From 231af4238990cf8e72c567eb2f7ae8ecdbf75a9c Mon Sep 17 00:00:00 2001 From: luoyang Date: Tue, 17 Jun 2025 14:44:09 +0800 Subject: [PATCH] adjust minddata doc --- .../source_en/features/dataset/overview.md | 148 +------- .../features/dataset/overview.ipynb | 259 +------------ tutorials/source_en/dataset/eager.md | 237 +++++++++++- tutorials/source_en/dataset/record.ipynb | 3 +- tutorials/source_en/dataset/sampler.md | 147 +++++++- tutorials/source_en/index.rst | 5 +- tutorials/source_zh_cn/dataset/eager.ipynb | 355 ++++++++++++++++-- tutorials/source_zh_cn/dataset/record.ipynb | 2 +- tutorials/source_zh_cn/dataset/sampler.ipynb | 287 ++++++++++++-- tutorials/source_zh_cn/index.rst | 7 +- 10 files changed, 983 insertions(+), 467 deletions(-) diff --git a/docs/mindspore/source_en/features/dataset/overview.md b/docs/mindspore/source_en/features/dataset/overview.md index be76383c1f..8cc2106eff 100644 --- a/docs/mindspore/source_en/features/dataset/overview.md +++ b/docs/mindspore/source_en/features/dataset/overview.md @@ -8,8 +8,6 @@ MindSpore Dataset provides two types of data processing capabilities: pipeline m 2. Lightweight mode: Users can perform data transform operations (e.g. Resize, Crop, HWC2CHW, etc.). Data processing of a single sample is performed. -This section focuses on two data processing modes. - ## Pipeline Mode Dataset pipeline defined by an API is used. After a training process is run, the dataset cyclically loads data from the dataset, processes data, and batch data, and then iterators for training. @@ -58,114 +56,15 @@ You can configure different parameters for loading [datasets](https://www.mindsp #### Dataset Combination -Dataset combination can combine multiple datasets in series/parallel mode to form a new dataset object. - -- Concatenate multiple datasets - - ```python - import mindspore.dataset as ds - - ds.config.set_seed(1234) - - data = [1, 2, 3] - dataset1 = ds.NumpySlicesDataset(data=data, column_names=["column_1"]) - - data = [4, 5, 6] - dataset2 = ds.NumpySlicesDataset(data=data, column_names=["column_1"]) - - dataset = dataset1.concat(dataset2) - for item in dataset.create_dict_iterator(): - print(item) - ``` - - ```text - {'column_1': Tensor(shape=[], dtype=Int32, value= 3)} - {'column_1': Tensor(shape=[], dtype=Int32, value= 2)} - {'column_1': Tensor(shape=[], dtype=Int32, value= 1)} - {'column_1': Tensor(shape=[], dtype=Int32, value= 6)} - {'column_1': Tensor(shape=[], dtype=Int32, value= 5)} - {'column_1': Tensor(shape=[], dtype=Int32, value= 4)} - ``` - -- Paralleling multiple datasets - - ```python - import mindspore.dataset as ds - - ds.config.set_seed(1234) - - data = [1, 2, 3] - dataset1 = ds.NumpySlicesDataset(data=data, column_names=["column_1"]) - - data = [4, 5, 6] - dataset2 = ds.NumpySlicesDataset(data=data, column_names=["column_2"]) - - dataset = dataset1.zip(dataset2) - for item in dataset.create_dict_iterator(): - print(item) - ``` - - ```text - {'column_1': Tensor(shape=[], dtype=Int32, value= 3), 'column_2': Tensor(shape=[], dtype=Int32, value= 6)} - {'column_1': Tensor(shape=[], dtype=Int32, value= 2), 'column_2': Tensor(shape=[], dtype=Int32, value= 5)} - {'column_1': Tensor(shape=[], dtype=Int32, value= 1), 'column_2': Tensor(shape=[], dtype=Int32, value= 4)} - ``` +Dataset combination can combine multiple datasets in series/parallel mode to form a new dataset object, see [Data Operation](https://www.mindspore.cn/tutorials/en/master/dataset/eager.html#data-operation). #### Dataset Segmentation -The dataset is divided into a training dataset and a validation dataset, which are used in a training process and a validation process, respectively. - -```python -import mindspore.dataset as ds - -data = [1, 2, 3, 4, 5, 6] -dataset = ds.NumpySlicesDataset(data=data, column_names=["column_1"], shuffle=False) - -train_dataset, eval_dataset = dataset.split([4, 2]) - -print(">>>> train dataset >>>>") -for item in train_dataset.create_dict_iterator(): - print(item) -``` - -```text ->>>> train dataset >>>> -{'column_1': Tensor(shape=[], dtype=Int32, value= 5)} -{'column_1': Tensor(shape=[], dtype=Int32, value= 2)} -{'column_1': Tensor(shape=[], dtype=Int32, value= 6)} -{'column_1': Tensor(shape=[], dtype=Int32, value= 1)} -``` - -```python -print(">>>> eval dataset >>>>") -for item in eval_dataset.create_dict_iterator(): - print(item) -``` - -```text ->>>> eval dataset >>>> -{'column_1': Tensor(shape=[], dtype=Int32, value= 3)} -{'column_1': Tensor(shape=[], dtype=Int32, value= 4)} -``` +The dataset is divided into a training dataset and a validation dataset, which are used in a training process and a validation process, respectively, see [Data Operation](https://www.mindspore.cn/tutorials/en/master/dataset/eager.html#data-operation). #### Dataset Saving -Re-save the dataset to the MindRecord data format. - -```python -import os -import mindspore.dataset as ds - -ds.config.set_seed(1234) - -data = [1, 2, 3, 4, 5, 6] -dataset = ds.NumpySlicesDataset(data=data, column_names=["column_1"]) -if os.path.exists("./train_dataset.mindrecord"): - os.remove("./train_dataset.mindrecord") -if os.path.exists("./train_dataset.mindrecord.db"): - os.remove("./train_dataset.mindrecord.db") -dataset.save("./train_dataset.mindrecord") -``` +Re-save the dataset to the MindRecord data format, see [Data Operation](https://www.mindspore.cn/tutorials/en/master/dataset/eager.html#data-operation). ### Data Transforms @@ -225,43 +124,10 @@ In addition, in the inference scenario, to achieve ultimate performance, you can You can directly use the data transform operation to process a piece of data. The return value is the data transform result. -Data transform operations ([vision transform](https://www.mindspore.cn/docs/en/master/api_python/mindspore.dataset.transforms.html#module-mindspore.dataset.vision), [nlp transform](https://www.mindspore.cn/docs/en/master/api_python/mindspore.dataset.transforms.html#module-mindspore.dataset.text), [audio transform](https://www.mindspore.cn/docs/en/master/api_python/mindspore.dataset.transforms.html#module-mindspore.dataset.audio)) can be used directly like calling a common function. Common usage is: first initialize the data transformation object, then call the data transformation operation method, pass in the data to be processed, and finally get the result of the process. - -```python -from download import download -from PIL import Image -import mindspore.dataset.vision as vision - -url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/banana.jpg" -download(url, './banana.jpg', replace=True) -``` - -```text -Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/banana.jpg (17 kB) - -file_sizes: 100%|██████████████████████████| 17.1k/17.1k [00:00<00:00, 2.14MB/s] -Successfully downloaded file to ./banana.jpg -'./banana.jpg' -``` - -```python -img_ori = Image.open("banana.jpg").convert("RGB") -print("Image.type: {}, Image.shape: {}".format(type(img_ori), img_ori.size)) -``` - -```text -Image.type: , Image.shape: (356, 200) -``` +Data transform operations ([vision transform](https://www.mindspore.cn/docs/en/master/api_python/mindspore.dataset.transforms.html#module-mindspore.dataset.vision), [nlp transform](https://www.mindspore.cn/docs/en/master/api_python/mindspore.dataset.transforms.html#module-mindspore.dataset.text), [audio transform](https://www.mindspore.cn/docs/en/master/api_python/mindspore.dataset.transforms.html#module-mindspore.dataset.audio)) can be used directly like calling a common function. Common usage is: first initialize the data transformation object, then call the data transformation operation method, pass in the data to be processed, and finally get the result of the process. For more examples, see [Lightweight Data Transformation](https://www.mindspore.cn/tutorials/en/master/dataset/eager.html#lightweight-data-transformation). -```python -# Apply Resize to input immediately -resize_op = vision.Resize(size=(320)) -img = resize_op(img_ori) -print("Image.type: {}, Image.shape: {}".format(type(img), img.size)) -``` +## Other Feature -```text -Image.type: , Image.shape: (569, 320) -``` +### Supporting Python Objects in Dataset Pipeline -For more examples, see [Lightweight Data Processing](https://www.mindspore.cn/tutorials/en/master/dataset/eager.html). +Dataset pipeline accepts any Python type as input for some operations(such as user-defined dataset `GeneratorDataset`, user-defined `map` augmentation operation, `batch(per_batch_map=...)`. See [Supporting Python Objects in Dataset Pipeline](https://www.mindspore.cn/tutorials/en/master/dataset/python_objects.html). diff --git a/docs/mindspore/source_zh_cn/features/dataset/overview.ipynb b/docs/mindspore/source_zh_cn/features/dataset/overview.ipynb index f1c503eaa4..90bafb8c4a 100644 --- a/docs/mindspore/source_zh_cn/features/dataset/overview.ipynb +++ b/docs/mindspore/source_zh_cn/features/dataset/overview.ipynb @@ -25,9 +25,7 @@ "\n", "1. 数据处理Pipeline模式：提供基于C++ Runtime的并发数据处理流水线（Pipeline）能力。用户通过定义数据集加载、数据变换、数据批处理（Batch）等流程，实现数据集的高效加载、高效处理、高效Batch。并发度可调、缓存可调等能力，实现为NPU卡训练提供零Bottle Neck的训练数据。\n", "\n", - "2. 数据处理轻量化模式：支持用户使用数据变换操作（如：Resize、Crop、HWC2CHW等）进行单个样本的数据处理。\n", - "\n", - "本章节后续重点讲述两种数据处理模式。" + "2. 数据处理轻量化模式：支持用户使用数据变换操作（如：Resize、Crop、HWC2CHW等）进行单个样本的数据处理。" ] }, { @@ -89,94 +87,8 @@ "\n", "#### 数据集组合\n", "\n", - "数据集组合可以将多个数据集以串联/并朕的方式组合起来，形成一个全新的dataset对象。\n", - "\n", - "- 串联多个数据集" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'column_1': Tensor(shape=[], dtype=Int32, value= 3)}\n", - "{'column_1': Tensor(shape=[], dtype=Int32, value= 2)}\n", - "{'column_1': Tensor(shape=[], dtype=Int32, value= 1)}\n", - "{'column_1': Tensor(shape=[], dtype=Int32, value= 6)}\n", - "{'column_1': Tensor(shape=[], dtype=Int32, value= 5)}\n", - "{'column_1': Tensor(shape=[], dtype=Int32, value= 4)}\n" - ] - } - ], - "source": [ - "import mindspore.dataset as ds\n", - "\n", - "ds.config.set_seed(1234)\n", - "\n", - "data = [1, 2, 3]\n", - "dataset1 = ds.NumpySlicesDataset(data=data, column_names=[\"column_1\"])\n", - "\n", - "data = [4, 5, 6]\n", - "dataset2 = ds.NumpySlicesDataset(data=data, column_names=[\"column_1\"])\n", - "\n", - "dataset = dataset1.concat(dataset2)\n", - "for item in dataset.create_dict_iterator():\n", - " print(item)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "- 并联多个数据集" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'column_1': Tensor(shape=[], dtype=Int32, value= 3), 'column_2': Tensor(shape=[], dtype=Int32, value= 6)}\n", - "{'column_1': Tensor(shape=[], dtype=Int32, value= 2), 'column_2': Tensor(shape=[], dtype=Int32, value= 5)}\n", - "{'column_1': Tensor(shape=[], dtype=Int32, value= 1), 'column_2': Tensor(shape=[], dtype=Int32, value= 4)}\n" - ] - } - ], - "source": [ - "import mindspore.dataset as ds\n", - "\n", - "ds.config.set_seed(1234)\n", - "\n", - "data = [1, 2, 3]\n", - "dataset1 = ds.NumpySlicesDataset(data=data, column_names=[\"column_1\"])\n", - "\n", - "data = [4, 5, 6]\n", - "dataset2 = ds.NumpySlicesDataset(data=data, column_names=[\"column_2\"])\n", - "\n", - "dataset = dataset1.zip(dataset2)\n", - "for item in dataset.create_dict_iterator():\n", - " print(item)" + "数据集组合可以将多个数据集以串联/并朕的方式组合起来，形成一个全新的dataset对象，详见[数据操作](https://www.mindspore.cn/tutorials/zh-CN/master/dataset/eager.html#数据操作)。\n", + "\n" ] }, { @@ -190,62 +102,7 @@ "source": [ "#### 数据集切分\n", "\n", - "将数据集切分成训练数据集和验证数据集，分别用于训练过程和验证过程。" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>>> train dataset >>>>\n", - "{'column_1': Tensor(shape=[], dtype=Int32, value= 5)}\n", - "{'column_1': Tensor(shape=[], dtype=Int32, value= 2)}\n", - "{'column_1': Tensor(shape=[], dtype=Int32, value= 6)}\n", - "{'column_1': Tensor(shape=[], dtype=Int32, value= 1)}\n" - ] - } - ], - "source": [ - "import mindspore.dataset as ds\n", - "\n", - "data = [1, 2, 3, 4, 5, 6]\n", - "dataset = ds.NumpySlicesDataset(data=data, column_names=[\"column_1\"], shuffle=False)\n", - "\n", - "train_dataset, eval_dataset = dataset.split([4, 2])\n", - "\n", - "print(\">>>> train dataset >>>>\")\n", - "for item in train_dataset.create_dict_iterator():\n", - " print(item)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>>> eval dataset >>>>\n", - "{'column_1': Tensor(shape=[], dtype=Int32, value= 3)}\n", - "{'column_1': Tensor(shape=[], dtype=Int32, value= 4)}\n" - ] - } - ], - "source": [ - "print(\">>>> eval dataset >>>>\")\n", - "for item in eval_dataset.create_dict_iterator():\n", - " print(item)" + "将数据集切分成训练数据集和验证数据集，分别用于训练过程和验证过程，详见[数据操作](https://www.mindspore.cn/tutorials/zh-CN/master/dataset/eager.html#数据操作)。" ] }, { @@ -259,31 +116,7 @@ "source": [ "#### 数据集保存\n", "\n", - "将数据集重新保存到MindRecord数据格式。" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import mindspore.dataset as ds\n", - "\n", - "ds.config.set_seed(1234)\n", - "\n", - "data = [1, 2, 3, 4, 5, 6]\n", - "dataset = ds.NumpySlicesDataset(data=data, column_names=[\"column_1\"])\n", - "if os.path.exists(\"./train_dataset.mindrecord\"):\n", - " os.remove(\"./train_dataset.mindrecord\")\n", - "if os.path.exists(\"./train_dataset.mindrecord.db\"):\n", - " os.remove(\"./train_dataset.mindrecord.db\")\n", - "dataset.save(\"./train_dataset.mindrecord\")" + "将数据集重新保存到MindRecord数据格式，详见[数据操作](https://www.mindspore.cn/tutorials/zh-CN/master/dataset/eager.html#数据操作)。" ] }, { @@ -359,81 +192,7 @@ "\n", "用户可以直接使用数据变换操作处理一条数据，返回值即是数据变换的结果。\n", "\n", - "数据变换操作（[vision数据变换](https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore.dataset.transforms.html#%E8%A7%86%E8%A7%89) ， [nlp数据变换](https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore.dataset.transforms.html#%E6%96%87%E6%9C%AC) ， [audio数据变换](https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore.dataset.transforms.html#%E9%9F%B3%E9%A2%91)）可以像调用普通函数一样直接来使用。常见用法是：先初始化数据变换对象，然后调用数据变换操作方法，传入需要处理的数据，最后得到处理的结果。" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/banana.jpg (17 kB)\n", - "\n", - "file_sizes: 100%|██████████████████████████| 17.1k/17.1k [00:00<00:00, 8.55MB/s]\n", - "Successfully downloaded file to ./banana.jpg\n" - ] - }, - { - "data": { - "text/plain": [ - "'./banana.jpg'" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from download import download\n", - "from PIL import Image\n", - "import mindspore.dataset.vision as vision\n", - "\n", - "url = \"https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/banana.jpg\"\n", - "download(url, './banana.jpg', replace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Image.type: , Image.shape: (356, 200)\n" - ] - } - ], - "source": [ - "\n", - "img_ori = Image.open(\"banana.jpg\").convert(\"RGB\")\n", - "print(\"Image.type: {}, Image.shape: {}\".format(type(img_ori), img_ori.size))" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Image.type: , Image.shape: (569, 320)\n" - ] - } - ], - "source": [ - "# Apply Resize to input immediately\n", - "resize_op = vision.Resize(size=(320))\n", - "img = resize_op(img_ori)\n", - "print(\"Image.type: {}, Image.shape: {}\".format(type(img), img.size))" + "数据变换操作（[vision数据变换](https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore.dataset.transforms.html#%E8%A7%86%E8%A7%89) ，[nlp数据变换](https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore.dataset.transforms.html#%E6%96%87%E6%9C%AC) ，[audio数据变换](https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore.dataset.transforms.html#%E9%9F%B3%E9%A2%91)）可以像调用普通函数一样直接来使用。常见用法是：先初始化数据变换对象，然后调用数据变换操作方法，传入需要处理的数据，最后得到处理的结果。示例详见[轻量化数据变换](https://www.mindspore.cn/tutorials/zh-CN/master/dataset/eager.html#轻量化数据变换)。" ] }, { @@ -441,7 +200,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "更多的示例请参考：[轻量化数据处理](https://www.mindspore.cn/tutorials/zh-CN/master/dataset/eager.html)。" + "## 其他特性\n", + "\n", + "### 数据处理管道支持Python对象\n", + "\n", + "数据处理管道中的特定操作（如自定义数据集GeneratorDataset、自定义map增强操作、自定义batch(per_batch_map=...)）支持任意Python类型对象作为输入。详见[数据处理管道支持Python对象](https://www.mindspore.cn/tutorials/zh-CN/master/dataset/python_objects.html)。" ] } ], diff --git a/tutorials/source_en/dataset/eager.md b/tutorials/source_en/dataset/eager.md index 454dbb4368..cb401dfc64 100644 --- a/tutorials/source_en/dataset/eager.md +++ b/tutorials/source_en/dataset/eager.md @@ -1,7 +1,226 @@ -# Lightweight Data Processing +# Data Operation/Data transformation [![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/dataset/eager.md) +## Data Operation + +`mindspore.dataset` provides a series of dataset operations. Users can use these dataset operations, such as [.shuffle](https://www.mindspore.cn/docs/en/master/api_python/dataset/dataset_method/operation/mindspore.dataset.Dataset.shuffle.html#mindspore.dataset.Dataset.shuffle) / [.filter](https://www.mindspore.cn/docs/en/master/api_python/dataset/dataset_method/operation/mindspore.dataset.Dataset.filter.html#mindspore.dataset.Dataset.filter) / [.skip](https://www.mindspore.cn/docs/en/master/api_python/dataset/dataset_method/operation/mindspore.dataset.Dataset.skip.html#mindspore.dataset.Dataset.skip) / [.take](https://www.mindspore.cn/docs/en/master/api_python/dataset/dataset_method/operation/mindspore.dataset.Dataset.take.html#mindspore.dataset.Dataset.take) / [.batch](https://www.mindspore.cn/docs/en/master/api_python/dataset/dataset_method/batch/mindspore.dataset.Dataset.batch.html#mindspore.dataset.Dataset.batch) / … to further shuffle, filter, skip, and batch combine datasets. + +Common data transformation operations include: + +- `.filter(...)`: Filter multiple data sets based on specified conditions and retain samples that meet the expected conditions. +- `.project(...)`: Sort multiple data columns or delete unnecessary data columns. +- `.rename(...)`: Rename specified data columns to facilitate data characteristic labelling. +- `.shuffle(...)`: Divide a data buffer and shuffle the data within the buffer. +- `.skip(...)`: Skip the first n samples in the dataset. +- `.take(...)`: Retrieve only the first n samples from the dataset. +- `.map(...)`: Data transformation, applying custom methods to enhance each sample. +- `.batch(...)`: Combine `batch_size` data points. + +The following example code demonstrates filter, skip, and batch data operations. + +```python +from mindspore.dataset import GeneratorDataset + +# Random-accessible object as input source +class MyDataset: + def __init__(self): + self._data = [1, 2, 3, 4, 5, 6] + def __getitem__(self, index): + return self._data[index] + def __len__(self): + return len(self._data) + +loader = MyDataset() + +# find sampler which value < 4 +dataset = GeneratorDataset(source=loader, column_names=["data"], shuffle=False) +filtered_dataset = dataset.filter(lambda x: x < 4, input_columns=["data"]) +print("filtered_dataset", list(filtered_dataset)) + +# skip first 3 samples +dataset = GeneratorDataset(source=loader, column_names=["data"], shuffle=False) +skipped_dataset = dataset.skip(3) +print("skipped_dataset", list(skipped_dataset)) + +# batch the dataset by batch_size=2 +dataset = GeneratorDataset(source=loader, column_names=["data"], shuffle=False) +batched_dataset = dataset.batch(2, num_parallel_workers=1) +print("batched_dataset", list(batched_dataset)) +``` + +```text +filtered_dataset [[Tensor(shape=[], dtype=Int64, value= 1)], [Tensor(shape=[], dtype=Int64, value= 2)], [Tensor(shape=[], dtype=Int64, value= 3)]] +skipped_dataset [[Tensor(shape=[], dtype=Int64, value= 4)], [Tensor(shape=[], dtype=Int64, value= 5)], [Tensor(shape=[], dtype=Int64, value= 6)]] +batched_dataset [[Tensor(shape=[2], dtype=Int64, value= [1, 2])], [Tensor(shape=[2], dtype=Int64, value= [3, 4])], [Tensor(shape=[2], dtype=Int64, value= [5, 6])]] +``` + +In addition, there are operations such as dataset combination, splitting, and saving. + +### Dataset Combination + +Dataset combination can combine multiple datasets in a serial/parallel manner to form a brand-new dataset object. + +```python +import mindspore.dataset as ds + +ds.config.set_seed(1234) + +# concat same column of two datasets +data = [1, 2, 3] +dataset1 = ds.NumpySlicesDataset(data=data, column_names=["column_1"]) + +data = [4, 5, 6] +dataset2 = ds.NumpySlicesDataset(data=data, column_names=["column_1"]) + +dataset = dataset1.concat(dataset2) +for item in dataset.create_dict_iterator(): + print("concated dataset", item) + + +# zip different columns of two datasets +data = [1, 2, 3] +dataset1 = ds.NumpySlicesDataset(data=data, column_names=["column_1"]) + +data = [4, 5, 6] +dataset2 = ds.NumpySlicesDataset(data=data, column_names=["column_2"]) + +dataset = dataset1.zip(dataset2) +for item in dataset.create_dict_iterator(): + print("zipped dataset", item) +``` + +```text +concated dataset {'column_1': Tensor(shape=[], dtype=Int64, value= 2)} +concated dataset {'column_1': Tensor(shape=[], dtype=Int64, value= 3)} +concated dataset {'column_1': Tensor(shape=[], dtype=Int64, value= 1)} +concated dataset {'column_1': Tensor(shape=[], dtype=Int64, value= 5)} +concated dataset {'column_1': Tensor(shape=[], dtype=Int64, value= 6)} +concated dataset {'column_1': Tensor(shape=[], dtype=Int64, value= 4)} +zipped dataset {'column_1': Tensor(shape=[], dtype=Int64, value= 2), 'column_2': Tensor(shape=[], dtype=Int64, value= 5)} +zipped dataset {'column_1': Tensor(shape=[], dtype=Int64, value= 3), 'column_2': Tensor(shape=[], dtype=Int64, value= 6)} +zipped dataset {'column_1': Tensor(shape=[], dtype=Int64, value= 1), 'column_2': Tensor(shape=[], dtype=Int64, value= 4)} +``` + +### Dataset Splitting + +Split the dataset into a training dataset and a validation dataset, which are used for the training process and validation process, respectively. + +```python +import mindspore.dataset as ds + +data = [1, 2, 3, 4, 5, 6] +dataset = ds.NumpySlicesDataset(data=data, column_names=["column_1"], shuffle=False) + +train_dataset, eval_dataset = dataset.split([4, 2]) + +print(">>>> train dataset >>>>") +for item in train_dataset.create_dict_iterator(): + print(item) + +print(">>>> eval dataset >>>>") +for item in eval_dataset.create_dict_iterator(): + print(item) +``` + +```text +>>>> train dataset >>>> +{'column_1': Tensor(shape=[], dtype=Int64, value= 6)} +{'column_1': Tensor(shape=[], dtype=Int64, value= 4)} +{'column_1': Tensor(shape=[], dtype=Int64, value= 1)} +{'column_1': Tensor(shape=[], dtype=Int64, value= 5)} +>>>> eval dataset >>>> +{'column_1': Tensor(shape=[], dtype=Int64, value= 3)} +{'column_1': Tensor(shape=[], dtype=Int64, value= 2)} +``` + +### Saving Datasets + +Resave the dataset in MindRecord data format. + +```python +import os +import mindspore.dataset as ds + +ds.config.set_seed(1234) + +data = [1, 2, 3, 4, 5, 6] +dataset = ds.NumpySlicesDataset(data=data, column_names=["column_1"]) + +if os.path.exists("./train_dataset.mindrecord"): + os.remove("./train_dataset.mindrecord") +if os.path.exists("./train_dataset.mindrecord.db"): + os.remove("./train_dataset.mindrecord.db") + +dataset.save("./train_dataset.mindrecord") +``` + +## Data Transformation + +In most cases, raw data cannot be directly loaded into a neural network for training. Instead, it must first undergo data preprocessing. +MindSpore provides various types of data transformations (Transforms) that can be used in conjunction with data processing pipelines to perform data preprocessing. + +These transformations can generally be used in two ways: 'data transformation based on data operation maps' and 'lightweight data transformation'. These are described below. + +### Data Transformation Based on `map` Data Operations + +- `mindspore.dataset` provides built-in data transformation operations for different data types such as images, text, and audio. All transformations can be passed to the `map` operation, which automatically transforms each sample using the `map` method. +- In addition to built-in data transformations, the `map` operation can also execute user-defined transformation operations. + +```python +# Download data from open datasets +from download import download +from mindspore.dataset import MnistDataset +import mindspore.dataset.vision as vision + +url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/" \ + "notebook/datasets/MNIST_Data.zip" +path = download(url, "./", kind="zip", replace=True) + +# create MNIST loader +train_dataset = MnistDataset("MNIST_Data/train", shuffle=False) + +# resize samples to (64, 64) using built-in transformation +train_dataset = train_dataset.map(operations=[vision.Resize((64, 64))], + input_columns=['image']) + +for data in train_dataset: + print(data[0].shape, data[0].dtype) + break +``` + +```text +Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/MNIST_Data.zip (10.3 MB) + +file_sizes: 100%|██████████████████████████| 10.8M/10.8M [00:01<00:00, 6.99MB/s] +Extracting zip file... +Successfully downloaded / unzipped to ./ +(64, 64, 1) UInt8 +``` + +```python +# create MNIST loader +train_dataset = MnistDataset("MNIST_Data/train", shuffle=False) + +def transform(img): + img = img / 255.0 + return img + +# apply normalize using customized transformation +train_dataset = train_dataset.map(operations=[transform], + input_columns=['image']) + +for data in train_dataset: + print(data[0].shape, data[0].dtype) + break +``` + +```text +(28, 28, 1) Float64 +``` + +### Lightweight Data Transformation + MindSpore provides a lightweight data processing way, called Eager mode. In the Eager mode, transforms is executed in the form of a functional call. The code will be simpler and the results are obtained immediately. It is recommended to be used in lightweight scenarios such as small data augmentation experiments and model inference. @@ -18,13 +237,9 @@ MindSpore currently supports executing various Transforms in the Eager mode, as - [transforms module](https://www.mindspore.cn/docs/en/master/api_python/mindspore.dataset.transforms.html#module-mindspore.dataset.transforms), general-purpose data transform implemented based on C++/Python/NumPy. -## Eager Mode - -The following is a brief introduction to the use of the Eager mode for each Transforms module. With the Eager mode, you only need to treat Transform itself as an executable function. - -### Data Preparation +The following sample code downloads the image data to the specified location. With the Eager mode, you only need to treat Transform itself as an executable function. -The following sample code downloads the image data to the specified location. +#### Data Preparation ```python from download import download @@ -44,7 +259,7 @@ Successfully downloaded file to ./banana.jpg './banana.jpg' ``` -### vision +#### vision This example will use Transform in the `mindspore.dataset.vision` module to transform a given image. @@ -93,7 +308,7 @@ Image.type: , Image.shape: (360, 360) ![eager_mode](./images/eager_mode.png) -### text +#### text This example will transform the given text by using the Transforms in the `text` module. @@ -121,7 +336,7 @@ Tokenize result: ['W' 'e' 'l' 'c' 'o' 'm' 'e' ' ' 't' 'o' ' ' 'B' 'e' 'i' 'j' 'i ToNumber result: [123456], type: int32 ``` -### audio +#### audio This example will transform the given audio by using the Transforms in the `audio` module. @@ -175,7 +390,7 @@ plot_waveform(transformed_waveform, sample_rate, title="BassBiquad Waveform") ![eager_mode_audio](./images/eager_mode_audio.png) -### transforms +#### transforms This example will transform the given data by using the general Transform in the `transforms` module. diff --git a/tutorials/source_en/dataset/record.ipynb b/tutorials/source_en/dataset/record.ipynb index 9a6e0b0edd..c773c10b32 100644 --- a/tutorials/source_en/dataset/record.ipynb +++ b/tutorials/source_en/dataset/record.ipynb @@ -5,7 +5,7 @@ "id": "f6392a05", "metadata": {}, "source": [ - "# Converting Dataset to MindRecord\n", + "# MindRecord Format Cnversion\n", "\n", "[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/dataset/record.ipynb)\n" ] @@ -397,4 +397,3 @@ "nbformat": 4, "nbformat_minor": 5 } - diff --git a/tutorials/source_en/dataset/sampler.md b/tutorials/source_en/dataset/sampler.md index fdc4ed9f56..8d3c2858a1 100644 --- a/tutorials/source_en/dataset/sampler.md +++ b/tutorials/source_en/dataset/sampler.md @@ -1,15 +1,156 @@ -# Data Sampling +# Data Loading and Sampling [![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_en/dataset/sampler.md) +## Data Loading + +Data is the foundation of training. The `mindspore.dataset` module provides APIs for customised loading of datasets, as well as loading classes for public datasets. + +### Customizing Dataset + +MindSpore supports loading data by constructing customized classes or customized generators. `GeneratorDataset` can help to load dataset based on the logic inside these classes/functions. + +`GeneratorDataset` supports constructing customized datasets from random-accessible objects, iterable objects and Python generator, which are explained in detail below. + +#### Random-accessible Dataset + +A random-accessible dataset implements the `__getitem__` and `__len__` methods, which represents a map from indices/keys to data samples. + +For example, when access a dataset with `dataset[idx]` , it should read the idx-th data inside the dataset content. + +```python +import numpy as np +from mindspore.dataset import GeneratorDataset + +# Random-accessible object as input source +class RandomAccessDataset: + def __init__(self): + self._data = np.ones((5, 2)) + self._label = np.zeros((5, 1)) + def __getitem__(self, index): + return self._data[index], self._label[index] + def __len__(self): + return len(self._data) + +loader = RandomAccessDataset() +dataset = GeneratorDataset(source=loader, column_names=["data", "label"]) + +for data in dataset: + print(data) +``` + +```text +[Tensor(shape=[2], dtype=Float64, value= [ 1.00000000e+00, 1.00000000e+00]), Tensor(shape=[1], dtype=Float64, value= [ 0.00000000e+00])] +[Tensor(shape=[2], dtype=Float64, value= [ 1.00000000e+00, 1.00000000e+00]), Tensor(shape=[1], dtype=Float64, value= [ 0.00000000e+00])] +[Tensor(shape=[2], dtype=Float64, value= [ 1.00000000e+00, 1.00000000e+00]), Tensor(shape=[1], dtype=Float64, value= [ 0.00000000e+00])] +[Tensor(shape=[2], dtype=Float64, value= [ 1.00000000e+00, 1.00000000e+00]), Tensor(shape=[1], dtype=Float64, value= [ 0.00000000e+00])] +[Tensor(shape=[2], dtype=Float64, value= [ 1.00000000e+00, 1.00000000e+00]), Tensor(shape=[1], dtype=Float64, value= [ 0.00000000e+00])] +``` + +docs/mindspore/source_en/features/dataset/overview.mdses where random access are expensive or forbidden. + +For example, when access a dataset with `iter(dataset)`, it should return a stream of data from a database or a remote server. + +The following constructs a simple iterator and loads it into `GeneratorDataset`. + +```python +# Iterator as input source +class IterableDataset(): + def __init__(self, start, end): + '''init the class object to hold the data''' + self.start = start + self.end = end + def __next__(self): + '''iter one data and return''' + return next(self.data) + def __iter__(self): + '''reset the iter''' + self.data = iter(range(self.start, self.end)) + return self + +loader = IterableDataset(1, 5) +dataset = GeneratorDataset(source=loader, column_names=["data"]) + +for d in dataset: + print(d) +``` + +```text +[Tensor(shape=[], dtype=Int32, value= 1)] +[Tensor(shape=[], dtype=Int32, value= 2)] +[Tensor(shape=[], dtype=Int32, value= 3)] +[Tensor(shape=[], dtype=Int32, value= 4)] +``` + +#### Generator + +Generator also belongs to iterable dataset types, and it can be a Python's generator to return data until the generator throws a `StopIteration` exception. + +Example constructs a generator and loads it into the `GeneratorDataset`. + +```python +# Generator +def my_generator(start, end): + for i in range(start, end): + yield i + +# since a generator instance can be only iterated once, we need to wrap it by lambda to generate multiple instances +dataset = GeneratorDataset(source=lambda: my_generator(3, 6), column_names=["data"]) + +for d in dataset: + print(d) +``` + +```text +[Tensor(shape=[], dtype=Int32, value= 3)] +[Tensor(shape=[], dtype=Int32, value= 4)] +[Tensor(shape=[], dtype=Int32, value= 5)] +``` + +### Loading Open Source Dataset + +MindSpore also supports parsing and reading open source classic datasets such as MNIST, CIFAR-10, CLUE, LJSpeech, etc. + +Take the MNIST dataset as an example. For more other datasets, please refer to [Open Source](https://www.mindspore.cn/docs/en/master/api_python/mindspore.dataset.loading.html#open-source). + +```python +# Download data from open datasets +from download import download +from mindspore.dataset import MnistDataset +import matplotlib.pyplot as plt + +url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/" \ + "notebook/datasets/MNIST_Data.zip" +path = download(url, "./", kind="zip", replace=True) + +# create MNIST loader +train_dataset = MnistDataset("MNIST_Data/train", shuffle=False) +print(type(train_dataset)) + +# visialize dataset content +figure = plt.figure(figsize=(4, 4)) +cols, rows = 3, 3 + +plt.subplots_adjust(wspace=0.5, hspace=0.5) + +for idx, (image, label) in enumerate(train_dataset.create_tuple_iterator()): + figure.add_subplot(rows, cols, idx + 1) + plt.title(int(label)) + plt.axis("off") + plt.imshow(image.asnumpy().squeeze(), cmap="gray") + if idx == cols * rows - 1: + break +plt.show() +``` + +## Samplers + To meet training requirements and solve problems such as too large datasets or uneven distribution of sample categories, MindSpore provides multiple samplers for different purposes to help users sample datasets. Users only need to import the sampler object when loading the dataset to implement data sampling. MindSpore provides multiple samplers, such as `RandomSampler`, `WeightedRandomSampler`, and `SubsetRandomSampler`. In addition, users can customize sampler classes as required. > For details about how to use the sampler, see [Sampler API](https://www.mindspore.cn/docs/en/master/api_python/mindspore.dataset.loading.html#sampler-1). -## Samplers - The following uses the CIFAR-10 dataset as an example to describe how to use several common MindSpore samplers. ![cifar10](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/tutorials/source_zh_cn/dataset/images/cifar10.jpg) diff --git a/tutorials/source_en/index.rst b/tutorials/source_en/index.rst index c29f4544b4..5fbbfecc31 100644 --- a/tutorials/source_en/index.rst +++ b/tutorials/source_en/index.rst @@ -30,11 +30,8 @@ MindSpore Tutorial :hidden: dataset/sampler - dataset/record dataset/eager - dataset/python_objects - dataset/augment - dataset/cache + dataset/record dataset/optimize .. toctree:: diff --git a/tutorials/source_zh_cn/dataset/eager.ipynb b/tutorials/source_zh_cn/dataset/eager.ipynb index a11c49629a..79f3d78adb 100644 --- a/tutorials/source_zh_cn/dataset/eager.ipynb +++ b/tutorials/source_zh_cn/dataset/eager.ipynb @@ -1,10 +1,11 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "# 轻量化数据处理\n", + "# 数据操作/数据变换\n", "\n", "[![下载Notebook](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_notebook.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/tutorials/zh_cn/dataset/mindspore_eager.ipynb) [![下载样例代码](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_download_code.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/tutorials/zh_cn/dataset/mindspore_eager.py) [![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/dataset/eager.ipynb)\n" ] @@ -14,11 +15,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "MindSpore提供了一种轻量化的数据处理执行方式，称为Eager模式。\n", + "## 数据操作\n", "\n", - "在Eager模式下，是以函数式调用的方式执行Transforms。因此代码编写会更为简洁且能立即执行得到运行结果，推荐在小型数据变换实验、模型推理等轻量化场景中使用。\n", + "`mindspore.dataset` 提供了一系列的数据集操作，用户通过这些数据集操作，如 [.shuffle](https://www.mindspore.cn/docs/zh-CN/master/api_python/dataset/dataset_method/operation/mindspore.dataset.Dataset.shuffle.html#mindspore.dataset.Dataset.shuffle) / [.filter](https://www.mindspore.cn/docs/zh-CN/master/api_python/dataset/dataset_method/operation/mindspore.dataset.Dataset.filter.html#mindspore.dataset.Dataset.filter) / [.skip](https://www.mindspore.cn/docs/zh-CN/master/api_python/dataset/dataset_method/operation/mindspore.dataset.Dataset.skip.html#mindspore.dataset.Dataset.skip) / [.take](https://www.mindspore.cn/docs/zh-CN/master/api_python/dataset/dataset_method/operation/mindspore.dataset.Dataset.take.html#mindspore.dataset.Dataset.take) / [.batch](https://www.mindspore.cn/docs/zh-CN/master/api_python/dataset/dataset_method/batch/mindspore.dataset.Dataset.batch.html#mindspore.dataset.Dataset.batch) / … 来实现数据集的进一步混洗、过滤、跳过、批处理组合等功能。\n", "\n", - "![eagermode1](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/tutorials/source_zh_cn/dataset/images/eager_mode.jpeg)" + "常用数据变换操作包括：\n", + "\n", + "- `.filter(...)`：通过指定条件，多数据进行过滤，保留满足预期条件的样本。\n", + "- `.project(...)`：对多个数据列进行排序，或删除不需要的数据列。\n", + "- `.rename(...)`： 对指定数据列进行重命名，便于标记数据特性。\n", + "- `.shuffle(...)`： 划分一个数据缓冲区，对落入缓冲区的数据进行混洗。\n", + "- `.skip(...)`： 跳过数据集的前n条样本。\n", + "- `.take(...)`： 只获取数据集的前n条样本。\n", + "- `.map(...)`：数据变换，通过自定义方法对每个样本进行变换增强。\n", + "- `.batch(...)`：对 `batch_size` 条数据进行组合。" ] }, { @@ -26,38 +36,328 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "MindSpore目前支持在Eager模式执行各种Transform，具体如下所示，更多数据变换接口参见API文档。\n", + "下面将通过示例代码展示filter、skip、batch数据操作。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "filtered_dataset [[Tensor(shape=[], dtype=Int64, value= 1)], [Tensor(shape=[], dtype=Int64, value= 2)], [Tensor(shape=[], dtype=Int64, value= 3)]]\n", + "skipped_dataset [[Tensor(shape=[], dtype=Int64, value= 4)], [Tensor(shape=[], dtype=Int64, value= 5)], [Tensor(shape=[], dtype=Int64, value= 6)]]\n", + "batched_dataset [[Tensor(shape=[2], dtype=Int64, value= [1, 2])], [Tensor(shape=[2], dtype=Int64, value= [3, 4])], [Tensor(shape=[2], dtype=Int64, value= [5, 6])]]\n" + ] + } + ], + "source": [ + "from mindspore.dataset import GeneratorDataset\n", + "\n", + "# Random-accessible object as input source\n", + "class MyDataset:\n", + " def __init__(self):\n", + " self._data = [1, 2, 3, 4, 5, 6]\n", + " def __getitem__(self, index):\n", + " return self._data[index]\n", + " def __len__(self):\n", + " return len(self._data)\n", + "\n", + "loader = MyDataset()\n", + "\n", + "# find sampler which value < 4\n", + "dataset = GeneratorDataset(source=loader, column_names=[\"data\"], shuffle=False)\n", + "filtered_dataset = dataset.filter(lambda x: x < 4, input_columns=[\"data\"])\n", + "print(\"filtered_dataset\", list(filtered_dataset))\n", + "\n", + "# skip dirst 3 samples\n", + "dataset = GeneratorDataset(source=loader, column_names=[\"data\"], shuffle=False)\n", + "skipped_dataset = dataset.skip(3)\n", + "print(\"skipped_dataset\", list(skipped_dataset))\n", + "\n", + "# batch the dataset by batch_size=2\n", + "dataset = GeneratorDataset(source=loader, column_names=[\"data\"], shuffle=False)\n", + "batched_dataset = dataset.batch(2, num_parallel_workers=1)\n", + "print(\"batched_dataset\", list(batched_dataset))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "除此之外，还有数据集组合、切分、保存等操作。\n", "\n", - "- [vision模块](https://mindspore.cn/docs/zh-CN/master/api_python/mindspore.dataset.transforms.html#视觉)，基于OpenCV/Pillow实现的数据变换。\n", + "### 数据集组合\n", "\n", - "- [text模块](https://mindspore.cn/docs/zh-CN/master/api_python/mindspore.dataset.transforms.html#文本)，基于Jieba/ICU4C等库实现的数据变换。\n", + "数据集组合可以将多个数据集以串联/并朕的方式组合起来，形成一个全新的dataset对象。" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "concated dataset {'column_1': Tensor(shape=[], dtype=Int64, value= 2)}\n", + "concated dataset {'column_1': Tensor(shape=[], dtype=Int64, value= 3)}\n", + "concated dataset {'column_1': Tensor(shape=[], dtype=Int64, value= 1)}\n", + "concated dataset {'column_1': Tensor(shape=[], dtype=Int64, value= 5)}\n", + "concated dataset {'column_1': Tensor(shape=[], dtype=Int64, value= 6)}\n", + "concated dataset {'column_1': Tensor(shape=[], dtype=Int64, value= 4)}\n", + "zipped dataset {'column_1': Tensor(shape=[], dtype=Int64, value= 2), 'column_2': Tensor(shape=[], dtype=Int64, value= 5)}\n", + "zipped dataset {'column_1': Tensor(shape=[], dtype=Int64, value= 3), 'column_2': Tensor(shape=[], dtype=Int64, value= 6)}\n", + "zipped dataset {'column_1': Tensor(shape=[], dtype=Int64, value= 1), 'column_2': Tensor(shape=[], dtype=Int64, value= 4)}\n" + ] + } + ], + "source": [ + "import mindspore.dataset as ds\n", "\n", - "- [audio模块](https://mindspore.cn/docs/zh-CN/master/api_python/mindspore.dataset.transforms.html#音频)，基于C++实现的数据变换。\n", + "ds.config.set_seed(1234)\n", + "\n", + "# concat same column of two datasets\n", + "data = [1, 2, 3]\n", + "dataset1 = ds.NumpySlicesDataset(data=data, column_names=[\"column_1\"])\n", + "\n", + "data = [4, 5, 6]\n", + "dataset2 = ds.NumpySlicesDataset(data=data, column_names=[\"column_1\"])\n", + "\n", + "dataset = dataset1.concat(dataset2)\n", + "for item in dataset.create_dict_iterator():\n", + " print(\"concated dataset\", item)\n", + "\n", + "\n", + "# zip different columns of two datasets\n", + "data = [1, 2, 3]\n", + "dataset1 = ds.NumpySlicesDataset(data=data, column_names=[\"column_1\"])\n", + "\n", + "data = [4, 5, 6]\n", + "dataset2 = ds.NumpySlicesDataset(data=data, column_names=[\"column_2\"])\n", + "\n", + "dataset = dataset1.zip(dataset2)\n", + "for item in dataset.create_dict_iterator():\n", + " print(\"zipped dataset\", item)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 数据集切分\n", + "\n", + "将数据集切分成训练数据集和验证数据集，分别用于训练过程和验证过程。" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>>> train dataset >>>>\n", + "{'column_1': Tensor(shape=[], dtype=Int64, value= 6)}\n", + "{'column_1': Tensor(shape=[], dtype=Int64, value= 4)}\n", + "{'column_1': Tensor(shape=[], dtype=Int64, value= 1)}\n", + "{'column_1': Tensor(shape=[], dtype=Int64, value= 5)}\n", + ">>>> eval dataset >>>>\n", + "{'column_1': Tensor(shape=[], dtype=Int64, value= 3)}\n", + "{'column_1': Tensor(shape=[], dtype=Int64, value= 2)}\n" + ] + } + ], + "source": [ + "import mindspore.dataset as ds\n", + "\n", + "data = [1, 2, 3, 4, 5, 6]\n", + "dataset = ds.NumpySlicesDataset(data=data, column_names=[\"column_1\"], shuffle=False)\n", + "\n", + "train_dataset, eval_dataset = dataset.split([4, 2])\n", + "\n", + "print(\">>>> train dataset >>>>\")\n", + "for item in train_dataset.create_dict_iterator():\n", + " print(item)\n", + "\n", + "print(\">>>> eval dataset >>>>\")\n", + "for item in eval_dataset.create_dict_iterator():\n", + " print(item)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 数据集保存\n", + "\n", + "将数据集重新保存到MindRecord数据格式。" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import mindspore.dataset as ds\n", + "\n", + "ds.config.set_seed(1234)\n", + "\n", + "data = [1, 2, 3, 4, 5, 6]\n", + "dataset = ds.NumpySlicesDataset(data=data, column_names=[\"column_1\"])\n", + "\n", + "if os.path.exists(\"./train_dataset.mindrecord\"):\n", + " os.remove(\"./train_dataset.mindrecord\")\n", + "if os.path.exists(\"./train_dataset.mindrecord.db\"):\n", + " os.remove(\"./train_dataset.mindrecord.db\")\n", + "\n", + "dataset.save(\"./train_dataset.mindrecord\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 数据变换\n", + "\n", + "通常情况下，直接加载的原始数据并不能直接送入神经网络进行训练，此时我们需要对其进行数据预处理。\n", + "MindSpore提供不同种类的数据变换（Transforms），配合数据处理Pipeline来实现数据预处理。\n", + "\n", + "这些变换通常有2种使用方法，分别为“基于数据操作map进行数据变换”与“轻量化数据变换”，下面分别进行介绍。" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 基于`map`数据操作进行数据变换\n", + "\n", + "- `mindspore.dataset` 提供了面向图像、文本、音频等不同数据类型的内置数据变换操作，所有的变换均可传到的 `map` 操作中，通过 `map` 方法自动对每条样本进行变换。\n", + "- 除了内置的数据变换外，`map` 操作也可以执行用户自定义的变换操作。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/MNIST_Data.zip (10.3 MB)\n", + "\n", + "file_sizes: 100%|██████████████████████████| 10.8M/10.8M [00:01<00:00, 6.99MB/s]\n", + "Extracting zip file...\n", + "Successfully downloaded / unzipped to ./\n", + "(64, 64, 1) UInt8\n" + ] + } + ], + "source": [ + "# Download data from open datasets\n", + "from download import download\n", + "from mindspore.dataset import MnistDataset\n", + "import mindspore.dataset.vision as vision\n", + "\n", + "url = \"https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/\" \\\n", + " \"notebook/datasets/MNIST_Data.zip\"\n", + "path = download(url, \"./\", kind=\"zip\", replace=True)\n", + "\n", + "# create MNIST loader\n", + "train_dataset = MnistDataset(\"MNIST_Data/train\", shuffle=False)\n", + "\n", + "# resize samples to (64, 64) using built-in transformation\n", + "train_dataset = train_dataset.map(operations=[vision.Resize((64, 64))],\n", + " input_columns=['image'])\n", + "\n", + "for data in train_dataset:\n", + " print(data[0].shape, data[0].dtype)\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(28, 28, 1) Float64\n" + ] + } + ], + "source": [ + "# create MNIST loader\n", + "train_dataset = MnistDataset(\"MNIST_Data/train\", shuffle=False)\n", + "\n", + "def transform(img):\n", + " img = img / 255.0\n", + " return img\n", "\n", - "- [transforms模块](https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore.dataset.transforms.html#通用)，基于C++/Python/NumPy实现的通用数据变换。" + "# apply normalize using customized transformation\n", + "train_dataset = train_dataset.map(operations=[transform],\n", + " input_columns=['image'])\n", + "\n", + "for data in train_dataset:\n", + " print(data[0].shape, data[0].dtype)\n", + " break" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Eager模式\n", + "### 轻量化数据变换\n", + "\n", + "MindSpore提供了一种轻量化的数据处理执行方式，称为Eager模式。\n", + "\n", + "在Eager模式下，是以函数式调用的方式执行Transforms。因此代码编写会更为简洁且能立即执行得到运行结果，推荐在小型数据变换实验、模型推理等轻量化场景中使用。\n", + "\n", + "![eagermode1](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/tutorials/source_zh_cn/dataset/images/eager_mode.jpeg)\n", + "\n", + "MindSpore目前支持在Eager模式执行各种Transform，具体如下所示，更多数据变换接口参见API文档。\n", + "\n", + "- [vision模块](https://mindspore.cn/docs/zh-CN/master/api_python/mindspore.dataset.transforms.html#视觉)，基于OpenCV/Pillow实现的数据变换。\n", + "\n", + "- [text模块](https://mindspore.cn/docs/zh-CN/master/api_python/mindspore.dataset.transforms.html#文本)，基于Jieba/ICU4C等库实现的数据变换。\n", + "\n", + "- [audio模块](https://mindspore.cn/docs/zh-CN/master/api_python/mindspore.dataset.transforms.html#音频)，基于C++实现的数据变换。\n", + "\n", + "- [transforms模块](https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore.dataset.transforms.html#通用)，基于C++/Python/NumPy实现的通用数据变换。\n", "\n", "下面将简要介绍各Transforms模块的Eager模式使用方法。使用Eager模式，只需要将Transform本身当成可执行函数即可。" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "### 数据准备\n", + "#### 数据准备\n", "\n", "以下示例代码将图片数据下载到指定位置。" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -66,7 +366,7 @@ "text": [ "Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/banana.jpg (17 kB)\n", "\n", - "file_sizes: 100%|██████████████████████████| 17.1k/17.1k [00:00<00:00, 8.67MB/s]\n", + "file_sizes: 100%|██████████████████████████| 17.1k/17.1k [00:00<00:00, 2.14MB/s]\n", "Successfully downloaded file to ./banana.jpg\n" ] }, @@ -76,7 +376,7 @@ "'./banana.jpg'" ] }, - "execution_count": 32, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -93,7 +393,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### vision\n", + "#### vision\n", "\n", "此示例将使用`mindspore.dataset.vision`模块中的Transform，对给定图像进行变换。\n", "\n", @@ -102,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -117,7 +417,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -165,7 +465,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### text\n", + "#### text\n", "\n", "此示例将使用`text`模块中Transforms，对给定文本进行变换。\n", "\n", @@ -174,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -208,7 +508,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### audio\n", + "#### audio\n", "\n", "此示例将使用`audio`模块中Transforms，对给定音频进行变换。\n", "\n", @@ -217,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -226,7 +526,7 @@ "text": [ "Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/84-121123-0000.wav (65 kB)\n", "\n", - "file_sizes: 100%|███████████████████████████| 67.0k/67.0k [00:00<00:00, 605kB/s]\n", + "file_sizes: 100%|███████████████████████████| 67.0k/67.0k [00:00<00:00, 756kB/s]\n", "Successfully downloaded file to ./84-121123-0000.wav\n" ] } @@ -270,12 +570,12 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -293,10 +593,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "### transforms\n", + "#### transforms\n", "\n", "此示例将使用`transforms`模块中通用Transform，对给定数据进行变换。\n", "\n", @@ -305,7 +606,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -351,7 +652,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.5" + "version": "3.10.16" }, "vscode": { "interpreter": { diff --git a/tutorials/source_zh_cn/dataset/record.ipynb b/tutorials/source_zh_cn/dataset/record.ipynb index 905aa8b769..019e285ad7 100644 --- a/tutorials/source_zh_cn/dataset/record.ipynb +++ b/tutorials/source_zh_cn/dataset/record.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 格式转换\n", + "# MindRecord格式转换\n", "\n", "[![下载Notebook](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_notebook.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/tutorials/zh_cn/dataset/mindspore_record.ipynb) \n", "[![下载样例代码](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_download_code.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/tutorials/zh_cn/dataset/mindspore_record.py) \n", diff --git a/tutorials/source_zh_cn/dataset/sampler.ipynb b/tutorials/source_zh_cn/dataset/sampler.ipynb index a8b90ebc76..5e3b576855 100644 --- a/tutorials/source_zh_cn/dataset/sampler.ipynb +++ b/tutorials/source_zh_cn/dataset/sampler.ipynb @@ -5,16 +5,247 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 数据采样" + "# 数据加载与采样" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "[![下载Notebook](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_notebook.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/tutorials/zh_cn/dataset/mindspore_sampler.ipynb) \n", "[![下载样例代码](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_download_code.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/tutorials/zh_cn/dataset/mindspore_sampler.py) \n", - "[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/dataset/sampler.ipynb)\n", + "[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/dataset/sampler.ipynb)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 数据加载\n", + "\n", + "数据是训练的基础，`mindspore.dataset` 模块提供了自定义方式加载数据集的API，也提供了常用的公开数据集的加载类。\n", + "\n", + "### 自定义数据集\n", + "\n", + "MindSpore可以构造自定义数据加载类或自定义数据集生成函数的方式来生成数据集，然后通过GeneratorDataset接口实现自定义方式的数据集加载。\n", + "\n", + "`GeneratorDataset` 支持通过可随机访问数据集对象、可迭代数据集对象和生成器(generator)构造自定义数据集，下面分别对其进行介绍。\n", + "\n", + "#### 可随机访问数据集\n", + "\n", + "可随机访问数据集是实现了 `__getitem__` 和 `__len__` 方法的数据集，表示可以通过索引/键直接访问对应位置的数据样本。\n", + "\n", + "例如，当使用 `dataset[idx]` 访问这样的数据集时，可以读取dataset内容中第 `idx` 个样本或标签。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Tensor(shape=[2], dtype=Float64, value= [ 1.00000000e+00, 1.00000000e+00]), Tensor(shape=[1], dtype=Float64, value= [ 0.00000000e+00])]\n", + "[Tensor(shape=[2], dtype=Float64, value= [ 1.00000000e+00, 1.00000000e+00]), Tensor(shape=[1], dtype=Float64, value= [ 0.00000000e+00])]\n", + "[Tensor(shape=[2], dtype=Float64, value= [ 1.00000000e+00, 1.00000000e+00]), Tensor(shape=[1], dtype=Float64, value= [ 0.00000000e+00])]\n", + "[Tensor(shape=[2], dtype=Float64, value= [ 1.00000000e+00, 1.00000000e+00]), Tensor(shape=[1], dtype=Float64, value= [ 0.00000000e+00])]\n", + "[Tensor(shape=[2], dtype=Float64, value= [ 1.00000000e+00, 1.00000000e+00]), Tensor(shape=[1], dtype=Float64, value= [ 0.00000000e+00])]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from mindspore.dataset import GeneratorDataset\n", + "\n", + "# Random-accessible object as input source\n", + "class RandomAccessDataset:\n", + " def __init__(self):\n", + " self._data = np.ones((5, 2))\n", + " self._label = np.zeros((5, 1))\n", + " def __getitem__(self, index):\n", + " return self._data[index], self._label[index]\n", + " def __len__(self):\n", + " return len(self._data)\n", + "\n", + "loader = RandomAccessDataset()\n", + "dataset = GeneratorDataset(source=loader, column_names=[\"data\", \"label\"])\n", + "\n", + "for data in dataset:\n", + " print(data)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 可迭代数据集\n", + "\n", + "可迭代的数据集是实现了 `__iter__` 和 `__next__` 方法的数据集，表示可以通过迭代的方式逐步获取数据样本。这种类型的数据集特别适用于随机访问成本太高或者不可行的情况。\n", + "\n", + "例如，当使用iter(dataset)的形式访问数据集时，可以读取从数据库、远程服务器返回的数据流。\n", + "\n", + "下面构造一个简单迭代器，并将其加载至 `GeneratorDataset` 。" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Tensor(shape=[], dtype=Int64, value= 1)]\n", + "[Tensor(shape=[], dtype=Int64, value= 2)]\n", + "[Tensor(shape=[], dtype=Int64, value= 3)]\n", + "[Tensor(shape=[], dtype=Int64, value= 4)]\n" + ] + } + ], + "source": [ + "# Iterator as input source\n", + "class IterableDataset():\n", + " def __init__(self, start, end):\n", + " '''init the class object to hold the data'''\n", + " self.start = start\n", + " self.end = end\n", + " def __next__(self):\n", + " '''iter one data and return'''\n", + " return next(self.data)\n", + " def __iter__(self):\n", + " '''reset the iter'''\n", + " self.data = iter(range(self.start, self.end))\n", + " return self\n", + "\n", + "loader = IterableDataset(1, 5)\n", + "dataset = GeneratorDataset(source=loader, column_names=[\"data\"])\n", + "\n", + "for d in dataset:\n", + " print(d)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 生成器\n", + "\n", + "生成器也属于可迭代的数据集类型，其直接依赖Python的生成器类型 `generator` 返回数据，直至生成器抛出 `StopIteration` 异常。\n", + "\n", + "下面构造一个生成器，并将其加载至 `GeneratorDataset` 。" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Tensor(shape=[], dtype=Int64, value= 3)]\n", + "[Tensor(shape=[], dtype=Int64, value= 4)]\n", + "[Tensor(shape=[], dtype=Int64, value= 5)]\n" + ] + } + ], + "source": [ + "# Generator\n", + "def my_generator(start, end):\n", + " for i in range(start, end):\n", + " yield i\n", + "\n", + "# since a generator instance can be only iterated once, we need to wrap it by lambda to generate multiple instances\n", + "dataset = GeneratorDataset(source=lambda: my_generator(3, 6), column_names=[\"data\"])\n", + "\n", + "for d in dataset:\n", + " print(d)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 常用的公开数据集的加载\n", + "\n", + "MindSpore也支持开源经典数据集的解析读取，如MNIST、CIFAR-10、CLUE、LJSpeech等。\n", + "\n", + "以MNIST数据集作为样例，更多其他数据集请参考 [开源数据集](https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore.dataset.loading.html#%E5%BC%80%E6%BA%90%E6%95%B0%E6%8D%AE%E9%9B%86%E5%8A%A0%E8%BD%BD) 。" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/MNIST_Data.zip (10.3 MB)\n", + "\n", + "file_sizes: 100%|██████████████████████████| 10.8M/10.8M [00:01<00:00, 7.65MB/s]\n", + "Extracting zip file...\n", + "Successfully downloaded / unzipped to ./\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Download data from open datasets\n", + "from download import download\n", + "from mindspore.dataset import MnistDataset\n", + "import matplotlib.pyplot as plt\n", + "\n", + "url = \"https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/\" \\\n", + " \"notebook/datasets/MNIST_Data.zip\"\n", + "path = download(url, \"./\", kind=\"zip\", replace=True)\n", + "\n", + "# create MNIST loader\n", + "train_dataset = MnistDataset(\"MNIST_Data/train\", shuffle=False)\n", + "print(type(train_dataset))\n", + "\n", + "# visialize dataset content\n", + "figure = plt.figure(figsize=(4, 4))\n", + "cols, rows = 3, 3\n", + "\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)\n", + "\n", + "for idx, (image, label) in enumerate(train_dataset.create_tuple_iterator()):\n", + " figure.add_subplot(rows, cols, idx + 1)\n", + " plt.title(int(label))\n", + " plt.axis(\"off\")\n", + " plt.imshow(image.asnumpy().squeeze(), cmap=\"gray\")\n", + " if idx == cols * rows - 1:\n", + " break\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 采样器\n", "\n", "为满足训练需求，解决诸如数据集过大或样本类别分布不均等问题，MindSpore提供了多种不同用途的采样器（Sampler），帮助用户对数据集进行不同形式的采样。用户只需在加载数据集时传入采样器对象，即可实现数据的采样。\n", "\n", @@ -22,8 +253,6 @@ "\n", "> 更多采样器的使用方法参见[采样器API文档](https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore.dataset.loading.html#%E9%87%87%E6%A0%B7%E5%99%A8-1)。\n", "\n", - "## 采样器\n", - "\n", "下面主要以CIFAR-10数据集为例，介绍几种常用MindSpore采样器的使用方法。\n", "\n", "![cifar10](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/tutorials/source_zh_cn/dataset/images/cifar10.jpg)\n", @@ -33,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -42,7 +271,7 @@ "text": [ "Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/cifar-10-binary.tar.gz (162.2 MB)\n", "\n", - "file_sizes: 100%|████████████████████████████| 170M/170M [00:16<00:00, 10.4MB/s]\n", + "file_sizes: 100%|████████████████████████████| 170M/170M [00:21<00:00, 7.85MB/s]\n", "Extracting tar.gz file...\n", "Successfully downloaded / unzipped to ./\n" ] @@ -57,6 +286,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -84,15 +314,15 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "With Replacement: 4 5 6 6 1 \n", - "Without Replacement: 4 1 5 6 2 " + "With Replacement: 1 4 5 8 7 \n", + "Without Replacement: 1 6 7 4 3 " ] } ], @@ -119,6 +349,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -133,7 +364,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -142,7 +373,7 @@ "text": [ "Image shape: (32, 32, 3) , Label: 6\n", "Image shape: (32, 32, 3) , Label: 6\n", - "Image shape: (32, 32, 3) , Label: 9\n", + "Image shape: (32, 32, 3) , Label: 6\n", "Image shape: (32, 32, 3) , Label: 9\n", "Image shape: (32, 32, 3) , Label: 9\n", "Image shape: (32, 32, 3) , Label: 6\n" @@ -150,7 +381,7 @@ }, { "data": { - "image/png": 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bM8DaG9ZvEDYjY1w/cGFMJiJr65TXZcVqnvgr0SK1Cpkpvi9nXGpXLl7gGoux7ISwWX+D6KLPruUaj2JBFhO0ldvAqcr78sRhrjlZs26TsOnoSbP24VdfFjajGZmITdViVMry+FNT/L6MxNPCxlYSHRZLfF5rNXm/Xw08+QAAAACAp2DxAQAAAABPweIDAAAAAJ6CxQcAAAAAPGXJCk7TLa0UmZVQpikeYd/rukw2k83x5DamUu2QiEhXksTYJJO0OEoSqHhcVgE1Sfa9eZaL24qGFEyFwzyRy2xh4vtEYlzI1uSTArSjZzKir1bl+zJSUrTX1sTHrbkkpDFrXCBZqkoBVVGpBFqtyTFqLmJHpegqBXSZzMnRleqlfjlHNYMLFB1FMKm2vQS+Wwe+21i++7vfvED+WT7k7+DVX1evv0lsE6lyP1x/wxphs25tL2tbFZnky9H5tSqSrJrsD0jf9fnSrG3WZMXYYn6StVMuot6aMu8Xr8hkaeH4ZdGXSvJkf6tWDwgbR/k/v5yVFanfeuUY36Ys7+8NO/6UtW/auErYlI9Iwek7Z86zdjQaFzapdIvSI8WjOeV3yjD4eUBwCgAAAIAlCxYfAAAAAPCUOS8+Xn75ZfrCF75A3d3dpGkaPf300+x7x3HoO9/5DnV1dVEkEqHt27fT22+/PV/jBeBDA98FjQp8Fyw35rz4KBaLdPPNN9O+fftcv/+nf/on+pd/+Rf64Q9/SK+88grFYjHasWMHVSqySBAAXgLfBY0KfBcsN+YsOL3nnnvonnvucf3OcRx65JFH6G//9m/p3nvvJSKiH//4x9TR0UFPP/00feUrX7n2A+l+olnCPC3gUnlSIaRUnYySzLTnV9Zbuu5S9VMR8oUiskri+KjM0lga52KcVc1SHGUovwXhmMySuG51Dx+juhER1XxyPlQxkN83LWwSQT4nLU2rhc3qNf2sfe7i74TNW6e58Crol1VQHUeKJms17nK6S5bMQJCfm+1SAdNW1H+apn9gmwi++z7wXfju1Ri7PEE+X10MesvN/4F9HwrJaqzNina0q1sKgSez3OeGzkwKm6rNhaK6JsWLPr+cT8tRrl9N/lmzDC5mdSy5n3iqlbUnClJ0rQflfWk7qkDYRTCsHC4elnM00N3H2mGf3I9O3C9v2rBS2KTTadH30/L/Y+3RESmm7Wnn1ZYtTd67AUXQnstxceu7mVRlVmE35lXzce7cORodHaXt27fP9KVSKbrtttvo0KFDrtsYhkG5XI59APAa+C5oVOC7oBGZ18XH6OgoERF1dPD8+x0dHTPfqezdu5dSqdTMp6+vz9UOgIUEvgsaFfguaEQW/W2XPXv20PT09MxnaGhosYcEwDUB3wWNCnwXLDbzmmSss/PdxECZTIa6urpm+jOZDG3atMl1m1AoRKGQTApTqdSInHpsVDPVZEEySUyxyB8dVk25tqrpPJZdKMn4d07p6+mT0+TU5HYrWnksd3W3jG2XKtymZ+3Nwibo8Fjb1LSsjBkRCWGIaIIHX/s6u4RJtsjjmKuulwmBkk1Rpb1e2EyN8fOfmpYx+oBLfFR3+LU21XKPRKSGyS1TXms1v5OjxF3V9h8Dvgvfnc3H1XcjsSbyz0qMFlCGknWpJBxqTrN2qSb1FKruNdKUkPuxlYmpyPl1XP5iVUye6CockUa6xpPG2bq0ibdwzUPQkboUX6RJ9DlB7ru2JhOIaRb3J90njx+IcQ1RJC41RTWD++7EZZmwryUmdTn3fn4Hax/5w3lhU1Aq3VaMMWFjlPlvWTqRZu2qSxXnqzGvTz5WrlxJnZ2ddPDgwZm+XC5Hr7zyCg0ODs7noQCYV+C7oFGB74JGZM5PPgqFAp05c2amfe7cOTp27Bg1NzdTf38/PfTQQ/T3f//3tGbNGlq5ciV9+9vfpu7ubrrvvvvmc9wAzBn4LmhU4LtguTHnxceRI0fo05/+9Ez74YcfJiKi+++/nx5//HH6m7/5GyoWi/SNb3yDstks3XnnnfTss89SOCxf3QPAS+C7oFGB74LlxpwXH3fdddcHxiQ1TaPvfve79N3vfvcjDQyA+Qa+CxoV+C5YbizZqraWZpE1K9mOY3HhltuNGAnz6qHxhEyCNDzGBTPnLklRjV9RWQUzw8KmkpHbrWnnIr3P3CUFce9c5iKmRI8UB7W28IqeV8akqCiddhHE2fz4QV1WjrwyxhMs+cNZYTOWHWHtyyMy4VIgwOc2nZQis3JZXiPHryRUcqkMaitCPl2TNpqSYGsRC4EK4Lt14LuN5budfSsoEKgLHdWxVioyH0gmx/+MBNOtwsascfGkW+K9coFfK9ORkkS/X4pkaz7eF03KBF7tLVnWdiZlteOqIg7WbHn8SCQi+lRXtR0pMraUitR6wKWqr48fr1CUwnBNUTSHXBIN5lzuuUi0mbU/ObhR2Jx65wJrHz8pX9Mu5LjoO6hUGTZdBNZXY9FftQUAAADAxwssPgAAAADgKVh8AAAAAMBTlqzmI5WKUSRcjxPW/DyWVCjIojeOyeNq03mZPOjCRR4PKxRkTDgS5muykXMyztkRlglgenpWsHa6Wxb9CeSV+HJYxj57b97KTUYvC5tITcbtLeJzUizKOeqK8jh91aXAkhaL8/HEuoVNIs1j+/kJGR+8kpkQfabGz7dSlUW9SOdB8FhIKvarZX7d1IJeFslYu1fAd2eZwHeFyVL2XUfzkaPV9QhqDL+UlzqEkKKDyOdcisZV+FyVcnI/AeW0EzGp72hrahZ9yWauIWpLS12G5ecFFsshqU2YXMF9xbBGhA2ZMoGYVVMSmKnJ0ojI0rmvai6aj3QzT2BmWy7HUq5HKiXPNahJEVE2n2Vtx5S/HZvW8/sinZDz/8wzvEDdWGactWs1mRjuauDJBwAAAAA8BYsPAAAAAHgKFh8AAAAA8BQsPgAAAADgKUtWcFqYnqRapS7E8le5QCmguaybFA2P3ydFPaUCF/I1JWTCo3SMi8TKU1K0194tK3P2bPwUax+/VBU2p8/wvtu7pIAqm+U2Hatl9VCdpBipqlQhTDtSkJe7woV0EZcqhF3NfExZSwqPAhu5OKqcleKs3/zbT0XfpSE+Rl9QihZJEdy55HsiU1k36yY/j8ockt3MN/DdOvBdabGUfZdqVXYKfptfz5RLtva+FD/n61elhU1cSaLnc7kHirksa1dKUnQdiclrvm4Nv+Z9K3qFjR7ggupCNits+rp4JeV152QF32SznIDmJp7UzO+Xgm5b8QNH3t4UjvHkd7WKS0VkZT8BlyRjFZJC6JZWLsQulOQ9WMxy4XVPm0wieN8XPsfaT//831kbScYAAAAAsGTB4gMAAAAAnoLFBwAAAAA8ZclqPnSNyDcrlGgpiXkcl0Q8OvF4k6XJwNqUEjLM5VwKSBk8ztmVkrH1LbPKW79P77ptrP1/9/9I2HQqSZB8VVng6PLZd/g2q24QNuGW60RfzOHagtKkjFlGbB7vrpZl7G88z/vSbTLhVEvnAGuXC7KYky67yAry5FFuxblMk8+/5pK4RnN4X63GXdlcxGpd8N1Z28B3hc1S9t07tm5iRQ5X3cA1O8OXZdK4nm6uuVi7ZrWw6WxrZ22fI+curyTCMlwSernNeTzGfTwel7oMX5BrTgK21DSVi1zT84kNK4TNwNoB0Wfa/MZ0XP6nr9lKcUmfPA9fQPGDivQDW9FU6H55LC3skqROsTNMqZ3x+5Rkd9WssGlTtCN3/skW1i5XDHrqpy/I47uAJx8AAAAA8BQsPgAAAADgKVh8AAAAAMBTsPgAAAAAgKcsWcGp5rz7eR9LEchoLslVVO2NU5aiGk3JXdTcEhU2nVEu6vnErWuFzfrbt4m+qStcWBiqySQ5q3p5AhxbHRARdbbz5C5uyWZKWSmYqta4nVmWl9ciLhh65/IlYfPG8SOsffs2eayWTp6oKpeXAsGAnFpqHeDiMNvlOlpVRZBnyONPj2VZ28jzgxnmtVdXnG/gu3Xgu43lu7fcuJZiswScN97CBaflDVJMGktxda70CiJH4yJI3ScTtDXHeFVVx+VfY7f/lm2bH7HmluhKuQcNQ4qlV1/Xz9qRoBRrl4vyvnB0xVc16buOUmnWdqSY1FLmyFYzkxFRtczHbdlyjLrfTdDOZy4/IcW8F84NsfYdd94ibEomF4ZHFXGr5iIkvhp48gEAAAAAT8HiAwAAAACegsUHAAAAADwFiw8AAAAAeMqSFZzaNYtsX31tVDa4qCioZFskIvL7uYjJp0ux13WdPEtiOCLXXwMr+lj75jtlRsiudRtF37FD+1m7v69J2HTeeBNrB9ukgMsfTbF2qVIQNuVcXvRlhrlgaCojBXmWkjUwkpDZAFtb+TwODb8mbDq6eli7VpJjdMqyuqJWnOLjcaTwSxVnRUJSnBbs5H25EBc6VarXLnyab+C7deC7jeW74ViMIrMEp/Ewrwoci7r8yfDzbLwuOknSVMGpJs/RVioZ26aUrroJNVUBd81F8qomRnVcqurG0zxTa82S+7Fsl3K0Nt+5Q1IwrKsDsOT5W8pvgEMuE1lTMuja8lghlzEGLH6+sYq0cTLcn8fOZoRN7zouOh/XlXtHLbv7AeDJBwAAAAA8BYsPAAAAAHgKFh8AAAAA8JQlq/kI+PwU8NWHN6VUq7QqMmYWifLKhT6X+FO7kphpaCQrbFZ/4k9Zu/emPxU2RDImbuaLrJ1KpIRN29pNrF30NwubE6/9jrWNclHY5HJZ0Td++SJr+yypGwiH+SXvWdkjbDau5VVHaz6ZyCbgS/N20KVKYqUi+koXeFVM26XqZ01ZEhd8Mj4ZbeFj6ujmiaPKlcVL1ATfrQPfbSzfjSebKBGva5IcJRlYySVpmmNwfYzhYlMscD+omtLGMPh1qNWk5sJ0qcaqVhIulWQCrVKR64xqttx3opn7fCKVFjbpRKvoCweDrG25VMwlTalGSzIRWkLRME1ckfupKBWybVveyxoFRZ9t8WuUTISEzYr+DtYul+S96yjVeVMJ7ssBF3+/GnjyAQAAAABPweIDAAAAAJ6CxQcAAAAAPAWLDwAAAAB4ypIVnFYrBumzEqhEQ3yoWtglkYrOxTCOJUU9kTjf7s+//OfC5vZ7PsPaydYOYZM5+6bo8ynHz+ZlBcSx86dYezgvxWUvPv00a8cjMlFRxZCJkTo7uGAqmZBiu3OXeDKnqi7nqLl7gLXX3rRZ2JDFBUuTWZkUquQirJwq8+NpjnTBSpmLwQouiYWcAhcErk8r+3DRfHkFfLcOfLexfPfn//ZLCofrwkcr8Cv2/dSUTDxVmB5nbbc8U6oINZOR+7GU7GTNbe3Cpqm1RfSFfPw6FCezwub029zncwXpg30rV7C2LyB9N5mQx1+5klfD7e3rlDaruDi6OST9KxHmx7OVasHvDor/BpguvxM+tUQ2EfmU43UMuAhnk/y+MB15f/sULWtzMx9jyCWp3tXAkw8AAAAAeMqcFh979+6lLVu2UCKRoPb2drrvvvvo1Cn+31ClUqFdu3ZRS0sLxeNx2rlzp+sqFwAvge+CRgW+C5Yjc1p8vPTSS7Rr1y46fPgw/fKXvyTTNOlzn/scFYv194G/9a1v0c9+9jN68skn6aWXXqLh4WH60pe+NO8DB2AuwHdBowLfBcuROWk+nn32WdZ+/PHHqb29nY4ePUqf/OQnaXp6mh577DF64okn6O677yYiov3799P69evp8OHDtG3btms+lu1UeaEhpYCO5pKApubwBDSaJoOP4RCPUW3aLGPCISXWd/KYLE41NfyO6DMMHsvNT00Km6EzJ1m74ESETcDi+4n7pUYgGZYx8bYmHjcfyYwKm5qSpKeUl7HPoXMXlZ4TwqZQ4El7wn4517WQjNlO1Pj8RyKyOFg0weck4pcJcfKlHD+WkvympvgLfLcOfBe+ezVe+NUrrMhhuncd+96x5Jy/9tsXWHtFb6+waW3hWonLl1yur3Le0ea0sKnq8t7JKFqgz2wdFDabNt7I2iVDJpHTA/zP4bmLF4TN6bflvfPGcX6PpVOycOTOv/gia99x41phE3T4s4Derj5hU1U0H5pasI7ci++ZSrE73e9SkC7N/Tmiy2cTto9rd1SFh38OK4qPpPmYnn5XlNbc/G6mw6NHj5JpmrR9+/YZm+uvv576+/vp0KFDrvswDINyuRz7ALDQwHdBowLfBcuBD734sG2bHnroIbrjjjtow4YNREQ0OjpKwWCQ0uk0s+3o6KDRUbnSJXo3nplKpWY+fX1ytQfAfALfBY0KfBcsFz704mPXrl10/PhxOnDgwEcawJ49e2h6enrmMzQ09Mc3AuAjAN8FjQp8FywXPlSej927d9MzzzxDL7/8MvXOiu91dnZStVqlbDbLVuGZTIY6O+W7z0REoVCIQiEZFwVgIYDvgkYFvguWE3NafDiOQw8++CA99dRT9OKLL9LKlSvZ95s3b6ZAIEAHDx6knTt3EhHRqVOn6OLFizQ4KEVAH4z93ue9Vo0LXfyBKKlYSpXJqkvlwI4UrwL43E+fETbNHVyk1u4m/CnJJEyBAL+Z4zGZJMavc8FQzCWRTWe7UuUyPyVsIj75wzExxpP9mFUpKkqEuSCu6pJs5+3XjrD2yFunhY1RK/OOgBQWWrrsi/UqYsOYzKikh7gYLGzL69hE/DzW38h9sVQ2iegPM2347qx9w3d5B3x3hvv+4j9RJFL3z1D7Gj62vAzjvP3GH1i7q1P6nK6IFyNh6V9Vm1+XtRvWCJumLikELrXy++LP7tkubFQhcNFFcGor2s2aI8WtlZrc7soVLs6+cG5YHj/Kz3f00oSwOX/ibdbWXSornx29wtpbP3ersFkx0C361GRkelhWvqWAIox38V3SuE1Q43MUDLhkmLsKc1p87Nq1i5544gn6yU9+QolEYiaemEqlKBKJUCqVoq997Wv08MMPU3NzMyWTSXrwwQdpcHBwToprAOYb+C5oVOC7YDkyp8XHo48+SkREd911F+vfv38//eVf/iUREX3ve98jXddp586dZBgG7dixg37wgx/My2AB+LDAd0GjAt8Fy5E5h13+GOFwmPbt20f79u370IMCYL6B74JGBb4LliNLtrCcbWtkzwrCBZVkRWG/jMeRknDF8clkRnaVJyoaH5cxzMIY74uY8h14m2RMuLmJx7vT3W3CpmYZrH15WB7fIf5jo+vyMlVrLgWFNB6Dj4WltkDNb+VzSXhFSoIrqyo1AroSIM2VZGy/GiqLvkQ3P/9iJCts8jaPpVeK8qWsluQq1m5VtAbF4uJV54Lv1oHvNpbvhgI6hYL1MZ9+6zj7Pjftcs2VxZFZleMvFIqsrWkyOVZYKUpmlvLCZnpMLsQyF/mbOr947hfCZirP9zVdkH6RSHJdRqqpWdjEklKvdOkS13i0t/YIm3CSa1V+9XM5xsm3X2dtS7nfiYjOjPKU+ZeKco7WrJdamVSS308pJakfEVEkypOMpWJS0xVQimJGo3w+qm735FVAYTkAAAAAeAoWHwAAAADwFCw+AAAAAOApWHwAAAAAwFOWrOBU10Kka/XhhUM8SYzjkoQpFuGimliiVdiUTJ64pSUhk634lX1XpzPCxtbldqUAF9t0dKwUNrYixlq3UVaA/O0LB/nxnZKwCbgItsoFbpdMyEQ+QaXsoE+TAqGCktzm3IgU5GWzfI4MrShs2tbKtW1PWkkU5ch5nBrn5xGsSOFTrEdJZlXiyW/KZZmkyivgu7OOD98VNkvZd/OTGaqV6+f5/E9+zr4fGr0kttFNLs59/XWXInXKNa+5iI5JuZ6/fOZ5YRIMSMHnpls+wdrVYELY5Ax+Xc5evCJsJibe5PupSP8aHj0v+s6d59vdeousNv2fdz3M2q8elgX/atM88VjOMIRNWRF0nz0i0+L/6uiI6Iv5uXg1EJSic5+S8TbhIjjtXTHA2vfu/Aprl0rXnmQMTz4AAAAA4ClYfAAAAADAU7D4AAAAAICnLFnNR8CvUdBfXxuVlPiXL+yShEkpWFUyZaIgn1L4JhSMCJtAgO87GJUJWVJJefzRMR5fL/XImHh733WsffnKuLC5ccsdrF0Yk4WKzp4+IfqKhSxr+33y/FMpHkvXSMY1Ry7z41284JKoKcTPP9khk0K1Ncu4vabE5LVJOY9NU9wte9plsp/eNJ/bMyd58qNyRSbo8Qr4bh34bmP5bmd7B0Wj9fNaM8C1P47LnPt13udz0fToPv5/rmNLbUBQvS8CYWHT3S0TeN21YwdrJ6LyeqbCvPjcyeN/EDanz7zD2p09A8Km4sj/132KXuv46beEzcnTvMBhdGC9sBke5mNsSjcJm/Yg1xlF4/I3YHL0guibuHyGtcfGpRasYinJ4tRKe0Q0kuX+fftnuE25LLe5GnjyAQAAAABPweIDAAAAAJ6CxQcAAAAAPAWLDwAAAAB4ypIVnLa36BQN19dG5gRPwFK2pPCpqOQKcnSZrMevJCpKJluETTDAk6uUizJpTiTgMnVV3nfkt78VJqvWKVUJL8kqkbpS4TQakslefD6ZbCcS4YKtYkGK9spl3leryQqU8Qjf9+23rBU2YSUJVM0nkwZZpkwwVR7ioj09L0Vl7VGeJOiWtTdKm3QHax8dOcfalapLEiOPgO/Wge82lu9OjU9RJVIXSG+77Xb2/e2f+pTYJhTiCav8Pvk/ra7zPtuR94BPqbZsVuU9UK7K6zJxic/fpItgd3J8krXPKuJSIqLhK9yf4+3dwoZC8pprQS44rdZkcrBfvvRr1l6x+iZh09fMxbRhl4rQUSXJmlGRVW3P5qSgO674vOVIHxudKrB2a+uAsCmZ/Lo9/9KrrG2a116RGU8+AAAAAOApWHwAAAAAwFOw+AAAAACAp2DxAQAAAABPWbKC097eIMUjdbFaSuNCnzNDUniUGeMZ2qqWFLbF4/yUiyWZAdGyufDG57JGmxybEH35AhfxVEy5b5/D+xJxmcUuM8rFUZeKFWFjOzKTXEcbFyBqthReTWV5lc9QTM5ROsVFc0EXAZmhisH8UlhYNOR21QK3i9nS5rq+Ttbu7pTCyqFLXPw4Mcb9wTAXrzIofLcOfLexfDcaDVF0lmh3Isev32uvHxXbtLdzP+holxWZTZNfz6mprDy4kkHW7+IDPSulCLSviV/zy6dlVddigYtA2zs6hU20Jc3avrDMclsqS3/u6upn7dFhWfl3fILfO13dspKy5vDfgILhkunWz33etKWvhCIy825IyTpbnRiT+9a5f3e4ZHitGlxQqgxZtD8IPPkAAAAAgKdg8QEAAAAAT8HiAwAAAACesmQ1H8l0gOLRegyqrMRFm9p96iZEMZ7sZTwjk71Uqjxm5Q/KuJ5iQrZLDNa05L6nyzwmHYvImHSlxGOG5YqsDFpVjme5HN9x5PkXcnyOkklZ8TCZ5FVOy2WpPxif4OcRj8sYoqYkDdJqLlUq/fL4ao6eYFCex8B1A3yMJbnvl18+ydqvn77C2jWXRF5eAd+tA99tLN8N+W0KBerHNypZ9v1vf3tQbOOY3C+SUTl3pqloisoyiZxf+V94xUCfsNmw7QbRt7qf60CyQ1JzMTrFfTXo4t+rW7gOZGysIGxuWrdB9N140zrWPvA/fyxs/MSr0ZouWqhqlfc5NRftT5jPoy8kz2Ng5SrRd2XoFO/Qpe9GFA3V+vUyQV+lxOekr6udtQ1DntfVwJMPAAAAAHgKFh8AAAAA8BQsPgAAAADgKVh8AAAAAMBTlqzg1Bf2kz9cH144yQU7zXG5bvKXuZAuEJHCrdyUcsqW3E8kzEU0VkDuxzKyoi8Y5fsO+IPCxufjwkLDpbpj1VQTucikTJpLMhdHESxZLtqfgJpQKSgFS9kpLtorV2Wym1Saix39ukslS5fzLxEXTGXGZVXGKSXhVb4oE179+4tv8f0o2kPbnkO2m3kGvlsHvttYvluqlIlmXzJlbnbc82diG7vKE2b5TFkx1VZEtI5PCh59ypyHFRE2EdFoVgpV89nTrD1ZlsfXwlwtfOrYWWEzcYgn3lq1cp2w2XLdGtFXVRKPRVz80lGSrLklK9N9/B605a1DZZvPo9+S57qiVwpOKwWeWPCGpBRiv3r0NdYevnBK2JSV8ttOid9v6v3/QeDJBwAAAAA8BYsPAAAAAHgKFh8AAAAA8JQlq/koFvyk2bNivL44+z4ekzGzQITHSmNqViAiSqV4zKyQkzHEQo4XfiqUXBI1VWRfIsiLSIUDsmBVzeCxfb9frv+CSlcgJOOjmia3iyqFx3SXq1tTYoTBiDRKpnmsdXJSxrbzSrw/2SwLaJVqMv739nkee3zrjSFh09HMY/IdvTL2Szo/fqtSUMyybbowde0Jb+YT+G4d+G5j+W4sFqBotK69SCnyk0SbTDxlKH4RdvmfNqhxPYcTcUniFuU2dkUm+crnc6LPF+Vz3r46LWxWR3mSsbfPvSNsSOO+GohK7cblkYuir6W16QPbRETVMtdKGIbUAhWVxGNGSZ6/aXCBkD8s/auju030XRjhvwuZi/L8KwU+pndOHBM2LS18305TM2+b154gD08+AAAAAOApWHwAAAAAwFPmtPh49NFHaePGjZRMJimZTNLg4CD94he/mPm+UqnQrl27qKWlheLxOO3cuZMymcwH7BEAb4DvgkYFvguWI3NafPT29tI//uM/0tGjR+nIkSN0991307333ksnTpwgIqJvfetb9LOf/YyefPJJeumll2h4eJi+9KUvLcjAAZgL8F3QqMB3wXJkToLTL3zhC6z9D//wD/Too4/S4cOHqbe3lx577DF64okn6O677yYiov3799P69evp8OHDtG3btjkNbHiIKDpLc2dkuQAv0SaTq4QjPJFLKi5MqLmZn3KhKCtjZrO8b2pCJhyamhBd5LO5YMl2ZLIgy1LEfrYU/6krQk2X2WZ8fnnpykrSKUdOEQVsPke10qQco1It1FKTOxFRtsBtqi4FGCddBJHnz/CJy04UhU21yHfWmeoUNutX9LC2eijTsun35+vnBt+d3Sf3Dd/lfFx9t1Q4Q2TN8lebX5eAJh0zk+FCxbdPnhc2YaVKcDCVFjat7Vyo2d2aEjZuCeFaUlww7FYUuKJUbW5vlxWhe7q5eHJkdFTYnD79pugbqK5kbVWAS0SUz/M5KpXkk6ncNBfTuglOrSp3Fl9IJgs7cbxV9FUNLqBub+8QNj0becXe9jZp09rG/TmsHL/iRVVby7LowIEDVCwWaXBwkI4ePUqmadL27dtnbK6//nrq7++nQ4cOXXU/hmFQLpdjHwAWEvguaFTgu2C5MOfFxxtvvEHxeJxCoRA98MAD9NRTT9ENN9xAo6OjFAwGKZ1OM/uOjg4adVlBvs/evXsplUrNfPr6+uZ8EgBcC/Bd0KjAd8FyY86Lj3Xr1tGxY8folVdeoW9+85t0//3308mTJz/0APbs2UPT09Mzn6Eh+e48APMBfBc0KvBdsNyYc5KxYDBI1113HRERbd68mX73u9/RP//zP9OXv/xlqlarlM1m2So8k8lQZ6eMe75PKBSiUEgmc7ECLWQF6v1m8Fb2vWHLuJpe44lkwikZb0638fh7ky6Dy80lHjTMTsqEONlxmTypXOTTadVkvJ0cvt6zazJAWVGKDgWDLkW+/PL4+QrfV7ngkszK4bG/hJ4QNrbOH8GapnSTUIxrAsIBeQ3TQZmoaRWlWfumm2XMct3Gm1l74D1/m83WbTxuf2mYx0eNao3o9+dZH3z3vT74LmvDd+s4VYNmy3905f9TvymvXVIpXnj08EvCZjTD/VtzmfOtWzez9p2Dtwqb6WmZnOv137/C2sWK9J3TF/ni6uz588KmXOLXxa0oYjgpE3jlcjyRXX5qXNgUc1xz4lIzjvw+3ptKyARi3Su5vqSppUvYtHfL6959y02s3exSWC6oFPvzuRT/UxOxqb8JfheN1dX4yHk+bNsmwzBo8+bNFAgE6ODBgzPfnTp1ii5evEiDg4Mf9TAAzDvwXdCowHdBozOnJx979uyhe+65h/r7+ymfz9MTTzxBL774Ij333HOUSqXoa1/7Gj388MPU3NxMyWSSHnzwQRocHJyz4hqA+Qa+CxoV+C5Yjsxp8XHlyhX66le/SiMjI5RKpWjjxo303HPP0Wc/+1kiIvre975Huq7Tzp07yTAM2rFjB/3gBz9YkIEDMBfgu6BRge+C5cicFh+PPfbYB34fDodp3759tG/fvg89IOe9/AKlCo+5lpW2FuDv/BMR2TaPgeslGVnzF5XtdPmSf7FsK215rFJFxrvLFR5Ltl1yFaiRLte4ucGPZ7nEHn1qzgUiKhv8+JWqHLfj8D6/i26gUuV9htt5aPxYPkfGBw1Tblit8XEHXGzUa18oyhhuWZkjQx3ze/t935/gu3Xgu/BdlfePVa5wPZKpXPOay1xVlG0sW/qFmjdGc6SNWePzUHHJl2EYUotjVHlf1cV3asq+bZcxOkqfm+bDdsltYxPvU/fz7r5k3hxpox5L7kfNtaOeFxGRabrMkTKXFUNeR1v/6JqP9/N8XMv5as61WHnIpUuX8NoXmDeGhoaot7fXk2PBd8F8At8Fjcq1+O6SW3zYtk3Dw8OUSCQon89TX18fDQ0NUTIpM9KB+SOXyy2ruXYch/L5PHV3d5PukhVxIYDvLg7w3Y8OfHdx+Dj77pxftV1odF2fWTFp2ruPvd4vqAQWnuU016mUTM+8kMB3F5flNNfw3Y8Xy2mur9V3vVlWAwAAAAC8BxYfAAAAAPCUJb34CIVC9Hd/93eumfjA/IK5nl8wn96BuZ5fMJ/e8XGe6yUnOAUAAADA8mZJP/kAAAAAwPIDiw8AAAAAeAoWHwAAAADwFCw+AAAAAOApS3bxsW/fPhoYGKBwOEy33XYbvfrqq4s9pIZn7969tGXLFkokEtTe3k733XcfnTp1itlUKhXatWsXtbS0UDwep507d1Imk1mkETcm8N35B77rDfDd+Qe+exWcJciBAwecYDDo/OhHP3JOnDjhfP3rX3fS6bSTyWQWe2gNzY4dO5z9+/c7x48fd44dO+Z8/vOfd/r7+51CoTBj88ADDzh9fX3OwYMHnSNHjjjbtm1zbr/99kUcdWMB310Y4LsLD3x3YYDvurMkFx9bt251du3aNdO2LMvp7u529u7du4ijWn5cuXLFISLnpZdechzHcbLZrBMIBJwnn3xyxubNN990iMg5dOjQYg2zoYDvegN8d/6B73oDfPddllzYpVqt0tGjR2n79u0zfbqu0/bt2+nQoUOLOLLlx/T0NBERNTc3ExHR0aNHyTRNNvfXX3899ff3Y+6vAfiud8B35xf4rnfAd99lyS0+xsfHybIs6ujoYP0dHR00Ojq6SKNafti2TQ899BDdcccdtGHDBiIiGh0dpWAwSOl0mtli7q8N+K43wHfnH/iuN8B36yy5qrbAG3bt2kXHjx+nX//614s9FADmBHwXNCrw3TpL7slHa2sr+Xw+ofTNZDLU2dm5SKNaXuzevZueeeYZeuGFF2bKaBMRdXZ2UrVapWw2y+wx99cGfHfhge8uDPDdhQe+y1lyi49gMEibN2+mgwcPzvTZtk0HDx6kwcHBRRxZ4+M4Du3evZueeuopev7552nlypXs+82bN1MgEGBzf+rUKbp48SLm/hqA7y4c8N2FBb67cMB3r8IiC15dOXDggBMKhZzHH3/cOXnypPONb3zDSafTzujo6GIPraH55je/6aRSKefFF190RkZGZj6lUmnG5oEHHnD6+/ud559/3jly5IgzODjoDA4OLuKoGwv47sIA31144LsLA3zXnSW5+HAcx/n+97/v9Pf3O8Fg0Nm6datz+PDhxR5Sw0NErp/9+/fP2JTLZeev//qvnaamJicajTpf/OIXnZGRkcUbdAMC351/4LveAN+df+C77miO4zheP20BAAAAwMeXJaf5AAAAAMDyBosPAAAAAHgKFh8AAAAA8BQsPgAAAADgKVh8AAAAAMBTsPgAAAAAgKdg8QEAAAAAT8HiAwAAAACegsUHAAAAADwFiw8AAAAAeAoWHwAAAADwFCw+AAAAAOApWHwAAAAAwFOw+AAAAACAp2DxAQAAAABPweIDAAAAAJ6CxQcAAAAAPAWLDwAAAAB4ChYfAAAAAPAULD4AAAAA4ClYfAAAAADAU7D4AAAAAICnYPEBAAAAAE/B4gMAAAAAnoLFBwAAAAA8BYsPAAAAAHgKFh8AAAAA8BQsPgAAAADgKVh8AAAAAMBTsPgAAAAAgKdg8QEAAAAAT8HiAwAAAACe4l+oHe/bt4/++3//7zQ6Oko333wzff/736etW7f+0e1s26bh4WFKJBKkadpCDQ8scxzHoXw+T93d3aTrc1tjw3fBYgLfBY3KnHzXWQAOHDjgBINB50c/+pFz4sQJ5+tf/7qTTqedTCbzR7cdGhpyiAgffOblMzQ0BN/FpyE/8F18GvVzLb6rOY7j0Dxz22230ZYtW+h//I//QUTvrqr7+vrowQcfpP/yX/7LB247PT1N6XSa/r/HnqBwNDrTP3z6GLMbv3BKbGtZ/EFOe+9aYdO7ch1rpzt6hU04wvdz5s1XhM3Fs8dFX61QZG2fJR8sJdJJ1vaHosJm87bbWXvVdfI8Krkp0ffmyddZ27arwsasVVj7rTdPCpv89ARrG1VD2NRMH2tPTZaFTaFUEX01i4+ptbVJ2KSbYqxtOwW5nxpvV8rcjU2zRr987mXKZrOUSqXE9lcDvvsu8N2Pp+9u3nob+f3+Wf38WoV0W2zbFOTj722SftHazPtaUjFhE9QDrO0LReRAfT7RNZWdZm2zJv+kpZV51C1T2Ki+UqlI3wlHQqLPIou1y+WisEmmErzDsYRNtcrH5HMJTPiU84/H4sImFpXz7w+EWbtiyPvL0ZQnFbo8frXKt6s5/ClZxajSt//lf12T78572KVardLRo0dpz549M326rtP27dvp0KFDwt4wDDKM+kXO5/NERBSORikSrTtoKMwnLxgMin2pP+DqNkREEeXCRF0unvoDHo7ImyAUkk6oq87j8gOubucPy/1EY/zGjCcSwsZvy5snGuXjtG15o1ZN7iyhkJxHI8h/BBySPzga8X37/XI8s3/E6hvymy4QkDZB5fiWI23UJ8OWyw/Ou3bX/ggZvlsHvvvx9F2/38/OXf1j59PlmPw+Pv5gQF67kDJXYWWeiIiCPt7nD0kb8sn5LCv70nU5n2FlX7r820+a6iu2NHIbt6VIJ22Xe0c9PjkyJKETH7eP5LHU6xFxmaNIWN4XgQDvc3Ota1l8+JTt1MVHff9/3HfnXXA6Pj5OlmVRR0cH6+/o6KDR0VFhv3fvXkqlUjOfvr6++R4SANcEfBc0KvBd0Ggs+tsue/bsoenp6ZnP0NDQYg8JgGsCvgsaFfguWGzmPezS2tpKPp+PMpkM689kMtTZ2SnsQ6GQ62PgfHaKzFmPBVvSzex7p61D3YQcP49Jd/WvEjaW8shXt0vCxi7xoGxlakLYOGUZE+5pbWft/r7rhE3fdStYu7tHxu3b2/m5BQJyfmppGdfr6+XzW6vJuF6lwuPb2SkZkx4fn2Rtf1CGAEjjj/+aWuQYwzEZS59W4v2hsHRB2+HzH/DLfeems6xdNfgjy5qpBNavAfhuHfjux9N333rrTdJmvaWQHR9n3ze7TWcL72y1ZKhNi3D/KtqTwqZg8XlwNBk+KFWkX5TKXJthWjLUNq7EC8J+GZqp1fh2Ppewg9uclSpc41Fz0StplRbW1mVkiv1mEBFF/HKyC4pWY9KSvhKNSj2NpuhpNJ9LSEt5O6VUkeHImqmEZxX/Nubgu/P+5CMYDNLmzZvp4MGDM322bdPBgwdpcHBwvg8HwLwB3wWNCnwXNBoLkufj4Ycfpvvvv59uvfVW2rp1Kz3yyCNULBbpr/7qrxbicADMG/Bd0KjAd0EjsSCLjy9/+cs0NjZG3/nOd2h0dJQ2bdpEzz77rBBDAbDUgO+CRgW+CxqJBctwunv3btq9e/eH34FpEs16Ba5q8FhTqSTjagNre1i7UJTvW1dNHu9ubpXvIvsDPBq1Zo3MVXD7tltFX4+SdyGVahM2pp+/vhV1eV1RDUdqamIAIioXZbzbUOJx0YiMrTeleex19aobhM2bbyp5KDSXd+INrjdIJWXOg4AM2dJ0jsekHZLX0bb5BExNyetYLvH4qJqtpuYSC71W4Lvw3Y+r74b9GumzX6dVLvGKFqlDGOjgftje1ixsIooOwe1VzLLB/btiyjwbjst2QfV1cpdXlx2b7yvVLP2rZqqvDMvX1C2XV3R9QUX3UJWaKrPGxx0Nutw7MX68sItNTeP+pDtS31IjOUfqK7LxmDz/QpHfF2ZN3jvqm9b5HM+xUjVdJugqLPrbLgAAAAD4eIHFBwAAAAA8BYsPAAAAAHgKFh8AAAAA8JQFE5x+VGqVCtVmiYu0GheyhIJSDDStJMRp6ZRJkPpv5MmT2vu6hY2aB59chDdqkSsiordGeEKn0tkxuZ3ORWqn3viDsNmyngvpPrl1i7BxqweYU8Q/Fy8MC5tgQK0zkhQ2rW1c/Hhx6G25nzAXLBVciinlcuOizx/giqVkUgqfymUufHLT36kJgUSdj3kvl3jtwHfrwHeFyZL23bBmka7Vx5dI8D8Ra3ukOLclwjNmBWzpX4VJ7juWLf/vLSsJ8nQX0W8yLesZ+RVhZnY6L22Uv3TNCXnt8jnuB9WKi1jYJfGWowg84zGZ5Mus8qR1ukv9l4CSwMxyKX7nV5SjhiFtgi5qad3mc2sUZHFHUpK8hVwSodVs7rvTRS7krdakAPZq4MkHAAAAADwFiw8AAAAAeAoWHwAAAADwlCWr+TDKJdJmJVCJR3i8N9kskyB94uZNrN23ao2wyStJj06dldUccyUety1ks8JmIisLdo2M8jha0iVRE+k8RvbMv/4fYRL4j3xN+KnBO6VNQMb6OjsVDYAj49bZKR4P/f1rrwsbv1IMLJaQsfWaEh+sFrLCxueytG1TEhBZlkzUNDHJx62TjM/6lSBuOs0THZmmnB+vgO/Wge82lu+mQz7yzSowFlF0CKmY1Cu1JXmRMsuWiabUHp/fRVCgFDYzbBfNgyreICK/kmjLMmRRQEe5oFeuZOUYlQRZ+ZIs3FhyuebxiOJjhjx/H/Ex6poU9vhC/HeiXJTamWiAH8vvop+quBTfKysF32wXYVG2wI+XLcn5L6iFK00+rzWXon5XA08+AAAAAOApWHwAAAAAwFOw+AAAAACAp2DxAQAAAABPWbKC01DIT6FQXchk+hLs+3JEJps5l+NCo2O/flXYTE7wipqXhzPCJqAkcgnoUkRj1KSoRxX6dLXJ6b0yeoG1k2qCISLKZ3OsffrcOWHT1dUq+gIBfryuvk5h0630XRyVosVTb/C+9i4pPjx/UREEmnKO7Krss5TKqG6VG0N+LmArV6SAK5lUhFd+vh/HJYmRV8B368B3G8t3W1Nh8s8SZyYCXBgaDkuhqO7j4sWIWmWWiEwl0Z7tUnnVcbgPVl2q01pVKYK0Hd7nuIhCHT/31XxVJhCzLH5uJRfxpJugMl/kx788Kfet3ofJgjx/c5T7ZXlaCl77W5VEg+0yGaGWmBZ9xhQXmRcKcozTeS44HZ+Wwt3zQ3zflo/ft7aLAPZq4MkHAAAAADwFiw8AAAAAeAoWHwAAAADwlCWr+YhE2ikSqSfouZLlyU3ODMl478kTx1lbD8jTs5RCPOW8jH35lPhc2cgJm2xe9uWLPCZ//tKbwiYW4fH/davXCRtSYvK/+dWLwmTFypWib+26tazd0pISNqEwn5NUUsat9RqP6xUNtyJQPOFUOSuLOVmWTJITjvCYeCEnt0sqiaFCLnHmqhL7LSkJgUzTpaKXR8B368B3G8t3O1ujFJyVACwZ5GOJR6XOR3NUHYaM+2tKIjCjLPUMuqIDaUlIH4jFwqIvN821EqmkTCyXVwrCXbgsk9gVDH6tgi75snqiLknOAlwbcX4iK2wMRym+55JkLJXk99ftN9wqbHIjXDvjlFz20xoQfUaJj7tQkPdFKMC36+tMCJv29g7WzuT4fVKzbLp4/JLYzg08+QAAAACAp2DxAQAAAABPweIDAAAAAJ6CxQcAAAAAPGXJCk7TTS0UicZm2meGTrPvR87L5EXRABeSTRenhE0hd4W1NVuqirJ5Lr7LlqX4zB+Sop7WjnbWjrgIpnoGbmbtPhdB2rk/HGJtnyaT5piWTF40Ns4Tydx003phc92aVfz4LkmY4ttuYe3X37oobIwKF34ZAZdETSSFX7bDBWyjo8PCJqhW0mxqFzZEXGxZLnPR12KK9uC7deC7jeW7TfEIhWYlFvNXs+z7kIsQOhrilXuNskwEZtr8nNLpJmHjKAmqqpb839g0XSq9xnnSvuExQ9i8c4ELkcfyco6Vgq20IiL9+74/2ST6erv48f/30bPC5tCZUdau2fK+8Ov8/PPZMTnGAj+3RELey2TJBGbhMLcLuty7UY3b1Cw5R/19vPp0YpKLrqumRS9DcAoAAACApQgWHwAAAADwFCw+AAAAAOApS1bzce7cUQqF67HZt945w74fHnlHbGMpSZcSqZiwWbdmgLU3rN8gbEbGeAz2wphM5tTW2SH6VqzmyZMSLTLem5ni+3LGZfz/4gUepx7LTgib9TeILvrsWh4nLxZkYSBbCbc7VRl7PHGYx+3XrNskbDp60qx9+NWXhc1oRiazUuPZlbI8/tQUjyNG4mlhYytJi4olPq+1mtQVeAV8tw58Ny1slrLvtjU1UzhY/7NQnuQaC12TfzIKJSX5XVVqBfyaUrTNlOeo/idcNuX8ppukFqdqca3E2UtSizOZU5Jz+WWyNJ+PjyAZlmNs98vEcuFJrsNYk5RFEUea+b4z2SvCxijx833t9Glho9e475gxOR+Ukvc36UqCvlRUmCRsPo8VlyJ+TpXfFwNt/Heq4nLtrwaefAAAAADAU7D4AAAAAICnYPEBAAAAAE/B4gMAAAAAnrJkBae/+80L5J+V0MbfwStorl5/k9gmUuVinPU3rBE269b2srZVkclWHJ2L3YokKyD6A7K6os+XZm2zJqtuFvOTrJ1yEejUFAHVxSsy4VQ4fln0pZI8cc+q1QPCxlHWm+WsrC751ivH+DZlmYRpw44/Ze2bNq4SNuUjUrT3zpnzrB2NxoVNKt2i9EjhVy7H58Qw+HkspmgPvlsHvttYvptuaaXIrCR0TfEI+17XZVKrrHI+plIhmYhIVxLL2SSvi6MkMIvHpZ+aJPvePMuFmUVDiqzDYe7Ps0W17xOJcRFmk0/699EzGdFXq/J9GSkpOG1r4uPWXJLYmTUu7i1Vpei6qFSxrdbkGDUXoa5SMJgCukxE5uhK5V2/nKOawcW1jnK/q+0PAk8+AAAAAOApWHwAAAAAwFPmvPh4+eWX6Qtf+AJ1d3eTpmn09NNPs+8dx6HvfOc71NXVRZFIhLZv305vv/32fI0XgA8NfBc0KvBdsNyY8+KjWCzSzTffTPv27XP9/p/+6Z/oX/7lX+iHP/whvfLKKxSLxWjHjh1UqciCQAB4CXwXNCrwXbDcmLPg9J577qF77rnH9TvHceiRRx6hv/3bv6V7772XiIh+/OMfU0dHBz399NP0la985ZqPM3Z5gny+ugDmlpv/A/s+FJIVLZsV/V1XtxT1TGZ5hrqhM5PCpmpzcZKuSQGYzy8FU5ajVFOsyem1DC4iciy5n3iqlbUnClJApQdlBkzbUcU+LuIf5XDxsJyjge4+1g775H504qKymzasFDbpdFr0/bT8/1h7dEQKEnvaeeVES5M/oAFFnJbLcYHgu9kouRANvvsu8F347lXR/USzRKVawKVqqkJIqZgaJXl9/cr/ubruUrFWucChiKysPD4qM4yWxvl1WNUsRamGchnCMZnhc93qHj5GdSMiqvnkfKgCYr9vWtgkFJ9vaVotbFav6Wftcxd/J2zeOs3F2kG/rODrOFLwW1PuZ90lw2sgyM/NdqmabSvKVU3TP7D9Qcyr5uPcuXM0OjpK27dvn+lLpVJ022230aFDh1y3MQyDcrkc+wDgNfBd0KjAd0EjMq+Lj9HRUSIi6ujgueU7OjpmvlPZu3cvpVKpmU9fX5+rHQALCXwXNCrwXdCILPrbLnv27KHp6emZz9DQ0GIPCYBrAr4LGhX4Llhs5jXJWGfnu8lVMpkMdXV1zfRnMhnatGmT6zahUIhCIZnQKBJrIv+sJCcBJXSbdakKGGpOs3apJmNWqv4q0pSQ+7GVBCwVGTd3XGauYvJkQeGINNI1ngDG1qVNvIXHjYOOjO37Ik2izwly4YCtySRMmsVjj7pPHj8Q4/HASFzGB2sGj71OXJbJd1piUttw7+d3sPaRP5wXNgWlWmjFGBM2RpnrD9KJNGtXXSoyfhDwXfjubD6uvlup1Iicug9ppproSia1KhZ5yKZqyv9pazrXYRRKUruRU/p6+uT1dWpyuxWt3OdXd0tdRqnCbXrW3ixsgg6/waam5XWIiCRyRDTBfbevs0uYZItc+7TqeplEMNkUVdrrhc3UGD//qWmpLwm4aKp0h19rUy0RTUSqxMMy5bVWc5M5ilZLbX8Q8/rkY+XKldTZ2UkHDx6c6cvlcvTKK6/Q4ODgfB4KgHkFvgsaFfguaETm/OSjUCjQmTNnZtrnzp2jY8eOUXNzM/X399NDDz1Ef//3f09r1qyhlStX0re//W3q7u6m++67bz7HDcCcge+CRgW+C5Ybc158HDlyhD796U/PtB9++GEiIrr//vvp8ccfp7/5m7+hYrFI3/jGNyibzdKdd95Jzz77LIXD8vUnALwEvgsaFfguWG7MefFx1113fWBcR9M0+u53v0vf/e53P9LAAJhv4LugUYHvguXGkq1q29m3ggKBulhMU5LSVCryvfRMjp9OMN0qbMwaF6C5JdEpF3iSFtOR0hi/X4q1aj7eF03KJEjtLVnWdiZl5cKqIvTRbHn8SCQi+pSihGQ7UjBkKdUl9YBLZVQfP16hKEVemqJOCrkkDcqNSSFfJNrM2p8c3ChsTr1zgbWPn5SvCxZyXMAVVCq1mi5iKa+A784aI3xX2Cxl37U0i6xZiaIci4/FbQEUCfPrGU/IBF7DY9xXzl2SQly/oswOZoaFTSUjt1vTzu+Dz9wlxZzvXObC50SPFBS3tvBqtFdcfCCddhFz2vz4QdWZiejKGE8O5g9nhc1YdoS1L4/IZGGBAJ/bdFIK08tleY0cv5IMzKWqra2IUHVN2qi/ZXMoYitY9FdtAQAAAPDxAosPAAAAAHgKFh8AAAAA8JQlq/lwNB85Wj12psZBS3kZyw0pseR8zqXwVoUX4inl5H4CSqgrEZMx8ramZtGXbObxwLa0jG1bfl4sqRyS8d3JFTxRk2GNCBsyZRImq6YkgVITThGRpfMYoeYSN0838yRQtuVyLOV6pFLyXIOaDAhm81nWdkwZ19y0nsde0wk5/888w4t8jWXGWbtWk0l0vAK+Wwe+21i+m0rFKBKua4tqfj5XhYIstuaYfLzTeZn46sJFrp8oFOTcRcL8f+GRc1Ib1RGWSeN6elawdrpbFgoM5BVtRFjqpXpv3spNRi8Lm0hNak4s4nNSLMo56opyjUnVpSijFovz8cS6hU0izf0rPyE1RVcyE6LP1Pj5VqqyIB3p3OdjIfmmVLXMr5tajM4ied9eDTz5AAAAAICnYPEBAAAAAE/B4gMAAAAAnoLFBwAAAAA8ZckKTqlWpdnaFb/NBWkpl6zBfSkudrl+VVrYxJWEOD5Nrr+KuSxrV0pSQBWJyYqH69ZwIV/fil5howe4OKqQzQqbvi5eFXHdOVkFNdksJ6C5iSeG8vulOMtWdHSO1OxROMYT2dQqLtUNlf0EXBI1VUiKmlpauaiqUJKCwGKWi6h62mRCoPu+8DnWfvrn/87ai5moCb47a7/wXWGzlH23MD1JtUpdROivclFzwMXnSLkOfp+8MKUC98OmhEzWlY5xvyhPScFpe7esKtuz8VOsffxSVdicPsP7bu+Soutsltt0rJaVb3WS17yqVC5OO1JMmrvCRaARl8rFXc18TFlLipUDG7mgupyVgu7f/NtPRd+lIT5GX1AKbkkRi7rkKiNTeV6hm/w8KnPwXTz5AAAAAICnYPEBAAAAAE/B4gMAAAAAnrJkNR93bN3EChatuoHH34YvywQwPd08ZrZ2zWph09nWzto+RyZFySvJhAyXpEhuhXniMR7HjMdlbNsX5HH7gC3jk+Uij899YsMKYTOwdkD0mTaPvzkua8uarRSK8snz8AW4W5gVGfyzldie7pfH0sIuCWcUO8OUsU+/T0lcU80KmzYl/n7nn2xh7XLFoKd++oI8vgfAd+vAd7PCZin7rq4RzZ5WS0kq5bgkkdKJz6elSc3HlDJVuZxL8TOD+1NXSupCtnz606Kvd9021v6/+38kbDqVBF6+qiyKePnsO3ybVTcIm3DLdaIv5nBdTGlS6pwiNtdqVMvyvhzP8750m0yW1tI5wNrlgiwAqcsusoI88Znbb4Bp8vnXXJLdaQ7vq9WU+20Olebw5AMAAAAAnoLFBwAAAAA8BYsPAAAAAHgKFh8AAAAA8JQlKzi95ca1FJslgrvxFi7aK2+QgrxYiittZKoXIkfjQhvdJ5OtNMd45UDHZYnmtmqzbX7EmlvCFUWkZhhS+LT6un7WjgSl8KpclMmjHF25nJq8vI5SrdN2pEDIUubIVrM7EVG1zMdt2XKMut9NnMZnLj8hhVcXzg2x9h133iJsSiYXeUUVgaDmIsb0CvhuHfhuY/mu5rz7eR9LueaaS0I2Va/rlKUQV1McurklKmw6o9znPnHrWmGz/vZtom/qChfFhmrSv1b18qR5tjogIups5wnh3BLUlbJSZF2tcTuzLH3XIi54fefyJWHzxvEjrH37Nnmslk6eZC2Xl+LWgJxaah3gPm67XEerqohJDXn86bEsaxt5fjDDvPaKzHjyAQAAAABPweIDAAAAAJ6CxQcAAAAAPAWLDwAAAAB4ypIVnIZjMYrMEu3Fw7zCXyzqMnQ/z6znojUjTRXtaVLcZStVCW1TipPcxG6qGKvmIhtUE8s5LlUi42me7bJmyf1YtktJT5vv3CEp/tHVAVjy/C0/FzI65DKRNSUbni2PFXIZY8Di5xurSBsnwwWBY2czwqZ3HReQjetcdCZKl3oIfHfWfuC7wmYp+65ds8j21c+zbPDrF1QyhRIR+ZU59+lSqHhdJ8/wGY5I3xlY0cfaN98ps5l2rdso+o4d2s/a/X1NwqbzxptYO9gmRd/+aIq1S5WCsCnn8qIvM8xFxlMZKSa1lEzDkYTMINzayudxaPg1YdPR1cPatZIco1OWFZm14hQfjyPF4qqgOxKSgvZgJ+/Lhfg9WKleu1gaTz4AAAAA4ClYfAAAAADAU7D4AAAAAICnLFnNRzzZRIl4Pb7oKAmVSi4JUByDx7oMF5tiocjaVVPaGAZPklOrybi16VLRUq0KWCrJJESlIo8Z1my570Qzjz0mUmlhk060ir5wMMjalkvVUdKUip4kE+kklHjkxBW5n4pS7dK2ZZxVo6Dosy1+jZKJkLBZ0d/B2uVSUdg4SoXTVIIn0Qn4XHQFHgHfndWG7wqbpey7AZ+fAr76n4UppdKqVZEx/UiUVzv2uWhW2pWkYkMjWWGz+hN/ytq9N/2psCGS18rM8zlOJVLCpm3tJtYu+puFzYnXfsfaRlleu1wuK/rGL19kbZ8lfS4c5n9qe1b2CJuNa3nF3JpPJr8L+NK8HXSprFypiL7SBV5J23apWFtTHkUUXPww2sLH1NHNk56VK0gyBgAAAIAlChYfAAAAAPAULD4AAAAA4ClYfAAAAADAU5as4PTn//ZLCofr4jEr8Cv2/dSUTN5TmB5nbbdcPaqQL5OR+7GUDE/Nbe3Cpqm1RfSFfHw6i5NZYXP67TdZO1eQSWL6Vq5gbV9AJntJJuTxV67kFUV7+zqlzSoudGoOSQFZIsyPZysVV98dFBcjmZYU//nUcpdE5FOO1zHgIj5MciGf6UgRk0/RAzY38zGGXBLkeAV8tw58t7F8t1oxSJ+VdC0a4n6hhV2Sr+l8/hyX+YzE+XZ//uU/Fza33/MZ1k62dgibzNk3RZ9POX42L6vajp0/xdrDeXldXnz6adaOR+R1qBjS5zs7uMA1mZBC0XOXeCKyqi7nqLl7gLXX3rRZ2JDF/WsyKxOalVxEwVNlfjzNkX/6K2UuIC+4JCN0ClzMuj6t7MNFJ3418OQDAAAAAJ6CxQcAAAAAPGVOi4+9e/fSli1bKJFIUHt7O91333106hR/nFWpVGjXrl3U0tJC8Xicdu7c6fp4GAAvge+CRgW+C5Yjc9J8vPTSS7Rr1y7asmUL1Wo1+q//9b/S5z73OTp58iTF3iuk9a1vfYt+/vOf05NPPkmpVIp2795NX/rSl+g3v/nNnAb2wq9eYQWL0r3r2PeOJWNvr/32BdZe0dsrbFpbeLz58qVRYVNTCk1Fm9PCpqrLBEsZJa73ma2DwmbTxhtZu2TIhDB6gF+WcxcvCJvTb78j+t44zgsRpVOyCNTOv/gia99x41phE3T4mrS3q0/YVJW4uaYW/SL3AmamUjBM97sU9UrzRFERXa6RbR8PLqrRWb/i2fDdOvBd+O7VsJ0qL06o+JPmkrSu5vBEV5om5y4c4rqWTZulniGk6INOHpOF1aaGpe8Yih/mpyaFzdCZk6xdcCLCJmDx/cT9Ut+SDEs9R1sT13yMZFzuSyWxXykvfwOGzl1Uek4Im0KBJ/oL++Vc10JS5zVR4/MficjCdtEEn5OIXybRy5dy/FhKwjz19+eDmNPi49lnn2Xtxx9/nNrb2+no0aP0yU9+kqanp+mxxx6jJ554gu6++24iItq/fz+tX7+eDh8+TNu2bZvL4QCYN+C7oFGB74LlyEfSfExPv6sqbm5+N1Xt0aNHyTRN2r59+4zN9ddfT/39/XTo0CHXfRiGQblcjn0AWGjgu6BRge+C5cCHXnzYtk0PPfQQ3XHHHbRhwwYiIhodHaVgMEjpdJrZdnR00OiofBRF9G48M5VKzXz6+uRjUgDmE/guaFTgu2C58KEXH7t27aLjx4/TgQMHPtIA9uzZQ9PT0zOfoaGhP74RAB8B+C5oVOC7YLnwoZKM7d69m5555hl6+eWXqXeWMK6zs5Oq1Spls1m2Cs9kMtTZKZMGERGFQiEKhaSw5b6/+E8UidQrIYba17DvS3m5on/7jT+wdlenXM3rigAsEpZJiKp2mbXXblgjbJq6pKin1MorLv7ZPduFjSrqKbqI9mxF/1ZzpMirUpPbXbnChVYXzg3L40f5+Y5emhA250+8zdq6S5XEs6NXWHvr524VNisGukWfmtBJD8vqoRRQRG62TMhDGrcJanyOggGXLF0E3yWC78J3r+67RPZ7n/daNS6O9QeipGIpFVKrLtWGO1Lcv5776TPCprmDCyzb3cTCJZlALBDg5xGPyfvCr3PxaMwl+V1nu1KhNT8lbCI+OWcTYzxBoFmVostEmN87VZcEfW+/doS1R946LWyMGr+/KSBFsZYu+2K9ilA2JrOB6SF+r4RdfLeJ+Hmsv3Ela5fKJhHx37KrMacnH47j0O7du+mpp56i559/nlau5AfevHkzBQIBOnjw4EzfqVOn6OLFizQ4KNXzAHgFfBc0KvBdsByZ05OPXbt20RNPPEE/+clPKJFIzMQTU6kURSIRSqVS9LWvfY0efvhham5upmQySQ8++CANDg5CcQ0WFfguaFTgu2A5MqfFx6OPPkpERHfddRfr379/P/3lX/4lERF973vfI13XaefOnWQYBu3YsYN+8IMfzMtgAfiwwHdBowLfBcuROS0+HJfEOyrhcJj27dtH+/bt+9CDIiIKBXQKBetRodNvHWff56Zl3Fwdn1mVca1CocjamiYTDIWVwk5mKS9spsfkXGQuctHWL577hbCZyvN9TRdkDDOR5DHLVFOzsIklZezx0iUeJ29v7RE24SSP9//q53KMk2+/ztpW1RQ2Z0Z59sRLRTlHa9ZLvUEqyWPGKSVBDxFRJMoT4KRiMj4bUApcRaN8PqpKMiT4bh34Lnz3ati2RvYs4U5QSbQV9ksNDylJ2hyfTMRlK9dhfFzeA4Ux3hcx5eu/Nkk9Q3MT12qku9uETc0yWPvysMs9SHyedV3+eazWXIoQavwax8JSF6PmZvO5JGsjJTmbVZX3l66IqnIlqUuphsqiL9HNz78YyQqbvM1/cypFqcpoSa5i7VZFJ1MsXntlOdR2AQAAAICnYPEBAAAAAE/B4gMAAAAAnoLFBwAAAAA85UMlGfOC/GSGauV6QpPnf/Jz9v3Q6CWxjW5yoc3rr7vUK1BEejUXAREpSX9++czzwiQYkKK5Tbd8grWrwYSwyRkl1j578YqwmZh4k++nIsVJw6PnRd+583y7W2+RlSP/866HWfvVw7L2Q22aJ2/KGYawKSvirLNHZIbEXx0dEX0xPxeeBYJSQOZTkh8lXER7vSsGWPvenV9h7VLpj4v0Fgr47qz9wHeFzVL2XV0Lka7V/yyEQzyplOOSQCwW4QLLWKJV2JRMnsCqJSETtPmVfVenM8LG1uV2pQD3sY6OlcLGVgTc6zbKqtG/feEga1edkrAJuIi8ywVul0zIJGdBpVSxT5P3RUFJiHduRIpJs1k+R4ZWFDZta+UzhZ60kuTMkfM4Nc7PI1iRvhvrURKxlXhCtXL52qva4skHAAAAADwFiw8AAAAAeAoWHwAAAADwlCWr+ehs76BotJ6sZs0Aj+M5JGNmfp33+Vzic7qPr7ccW8ZXg2ElSU4gLGy6u2USpLt27GDtRFQmm0mFeYGlk8dlEZ7TZ95h7c6eAWFTceS60afEXo+ffkvYnDzNixVFB9YLm+FhPsamdJOwaQ/ymGE0HhE2k6MXRN/E5TOsPTYu47oVS0m4pVYrI6KRLHfd2z/DbcpluY1XwHfrwHcby3cDfo2C/vr1KSmaGZ/qX0RkK8XWSqZMcuVTiuWFgnLOAwG+72BUJnFLJeXxR8f4dSj1SD1He991rH35yriwuXHLHaxdGJPFDc+ePiH6ioUsa/t98vxTKa4D0Vx+A0Yu8+NdvOCSZCzEzz/ZIe/TtmapOdEUPYk2KeexaYr7ZU+7TBDYm+Zze+YkT9ZWrsikflcDTz4AAAAA4ClYfAAAAADAU7D4AAAAAICnYPEBAAAAAE9ZsoLTqfEpqkTqYqdtt93Ovr/9U58S24RCPOmP3yfXVrrO+2xHCn98SuVEsyoTp5SrMgHNxKVzrD3pIr6ZHJ9k7bOKQI+IaPgKF/HE27uFDYWkkFALcvFRtSYTLP3ypV+z9orVNwmbvmYuSAy7VHeMKomqjIqsDHo2J8VZcSUBj+XIpEWjUwXWbm0dEDYlk1+35196lbVN89qrK8438N068N0BYbOUfbe9RadouO5n5gRP2la2pM8VlTxXji59zq8k2UomW4RNMMCTWpWLMtFeJODyJ6vK+4789rfCZNU6pZLxJVnVVleq80ZDMsmWzycT9EUiXLxZLEjBabnM+2o1eY3jEb7v229ZK2zCig/WfNIHLVPe3+UhLjjV8/IebI/yxIK3rL1R2qQ7WPvoCP/dqFRdEh9eBTz5AAAAAICnYPEBAAAAAE/B4gMAAAAAnoLFBwAAAAA8ZckKTqPREEVnCXAmclww89rrR8U27e08m2FHu6yuaJpcSDc1lZUHV7LB+W0pvutZKYV0fU1csHP5tKyMWSxwIV17R6ewibakWdsXlhnrSuWK6Ovq6mft0WFZPXV8gmfN6+qWVRE1h2cjLBguWev8XBxl2lJkForILHohJXNndWJM7lvnQq8OlyyZVYMLtpQhi7aXwHfrwHcHhMlS9t3e3iDFI/VzSGlcmHhmSIoZM2N8wFVLijLjcf6npliS2Tstm4t1fS7/G0+OTYi+fIGLHCum3LfP4X2JuMx8mxnlgupLRemntiOzz3a0cfGs5nLPTWV5hdpQTM5ROsXvwaCL6NxQBeR+KYotGnK7aoHbxWxpc10fv5+7O6UoeOgSF+5OjHF/MExUtQUAAADAEgWLDwAAAAB4ChYfAAAAAPCUJav5CPltCgXqCW2MSpZ9/9vfHhTbOCaP0SWjsnKiaSrxwbJMCONX1mQrBvqEzYZtN4i+1f08lp4dknHr0SleTTEYkbG/1S089jY2VhA2N63bIPpuvGkdax/4nz8WNn7iFT1Nl7hmtcr7nJpLHC/M59EXkucxsHKV6LsydIp36D5hE1HioevXy2Q7lRKfk76udtY2DHleXgHfrQPfbSzfTaYDFI/W9QFlJabf1C7PmWI8Qdx4RiaIq1S5zsUflFogxYRsF/2Aacl9T5e5niLm4peVEp/TckVWta0qx7Ncju848vwLOT5HyaS8d5NJXqG3XJbamfEJfh7xuNQdaUqiQa3mUtnaL4+v5vULBuV5DFw3wMdYkvt++eWTrP366SusXXNJQnc18OQDAAAAAJ6CxQcAAAAAPAWLDwAAAAB4ChYfAAAAAPCUJSs4LVXKRLPzuShCmx33/JnYxq7ypEM+U1bYsxVBjOOTwhufnwvbwoqgiohoNCvFfvnsadaeLMvja2Gu/Dl17KywmTjEkxetWrlO2Gy5bo3oqyrJmyJBKbxylERVbgmfdB93C1vm1aGyzefRb8lzXdErRXuVAk8SdENSiqpePfoaaw9fOCVsykopTafExVrVRawMCt+tA99tLN/1hf3kD9fnMJzk/tQcl/+v+stcBBqISNFhbkr5U2PJ/UTCXHhrBeR+LCMr+oJRvu+Acg8QEfl8/D4wXCpCq/PuuCQU01wSwDmKyNly0QsH1GRgLv6dneJ+UK7KZGWpNBfq+nWX6tcu518i7uOZcVnJeUpJ1pYvymRt//7iW3w/im7Wtq89Qx6efAAAAADAU7D4AAAAAICnYPEBAAAAAE9ZspqPWCxA0Wg9dpVSQkmJNpm8xzB47DHssrYKajwe5kRcErJEuY1dkYmS8vmc6PNFeTyufXVa2KyO8uQ2b597R9iQxmP5gaiMD14euSj6WlqbPrBNRFQt83izYci4XlFJ3mSU5PmbBg/2+cNSW9DR3Sb6LozwwkSZi/L8KwU+pndOHBM2LS18305TM2+b157sZr6B79aB7x4TNkvZd4sFP2n2LH2CL86+j8ekoCEQ4Q4eUzNaEVEqxc+pkJO6o0KOz2+h5JJkrCL7EkFeAC0ckMXWasr95fe73F9KVyAkNVWaJreLKkXzdJe/qjVFVxSMSKNkmvvh5KTUZeQVrUqyWRZ/K9WkZujt81yv9NYbQ8Kmo5n/BnT0yvuCdH78VqUYnmXbdGHq2pLk4ckHAAAAADwFiw8AAAAAeMqcFh+PPvoobdy4kZLJJCWTSRocHKRf/OIXM99XKhXatWsXtbS0UDwep507d1Imk/mAPQLgDfBd0KjAd8FyZE6Lj97eXvrHf/xHOnr0KB05coTuvvtuuvfee+nEiRNERPStb32Lfvazn9GTTz5JL730Eg0PD9OXvvSlBRk4AHMBvgsaFfguWI7MSXD6hS98gbX/4R/+gR599FE6fPgw9fb20mOPPUZPPPEE3X333UREtH//flq/fj0dPnyYtm3bNqeBlQpniKxZwiWbr5MCWpxUMhku9nr75HlhE1Yq/gVTaWHT2s7Fbt2tKWHjltylJcXFP24F/ipKBcb2dlndsaebC9BGRkeFzenTb4q+gepK1lZFjERE+Tyfo1JJ/oeUm+aCRDfRnlXlgjFfSCZcOnG8VfRVDS6Gam/vEDY9G3nV0/Y2adPaxqunhpXjV5TKoPDdOvBd+O7VGB4iis5yXSPLxaOJNpmQLRzhybBS0r2puZn/qSkUZVXXbJb3TU3IZFlTE6KLfDYXhtqOTHRlWYpQ1ZbCVfWu0HSZZMznl38yy0rCNEdOEQVsPke10qQco1Lp1lITkxFRtsBtqi5FmyddxLznz/CJy04UhU21yHfWmeoUNutX9LC2eijTsun35+W5ufGhNR+WZdGBAweoWCzS4OAgHT16lEzTpO3bt8/YXH/99dTf30+HDh266n4Mw6BcLsc+ACwk8F3QqMB3wXJhzouPN954g+LxOIVCIXrggQfoqaeeohtuuIFGR0cpGAxSOp1m9h0dHTTq8t/P++zdu5dSqdTMp6+vb84nAcC1AN8FjQp8Fyw35rz4WLduHR07doxeeeUV+uY3v0n3338/nTx58kMPYM+ePTQ9PT3zGRqS7x8DMB/Ad0GjAt8Fy405JxkLBoN03XXXERHR5s2b6Xe/+x398z//M335y1+marVK2WyWrcIzmQx1dsrY0fuEQiEKhVyKSFUNmh3K05V1kt+UCWCSSiGio4dfEjajGZ4oSQvIY2/dupm17xy8VdhMT8sER6///hXWLlZkspXTF/lNfvb8eWFTLvG4nluBo3BSJkHK5XhSmvzUuLAp5njc3qXuFvl9vDeVkMlmulfyGH1TS5ewae+W1737lptYu9mlOFdQKZjmcymgpiazIkfxD5d4KXz3XeC78N2r+a4VaCFrll+ZQe4/hi21OHqNX6twSl6ZdBvXjjTpUhjRXOL3QHZSJtHLjsv5LBf5nzGrJrUi6hzbNSlqqiiFCoNBlwJ1fnn8fIXvq1xwScTmcL1QQk8IG1vnoS/TlH+eQzGuZwm7/AakgzLJ2CpKs/ZNN0vfXbfxZtYeeM/fZrN1G7+/Lw1zTZVRrRH9/rzYzo2PnOfDtm0yDIM2b95MgUCADh48OPPdqVOn6OLFizQ4OPhRDwPAvAPfBY0KfBc0OnN68rFnzx665557qL+/n/L5PD3xxBP04osv0nPPPUepVIq+9rWv0cMPP0zNzc2UTCbpwQcfpMHBwTkrrgGYb+C7oFGB74LlyJwWH1euXKGvfvWrNDIyQqlUijZu3EjPPfccffaznyUiou9973uk6zrt3LmTDMOgHTt20A9+8IMFGTgAcwG+CxoV+C5Yjsxp8fHYY4994PfhcJj27dtH+/bt+9ADct57R7tc4bFFU4kQ1RwZe6so21i2jOup74BrjrQxazweWXHJOWAYMq5mVHlftWoKm5qyb9tljI7S5xY3t13eU7eJ96n7eXdf8h14aaMeS+5HfW9ePS8iItN0mSNlLiuGvI62/tHj5u/nSnj/fOG7deC78F2V949VqvDzLittLSD9wrb5/Oklec39RWU7XfpAsWwrbXmsUkVez3KFX3TbJc+GqjBw1XwY/HiWi+/61HwhRFQ2+PErLveO4/A+v4vmpVLlfYbbeWj8WD6X3xLDlBtWa3zcARcb9doXilK7UlbmyFDH/N5+r+Ve1ZxrsfKQS5cu4bUvMG8MDQ1Rb2+vJ8eC74L5BL4LGpVr8d0lt/iwbZuGh4cpkUhQPp+nvr4+GhoaomRSZlME80cul1tWc+04DuXzeeru7ibdJaPnQgDfXRzgux8d+O7i8HH23Tm/arvQ6Lo+s2LStHcfe71fUAksPMtprlMpmVp8IYHvLi7Laa7hux8vltNcX6vverOsBgAAAAB4Dyw+AAAAAOApS3rxEQqF6O/+7u9cM/GB+QVzPb9gPr0Dcz2/YD694+M810tOcAoAAACA5c2SfvIBAAAAgOUHFh8AAAAA8BQsPgAAAADgKVh8AAAAAMBTluziY9++fTQwMEDhcJhuu+02evXVVxd7SA3P3r17acuWLZRIJKi9vZ3uu+8+OnXqFLOpVCq0a9cuamlpoXg8Tjt37qRMJrNII25M4LvzD3zXG+C78w989yo4S5ADBw44wWDQ+dGPfuScOHHC+frXv+6k02knk8ks9tAamh07djj79+93jh8/7hw7dsz5/Oc/7/T39zuFQmHG5oEHHnD6+vqcgwcPOkeOHHG2bdvm3H777Ys46sYCvrswwHcXHvjuwgDfdWdJLj62bt3q7Nq1a6ZtWZbT3d3t7N27dxFHtfy4cuWKQ0TOSy+95DiO42SzWScQCDhPPvnkjM2bb77pEJFz6NChxRpmQwHf9Qb47vwD3/UG+O67LLmwS7VapaNHj9L27dtn+nRdp+3bt9OhQ4cWcWTLj+npaSIiam5uJiKio0ePkmmabO6vv/566u/vx9xfA/Bd74Dvzi/wXe+A777Lklt8jI+Pk2VZ1NHRwfo7OjpodHR0kUa1/LBtmx566CG64447aMOGDURENDo6SsFgkNLpNLPF3F8b8F1vgO/OP/Bdb4Dv1llyVW2BN+zatYuOHz9Ov/71rxd7KADMCfguaFTgu3WW3JOP1tZW8vl8QumbyWSos7NzkUa1vNi9ezc988wz9MILL8yU0SYi6uzspGq1Stlsltlj7q8N+O7CA99dGOC7Cw98l7PkFh/BYJA2b95MBw8enOmzbZsOHjxIg4ODiziyxsdxHNq9ezc99dRT9Pzzz9PKlSvZ95s3b6ZAIMDm/tSpU3Tx4kXM/TUA31044LsLC3x34YDvXoVFFry6cuDAAScUCjmPP/64c/LkSecb3/iGk06nndHR0cUeWkPzzW9+00mlUs6LL77ojIyMzHxKpdKMzQMPPOD09/c7zz//vHPkyBFncHDQGRwcXMRRNxbw3YUBvrvwwHcXBviuO0ty8eE4jvP973/f6e/vd4LBoLN161bn8OHDiz2khoeIXD/79++fsSmXy85f//VfO01NTU40GnW++MUvOiMjI4s36AYEvjv/wHe9Ab47/8B33dEcx3G8ftoCAAAAgI8vS07zAQAAAIDlDRYfAAAAAPAULD4AAAAA4ClYfAAAAADAU7D4AAAAAICnYPEBAAAAAE/B4gMAAAAAnoLFBwAAAAA8BYsPAAAAAHgKFh8AAAAA8BQsPgAAAADgKVh8AAAAAMBT/n9EQBvvHDWTZQAAAABJRU5ErkJggg==", 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", 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" ] @@ -186,6 +417,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -200,24 +432,24 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Image shape: (32, 32, 3) , Label: 9\n", - "Image shape: (32, 32, 3) , Label: 1\n", "Image shape: (32, 32, 3) , Label: 4\n", - "Image shape: (32, 32, 3) , Label: 9\n", + "Image shape: (32, 32, 3) , Label: 1\n", "Image shape: (32, 32, 3) , Label: 6\n", + "Image shape: (32, 32, 3) , Label: 9\n", + "Image shape: (32, 32, 3) , Label: 9\n", "Image shape: (32, 32, 3) , Label: 1\n" ] }, { "data": { - "image/png": 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", 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" ] @@ -239,6 +471,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -253,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -274,7 +507,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -294,6 +527,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -317,7 +551,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -346,6 +580,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -364,7 +599,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -394,6 +629,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -408,7 +644,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -441,6 +677,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -450,9 +687,9 @@ ], "metadata": { "kernelspec": { - "display_name": "MindSpore", + "display_name": "ly310", "language": "python", - "name": "mindspore" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -464,7 +701,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.5" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/tutorials/source_zh_cn/index.rst b/tutorials/source_zh_cn/index.rst index 177d639da0..d4c7396fb6 100644 --- a/tutorials/source_zh_cn/index.rst +++ b/tutorials/source_zh_cn/index.rst @@ -30,12 +30,9 @@ MindSpore教程 :hidden: dataset/sampler - dataset/record dataset/eager - dataset/python_objects - dataset/augment - dataset/cache - dataset/optimize + dataset/record + dataset/optimize .. toctree:: :glob: -- Gitee