord文件 - PARTITION_NUM = 1 - - ################################ 创建CSV文件 ################################ - - # 生成CSV文件 - def generate_csv(): - headers = ["id", "name", "math", "english"] - rows = [(1, "Lily", 78.5, 90), - (2, "Lucy", 99, 85.2), - (3, "Mike", 65, 71), - (4, "Tom", 95, 99), - (5, "Jeff", 85, 78.5)] - with open(CSV_FILE_NAME, 'w', encoding='utf-8') as f: - writer = csv.writer(f) - writer.writerow(headers) - writer.writerows(rows) - - generate_csv() - - if os.path.exists(MINDRECORD_FILE_NAME): - os.remove(MINDRECORD_FILE_NAME) - os.remove(MINDRECORD_FILE_NAME + ".db") - - ################################ CSV 转 MindRecord文件 ################################ - - # 调用CsvToMR工具，初始化 - csv_transformer = CsvToMR(CSV_FILE_NAME, MINDRECORD_FILE_NAME, partition_number=PARTITION_NUM) - # 执行转换操作 - csv_transformer.transform() - - assert os.path.exists(MINDRECORD_FILE_NAME) - assert os.path.exists(MINDRECORD_FILE_NAME + ".db") - - ############################### 读取MindRecord文件 ################################ - - data_set = ds.MindDataset(dataset_file=MINDRECORD_FILE_NAME) # 创建读取对象，默认开启shuffle - count = 0 - for item in data_set.create_dict_iterator(output_numpy=True): # 循环读取MindRecord中所有数据 - print("sample: {}".format(item)) - count += 1 - print("Got {} samples".format(count)) - ``` +data_set = ds.MindDataset(dataset_file=MINDRECORD_FILE_NAME) +count = 0 +for item in data_set.create_dict_iterator(): + print("sample: {}".format(item)) + count += 1 +print("Got {} samples".format(count)) +``` ### 转换TFRecord数据集 - ```python - """ - 示例说明：本示例通过TF创建一个TFRecord文件，然后通过MindSpore中TFRecordToMR工具， - 将TFRecord文件转换为MindRecord文件，并最终通过MindDataset将其读取出来。 - 详细步骤： - 1. 创建一个包含10条记录，且样本格式为： - feature_dict = {"file_name": tf.io.FixedLenFeature([], tf.string), - "image_bytes": tf.io.FixedLenFeature([], tf.string), - "int64_scalar": tf.io.FixedLenFeature([], tf.int64), - "float_scalar": tf.io.FixedLenFeature([], tf.float32), - "int64_list": tf.io.FixedLenFeature([6], tf.int64), - "float_list": tf.io.FixedLenFeature([7], tf.float32)} - 的TFRecord文件； - 2. 使用TFRecordToMR工具将TFRecord转换为MindRecord； - 3. 使用MindDataset读取MindRecord文件，并通过Decode算子对其image_bytes字段进行解码。 - """ - - import collections - from io import BytesIO - import os - import mindspore.dataset as ds - from mindspore.mindrecord import TFRecordToMR - import mindspore.dataset.vision.c_transforms as vision - from PIL import Image - import tensorflow as tf # 需要tensorflow >= 2.1.0 - - TFRECORD_FILE_NAME = "test.tfrecord" # 创建的TFRecord文件 - MINDRECORD_FILE_NAME = "test.mindrecord" # 转换后的MindRecord文件 - PARTITION_NUM = 1 - - ################################ 创建TFRecord文件 ################################ - - # 生成TFRecord文件 - def generate_tfrecord(): - def create_int_feature(values): - if isinstance(values, list): - feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values))) # values: [int, int, int] - else: - feature = tf.train.Feature(int64_list=tf.train.Int64List(value=[values])) # values: int - return feature - - def create_float_feature(values): - if isinstance(values, list): - feature = tf.train.Feature(float_list=tf.train.FloatList(value=list(values))) # values: [float, float] - else: - feature = tf.train.Feature(float_list=tf.train.FloatList(value=[values])) # values: float - return feature - - def create_bytes_feature(values): - if isinstance(values, bytes): - white_io = BytesIO() - Image.new('RGB', (10, 10), (255, 255, 255)).save(white_io, 'JPEG') # 图片大小可以不同 - image_bytes = white_io.getvalue() - feature = tf.train.Feature(bytes_list=tf.train.BytesList(value=[image_bytes])) # values: bytes - else: - # values: string - feature = tf.train.Feature(bytes_list=tf.train.BytesList(value=[bytes(values, encoding='utf-8')])) - return feature - - writer = tf.io.TFRecordWriter(TFRECORD_FILE_NAME) - - example_count = 0 - for i in range(10): - file_name = "000" + str(i) + ".jpg" - image_bytes = bytes(str("aaaabbbbcccc" + str(i)), encoding="utf-8") - int64_scalar = i - float_scalar = float(i) - int64_list = [i, i+1, i+2, i+3, i+4, i+1234567890] - float_list = [float(i), float(i+1), float(i+2.8), float(i+3.2), - float(i+4.4), float(i+123456.9), float(i+98765432.1)] - - features = collections.OrderedDict() - features["file_name"] = create_bytes_feature(file_name) - features["image_bytes"] = create_bytes_feature(image_bytes) - features["int64_scalar"] = create_int_feature(int64_scalar) - features["float_scalar"] = create_float_feature(float_scalar) - features["int64_list"] = create_int_feature(int64_list) - features["float_list"] = create_float_feature(float_list) - - tf_example = tf.train.Example(features=tf.train.Features(feature=features)) - writer.write(tf_example.SerializeToString()) - example_count += 1 - writer.close() - print("Write {} rows in tfrecord.".format(example_count)) - - generate_tfrecord() - - ################################ TFRecord 转 MindRecord文件 ################################ - - feature_dict = {"file_name": tf.io.FixedLenFeature([], tf.string), - "image_bytes": tf.io.FixedLenFeature([], tf.string), - "int64_scalar": tf.io.FixedLenFeature([], tf.int64), - "float_scalar": tf.io.FixedLenFeature([], tf.float32), - "int64_list": tf.io.FixedLenFeature([6], tf.int64), - "float_list": tf.io.FixedLenFeature([7], tf.float32), - } - - if os.path.exists(MINDRECORD_FILE_NAME): - os.remove(MINDRECORD_FILE_NAME) - os.remove(MINDRECORD_FILE_NAME + ".db") - - # 调用TFRecordToMR工具，初始化 - tfrecord_transformer = TFRecordToMR(TFRECORD_FILE_NAME, MINDRECORD_FILE_NAME, feature_dict, ["image_bytes"]) - # 执行转换操作 - tfrecord_transformer.transform() - - assert os.path.exists(MINDRECORD_FILE_NAME) - assert os.path.exists(MINDRECORD_FILE_NAME + ".db") - - ############################### 读取MindRecord文件 ################################ - - data_set = ds.MindDataset(dataset_file=MINDRECORD_FILE_NAME) # 创建读取对象，默认开启shuffle - decode_op = vision.Decode() - data_set = data_set.map(operations=decode_op, input_columns=["image_bytes"], num_parallel_workers=2) # 解码图像字段 - count = 0 - for item in data_set.create_dict_iterator(output_numpy=True): # 循环读取MindRecord中所有数据 - print("sample: {}".format(item)) - count += 1 - print("Got {} samples".format(count)) - ``` +> 目前只支持TensorFlow 2.1.0及以上版本。 + +本示例首先通过TensorFlow创建一个TFRecord文件，然后通过`TFRecordToMR`工具类将TFRecord文件转换为MindRecord，最后通过`MindDataset`将其读取出来，并使用`Decode`算子对`image_bytes`字段进行解码。 + +```python +import collections +from io import BytesIO +import os +import mindspore.dataset as ds +from mindspore.mindrecord import TFRecordToMR +import mindspore.dataset.vision.c_transforms as vision +from PIL import Image +import tensorflow as tf + +TFRECORD_FILE_NAME = "test.tfrecord" +MINDRECORD_FILE_NAME = "test.mindrecord" +PARTITION_NUM = 1 + +def generate_tfrecord(): + def create_int_feature(values): + if isinstance(values, list): + feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values))) + else: + feature = tf.train.Feature(int64_list=tf.train.Int64List(value=[values])) + return feature + + def create_float_feature(values): + if isinstance(values, list): + feature = tf.train.Feature(float_list=tf.train.FloatList(value=list(values))) + else: + feature = tf.train.Feature(float_list=tf.train.FloatList(value=[values])) + return feature + + def create_bytes_feature(values): + if isinstance(values, bytes): + white_io = BytesIO() + Image.new('RGB', (10, 10), (255, 255, 255)).save(white_io, 'JPEG') + image_bytes = white_io.getvalue() + feature = tf.train.Feature(bytes_list=tf.train.BytesList(value=[image_bytes])) + else: + feature = tf.train.Feature(bytes_list=tf.train.BytesList(value=[bytes(values, encoding='utf-8')])) + return feature + + writer = tf.io.TFRecordWriter(TFRECORD_FILE_NAME) + + example_count = 0 + for i in range(10): + file_name = "000" + str(i) + ".jpg" + image_bytes = bytes(str("aaaabbbbcccc" + str(i)), encoding="utf-8") + int64_scalar = i + float_scalar = float(i) + int64_list = [i, i+1, i+2, i+3, i+4, i+1234567890] + float_list = [float(i), float(i+1), float(i+2.8), float(i+3.2), + float(i+4.4), float(i+123456.9), float(i+98765432.1)] + + features = collections.OrderedDict() + features["file_name"] = create_bytes_feature(file_name) + features["image_bytes"] = create_bytes_feature(image_bytes) + features["int64_scalar"] = create_int_feature(int64_scalar) + features["float_scalar"] = create_float_feature(float_scalar) + features["int64_list"] = create_int_feature(int64_list) + features["float_list"] = create_float_feature(float_list) + + tf_example = tf.train.Example(features=tf.train.Features(feature=features)) + writer.write(tf_example.SerializeToString()) + example_count += 1 + writer.close() + print("Write {} rows in tfrecord.".format(example_count)) + +generate_tfrecord() + +feature_dict = {"file_name": tf.io.FixedLenFeature([], tf.string), + "image_bytes": tf.io.FixedLenFeature([], tf.string), + "int64_scalar": tf.io.FixedLenFeature([], tf.int64), + "float_scalar": tf.io.FixedLenFeature([], tf.float32), + "int64_list": tf.io.FixedLenFeature([6], tf.int64), + "float_list": tf.io.FixedLenFeature([7], tf.float32), + } + +if os.path.exists(MINDRECORD_FILE_NAME): + os.remove(MINDRECORD_FILE_NAME) + os.remove(MINDRECORD_FILE_NAME + ".db") + +tfrecord_transformer = TFRecordToMR(TFRECORD_FILE_NAME, MINDRECORD_FILE_NAME, feature_dict, ["image_bytes"]) +tfrecord_transformer.transform() + +assert os.path.exists(MINDRECORD_FILE_NAME) +assert os.path.exists(MINDRECORD_FILE_NAME + ".db") + +data_set = ds.MindDataset(dataset_file=MINDRECORD_FILE_NAME) +decode_op = vision.Decode() +data_set = data_set.map(operations=decode_op, input_columns=["image_bytes"], num_parallel_workers=2) +count = 0 +for item in data_set.create_dict_iterator(): + print("sample: {}".format(item)) + count += 1 +print("Got {} samples".format(count)) +``` diff --git a/api/source_zh_cn/programming_guide/dataset_loading.md b/api/source_zh_cn/programming_guide/dataset_loading.md index 9cec789095..2253545df4 100644 --- a/api/source_zh_cn/programming_guide/dataset_loading.md +++ b/api/source_zh_cn/programming_guide/dataset_loading.md @@ -4,8 +4,7 @@ - [数据集加载](#数据集加载) - [概述](#概述) - - [经典数据集加载](#经典数据集加载) - - [MNIST数据集](#mnist数据集) + - [常用数据集加载](#常用数据集加载) - [CIFAR10/100数据集](#cifar10100数据集) - [VOC数据集](#voc数据集) - [COCO数据集](#coco数据集) @@ -13,8 +12,7 @@ - [MindRecord数据格式](#mindrecord数据格式) - [Manifest数据格式](#manifest数据格式) - [TFRecord数据格式](#tfrecord数据格式) - - [Numpy数据格式](#numpy数据格式) - - [text数据格式](#text数据格式) + - [NumPy数据格式](#numpy数据格式) - [CSV数据格式](#csv数据格式) - [自定义数据集加载](#自定义数据集加载) - [构造数据集生成函数](#构造数据集生成函数) @@ -27,15 +25,15 @@ ## 概述 -MindSpore支持加载图像领域常用的经典数据集，用户可以直接使用`mindspore.dataset`中对应的类实现数据集的加载。目前支持的经典数据集及对应的数据集类如下表所示。 +MindSpore支持加载图像领域常用的数据集，用户可以直接使用`mindspore.dataset`中对应的类实现数据集的加载。目前支持的常用数据集及对应的数据集类如下表所示。 | 图像数据集 | 数据集类 | 数据集简介 | | ---- | ---- | ---- | | MNIST | MnistDataset | MNIST是一个大型手写数字图像数据集，拥有60,000张训练图像和10,000张测试图像，常用于训练各种图像处理系统。 | | CIFAR-10 | Cifar10Dataset | CIFAR-10是一个微小图像数据集，包含10种类别下的60,000张32x32大小彩色图像，平均每种类别6,000张，其中5,000张为训练集，1,000张为测试集。 | | CIFAR-100 | Cifar100Dataset | CIFAR-100与CIFAR-10类似，但拥有100种类别，平均每种类别600张，其中500张为训练集，100张为测试集。 | -|CelebA | CelebADataset | CelebA是一个大型人脸图像数据集，包含超过200,000张名人人脸图像，每张图像拥有40个特征标记。 | -| PASCAL-VOC | VOCDataset | PASCAL-VOC是一个经典图像数据集，被广泛用于目标检测、图像分割等计算机视觉领域。 | +| CelebA | CelebADataset | CelebA是一个大型人脸图像数据集，包含超过200,000张名人人脸图像，每张图像拥有40个特征标记。 | +| PASCAL-VOC | VOCDataset | PASCAL-VOC是一个常用图像数据集，被广泛用于目标检测、图像分割等计算机视觉领域。 | | COCO | CocoDataset | COCO是一个大型目标检测、图像分割、姿态估计数据集。 | | CLUE | CLUEDataset | CLUE是一个大型中文语义理解数据集。 | @@ -45,65 +43,39 @@ MindSpore还支持加载多种数据存储格式下的数据集，用户可以 | ---- | ---- | ---- | | MindRecord | MindDataset | MindRecord是MindSpore的自研数据格式，具有读写高效、易于分布式处理等优势。 | | Manifest | ManifestDataset | Manifest是华为ModelArts支持的一种数据格式，描述了原始文件和标注信息，可用于标注、训练、推理场景。 | -| TFRecord | TFRecordDataset | TFRecord是Tensorflow定义的一种二进制数据文件格式。 | -| Numpy | NumpySlicesDataset | Numpy数据源指的是已经读入内存中的Numpy arrays格式数据集。 | +| TFRecord | TFRecordDataset | TFRecord是TensorFlow定义的一种二进制数据文件格式。 | +| NumPy | NumpySlicesDataset | NumPy数据源指的是已经读入内存中的NumPy arrays格式数据集。 | | Text File | TextFileDataset | Text File指的是常见的文本格式数据。 | | CSV File | CSVDataset | CSV指逗号分隔值，其文件以纯文本形式存储表格数据。 | -MindSpore也同样支持使用GeneratorDataset自定义数据集的加载方式，用户可以根据需要实现自己的数据集类。 +MindSpore也同样支持使用`GeneratorDataset`自定义数据集的加载方式，用户可以根据需要实现自己的数据集类。 -更多详细的数据集加载接口说明，参见[API文档](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.dataset.html)。 +> 更多详细的数据集加载接口说明，参见[API文档](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.dataset.html)。 -## 经典数据集加载 +## 常用数据集加载 -### MNIST数据集 +下面将介绍几种常用数据集的加载方式。 -```python -# 通过MNIST API读取、解析MNIST数据集，并构建数据管道 - -import mindspore.dataset as ds - -# 下载MNIST数据集，将其解压到MnistData目录 -DATA_DIR = "MnistData/" - -# 使用MnistDataset读取数据集，指定num_samples以获取5个样本数据 -# shuffle参数为True时，是随机获取5个样本，每次运行的label结果可能不一致 -dataset = ds.MnistDataset(DATA_DIR, num_samples=5, shuffle=True) - -# 启动数据管道，输出5个样本数据 -for data in dataset.create_dict_iterator(): - print("Image shape:", data['image'].shape, ", Label:", data['label']) -``` +### CIFAR10/100数据集 -``` -Image shape: (28, 28, 1) , Label: 4 -Image shape: (28, 28, 1) , Label: 9 -Image shape: (28, 28, 1) , Label: 4 -Image shape: (28, 28, 1) , Label: 0 -Image shape: (28, 28, 1) , Label: 9 -``` +下面的样例通过`Cifar10Dataset`接口加载CIFAR-10数据集，使用顺序采样器获取其中5个样本，然后展示了对应图片的形状和标签。 -### CIFAR10/100数据集 +CIFAR-100数据集和MNIST数据集的加载方式也与之类似。 ```python -# 通过Cifar API读取、解析CIFAR数据集，并构建数据管道（以CIFAR10数据集为例） - import mindspore.dataset as ds -# 下载CIFAR10数据集，将其解压到CIFAR10Data目录 DATA_DIR = "Cifar10Data/" -# 指定一个顺序采样器SequentialSampler，按照读取顺序获取5个样本数据 sampler = ds.SequentialSampler(num_samples=5) - -# 使用CIFAR10Dataset读取数据集，指定sampler为上述采样器 dataset = ds.Cifar10Dataset(DATA_DIR, sampler=sampler) -# 启动数据管道，输出5个样本数据 for data in dataset.create_dict_iterator(): print("Image shape:", data['image'].shape, ", Label:", data['label']) ``` +输出结果如下： + ``` Image shape: (32, 32, 3) , Label: 0 Image shape: (32, 32, 3) , Label: 1 @@ -114,34 +86,30 @@ Image shape: (32, 32, 3) , Label: 4 ### VOC数据集 -```python -# 通过VOC API读取、解析VOC数据集，并构建数据管道 +下面的样例通过`VOCDataset`接口加载VOC2012数据集，分别演示了将任务指定为分割（Segmentation）和检测（Detection）时的原始图像形状和目标形状。 +```python import mindspore.dataset as ds -# 下载VOC数据集，将其解压到VOC2012目录 DATA_DIR = "VOC2012/" -# 使用VOCDataset读取数据集，指定为Segmentation任务，同时指定num_samples以获取2个样本数据 -# decode参数会将读取的图像解码 dataset = ds.VOCDataset(DATA_DIR, task="Segmentation", usage="train", num_samples=2, decode=True, shuffle=False) + print("[Segmentation]:") for data in dataset.create_dict_iterator(): - # 原图像 print("image shape:", data["image"].shape) - # 分割后图像 print("target shape:", data["target"].shape) -# 接下来是Detection任务 dataset = ds.VOCDataset(DATA_DIR, task="Detection", usage="train", num_samples=1, decode=True, shuffle=False) + print("[Detection]:") for data in dataset.create_dict_iterator(): - # 原图像 print("image shape:", data["image"].shape) - # 目标框 print("bbox shape:", data["bbox"].shape) ``` +输出结果如下： + ``` [Segmentation]: image shape: (281, 500, 3) @@ -155,39 +123,35 @@ bbox shape: (2, 4) ### COCO数据集 -```python -# 通过Coco API读取、解析Coco数据集，并构建数据管道 +下面的样例通过`CocoDataset`接口加载COCO数据集，分别演示了将任务指定为目标检测（Detection）、背景分割（Stuff）、关键点检测（Keypoint）和全景分割（Panoptic）时获取到的不同数据。 +```python import mindspore.dataset as ds -# 下载Coco数据集，将其解压到CocoData目录 DATA_DIR = "COCO/train/" ANNOTATION_FILE = "COCO/annotations/train.json" KEYPOINT_FILE = "COCO/annotations/key_point.json" PANOPTIC_FILE = "COCO/annotations/panoptic.json" -# 使用CocoDataset读取数据集，指定为Detection任务，同时指定num_samples以获取1个样本数据 dataset = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Detection", num_samples=1) for data in dataset.create_dict_iterator(): print("Detection:", data.keys()) -# 让我们来观察一下，在指定Coco不同任务时，我们获取到的不同数据 -# Stuff 任务 dataset = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Stuff", num_samples=1) for data in dataset.create_dict_iterator(): print("Stuff:", data.keys()) -# Keypoint 任务 dataset = ds.CocoDataset(DATA_DIR, annotation_file=KEYPOINT_FILE, task="Keypoint", num_samples=1) for data in dataset.create_dict_iterator(): print("Keypoint:", data.keys()) -# Panoptic 任务 dataset = ds.CocoDataset(DATA_DIR, annotation_file=PANOPTIC_FILE, task="Panoptic", num_samples=1) for data in dataset.create_dict_iterator(): print("Panoptic:", data.keys()) ``` +输出结果如下： + ``` Detection: dict_keys(['bbox', 'image', 'iscrowd', 'category_id']) Stuff: dict_keys(['segmentation', 'iscrowd', 'image']) @@ -195,49 +159,51 @@ Keypoint: dict_keys(['keypoints', 'num_keypoints', 'image']) Panoptic: dict_keys(['bbox', 'image', 'area', 'category_id', 'iscrowd']) ``` -> 更多经典数据集加载接口说明，参见对应[API文档](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.dataset.html)。 - ## 特定格式数据集加载 +下面将介绍几种特定格式数据集文件的加载方式。 + ### MindRecord数据格式 -MindRecord是MindSpore的自研数据格式，具有更好的性能和特性。 +MindRecord是MindSpore定义的一种数据格式，使用MindRecord能够获得更好的性能提升。 ->阅读[数据格式转换](https://www.mindspore.cn/api/zh-CN/master/programming_guide/dataset_conversion.html)章节，了解如何将数据集转化为MindSpore数据格式。 +> 阅读[数据格式转换](https://www.mindspore.cn/api/zh-CN/master/programming_guide/dataset_conversion.html)章节，了解如何将数据集转化为MindSpore数据格式。 + +下面的样例通过`MindDataset`接口加载MindRecord文件，并展示已加载数据的标签。 ```python import mindspore.dataset as ds -# 指定MindRecord数据格式地址 DATA_DIR = "mindrecord_dataset_path" mindrecord_dataset = ds.MindDataset(DATA_DIR) -# 启动数据管道读取 for data in mindrecord_dataset.create_dict_iterator(output_numpy=True): print(data["label"]) ``` ### Manifest数据格式 -Manifest是华为ModelArts支持的数据格式文件，详细说明请参见相关[文档](https://support.huaweicloud.com/engineers-modelarts/modelarts_23_0009.html)。 +Manifest是华为ModelArts支持的数据格式文件，详细说明请参见[Manifest文档](https://support.huaweicloud.com/engineers-modelarts/modelarts_23_0009.html)。 + +下面的样例通过`ManifestDataset`接口加载Manifest文件，并展示已加载数据的标签。 ```python import mindspore.dataset as ds -# 指定Manifest数据集地址 DATA_DIR = "manifest_dataset_path" manifest_dataset = ds.ManifestDataset(DATA_DIR) -# 启动数据管道读取 for data in manifest_dataset.create_dict_iterator(): print(data["label"]) ``` ### TFRecord数据格式 -TFRecord是Tensorflow定义的一种二进制数据文件格式。 +TFRecord是TensorFlow定义的一种二进制数据文件格式。 -1. 传入数据集路径或`.tfrecord`文件列表，创建TFRecordDataset对象。 +下面的样例通过`TFRecordDataset`接口加载TFRecord文件，并介绍了两种不同的数据集格式设定方案。 + +1. 传入数据集路径或TFRecord文件列表，创建`TFRecordDataset`对象。 ```python import mindspore.dataset as ds @@ -246,77 +212,76 @@ TFRecord是Tensorflow定义的一种二进制数据文件格式。 dataset = ds.TFRecordDataset(DATA_DIR) ``` -2. 用户可以选择通过创建Schema文件或Schema类，设定数据集格式及特征。 - - - 创建Schema文件 - - Schema文件示例： - - ``` - { - "datasetType": "TF", - "numRows": 3, - "columns": { - "image": { - "type": "uint8", - "rank": 1 - }, - "label" : { - "type": "int64", - "rank": 1 +2. 用户可以通过编写Schema文件或创建Schema对象，设定数据集格式及特征。 + + - 编写Schema文件 + + 将数据集格式和特征按JSON格式写入Schema文件，示例如下： + + ``` + { + "datasetType": "TF", + "numRows": 3, + "columns": { + "image": { + "type": "uint8", + "rank": 1 + }, + "label" : { + "type": "int64", + "rank": 1 + } } } - } - ``` - - - `datasetType`: 数据格式的类型，这里`TF`是指TFrecord数据格式。 + ``` + - `datasetType`: 数据格式的类型，这里`TF`是指TFRecord数据格式。 - `columns`：列信息字段，需要根据数据集的实际列名定义，上面Schema文件示例中，数据集列为`image`和`label`两列。 - - `numRows`：行数信息字段，控制加载数据的最大行数。如果定义的行数大于实际行数，加载时则以实际行数为准。 - 在创建TFRecordDataset时将Schema文件路径传入。 + 然后在创建`TFRecordDataset`时将Schema文件路径传入。 - ```python - DATA_DIR = "tfrecord_dataset_path" - SCHEMA_DIR = "dataset_schema_path/schema.json" - dataset = ds.TFRecordDataset(DATA_DIR, schema=SCHEMA_DIR) - ``` + ```python + DATA_DIR = "tfrecord_dataset_path" + SCHEMA_DIR = "dataset_schema_path/schema.json" + dataset = ds.TFRecordDataset(DATA_DIR, schema=SCHEMA_DIR) + ``` - - 创建Schema类 + - 创建Schema对象 - ```python - import mindspore.common.dtype as mstype - schema = ds.Schema() - schema.add_column('image', de_type=mstype.uint8) - schema.add_column('label', de_type=mstype.int32) - dataset = ds.TFRecordDataset(DATA_DIR, schema=schema) - ``` + 创建Schema对象，为其添加自定义字段，然后在创建数据集对象时传入。 -### Numpy数据格式 + ```python + import mindspore.common.dtype as mstype + schema = ds.Schema() + schema.add_column('image', de_type=mstype.uint8) + schema.add_column('label', de_type=mstype.int32) + dataset = ds.TFRecordDataset(DATA_DIR, schema=schema) + ``` -如果所有数据已经读入内存，可以直接使用NumpySlicesDataset类将其加载。 +### NumPy数据格式 -- 加载Numpy arrays数据 +如果所有数据已经读入内存，可以直接使用`NumpySlicesDataset`类将其加载。 - ```python - # 从Numpy arrays构建数据管道 +下面的样例分别介绍了通过`NumpySlicesDataset`加载arrays数据、 list数据和dict数据的方式。 + +- 加载NumPy arrays数据 + ```python import numpy as np import mindspore.dataset as ds - # 使用numpy构建一个数组 features, labels = np.random.sample((5, 2)), np.random.sample((5, 1)) - # 从numpy中构建数据管道 - # 注意：传入参数需要是一个tuple，即是(features, labels)；column_names用于指定生成的数据集名称为col1, col2 + data = (features, labels) dataset = ds.NumpySlicesDataset(data, column_names=["col1", "col2"], shuffle=False) - # 启动数据管道 for data in dataset: print(data[0], " ", data[1]) ``` + 输出结果如下： + ``` [0.49893939 0.36348882] [0.15234002] [0.83845534 0.19721032] [0.94602561] @@ -328,22 +293,19 @@ TFRecord是Tensorflow定义的一种二进制数据文件格式。 - 加载Python list数据 ```python - # 从Python list构建数据管道 import mindspore.dataset as ds - # 构建一个list data1 = [[1, 2], [3, 4]] - # 从list中构建数据管道 - # column_names用于指定生成的数据集名称为col1 dataset = ds.NumpySlicesDataset(data1, column_names=["col1"], shuffle=False) - # 启动数据管道 for data in dataset: print(data[0]) ``` + 输出结果如下： + ``` [1 2] [3 4] @@ -352,60 +314,42 @@ TFRecord是Tensorflow定义的一种二进制数据文件格式。 - 加载Python dict数据 ```python - # 从Python dict构建数据管道 - import mindspore.dataset as ds - # 构建一个dict data1 = {"a": [1, 2], "b": [3, 4]} - # 从dict中构建数据管道 - # column_names用于指定生成的数据集名称为col1, col2 dataset = ds.NumpySlicesDataset(data1, column_names=["col1", "col2"], shuffle=False) - # 启动数据管道 for data in dataset.create_dict_iterator(): print(data) ``` + 输出结果如下： + ``` {'col1': Tensor(shape=[], dtype=Int64, value= 1), 'col2': Tensor(shape=[], dtype=Int64, value= 3)} {'col1': Tensor(shape=[], dtype=Int64, value= 2), 'col2': Tensor(shape=[], dtype=Int64, value= 4)} ``` -### text数据格式 - -```python -import mindspore.dataset as ds +### CSV数据格式 -# 指定text数据格式地址 -DATA_DIR = "text_file_path" -text_dataset = ds.TextFileDataset(DATA_DIR) +下面的样例通过`CSVDataset`加载CSV格式数据集文件，并展示了已加载数据的标签。 -# 启动数据管道读取 -for data in text_dataset.create_dict_iterator(output_numpy=True): - print(data["text"]) -``` - -### CSV数据格式 +Text格式数据集文件的加载方式与CSV文件类似。 ```python import mindspore.dataset as ds -# 指定CSV数据格式地址 DATA_DIR = "csv_file_path" csv_dataset = ds.CSVDataset(DATA_DIR) -# 启动数据管道读取 for data in csv_dataset.create_dict_iterator(output_numpy=True): print(data["1"]) ``` ->更多数据格式文件加载说明，参见对应[API文档](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.dataset.html)。 - ## 自定义数据集加载 -对于目前MindSpore不支持直接加载的数据集，可以通过构造GeneratorDataset对象实现自定义方式的加载，或者将其转换成MindRecord数据格式。目前自定义数据集加载有以下几种方式。 +对于目前MindSpore不支持直接加载的数据集，可以通过构造`GeneratorDataset`对象实现自定义方式的加载，或者将其转换成MindRecord数据格式。目前自定义数据集加载有以下几种方式。 ### 构造数据集生成函数 @@ -415,23 +359,22 @@ for data in csv_dataset.create_dict_iterator(output_numpy=True): import numpy as np import mindspore.dataset as ds -# 随机生成一个数据集 np.random.seed(58) data = np.random.sample((5, 2)) label = np.random.sample((5, 1)) -# 自定义数据返回方式 def GeneratorFunc(): for i in range(5): yield (data[i], label[i]) -# 构建自定义数据集对象 dataset = ds.GeneratorDataset(GeneratorFunc, ["data", "label"]) for data in dataset.create_dict_iterator(): print(data["data"], data["label"]) ``` +输出结果如下： + ``` [0.36510558 0.45120592] [0.78888122] [0.49606035 0.07562207] [0.38068183] @@ -476,6 +419,8 @@ for data in dataset.create_dict_iterator(): print(data["data"], data["label"]) ``` +输出结果如下： + ``` [0.36510558 0.45120592] [0.78888122] [0.49606035 0.07562207] [0.38068183] @@ -511,6 +456,8 @@ for data in dataset.create_dict_iterator(): print(data["data"], data["label"]) ``` +输出结果如下： + ``` [0.36510558 0.45120592] [0.78888122] [0.49606035 0.07562207] [0.38068183] @@ -549,6 +496,8 @@ for data in dataset.create_dict_iterator(): print(data["data"], data["label"]) ``` +输出结果如下： + ``` [0.36510558 0.45120592] [0.78888122] [0.57176158 0.28963401] [0.16271622] diff --git 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z@bF9RiL;skS4Md>bxrcr}j{R)2vn2JLy3KO@x(#~gOiQ>#%xm}2<+t2GI8&% z$TNL3xL9Wre^B?Q@4qeACR#x`ORHdaOJw2gK5J -- [实现简单线性函数拟合](#实现简单线性函数拟合) - - [概述](#概述) - - [环境准备](#环境准备) - - [生成数据集](#生成数据集) - - [定义数据集生成函数](#定义数据集生成函数) - - [生成测试数据](#生成测试数据) +- [概述](#概述) +- [环境准备](#环境准备) +- [生成数据集](#生成数据集) + - [定义数据集生成函数](#定义数据集生成函数) + - [定义数据增强函数](#定义数据增强函数) +- [定义训练网络](#定义训练网络) +- [定义前向传播网络与反向传播网络并关联](#定义前向传播网络与反向传播网络并关联) - [定义前向传播网络](#定义前向传播网络) - - [初始化网络模型](#初始化网络模型) - - [查看初始化的网络模型](#查看初始化的网络模型) - - [定义损失函数](#定义损失函数) - - [损失函数与网络结合](#损失函数与网络结合) - [定义反向传播网络](#定义反向传播网络) - - [实现梯度函数](#实现梯度函数) - - [反向传播更新权重](#反向传播更新权重) - - [定义模型拟合过程可视化函数](#定义模型拟合过程可视化函数) - - [执行训练](#执行训练) - - [总结](#总结) + - [关联前向和反向传播网络](#关联前向和反向传播网络) +- [拟合过程可视化准备](#拟合过程可视化准备) + - [定义绘图函数](#定义绘图函数) + - [定义回调函数](#定义回调函数) +- [执行训练](#执行训练) +- [总结](#总结) @@ -30,374 +26,335 @@    + ## 概述 回归问题算法通常是利用一系列属性来预测一个值，预测的值是连续的。例如给出一套房子的一些特征数据，如面积、卧室数等等来预测房价，利用最近一周的气温变化和卫星云图来预测未来的气温情况等。如果一套房子实际价格为500万元，通过回归分析的预测值为499万元，则认为这是一个比较好的回归分析。在机器学习问题中，常见的回归分析有线性回归、多项式回归、逻辑回归等。本例子介绍线性回归算法，并通过MindSpore进行线性回归AI训练体验。 -主要流程如下： +整体流程如下： 1. 生成数据集 -2. 定义前向传播网络 -3. 定义反向传播网络 -4. 定义线性拟合过程的可视化函数 +2. 定义训练网络 +3. 定义前向传播网络与反向传播网络并关联 +4. 拟合过程可视化准备 5. 执行训练 -本次样例源代码请参考：。 - ## 环境准备 -系统：Ubuntu18.04 +系统：Windows 10 X86，Linux Ubuntu 18.04 -MindSpore版本：GPU +MindSpore版本：CPU-Windows，CPU-Linux 设置MindSpore运行配置 第三方支持包：`matplotlib`，未安装此包的，可使用命令`pip install matplotlib`预先安装。 + ```python from mindspore import context -context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU") +context.set_context(mode=context.GRAPH_MODE, device_target="CPU") ``` -`PYNATIVE_MODE`：自定义调试模式。 +`GRAPH_MODE`：自定义调试模式。 -`device_target`：设置MindSpore的训练硬件为GPU。 +`device_target`：设置MindSpore的训练硬件为CPU。 ## 生成数据集 ### 定义数据集生成函数 -`get_data`用于生成训练数据集和测试数据集。由于拟合的是线性数据，假定要拟合的目标函数为：$y=2x+3$，那么我们需要的训练数据集应随机分布于函数周边，这里采用了$y=2x+3+noise$的方式生成，其中`noise`为遵循标准正态分布规律的随机数值。 +`get_data`用于生成训练数据集和测试数据集。由于拟合的是线性数据，假定要拟合的目标函数为：$f(x)=2x+3$，那么我们需要的训练数据集应随机分布于函数周边，这里采用了$f(x)=2x+3+noise$的方式生成，其中`noise`为遵循标准正态分布规律的随机数值。 ```python import numpy as np -import mindspore as ms -from mindspore import Tensor - -def get_data(num,w=2.0, b=3.0): - np_x = np.ones([num, 1]) - np_y = np.ones([num, 1]) + +def get_data(num, w=2.0, b=3.0): for i in range(num): x = np.random.uniform(-10.0, 10.0) - np_x[i] = x noise = np.random.normal(0, 1) y = x * w + b + noise - np_y[i] = y - return Tensor(np_x,ms.float32), Tensor(np_y,ms.float32) + yield np.array([x]).astype(np.float32), np.array([y]).astype(np.float32) ``` -数据生成函数将有以下两个作用。 - -1. 生成训练数据，对模型函数进行训练。 -2. 生成验证数据，在训练结束后，对模型函数进行精度验证。 - -### 生成测试数据 - -使用数据生成函数`get_data`随机生成50组验证数据，并可视化展示。 +使用`get_data`生成50组测试数据，可视化展示。 ```python import matplotlib.pyplot as plt -eval_x, eval_label = get_data(50) -x1, y1 = eval_x.asnumpy(), eval_label.asnumpy() -plt.scatter(x1, y1, color="red", s=5) -plt.title("Eval_data") +eval_data = list(get_data(50)) +x_target_label = np.array([-10, 10, 0.1]) +y_target_label = x_target_label * 2 + 3 +x_eval_label,y_eval_label = zip(*eval_data) + +plt.scatter(x_eval_label, y_eval_label, color="red", s=5) +plt.plot(x_target_label, y_target_label, color="green") +plt.title("Eval data") plt.show() ``` 输出结果： -![png](./images/linear_regression_eval_datasets.png) +![png](./images/linear_regression_eval_datasets.png) -## 定义前向传播网络 -### 初始化网络模型 +上图中绿色线条部分为目标函数，红点部分为验证数据`eval_data`。 -使用`nn.Dense`定义了网络模型，即为线性模型， +### 定义数据增强函数 -$$y=wx+b\tag{1}$$ +先使用MindSpore的数据转换函数`GeneratorDataset`转换成适应MindSpore训练的数据类型，然后再使用`batch`、`repeat`对数据进行增强操作，操作解释如下： -其中，权重值$w$对应`weight`，$b$对应`bias`，并将其打印出来。 +- `ds.GeneratorDataset`：将生成的数据转换为MindSpore的数据集，并且将生成的数据的x，y值存入到`data`和`label`的数组中。 +- `batch`：将`batch_size`个数据组合成一个batch。 +- `repeat`：将数据集数量倍增。 ```python -from mindspore.common.initializer import TruncatedNormal -from mindspore import nn +from mindspore import dataset as ds -net = nn.Dense(1,1,TruncatedNormal(0.02),TruncatedNormal(0.02)) -print("weight:", net.weight.set_data([0][0]), "bias:", net.bias.set_data([0])) +def create_dataset(num_data, batch_size=16, repeat_size=1): + input_data = ds.GeneratorDataset(list(get_data(num_data)), column_names=['data', 'label']) + input_data = input_data.batch(batch_size) + input_data = input_data.repeat(repeat_size) + return input_data ``` -输出结果： +使用数据集增强函数生成训练数据，并查看训练数据的格式。 - weight: -0.00034249047 bias: -0.019308656 - - -### 查看初始化的网络模型 -我们将验证数据集和初始化的模型函数可视化。 +```python +num_data = 1600 +batch_size = 16 +repeat_size = 1 +ds_train = create_dataset(num_data, batch_size=batch_size, repeat_size=repeat_size) +print("The dataset size of ds_train:", ds_train.get_dataset_size()) +dict_datasets = ds_train.create_dict_iterator().get_next() -```python -x = np.arange(-10, 10, 0.1) -y = x * (net.weight.set_data([0][0]).asnumpy()) + (net.bias.set_data([0]).asnumpy()) -plt.scatter(x1, y1, color="red", s=5) -plt.plot(x, y, "blue") -plt.title("Eval data and net") -plt.show() +print(dict_datasets.keys()) +print("The x label value shape:", dict_datasets["data"].shape) +print("The y label value shape:", dict_datasets["label"].shape) ``` 输出结果： -![png](./images/model_net_and_eval_datasets.png) - - -红色的点：为之前生成的50组验证数据集。 - -蓝色的线：初始化的模型网络。 - -### 定义损失函数 - -我们的网络模型表达式为： - -$$h(x)=wx+b\tag{2}$$ - -一般地，数学上对线性回归模型采用均方差的方式来判断模型是否拟合得很好，即均方差的值$J(w)$值越小，函数模型便拟合得越好，验证数据代入后，预测得到的y值就越准确。公式2对应m个数据的均方差公式为： - -$$J(w)=\frac{1}{m}\sum_{i=1}^m(h(x_i)-y^{(i)})^2\tag{3}$$ - -为了方便后续的计算，我们采用0.5倍的均方差的表达式来进行计算，均方差值整体缩小至0.5倍的计算方式对判断模型拟合的好坏没有影响。 - -$$J(w)=\frac{1}{2m}\sum_{i=1}^m(h(x_i)-y^{(i)})^2\tag{4}$$ - -公式4即为网络训练中的损失函数，其中参数： + The dataset size of ds_train: 100 + dict_keys(['data', 'label']) + The x label value shape: (16, 1) + The y label value shape: (16, 1) + -- $J(w)$为均方差。 +通过定义的`create_dataset`将生成的1600个数据增强为了100组shape为16x1的数据集。 -- $m$为样本数据的数量。 +## 定义训练网络 -- $h(x_i)$为第$i$个数据的$x_i$值代入模型网络（公式2）后的预测值。 +在MindSpore中使用`nn.Dense`生成单个数据输入，单个数据暑促和的线性模型为： -- $y^{(i)}$为第$i$个数据中的$y$值（label值）。 +$$f(x)=wx+b\tag{1}$$ -在MindSpore中定义损失函数的方法如下。 +并使用Normal算子随机初始化权重$w$和$b$。 ```python -from mindspore.ops import operations as P - -class MyLoss(nn.loss.loss._Loss): - def __init__(self,reduction='mean'): - super().__init__(reduction) - self.square = P.Square() - def construct(self, data, label): - x = self.square(data-label) * 0.5 - return self.get_loss(x) -``` - -其中: - -- `nn.loss.loss._Loss`：是MindSpore自定义loss算子的一个基类。 +from mindspore.common.initializer import Normal +from mindspore import nn -- `P.Square`：MindSpore训练的框架中的平方算子，算子需要注册过才能在框架的计算图中使用。 +class LinearNet(nn.Cell): + def __init__(self): + super(LinearNet, self).__init__() + self.fc = nn.Dense(1, 1, Normal(0.02), Normal(0.02)) -### 损失函数与网络结合 + def construct(self, x): + x = self.fc(x) + return x +``` -接下来我们需要将loss函数的表达式和网络net关联在一起，在MindSpore中需要`nn.WithLossCell`，实现方法如下： +调用网络查看初始化的模型参数。 ```python -criterion = MyLoss() -loss_opeartion = nn.WithLossCell(net, criterion) +net = LinearNet() +model_params = net.trainable_params() +print(model_params) ``` -其中： - -- `net`：网络模型。 - -- `criterion`：即为实例化的loss函数。 - -上述从数据代入到计算出loss值的过程为AI训练中的前向传播过程。 - -## 定义反向传播网络 +输出结果： -有了损失函数后，我们如何使得损失函数最小呢？我们可以将公式1代入到损失函数公式4中展开： + [Parameter (name=fc.weight, value=Tensor(shape=[1, 1], dtype=Float32, + [[-7.35660456e-003]])), Parameter (name=fc.bias, value=Tensor(shape=[1], dtype=Float32, [-7.35660456e-003]))] + -$$J(w,b)=\frac{1}{2m}\sum_{i=1}^m(wx_i+b-y^{(i)})^2\tag{5}$$ +初始化网络模型后，接下来将初始化的网络函数和训练数据集进行可视化，了解拟合前的模型函数情况。 -公式5可以将$J(w)$看作为凹函数，对权重值$w$微分可求得： -$$\frac{\partial{J(w)}}{\partial{w}}=\frac{1}{m}\sum_{i=1}^mx_i(wx_i+b-y^{(i)})\tag{6}$$ +```python +from mindspore import Tensor +x_model_label = np.array([-10, 10, 0.1]) +y_model_label = (x_model_label * Tensor(model_params[0]).asnumpy()[0][0] + + Tensor(model_params[1]).asnumpy()[0]) -由凹函数的特性可以知道，当公式6等于0时，损失函数有最小值： +plt.scatter(x_eval_label, y_eval_label, color="red", s=5) +plt.plot(x_model_label, y_model_label, color="blue") +plt.plot(x_target_label, y_target_label, color="green") +plt.show() +``` -$$\sum_{i=1}^mx_i(wx_i+b-y^{(i)})=0\tag{7}$$ +输出结果： -假设有一个$w_{min}$使得公式7成立。我们如何将初始的权重$w_{s}$逐步的变成$w_{min}$，在这里采取迭代法，也就是梯度下降方法 -当权重$w_{s}w_{min}$，权重值需要左移即权重值变小接近$w_{min}$，才能使得损失函数逐步的变小，由凹函数的性质可知，在$w_{s}$处的导数为正（损失函数在$w_{min}$右边单调上升），公式8的值为正。其权重的更新公式为： +- $h(x_i)$为第$i$个数据的$x_i$值代入模型网络（公式1）后的预测值。 -$$w_{ud}=w_{s}-\alpha\frac{\partial{J(w_{s})}}{\partial{w}}\tag{10}$$ +- $y^{(i)}$为第$i$个数据中的$y^{(i)}$值（label值）。 +### 定义前向传播网络 -当$w_{s}=w_{min}$时，到$\frac{\partial{J(w_{s})}}{\partial{w}}$=0，即梯度消失，其表达式也可写为公式9的样式。 +前向传播网络包含两个部分，其中： -在考虑了全区间的情况后，可以得出权重$w$的更新公式即为： +1. 将参数带入到模型网络中得出预测值。 +2. 使用预测值和训练数据计算出loss值。 -$$w_{ud}=w_{s}-\alpha\frac{\partial{J(w_{s})}}{\partial{w}}\tag{11}$$ +在MindSpore中使用如下方式实现。 -当权重$w$在更新的过程中假如临近$w_{min}$在增加或者减少一个$\Delta{w}$，从左边或者右边越过了$w_{min}$，公式11都会使权重往反的方向移动，那么最终$w_{s}$的值会在$w_{min}$附近来回迭代，在实际训练中我们也是这样采用迭代的方式取得最优权重$w$，使得损失函数无限逼近局部最小值。 -同理：对于公式5中的另一个权重$b$容易得出其更新公式为： +```python +net = LinearNet() +net_loss = nn.loss.MSELoss() +``` -$$b_{ud}=b_{s}-\alpha\frac{\partial{J(b_{s})}}{\partial{b}}\tag{12}$$ +### 定义反向传播网络 +反向传播网络的目标是不断变换权重值，使得loss值取得最小值，一般的在线性网络中采用权重更新公式： -当所有的权重更新完成后，将新的权重赋值给初始权重：即$w_{s}$=$w_{ud}$，$b_{s}$=$b_{ud}$。将新的初始权重传递回到模型函数中，这样就完成了反向传播的过程。 +$$w_{t}=w_{t-1}-\alpha\frac{\partial{J(w_{t-1})}}{\partial{w}}\tag{3}$$ -> 当遇到多项式的回归模型时，上述梯度方法也适用，由于权重数量的增加，需要将权重的名称更新为$w_0,w_1,w_2,...,w_n$，引入矩阵的表达方式，公式将会更加简洁，这里就不多介绍了。 +公式3参数解释： -### 实现梯度函数 +- $w_{t}$为迭代后的权重值。 +- $w_{t-1}$为迭代前的权重值。 +- $\alpha$为学习率。 +- $\frac{\partial{J(w_{t-1})}}{\partial{w}}$为损失函数对权重$w_{t-1}$的微分。 -在MindSpore中的所有要编入计算图的类都需要继承`nn.Cell`算子。MindSpore的梯度计算函数采用如下方式。 +函数中所有的权重值更新完成后，将值传入到模型函数中，这个过程就是反向传播过程，实现此过程需要使用MindSpore中的优化器函数，如下： ```python -from mindspore.ops import composite as C - -class GradWrap(nn.Cell): - """ GradWrap definition """ - def __init__(self, network): - super().__init__(auto_prefix=False) - self.network = network - self.weights = ms.ParameterTuple(filter(lambda x: x.requires_grad, - network.get_parameters())) - - def construct(self, data, label): - weights = self.weights - return C.GradOperation(get_by_list=True) \ - (self.network, weights)(data, label) - +opt = nn.Momentum(net.trainable_params(), learning_rate=0.005, momentum=0.9) ``` -上述代码中`GradWrap`实现的是对各个权重的微分$\frac{\partial{J(w)}}{\partial{w}}$，其展开式子参考公式6。 - -### 反向传播更新权重 +### 关联前向和反向传播网络 -`nn.RMSProp`为完成权重更新的函数，更新方式大致为公式11，但是考虑的因素更多，具体信息请参考[官网说明](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html?highlight=rmsprop#mindspore.nn.RMSProp)。 +定义完成前向传播和反向传播后，在MindSpore中需要调用`Model`函数，将前面定义的网络，损失函数，优化器函数关联起来，使之变成完整的计算网络。 ```python -train_network = GradWrap(loss_opeartion) -train_network.set_train() -optim = nn.RMSProp(params=net.trainable_params(),learning_rate=0.02) +from mindspore.train import Model + +model = Model(net, net_loss, opt) ``` -通过以上操作，我们就完成了前向传播网络和反向传播网络的定义，接下来可以加载训练数据进行线性拟合了。 +## 拟合过程可视化准备 -## 定义模型拟合过程可视化函数 +### 定义绘图函数 -定义一个可视化函数`plot_model_and_datasets`，将模型函数和验证数据集打印出来，观察其变化。 +为了使得整个训练过程更容易理解，需要将训练过程的测试数据、目标函数和模型网络进行可视化，这里定义了可视化函数，将在每个step训练结束后调用，展示模型网络的拟合过程。 ```python -import time +import matplotlib.pyplot as plt +import time -def plot_model_and_datasets(weight, bias, data_x, data_y): +def plot_model_and_datasets(net, eval_data): + weight = net.trainable_params()[0] + bias = net.trainable_params()[1] x = np.arange(-10, 10, 0.1) - y = x * ((weight[0][0]).asnumpy()) + ((bias[0]).asnumpy()) - plt.scatter(x1,y1,color="red",s=5) - plt.scatter(data_x.asnumpy(), data_y.asnumpy(), color="black", s=5) - plt.plot(x, y, "blue") + y = x * Tensor(weight).asnumpy()[0][0] + Tensor(bias).asnumpy()[0] + x1, y1 = zip(*eval_data) + x_target = x + y_target = x_target * 2 + 3 + plt.axis([-11, 11, -20, 25]) + plt.scatter(x1, y1, color="red", s=5) + plt.plot(x, y, color="blue") + plt.plot(x_target, y_target, color="green") plt.show() time.sleep(0.02) ``` -上述函数的参数： - -- `weight`：模型函数的权重，即$w$。 +### 定义回调函数 -- `bias`：模型函数的权重，即$b$。 +MindSpore提供的工具，可对模型训练过程进行自定义控制，这里在`step_end`中调用可视化函数，展示拟合过程。更多的使用可参考[官网说明](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/customized_debugging_information.html#callback) -- `data_x`：训练数据的$x$值。 +- `display.clear_output`：清除打印内容，实现动态拟合效果。 -- `data_y`：训练数据的$y$值。 -> 可视化过程中，红色的点是验证数据集，黑色的点是单个batch的训练数据，蓝色的线条是正在训练的回归模型。 +```python +from IPython import display +from mindspore.train.callback import Callback + +class ImageShowCallback(Callback): + def __init__(self, net, eval_data): + self.net = net + self.eval_data = eval_data + + def step_end(self, run_context): + plot_model_and_datasets(self.net, self.eval_data) + display.clear_output(wait=True) +``` ## 执行训练 -其训练过程如下： - -1. 设置训练的迭代次数`step_size`。 -2. 设置单次迭代的训练数据量`batch_size`。 -3. 正向传播训练`grads`。 -4. 反向传播训练`optim`。 -5. 图形展示模型函数和数据集。 -6. 清除本轮迭代的输出`display.clear_output`，起到动态可视化效果。 +完成以上过程后，可以使用训练数`ds_train`对模型训练，这里调用`model.train`进行，其中参数解释： -迭代完成后，输出网络模型的权重值$w$和$b$。 +- `epoch`：训练迭代的整个数据集的次数，int类型。 +- `ds_train`：训练数据集。 +- `callbacks`：训练过程中需要调用的回调函数。 +- `dataset_sink_model`：数据集下沉模式，bool类型，支持Ascend，GPU计算平台，本例为CPU计算平台设置为False。 ```python -from IPython import display -step_size = 200 -batch_size = 16 +from mindspore.train.callback import LossMonitor -for i in range(step_size): - data_x,data_y = get_data(batch_size) - grads = train_network(data_x,data_y) - optim(grads) - plot_model_and_datasets(net.weight.data, - net.bias.data, data_x, data_y) - display.clear_output(wait=True) - -output = net(eval_x) -loss_output = criterion(output, eval_label) -print("loss_value:", loss_output.asnumpy()) -plot_model_and_datasets(net.weight.data, net.bias.data, data_x,data_y) -print("weight:", net.weight.set_data([0][0]), "bias:", net.bias.set_data([0])) +epoch = 1 +imageshow_cb = ImageShowCallback(net, eval_data) +model.train(epoch, ds_train, callbacks=[imageshow_cb], dataset_sink_mode=False) + +plot_model_and_datasets(net,eval_data) +print(net.trainable_params()[0], "\n%s" % net.trainable_params()[1]) ``` 输出结果： - loss_value: 0.42879593 - - - ![gif](./images/linear_regression.gif) - weight: 1.9990227 bias: 2.9115517 + Parameter (name=fc.weight, value=[[2.0065749]]) + Parameter (name=fc.bias, value=[3.0089042]) -可以看到最终得到的线性拟合的权重值非常接近目标函数权重weight=2、bias=3。 +训练完成后打印出最终模型的权重参数，其中weight接近于2.0，bias接近于3.0，模型训练完成，符合预期。 ## 总结 diff --git a/tutorials/tutorial_code/linear_regression.py b/tutorials/tutorial_code/linear_regression.py index 6e53c6ae09..35003f67b2 100644 --- a/tutorials/tutorial_code/linear_regression.py +++ b/tutorials/tutorial_code/linear_regression.py @@ -1,74 +1,53 @@ import numpy as np -import mindspore as ms -from mindspore.ops import composite as C -from mindspore.ops import operations as P -from mindspore import Tensor -from mindspore import context -from mindspore.common.initializer import TruncatedNormal +from mindspore import dataset as ds +from mindspore.common.initializer import Normal from mindspore import nn - -context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU") +from mindspore.train import Model +from mindspore.train.callback import LossMonitor +from mindspore import context + +context.set_context(mode=context.GRAPH_MODE, device_target="CPU") + -# Generating training data sets def get_data(num, w=2.0, b=3.0): - np_x = np.ones([num, 1]) - np_y = np.ones([num, 1]) for i in range(num): - x = np.random.uniform(-10.0, 10.0) - np_x[i] = x + x = np.random.uniform(-10.0, 10.0) noise = np.random.normal(0, 1) - y = x * w + b + noise - np_y[i]=y - return Tensor(np_x,ms.float32), Tensor(np_y,ms.float32) - -# Define the form of loss function: 1/2 * (y - y')^2 -class MyLoss(nn.loss.loss._Loss): - def __init__(self, reduction='mean'): - super().__init__(reduction) - self.square = P.Square() - def construct(self, data, label): - x = self.square(data - label) * 0.5 - return self.get_loss(x) - -# Gradient function -class GradWrap(nn.Cell): - """ GradWrap definition """ - def __init__(self, network): - super().__init__(auto_prefix=False) - self.network = network - self.weights = ms.ParameterTuple(filter(lambda x: x.requires_grad, - network.get_parameters())) - - def construct(self, data, label): - weights = self.weights - return C.GradOperation(get_by_list=True) \ - (self.network, weights)(data, label) - -# Initializing model functions -net = nn.Dense(1, 1, TruncatedNormal(0.02), TruncatedNormal(0.02)) - -# Loss function -criterion = MyLoss() -loss_opeartion = nn.WithLossCell(net, criterion) -train_network = GradWrap(loss_opeartion) -train_network.set_train() - -# Defining optimization -optim = nn.RMSProp(params=net.trainable_params(), learning_rate=0.02) - -# Executive Training -step_size = 200 -batch_size = 16 -for i in range(step_size): - data_x, data_y = get_data(batch_size) - grads = train_network(data_x, data_y) - optim(grads) - - # Print loss value per 10 step - if i%10 == 0: - output = net(data_x) - loss_output = criterion(output, data_y) - print(loss_output.asnumpy()) - -# Print final weight parameters -print("weight:", net.weight.set_data([0][0]), "bias:", net.bias.set_data([0])) \ No newline at end of file + y = x * w + b + noise + yield np.array([x]).astype(np.float32), np.array([y]).astype(np.float32) + + +def create_dataset(num_data, batch_size=16, repeat_size=1): + input_data = ds.GeneratorDataset(list(get_data(num_data)), column_names=['data','label']) + input_data = input_data.batch(batch_size) + input_data = input_data.repeat(repeat_size) + return input_data + + +class LinearNet(nn.Cell): + def __init__(self): + super(LinearNet, self).__init__() + self.fc = nn.Dense(1, 1, Normal(0.02), Normal(0.02)) + + def construct(self, x): + x = self.fc(x) + return x + + +if __name__ == "__main__": + + num_data = 1600 + batch_size = 16 + repeat_size = 1 + lr = 0.005 + momentum = 0.9 + + net = LinearNet() + net_loss = nn.loss.MSELoss() + opt = nn.Momentum(net.trainable_params(), lr, momentum) + model = Model(net, net_loss, opt) + + ds_train = create_dataset(num_data, batch_size=batch_size, repeat_size=repeat_size) + model.train(1, ds_train, callbacks=LossMonitor(), dataset_sink_mode=False) + + print(net.trainable_params()[0], "\n%s" % net.trainable_params()[1]) \ No newline at end of file -- Gitee From 82cd4b474f8aea4684f950c7630b974e572fbb6e Mon Sep 17 00:00:00 2001 From: Ting Wang Date: Tue, 15 Sep 2020 21:11:46 +0800 Subject: [PATCH 040/156] update programming guide Signed-off-by: Ting Wang --- api/source_zh_cn/index.rst | 3 ++ .../programming_guide/advanced_use.rst | 9 +++++ .../programming_guide/callback.md | 9 +++-- api/source_zh_cn/programming_guide/cell.md | 4 ++ .../programming_guide/compute_component.rst | 1 + api/source_zh_cn/programming_guide/context.md | 4 +- .../execution_management.rst | 9 +++++ api/source_zh_cn/programming_guide/optim.md | 8 ++-- .../programming_guide/security_and_privacy.md | 40 +++++++++---------- api/source_zh_cn/programming_guide/train.md | 1 - .../programming_guide/user-defined.rst | 7 ++++ 11 files changed, 64 insertions(+), 31 deletions(-) create mode 100644 api/source_zh_cn/programming_guide/advanced_use.rst create mode 100644 api/source_zh_cn/programming_guide/execution_management.rst create mode 100644 api/source_zh_cn/programming_guide/user-defined.rst diff --git a/api/source_zh_cn/index.rst b/api/source_zh_cn/index.rst index 8763f86224..34b1223c63 100644 --- a/api/source_zh_cn/index.rst +++ b/api/source_zh_cn/index.rst @@ -13,6 +13,9 @@ MindSpore API programming_guide/api_structure programming_guide/data_type programming_guide/compute_component + programming_guide/execution_management + programming_guide/auto_parallel_context + programming_guide/advanced_use .. toctree:: :maxdepth: 1 diff --git a/api/source_zh_cn/programming_guide/advanced_use.rst b/api/source_zh_cn/programming_guide/advanced_use.rst new file mode 100644 index 0000000000..2102ce044e --- /dev/null +++ b/api/source_zh_cn/programming_guide/advanced_use.rst @@ -0,0 +1,9 @@ +执行管理 +=========== + +.. toctree:: + :maxdepth: 1 + + train + user_defined + security_and_privacy \ No newline at end of file diff --git a/api/source_zh_cn/programming_guide/callback.md b/api/source_zh_cn/programming_guide/callback.md index 5717a72a47..d46164fc7b 100644 --- a/api/source_zh_cn/programming_guide/callback.md +++ b/api/source_zh_cn/programming_guide/callback.md @@ -1,10 +1,11 @@ -# MindSpore Callback回调函数机制 +# Callback -- [概述](#概述) -- [MindSpore内置回调函数](#mindspore内置回调函数) -- [MindSpore自定义回调函数](#mindspore自定义回调函数) +- [Callback](#callback) + - [概述](#概述) + - [MindSpore内置回调函数](#mindspore内置回调函数) + - [MindSpore自定义回调函数](#mindspore自定义回调函数) diff --git a/api/source_zh_cn/programming_guide/cell.md b/api/source_zh_cn/programming_guide/cell.md index 76b78ef77c..207ab17ee2 100644 --- a/api/source_zh_cn/programming_guide/cell.md +++ b/api/source_zh_cn/programming_guide/cell.md @@ -15,6 +15,7 @@ - [损失函数](#损失函数) - [内置损失函数](#内置损失函数) - [应用实例](#应用实例-1) + - [优化算法](#优化算法) - [构建自定义网络](#构建自定义网络) @@ -304,6 +305,9 @@ print(loss(input_data, target_data)) 此用例构造了两个Tensor数据，利用`nn.L1Loss()`接口定义了loss，将`input_data`和`target_data`传入loss，执行L1Loss的计算，结果为1.5。若loss = nn.L1Loss(reduction='sum')，则结果为9.0。若loss = nn.L1Loss(reduction='none')，结果为[[1. 0. 2.] [1. 2. 3.]]。 +## 优化算法 + +`mindspore.nn.optim`是MindSpore框架中实现各种优化算法的模块，详细说明参见[optim](https://www.mindspore.cn/api/zh-CN/master/programming_guide/optim.html)。 ## 构建自定义网络 diff --git a/api/source_zh_cn/programming_guide/compute_component.rst b/api/source_zh_cn/programming_guide/compute_component.rst index de586587e0..16a1aee634 100644 --- a/api/source_zh_cn/programming_guide/compute_component.rst +++ b/api/source_zh_cn/programming_guide/compute_component.rst @@ -4,5 +4,6 @@ .. toctree:: :maxdepth: 1 + parameter cell network_component \ No newline at end of file diff --git a/api/source_zh_cn/programming_guide/context.md b/api/source_zh_cn/programming_guide/context.md index 12f3d3571c..81f4a27ce6 100644 --- a/api/source_zh_cn/programming_guide/context.md +++ b/api/source_zh_cn/programming_guide/context.md @@ -1,8 +1,8 @@ -# 运行管理context +# context -- [运行管理context](#运行管理context) +- [context](#context) - [概述](#概述) - [执行模式管理](#执行模式管理) - [模式选择](#模式选择) diff --git a/api/source_zh_cn/programming_guide/execution_management.rst b/api/source_zh_cn/programming_guide/execution_management.rst new file mode 100644 index 0000000000..b57742c1c7 --- /dev/null +++ b/api/source_zh_cn/programming_guide/execution_management.rst @@ -0,0 +1,9 @@ +执行管理 +=========== + +.. toctree:: + :maxdepth: 1 + + context + run + callback \ No newline at end of file diff --git a/api/source_zh_cn/programming_guide/optim.md b/api/source_zh_cn/programming_guide/optim.md index 28b3a65289..69ca81fdeb 100644 --- a/api/source_zh_cn/programming_guide/optim.md +++ b/api/source_zh_cn/programming_guide/optim.md @@ -1,13 +1,13 @@ -# Optim +# optim -- [Optim](#Optim) +- [optim](#optim) - [概述](#概述) - [学习率](#学习率) - [dynamic_lr](#dynamic_lr) - [learning_rate_schedule](#learning_rate_schedule) - - [optimzer](#optimzer) + - [Optimzer](#optimzer) - [如何使用](#如何使用) - [内置优化器](#内置优化器) @@ -98,7 +98,7 @@ if __name__ == '__main__': ``` -## optimzer +## Optimzer ### 如何使用 为了使用`mindspore.nn.optim`，我们需要构建一个`Optimizer`对象。这个对象能够保持当前参数状态并基于计算得到的梯度进行参数更新。 diff --git a/api/source_zh_cn/programming_guide/security_and_privacy.md b/api/source_zh_cn/programming_guide/security_and_privacy.md index 8b6d6c20c9..7fb6c5d187 100644 --- a/api/source_zh_cn/programming_guide/security_and_privacy.md +++ b/api/source_zh_cn/programming_guide/security_and_privacy.md @@ -2,60 +2,60 @@ -- [AI安全与隐私保护](AI安全与隐私保护) +- [AI安全与隐私保护](#ai安全与隐私保护) - [概述](#概述) - [对抗鲁棒性](#对抗鲁棒性) - - [Attack](#Attack) - - [Defense](#Defense) - - [Detector](#Detector) + - [Attack](#attack) + - [Defense](#defense) + - [Detector](#detector) - [模型安全测试](#模型安全测试) - - [Fuzzer](#Fuzzer) + - [Fuzzer](#fuzzer) - [差分隐私训练](#差分隐私训练) - - [DPModel](#DPModel) + - [DPModel](#dpmodel) - [隐私泄露风险评估](#隐私泄露风险评估) - - [MembershipInference](#MembershipInference) + - [MembershipInference](#membershipinference) -## 概述 + -本篇是AI安全与隐私保护的编程指南。 +## 概述 -AI作为一种通用技术，在带来巨大机遇和效益的同时也面临着新的安全与隐私保护的挑战。MindArmour是MindSpore的一个子项目，为MindSpore提供安全与隐私保护能力，主要包括对抗鲁棒性、模型安全测试、差分隐私训练、隐私泄露风险评估等技术。 +本篇主要介绍AI安全与隐私保护。AI作为一种通用技术，在带来巨大机遇和效益的同时也面临着新的安全与隐私保护的挑战。MindArmour是MindSpore的一个子项目，为MindSpore提供安全与隐私保护能力，主要包括对抗鲁棒性、模型安全测试、差分隐私训练、隐私泄露风险评估等技术。 ## 对抗鲁棒性 ### Attack -Attack基类定义了对抗样本生成的使用接口，其子类实现了各种具体的生成算法，支持安全工作人员快速高效地生成对抗样本，用于攻击AI模型，以评估模型的鲁棒性。 +`Attack`基类定义了对抗样本生成的使用接口，其子类实现了各种具体的生成算法，支持安全工作人员快速高效地生成对抗样本，用于攻击AI模型，以评估模型的鲁棒性。 ### Defense -Defense基类定义了对抗训练的使用接口，其子类实现了各种具体的对抗训练算法，增强模型的对抗鲁棒性。 +`Defense`基类定义了对抗训练的使用接口，其子类实现了各种具体的对抗训练算法，增强模型的对抗鲁棒性。 ### Detector -Detector基类定义了对抗样本检测的使用借口，其子类实现了各种具体的检测算法，增强模型的对抗鲁棒性。 +`Detector`基类定义了对抗样本检测的使用借口，其子类实现了各种具体的检测算法，增强模型的对抗鲁棒性。 -详细内容，请参考[对抗鲁棒性官网教程](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/model_security.html) +详细内容，请参考[对抗鲁棒性官网教程](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/model_security.html)。 ## 模型安全测试 ### Fuzzer -Fuzzer类基于神经元覆盖率增益控制fuzzing流程，采用自然扰动和对抗样本生成方法作为变异策略，激活更多的神经元，从而探索不同类型的模型输出结果、错误行为，指导用户增强模型鲁棒性。 +`Fuzzer`类基于神经元覆盖率增益控制fuzzing流程，采用自然扰动和对抗样本生成方法作为变异策略，激活更多的神经元，从而探索不同类型的模型输出结果、错误行为，指导用户增强模型鲁棒性。 -详细内容，请参考[模型安全测试官网教程](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/fuzzer.html) +详细内容，请参考[模型安全测试官网教程](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/fuzzer.html)。 ## 差分隐私训练 ### DPModel -DPModel继承了mindspore.Model，提供了差分隐私训练的入口函数。 +`DPModel`继承了`mindspore.Model`，提供了差分隐私训练的入口函数。 -详细内容，请参考[差分隐私官网教程](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/differential_privacy.html) +详细内容，请参考[差分隐私官网教程](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/differential_privacy.html)。 ## 隐私泄露风险评估 ### MembershipInference -MembershipInference类提供了一种模型逆向分析方法，能够基于模型对样本的预测信息，推测某个样本是否在模型的训练集中，以此评估模型的隐私泄露风险。 +`MembershipInference`类提供了一种模型逆向分析方法，能够基于模型对样本的预测信息，推测某个样本是否在模型的训练集中，以此评估模型的隐私泄露风险。 -详细内容，请参考[隐私泄露风险评估官方教程](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/membership_inference.html) +详细内容，请参考[隐私泄露风险评估官方教程](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/membership_inference.html)。 diff --git a/api/source_zh_cn/programming_guide/train.md b/api/source_zh_cn/programming_guide/train.md index e9c7db1821..3cef6dc402 100644 --- a/api/source_zh_cn/programming_guide/train.md +++ b/api/source_zh_cn/programming_guide/train.md @@ -6,7 +6,6 @@ - [概述](#概述) - [自定义训练网络](#自定义训练网络) - [自定义训练循环](#自定义训练循环) - - [梯度累积](#梯度累积) - [边训练边推理](#边训练边推理) - [on-device执行](#on-device执行) - [计算图下沉](#计算图下沉) diff --git a/api/source_zh_cn/programming_guide/user-defined.rst b/api/source_zh_cn/programming_guide/user-defined.rst new file mode 100644 index 0000000000..b710df2302 --- /dev/null +++ b/api/source_zh_cn/programming_guide/user-defined.rst @@ -0,0 +1,7 @@ +自定义 +=========== + +.. toctree:: + :maxdepth: 1 + + 自定义算子 \ No newline at end of file -- Gitee From 4bb0949d72514276c0b3adec9ac1228ccccd89f5 Mon Sep 17 00:00:00 2001 From: caozhou Date: Tue, 15 Sep 2020 21:22:44 +0800 Subject: [PATCH 041/156] perfect overview --- api/source_zh_cn/programming_guide/api_structure.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/api/source_zh_cn/programming_guide/api_structure.md b/api/source_zh_cn/programming_guide/api_structure.md index 9a42ef6642..1827ed6d59 100644 --- a/api/source_zh_cn/programming_guide/api_structure.md +++ b/api/source_zh_cn/programming_guide/api_structure.md @@ -14,9 +14,11 @@ MindSpore源于全产业的最佳实践，向数据科学家和算法工程师提供了统一的模型训练、推理和导出等接口，支持端、边、云等不同场景下的灵活部署，推动深度学习和科学计算等领域繁荣发展。 -MindSpore提供了动态图和静态图统一的编码方式，用户无需开发多套代码，仅变更一行代码便可切换动态图/静态图模式，从而拥有更轻松的开发调试及性能体验。 +神经网络模型通常基于梯度下降算法进行训练，但手动求导过程复杂且梯度难以计算。MindSpore采用函数式可微分编程架构，用户可聚焦于模型算法的数学原生表达，无需进行手动求导。MindSpore的基于源码转换（Source Code Transformation, SCT）的自动微分（Automatic Differentiation）机制能够将Python代码转换为函数中间表达（Intermediate Representation, IR），函数中间表达构造出能够在不同设备解析和执行的计算图，并且在执行该计算图前，应用了多种软硬件协同优化技术，端、边、云等不同场景下的性能和效率得到针对性的提升。此外，MindSpore提供了Python编程范式，用户使用Python原生控制逻辑即可构建复杂的神经网络模型，AI编程变得简单。 -此外，由于MindSpore统一了单机和分布式训练的编码方式，开发者无需编写复杂的分布式策略，在单机代码中添加少量代码即可实现分布式训练，大大降低了AI开发门槛。 +目前主流的深度学习框架的执行模式有两种，分别为静态图模式和动态图模式。静态图模式拥有较高的训练性能，但难以调试。动态图模式相较于静态图模式虽然易于调试，但难以高效执行。MindSpore提供了动态图和静态图统一的编码方式。大大增加了静态图和动态图的可兼容性，用户无需开发多套代码，仅变更一行代码便可切换动态图/静态图模式，例如设置`context.set_context(mode=context.PYNATIVE_MODE)`即可从静态图模式切换成动态图模式，从而拥有更轻松的开发调试及性能体验。 + +随着神经网络模型和数据集的规模不断增加，分布式并行训练成为了神经网络训练的常见做法，但分布式并行训练的策略选择和编写十分复杂，这制约着深度学习的发展。MindSpore统一了单机和分布式训练的编码方式，开发者无需编写复杂的分布式策略，在单机代码中添加少量代码即可实现分布式训练，例如设置`context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL)`便可自动建立代价模型，为用户选择一种较优的并行模式，大大降低了AI开发门槛，提高了神经网络训练效率。 ## 层次结构 @@ -26,12 +28,12 @@ MindSpore向用户提供了3个不同层次的API，支撑用户进行网络构 - Low-Level Python API - 第一层为低阶API，主要包括张量定义、基础算子、自动微分等模块，用户可使用低阶API轻松实现张量操作和求导计算。 + 第一层为低阶API，主要包括张量定义、基础算子、自动微分等模块，用户可使用低阶API轻松实现张量定义和求导计算，例如用户可通过`Tensor`接口自定义张量，使用`grad_all`算子计算函数在指定处的导数。 - Medium-Level Python API - 第二层为中阶API，其封装了低价API，提供网络层、优化器、损失函数等模块，用户可通过中阶API灵活构建神经网络和控制执行流程，快速实现模型算法逻辑。 + 第二层为中阶API，其封装了低价API，提供网络层、优化器、损失函数等模块，用户可通过中阶API灵活构建神经网络和控制执行流程，快速实现模型算法逻辑，例如用户可调用`Cell`接口构建神经网络模型和计算逻辑，通过使用`loss`模块和`Optimizer`接口为神经网络模型添加损失函数和优化方式。 - High-Level Python API - 第三层为高阶API，其在中阶API的基础上又提供了训练推理的管理、Callback、混合精度训练等高级接口，方便用户控制整网的执行流程和实现神经网络的训练及推理。 + 第三层为高阶API，其在中阶API的基础上又提供了训练推理的管理、Callback、混合精度训练等高级接口，方便用户控制整网的执行流程和实现神经网络的训练及推理，例如用户使用`Model`接口，指定要训练的神经网络模型和相关的训练设置，即可对神经网络模型进行训练。 -- Gitee From cf788c3f9599569175a20f71aa37b5272739f5ae Mon Sep 17 00:00:00 2001 From: Ting Wang Date: Tue, 15 Sep 2020 21:29:04 +0800 Subject: [PATCH 042/156] update content Signed-off-by: Ting Wang --- api/source_zh_cn/index.rst | 2 +- ...o_parallel_context.md => auto_parallel.md} | 33 ++++++++++--------- .../programming_guide/parameter.md | 2 +- .../{user-defined.rst => user_defined.rst} | 0 4 files changed, 19 insertions(+), 18 deletions(-) rename api/source_zh_cn/programming_guide/{auto_parallel_context.md => auto_parallel.md} (92%) rename api/source_zh_cn/programming_guide/{user-defined.rst => user_defined.rst} (100%) diff --git a/api/source_zh_cn/index.rst b/api/source_zh_cn/index.rst index 34b1223c63..4e58d830f0 100644 --- a/api/source_zh_cn/index.rst +++ b/api/source_zh_cn/index.rst @@ -14,7 +14,7 @@ MindSpore API programming_guide/data_type programming_guide/compute_component programming_guide/execution_management - programming_guide/auto_parallel_context + programming_guide/auto_parallel programming_guide/advanced_use .. toctree:: diff --git a/api/source_zh_cn/programming_guide/auto_parallel_context.md b/api/source_zh_cn/programming_guide/auto_parallel.md similarity index 92% rename from api/source_zh_cn/programming_guide/auto_parallel_context.md rename to api/source_zh_cn/programming_guide/auto_parallel.md index 3a1cad9cee..0f96d49aea 100644 --- a/api/source_zh_cn/programming_guide/auto_parallel_context.md +++ b/api/source_zh_cn/programming_guide/auto_parallel.md @@ -6,31 +6,32 @@ - [概述](#概述) - [分布式并行配置](#分布式并行配置) - [通用配置](#通用配置) - - [device_num](#device_num) - - [global_rank](#global_rank) - - [gradients_mean](#gradients_mean) - - [parallel_mode](#parallel_mode) + - [device_num](#device_num) + - [global_rank](#global_rank) + - [gradients_mean](#gradients_mean) + - [parallel_mode](#parallel_mode) + - [all_reduce_fusion_config](#all_reduce_fusion_config) - [自动并行配置](#自动并行配置) - - [gradient_fp32_sync](#gradient_fp32-sync) - - [loss_repeated_mean](#loss_repeated_mean) - - [auto_parallel_search_mode](#auto_parallel_search_mode) - - [strategy_ckpt_load_file](#strategy_ckpt_load_file) - - [strategy_ckpt_save_file](#strategy_ckpt_save_file) - - [full_batch](#full_batch) + - [gradient_fp32_sync](#gradient_fp32_sync) + - [loss_repeated_mean](#loss_repeated_mean) + - [auto_parallel_search_mode](#auto_parallel_search_mode) + - [strategy_ckpt_load_file](#strategy_ckpt_load_file) + - [strategy_ckpt_save_file](#strategy_ckpt_save_file) + - [full_batch](#full_batch) - [数据并行配置](#数据并行配置) - - [enable_parallel_optimizer](#enable_parallel_optimizer) + - [enable_parallel_optimizer](#enable_parallel_optimizer) - [混合并行配置](#混合并行配置) - - [layerwise_parallel](#layerwise_parallel) + - [layerwise_parallel](#layerwise_parallel) - [分布式通信接口](#分布式通信接口) - [init](#init) - - [get_rank](#get_rank) - [get_group_size](#get_group_size) - - [数据并行](#数据并行) - - [自动并行](#自动并行) + - [get_rank](#get_rank) + - [数据并行](#数据并行) + - [自动并行](#自动并行) - + ## 概述 diff --git a/api/source_zh_cn/programming_guide/parameter.md b/api/source_zh_cn/programming_guide/parameter.md index 03b7d35e50..989f445332 100644 --- a/api/source_zh_cn/programming_guide/parameter.md +++ b/api/source_zh_cn/programming_guide/parameter.md @@ -35,7 +35,7 @@ def __init__(self, default_input, name, requires_grad=True, layerwise_parallel=F 当`layerwise_parallel`（混合并行）配置为True时，参数广播和参数梯度聚合时会过滤掉该参数。 -有关分布式并行的相关配置，可以参考文档：。 +有关分布式并行的相关配置，可以参考文档：。 下例通过三种不同的数据类型构造了`Parameter`，三个`Parameter`都需要更新，都不采用layerwise并行。如下： ``` diff --git a/api/source_zh_cn/programming_guide/user-defined.rst b/api/source_zh_cn/programming_guide/user_defined.rst similarity index 100% rename from api/source_zh_cn/programming_guide/user-defined.rst rename to api/source_zh_cn/programming_guide/user_defined.rst -- Gitee From 4c8f3d1ca8da097693dd7b2f23eb6bd8a69969de Mon Sep 17 00:00:00 2001 From: zhangz0911gm Date: Tue, 15 Sep 2020 00:02:47 -0400 Subject: [PATCH 043/156] fixing operator.md in programming_guide --- .../programming_guide/operator.md | 157 +++++------------- 1 file changed, 39 insertions(+), 118 deletions(-) diff --git a/api/source_zh_cn/programming_guide/operator.md b/api/source_zh_cn/programming_guide/operator.md index 8bb948a9e1..52839d407d 100644 --- a/api/source_zh_cn/programming_guide/operator.md +++ b/api/source_zh_cn/programming_guide/operator.md @@ -7,17 +7,13 @@ - [张量操作](#张量操作) - [标量运算](#标量运算) - [加法](#加法) - - [Element-wise除法](#element-wise除法) - [Element-wise乘法](#element-wise乘法) - [求三角函数](#求三角函数) - [向量运算](#向量运算) - - [Concat](#concat) - [Squeeze](#squeeze) - - [求Sparse2Dense(改变tensor维度使其变稠密)](#求sparse2dense改变tensor维度使其变稠密) - - [ScalarCast](#scalarcast) + - [求Sparse2Dense](#求sparse2dense) - [矩阵运算](#矩阵运算) - [矩阵乘法](#矩阵乘法) - - [常见范数](#常见范数) - [广播机制](#广播机制) - [网络操作](#网络操作) - [特征提取](#特征提取) @@ -71,7 +67,7 @@ 有些标量运算符对常用的数学运算符进行了重载。并且支持类似NumPy的广播特性。 -以下代码实现了XXX（TODO）： +以下代码实现了对input_x作乘方数为input_y的乘方操作： ```python import numpy as np import mindspore @@ -99,43 +95,9 @@ print(input_x + input_y) [4.0 5.0 7.0] ``` -除了普通加外，还有element-wise加法： -```python -import TODO - -net = NetAddN() -input_x = Tensor(np.array([1, 2, 3]), mindspore.float32) -input_y = Tensor(np.array([4, 5, 6]), mindspore.float32) -net(input_x, input_y, input_x, input_y)[10.0, 14.0, 18.0] -``` - -输出如下：（TODO） - -#### Element-wise除法 - -以下代码实现了XXX（TODO）： -```python -input_x = Tensor(np.array([-4.0, 5.0, 6.0]), mindspore.float32) -input_y = Tensor(np.array([3.0, 2.0, 3.0]), mindspore.float32) -div = P.Div() -div(input_x, input_y) -``` - -输出如下：（TODO） - -以下代码实现了XXX（TODO）： -```python -input_x = Tensor(np.array([2, 4, -1]), mindspore.int32)) -input_y = Tensor(np.array([3, 3, 3]), mindspore.int32) -floor_div = P.FloorDiv() -floor_div(input_x, input_y)[0, 1, -1] -``` - -输出如下：（TODO） - #### Element-wise乘法 -以下代码实现了XXX（TODO）： +以下代码实现了Element-wise乘法示例： ```python input_x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32) input_y = Tensor(np.array([4.0, 5.0, 6.0]), mindspore.float32) @@ -150,45 +112,35 @@ mul(input_x, input_y) #### 求三角函数 -以下代码实现了XXX（TODO）： +以下代码实现了Acos： ```python acos = P.ACos() input_x = Tensor(np.array([0.74, 0.04, 0.30, 0.56]), mindspore.float32) output = acos(input_x) ``` -输出如下：（TODO） +输出如下：[42.268584, 87.707557, 72.542397，55.944202] ### 向量运算 向量运算符只在一个特定轴上运算，将一个向量映射到一个标量或者另外一个向量。 -#### Concat - -以下代码实现了XXX（TODO）： -```python -data1 = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32)) -data2 = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32)) -op = P.Concat() -output = op((data1, data2)) -``` - -输出如下：（TODO） - #### Squeeze -以下代码实现了XXX（TODO）： +以下代码实现了压缩第3个通道维度为1的通道： ```python input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32) squeeze = P.Squeeze(2) output = squeeze(input_tensor) ``` -输出如下：（TODO） +输出如下：[[1, 1], + [1, 1], + [1, 1]] -#### 求Sparse2Dense(改变tensor维度使其变稠密) +#### 求Sparse2Dense -以下代码实现了XXX（TODO）： +以下代码实现了对Sparse2Dense示例： ```python indices = Tensor([[0, 1], [1, 2]]) values = Tensor([1, 2], dtype=ms.float32) @@ -196,17 +148,9 @@ dense_shape = (3, 4) out = P.SparseToDense()(indices, values, dense_shape) ``` -输出如下：（TODO） - -#### ScalarCast - -以下代码实现了XXX（TODO）： -```python -scalar_cast = P.ScalarCast() -output = scalar_cast(255.0, mindspore.int32) -``` - -输出如下：（TODO） +输出如下：[[0, 1, 0, 0], + [0, 0, 2, 0], + [0, 0, 0, 0]] ### 矩阵运算 @@ -214,7 +158,7 @@ output = scalar_cast(255.0, mindspore.int32) #### 矩阵乘法 -以下代码实现了XXX（TODO）： +以下代码实现了input_x 和 input_y的矩阵乘法： ```python input_x = Tensor(np.ones(shape=[1, 3]), mindspore.float32) input_y = Tensor(np.ones(shape=[3, 4]), mindspore.float32) @@ -222,33 +166,19 @@ matmul = P.MatMul() output = matmul(input_x, input_y) ``` -输出如下：（TODO） - -#### 常见范数 - -以下代码实现了XXX（TODO）： -```python -input_x = Tensor(np.ones([128, 64, 32, 64]), mindspore.float32) -scale = Tensor(np.ones([64]), mindspore.float32) -bias = Tensor(np.ones([64]), mindspore.float32) -mean = Tensor(np.ones([64]), mindspore.float32) -variance = Tensor(np.ones([64]), mindspore.float32) -batch_norm = P.BatchNorm() -output = batch_norm(input_x, scale, bias, mean, variance) -``` - -输出如下：（TODO） +输出如下：[3, 12] #### 广播机制 -广播表示XXX（TODO）。 +广播表示输入各变量channel数目不一致时，改变他们的channel 数以得到结果。 -以下代码实现了XXX（TODO）： +以下代码实现了广播机制的示例： ```python from mindspore import Tensor from mindspore.communication import init import mindspore.nn as nn import mindspore.ops.operations as P + init() class Net(nn.Cell): def __init__(self): @@ -263,19 +193,17 @@ net = Net() output = net(input_) ``` -输出如下：（TODO） - ## 网络操作 网络操作包括特征提取、激活函数、LossFunction、优化算法等。 ### 特征提取 -特征提取是XXX（TODO）。 +特征提取是机器学习中的常见操作，核心是提取较原输入更且代表性的Tensor表达。 #### 卷积操作 -以下代码实现了XXX（TODO）： +以下代码实现了常见卷积操作之一的2D convolution 操作： ```python input = Tensor(np.ones([10, 32, 32, 32]), mindspore.float32) weight = Tensor(np.ones([32, 32, 3, 3]), mindspore.float32)) @@ -285,7 +213,8 @@ conv2d(input, weight) #### 卷积的反向传播算子操作 -以下代码实现了XXX（TODO）： +以下代码实现了反向梯度算子传播操作的具体代码，输出存于dout， weight： + ```python dout = Tensor(np.ones([10, 32, 30, 30]), mindspore.float32) weight = Tensor(np.ones([32, 32, 3, 3]), mindspore.float32) @@ -296,7 +225,7 @@ conv2d_backprop_input(dout, weight, F.shape(x)) ### 激活函数 -以下代码实现了XXX（TODO）： +以下代码实现Softmax激活函数计算： ```python input_x = Tensor(np.array([1, 2, 3, 4, 5]), mindspore.float32) softmax = P.Softmax() @@ -312,7 +241,7 @@ softmax(input_x) #### L1Loss -以下代码实现了XXX（TODO）： +以下代码实现了L1 loss function： ```python loss = P.SmoothL1Loss() input_data = Tensor(np.array([1, 2, 3]), mindspore.float32) @@ -329,7 +258,7 @@ loss(input_data, target_data) #### SGD -以下代码实现了XXX（TODO）： +以下代码实现了SGD梯度下降算法的具体实现，输出是result： ```python sgd = P.SGD() parameters = Tensor(np.array([2, -0.5, 1.7, 4]), mindspore.float32) @@ -341,8 +270,6 @@ stat = Tensor(np.array([1.5, -0.3, 0.2, -0.7]), mindspore.float32) result = sgd(parameters, gradient, learning_rate, accum, momentum, stat) ``` -输出如下：（TODO） - ## 数组操作 数组操作指操作对象是一些数组的操作。 @@ -351,47 +278,43 @@ result = sgd(parameters, gradient, learning_rate, accum, momentum, stat) 返回跟输入的数据类型一致的并且适配Mindspore的Tensor变量，常用于Mindspore工程内。 -以下代码实现了XXX（TODO）： ```python input_tensor = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32) type = P.DType()(input_tensor) ``` -输出如下：（TODO） - ### Cast 转换输入的数据类型并且输出与目标数据类型相同的变量。 -以下代码实现了XXX（TODO）： ```python input_np = np.random.randn(2, 3, 4, 5).astype(np.float32) input_x = Tensor(input_np) type_dst = mindspore.float16 cast = P.Cast() result = cast(input_x, type_dst) +print(result.type()) ``` - -输出如下：（TODO） +输出结果: mindspore.float16 ### Shape 返回输入数据的形状。 -以下代码实现了XXX（TODO）： +以下代码实现了返回输入数据input_tensor的操作： ```python input_tensor = Tensor(np.ones(shape=[3, 2, 1]), mindspore.float32) shape = P.Shape() output = shape(input_tensor) ``` -输出如下：（TODO） +输出如下： [3, 2, 1] ## 图像操作 图像操作包括图像预处理操作，如图像剪切（Crop，便于得到大量训练样本）和大小变化（Reise，用于构建图像金子塔等）。 -以下代码实现了XXX（TODO）： +以下代码实现了Crop和Resize操作： ```python class CropAndResizeNet(nn.Cell): def __init__(self, crop_size): @@ -416,8 +339,6 @@ output = crop_and_resize(Tensor(image), Tensor(boxes), Tensor(box_index)) print(output.asnumpy()) ``` -输出如下：（TODO） - ## 编码运算 编码运算包括BoundingBox Encoding、BoundingBox Decoding、IOU计算等。 @@ -426,14 +347,13 @@ print(output.asnumpy()) 对物体所在区域方框进行编码，得到类似PCA的更精简信息，以便做后续类似特征提取，物体检测，图像恢复等任务。 -以下代码实现了XXX（TODO）： +以下代码实现了对anchor_box和groundtruth_box的boundingbox encode： ```python anchor_box = Tensor([[4,1,2,1],[2,2,2,3]],mindspore.float32) groundtruth_box = Tensor([[3,1,2,2],[1,2,1,4]],mindspore.float32) boundingbox_encode = P.BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0)) boundingbox_encode(anchor_box, groundtruth_box) ``` - 输出如下: ``` @@ -445,7 +365,7 @@ boundingbox_encode(anchor_box, groundtruth_box) 编码器对区域位置信息解码之后，用此算子进行解码。 -以下代码实现了XXX（TODO）： +以下代码实现了： ```python anchor_box = Tensor([[4,1,2,1],[2,2,2,3]],mindspore.float32) deltas = Tensor([[3,1,2,2],[1,s2,1,4]],mindspore.float32) @@ -464,22 +384,23 @@ boundingbox_decode(anchor_box, deltas) 计算预测的物体所在方框和真实物体所在方框的交集区域与并集区域的占比大小，常作为一种损失函数，用以优化模型。 -以下代码实现了XXX（TODO）： +以下代码实现了计算两个变量anchor_boxes和gt_boxes之间的IOU，以out输出： ```python iou = P.IOU() anchor_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16) gt_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16) +out = iou(anchor_boxes, gt_boxes) ``` ## 调试操作 -调试操作指的是用于调试网络的一些常用算子及其操作，例如Debug等。 +调试操作指的是用于调试网络的一些常用算子及其操作，例如Debug等, 此操作非常方便，对入门深度学习重要，极大提高学习者的学习体验。 ### Debug 输出Tensor变量的数值，方便用户随时随地打印想了解或者debug必需的某变量数值。 -以下代码实现了XXX（TODO）： +以下代码实现了输出x这一变量的值： ```python class DebugNN(nn.Cell): def __init__(self,): @@ -495,7 +416,7 @@ class DebugNN(nn.Cell): 打印中间变量的梯度，是比较常用的算子，目前仅支持Pynative模式。 -以下代码实现了XXX（TODO）： +以下代码实现了打印中间变量(例中x,y)的梯度： ```python def hook_fn(grad_out): print(grad_out) @@ -523,7 +444,7 @@ backward(1, 2) 完成在训练时的量化和反量化操作。 -以下代码实现了XXX（TODO）： +以下代码实现了设置量化所需输入三个参数，输入tensor， 输出tensor和量化结果范围，最后对它实现量化,量化结果为output_tensor： ```python input_tensor = Tensor(np.random.rand(3, 16, 5, 5), mstype.float32) min_tensor = Tensor(np.array([-6]), mstype.float32) -- Gitee From 732d9096b777ef5b2169fbedf9e575f4c37e7069 Mon Sep 17 00:00:00 2001 From: Ting Wang Date: Tue, 15 Sep 2020 21:46:49 +0800 Subject: [PATCH 044/156] update pg Signed-off-by: Ting Wang --- api/source_zh_cn/programming_guide/advanced_use.rst | 2 +- api/source_zh_cn/programming_guide/compute_component.rst | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/api/source_zh_cn/programming_guide/advanced_use.rst b/api/source_zh_cn/programming_guide/advanced_use.rst index 2102ce044e..33edc062bc 100644 --- a/api/source_zh_cn/programming_guide/advanced_use.rst +++ b/api/source_zh_cn/programming_guide/advanced_use.rst @@ -1,4 +1,4 @@ -执行管理 +进阶用法 =========== .. toctree:: diff --git a/api/source_zh_cn/programming_guide/compute_component.rst b/api/source_zh_cn/programming_guide/compute_component.rst index 16a1aee634..58d676eacf 100644 --- a/api/source_zh_cn/programming_guide/compute_component.rst +++ b/api/source_zh_cn/programming_guide/compute_component.rst @@ -4,6 +4,7 @@ .. toctree:: :maxdepth: 1 + operator parameter cell network_component \ No newline at end of file -- Gitee From d35b66495f03c4e95c36a4dda4db053867f1d518 Mon Sep 17 00:00:00 2001 From: Ting Wang Date: Tue, 15 Sep 2020 23:38:36 +0800 Subject: [PATCH 045/156] update doc name Signed-off-by: Ting Wang --- .../programming_guide/callback.md | 10 +-- api/source_zh_cn/programming_guide/cell.md | 34 ++++----- api/source_zh_cn/programming_guide/context.md | 24 +++--- .../programming_guide/operator.md | 74 +++++++++---------- api/source_zh_cn/programming_guide/optim.md | 18 ++--- 5 files changed, 80 insertions(+), 80 deletions(-) diff --git a/api/source_zh_cn/programming_guide/callback.md b/api/source_zh_cn/programming_guide/callback.md index d46164fc7b..ab2e28dfbd 100644 --- a/api/source_zh_cn/programming_guide/callback.md +++ b/api/source_zh_cn/programming_guide/callback.md @@ -1,11 +1,11 @@ -# Callback +# Callback机制 -- [Callback](#callback) - - [概述](#概述) - - [MindSpore内置回调函数](#mindspore内置回调函数) - - [MindSpore自定义回调函数](#mindspore自定义回调函数) +- [Callback机制](#callback机制) + - [概述](#概述) + - [MindSpore内置回调函数](#mindspore内置回调函数) + - [MindSpore自定义回调函数](#mindspore自定义回调函数) diff --git a/api/source_zh_cn/programming_guide/cell.md b/api/source_zh_cn/programming_guide/cell.md index 207ab17ee2..5c4826caf4 100644 --- a/api/source_zh_cn/programming_guide/cell.md +++ b/api/source_zh_cn/programming_guide/cell.md @@ -1,22 +1,22 @@ -# Cell +# Cell及其继承类 -- [Cell](#cell) - - [概述](#概述) - - [关键成员函数](#关键成员函数) - - [construct方法](#construct方法) - - [parameters_dict](#parameters_dict) - - [cells_and_names](#cells_and_names) - - [set_grad](#set_grad) - - [模型层](#模型层) - - [内置模型层](#内置模型层) - - [应用实例](#应用实例) - - [损失函数](#损失函数) - - [内置损失函数](#内置损失函数) - - [应用实例](#应用实例-1) - - [优化算法](#优化算法) - - [构建自定义网络](#构建自定义网络) +- [Cell及其继承类](#cell及其继承类) + - [概述](#概述) + - [关键成员函数](#关键成员函数) + - [construct方法](#construct方法) + - [parameters_dict](#parameters_dict) + - [cells_and_names](#cells_and_names) + - [set_grad](#set_grad) + - [模型层](#模型层) + - [内置模型层](#内置模型层) + - [应用实例](#应用实例) + - [损失函数](#损失函数) + - [内置损失函数](#内置损失函数) + - [应用实例](#应用实例-1) + - [优化算法](#优化算法) + - [构建自定义网络](#构建自定义网络) @@ -307,7 +307,7 @@ print(loss(input_data, target_data)) ## 优化算法 -`mindspore.nn.optim`是MindSpore框架中实现各种优化算法的模块，详细说明参见[optim](https://www.mindspore.cn/api/zh-CN/master/programming_guide/optim.html)。 +`mindspore.nn.optim`是MindSpore框架中实现各种优化算法的模块，详细说明参见[优化算法](https://www.mindspore.cn/api/zh-CN/master/programming_guide/optim.html)。 ## 构建自定义网络 diff --git a/api/source_zh_cn/programming_guide/context.md b/api/source_zh_cn/programming_guide/context.md index 81f4a27ce6..9cc81c384f 100644 --- a/api/source_zh_cn/programming_guide/context.md +++ b/api/source_zh_cn/programming_guide/context.md @@ -1,18 +1,18 @@ -# context +# 运行管理 -- [context](#context) - - [概述](#概述) - - [执行模式管理](#执行模式管理) - - [模式选择](#模式选择) - - [模式切换](#模式切换) - - [硬件管理](#硬件管理) - - [分布式管理](#分布式管理) - - [维测管理](#维测管理) - - [采集profiling数据](#采集profiling数据) - - [异步数据dump功能](#异步数据dump功能) - - [print算子落盘](#print算子落盘) +- [运行管理](#运行管理) + - [概述](#概述) + - [执行模式管理](#执行模式管理) + - [模式选择](#模式选择) + - [模式切换](#模式切换) + - [硬件管理](#硬件管理) + - [分布式管理](#分布式管理) + - [维测管理](#维测管理) + - [采集profiling数据](#采集profiling数据) + - [异步数据dump功能](#异步数据dump功能) + - [print算子落盘](#print算子落盘) diff --git a/api/source_zh_cn/programming_guide/operator.md b/api/source_zh_cn/programming_guide/operator.md index 52839d407d..d05c84e63c 100644 --- a/api/source_zh_cn/programming_guide/operator.md +++ b/api/source_zh_cn/programming_guide/operator.md @@ -1,43 +1,43 @@ -# 算子组件 +# 算子 -- [算子组件](#算子组件) - - [概述](#概述) - - [张量操作](#张量操作) - - [标量运算](#标量运算) - - [加法](#加法) - - [Element-wise乘法](#element-wise乘法) - - [求三角函数](#求三角函数) - - [向量运算](#向量运算) - - [Squeeze](#squeeze) - - [求Sparse2Dense](#求sparse2dense) - - [矩阵运算](#矩阵运算) - - [矩阵乘法](#矩阵乘法) - - [广播机制](#广播机制) - - [网络操作](#网络操作) - - [特征提取](#特征提取) - - [卷积操作](#卷积操作) - - [卷积的反向传播算子操作](#卷积的反向传播算子操作) - - [激活函数](#激活函数) - - [LossFunction](#lossfunction) - - [L1Loss](#l1loss) - - [优化算法](#优化算法) - - [SGD](#sgd) - - [数组操作](#数组操作) - - [DType](#dtype) - - [Cast](#cast) - - [Shape](#shape) - - [图像操作](#图像操作) - - [编码运算](#编码运算) - - [BoundingBoxEncode](#boundingboxencode) - - [BoundingBoxDecode](#boundingboxdecode) - - [IOU计算](#iou计算) - - [调试操作](#调试操作) - - [Debug](#debug) - - [HookBackward](#hookbackward) - - [量化操作](#量化操作) - - [MinMaxUpdatePerLayer](#minmaxupdateperlayer) +- [算子](#算子) + - [概述](#概述) + - [张量操作](#张量操作) + - [标量运算](#标量运算) + - [加法](#加法) + - [Element-wise乘法](#element-wise乘法) + - [求三角函数](#求三角函数) + - [向量运算](#向量运算) + - [Squeeze](#squeeze) + - [求Sparse2Dense](#求sparse2dense) + - [矩阵运算](#矩阵运算) + - [矩阵乘法](#矩阵乘法) + - [广播机制](#广播机制) + - [网络操作](#网络操作) + - [特征提取](#特征提取) + - [卷积操作](#卷积操作) + - [卷积的反向传播算子操作](#卷积的反向传播算子操作) + - [激活函数](#激活函数) + - [LossFunction](#lossfunction) + - [L1Loss](#l1loss) + - [优化算法](#优化算法) + - [SGD](#sgd) + - [数组操作](#数组操作) + - [DType](#dtype) + - [Cast](#cast) + - [Shape](#shape) + - [图像操作](#图像操作) + - [编码运算](#编码运算) + - [BoundingBoxEncode](#boundingboxencode) + - [BoundingBoxDecode](#boundingboxdecode) + - [IOU计算](#iou计算) + - [调试操作](#调试操作) + - [Debug](#debug) + - [HookBackward](#hookbackward) + - [量化操作](#量化操作) + - [MinMaxUpdatePerLayer](#minmaxupdateperlayer) diff --git a/api/source_zh_cn/programming_guide/optim.md b/api/source_zh_cn/programming_guide/optim.md index 69ca81fdeb..c2268ce55d 100644 --- a/api/source_zh_cn/programming_guide/optim.md +++ b/api/source_zh_cn/programming_guide/optim.md @@ -1,15 +1,15 @@ -# optim +# 优化算法 -- [optim](#optim) - - [概述](#概述) - - [学习率](#学习率) - - [dynamic_lr](#dynamic_lr) - - [learning_rate_schedule](#learning_rate_schedule) - - [Optimzer](#optimzer) - - [如何使用](#如何使用) - - [内置优化器](#内置优化器) +- [优化算法](#优化算法) + - [概述](#概述) + - [学习率](#学习率) + - [dynamic_lr](#dynamic_lr) + - [learning_rate_schedule](#learning_rate_schedule) + - [Optimzer](#optimzer) + - [如何使用](#如何使用) + - [内置优化器](#内置优化器) -- Gitee From 81c70eece413409ec580d9abd9d65aa71879c6a3 Mon Sep 17 00:00:00 2001 From: zhangyi Date: Wed, 16 Sep 2020 09:17:06 +0800 Subject: [PATCH 046/156] fix api contents of mindspore.dataset.config --- .../api/python/mindspore/mindspore.dataset.rst | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/api/source_zh_cn/api/python/mindspore/mindspore.dataset.rst b/api/source_zh_cn/api/python/mindspore/mindspore.dataset.rst index c5e1cdf7d3..1437a22b8e 100644 --- a/api/source_zh_cn/api/python/mindspore/mindspore.dataset.rst +++ b/api/source_zh_cn/api/python/mindspore/mindspore.dataset.rst @@ -5,3 +5,10 @@ mindspore.dataset :members: :inherited-members: :exclude-members: get_args, read_dir + +mindspore.dataset.config +------------------------ + +.. automodule:: mindspore.dataset.config + :members: + \ No newline at end of file -- Gitee From 701b0f5c653a09ce3bf877493f5f7228439875c0 Mon Sep 17 00:00:00 2001 From: lvmingfu <630944715@qq.com> Date: Wed, 16 Sep 2020 09:25:48 +0800 Subject: [PATCH 047/156] fix notebook files in tutorials for master --- .../data_loading_enhancement.ipynb | 2 +- tutorials/notebook/model_security.ipynb | 10 +++++----- .../synchronization_training_and_evaluation.ipynb | 4 ++-- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/tutorials/notebook/data_loading_enhance/data_loading_enhancement.ipynb b/tutorials/notebook/data_loading_enhance/data_loading_enhancement.ipynb index 125d1cc4f1..60aaa92e94 100644 --- a/tutorials/notebook/data_loading_enhance/data_loading_enhancement.ipynb +++ b/tutorials/notebook/data_loading_enhance/data_loading_enhancement.ipynb @@ -537,7 +537,7 @@ "metadata": {}, "outputs": [], "source": [ - "from mindspore.dataset.transforms.py_transforms import Compose" + "from mindspore.dataset.transforms.py_transforms import Compose\n", "import mindspore.dataset.vision.py_transforms as transforms" ] }, diff --git a/tutorials/notebook/model_security.ipynb b/tutorials/notebook/model_security.ipynb index 53f79dfe40..79c458f78c 100644 --- a/tutorials/notebook/model_security.ipynb +++ b/tutorials/notebook/model_security.ipynb @@ -214,16 +214,16 @@ " # apply map operations on images\n", " if not sparse:\n", " one_hot_enco = C.OneHot(10)\n", - " ds1 = ds1.map(operations=one_hot_enco,\n", input_columns=\"label\", + " ds1 = ds1.map(operations=one_hot_enco, input_columns=\"label\",\n", " num_parallel_workers=num_parallel_workers)\n", " type_cast_op = C.TypeCast(mstype.float32)\n", - " ds1 = ds1.map(operations=type_cast_op,\n", input_columns=\"label\", + " ds1 = ds1.map(operations=type_cast_op, input_columns=\"label\",\n", " num_parallel_workers=num_parallel_workers)\n", - " ds1 = ds1.map(operations=resize_op,\n", input_columns=\"image\", + " ds1 = ds1.map(operations=resize_op, input_columns=\"image\",\n", " num_parallel_workers=num_parallel_workers)\n", - " ds1 = ds1.map(operations=rescale_op,\n", input_columns=\"image\", + " ds1 = ds1.map(operations=rescale_op,input_columns=\"image\",\n", " num_parallel_workers=num_parallel_workers)\n", - " ds1 = ds1.map(operations=hwc2chw_op,\n", input_columns=\"image\", + " ds1 = ds1.map(operations=hwc2chw_op, input_columns=\"image\",\n", " num_parallel_workers=num_parallel_workers)\n", "\n", " # apply DatasetOps\n", diff --git a/tutorials/notebook/synchronization_training_and_evaluation.ipynb b/tutorials/notebook/synchronization_training_and_evaluation.ipynb index d3670b2382..8c22d397b8 100644 --- a/tutorials/notebook/synchronization_training_and_evaluation.ipynb +++ b/tutorials/notebook/synchronization_training_and_evaluation.ipynb @@ -113,8 +113,8 @@ "\n", " # apply map operations on images\n", " mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns=\"label\", num_parallel_workers=num_parallel_workers)\n", - " mnist_ds = mnist_ds.map(operations=[resize_op,rescale_op,rescale_nml_op,hwc2chw_op],\n", input_columns=\"image\", - " num_parallel_workers=num_parallel_workers)\n", + " mnist_ds = mnist_ds.map(operations=[resize_op,rescale_op,rescale_nml_op,hwc2chw_op],\n", + " input_columns=\"image\", num_parallel_workers=num_parallel_workers)\n", "\n", " # apply DatasetOps\n", " buffer_size = 10000\n", -- Gitee From ad5d61014a1696897d232e5cf437a0c0ff563c82 Mon Sep 17 00:00:00 2001 From: caifubi Date: Wed, 16 Sep 2020 09:58:51 +0800 Subject: [PATCH 048/156] fix ide_deamon path --- .../advanced_use/customized_debugging_information.md | 2 +- .../advanced_use/customized_debugging_information.md | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/tutorials/source_en/advanced_use/customized_debugging_information.md b/tutorials/source_en/advanced_use/customized_debugging_information.md index 996b1baac7..878d7a067f 100644 --- a/tutorials/source_en/advanced_use/customized_debugging_information.md +++ b/tutorials/source_en/advanced_use/customized_debugging_information.md @@ -338,7 +338,7 @@ The input and output of the operator can be saved for debugging through the data ``` - `dump_mode`：0:dump all kernels in graph, 1: dump kernels in kernels list. - - `path`：Relative path where dump data saves. eg:data will be saved in `/var/log/npu/ide_deam/dump/relative_path`. + - `path`：Relative path where dump data saves. eg:data will be saved in `/var/log/npu/ide_daemon/dump/relative_path`. - `net_name`：net name eg:ResNet50. - `iteration`：Specify the iterations to dump. Iteration should be set to 0 when dataset_sink_mode is False and data of every iteration will be dumped. - `input_output`：0:dump input and output of kernel, 1:dump input of kernel, 2:dump output of kernel. diff --git a/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md b/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md index 57f34688e9..73764773a6 100644 --- a/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md +++ b/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md @@ -328,7 +328,7 @@ val:[[1 1] { "common_dump_settings": { "dump_mode": 0, - "path": "/test", + "path": "/relative_path", "net_name": "ResNet50", "iteration": 0, "input_output": 0, @@ -343,7 +343,7 @@ val:[[1 1] ``` - `dump_mode`：设置成0，表示Dump出改网络中的所有算子；设置成1，表示Dump`"kernels"`里面指定的算子。 - - `path`：Dump保存数据的相对路径，异步Dump生成的数据都会保存在`/var/log/npu/ide_deam/dump/`目录下。 + - `path`：Dump保存数据的相对路径，异步Dump生成的数据都会保存在`/var/log/npu/ide_daemon/dump/relative_path`目录下。 - `net_name`：自定义的网络名称，例如："ResNet50"。 - `iteration`：指定需要Dump的迭代。非数据下沉模式下，`iteration`需要设置成0，并且会Dump出每个迭代的数据。 - `input_output`：设置成0，表示Dump出算子的输入和算子的输出；设置成1，表示Dump出算子的输入；设置成2，表示Dump出算子的输出。 -- Gitee From 9bea2df688c027d40c2d80c194d2712cafbaec0d Mon Sep 17 00:00:00 2001 From: xiefangqi Date: Wed, 16 Sep 2020 11:22:20 +0800 Subject: [PATCH 049/156] md iterator api change --- .../programming_guide/augmentation.md | 36 ++++---- .../programming_guide/dataset_conversion.md | 6 +- .../programming_guide/dataset_loading.md | 18 ++-- .../programming_guide/pipeline.md | 86 +++++++++---------- .../programming_guide/probability.md | 16 ++-- .../programming_guide/tokenizer.md | 36 ++++---- .../computer_vision_application.ipynb | 2 +- ...ert_dataset_to_mindspore_data_format.ipynb | 15 ++-- .../data_loading_enhancement.ipynb | 14 +-- .../notebook/debugging_in_pynative_mode.ipynb | 4 +- tutorials/notebook/loading_dataset.ipynb | 42 ++++----- ...nsight_image_histogram_scalar_tensor.ipynb | 4 +- tutorials/notebook/mixed_precision.ipynb | 4 +- tutorials/notebook/model_security.ipynb | 8 +- tutorials/notebook/nlp_application.ipynb | 4 +- ..._the_performance_of_data_preparation.ipynb | 16 ++-- tutorials/notebook/quick_start.ipynb | 8 +- .../source_en/advanced_use/model_security.md | 4 +- ...ize_the_performance_of_data_preparation.md | 30 ++++--- .../data_processing_and_augmentation.md | 14 +-- .../data_preparation/loading_the_datasets.md | 80 ++++++++--------- .../advanced_use/auto_augmentation.md | 2 +- .../advanced_use/deep_probability_program.md | 12 +-- tutorials/source_zh_cn/advanced_use/fuzzer.md | 6 +- .../advanced_use/model_security.md | 4 +- ...ize_the_performance_of_data_preparation.md | 16 ++-- tutorials/source_zh_cn/use/image_loading.md | 8 +- tutorials/source_zh_cn/use/text_loading.md | 10 +-- .../model_safety/mnist_attack_fgsm.py | 4 +- .../model_safety/mnist_defense_nad.py | 4 +- 30 files changed, 259 insertions(+), 254 deletions(-) diff --git a/api/source_zh_cn/programming_guide/augmentation.md b/api/source_zh_cn/programming_guide/augmentation.md index 35920d2dfb..6d20f22c24 100644 --- a/api/source_zh_cn/programming_guide/augmentation.md +++ b/api/source_zh_cn/programming_guide/augmentation.md @@ -96,12 +96,12 @@ num_samples = len(image_list1) + len(image_list2) for i in range(num_samples): if i < len(image_list1): plt.subplot(2, len(image_list1), i + 1) - plt.imshow(image_list1[i]) - plt.title(label_list1[i]) + plt.imshow(image_list1[i].asnumpy()) + plt.title(label_list1[i].asnumpy()) else: plt.subplot(2, len(image_list2), i + 1) - plt.imshow(image_list2[i % len(image_list2)]) - plt.title(label_list2[i % len(image_list2)]) + plt.imshow(image_list2[i % len(image_list2)].asnumpy()) + plt.title(label_list2[i % len(image_list2)].asnumpy()) plt.show() ``` @@ -167,12 +167,12 @@ num_samples = len(image_list1) + len(image_list2) for i in range(num_samples): if i < len(image_list1): plt.subplot(2, len(image_list1), i + 1) - plt.imshow(image_list1[i]) - plt.title(label_list1[i]) + plt.imshow(image_list1[i].asnumpy()) + plt.title(label_list1[i].asnumpy()) else: plt.subplot(2, len(image_list2), i + 1) - plt.imshow(image_list2[i % len(image_list2)]) - plt.title(label_list2[i % len(image_list2)]) + plt.imshow(image_list2[i % len(image_list2)].asnumpy()) + plt.title(label_list2[i % len(image_list2)].asnumpy()) plt.show() ``` @@ -236,12 +236,12 @@ num_samples = len(image_list1) + len(image_list2) for i in range(num_samples): if i < len(image_list1): plt.subplot(2, len(image_list1), i + 1) - plt.imshow(image_list1[i].squeeze(), cmap=plt.cm.gray) - plt.title(label_list1[i]) + plt.imshow(image_list1[i].asnumpy().squeeze(), cmap=plt.cm.gray) + plt.title(label_list1[i].asnumpy()) else: plt.subplot(2, len(image_list2), i + 1) - plt.imshow(image_list2[i % len(image_list2)].squeeze(), cmap=plt.cm.gray) - plt.title(label_list2[i % len(image_list2)]) + plt.imshow(image_list2[i % len(image_list2)].asnumpy().squeeze(), cmap=plt.cm.gray) + plt.title(label_list2[i % len(image_list2)].asnumpy()) plt.show() ``` @@ -307,12 +307,12 @@ num_samples = len(image_list1) + len(image_list2) for i in range(num_samples): if i < len(image_list1): plt.subplot(2, len(image_list1), i + 1) - plt.imshow(image_list1[i].squeeze(), cmap=plt.cm.gray) - plt.title(label_list1[i]) + plt.imshow(image_list1[i].asnumpy().squeeze(), cmap=plt.cm.gray) + plt.title(label_list1[i].asnumpy()) else: plt.subplot(2, len(image_list2), i + 1) - plt.imshow(image_list2[i % len(image_list2)].squeeze(), cmap=plt.cm.gray) - plt.title(label_list2[i % len(image_list2)]) + plt.imshow(image_list2[i % len(image_list2)].asnumpy().squeeze(), cmap=plt.cm.gray) + plt.title(label_list2[i % len(image_list2)].asnumpy()) plt.show() ``` @@ -376,8 +376,8 @@ for data in dataset2.create_dict_iterator(): num_samples = len(image_list) for i in range(num_samples): plt.subplot(1, len(image_list), i + 1) - plt.imshow(image_list[i].transpose(1, 2, 0)) - plt.title(label_list[i]) + plt.imshow(image_list[i].asnumpy().transpose(1, 2, 0)) + plt.title(label_list[i].asnumpy()) plt.show() ``` diff --git a/api/source_zh_cn/programming_guide/dataset_conversion.md b/api/source_zh_cn/programming_guide/dataset_conversion.md index 255bab0f4d..29f7557b12 100644 --- a/api/source_zh_cn/programming_guide/dataset_conversion.md +++ b/api/source_zh_cn/programming_guide/dataset_conversion.md @@ -93,7 +93,7 @@ decode_op = vision.Decode() data_set = data_set.map(operations=decode_op, input_columns=["data"], num_parallel_workers=2) # 解码data字段 count = 0 - for item in data_set.create_dict_iterator(): # 循环读取MindRecord中所有数据 + for item in data_set.create_dict_iterator(output_numpy=True): # 循环读取MindRecord中所有数据 print("sample: {}".format(item)) count += 1 print("Got {} samples".format(count)) @@ -391,7 +391,7 @@ MindSpore提供转换常见数据集的工具类，能够将常见的经典数 data_set = ds.MindDataset(dataset_file=MINDRECORD_FILE_NAME) # 创建读取对象，默认开启shuffle count = 0 - for item in data_set.create_dict_iterator(): # 循环读取MindRecord中所有数据 + for item in data_set.create_dict_iterator(output_numpy=True): # 循环读取MindRecord中所有数据 print("sample: {}".format(item)) count += 1 print("Got {} samples".format(count)) @@ -514,7 +514,7 @@ MindSpore提供转换常见数据集的工具类，能够将常见的经典数 decode_op = vision.Decode() data_set = data_set.map(operations=decode_op, input_columns=["image_bytes"], num_parallel_workers=2) # 解码图像字段 count = 0 - for item in data_set.create_dict_iterator(): # 循环读取MindRecord中所有数据 + for item in data_set.create_dict_iterator(output_numpy=True): # 循环读取MindRecord中所有数据 print("sample: {}".format(item)) count += 1 print("Got {} samples".format(count)) diff --git a/api/source_zh_cn/programming_guide/dataset_loading.md b/api/source_zh_cn/programming_guide/dataset_loading.md index e3695494d5..9cec789095 100644 --- a/api/source_zh_cn/programming_guide/dataset_loading.md +++ b/api/source_zh_cn/programming_guide/dataset_loading.md @@ -124,7 +124,7 @@ DATA_DIR = "VOC2012/" # 使用VOCDataset读取数据集，指定为Segmentation任务，同时指定num_samples以获取2个样本数据 # decode参数会将读取的图像解码 -dataset = ds.VOCDataset(DATA_DIR, task="Segmentation", mode="train", num_samples=2, decode=True, shuffle=False) +dataset = ds.VOCDataset(DATA_DIR, task="Segmentation", usage="train", num_samples=2, decode=True, shuffle=False) print("[Segmentation]:") for data in dataset.create_dict_iterator(): # 原图像 @@ -133,7 +133,7 @@ for data in dataset.create_dict_iterator(): print("target shape:", data["target"].shape) # 接下来是Detection任务 -dataset = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", num_samples=1, decode=True, shuffle=False) +dataset = ds.VOCDataset(DATA_DIR, task="Detection", usage="train", num_samples=1, decode=True, shuffle=False) print("[Detection]:") for data in dataset.create_dict_iterator(): # 原图像 @@ -213,7 +213,7 @@ DATA_DIR = "mindrecord_dataset_path" mindrecord_dataset = ds.MindDataset(DATA_DIR) # 启动数据管道读取 -for data in mindrecord_dataset.create_dict_iterator(): +for data in mindrecord_dataset.create_dict_iterator(output_numpy=True): print(data["label"]) ``` @@ -369,8 +369,8 @@ TFRecord是Tensorflow定义的一种二进制数据文件格式。 ``` ``` - {'col1': array(1, dtype=int64), 'col2': array(3, dtype=int64)} - {'col1': array(2, dtype=int64), 'col2': array(4, dtype=int64)} + {'col1': Tensor(shape=[], dtype=Int64, value= 1), 'col2': Tensor(shape=[], dtype=Int64, value= 3)} + {'col1': Tensor(shape=[], dtype=Int64, value= 2), 'col2': Tensor(shape=[], dtype=Int64, value= 4)} ``` ### text数据格式 @@ -383,8 +383,8 @@ DATA_DIR = "text_file_path" text_dataset = ds.TextFileDataset(DATA_DIR) # 启动数据管道读取 -for data in text_dataset.create_dict_iterator(): - print(data["label"]) +for data in text_dataset.create_dict_iterator(output_numpy=True): + print(data["text"]) ``` ### CSV数据格式 @@ -397,8 +397,8 @@ DATA_DIR = "csv_file_path" csv_dataset = ds.CSVDataset(DATA_DIR) # 启动数据管道读取 -for data in csv_dataset.create_dict_iterator(): - print(data["label"]) +for data in csv_dataset.create_dict_iterator(output_numpy=True): + print(data["1"]) ``` >更多数据格式文件加载说明，参见对应[API文档](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.dataset.html)。 diff --git a/api/source_zh_cn/programming_guide/pipeline.md b/api/source_zh_cn/programming_guide/pipeline.md index bb87c70caf..55bc2bc5c7 100644 --- a/api/source_zh_cn/programming_guide/pipeline.md +++ b/api/source_zh_cn/programming_guide/pipeline.md @@ -71,11 +71,11 @@ for data in dataset1.create_dict_iterator(): ``` ``` -{'data': array([0, 1, 2], dtype=int64)} -{'data': array([2, 3, 4], dtype=int64)} -{'data': array([3, 4, 5], dtype=int64)} -{'data': array([1, 2, 3], dtype=int64)} -{'data': array([4, 5, 6], dtype=int64)} +{'data': Tensor(shape=[3], dtype=int64, value=[0, 1, 2])} +{'data': Tensor(shape=[3], dtype=int64, value=[2, 3, 4])} +{'data': Tensor(shape=[3], dtype=int64, value=[3, 4, 5])} +{'data': Tensor(shape=[3], dtype=int64, value=[1, 2, 3])} +{'data': Tensor(shape=[3], dtype=int64, value=[4, 5, 6])} ``` ### map @@ -119,17 +119,17 @@ for data in dataset.create_dict_iterator(): ``` ``` -{'data': array([0, 1, 2], dtype=int64)} -{'data': array([1, 2, 3], dtype=int64)} -{'data': array([2, 3, 4], dtype=int64)} -{'data': array([3, 4, 5], dtype=int64)} -{'data': array([4, 5, 6], dtype=int64)} - -{'data': array([0, 2, 4], dtype=int64)} -{'data': array([2, 4, 6], dtype=int64)} -{'data': array([4, 6, 8], dtype=int64)} -{'data': array([ 6, 8, 10], dtype=int64)} -{'data': array([ 8, 10, 12], dtype=int64)} +{'data': Tensor(shape=[3], dtype=int64, value=[0, 1, 2])} +{'data': Tensor(shape=[3], dtype=int64, value=[1, 2, 3])} +{'data': Tensor(shape=[3], dtype=int64, value=[2, 3, 4])} +{'data': Tensor(shape=[3], dtype=int64, value=[3, 4, 5])} +{'data': Tensor(shape=[3], dtype=int64, value=[4, 5, 6])} + +{'data': Tensor(shape=[3], dtype=int64, value=[0, 2, 4])} +{'data': Tensor(shape=[3], dtype=int64, value=[2, 4, 6])} +{'data': Tensor(shape=[3], dtype=int64, value=[4, 6, 8])} +{'data': Tensor(shape=[3], dtype=int64, value=[ 6, 8, 10])} +{'data': Tensor(shape=[3], dtype=int64, value=[ 8, 10, 12])} ``` ### batch @@ -171,12 +171,12 @@ for data in dataset2.create_dict_iterator(): ``` ``` -{'data': array([[0, 1, 2], [1, 2, 3]], dtype=int64)} -{'data': array([[2, 3, 4], [3, 4, 5]], dtype=int64)} -{'data': array([[4, 5, 6]], dtype=int64)} +{'data': Tensor(shape=[2, 3], dtype=int64, value=[[0, 1, 2], [1, 2, 3]])} +{'data': Tensor(shape=[2, 3], dtype=int64, value=[[2, 3, 4], [3, 4, 5]])} +{'data': Tensor(shape=[1, 3], dtype=int64, value=[[4, 5, 6]])} -{'data': array([[0, 1, 2], [1, 2, 3]], dtype=int64)} -{'data': array([[2, 3, 4], [3, 4, 5]], dtype=int64)} +{'data': Tensor(shape=[2, 3], dtype=int64, value=[[0, 1, 2], [1, 2, 3]])} +{'data': Tensor(shape=[2, 3], dtype=int64, value=[[2, 3, 4], [3, 4, 5]])} ``` ### repeat @@ -209,16 +209,16 @@ for data in dataset1.create_dict_iterator(): ``` ``` -{'data': array([0, 1, 2], dtype=int64)} -{'data': array([1, 2, 3], dtype=int64)} -{'data': array([2, 3, 4], dtype=int64)} -{'data': array([3, 4, 5], dtype=int64)} -{'data': array([4, 5, 6], dtype=int64)} -{'data': array([0, 1, 2], dtype=int64)} -{'data': array([1, 2, 3], dtype=int64)} -{'data': array([2, 3, 4], dtype=int64)} -{'data': array([3, 4, 5], dtype=int64)} -{'data': array([4, 5, 6], dtype=int64)} +{'data': Tensor(shape=[3], dtype=int64, value=[0, 1, 2])} +{'data': Tensor(shape=[3], dtype=int64, value=[1, 2, 3])} +{'data': Tensor(shape=[3], dtype=int64, value=[2, 3, 4])} +{'data': Tensor(shape=[3], dtype=int64, value=[3, 4, 5])} +{'data': Tensor(shape=[3], dtype=int64, value=[4, 5, 6])} +{'data': Tensor(shape=[3], dtype=int64, value=[0, 1, 2])} +{'data': Tensor(shape=[3], dtype=int64, value=[1, 2, 3])} +{'data': Tensor(shape=[3], dtype=int64, value=[2, 3, 4])} +{'data': Tensor(shape=[3], dtype=int64, value=[3, 4, 5])} +{'data': Tensor(shape=[3], dtype=int64, value=[4, 5, 6])} ``` ### zip @@ -259,10 +259,10 @@ for data in dataset3.create_dict_iterator(): ``` ``` -{'data1': array([0, 1, 2], dtype=int64), 'data2': array([1, 2], dtype=int64)} -{'data1': array([1, 2, 3], dtype=int64), 'data2': array([1, 2], dtype=int64)} -{'data1': array([2, 3, 4], dtype=int64), 'data2': array([1, 2], dtype=int64)} -{'data1': array([3, 4, 5], dtype=int64), 'data2': array([1, 2], dtype=int64)} +{'data1': Tensor(shape=[3], dtype=int64, value= [0, 1, 2]), 'data2': Tensor(shape=[2], dtype=int64, value= [1, 2])} +{'data1': Tensor(shape=[3], dtype=int64, value= [1, 2, 3]), 'data2': Tensor(shape=[2], dtype=int64, value= [1, 2])} +{'data1': Tensor(shape=[3], dtype=int64, value= [2, 3, 4]), 'data2': Tensor(shape=[2], dtype=int64, value= [1, 2])} +{'data1': Tensor(shape=[3], dtype=int64, value= [3, 4, 5]), 'data2': Tensor(shape=[2], dtype=int64, value= [1, 2])} ``` ### concat @@ -305,10 +305,10 @@ for data in dataset3.create_dict_iterator(): ``` ``` -{'data1': array([0, 0, 0], dtype=int64)} -{'data1': array([0, 0, 0], dtype=int64)} -{'data1': array([1, 2, 3], dtype=int64)} -{'data1': array([1, 2, 3], dtype=int64)} +{'data1': Tensor(shape=[3], dtype=int64, value= [0, 0, 0])} +{'data1': Tensor(shape=[3], dtype=int64, value= [0, 0, 0])} +{'data1': Tensor(shape=[3], dtype=int64, value= [1, 2, 3])} +{'data1': Tensor(shape=[3], dtype=int64, value= [1, 2, 3])} ``` ### project @@ -348,9 +348,9 @@ for data in dataset.create_dict_iterator(): ``` ``` -{'data1': array([1, 2, 3], dtype=int64), 'data2': array([7, 8, 9], dtype=int64)} -{'data1': array([1, 2, 3], dtype=int64), 'data2': array([7, 8, 9], dtype=int64)} +{'data1': Tensor(shape=[3], dtype=int64, value= [1, 2, 3]), 'data2': Tensor(shape=[3], dtype=int64, value= [7, 8, 9])} +{'data1': Tensor(shape=[3], dtype=int64, value= [1, 2, 3]), 'data2': Tensor(shape=[3], dtype=int64, value= [7, 8, 9])} -{'data1': array([1, 2, 3], dtype=int64)} -{'data1': array([1, 2, 3], dtype=int64)} +{'data1': Tensor(shape=[3], dtype=int64, value= [1, 2, 3])} +{'data1': Tensor(shape=[3], dtype=int64, value= [1, 2, 3])} ``` diff --git a/api/source_zh_cn/programming_guide/probability.md b/api/source_zh_cn/programming_guide/probability.md index c8437fee82..67f2c23543 100644 --- a/api/source_zh_cn/programming_guide/probability.md +++ b/api/source_zh_cn/programming_guide/probability.md @@ -859,7 +859,7 @@ trained_loss = vi.get_train_loss() ```python generated_sample = vae.generate_sample(64, IMAGE_SHAPE) for sample in ds_train.create_dict_iterator(): - sample_x = Tensor(sample['image'], dtype=mstype.float32) + sample_x = Tensor(sample['image'].asnumpy(), dtype=mstype.float32) reconstructed_sample = vae.reconstruct_sample(sample_x) print('The shape of the generated sample is ', generated_sample.shape) ``` @@ -873,8 +873,8 @@ ConditionalVAE训练过程和vae的过程类似，但需要注意的是使用训 sample_label = Tensor([i for i in range(0, 8)] * 8, dtype=mstype.int32) generated_sample = cvae.generate_sample(sample_label, 64, IMAGE_SHAPE) for sample in ds_train.create_dict_iterator(): - sample_x = Tensor(sample['image'], dtype=mstype.float32) - sample_y = Tensor(sample['label'], dtype=mstype.int32) + sample_x = Tensor(sample['image'].asnumpy(), dtype=mstype.float32) + sample_y = Tensor(sample['label'].asnumpy(), dtype=mstype.int32) reconstructed_sample = cvae.reconstruct_sample(sample_x, sample_y) print('The shape of the generated sample is ', generated_sample.shape) ``` @@ -989,8 +989,8 @@ def train_model(train_net, net, dataset): accs = [] loss_sum = 0 for i, data in enumerate(dataset.create_dict_iterator()): - train_x = Tensor(data['image'].astype(np.float32)) - label = Tensor(data['label'].astype(np.int32)) + train_x = Tensor(data['image'].asnumpy().astype(np.float32)) + label = Tensor(data['label'].asnumpy().astype(np.int32)) loss = train_net(train_x, label) output = net(train_x) log_output = P.LogSoftmax(axis=1)(output) @@ -1006,8 +1006,8 @@ def train_model(train_net, net, dataset): def validate_model(net, dataset): accs = [] for _, data in enumerate(dataset.create_dict_iterator()): - train_x = Tensor(data['image'].astype(np.float32)) - label = Tensor(data['label'].astype(np.int32)) + train_x = Tensor(data['image'].asnumpy().astype(np.float32)) + label = Tensor(data['label'].asnumpy().astype(np.int32)) output = net(train_x) log_output = P.LogSoftmax(axis=1)(output) acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy()) @@ -1404,7 +1404,7 @@ if __name__ == '__main__': ale_uncer_model_path=None, save_model=False) for eval_data in ds_eval.create_dict_iterator(): - eval_data = Tensor(eval_data['image'], mstype.float32) + eval_data = Tensor(eval_data['image'].asnumpy(), mstype.float32) epistemic_uncertainty = evaluation.eval_epistemic_uncertainty(eval_data) aleatoric_uncertainty = evaluation.eval_aleatoric_uncertainty(eval_data) print('The shape of epistemic uncertainty is ', epistemic_uncertainty.shape) diff --git a/api/source_zh_cn/programming_guide/tokenizer.md b/api/source_zh_cn/programming_guide/tokenizer.md index 225ce08707..65c73d8a7d 100644 --- a/api/source_zh_cn/programming_guide/tokenizer.md +++ b/api/source_zh_cn/programming_guide/tokenizer.md @@ -53,7 +53,7 @@ dataset = ds.NumpySlicesDataset(input_list, column_names=["text"], shuffle=False print("------------------------before tokenize----------------------------") # 输出分词之前的数据 -for data in dataset.create_dict_iterator(): +for data in dataset.create_dict_iterator(output_numpy=True): print(text.to_str(data['text'])) #打印分词后的数据输出 @@ -65,7 +65,7 @@ basic_tokenizer = text.BasicTokenizer() dataset = dataset.map(operations=basic_tokenizer) -for i in dataset.create_dict_iterator(num_epochs=1): +for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True): token = text.to_str(i['text']) print(token) ``` @@ -104,7 +104,7 @@ dataset = ds.NumpySlicesDataset(input_list, column_names=["text"], shuffle=False print("------------------------before tokenize----------------------------") # 输出分词之前的数据 -for data in dataset.create_dict_iterator(): +for data in dataset.create_dict_iterator(output_numpy=True): print(text.to_str(data['text'])) # 字符串列表，其中每个元素都是字符串类型的单词。 @@ -126,7 +126,7 @@ print("------------------------after tokenize-----------------------------") dataset = dataset.map(operations=tokenizer_op) -for i in dataset.create_dict_iterator(num_epochs=1): +for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True): token = text.to_str(i['text']) print(token) ``` @@ -171,7 +171,7 @@ dataset = ds.NumpySlicesDataset(input_list, column_names=["text"], shuffle=False print("------------------------before tokenize----------------------------") # 输出分词之前的数据 -for data in dataset.create_dict_iterator(): +for data in dataset.create_dict_iterator(output_numpy=True): print(text.to_str(data['text'])) tokenizer_op = text.JiebaTokenizer(HMM_FILE, MP_FILE) @@ -181,7 +181,7 @@ print("------------------------after tokenize-----------------------------") dataset = dataset.map(operations=jieba_op, input_columns=["text"], num_parallel_workers=1) -for i in dataset.create_dict_iterator(num_epochs=1): +for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True): token = text.to_str(i['text']) print(token) ``` @@ -212,7 +212,7 @@ dataset = ds.NumpySlicesDataset(input_list, column_names=["text"], shuffle=False print("------------------------before tokenize----------------------------") # 输出分词之前的数据 -for data in dataset.create_dict_iterator(): +for data in dataset.create_dict_iterator(output_numpy=True): print(text.to_str(data['text'])) tokenizer_op = text.RegexTokenizer(delim_pattern) @@ -222,7 +222,7 @@ print("------------------------after tokenize-----------------------------") dataset = dataset.map(operations=tokenizer_op) -for i in dataset.create_dict_iterator(num_epochs=1): +for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True): token = text.to_str(i['text']).tolist() print(token) ``` @@ -250,7 +250,7 @@ dataset = ds.NumpySlicesDataset(input_list, column_names=["text"], shuffle=False print("------------------------before tokenize----------------------------") # 输出分词之前的数据 -for data in dataset.create_dict_iterator(): +for data in dataset.create_dict_iterator(output_numpy=True): print(text.to_str(data['text'])) # 从文件数据中构建一个vocab对象 @@ -262,7 +262,7 @@ print("------------------------after tokenize-----------------------------") dataset = dataset.map(operations=tokenizer_op) -for i in dataset.create_dict_iterator(num_epochs=1): +for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True): token = text.to_str(i['text']) print(token) ``` @@ -290,7 +290,7 @@ dataset = ds.NumpySlicesDataset(input_list, column_names=["text"], shuffle=False print("------------------------before tokenize----------------------------") # 输出分词之前的数据 -for data in dataset.create_dict_iterator(): +for data in dataset.create_dict_iterator(output_numpy=True): print(text.to_str(data['text'])) tokenizer_op = text.UnicodeCharTokenizer() @@ -300,7 +300,7 @@ print("------------------------after tokenize-----------------------------") dataset = dataset.map(operations=tokenizer_op) -for i in dataset.create_dict_iterator(num_epochs=1): +for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True): token = text.to_str(i['text']).tolist() print(token) ``` @@ -332,7 +332,7 @@ dataset = ds.NumpySlicesDataset(input_list, column_names=["text"], shuffle=False print("------------------------before tokenize----------------------------") # 输出分词之前的数据 -for data in dataset.create_dict_iterator(): +for data in dataset.create_dict_iterator(output_numpy=True): print(text.to_str(data['text'])) tokenizer_op = text.UnicodeScriptTokenizer() @@ -342,7 +342,7 @@ print("------------------------after tokenize-----------------------------") dataset = dataset.map(operations=tokenizer_op) -for i in dataset.create_dict_iterator(num_epochs=1): +for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True): token = text.to_str(i['text']).tolist() print(token) ``` @@ -374,7 +374,7 @@ dataset = ds.NumpySlicesDataset(input_list, column_names=["text"], shuffle=False print("------------------------before tokenize----------------------------") # 输出分词之前的数据 -for data in dataset.create_dict_iterator(): +for data in dataset.create_dict_iterator(output_numpy=True): print(text.to_str(data['text'])) tokenizer_op = text.WhitespaceTokenizer() @@ -384,7 +384,7 @@ print("------------------------after tokenize-----------------------------") dataset = dataset.map(operations=tokenizer_op) -for i in dataset.create_dict_iterator(num_epochs=1): +for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True): token = text.to_str(i['text']).tolist() print(token) ``` @@ -417,7 +417,7 @@ dataset = ds.NumpySlicesDataset(input_list, column_names=["text"], shuffle=False print("------------------------before tokenize----------------------------") # 输出分词之前的数据 -for data in dataset.create_dict_iterator(): +for data in dataset.create_dict_iterator(output_numpy=True): print(text.to_str(data['text'])) #打印分词后的数据输出 @@ -432,7 +432,7 @@ tokenizer_op = text.WordpieceTokenizer(vocab=vocab) dataset = dataset.map(operations=tokenizer_op) -for i in dataset.create_dict_iterator(num_epochs=1): +for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True): token = text.to_str(i['text']) print(token) ``` diff --git a/tutorials/notebook/computer_vision_application.ipynb b/tutorials/notebook/computer_vision_application.ipynb index 17065fcbb5..2b2f978b16 100644 --- a/tutorials/notebook/computer_vision_application.ipynb +++ b/tutorials/notebook/computer_vision_application.ipynb @@ -314,7 +314,7 @@ "import matplotlib.pyplot as plt\n", "dataset_show = create_dataset()\n", "iterator_show= dataset_show.create_dict_iterator()\n", - "images = iterator_show.get_next()[\"image\"]\n", + "images = iterator_show.get_next()[\"image\"].asnumpy()\n", "# Images[0].shape is (3,224,224).We need transpose as (224,224,3) for using in plt.show().\n", "picture_show = np.transpose(images[0],(1,2,0))\n", "plt.imshow(picture_show)\n" diff --git a/tutorials/notebook/convert_dataset_to_mindspore_data_format/convert_dataset_to_mindspore_data_format.ipynb b/tutorials/notebook/convert_dataset_to_mindspore_data_format/convert_dataset_to_mindspore_data_format.ipynb index f34bc0c817..0fea6b2a76 100644 --- a/tutorials/notebook/convert_dataset_to_mindspore_data_format/convert_dataset_to_mindspore_data_format.ipynb +++ b/tutorials/notebook/convert_dataset_to_mindspore_data_format/convert_dataset_to_mindspore_data_format.ipynb @@ -194,7 +194,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'data': array([255, 216, 255, 224, 0, 16, 74, 70, 73, 70, 0, 1, 1,\n", + "{'data': Tensor(shape=[803], dtype=UInt8, value= [255, 216, 255, 224, 0, 16, 74, 70, 73, 70, 0, 1, 1,\n", " 0, 0, 1, 0, 1, 0, 0, 255, 219, 0, 67, 0, 2,\n", " 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 2,\n", " 2, 4, 3, 2, 2, 2, 2, 5, 4, 4, 3, 4, 6,\n", @@ -250,8 +250,7 @@ " 143, 6, 252, 112, 209, 62, 35, 120, 247, 224, 174, 137, 168,\n", " 77, 241, 3, 92, 240, 206, 167, 29, 245, 142, 155, 115, 114,\n", " 80, 27, 5, 157, 73, 203, 164, 139, 42, 249, 103, 12, 145,\n", - " 195, 22, 229, 5, 128, 31, 149, 148, 81, 69, 21, 255, 217],\n", - " dtype=uint8), 'label': array(3, dtype=int64)}\n" + " 195, 22, 229, 5, 128, 31, 149, 148, 81, 69, 21, 255, 217]), 'label': Tensor(shape=[], dtype=Int64, value= 3)}\n" ] } ], @@ -376,7 +375,7 @@ "# create MindDataset for reading data\n", "csv_data_set = ds.MindDataset(dataset_file=csv_mindrecord_path)\n", "# create a dictionary iterator and read a data record through the iterator\n", - "print(next(csv_data_set.create_dict_iterator()))" + "print(next(csv_data_set.create_dict_iterator(output_numpy=True)))" ] }, { @@ -493,7 +492,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'data': array([255, 216, 255, ..., 35, 255, 217], dtype=uint8), 'id': array(30707, dtype=int64), 'label': array(4, dtype=int64)}\n" + "{'data': Tensor(shape=[1431], dtype=UInt8, value= [255, 216, 255, ..., 35, 255, 217]), 'id': Tensor(shape=[], dtype=Int64, value= 30707), 'label': Tensor(shape=[], dtype=Int64, value= 4)}\n" ] } ], @@ -620,7 +619,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'data': array([255, 216, 255, ..., 127, 255, 217], dtype=uint8), 'fine_label': array(88, dtype=int64), 'coarse_label': array(8, dtype=int64), 'id': array(10349, dtype=int64)}\n" + "{'data': Tensor(shape=[1374], dtype=UInt8, value= [255, 216, 255, ..., 127, 255, 217]), 'fine_label': Tensor(shape=[], dtype=Int64, value= 88), 'coarse_label': Tensor(shape=[], dtype=Int64, value= 8), 'id': Tensor(shape=[], dtype=Int64, value= 10349)}\n" ] } ], @@ -767,7 +766,7 @@ "# create MindDataset for reading data\n", "imagenet_data_set = ds.MindDataset(dataset_file=file_name)\n", "# create a dictionary iterator and read a data record through the iterator\n", - "print(next(imagenet_data_set.create_dict_iterator()))" + "print(next(imagenet_data_set.create_dict_iterator(output_numpy=True)))" ] }, { @@ -938,7 +937,7 @@ "# create MindDataset for reading data\n", "define_data_set = ds.MindDataset(dataset_file=file_name)\n", "# create a dictionary iterator and read a data record through the iterator\n", - "print(next(define_data_set.create_dict_iterator()))" + "print(next(define_data_set.create_dict_iterator(output_numpy=True)))" ] }, { diff --git a/tutorials/notebook/data_loading_enhance/data_loading_enhancement.ipynb b/tutorials/notebook/data_loading_enhance/data_loading_enhancement.ipynb index 60aaa92e94..6733e3aa75 100644 --- a/tutorials/notebook/data_loading_enhance/data_loading_enhancement.ipynb +++ b/tutorials/notebook/data_loading_enhance/data_loading_enhancement.ipynb @@ -383,11 +383,11 @@ "[ 3 2 1 0 -1]\n", "[4 3 2 1 0]\n", "After zip:\n", - "{'data': array([0, 1, 2, 3, 4], dtype=int32), 'data2': array([ 0, -1, -2, -3, -4], dtype=int32)}\n", - "{'data': array([1, 2, 3, 4, 5], dtype=int32), 'data2': array([ 1, 0, -1, -2, -3], dtype=int32)}\n", - "{'data': array([2, 3, 4, 5, 6], dtype=int32), 'data2': array([ 2, 1, 0, -1, -2], dtype=int32)}\n", - "{'data': array([3, 4, 5, 6, 7], dtype=int32), 'data2': array([ 3, 2, 1, 0, -1], dtype=int32)}\n", - "{'data': array([4, 5, 6, 7, 8], dtype=int32), 'data2': array([4, 3, 2, 1, 0], dtype=int32)}\n" + "{'data': Tensor(shape=[5], dtype=Int64, value= [0, 1, 2, 3, 4]), 'data2': Tensor(shape=[5], dtype=Int64, value= [ 0, -1, -2, -3, -4])}\n", + "{'data': Tensor(shape=[5], dtype=Int64, value= [1, 2, 3, 4, 5]), 'data2': Tensor(shape=[5], dtype=Int64, value= [ 1, 0, -1, -2, -3])}\n", + "{'data': Tensor(shape=[5], dtype=Int64, value= [2, 3, 4, 5, 6]), 'data2': Tensor(shape=[5], dtype=Int64, value= [ 2, 1, 0, -1, -2])}\n", + "{'data': Tensor(shape=[5], dtype=Int64, value= [3, 4, 5, 6, 7]), 'data2': Tensor(shape=[5], dtype=Int64, value= [ 3, 2, 1, 0, -1])}\n", + "{'data': Tensor(shape=[5], dtype=Int64, value= [4, 5, 6, 7, 8]), 'data2': Tensor(shape=[5], dtype=Int64, value= [4, 3, 2, 1, 0])}\n" ] } ], @@ -518,7 +518,7 @@ ], "source": [ "for data in ds2.create_dict_iterator():\n", - " imgplot_resized = plt.imshow(data[\"image\"])\n", + " imgplot_resized = plt.imshow(data[\"image\"].asnumpy())\n", " plt.show()" ] }, @@ -589,7 +589,7 @@ "compose = Compose(transforms_list)\n", "ds4 = ds3.map(operations=compose, input_columns=\"image\")\n", "for data in ds4.create_dict_iterator():\n", - " imgplot_resized = plt.imshow(data[\"image\"].transpose(1, 2, 0))\n", + " imgplot_resized = plt.imshow(data[\"image\"].asnumpy().transpose(1, 2, 0))\n", " plt.show()" ] }, diff --git a/tutorials/notebook/debugging_in_pynative_mode.ipynb b/tutorials/notebook/debugging_in_pynative_mode.ipynb index c5e9993c98..1e93c5bcc4 100644 --- a/tutorials/notebook/debugging_in_pynative_mode.ipynb +++ b/tutorials/notebook/debugging_in_pynative_mode.ipynb @@ -187,8 +187,8 @@ "datas = create_dataset(train_data_path)\n", "data1 = datas.create_dict_iterator()\n", "data= data1.get_next()\n", - "images = data[\"image\"]\n", - "labels = data[\"label\"]\n", + "images = data[\"image\"].asnumpy()\n", + "labels = data[\"label\"].asnumpy()\n", "print(images.shape)\n", "count = 1\n", "for i in images:\n", diff --git a/tutorials/notebook/loading_dataset.ipynb b/tutorials/notebook/loading_dataset.ipynb index d1865e5fd7..dab7c92b1d 100644 --- a/tutorials/notebook/loading_dataset.ipynb +++ b/tutorials/notebook/loading_dataset.ipynb @@ -283,7 +283,7 @@ "count = 0\n", "for data in cifar10_dataset.create_dict_iterator():\n", "# In CIFAR-10 dataset, each dictionary of data has keys \"image\" and \"label\".\n", - " image = data[\"image\"]\n", + " image = data[\"image\"].asnumpy()\n", " print(f\"The data of image {count+1} is below:\")\n", " print(image)\n", " plt.figure(count)\n", @@ -491,27 +491,27 @@ "output_type": "stream", "text": [ "dataset1:\n", - "[array([0], dtype=int32)]\n", - "[array([1], dtype=int32)]\n", - "[array([2], dtype=int32)]\n", - "[array([3], dtype=int32)]\n", - "[array([4], dtype=int32)]\n", - "[array([5], dtype=int32)]\n", - "[array([6], dtype=int32)]\n", - "[array([7], dtype=int32)]\n", - "[array([8], dtype=int32)]\n", - "[array([9], dtype=int32)]\n", + "[Tensor(shape=[1], dtype=int32, value= [0])]\n", + "[Tensor(shape=[1], dtype=int32, value= [1])]\n", + "[Tensor(shape=[1], dtype=int32, value= [2])]\n", + "[Tensor(shape=[1], dtype=int32, value= [3])]\n", + "[Tensor(shape=[1], dtype=int32, value= [4])]\n", + "[Tensor(shape=[1], dtype=int32, value= [5])]\n", + "[Tensor(shape=[1], dtype=int32, value= [6])]\n", + "[Tensor(shape=[1], dtype=int32, value= [7])]\n", + "[Tensor(shape=[1], dtype=int32, value= [8])]\n", + "[Tensor(shape=[1], dtype=int32, value= [9])]\n", "dataset2:\n", - "[array([0], dtype=int64)]\n", - "[array([1], dtype=int64)]\n", - "[array([2], dtype=int64)]\n", - "[array([3], dtype=int64)]\n", - "[array([4], dtype=int64)]\n", - "[array([5], dtype=int64)]\n", - "[array([6], dtype=int64)]\n", - "[array([7], dtype=int64)]\n", - "[array([8], dtype=int64)]\n", - "[array([9], dtype=int64)]\n" + "[Tensor(shape=[1], dtype=int64, value= [0])]\n", + "[Tensor(shape=[1], dtype=int64, value= [1])]\n", + "[Tensor(shape=[1], dtype=int64, value= [2])]\n", + "[Tensor(shape=[1], dtype=int64, value= [3])]\n", + "[Tensor(shape=[1], dtype=int64, value= [4])]\n", + "[Tensor(shape=[1], dtype=int64, value= [5])]\n", + "[Tensor(shape=[1], dtype=int64, value= [6])]\n", + "[Tensor(shape=[1], dtype=int64, value= [7])]\n", + "[Tensor(shape=[1], dtype=int64, value= [8])]\n", + "[Tensor(shape=[1], dtype=int64, value= [9])]\n" ] } ], diff --git a/tutorials/notebook/mindinsight/mindinsight_image_histogram_scalar_tensor.ipynb b/tutorials/notebook/mindinsight/mindinsight_image_histogram_scalar_tensor.ipynb index 3b0654081e..f1fb22e942 100644 --- a/tutorials/notebook/mindinsight/mindinsight_image_histogram_scalar_tensor.ipynb +++ b/tutorials/notebook/mindinsight/mindinsight_image_histogram_scalar_tensor.ipynb @@ -240,8 +240,8 @@ "ds_iterator = ds_train.create_dict_iterator()\n", "ds_iterator.get_next()\n", "batch_1 = ds_iterator.get_next()\n", - "batch_image = batch_1[\"image\"]\n", - "batch_label = batch_1[\"label\"]\n", + "batch_image = batch_1[\"image\"].asnumpy()\n", + "batch_label = batch_1[\"label\"].asnumpy()\n", "%matplotlib inline\n", "plt.figure(dpi=144)\n", "for i,image in enumerate(batch_image):\n", diff --git a/tutorials/notebook/mixed_precision.ipynb b/tutorials/notebook/mixed_precision.ipynb index a595551727..69aa651439 100644 --- a/tutorials/notebook/mixed_precision.ipynb +++ b/tutorials/notebook/mixed_precision.ipynb @@ -169,7 +169,7 @@ "datas = dict1.get_next()\n", "image = datas[\"image\"]\n", "print(\"the tensor of image is:\", image.shape)\n", - "plt.imshow(np.array(image))\n", + "plt.imshow(np.array(image.asnumpy()))\n", "plt.show()" ] }, @@ -282,7 +282,7 @@ "print(\"the cifar dataset size is:\", ds.get_dataset_size())\n", "dict1 = ds.create_dict_iterator()\n", "datas = dict1.get_next()\n", - "image = datas[\"image\"]\n", + "image = datas[\"image\"].asnumpy()\n", "single_pic = np.transpose(image[0], (1,2,0))\n", "print(\"the tensor of image is:\", image.shape)\n", "plt.imshow(np.array(single_pic))\n", diff --git a/tutorials/notebook/model_security.ipynb b/tutorials/notebook/model_security.ipynb index 79c458f78c..bac7061f0b 100644 --- a/tutorials/notebook/model_security.ipynb +++ b/tutorials/notebook/model_security.ipynb @@ -281,8 +281,8 @@ "ds_iterator = ds_train.create_dict_iterator()\n", "ds_iterator.get_next()\n", "batch_1 = ds_iterator.get_next()\n", - "batch_image = batch_1[\"image\"]\n", - "batch_label = batch_1[\"label\"]\n", + "batch_image = batch_1[\"image\"].asnumpy()\n", + "batch_label = batch_1[\"label\"].asnumpy()\n", "%matplotlib inline\n", "plt.figure(dpi=144)\n", "for i,image in enumerate(batch_image):\n", @@ -506,8 +506,8 @@ "i = 0\n", "for data in ds_test.create_tuple_iterator():\n", " i += 1\n", - " images = data[0].astype(np.float32)\n", - " labels = data[1]\n", + " images = data[0].asnumpy().astype(np.float32)\n", + " labels = data[1].asnumpy()\n", " test_images.append(images)\n", " test_labels.append(labels)\n", " pred_labels = np.argmax(model.predict(Tensor(images)).asnumpy(),\n", diff --git a/tutorials/notebook/nlp_application.ipynb b/tutorials/notebook/nlp_application.ipynb index 02cf130217..47fa6ebe6d 100644 --- a/tutorials/notebook/nlp_application.ipynb +++ b/tutorials/notebook/nlp_application.ipynb @@ -652,8 +652,8 @@ ], "source": [ "iterator = ds_train.create_dict_iterator().get_next()\n", - "first_batch_label = iterator[\"label\"]\n", - "first_batch_first_feature = iterator[\"feature\"][0]\n", + "first_batch_label = iterator[\"label\"].asnumpy()\n", + "first_batch_first_feature = iterator[\"feature\"].asnumpy()[0]\n", "print(f\"The first batch contains label below:\\n{first_batch_label}\\n\")\n", "print(f\"The feature of the first item in the first batch is below vector:\\n{first_batch_first_feature}\")" ] diff --git a/tutorials/notebook/optimize_the_performance_of_data_preparation/optimize_the_performance_of_data_preparation.ipynb b/tutorials/notebook/optimize_the_performance_of_data_preparation/optimize_the_performance_of_data_preparation.ipynb index 4e92276777..20dbba8db2 100644 --- a/tutorials/notebook/optimize_the_performance_of_data_preparation/optimize_the_performance_of_data_preparation.ipynb +++ b/tutorials/notebook/optimize_the_performance_of_data_preparation/optimize_the_performance_of_data_preparation.ipynb @@ -210,7 +210,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'image': array([[[235, 235, 235],\n", + "{'image': Tensor(shape=[32, 32, 3], dtype=UInt8, value=\n", + " [[[235, 235, 235],\n", " [230, 230, 230],\n", " [234, 234, 234],\n", " ...,\n", @@ -258,7 +259,7 @@ " ...,\n", " [120, 120, 119],\n", " [146, 146, 146],\n", - " [177, 174, 190]]], dtype=uint8), 'label': array(9, dtype=uint32)}\n" + " [177, 174, 190]]]), 'label': Tensor(shape=[], dtype=UInt32, value= 9)}\n" ] } ], @@ -287,7 +288,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'data': array([255, 216, 255, ..., 63, 255, 217], dtype=uint8), 'id': array(30474, dtype=int64), 'label': array(2, dtype=int64)}\n" + "{'data': Tensor(shape=[1431], dtype=UInt8, value= [255, 216, 255, ..., 63, 255, 217]), 'id': Tensor(shape=[], dtype=Int64, value= 30474), 'label': Tensor(shape=[], dtype=Int64, value= 2)}\n" ] } ], @@ -323,7 +324,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'data': array([0], dtype=int64)}\n" + "{'data': Tensor(shape=[1], dtype=Int64, value= [0])}\n" ] } ], @@ -400,7 +401,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'image': array([[[254, 254, 254],\n", + "{'image': Tensor(shape=[32, 32, 3], dtype=UInt8, value=\n", + " [[[254, 254, 254],\n", " [255, 255, 254],\n", " [255, 255, 254],\n", " ...,\n", @@ -448,7 +450,7 @@ " ...,\n", " [ 64, 61, 63],\n", " [ 63, 58, 60],\n", - " [ 61, 56, 58]]], dtype=uint8), 'label': array(9, dtype=uint32)}\n" + " [ 61, 56, 58]]], dtype=uint8), 'label': Tensor(shape=[], dtype=UInt32, value= 9)}\n" ] } ], @@ -611,7 +613,7 @@ "cifar10_dataset = cifar10_dataset.map(operations=transforms,input_columns=\"image\",num_parallel_workers=4)\n", "\n", "data = next(cifar10_dataset.create_dict_iterator())\n", - "plt.imshow(data[\"image\"])\n", + "plt.imshow(data[\"image\"].asnumpy())\n", "plt.show()" ] }, diff --git a/tutorials/notebook/quick_start.ipynb b/tutorials/notebook/quick_start.ipynb index 18590d37b5..83c2d44138 100644 --- a/tutorials/notebook/quick_start.ipynb +++ b/tutorials/notebook/quick_start.ipynb @@ -250,8 +250,8 @@ "\n", "dic_ds = mnist_ds.create_dict_iterator()\n", "item = dic_ds.get_next()\n", - "img = item[\"image\"]\n", - "label = item[\"label\"]\n", + "img = item[\"image\"].asnumpy()\n", + "label = item[\"label\"].asnumpy()\n", "\n", "print(\"The item of mnist_ds:\", item.keys())\n", "print(\"Tensor of image in item:\", img.shape) \n", @@ -1062,8 +1062,8 @@ "source": [ "ds_test = create_dataset(test_data_path).create_dict_iterator()\n", "data = ds_test.get_next()\n", - "images = data[\"image\"]\n", - "labels = data[\"label\"]\n", + "images = data[\"image\"].asnumpy()\n", + "labels = data[\"label\"].asnumpy()\n", "\n", "output = model.predict(Tensor(data['image']))\n", "prb = output.asnumpy()\n", diff --git a/tutorials/source_en/advanced_use/model_security.md b/tutorials/source_en/advanced_use/model_security.md index ecfab36032..f94c024155 100644 --- a/tutorials/source_en/advanced_use/model_security.md +++ b/tutorials/source_en/advanced_use/model_security.md @@ -198,8 +198,8 @@ The LeNet model is used as an example. You can also create and train your own mo inputs = [] labels = [] for data in ds_test.create_tuple_iterator(): - inputs.append(data[0].astype(np.float32)) - labels.append(data[1]) + inputs.append(data[0].asnumpy().astype(np.float32)) + labels.append(data[1].asnumpy()) test_inputs = np.concatenate(inputs) test_labels = np.concatenate(labels) ``` diff --git a/tutorials/source_en/advanced_use/optimize_the_performance_of_data_preparation.md b/tutorials/source_en/advanced_use/optimize_the_performance_of_data_preparation.md index bc2dce74e7..2af061f9a3 100644 --- a/tutorials/source_en/advanced_use/optimize_the_performance_of_data_preparation.md +++ b/tutorials/source_en/advanced_use/optimize_the_performance_of_data_preparation.md @@ -132,7 +132,8 @@ Based on the preceding suggestions of data loading performance optimization, the The output is as follows: ``` - {'image': array([[[235, 235, 235], + {'image': Tensor(shape=[32, 32, 3], dtype=UInt8, value= + [[[235, 235, 235], [230, 230, 230], [234, 234, 234], ..., @@ -142,7 +143,7 @@ Based on the preceding suggestions of data loading performance optimization, the ..., [120, 120, 119], [146, 146, 146], - [177, 174, 190]]], dtype=uint8), 'label': array(9, dtype=uint32)} + [177, 174, 190]]]), 'label': Tensor(shape=[], dtype=UInt32, value= 9)} ``` 2. Use the `Cifar10ToMR` class to convert the CIFAR-10 dataset into MindSpore data format. In this example, the CIFAR-10 dataset in Python file format is used. Then use the `MindDataset` class to load the dataset in MindSpore data format. The multi-thread optimization solution is used for data loading. Four threads are enabled to concurrently complete the task. Finally, a dictionary iterator is created for data and a data record is read through the iterator. @@ -166,7 +167,7 @@ Based on the preceding suggestions of data loading performance optimization, the The output is as follows: ``` - {'data': array([255, 216, 255, ..., 63, 255, 217], dtype=uint8), 'id': array(30474, dtype=int64), 'label': array(2, dtype=int64)} + {'data': Tensor(shape=[1431], dtype=UInt8, value= [255, 216, 255, ..., 63, 255, 217]), 'id': Tensor(shape=[], dtype=Int64, value= 30474, dtype=int64), 'label': Tensor(shape=[], dtype=Int64, value= 2)} ``` 3. The `GeneratorDataset` class is used to load the user-defined dataset, and the multi-process optimization solution is used. Four processes are enabled to concurrently complete the task. Finally, a dictionary iterator is created for the data, and a data record is read through the iterator. @@ -185,7 +186,7 @@ Based on the preceding suggestions of data loading performance optimization, the The output is as follows: ``` - {'data': array([0], dtype=int64)} + {'data': Tensor(shape=[1], dtype=Int64, value= [0])} ``` ## Optimizing the Shuffle Performance @@ -218,17 +219,18 @@ Based on the preceding shuffle performance optimization suggestions, the `shuffl The output is as follows: ``` - {'image': array([[[254, 254, 254], - [255, 255, 254], - [255, 255, 254], + {'image': Tensor(shape=[32, 32, 3], dtype=UInt8, value= + [[[235, 235, 235], + [230, 230, 230], + [234, 234, 234], ..., - [232, 234, 244], - [226, 230, 242], - [228, 232, 243]], + [248, 248, 248], + [248, 248, 248], + [249, 249, 249]], ..., - [ 64, 61, 63], - [ 63, 58, 60], - [ 61, 56, 58]]], dtype=uint8), 'label': array(9, dtype=uint32)} + [120, 120, 119], + [146, 146, 146], + [177, 174, 190]]]), 'label': Tensor(shape=[], dtype=UInt32, value= 9)} ``` 2. Use the `shuffle` function to shuffle data. Set `buffer_size` to 3 and use the `GeneratorDataset` class to generate data. @@ -312,7 +314,7 @@ Based on the preceding suggestions of data augmentation performance optimization cifar10_dataset = cifar10_dataset.map(operations=transforms, input_columns="image", num_parallel_workers=4) data = next(cifar10_dataset.create_dict_iterator()) - plt.imshow(data["image"]) + plt.imshow(data["image"].asnumpy()) plt.show() ``` diff --git a/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md b/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md index 74a9ad497e..74aa42eea8 100644 --- a/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md +++ b/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md @@ -256,11 +256,11 @@ def zip(self, datasets): ``` The output is as follows: ``` - {'data': array([0, 1, 2], dtype=int64), 'data2': array([-3, -2, -1], dtype=int64)} - {'data': array([1, 2, 3], dtype=int64), 'data2': array([-2, -1, 0], dtype=int64)} - {'data': array([2, 3, 4], dtype=int64), 'data2': array([-1, 0, 1], dtype=int64)} - {'data': array([3, 4, 5], dtype=int64), 'data2': array([0, 1, 2], dtype=int64)} - {'data': array([4, 5, 6], dtype=int64), 'data2': array([1, 2, 3], dtype=int64)} + {'data': Tensor(shape=[3], dtype=Int64, value= [0, 1, 2]), 'data2': Tensor(shape=[3], dtype=Int64, value= [-3, -2, -1])} + {'data': Tensor(shape=[3], dtype=Int64, value= [1, 2, 3]), 'data2': Tensor(shape=[3], dtype=Int64, value= [-2, -1, 0])} + {'data': Tensor(shape=[3], dtype=Int64, value= [2, 3, 4]), 'data2': Tensor(shape=[3], dtype=Int64, value= [-1, 0, 1])} + {'data': Tensor(shape=[3], dtype=Int64, value= [3, 4, 5]), 'data2': Tensor(shape=[3], dtype=Int64, value= [0, 1, 2])} + {'data': Tensor(shape=[3], dtype=Int64, value= [4, 5, 6]), 'data2': Tensor(shape=[3], dtype=Int64, value= [1, 2, 3])} ``` ## Data Augmentation During image training, especially when the dataset size is relatively small, you can preprocess images by using a series of data augmentation operations, thereby enriching the datasets. @@ -291,7 +291,7 @@ Data augmentation requires the `map` function. For details about how to use the dataset.map(operations=resize_op, input_columns="image") for data in dataset.create_dict_iterator(): - imgplot_resized = plt.imshow(data["image"]) + imgplot_resized = plt.imshow(data["image"].asnumpy()) plt.show() ``` The running result shows that the original image is changed from 1024 x 683 pixels to 500 x 500 pixels after data processing by using `Resize`. @@ -326,7 +326,7 @@ Figure 2: Image after its size is reset dataset = dataset.map(operations=compose, input_columns="image") for data in dataset.create_dict_iterator(): print(data["image"]) - imgplot_resized = plt.imshow(data["image"].transpose(1, 2, 0)) + imgplot_resized = plt.imshow(data["image"].asnumpy().transpose(1, 2, 0)) plt.show() ``` diff --git a/tutorials/source_en/use/data_preparation/loading_the_datasets.md b/tutorials/source_en/use/data_preparation/loading_the_datasets.md index ad339502f6..72aeb988d0 100644 --- a/tutorials/source_en/use/data_preparation/loading_the_datasets.md +++ b/tutorials/source_en/use/data_preparation/loading_the_datasets.md @@ -202,27 +202,27 @@ Define a `Generator` and transfer it to `GeneratorDataset` to create a dataset a The output is as follows: ``` dataset1: - [array([0], dtype=int64)] - [array([1], dtype=int64)] - [array([2], dtype=int64)] - [array([3], dtype=int64)] - [array([4], dtype=int64)] - [array([5], dtype=int64)] - [array([6], dtype=int64)] - [array([7], dtype=int64)] - [array([8], dtype=int64)] - [array([9], dtype=int64)] + [Tensor(shape=[1], dtype=Int64, value= [0])] + [Tensor(shape=[1], dtype=Int64, value= [1])] + [Tensor(shape=[1], dtype=Int64, value= [2])] + [Tensor(shape=[1], dtype=Int64, value= [3])] + [Tensor(shape=[1], dtype=Int64, value= [4])] + [Tensor(shape=[1], dtype=Int64, value= [5])] + [Tensor(shape=[1], dtype=Int64, value= [6])] + [Tensor(shape=[1], dtype=Int64, value= [7])] + [Tensor(shape=[1], dtype=Int64, value= [8])] + [Tensor(shape=[1], dtype=Int64, value= [9])] dataset2: - [array([0], dtype=int64)] - [array([1], dtype=int64)] - [array([2], dtype=int64)] - [array([3], dtype=int64)] - [array([4], dtype=int64)] - [array([5], dtype=int64)] - [array([6], dtype=int64)] - [array([7], dtype=int64)] - [array([8], dtype=int64)] - [array([9], dtype=int64)] + [Tensor(shape=[1], dtype=Int64, value= [0])] + [Tensor(shape=[1], dtype=Int64, value= [1])] + [Tensor(shape=[1], dtype=Int64, value= [2])] + [Tensor(shape=[1], dtype=Int64, value= [3])] + [Tensor(shape=[1], dtype=Int64, value= [4])] + [Tensor(shape=[1], dtype=Int64, value= [5])] + [Tensor(shape=[1], dtype=Int64, value= [6])] + [Tensor(shape=[1], dtype=Int64, value= [7])] + [Tensor(shape=[1], dtype=Int64, value= [8])] + [Tensor(shape=[1], dtype=Int64, value= [9])] ``` - Create an iterator whose return value is a dictionary type. As shown in the following, create the iterators for `dataset1` and `dataset2`, and print the output. @@ -238,25 +238,25 @@ Define a `Generator` and transfer it to `GeneratorDataset` to create a dataset a The output is as follows: ``` dataset1: - {'data': array([0], dtype=int64)} - {'data': array([1], dtype=int64)} - {'data': array([2], dtype=int64)} - {'data': array([3], dtype=int64)} - {'data': array([4], dtype=int64)} - {'data': array([5], dtype=int64)} - {'data': array([6], dtype=int64)} - {'data': array([7], dtype=int64)} - {'data': array([8], dtype=int64)} - {'data': array([9], dtype=int64)} + {'data': Tensor(shape=[], dtype=Int64, value= [0])} + {'data': Tensor(shape=[], dtype=Int64, value= [1])} + {'data': Tensor(shape=[], dtype=Int64, value= [2])} + {'data': Tensor(shape=[], dtype=Int64, value= [3])} + {'data': Tensor(shape=[], dtype=Int64, value= [4])} + {'data': Tensor(shape=[], dtype=Int64, value= [5])} + {'data': Tensor(shape=[], dtype=Int64, value= [6])} + {'data': Tensor(shape=[], dtype=Int64, value= [7])} + {'data': Tensor(shape=[], dtype=Int64, value= [8])} + {'data': Tensor(shape=[], dtype=Int64, value= [9])} dataset2: - {'data': array([0], dtype=int64)} - {'data': array([1], dtype=int64)} - {'data': array([2], dtype=int64)} - {'data': array([3], dtype=int64)} - {'data': array([4], dtype=int64)} - {'data': array([5], dtype=int64)} - {'data': array([6], dtype=int64)} - {'data': array([7], dtype=int64)} - {'data': array([8], dtype=int64)} - {'data': array([9], dtype=int64)} + {'data': Tensor(shape=[], dtype=Int64, value= [0])} + {'data': Tensor(shape=[], dtype=Int64, value= [1])} + {'data': Tensor(shape=[], dtype=Int64, value= [2])} + {'data': Tensor(shape=[], dtype=Int64, value= [3])} + {'data': Tensor(shape=[], dtype=Int64, value= [4])} + {'data': Tensor(shape=[], dtype=Int64, value= [5])} + {'data': Tensor(shape=[], dtype=Int64, value= [6])} + {'data': Tensor(shape=[], dtype=Int64, value= [7])} + {'data': Tensor(shape=[], dtype=Int64, value= [8])} + {'data': Tensor(shape=[], dtype=Int64, value= [9])} ``` \ No newline at end of file diff --git a/tutorials/source_zh_cn/advanced_use/auto_augmentation.md b/tutorials/source_zh_cn/advanced_use/auto_augmentation.md index 9a159b0529..0c6114dc5f 100644 --- a/tutorials/source_zh_cn/advanced_use/auto_augmentation.md +++ b/tutorials/source_zh_cn/advanced_use/auto_augmentation.md @@ -219,7 +219,7 @@ imagenet_policy = [ step_num += 1 for index in range(rows): fig.add_subplot(rows, columns, ep_num * rows + index + 1) - plt.imshow(data['image'][index]) + plt.imshow(data['image'].asnumpy()[index]) plt.show() ``` diff --git a/tutorials/source_zh_cn/advanced_use/deep_probability_program.md b/tutorials/source_zh_cn/advanced_use/deep_probability_program.md index 14079f1b17..cc6464cfa9 100644 --- a/tutorials/source_zh_cn/advanced_use/deep_probability_program.md +++ b/tutorials/source_zh_cn/advanced_use/deep_probability_program.md @@ -143,8 +143,8 @@ def train_model(train_net, net, dataset): accs = [] loss_sum = 0 for _, data in enumerate(dataset.create_dict_iterator()): - train_x = Tensor(data['image'].astype(np.float32)) - label = Tensor(data['label'].astype(np.int32)) + train_x = Tensor(data['image'].asnumpy().astype(np.float32)) + label = Tensor(data['label'].asnumpy().astype(np.int32)) loss = train_net(train_x, label) output = net(train_x) log_output = P.LogSoftmax(axis=1)(output) @@ -160,8 +160,8 @@ def train_model(train_net, net, dataset): def validate_model(net, dataset): accs = [] for _, data in enumerate(dataset.create_dict_iterator()): - train_x = Tensor(data['image'].astype(np.float32)) - label = Tensor(data['label'].astype(np.int32)) + train_x = Tensor(data['image'].asnumpy().astype(np.float32)) + label = Tensor(data['label'].asnumpy().astype(np.int32)) output = net(train_x) log_output = P.LogSoftmax(axis=1)(output) acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy()) @@ -252,7 +252,7 @@ trained_loss = vi.get_train_loss() IMAGE_SHAPE = (-1, 1, 32, 32) generated_sample = vae.generate_sample(64, IMAGE_SHAPE) for sample in ds_train.create_dict_iterator(): - sample_x = Tensor(sample['image'], dtype=mstype.float32) + sample_x = Tensor(sample['image'].asnumpy(), dtype=mstype.float32) reconstructed_sample = vae.reconstruct_sample(sample_x) ``` @@ -606,7 +606,7 @@ evaluation = UncertaintyEvaluation(model=network, ale_uncer_model_path=None, save_model=False) for eval_data in ds_eval.create_dict_iterator(): - eval_data = Tensor(eval_data['image'], mstype.float32) + eval_data = Tensor(eval_data['image'].asnumpy(), mstype.float32) epistemic_uncertainty = evaluation.eval_epistemic_uncertainty(eval_data) aleatoric_uncertainty = evaluation.eval_aleatoric_uncertainty(eval_data) ``` diff --git a/tutorials/source_zh_cn/advanced_use/fuzzer.md b/tutorials/source_zh_cn/advanced_use/fuzzer.md index 516b9a7467..153af4b785 100644 --- a/tutorials/source_zh_cn/advanced_use/fuzzer.md +++ b/tutorials/source_zh_cn/advanced_use/fuzzer.md @@ -72,7 +72,7 @@ context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target) ds = generate_mnist_dataset(data_list, batch_size, sparse=False) train_images = [] for data in ds.create_tuple_iterator(): - images = data[0].astype(np.float32) + images = data[0].asnumpy().astype(np.float32) train_images.append(images) train_images = np.concatenate(train_images, axis=0) @@ -83,8 +83,8 @@ context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target) test_images = [] test_labels = [] for data in ds.create_tuple_iterator(): - images = data[0].astype(np.float32) - labels = data[1] + images = data[0].asnumpy().astype(np.float32) + labels = data[1].asnumpy() test_images.append(images) test_labels.append(labels) test_images = np.concatenate(test_images, axis=0) diff --git a/tutorials/source_zh_cn/advanced_use/model_security.md b/tutorials/source_zh_cn/advanced_use/model_security.md index 52d4a2fd09..9e6965463b 100644 --- a/tutorials/source_zh_cn/advanced_use/model_security.md +++ b/tutorials/source_zh_cn/advanced_use/model_security.md @@ -198,8 +198,8 @@ def generate_mnist_dataset(data_path, batch_size=32, repeat_size=1, inputs = [] labels = [] for data in ds_test.create_tuple_iterator(): - inputs.append(data[0].astype(np.float32)) - labels.append(data[1]) + inputs.append(data[0].asnumpy().astype(np.float32)) + labels.append(data[1].asnumpy()) test_inputs = np.concatenate(inputs) test_labels = np.concatenate(labels) ``` diff --git a/tutorials/source_zh_cn/advanced_use/optimize_the_performance_of_data_preparation.md b/tutorials/source_zh_cn/advanced_use/optimize_the_performance_of_data_preparation.md index 83125c64b7..956744a85f 100644 --- a/tutorials/source_zh_cn/advanced_use/optimize_the_performance_of_data_preparation.md +++ b/tutorials/source_zh_cn/advanced_use/optimize_the_performance_of_data_preparation.md @@ -134,7 +134,8 @@ MindSpore为用户提供了多种数据加载方式，其中包括常用数据 输出： ``` - {'image': array([[[235, 235, 235], + {'image': Tensor(shape=[32, 32, 3], dtype=UInt8, value= + [[[235, 235, 235], [230, 230, 230], [234, 234, 234], ..., @@ -144,7 +145,7 @@ MindSpore为用户提供了多种数据加载方式，其中包括常用数据 ..., [120, 120, 119], [146, 146, 146], - [177, 174, 190]]], dtype=uint8), 'label': array(9, dtype=uint32)} + [177, 174, 190]]], dtype=uint8), 'label': Tensor(shape=[], dtype=UInt32, value= 9)} ``` 2. 使用`Cifar10ToMR`这个类将CIFAR-10数据集转换为MindSpore数据格式，这里使用的是CIFAR-10 python文件格式的数据集，然后使用`MindDataset`类加载MindSpore数据格式数据集，加载数据采取多线程优化方案，开启了4个线程并发完成任务，最后对数据创建了字典迭代器，并通过迭代器读取了一条数据记录。 @@ -168,7 +169,7 @@ MindSpore为用户提供了多种数据加载方式，其中包括常用数据 输出： ``` - {'data': array([255, 216, 255, ..., 63, 255, 217], dtype=uint8), 'id': array(30474, dtype=int64), 'label': array(2, dtype=int64)} + {'data': Tensor(shape=[1431], dtype=UInt8, value= [255, 216, 255, ..., 63, 255, 217]), 'id': Tensor(shape=[], dtype=Int64, value= 30474), 'label': Tensor(shape=[], dtype=Int64, value= 2)} ``` 3. 使用`GeneratorDataset`类加载自定义数据集，并且采取多进程优化方案，开启了4个进程并发完成任务，最后对数据创建了字典迭代器，并通过迭代器读取了一条数据记录。 @@ -187,7 +188,7 @@ MindSpore为用户提供了多种数据加载方式，其中包括常用数据 输出： ``` - {'data': array([0], dtype=int64)} + {'data': Tensor(shape=[1], dtype=Int64, value= [0])} ``` ## shuffle性能优化 @@ -220,7 +221,8 @@ shuffle性能优化建议如下： 输出： ``` - {'image': array([[[254, 254, 254], + {'image': Tensor(shape=[32, 32, 3], dtype=UInt8, value= + [[[254, 254, 254], [255, 255, 254], [255, 255, 254], ..., @@ -230,7 +232,7 @@ shuffle性能优化建议如下： ..., [ 64, 61, 63], [ 63, 58, 60], - [ 61, 56, 58]]], dtype=uint8), 'label': array(9, dtype=uint32)} + [ 61, 56, 58]]]), 'label': Tensor(shape=[], dtype=UInt32, value= 9)} ``` 2. 使用`shuffle`函数进行数据混洗，参数`buffer_size`设置为3，数据采用`GeneratorDataset`类自定义生成。 @@ -314,7 +316,7 @@ shuffle性能优化建议如下： cifar10_dataset = cifar10_dataset.map(operations=transforms, input_columns="image", num_parallel_workers=4) data = next(cifar10_dataset.create_dict_iterator()) - plt.imshow(data["image"]) + plt.imshow(data["image"].asnumpy()) plt.show() ``` diff --git a/tutorials/source_zh_cn/use/image_loading.md b/tutorials/source_zh_cn/use/image_loading.md index db5545aeb2..eb24b619d8 100644 --- a/tutorials/source_zh_cn/use/image_loading.md +++ b/tutorials/source_zh_cn/use/image_loading.md @@ -63,8 +63,8 @@ MindSpore目前支持的数据采样器及其详细使用方法，可参考编 # 创建迭代器并展示样本及标签 mnist_it = mnist_dataset.create_dict_iterator() data = mnist_it.get_next() - plt.imshow(data['image'].squeeze(), cmap=plt.cm.gray) - plt.title(data['label'], fontsize=20) + plt.imshow(data['image'].asnumpy().squeeze(), cmap=plt.cm.gray) + plt.title(data['label'].asnumpy(), fontsize=20) plt.show() ``` @@ -175,8 +175,8 @@ for data in mnist_dataset.create_dict_iterator(): ```python mnist_it = ds4.create_dict_iterator() data = mnist_it.get_next() - plt.imshow(data['image'].squeeze(), cmap=plt.cm.gray) - plt.title(data['label'], fontsize=20) + plt.imshow(data['image'].asnumpy().squeeze(), cmap=plt.cm.gray) + plt.title(data['label'].asnumpy(), fontsize=20) plt.show() ``` diff --git a/tutorials/source_zh_cn/use/text_loading.md b/tutorials/source_zh_cn/use/text_loading.md index 340d2c0828..084c4d3cf1 100644 --- a/tutorials/source_zh_cn/use/text_loading.md +++ b/tutorials/source_zh_cn/use/text_loading.md @@ -64,7 +64,7 @@ MindSpore目前支持的数据采样器及其详细使用方法，可参考编 2. 创建迭代器，通过迭代器获取数据。 ```python - for data in dataset.create_dict_iterator(): + for data in dataset.create_dict_iterator(output_numpy=True): print(text.to_str(data['text'])) ``` @@ -94,7 +94,7 @@ MindSpore目前支持的数据采样器及其详细使用方法，可参考编 2. 原始数据输出效果。 ```python - for data in dataset.create_dict_iterator(): + for data in dataset.create_dict_iterator(output_numpy=True): print(text.to_str(data['text']).tolist()) ``` @@ -111,7 +111,7 @@ MindSpore目前支持的数据采样器及其详细使用方法，可参考编 4. 执行之后输出效果。 ```python - for data in dataset.create_dict_iterator(): + for data in dataset.create_dict_iterator(output_numpy=True): print(text.to_str(data['text']).tolist()) ``` @@ -136,7 +136,7 @@ MindSpore目前支持的数据采样器及其详细使用方法，可参考编 2. 数据输出效果。 ```python - for data in dataset.create_dict_iterator(): + for data in dataset.create_dict_iterator(output_numpy=True): print(text.to_str(data['text']).tolist()) ``` @@ -177,7 +177,7 @@ MindSpore目前支持的数据采样器及其详细使用方法，可参考编 3. 创建迭代器，通过迭代器获取数据。 ```python - for i in dataset.create_dict_iterator(num_epochs=1): + for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True): token = text.to_str(i['text']).tolist() print(token) ``` diff --git a/tutorials/tutorial_code/model_safety/mnist_attack_fgsm.py b/tutorials/tutorial_code/model_safety/mnist_attack_fgsm.py index 9561b5cebd..743b083ace 100644 --- a/tutorials/tutorial_code/model_safety/mnist_attack_fgsm.py +++ b/tutorials/tutorial_code/model_safety/mnist_attack_fgsm.py @@ -72,8 +72,8 @@ def test_fast_gradient_sign_method(): i = 0 for data in ds.create_tuple_iterator(): i += 1 - images = data[0].astype(np.float32) - labels = data[1] + images = data[0].asnumpy().astype(np.float32) + labels = data[1].asnumpy() test_images.append(images) test_labels.append(labels) pred_labels = np.argmax(model.predict(Tensor(images)).asnumpy(), diff --git a/tutorials/tutorial_code/model_safety/mnist_defense_nad.py b/tutorials/tutorial_code/model_safety/mnist_defense_nad.py index d587f960ac..3c19eeed38 100644 --- a/tutorials/tutorial_code/model_safety/mnist_defense_nad.py +++ b/tutorials/tutorial_code/model_safety/mnist_defense_nad.py @@ -71,8 +71,8 @@ def test_nad_method(): inputs = [] labels = [] for data in ds_test.create_tuple_iterator(): - inputs.append(data[0].astype(np.float32)) - labels.append(data[1]) + inputs.append(data[0].asnumpy().astype(np.float32)) + labels.append(data[1].asnumpy()) inputs = np.concatenate(inputs) labels = np.concatenate(labels) -- Gitee From 88a6965a4c56af0347ec96e0f522673644f4787a Mon Sep 17 00:00:00 2001 From: liyong Date: Wed, 16 Sep 2020 11:00:33 +0800 Subject: [PATCH 050/156] fix autoaug doc --- .../advanced_use/auto_augmentation.md | 234 +++++++++--------- 1 file changed, 123 insertions(+), 111 deletions(-) diff --git a/tutorials/source_zh_cn/advanced_use/auto_augmentation.md b/tutorials/source_zh_cn/advanced_use/auto_augmentation.md index 9a159b0529..9f0c3dac11 100644 --- a/tutorials/source_zh_cn/advanced_use/auto_augmentation.md +++ b/tutorials/source_zh_cn/advanced_use/auto_augmentation.md @@ -1,6 +1,6 @@ # 自动数据增强 -`Linux` `Ascend` `GPU` `CPU` `中级` `高级` +`Linux` `Ascend` `GPU` `CPU` `中级` `高级` `数据准备` @@ -15,11 +15,12 @@ ## 概述 -AutoAugment是在一系列图像增强子策略的搜索空间中通过搜索算法找到适合特定数据集的图像增强方案，针对ImageNet数据集的数据增强增强策略包含25条子策略，每条子策略中包含两种变换，针对一个batch中的每张图像随机挑选一个子策略的组合，以预定的概率来决定是否执行子策略中的每种变换。 +自动数据增强（AutoAugment）[1]是在一系列图像增强子策略的搜索空间中，通过搜索算法找到适合特定数据集的图像增强方案。MindSpore的`c_transforms`模块提供了丰富的C++算子来实现AutoAugment，用户也可以自定义函数或者算子来实现。 +更多MindSpore算子的详细说明参见[API文档](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.dataset.vision.html)。 -MindSpore的`c_transforms`模块提供了丰富的c_vision++算子来实现AutoAugment，用户也可以自定义函数或者算子来实现。更多MindSpore算子的详细说明参见[API文档](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.dataset.vision.html)。 +MindSpore算子和AutoAugment中的算子的对应关系如下： -| AutoAugment增强算子 | MindSpore算子 |描述 | +| AutoAugment算子 | MindSpore算子 |描述 | |:-------------------:|:------|--------------| |shearX|RandomAffine|横向剪切| |shearY|RandomAffine|纵向剪切| @@ -36,115 +37,25 @@ MindSpore的`c_transforms`模块提供了丰富的c_vision++算子来实现AutoA |equalize|Equalize|均衡图像直方图| |invert|Invert|反转图像| -ImageNet数据集的增强策略定义如下： - -```python -# define autoAugment operators - -PARAMETER_MAX = 10 - -def float_parameter(level, maxval): - return float(level) * maxval / PARAMETER_MAX - -def int_parameter(level, maxval): - return int(level * maxval / PARAMETER_MAX) - -def shear_x(level): - v = float_parameter(level, 0.3) - return c_transforms.RandomChoice([c_vision.RandomAffine(degrees=0, shear=(-v,-v)), c_vision.RandomAffine(degrees=0, shear=(v, v))]) - -def shear_y(level): - v = float_parameter(level, 0.3) - return c_transforms.RandomChoice([c_vision.RandomAffine(degrees=0, shear=(0, 0, -v,-v)), c_vision.RandomAffine(degrees=0, shear=(0, 0, v, v))]) - -def translate_x(level): - v = float_parameter(level, 150 / 331) - return c_transforms.RandomChoice([c_vision.RandomAffine(degrees=0, translate=(-v,-v)), c_vision.RandomAffine(degrees=0, translate=(v, v))]) - -def translate_y(level): - v = float_parameter(level, 150 / 331) - return c_transforms.RandomChoice([c_vision.RandomAffine(degrees=0, translate=(0, 0, -v,-v)), c_vision.RandomAffine(degrees=0, translate=(0, 0, v, v))]) - -def color_impl(level): - v = float_parameter(level, 1.8) + 0.1 - return c_vision.RandomColor(degrees=(v, v)) - -def rotate_impl(level): - v = int_parameter(level, 30) - return c_transforms.RandomChoice([c_vision.RandomRotation(degrees=(-v, -v)), c_vision.RandomRotation(degrees=(v, v))]) - -def solarize_impl(level): - level = int_parameter(level, 256) - v = 256 - level - return c_vision.RandomSolarize(threshold=(0, v)) - -def posterize_impl(level): - level = int_parameter(level, 4) - v = 4 - level - return c_vision.RandomPosterize(bits=(v, v)) - -def contrast_impl(level): - v = float_parameter(level, 1.8) + 0.1 - return c_vision.RandomColorAdjust(contrast=(v, v)) - -def autocontrast_impl(level): - return c_vision.AutoContrast() - -def sharpness_impl(level): - v = float_parameter(level, 1.8) + 0.1 - return c_vision.RandomSharpness(degrees=(v, v)) - -def brightness_impl(level): - v = float_parameter(level, 1.8) + 0.1 - return c_vision.RandomColorAdjust(brightness=(v, v)) - -# define AutoAugment policy -imagenet_policy = [ - [(posterize_impl(8), 0.4), (rotate_impl(9), 0.6)], - [(solarize_impl(5), 0.6), (autocontrast_impl(5), 0.6)], - [(c_vision.Equalize(), 0.8), (c_vision.Equalize(), 0.6)], - [(posterize_impl(7), 0.6), (posterize_impl(6), 0.6)], - [(c_vision.Equalize(), 0.4), (solarize_impl(4), 0.2)], - - [(c_vision.Equalize(), 0.4), (rotate_impl(8), 0.8)], - [(solarize_impl(3), 0.6), (c_vision.Equalize(), 0.6)], - [(posterize_impl(5), 0.8), (c_vision.Equalize(), 1.0)], - [(rotate_impl(3), 0.2), (solarize_impl(8), 0.6)], - [(c_vision.Equalize(), 0.6), (posterize_impl(6), 0.4)], - - [(rotate_impl(8), 0.8), (color_impl(0), 0.4)], - [(rotate_impl(9), 0.4), (c_vision.Equalize(), 0.6)], - [(c_vision.Equalize(), 0.0), (c_vision.Equalize(), 0.8)], - [(c_vision.Invert(), 0.6), (c_vision.Equalize(), 1.0)], - [(color_impl(4), 0.6), (contrast_impl(8), 1.0)], - - [(rotate_impl(8), 0.8), (color_impl(2), 1.0)], - [(color_impl(8), 0.8), (solarize_impl(7), 0.8)], - [(sharpness_impl(7), 0.4), (c_vision.Invert(), 0.6)], - [(shear_x(5), 0.6), (c_vision.Equalize(), 1.0)], - [(color_impl(0), 0.4), (c_vision.Equalize(), 0.6)], - - [(c_vision.Equalize(), 0.4), (solarize_impl(4), 0.2)], - [(solarize_impl(5), 0.6), (autocontrast_impl(5), 0.6)], - [(c_vision.Invert(), 0.6), (c_vision.Equalize(), 1.0)], - [(color_impl(4), 0.6), (contrast_impl(8), 1.0)], - [(c_vision.Equalize(), 0.8), (c_vision.Equalize(), 0.6)], - ] -``` - ## ImageNet自动数据增强 +本教程以在ImageNet数据集上实现AutoAugment作为示例。 + +针对ImageNet数据集的数据增强策略包含25条子策略，每条子策略中包含两种变换，针对一个batch中的每张图像随机挑选一个子策略的组合，以预定的概率来决定是否执行子策略中的每种变换。 -用户可以使用Mindspore提供的`RandomSelectSubpolicy`接口来实现自动数据增强， +用户可以使用MindSpore中`c_transforms`模块的`RandomSelectSubpolicy`接口来实现AutoAugment， 在ImageNet分类训练中标准的数据增强方式分以下几个步骤: -1. `RandomCropDecodeResize`：随机裁剪后进行解码。 -2. `RandomHorizontalFlip`：水平方向上随机翻转。 -3. `Normalize`：归一化。 -4. `HWC2CHW`：shape变换。 +一. `RandomCropDecodeResize`：随机裁剪后进行解码。 + +二. `RandomHorizontalFlip`：水平方向上随机翻转。 + +三. `Normalize`：归一化。 -在步骤1后插入AutoAugment变换，如下所示： +四. `HWC2CHW`：shape变换。 -1. 引入mindspore数据增强模块。 +在步骤一后插入AutoAugment变换，如下所示： + +1. 引入MindSpore数据增强模块。 ```python import mindspore.common.dtype as mstype @@ -154,7 +65,108 @@ imagenet_policy = [ import matplotlib.pyplot as plt ``` -2. 在`RandomCropDecodeResize`操作后插入AutoAugment变换。 +2. 定义MindSpore算子到AutoAugment算子的映射： + + ```python + # define AutoAugment operators + + PARAMETER_MAX = 10 + + def float_parameter(level, maxval): + return float(level) * maxval / PARAMETER_MAX + + def int_parameter(level, maxval): + return int(level * maxval / PARAMETER_MAX) + + def shear_x(level): + v = float_parameter(level, 0.3) + return c_transforms.RandomChoice([c_vision.RandomAffine(degrees=0, shear=(-v,-v)), c_vision.RandomAffine(degrees=0, shear=(v, v))]) + + def shear_y(level): + v = float_parameter(level, 0.3) + return c_transforms.RandomChoice([c_vision.RandomAffine(degrees=0, shear=(0, 0, -v,-v)), c_vision.RandomAffine(degrees=0, shear=(0, 0, v, v))]) + + def translate_x(level): + v = float_parameter(level, 150 / 331) + return c_transforms.RandomChoice([c_vision.RandomAffine(degrees=0, translate=(-v,-v)), c_vision.RandomAffine(degrees=0, translate=(v, v))]) + + def translate_y(level): + v = float_parameter(level, 150 / 331) + return c_transforms.RandomChoice([c_vision.RandomAffine(degrees=0, translate=(0, 0, -v,-v)), c_vision.RandomAffine(degrees=0, translate=(0, 0, v, v))]) + + def color_impl(level): + v = float_parameter(level, 1.8) + 0.1 + return c_vision.RandomColor(degrees=(v, v)) + + def rotate_impl(level): + v = int_parameter(level, 30) + return c_transforms.RandomChoice([c_vision.RandomRotation(degrees=(-v, -v)), c_vision.RandomRotation(degrees=(v, v))]) + + def solarize_impl(level): + level = int_parameter(level, 256) + v = 256 - level + return c_vision.RandomSolarize(threshold=(0, v)) + + def posterize_impl(level): + level = int_parameter(level, 4) + v = 4 - level + return c_vision.RandomPosterize(bits=(v, v)) + + def contrast_impl(level): + v = float_parameter(level, 1.8) + 0.1 + return c_vision.RandomColorAdjust(contrast=(v, v)) + + def autocontrast_impl(level): + return c_vision.AutoContrast() + + def sharpness_impl(level): + v = float_parameter(level, 1.8) + 0.1 + return c_vision.RandomSharpness(degrees=(v, v)) + + def brightness_impl(level): + v = float_parameter(level, 1.8) + 0.1 + return c_vision.RandomColorAdjust(brightness=(v, v)) + + ``` + +3. 定义ImageNet数据集的AutoAugment策略： + ```python + # define AutoAugment policy + imagenet_policy = [ + [(posterize_impl(8), 0.4), (rotate_impl(9), 0.6)], + [(solarize_impl(5), 0.6), (autocontrast_impl(5), 0.6)], + [(c_vision.Equalize(), 0.8), (c_vision.Equalize(), 0.6)], + [(posterize_impl(7), 0.6), (posterize_impl(6), 0.6)], + [(c_vision.Equalize(), 0.4), (solarize_impl(4), 0.2)], + + [(c_vision.Equalize(), 0.4), (rotate_impl(8), 0.8)], + [(solarize_impl(3), 0.6), (c_vision.Equalize(), 0.6)], + [(posterize_impl(5), 0.8), (c_vision.Equalize(), 1.0)], + [(rotate_impl(3), 0.2), (solarize_impl(8), 0.6)], + [(c_vision.Equalize(), 0.6), (posterize_impl(6), 0.4)], + + [(rotate_impl(8), 0.8), (color_impl(0), 0.4)], + [(rotate_impl(9), 0.4), (c_vision.Equalize(), 0.6)], + [(c_vision.Equalize(), 0.0), (c_vision.Equalize(), 0.8)], + [(c_vision.Invert(), 0.6), (c_vision.Equalize(), 1.0)], + [(color_impl(4), 0.6), (contrast_impl(8), 1.0)], + + [(rotate_impl(8), 0.8), (color_impl(2), 1.0)], + [(color_impl(8), 0.8), (solarize_impl(7), 0.8)], + [(sharpness_impl(7), 0.4), (c_vision.Invert(), 0.6)], + [(shear_x(5), 0.6), (c_vision.Equalize(), 1.0)], + [(color_impl(0), 0.4), (c_vision.Equalize(), 0.6)], + + [(c_vision.Equalize(), 0.4), (solarize_impl(4), 0.2)], + [(solarize_impl(5), 0.6), (autocontrast_impl(5), 0.6)], + [(c_vision.Invert(), 0.6), (c_vision.Equalize(), 1.0)], + [(color_impl(4), 0.6), (contrast_impl(8), 1.0)], + [(c_vision.Equalize(), 0.8), (c_vision.Equalize(), 0.6)], + ] + + ``` + +4. 在`RandomCropDecodeResize`操作后插入AutoAugment变换。 ```python def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, shuffle=True, num_samples=5, target="Ascend"): @@ -200,7 +212,7 @@ imagenet_policy = [ return ds ``` -3. 验证自动数据增强效果。 +5. 验证自动数据增强效果。 ```python # path to imagefolder directory. This directory needs to contain sub-directories which contain the images @@ -223,12 +235,12 @@ imagenet_policy = [ plt.show() ``` - >为了更好演示效果从数据集中只读取5张图片并且不进行`shuffle`，并且为了更好显示图片不进行`Normalize`和`HWC2CHW`操作。 + >为了更好演示效果，从数据集中只读取5张图片并且不进行`shuffle`且不进行`Normalize`和`HWC2CHW`操作。 - 运行结果可以看到，batch中每张图像的增强效果，X方向表示1个batch的5张图像，Y方向表示5个bacth。 ![augment](./images/auto_augmentation.png) + 运行结果可以看到，batch中每张图像的增强效果，X方向表示1个batch的5张图像，Y方向表示5个bacth。 ## 参考文献 [1] [AutoAugment: Learning Augmentation Policies from Data](https://arxiv.org/abs/1805.09501) -- Gitee From 7510e247bca701ddfd76f3f8bad7f7fb28e260bf Mon Sep 17 00:00:00 2001 From: pkuliuliu Date: Wed, 16 Sep 2020 15:16:31 +0800 Subject: [PATCH 051/156] add diff_privacy design in english --- docs/source_en/design.rst | 3 +- .../mindarmour/differential_privacy_design.md | 71 ++++++++++++++++++ .../design/mindarmour/images/dp_arch.png | Bin 0 -> 61680 bytes 3 files changed, 73 insertions(+), 1 deletion(-) create mode 100644 docs/source_en/design/mindarmour/differential_privacy_design.md create mode 100644 docs/source_en/design/mindarmour/images/dp_arch.png diff --git a/docs/source_en/design.rst b/docs/source_en/design.rst index 359add5edc..e0a79ac16b 100644 --- a/docs/source_en/design.rst +++ b/docs/source_en/design.rst @@ -8,4 +8,5 @@ Design design/mindspore/ir design/mindinsight/training_visual_design design/mindinsight/graph_visual_design - design/mindinsight/tensor_visual_design \ No newline at end of file + design/mindinsight/tensor_visual_design + design/mindarmour/differential_privacy_design.md diff --git a/docs/source_en/design/mindarmour/differential_privacy_design.md b/docs/source_en/design/mindarmour/differential_privacy_design.md new file mode 100644 index 0000000000..71f4b5e5cf --- /dev/null +++ b/docs/source_en/design/mindarmour/differential_privacy_design.md @@ -0,0 +1,71 @@ +# Differential Privacy + +`Ascend` `Model Development` `Model Optimization` `Framework Development` `Enterprise` `Expert` `Contributor` + + + +- [Differential Privacy](#differential-privacy) + - [Overall Design](#overall-design) + - [DP Optimizer](#dp-optimizer) + - [DP Mechanisms](#dp-mechanisms) + - [Monitor](#monitor) + - [Code Implementation](#code-implementation) + - [References](#references) + + + + + +## Overall Design + +The Differential-Privacy module of MindArmour implements the differential privacy training capability. Model training consists of building training dataset, computing loss, computing gradient, and updating model parameters. Currently, the differential privacy training of MindArmour focuses on the gradient computing process and uses the corresponding algorithm to clip and add noise to the gradient. In this way, user data privacy is protected. + +![dp_arch](./images/dp_arch.png) + +

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[PATCH 015/156] add lite docs image_classification and object-detection --- lite/docs/source_en/image_classification.md | 33 ++++++++++++++++++ .../images/image_classification_result.png | Bin 0 -> 338804 bytes .../source_en/images/object_detection.png | Bin 0 -> 164013 bytes lite/docs/source_en/object_detection.md | 29 +++++++++++++++ .../docs/source_zh_cn/image_classification.md | 33 ++++++++++++++++++ .../images/image_classification_result.png | Bin 0 -> 338804 bytes .../source_zh_cn/images/object_detection.png | Bin 0 -> 164013 bytes lite/docs/source_zh_cn/object_detection.md | 29 +++++++++++++++ 8 files changed, 124 insertions(+) create mode 100644 lite/docs/source_en/image_classification.md create mode 100644 lite/docs/source_en/images/image_classification_result.png create mode 100644 lite/docs/source_en/images/object_detection.png create mode 100644 lite/docs/source_en/object_detection.md create mode 100644 lite/docs/source_zh_cn/image_classification.md create mode 100644 lite/docs/source_zh_cn/images/image_classification_result.png create mode 100644 lite/docs/source_zh_cn/images/object_detection.png create mode 100644 lite/docs/source_zh_cn/object_detection.md diff --git a/lite/docs/source_en/image_classification.md b/lite/docs/source_en/image_classification.md new file mode 100644 index 0000000000..4f478abb72 --- /dev/null +++ b/lite/docs/source_en/image_classification.md @@ -0,0 +1,33 @@ +# Image classification + + + +## Image classification introduction + +Image classification is to identity what an image represents, to predict the object list and the probabilites. For example，the following tabel shows the classification results after mode inference. + +![image_classification](images/image_classification_result.png) + +| Category | Probability | +| ---------- | ----------- | +| plant | 0.9359 | +| flower | 0.8641 | +| tree | 0.8584 | +| houseplant | 0.7867 | + +Using MindSpore Lite to realize image classification [example](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/lite/image_classification). + +## Image classification model list + +The following table shows the data of some image classification models using MindSpore Lite inference. + +> The performance of the table below is tested on the mate30. + +| model name | link | size | precision | CPU 4 thread delay | +|-----------------------|----------|----------|----------|-----------| +| MobileNetV2 | | | | | +| LeNet | | | | | +| AlexNet | | | | | +| GoogleNet | | | | | +| ResNext50 | | | | | + diff --git a/lite/docs/source_en/images/image_classification_result.png 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