From 0a27cdccebbe65e0bc4f056e7d5a3d33fe1fb77e Mon Sep 17 00:00:00 2001 From: zhangquan <2134922008@qq.com> Date: Sun, 5 Jun 2022 16:28:45 +0800 Subject: [PATCH] =?UTF-8?q?=E3=80=90=E4=BC=97=E6=99=BA=E3=80=91=E3=80=90?= =?UTF-8?q?=E6=B8=85=E5=8D=8E=E5=A4=A7=E5=AD=A6=E3=80=91=E3=80=90ID1221?= =?UTF-8?q?=E3=80=91=E3=80=90Circleloss=E3=80=91NPU=E7=B2=BE=E5=BA=A6?= =?UTF-8?q?=E8=BE=BE=E6=A0=87=EF=BC=8C=E6=8F=90=E4=BA=A4=E7=B2=BE=E5=BA=A6?= =?UTF-8?q?=E9=AA=8C=E6=94=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../README.md | 97 +- .../{debug.py => circle_loss.py} | 39 +- .../CircleLoss_ID1221_for_TensorFlow/data.py | 413 -- .../data/learning_rate_circle_loss.txt | 6 + .../data/pairs.txt | 6001 +++++++++++++++++ .../CircleLoss_ID1221_for_TensorFlow/demo.py | 138 - .../CircleLoss_ID1221_for_TensorFlow/eval.py | 265 - .../facenet.py | 598 ++ .../{config.py => lfw.py} | 100 +- .../CircleLoss_ID1221_for_TensorFlow/loss.png | Bin 0 -> 40371 bytes .../CircleLoss_ID1221_for_TensorFlow/loss.py | 170 - 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# CircleLoss **Circle Loss: A Unified Perspective of Pair Similarity Optimization** @@ -14,57 +12,96 @@ ArXiv: [https://arxiv.org/abs/2002.10857](https://arxiv.org/abs/2002.10857) ![image-20210906114857004](https://picbed-1301760901.cos.ap-guangzhou.myqcloud.com/image-20210906114857004.png) -## 1. 依赖库安装 -见Requirements.txt， 需要安装 numpy， scikit-image， tensorflow等库。 +- 适配昇腾 AI 处理器的实现： + + + https://gitee.com/ascend/ModelZoo-TensorFlow/tree/master/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow + + + + -## 2. 数据集 +- 精度 -模型训练采用MARKET1501数据集，将文件夹解压到 /Data/ 下 +| - | 论文 | GPU （v100） | NPU | +|-----|-------|------------|-------| +| acc | 0.997 | 0.941 | 0.942 | -https://www.kaggle.com/pengcw1/market-1501/data +- 性能 -## 3. 训练 +| batchsize |image_size| GPU （v100） | NPU | +|---|---|----|---| +| 30 | 112 x 112| 0.075 s/step | 0.35 s/step | -将模型预训练权重放置到 /checkpoint/ 下 -训练脚本为`train_npu.py`，跳转至项目根目录下，直接执行以下命令即可： +- ## 5.Loss曲线 -```bash -bash script/train.sh -``` +![loss曲线](loss.png) +- 训练超参 + - Batch size: 30 + - Learning rate(LR): 0.01 + - Optimizer: Adam + - Weight decay: 1e-6 + - Loss: Circle loss + +## 支持特性 + +| 特性列表 | 是否支持 | +|-------|------| +| 分布式训练 | 否 | +| 混合精度 | 否 | +| 并行数据 | 否 | + + +## 1. 依赖库安装 -## 4. NPU性能 +见requirements.txt， 需要安装 numpy， scikit-image， tensorflow等库。 +## 2. 模型运行 +- 数据集和预训练模型获取 + 链接：https://pan.baidu.com/s/1Rs3KefgNNELa5V0YNtdlfw + 提取码：vyux + 数据集文件为分卷压缩格式，将所有卷放入同一文件夹解压得到dataset.zip文件 + 将dataset.zip文件解压到代码所在目录即可 + 模型参数文件也保存到当前代码目录 -## 5.Loss曲线 +- 模型精度评估 -![image-20211206125601106](https://picbed-1301760901.cos.ap-guangzhou.myqcloud.com/image-20211206125601106.png) + 1. 配置数据路径参数。 -## 6. 精度对比 + 首先在脚本train_performance_1p.sh中，配置训练数据集路径和预训练模型路径，请用户根据实际路径配置，示例如下： -论文中的测试精度如下所示 + ``` + --lfw_dir ./dataset/lfw-deepfunneled_align + --data_dir ./dataset/MS-Celeb-1M_clean_align + --pretrained_model ./20220602-013518/model-20220602-013518.ckpt-258 + ``` -![image-20211206124710842](https://picbed-1301760901.cos.ap-guangzhou.myqcloud.com/image-20211206124710842.png) + 2. 启动模型精度评估。 -使用NPU训练后得到的测试集精度如下所示： + ``` + bash train_performance_1p.sh + ``` -表格：Market-1501数据集上Circle loss 应用于ResNet50训练的测试结果，其中包括了R-1准确率（%）和mAP（%） -| Method | R-1 | mAP | -| ----------- | ---- | ---- | -| 原论文精度 | 94.2 | 84.9 | -| GPU复现精度 | 81.0 | 62.1 | -| NPU复现精度 | 80.9 | 61.5 | +- 模型性能评估。 -## Contact + 1. 配置数据路径参数。 -Please assign issues to [@louis-yx](https://gitee.com/louis-yx) if you have any question. + 首先在脚本train_1p.sh中，配置数据集路径和模型参数路径，请用户根据实际路径配置，示例如下： -## Credits + ``` + --lfw_dir ./dataset/lfw-deepfunneled_align + --data_dir ./dataset/MS-Celeb-1M_clean_align + --pretrained_model ./20220602-013518/model-20220602-013518.ckpt-258 + ``` -This implementation is derived from [Person_reID_baseline_pytorch](https://github.com/layumi/Person_reID_baseline_pytorch) by [layumi](https://github.com/layumi). + 2. 启动模型性能评估。 + ``` + bash train_1p.sh + ``` diff --git a/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/debug.py b/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/circle_loss.py similarity index 59% rename from TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/debug.py rename to TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/circle_loss.py index 3735c926a..2e8d1ffd4 100644 --- a/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/debug.py +++ b/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/circle_loss.py @@ -25,14 +25,33 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + +from npu_bridge.npu_init import * import tensorflow as tf -a = tf.constant([1, 2, 3, 4], dtype = tf.float64) -def reducelogsumexp(x): - x = tf.math.exp(x) - x = tf.math.reduce_sum(x) - x = tf.math.log(x) - return x -res=reducelogsumexp(a) -apires=tf.math.reduce_logsumexp(a) -with tf.Session() as sess: - print(sess.run([res,apires])) \ No newline at end of file +import numpy as np +import tensorflow.compat.v1 as tf +tf.disable_eager_execution() + + +def circle_loss(y_true, + y_pred, + gamma = 256, + margin = 0.25, + batch_size=None): + O_p = 1 + margin + O_n = -margin + Delta_p = 1 - margin + Delta_n = margin + if batch_size: + batch_size = batch_size + batch_idxs = tf.expand_dims( + tf.range(0, batch_size, dtype=tf.int32), 1) # shape [batch,1] + alpha_p = tf.nn.relu(O_p - tf.stop_gradient(y_pred)) + alpha_n = tf.nn.relu(tf.stop_gradient(y_pred) - O_n) + # yapf: disable + y_true = tf.cast(y_true, tf.float32) + y_pred = (y_true * (alpha_p * (y_pred - Delta_p)) + + (1 - y_true) * (alpha_n * (y_pred - Delta_n))) * gamma + # yapf: enable + return tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=y_pred)) + diff --git a/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/data.py b/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/data.py deleted file mode 100644 index dcdaedc51..000000000 --- a/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/data.py +++ /dev/null @@ -1,413 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -# from src.dataflow.market import MarketTriplet -# import skimage -# import skimage.transform -import numpy as np - - -def get_file_list(file_dir, file_ext, sub_name=None): - """ Get file list in a directory with sepcific filename and extension - - Args: - file_dir (str): directory of files - file_ext (str): filename extension - sub_name (str): Part of filename. Can be None. - - Return: - List of filenames under ``file_dir`` as well as subdirectories - - """ - re_list = [] - - if sub_name is None: - return np.array([os.path.join(root, name) - for root, dirs, files in os.walk(file_dir) - for name in sorted(files) if name.lower().endswith(file_ext)]) - else: - return np.array([os.path.join(root, name) - for root, dirs, files in os.walk(file_dir) - for name in sorted(files) - if name.lower().endswith(file_ext) and sub_name.lower() in name.lower()]) - - - -# def loadMarketForTest(data_dir='', rescale_im=[384, 128]): -# def normalize_im(im): -# im = skimage.transform.resize( -# im, rescale_im, -# mode='constant', preserve_range=True) -# return np.clip(im/255.0, 0., 1.) - -# query_dir = os.path.join(data_dir, 'query') -# query_data = MarketTripletForTest( -# n_class=None, -# data_dir=query_dir, -# batch_dict_name=['im', 'label','camid'], -# shuffle=True, -# pf=normalize_im) -# # query_data.setup(epoch_val=0, sample_n_class=10, sample_per_class=1) - -# test_dir = os.path.join(data_dir, 'bounding_box_test') -# test_data = MarketTripletForTest( -# n_class=None, -# data_dir=test_dir, -# batch_dict_name=['im', 'label','camid'], -# shuffle=True, -# pf=normalize_im) -# # test_data.setup(epoch_val=0, sample_n_class=750, sample_per_class=2) -# return query_data,test_data - -# query_data,test_data = loadMarketForTest(market_dir, rescale_im=im_size) - - - -# def loadMarket(P,K,data_dir='', rescale_im=[128, 64]): -# def normalize_im(im): -# im = skimage.transform.resize( -# im, rescale_im, -# mode='constant', preserve_range=True) -# return np.clip(im/255.0, 0., 1.) -# -# train_dir = os.path.join(data_dir, 'bounding_box_train') -# train_data = MarketTriplet( -# n_class=None, -# data_dir=train_dir, -# batch_dict_name=['im', 'label'], -# shuffle=True, -# pf=normalize_im) -# train_data.setup(epoch_val=0, sample_n_class=P, sample_per_class=K) -# -# test_dir = os.path.join(data_dir, 'bounding_box_test') -# test_data = MarketTriplet( -# n_class=None, -# data_dir=test_dir, -# batch_dict_name=['im', 'label'], -# shuffle=True, -# pf=normalize_im) -# test_data.setup(epoch_val=0, sample_n_class=P, sample_per_class=K) -# -# data = {} -# data['test'] = test_data -# data['train'] = train_data -# return data - -import numpy as np -import random -import tensorflow as tf - -const_mean = np.zeros([256,128,3]) -const_std = np.zeros([256,128,3]) -mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225] -for i in range(3): - const_mean[:,:,i] = mean[i] - const_std[:,:,i] = std[i] -const_mean = tf.constant(const_mean,dtype=tf.float32) -const_std = tf.constant(const_std,dtype=tf.float32) - -# mean_tf1 = mean[0]*tf.ones(shape=[256,128,1]) -# mean_tf2 = mean[1]*tf.ones(shape=[256,128,1]) -# mean_tf3 = mean[2]*tf.ones(shape=[256,128,1]) -# std_tf1 = std[0]*tf.ones(shape=[256,128,1]) -# std_tf2 = std[1]*tf.ones(shape=[256,128,1]) -# std_tf3 = std[2]*tf.ones(shape=[256,128,1]) -# mean = tf.concat([mean_tf1,mean_tf2,mean_tf3],axis=2) -# std = tf.concat([std_tf1,std_tf2,std_tf3],axis=2) - -def norm_trans(im): - im = (im-const_mean)/const_std - return im -def random_erasing(img, probability = 0.5, sl = 0.02, sh = 0.4, r1 = 0.3): - ''' - img is a 3-D variable (ex: tf.Variable(image, validate_shape=False) ) and HWC order - ''' - # HWC order - import random - if random.uniform(0, 1) > probability: - return img - # print('erasing') - height = int(img.shape[0]) - width = int(img.shape[1]) - channel = int(img.shape[2]) - area = height*width - - erase_area_low_bound = round(np.sqrt(sl * area * r1)) - erase_area_up_bound = round((sh * area) / r1) - h_upper_bound = min(erase_area_up_bound, height) - w_upper_bound = min(erase_area_up_bound, width) - - h = np.random.randint(low=erase_area_low_bound,high=h_upper_bound) - w = np.random.randint(low=erase_area_low_bound,high=w_upper_bound) - - x1 = np.random.randint(low=0,high=height+1-h) - y1 = np.random.randint(low=0,high=width+1-w) - - mask = np.ones([height,width,channel]) - mask[x1:x1+h, y1:y1+w, :] = 0 - # print(f'erasing {h}x{w} pixel!') - masktensor = tf.constant(mask,dtype=tf.float32) - return img*masktensor - - - - -# from src.utils.dataflow import get_file_list -def tf_dataset_PK(P=4,K=4,erasing_prob=0.5,resize_image=[256,128],epoch_size=60,path='/home/nanshen/xutan/yixin/market1501/Market-1501-v15.09.15/bounding_box_train'): - imlist = get_file_list(path, '.jpg') - print(len(imlist)) - label = [] - for im_path in imlist: - import ntpath - head, tail = ntpath.split(im_path) - class_label = tail.split('_')[0] - label.append(int(class_label)) - from sklearn import preprocessing - le = preprocessing.LabelEncoder() - le.fit(label) - label_ = le.transform(label) - # print(f'加载数据中一共有{len(le.classes_)}个类') - # 函数的功能时将filename对应的图片文件读进来，并缩放到统一的大小 - def _parse_function(filename, label): - image_string = tf.read_file(filename) - image_decoded = tf.image.decode_jpeg(image_string,channels=3) - image_resized = tf.image.resize(image_decoded, resize_image,method=tf.image.ResizeMethod.BICUBIC) - # print(image_resized.shape) - # im = np.pad(image_resized,pad_width=((10,10),(10,10),(0,0)),mode='constant') - image_padded = tf.image.pad_to_bounding_box(image_resized, 10,10,resize_image[0]+20,resize_image[1]+20) - # im = random_crop(im,[256, 128]) - crop = tf.image.random_crop(image_padded,[resize_image[0],resize_image[1],3]) - rd = np.random.rand() - if rd < 0.5: - flip = tf.image.flip_left_right(crop) - else: - flip = crop - im = tf.clip_by_value(flip/255.0,0,1.0) - # norm - im = norm_trans(im) - # erasing - im = random_erasing(im,probability=erasing_prob) - return im, label - - imlists_dict = {} - for i in range(751): - imlists_dict[i] = [] - for indxi,labeli in enumerate(label_): - imlists_dict[labeli].append(imlist[indxi]) - - datasets = [] - for i in range(751): - imlist_ind = imlists_dict[i] - # 图片文件的列表 - filenames = tf.constant(imlist_ind) - # label[i]就是图片filenames[i]的label - label_ind = [i]*len(imlist_ind) - labels = tf.constant(label_ind) - - # 此时dataset中的一个元素是(filename, label) - dataset = tf.data.Dataset.from_tensor_slices((filenames, labels)) - - # 此时dataset中的一个元素是(image_resized, label) - dataset = dataset.map(_parse_function) - - # 此时dataset中的一个元素是(image_resized_batch, label_batch) - dataset = dataset.shuffle(buffer_size=1000).batch(K).repeat(epoch_size) - - datasets.append(dataset) - - return [len(imlist),datasets,le] - -# from src.utils.dataflow import get_file_list -def tf_dataset(erasing_prob=0.5,resize_image=[256,128],epoch_size=60,batch_size=32,path='/home/nanshen/xutan/yixin/market1501/Market-1501-v15.09.15/bounding_box_train'): - imlist = get_file_list(path, '.jpg') - print(len(imlist)) - label = [] - for im_path in imlist: - import ntpath - head, tail = ntpath.split(im_path) - class_label = tail.split('_')[0] - label.append(int(class_label)) - from sklearn import preprocessing - le = preprocessing.LabelEncoder() - le.fit(label) - label_ = le.transform(label) - # print(f'加载数据中一共有{len(le.classes_)}个类') - # 函数的功能时将filename对应的图片文件读进来，并缩放到统一的大小 - def _parse_function(filename, label): - image_string = tf.read_file(filename) - image_decoded = tf.image.decode_jpeg(image_string,channels=3) - image_resized = tf.image.resize(image_decoded, resize_image,method=tf.image.ResizeMethod.BICUBIC) - # print(image_resized.shape) - # im = np.pad(image_resized,pad_width=((10,10),(10,10),(0,0)),mode='constant') - image_padded = tf.image.pad_to_bounding_box(image_resized, 10,10,resize_image[0]+20,resize_image[1]+20) - # im = random_crop(im,[256, 128]) - crop = tf.image.random_crop(image_padded,[resize_image[0],resize_image[1],3]) - rd = np.random.rand() - if rd < 0.5: - flip = tf.image.flip_left_right(crop) - else: - flip = crop - im = tf.clip_by_value(flip/255.0,0,1.0) - # norm - im = norm_trans(im) - # erasing - im = random_erasing(im,probability=erasing_prob) - return im, label - - # 图片文件的列表 - filenames = tf.constant(imlist) - # label[i]就是图片filenames[i]的label - labels = tf.constant(label_) - - # 此时dataset中的一个元素是(filename, label) - dataset = tf.data.Dataset.from_tensor_slices((filenames, labels)) - - # 此时dataset中的一个元素是(image_resized, label) - dataset = dataset.map(_parse_function) - - # 此时dataset中的一个元素是(image_resized_batch, label_batch) - dataset = dataset.shuffle(buffer_size=1000).batch(batch_size).repeat(epoch_size) - - return [len(imlist),dataset,le] - -# from src.utils.dataflow import get_file_list -def tf_val_dataset(resize_image=[256,128],epoch_size=60,batch_size=32,datapath='/home/liulizhao/projects/liuyixin/Market-1501-v15.09.15/'): - queryimlist = get_file_list(datapath+'query', '.jpg') - galleyimlist = get_file_list(datapath+'bounding_box_test', '.jpg') - imlist = list(queryimlist) + list(galleyimlist) - label = [] - for im_path in imlist: - import ntpath - head, tail = ntpath.split(im_path) - class_label = tail.split('_')[0] - label.append(int(class_label)) - from sklearn import preprocessing - le = preprocessing.LabelEncoder() - le.fit(label) - label_ = le.transform(label) - # 函数的功能时将filename对应的图片文件读进来，并缩放到统一的大小 - def _parse_function(filename, label): - image_string = tf.read_file(filename) - image_decoded = tf.image.decode_jpeg(image_string,channels=3) - image_resized = tf.image.resize(image_decoded, resize_image,method=tf.image.ResizeMethod.BICUBIC) - # print(image_resized.shape) - # im = np.pad(image_resized,pad_width=((10,10),(10,10),(0,0)),mode='constant') - # image_padded = tf.image.pad_to_bounding_box(image_resized, 10,10,resize_image[0]+20,resize_image[1]+20) - # im = random_crop(im,[256, 128]) - # crop = tf.image.random_crop(image_padded,[resize_image[0],resize_image[1],3]) - # rd = np.random.rand() - # if rd < 0.5: - # flip = tf.image.flip_left_right(crop) - # else: - # flip = crop - im = tf.clip_by_value(image_resized/255.0,0,1.0) - # norm - im = norm_trans(im) - return im, label - - # 图片文件的列表 - filenames = tf.constant(imlist) - # label[i]就是图片filenames[i]的label - labels = tf.constant(label_) - - # 此时dataset中的一个元素是(filename, label) - dataset = tf.data.Dataset.from_tensor_slices((filenames, labels)) - - # 此时dataset中的一个元素是(image_resized, label) - dataset = dataset.map(_parse_function) - - # 此时dataset中的一个元素是(image_resized_batch, label_batch) - dataset = dataset.shuffle(buffer_size=1000).batch(batch_size).repeat(epoch_size) - - return dataset - - -import random -def random_crop(image, crop_shape): - img_h = image.shape[0] - img_w = image.shape[1] - # img_d = image.shape[2] - nh = random.randint(0, img_h - crop_shape[0]) - nw = random.randint(0, img_w - crop_shape[1]) - image_crop = image[nh:nh + crop_shape[0], nw:nw + crop_shape[1],:] - return image_crop - -# def loadMarketForTrain(P,K,data_dir='', rescale_im=[128, 64]): -# def process_im(im): -# im = skimage.transform.resize( -# im, rescale_im, -# mode='constant', preserve_range=True) -# # pad -# im = np.pad(im,pad_width=((10,10),(10,10),(0,0)),mode='constant') -# # random crop -# im = random_crop(im,rescale_im) -# # horizontalFlip -# im = np.fliplr(im) -# # to tensor -# im = np.clip(im/255.0, 0., 1.) -# # normalize -# mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225] -# for i in range(3): -# im[:,:,i] = (im[:,:,i]-mean[i])/std[i] -# return im -# -# train_dir = os.path.join(data_dir, 'bounding_box_train') -# train_data = MarketTriplet( -# n_class=None, -# data_dir=train_dir, -# batch_dict_name=['im', 'label'], -# shuffle=True, -# pf=process_im) -# train_data.setup(epoch_val=0, sample_n_class=P, sample_per_class=K) -# -# return train_data - -def feed_dict(data,train,x,y_,lr,lr_new): - """定义训练和测试操作""" - if train: - dict_ = data['train'].next_batch_dict() - xs = dict_['im'] - ys = dict_['label'] - else: - dict_ = data['test'].next_batch_dict() - xs = dict_['im'] - ys = dict_['label'] - return {x: xs, y_: ys,lr:lr_new} - -def get_next_batch(data): - dict_ = data.next_batch_dict() - xs = dict_['im'] - ys = dict_['label'] - return xs,ys - -if __name__ == '__main__': - valset = tf_val_dataset(batch_size=128,epoch_size=None) - testiterator = valset.make_one_shot_iterator() - testnext = testiterator.get_next() - \ No newline at end of file diff --git a/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/data/learning_rate_circle_loss.txt b/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/data/learning_rate_circle_loss.txt new file mode 100644 index 000000000..87d6bfbb4 --- /dev/null +++ b/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/data/learning_rate_circle_loss.txt @@ -0,0 +1,6 @@ +# Learning rate schedule +# Maps an epoch number to a learning rate +0: 0.1 +400: 0.01 +700: 0.001 +900: 0.0001 \ No newline at end of file diff --git a/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/data/pairs.txt b/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/data/pairs.txt new file mode 100644 index 000000000..0b3cb5482 --- /dev/null +++ b/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/data/pairs.txt @@ -0,0 +1,6001 @@ +10 300 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+Sergei_Alexandrovitch_Ordzhonikidze 1 Yolanda_King 1 +Shane_Loux 1 Val_Ackerman 1 +Shawn_Marion 1 Shirley_Jones 1 +Slobodan_Milosevic 2 Sok_An 1 diff --git a/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/demo.py b/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/demo.py deleted file mode 100644 index 714aeba9d..000000000 --- a/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/demo.py +++ /dev/null @@ -1,138 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -import scipy.io -import torch -import numpy as np -import os -from torchvision import datasets -import matplotlib -matplotlib.use('agg') -import matplotlib.pyplot as plt -####################################################################### -# Evaluate -parser = argparse.ArgumentParser(description='Demo') -parser.add_argument('--query_index', default=777, type=int, help='test_image_index') -parser.add_argument('--test_dir',default='/home/liulizhao/projects/liuyixin/Market-1501-v15.09.15/pytorch',type=str, help='./test_data') -opts = parser.parse_args() - -data_dir = opts.test_dir -image_datasets = {x: datasets.ImageFolder( os.path.join(data_dir,x) ) for x in ['gallery','query']} - -##################################################################### -#Show result -def imshow(path, title=None): - """Imshow for Tensor.""" - im = plt.imread(path) - plt.imshow(im) - if title is not None: - plt.title(title) - plt.pause(0.001) # pause a bit so that plots are updated - -###################################################################### -result = scipy.io.loadmat('/home/liulizhao/projects/liuyixin/projects/checkpoint/pytorch_result.mat') -query_feature = torch.FloatTensor(result['query_f']) -query_cam = result['query_cam'][0] -query_label = result['query_label'][0] -gallery_feature = torch.FloatTensor(result['gallery_f']) -gallery_cam = result['gallery_cam'][0] -gallery_label = result['gallery_label'][0] - -multi = os.path.isfile('/home/liulizhao/projects/liuyixin/projects/checkpoint/multi_query.mat') - -if multi: - m_result = scipy.io.loadmat('multi_query.mat') - mquery_feature = torch.FloatTensor(m_result['mquery_f']) - mquery_cam = m_result['mquery_cam'][0] - mquery_label = m_result['mquery_label'][0] - mquery_feature = mquery_feature.cuda() - -# query_feature = query_feature.cuda() -# gallery_feature = gallery_feature.cuda() - -####################################################################### -# sort the images -def sort_img(qf, ql, qc, gf, gl, gc): - query = qf.view(-1,1) - # print(query.shape) - score = torch.mm(gf,query) - score = score.squeeze(1).cpu() - score = score.numpy() - # predict index - index = np.argsort(score) #from small to large - index = index[::-1] - # index = index[0:2000] - # good index - query_index = np.argwhere(gl==ql) - #same camera - camera_index = np.argwhere(gc==qc) - - #good_index = np.setdiff1d(query_index, camera_index, assume_unique=True) - junk_index1 = np.argwhere(gl==-1) - junk_index2 = np.intersect1d(query_index, camera_index) - junk_index = np.append(junk_index2, junk_index1) - - mask = np.in1d(index, junk_index, invert=True) - index = index[mask] - return index - -i = opts.query_index -index = sort_img(query_feature[i],query_label[i],query_cam[i],gallery_feature,gallery_label,gallery_cam) - -######################################################################## -# Visualize the rank result - -query_path, _ = image_datasets['query'].imgs[i] -query_label = query_label[i] -print(query_path) -print('Top 10 images are as follow:') -try: # Visualize Ranking Result - # Graphical User Interface is needed - fig = plt.figure(figsize=(16,4)) - ax = plt.subplot(1,11,1) - ax.axis('off') - imshow(query_path,'query') - for i in range(10): - ax = plt.subplot(1,11,i+2) - ax.axis('off') - img_path, _ = image_datasets['gallery'].imgs[index[i]] - label = gallery_label[index[i]] - imshow(img_path) - if label == query_label: - ax.set_title('%d'%(i+1), color='green') - else: - ax.set_title('%d'%(i+1), color='red') - print(img_path) -except RuntimeError: - for i in range(10): - img_path = image_datasets.imgs[index[i]] - print(img_path[0]) - print('If you want to see the visualization of the ranking result, graphical user interface is needed.') - -fig.savefig("/home/liulizhao/projects/liuyixin/projects/show.png") diff --git a/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/eval.py b/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/eval.py deleted file mode 100644 index c2c69b14d..000000000 --- a/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/eval.py +++ /dev/null @@ -1,265 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# #!/usr/bin/python -# # -*- encoding: utf-8 -*- -# import sys -# import os.path as osp -# import logging -# import pickle -# from tqdm import tqdm -# import numpy as np -# from data import loadMarketForTest - -# FORMAT = '%(levelname)s %(filename)s(%(lineno)d): %(message)s' -# logging.basicConfig(level=logging.INFO, format=FORMAT, stream=sys.stdout) -# logger = logging.getLogger(__name__) - -# # import os -# # os.environ['CUDA_VISBLE_DEVICES'] = '3' - -# import tensorflow as tf - -# sess=tf.Session() -# #先加载图和参数变量 -# saver = tf.train.import_meta_graph('/home/liulizhao/projects/liuyixin/projects/logs/fc2_adjustlr/model.ckpt-20500.meta') -# saver.restore(sess, tf.train.latest_checkpoint('/home/liulizhao/projects/liuyixin/projects/logs/fc2_adjustlr/')) -# # 访问placeholders变量，并且创建feed-dict来作为placeholders的新值 -# graph = tf.get_default_graph() -# x = graph.get_tensor_by_name("inputs:0") -# y = graph.get_tensor_by_name("labels:0") -# features = graph.get_tensor_by_name("features:0") - -# # 访问placeholders变量，并且创建feed-dict来作为placeholders的新值 -# graph = tf.get_default_graph() - -# def embed(): -# q,t = loadMarketForTest(data_dir='/home/liulizhao/projects/liuyixin/Market-1501-v15.09.15') -# query_embds, query_pids, query_camids = q.batch_sample(10,2,all=False) -# gallery_embds, gallery_pids, gallery_camids = t.batch_sample(750,3,all=False) - -# f1 = {x:query_embds,y:query_pids} -# query_embds = sess.run(features,f1) -# f2 = {x:gallery_embds,y:gallery_pids} -# gallery_embds = sess.run(features,f2) - -# embd_res = (query_embds, query_pids, query_camids,gallery_embds, gallery_pids, gallery_camids) - -# with open('/home/liulizhao/projects/liuyixin/projects/checkpoint/embds.pkl', 'wb') as fw: -# pickle.dump(embd_res, fw) -# # logger.info('embedding done, dump to: /home/liulizhao/projects/liuyixin/projects/checkpoint/embds.pkl') -# return embd_res - - -# def evaluate(embd_res, cmc_max_rank = 1): -# query_embds, query_pids, query_camids, gallery_embds, gallery_pids, gallery_camids = embd_res - -# ## compute distance matrix -# logger.info('compute distance matrix') -# dist_mtx = np.matmul(query_embds, gallery_embds.T) -# dist_mtx = 1.0 / (dist_mtx + 1) -# n_q, n_g = dist_mtx.shape - -# logger.info('start evaluating ...') -# indices = np.argsort(dist_mtx, axis = 1) -# matches = gallery_pids[indices] == query_pids[:, np.newaxis] -# matches = matches.astype(np.int32) -# all_aps = [] -# all_cmcs = [] -# for query_idx in tqdm(range(n_q)): -# query_pid = query_pids[query_idx] -# query_camid = query_camids[query_idx] - -# ## exclude duplicated gallery pictures -# order = indices[query_idx] -# pid_diff = gallery_pids[order] != query_pid -# camid_diff = gallery_camids[order] != query_camid -# useful = gallery_pids[order] != -1 -# keep = np.logical_or(pid_diff, camid_diff) -# keep = np.logical_and(keep, useful) -# match = matches[query_idx][keep] - -# if not np.any(match): continue - -# ## compute cmc -# cmc = match.cumsum() -# cmc[cmc > 1] = 1 -# all_cmcs.append(cmc[:cmc_max_rank]) - -# ## compute map -# num_real = match.sum() -# match_cum = match.cumsum() -# match_cum = [el / (1.0 + i) for i, el in enumerate(match_cum)] -# match_cum = np.array(match_cum) * match -# ap = match_cum.sum() / num_real -# all_aps.append(ap) - -# assert len(all_aps) > 0, "NO QUERRY APPEARS IN THE GALLERY" -# mAP = sum(all_aps) / len(all_aps) -# all_cmcs = np.array(all_cmcs, dtype = np.float32) -# cmc = np.mean(all_cmcs, axis = 0) - -# return cmc, mAP - - -# if __name__ == '__main__': -# embd_res = embed() -# with open('/home/liulizhao/projects/liuyixin/projects/checkpoint/embds.pkl', 'rb') as fr: -# embd_res = pickle.load(fr) - -# cmc, mAP = evaluate(embd_res) -# print('cmc is: {}, map is: {}'.format(cmc, mAP)) - - -import scipy.io -import torch -import numpy as np -#import time -import os - - -import argparse -parser = argparse.ArgumentParser(description='test') -parser.add_argument('--filename',default='pytorch_result.mat', type=str) -parser.add_argument('--logdir',default='pytorch_result.mat', type=str) -opt = parser.parse_args() - - -####################################################################### -# Evaluate -def evaluate(qf,ql,qc,gf,gl,gc): - query = qf.view(-1,1) - # print(query.shape) - score = torch.mm(gf,query) - score = score.squeeze(1).cpu() - score = score.numpy() - # predict index - index = np.argsort(score) #from small to large - index = index[::-1] - # index = index[0:2000] - # good index - query_index = np.argwhere(gl==ql) - camera_index = np.argwhere(gc==qc) - - good_index = np.setdiff1d(query_index, camera_index, assume_unique=True) - junk_index1 = np.argwhere(gl==-1) - junk_index2 = np.intersect1d(query_index, camera_index) - junk_index = np.append(junk_index2, junk_index1) #.flatten()) - - CMC_tmp = compute_mAP(index, good_index, junk_index) - return CMC_tmp - - -def compute_mAP(index, good_index, junk_index): - ap = 0 - cmc = torch.IntTensor(len(index)).zero_() - if good_index.size==0: # if empty - cmc[0] = -1 - return ap,cmc - - # remove junk_index - mask = np.in1d(index, junk_index, invert=True) - index = index[mask] - - # find good_index index - ngood = len(good_index) - mask = np.in1d(index, good_index) - rows_good = np.argwhere(mask==True) - rows_good = rows_good.flatten() - - cmc[rows_good[0]:] = 1 - for i in range(ngood): - d_recall = 1.0/ngood - precision = (i+1)*1.0/(rows_good[i]+1) - if rows_good[i]!=0: - old_precision = i*1.0/rows_good[i] - else: - old_precision=1.0 - ap = ap + d_recall*(old_precision + precision)/2 - - return ap, cmc - -###################################################################### -result = scipy.io.loadmat(opt.filename) -query_feature = torch.FloatTensor(result['query_f']) -query_cam = result['query_cam'][0] -query_label = result['query_label'][0] -gallery_feature = torch.FloatTensor(result['gallery_f']) -gallery_cam = result['gallery_cam'][0] -gallery_label = result['gallery_label'][0] - -multi = os.path.isfile('multi_query.mat') - -if multi: - m_result = scipy.io.loadmat('multi_query.mat') - mquery_feature = torch.FloatTensor(m_result['mquery_f']) - mquery_cam = m_result['mquery_cam'][0] - mquery_label = m_result['mquery_label'][0] - mquery_feature = mquery_feature.cuda() - -# query_feature = query_feature.cuda() -# gallery_feature = gallery_feature.cuda() - -print(query_feature.shape) -CMC = torch.IntTensor(len(gallery_label)).zero_() -ap = 0.0 -#print(query_label) -for i in range(len(query_label)): - ap_tmp, CMC_tmp = evaluate(query_feature[i],query_label[i],query_cam[i],gallery_feature,gallery_label,gallery_cam) - if CMC_tmp[0]==-1: - continue - CMC = CMC + CMC_tmp - ap += ap_tmp - #print(i, CMC_tmp[0]) - -CMC = CMC.float() -CMC = CMC/len(query_label) #average CMC -result_path = opt.logdir+"result.txt" -result_str = 'Rank@1:%f Rank@5:%f Rank@10:%f mAP:%f'%(CMC[0],CMC[4],CMC[9],ap/len(query_label)) -print(result_str) -with open(result_path,'a') as f: #设置文件对象 - f.write(result_str) #将字符串写入文件中 - -# multiple-query -CMC = torch.IntTensor(len(gallery_label)).zero_() -ap = 0.0 -if multi: - for i in range(len(query_label)): - mquery_index1 = np.argwhere(mquery_label==query_label[i]) - mquery_index2 = np.argwhere(mquery_cam==query_cam[i]) - mquery_index = np.intersect1d(mquery_index1, mquery_index2) - mq = torch.mean(mquery_feature[mquery_index,:], dim=0) - ap_tmp, CMC_tmp = evaluate(mq,query_label[i],query_cam[i],gallery_feature,gallery_label,gallery_cam) - if CMC_tmp[0]==-1: - continue - CMC = CMC + CMC_tmp - ap += ap_tmp - #print(i, CMC_tmp[0]) - CMC = CMC.float() - CMC = CMC/len(query_label) #average CMC - print('multi Rank@1:%f Rank@5:%f Rank@10:%f mAP:%f'%(CMC[0],CMC[4],CMC[9],ap/len(query_label))) diff --git a/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/facenet.py b/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/facenet.py new file mode 100644 index 000000000..335eec2ee --- /dev/null +++ b/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/facenet.py @@ -0,0 +1,598 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from npu_bridge.npu_init import * + +import os +from subprocess import Popen, PIPE +import numpy as np +import tensorflow.compat.v1 as tf +tf.disable_eager_execution() +from scipy import misc +from sklearn.model_selection import KFold +from scipy import interpolate +import tensorflow.compat.v1.train as training +import random +import re +from tensorflow.compat.v1 import gfile +import math +from six import iteritems +import imageio +from tqdm import tqdm +from PIL import Image + + +def triplet_loss(anchor, positive, negative, alpha): + with tf.variable_scope('triplet_loss'): + pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), 1) + neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), 1) + + basic_loss = tf.add(tf.subtract(pos_dist, neg_dist), alpha) + loss = tf.reduce_mean(tf.maximum(basic_loss, 0.0), 0) + + return loss + + +def center_loss(features, label, alfa, nrof_classes): + + nrof_features = features.get_shape()[1] + centers = tf.get_variable('centers', [nrof_classes, nrof_features], dtype=tf.float32, + initializer=tf.constant_initializer(0), trainable=False) + label = tf.reshape(label, [-1]) + centers_batch = tf.gather(centers, label) + diff = (1 - alfa) * (centers_batch - features) + centers = tf.scatter_sub(centers, label, diff) + with tf.control_dependencies([centers]): + loss = tf.reduce_mean(tf.square(features - centers_batch)) + return loss, centers + + +def get_image_paths_and_labels(dataset): + image_paths_flat = [] + labels_flat = [] + for i in range(len(dataset)): + image_paths_flat += dataset[i].image_paths + labels_flat += [i] * len(dataset[i].image_paths) + return image_paths_flat, labels_flat + + +def shuffle_examples(image_paths, labels): + shuffle_list = list(zip(image_paths, labels)) + random.shuffle(shuffle_list) + image_paths_shuff, labels_shuff = zip(*shuffle_list) + return image_paths_shuff, labels_shuff + + +def random_rotate_image(image): + angle = np.random.uniform(low=-10.0, high=10.0) + return misc.imrotate(image, angle, 'bicubic') + +RANDOM_ROTATE = 1 +RANDOM_CROP = 2 +RANDOM_FLIP = 4 +FIXED_STANDARDIZATION = 8 +FLIP = 16 + +def create_input_pipeline(input_queue, image_size, nrof_preprocess_threads, batch_size_placeholder): + images_and_labels_list = [] + for _ in range(nrof_preprocess_threads): + filenames, label, control = input_queue.dequeue() + images = [] + for filename in tf.unstack(filenames): + file_contents = tf.read_file(filename) + image = tf.image.decode_image(file_contents, 3) + # image = tf.cast(image, tf.float32) + # image = tf.cond(get_control_flag(control[0], RANDOM_ROTATE), + # lambda: tf.py_func(random_rotate_image, [image], tf.uint8), + # lambda: tf.identity(image)) + image = tf.cond(get_control_flag(control[0], RANDOM_CROP), + lambda: tf.random_crop(image, image_size + (3,)), + lambda: tf.image.resize_image_with_crop_or_pad(image, image_size[0], image_size[1])) + image = tf.cond(get_control_flag(control[0], RANDOM_FLIP), + lambda: tf.image.random_flip_left_right(image), + lambda: tf.identity(image)) + image = tf.cond(get_control_flag(control[0], FIXED_STANDARDIZATION), + lambda: (tf.cast(image, tf.float32) - 127.5) / 128.0, + lambda: tf.cast(tf.image.per_image_standardization(image),tf.float32)) + image = tf.cond(get_control_flag(control[0], FLIP), + lambda: tf.image.flip_left_right(image), + lambda: tf.identity(image)) + # pylint: disable=no-member + image.set_shape(image_size + (3,)) + images.append(image) + images_and_labels_list.append([images, label]) + + image_batch, label_batch = tf.train.batch_join( + images_and_labels_list, batch_size=batch_size_placeholder, + shapes=[image_size + (3,), ()], enqueue_many=True, + capacity=4 * nrof_preprocess_threads * 100, + allow_smaller_final_batch=True) + + print("image_batch:",image_batch) + print("label_batch:",label_batch) + + return image_batch, label_batch + +def get_control_flag(control, field): + return tf.equal(tf.mod(tf.floor_div(control, field), 2), 1) + +def _add_loss_summaries(total_loss): + + loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg') + losses = tf.get_collection('losses') + loss_averages_op = loss_averages.apply(losses + [total_loss]) + + for l in losses + [total_loss]: + tf.summary.scalar(l.op.name + ' (raw)', l) + tf.summary.scalar(l.op.name, loss_averages.average(l)) + + return loss_averages_op + +def train(total_loss, global_step, optimizer, learning_rate, moving_average_decay, update_gradient_vars, + log_histograms=True): + + loss_averages_op = _add_loss_summaries(total_loss) + + with tf.control_dependencies([loss_averages_op]): + if optimizer == 'ADAGRAD': + opt = tf.train.AdagradOptimizer(learning_rate) + elif optimizer == 'ADADELTA': + opt = tf.train.AdadeltaOptimizer(learning_rate, rho=0.9, epsilon=1e-6) + elif optimizer == 'ADAM': + opt = tf.train.AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999, epsilon=0.1) + elif optimizer == 'RMSPROP': + opt = tf.train.RMSPropOptimizer(learning_rate, decay=0.9, momentum=0.9, epsilon=1.0) + elif optimizer == 'MOM': + opt = tf.train.MomentumOptimizer(learning_rate, 0.9, use_nesterov=True) + elif optimizer == "GD": + opt = tf.train.GradientDescentOptimizer(learning_rate) + else: + raise ValueError('Invalid optimization algorithm') + + grads = opt.compute_gradients(total_loss, update_gradient_vars) + + apply_gradient_op = opt.apply_gradients(grads, global_step=global_step) + + if log_histograms: + for var in tf.trainable_variables(): + tf.summary.histogram(var.op.name, var) + + if log_histograms: + for grad, var in grads: + if grad is not None: + tf.summary.histogram(var.op.name + '/gradients', grad) + + variable_averages = tf.train.ExponentialMovingAverage( + moving_average_decay, global_step) + variables_averages_op = variable_averages.apply(tf.trainable_variables()) + + with tf.control_dependencies([apply_gradient_op, variables_averages_op]): + train_op = tf.no_op(name='train') + + return train_op + + +def prewhiten(x): + mean = np.mean(x) + std = np.std(x) + std_adj = np.maximum(std, 1.0 / np.sqrt(x.size)) + y = np.multiply(np.subtract(x, mean), 1 / std_adj) + return y + + +def crop(image, random_crop, image_size): + if image.shape[1] > image_size: + sz1 = int(image.shape[1] // 2) + sz2 = int(image_size // 2) + if random_crop: + diff = sz1 - sz2 + (h, v) = (np.random.randint(-diff, diff + 1), np.random.randint(-diff, diff + 1)) + else: + (h, v) = (0, 0) + image = image[(sz1 - sz2 + v):(sz1 + sz2 + v), (sz1 - sz2 + h):(sz1 + sz2 + h), :] + return image + + +def flip(image, random_flip): + if random_flip and np.random.choice([True, False]): + image = np.fliplr(image) + return image + + +def to_rgb(img): + w, h = img.shape + ret = np.empty((w, h, 3), dtype=np.uint8) + ret[:, :, 0] = ret[:, :, 1] = ret[:, :, 2] = img + return ret + +def load_data(image_paths, do_random_crop=False, do_random_flip=True, image_size=112, do_prewhiten=False): + nrof_samples = len(image_paths) + images = np.zeros((nrof_samples, image_size, image_size, 3)) + # (tf.cast(image, tf.float32) - 127.5) / 128.0, + for i in range(nrof_samples): + print("image_path:",image_paths[i]) + img = Image.open(image_paths[i]) + if img.ndim == 2: + img = to_rgb(img) + if do_prewhiten: + img = prewhiten(img) + if do_random_crop: + img = crop(img, do_random_crop, image_size) + if do_random_flip: + img = flip(img, do_random_flip) + + img=(img-127.5)/128.0 + images[i, :, :, :] = img + return images + + +def get_label_batch(label_data, batch_size, batch_index): + nrof_examples = np.size(label_data, 0) + j = batch_index * batch_size % nrof_examples + if j + batch_size <= nrof_examples: + batch = label_data[j:j + batch_size] + else: + x1 = label_data[j:nrof_examples] + x2 = label_data[0:nrof_examples - j] + batch = np.vstack([x1, x2]) + batch_int = batch.astype(np.int64) + return batch_int + + +def get_batch(image_data, batch_size, batch_index): + nrof_examples = np.size(image_data, 0) + j = batch_index * batch_size % nrof_examples + if j + batch_size <= nrof_examples: + batch = image_data[j:j + batch_size, :, :, :] + else: + x1 = image_data[j:nrof_examples, :, :, :] + x2 = image_data[0:nrof_examples - j, :, :, :] + batch = np.vstack([x1, x2]) + batch_float = batch.astype(np.float32) + return batch_float + + +def get_triplet_batch(triplets, batch_index, batch_size): + ax, px, nx = triplets + a = get_batch(ax, int(batch_size / 3), batch_index) + p = get_batch(px, int(batch_size / 3), batch_index) + n = get_batch(nx, int(batch_size / 3), batch_index) + batch = np.vstack([a, p, n]) + return batch + + +def get_learning_rate_from_file(filename, epoch): + with open(filename, 'r') as f: + for line in f.readlines(): + line = line.split('#', 1)[0] + if line: + par = line.strip().split(':') + e = int(par[0]) + if par[1] == '-': + lr = -1 + else: + lr = float(par[1]) + if e <= epoch: + learning_rate = lr + else: + return learning_rate + + +class ImageClass(): + def __init__(self, name, image_paths): + + self.name = name + + self.image_paths = image_paths + + def __str__(self): + + return self.name + ', ' + str(len(self.image_paths)) + ' images' + + def __len__(self): + + return len(self.image_paths) + + +def get_dataset(path, has_class_directories=True): + print("Get dataset.........") + dataset = [] + + path_exp = os.path.expanduser(path) + + classes = [path for path in tqdm(os.listdir(path_exp)) \ + if os.path.isdir(os.path.join(path_exp, path))] + + classes.sort() + + nrof_classes = len(classes) + + for i in tqdm(range(nrof_classes)): + + class_name = classes[i] + + facedir = os.path.join(path_exp, class_name) + + image_paths = get_image_paths(facedir) + + dataset.append(ImageClass(class_name, image_paths)) + return dataset + + +def get_image_paths(facedir): + image_paths = [] + if os.path.isdir(facedir): + images = os.listdir(facedir) + image_paths = [os.path.join(facedir, img) for img in images] + return image_paths + + +def split_dataset(dataset, split_ratio, min_nrof_images_per_class, mode): + if mode == 'SPLIT_CLASSES': + nrof_classes = len(dataset) + class_indices = np.arange(nrof_classes) + np.random.shuffle(class_indices) + split = int(round(nrof_classes * (1 - split_ratio))) + train_set = [dataset[i] for i in class_indices[0:split]] + test_set = [dataset[i] for i in class_indices[split:-1]] + elif mode == 'SPLIT_IMAGES': + train_set = [] + test_set = [] + for cls in dataset: + paths = cls.image_paths + np.random.shuffle(paths) + nrof_images_in_class = len(paths) + split = int(math.floor(nrof_images_in_class * (1 - split_ratio))) + if split == nrof_images_in_class: + split = nrof_images_in_class - 1 + if split >= min_nrof_images_per_class and nrof_images_in_class - split >= 1: + train_set.append(ImageClass(cls.name, paths[:split])) + test_set.append(ImageClass(cls.name, paths[split:])) + else: + raise ValueError('Invalid train/test split mode "%s"' % mode) + return train_set, test_set + + +def load_model(model, input_map=None): + + model_exp = os.path.expanduser(model) + if (os.path.isfile(model_exp)): + print('Model filename: %s' % model_exp) + with gfile.FastGFile(model_exp, 'rb') as f: + graph_def = tf.GraphDef() + graph_def.ParseFromString(f.read()) + tf.import_graph_def(graph_def, input_map=input_map, name='') + else: + print('Model directory: %s' % model_exp) + meta_file, ckpt_file = get_model_filenames(model_exp) + + print('Metagraph file: %s' % meta_file) + print('Checkpoint file: %s' % ckpt_file) + + saver = tf.train.import_meta_graph(os.path.join(model_exp, meta_file), input_map=input_map) + saver.restore(tf.get_default_session(), os.path.join(model_exp, ckpt_file)) + + +def get_model_filenames(model_dir): + files = os.listdir(model_dir) + meta_files = [s for s in files if s.endswith('.meta')] + if len(meta_files) == 0: + raise ValueError('No meta file found in the model directory (%s)' % model_dir) + elif len(meta_files) > 1: + raise ValueError('There should not be more than one meta file in the model directory (%s)' % model_dir) + meta_file = meta_files[0] + ckpt = tf.train.get_checkpoint_state(model_dir) + if ckpt and ckpt.model_checkpoint_path: + ckpt_file = os.path.basename(ckpt.model_checkpoint_path) + return meta_file, ckpt_file + + meta_files = [s for s in files if '.ckpt' in s] + max_step = -1 + for f in files: + step_str = re.match(r'(^model-[\w\- ]+.ckpt-(\d+))', f) + if step_str is not None and len(step_str.groups()) >= 2: + step = int(step_str.groups()[1]) + if step > max_step: + max_step = step + ckpt_file = step_str.groups()[0] + return meta_file, ckpt_file + + +def distance(embeddings1, embeddings2, distance_metric=0): + if distance_metric == 0: + + diff = np.subtract(embeddings1, embeddings2) + dist = np.sum(np.square(diff), 1) + elif distance_metric == 1: + + dot = np.sum(np.multiply(embeddings1, embeddings2), axis=1) + norm = np.linalg.norm(embeddings1, axis=1) * np.linalg.norm(embeddings2, axis=1) + similarity = dot / norm + dist = np.arccos(similarity) / math.pi + else: + raise 'Undefined distance metric %d' % distance_metric + + return dist + + +def calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, nrof_folds=10, distance_metric=0, + subtract_mean=False): + assert (embeddings1.shape[0] == embeddings2.shape[0]) + assert (embeddings1.shape[1] == embeddings2.shape[1]) + nrof_pairs = min(len(actual_issame), embeddings1.shape[0]) + nrof_thresholds = len(thresholds) + k_fold = KFold(n_splits=nrof_folds, shuffle=False) + + tprs = np.zeros((nrof_folds, nrof_thresholds)) + fprs = np.zeros((nrof_folds, nrof_thresholds)) + accuracy = np.zeros((nrof_folds)) + + indices = np.arange(nrof_pairs) + + for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): + if subtract_mean: + mean = np.mean(np.concatenate([embeddings1[train_set], embeddings2[train_set]]), axis=0) + else: + mean = 0.0 + dist = distance(embeddings1 - mean, embeddings2 - mean, distance_metric) + + acc_train = np.zeros((nrof_thresholds)) + for threshold_idx, threshold in enumerate(thresholds): + _, _, acc_train[threshold_idx] = calculate_accuracy(threshold, dist[train_set], actual_issame[train_set]) + best_threshold_index = np.argmax(acc_train) + for threshold_idx, threshold in enumerate(thresholds): + tprs[fold_idx, threshold_idx], fprs[fold_idx, threshold_idx], _ = calculate_accuracy(threshold, + dist[test_set], + actual_issame[ + test_set]) + _, _, accuracy[fold_idx] = calculate_accuracy(thresholds[best_threshold_index], dist[test_set], + actual_issame[test_set]) + + tpr = np.mean(tprs, 0) + fpr = np.mean(fprs, 0) + return tpr, fpr, accuracy + + +def calculate_accuracy(threshold, dist, actual_issame): + predict_issame = np.less(dist, threshold) + tp = np.sum(np.logical_and(predict_issame, actual_issame)) + fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame))) + tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame))) + fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame)) + + tpr = 0 if (tp + fn == 0) else float(tp) / float(tp + fn) + fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn) + acc = float(tp + tn) / dist.size + return tpr, fpr, acc + + +def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10, distance_metric=0, + subtract_mean=False): + assert (embeddings1.shape[0] == embeddings2.shape[0]) + assert (embeddings1.shape[1] == embeddings2.shape[1]) + nrof_pairs = min(len(actual_issame), embeddings1.shape[0]) + nrof_thresholds = len(thresholds) + k_fold = KFold(n_splits=nrof_folds, shuffle=False) + + val = np.zeros(nrof_folds) + far = np.zeros(nrof_folds) + + indices = np.arange(nrof_pairs) + + for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): + if subtract_mean: + mean = np.mean(np.concatenate([embeddings1[train_set], embeddings2[train_set]]), axis=0) + else: + mean = 0.0 + dist = distance(embeddings1 - mean, embeddings2 - mean, distance_metric) + + far_train = np.zeros(nrof_thresholds) + for threshold_idx, threshold in enumerate(thresholds): + _, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set]) + if np.max(far_train) >= far_target: + f = interpolate.interp1d(far_train, thresholds, kind='slinear') + threshold = f(far_target) + else: + threshold = 0.0 + + val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set]) + + val_mean = np.mean(val) + far_mean = np.mean(far) + val_std = np.std(val) + return val_mean, val_std, far_mean + + +def calculate_val_far(threshold, dist, actual_issame): + predict_issame = np.less(dist, threshold) + true_accept = np.sum(np.logical_and(predict_issame, actual_issame)) + false_accept = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame))) + n_same = np.sum(actual_issame) + n_diff = np.sum(np.logical_not(actual_issame)) + val = float(true_accept) / float(n_same) + far = float(false_accept) / float(n_diff) + return val, far + + +def store_revision_info(src_path, output_dir, arg_string): + try: + cmd = ['git', 'rev-parse', 'HEAD'] + gitproc = Popen(cmd, stdout=PIPE, cwd=src_path) + (stdout, _) = gitproc.communicate() + git_hash = stdout.strip() + except OSError as e: + git_hash = ' '.join(cmd) + ': ' + e.strerror + + try: + cmd = ['git', 'diff', 'HEAD'] + gitproc = Popen(cmd, stdout=PIPE, cwd=src_path) + (stdout, _) = gitproc.communicate() + git_diff = stdout.strip() + except OSError as e: + git_diff = ' '.join(cmd) + ': ' + e.strerror + + rev_info_filename = os.path.join(output_dir, 'revision_info.txt') + with open(rev_info_filename, "w") as text_file: + text_file.write('arguments: %s\n--------------------\n' % arg_string) + text_file.write('tensorflow version: %s\n--------------------\n' % tf.__version__) + text_file.write('git hash: %s\n--------------------\n' % git_hash) + text_file.write('%s' % git_diff) + + +def list_variables(filename): + reader = training.NewCheckpointReader(filename) + variable_map = reader.get_variable_to_shape_map() + names = sorted(variable_map.keys()) + return names + + +def put_images_on_grid(images, shape=(16, 8)): + nrof_images = images.shape[0] + img_size = images.shape[1] + bw = 3 + img = np.zeros((shape[1] * (img_size + bw) + bw, shape[0] * (img_size + bw) + bw, 3), np.float32) + for i in range(shape[1]): + x_start = i * (img_size + bw) + bw + for j in range(shape[0]): + img_index = i * shape[0] + j + if img_index >= nrof_images: + break + y_start = j * (img_size + bw) + bw + img[x_start:x_start + img_size, y_start:y_start + img_size, :] = images[img_index, :, :, :] + if img_index >= nrof_images: + break + return img + + +def write_arguments_to_file(args, filename): + with open(filename, 'w') as f: + for key, value in iteritems(vars(args)): + f.write('%s: %s\n' % (key, str(value))) + diff --git a/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/config.py b/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/lfw.py similarity index 33% rename from TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/config.py rename to TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/lfw.py index 8203193a3..20d57f00f 100644 --- a/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/config.py +++ b/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/lfw.py @@ -25,46 +25,68 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from npu_bridge.npu_init import * -import argparse -arg = argparse.ArgumentParser() -import time -timestamp = int(time.time()) -arg.add_argument('-e','--expriment_name',type=str,default=f'test_{timestamp}') -arg.add_argument('-g','--gpu',type=str,default='0,1') -arg.add_argument('-w','--warmup',type=int,default=5) -arg.add_argument('-b','--batch_size',type=int,default=32) -arg.add_argument('-epoch','--epoch_size',type=int,default=120) -arg.add_argument('-modelname','--modelname',type=str,default='softmaxcircleloss') -arg.add_argument('-lr','--init_lr',type=float,default=0.02) -arg.add_argument('-lamda','--lamda',type=float,default=1) -arg.add_argument('-drop','--droprate',type=float,default=0.5) +import os +import numpy as np +import facenet -# for huawei npu -arg.add_argument("--train_url", type=str, default="./output") -arg.add_argument("--data_url", type=str, default="./dataset") -arg.add_argument("--ckpt_url", type=str, default="./ckpt") -args = arg.parse_args() -# args.expriment_name = args.expriment_name+"_"+str(timestamp) -homedir = "/home/nanshen/xutan/yixin/circlesoftmax" -log_dir = f'{homedir}/logs/{args.expriment_name}/' -im_size = [256, 128] -market_dir = 'Data/Market-1501-v15.09.15' -# P=4 -warmup = args.warmup -# K=4 -m=0.25 -gamma=32 -lamda = args.lamda -num_classes=751 -epoch_size = args.epoch_size -batch_size = args.batch_size -embedding_size=128 -resnet_model_path = '/home/nanshen/xutan/yixin/resnet_v1_50.ckpt' # Path to the pretrained model -model_save_path = log_dir + args.modelname # Path to the model.ckpt-(num_steps) will be saved -num_bottleneck = 512 -import os -os.environ["CUDA_VISIBLE_DEVICES"]=args.gpu -init_lr = args.init_lr +def evaluate(embeddings, actual_issame, nrof_folds=10, distance_metric=0, subtract_mean=False): + thresholds = np.arange(0, 4, 0.01) + embeddings1 = embeddings[0::2] + embeddings2 = embeddings[1::2] + tpr, fpr, accuracy = facenet.calculate_roc(thresholds, embeddings1, embeddings2, + np.asarray(actual_issame), nrof_folds=nrof_folds, + distance_metric=distance_metric, subtract_mean=subtract_mean) + thresholds = np.arange(0, 4, 0.001) + val, val_std, far = facenet.calculate_val(thresholds, embeddings1, embeddings2, + np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds, + distance_metric=distance_metric, subtract_mean=subtract_mean) + return tpr, fpr, accuracy, val, val_std, far + + +def get_paths(lfw_dir, pairs): + nrof_skipped_pairs = 0 + path_list = [] + issame_list = [] + for pair in pairs: + if len(pair) == 3: + path0 = add_extension(os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1]))) + path1 = add_extension(os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[2]))) + issame = True + elif len(pair) == 4: + path0 = add_extension(os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1]))) + path1 = add_extension(os.path.join(lfw_dir, pair[2], pair[2] + '_' + '%04d' % int(pair[3]))) + issame = False + if os.path.exists(path0) and os.path.exists(path1): # Only add the pair if both paths exist + path_list += (path0, path1) + issame_list.append(issame) + else: + nrof_skipped_pairs += 1 + if nrof_skipped_pairs > 0: + print('Skipped %d image pairs' % nrof_skipped_pairs) + + return path_list, issame_list + + +def add_extension(path): + if os.path.exists(path + '.jpg'): + return path + '.jpg' + elif os.path.exists(path + '.png'): + return path + '.png' + else: + raise RuntimeError('No file "%s" with extension png or jpg.' % path) + + +def read_pairs(pairs_filename): + pairs = [] + with open(pairs_filename, 'r') as f: + for line in f.readlines()[1:]: + pair = line.strip().split() + pairs.append(pair) + return np.array(pairs) diff --git a/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/loss.png b/TensorFlow/contrib/cv/CircleLoss_ID1221_for_TensorFlow/loss.png new file mode 100644 index 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