From ed0360eac2059c490408ad578051a325c508408c Mon Sep 17 00:00:00 2001 From: dengkaipeng Date: Wed, 1 Apr 2020 07:57:44 +0000 Subject: [PATCH] add image and dataset --- tsm/README.md | 4 +- tsm/dataset/README.md | 78 ++++++++++++++++++++++ tsm/dataset/kinetics/generate_label.py | 44 +++++++++++++ tsm/dataset/kinetics/video2pkl.py | 87 +++++++++++++++++++++++++ tsm/images/temporal_shift.png | Bin 0 -> 170430 bytes 5 files changed, 211 insertions(+), 2 deletions(-) create mode 100644 tsm/dataset/README.md create mode 100644 tsm/dataset/kinetics/generate_label.py create mode 100644 tsm/dataset/kinetics/video2pkl.py create mode 100644 tsm/images/temporal_shift.png diff --git a/tsm/README.md b/tsm/README.md index 68e7c37..bb5d148 100644 --- a/tsm/README.md +++ b/tsm/README.md @@ -14,7 +14,7 @@ Temporal Shift Module是由MIT和IBM Watson AI Lab的Ji Lin,Chuang Gan和Song Han等人提出的通过时间位移来提高网络视频理解能力的模块,其位移操作原理如下图所示。

-
+
Temporal shift module

@@ -44,7 +44,7 @@ TSM模型是将Temporal Shift Module插入到ResNet网络中构建的视频分 ### 数据准备 -TSM的训练数据采用由DeepMind公布的Kinetics-400动作识别数据集。数据下载及准备请参考[数据说明](https://github.com/PaddlePaddle/models/blob/release/1.7/PaddleCV/video/data/dataset/README.md) +TSM的训练数据采用由DeepMind公布的Kinetics-400动作识别数据集。数据下载及准备请参考[数据说明](./dataset/README.md) #### 小数据集验证 diff --git a/tsm/dataset/README.md b/tsm/dataset/README.md new file mode 100644 index 0000000..55613ef --- /dev/null +++ b/tsm/dataset/README.md @@ -0,0 +1,78 @@ +# 数据使用说明 + +## Kinetics数据集 + +Kinetics数据集是DeepMind公开的大规模视频动作识别数据集,有Kinetics400与Kinetics600两个版本。这里使用Kinetics400数据集,具体的数据预处理过程如下。 + +### mp4视频下载 +在Code\_Root目录下创建文件夹 + + cd $Code_Root/data/dataset && mkdir kinetics + + cd kinetics && mkdir data_k400 && cd data_k400 + + mkdir train_mp4 && mkdir val_mp4 + +ActivityNet官方提供了Kinetics的下载工具,具体参考其[官方repo ](https://github.com/activitynet/ActivityNet/tree/master/Crawler/Kinetics)即可下载Kinetics400的mp4视频集合。将kinetics400的训练与验证集合分别下载到data/dataset/kinetics/data\_k400/train\_mp4与data/dataset/kinetics/data\_k400/val\_mp4。 + +### mp4文件预处理 + +为提高数据读取速度,提前将mp4文件解帧并打pickle包,dataloader从视频的pkl文件中读取数据(该方法耗费更多存储空间)。pkl文件里打包的内容为(video-id, label, [frame1, frame2,...,frameN])。 + +在 data/dataset/kinetics/data\_k400目录下创建目录train\_pkl和val\_pkl + + cd $Code_Root/data/dataset/kinetics/data_k400 + + mkdir train_pkl && mkdir val_pkl + +进入$Code\_Root/data/dataset/kinetics目录,使用video2pkl.py脚本进行数据转化。首先需要下载[train](https://github.com/activitynet/ActivityNet/tree/master/Crawler/Kinetics/data/kinetics-400_train.csv)和[validation](https://github.com/activitynet/ActivityNet/tree/master/Crawler/Kinetics/data/kinetics-400_val.csv)数据集的文件列表。 + +首先生成预处理需要的数据集标签文件 + + python generate_label.py kinetics-400_train.csv kinetics400_label.txt + +然后执行如下程序: + + python video2pkl.py kinetics-400_train.csv $Source_dir $Target_dir 8 #以8个进程为例 + +- 该脚本依赖`ffmpeg`库,请预先安装`ffmpeg` + +对于train数据, + + Source_dir = $Code_Root/data/dataset/kinetics/data_k400/train_mp4 + + Target_dir = $Code_Root/data/dataset/kinetics/data_k400/train_pkl + +对于val数据, + + Source_dir = $Code_Root/data/dataset/kinetics/data_k400/val_mp4 + + Target_dir = $Code_Root/data/dataset/kinetics/data_k400/val_pkl + +这样即可将mp4文件解码并保存为pkl文件。 + +### 生成训练和验证集list +·· + cd $Code_Root/data/dataset/kinetics + + ls $Code_Root/data/dataset/kinetics/data_k400/train_pkl/* > train.list + + ls $Code_Root/data/dataset/kinetics/data_k400/val_pkl/* > val.list + + ls $Code_Root/data/dataset/kinetics/data_k400/val_pkl/* > test.list + + ls $Code_Root/data/dataset/kinetics/data_k400/val_pkl/* > infer.list + +即可生成相应的文件列表,train.list和val.list的每一行表示一个pkl文件的绝对路径,示例如下: + + /ssd1/user/models/PaddleCV/PaddleVideo/data/dataset/kinetics/data_k400/train_pkl/data_batch_100-097 + /ssd1/user/models/PaddleCV/PaddleVideo/data/dataset/kinetics/data_k400/train_pkl/data_batch_100-114 + /ssd1/user/models/PaddleCV/PaddleVideo/data/dataset/kinetics/data_k400/train_pkl/data_batch_100-118 + ... + +或者 + + /ssd1/user/models/PaddleCV/PaddleVideo/data/dataset/kinetics/data_k400/val_pkl/data_batch_102-085 + /ssd1/user/models/PaddleCV/PaddleVideo/data/dataset/kinetics/data_k400/val_pkl/data_batch_102-086 + /ssd1/user/models/PaddleCV/PaddleVideo/data/dataset/kinetics/data_k400/val_pkl/data_batch_102-090 + ... diff --git a/tsm/dataset/kinetics/generate_label.py b/tsm/dataset/kinetics/generate_label.py new file mode 100644 index 0000000..d7608e8 --- /dev/null +++ b/tsm/dataset/kinetics/generate_label.py @@ -0,0 +1,44 @@ +# Copyright (c) 2020 PaddlePaddle 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. +import sys + +# kinetics-400_train.csv should be down loaded first and set as sys.argv[1] +# sys.argv[2] can be set as kinetics400_label.txt +# python generate_label.py kinetics-400_train.csv kinetics400_label.txt + +num_classes = 400 + +fname = sys.argv[1] +outname = sys.argv[2] +fl = open(fname).readlines() +fl = fl[1:] +outf = open(outname, 'w') + +label_list = [] +for line in fl: + label = line.strip().split(',')[0].strip('"') + if label in label_list: + continue + else: + label_list.append(label) + +assert len(label_list + ) == num_classes, "there should be {} labels in list, but ".format( + num_classes, len(label_list)) + +label_list.sort() +for i in range(num_classes): + outf.write('{} {}'.format(label_list[i], i) + '\n') + +outf.close() diff --git a/tsm/dataset/kinetics/video2pkl.py b/tsm/dataset/kinetics/video2pkl.py new file mode 100644 index 0000000..78d1b09 --- /dev/null +++ b/tsm/dataset/kinetics/video2pkl.py @@ -0,0 +1,87 @@ +# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# +#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 +import sys +import glob +try: + import cPickle as pickle +except: + import pickle +from multiprocessing import Pool + +# example command line: python generate_k400_pkl.py kinetics-400_train.csv 8 +# +# kinetics-400_train.csv is the training set file of K400 official release +# each line contains laebl,youtube_id,time_start,time_end,split,is_cc + +assert (len(sys.argv) == 5) + +f = open(sys.argv[1]) +source_dir = sys.argv[2] +target_dir = sys.argv[3] +num_threads = sys.argv[4] +all_video_entries = [x.strip().split(',') for x in f.readlines()] +all_video_entries = all_video_entries[1:] +f.close() + +category_label_map = {} +f = open('kinetics400_label.txt') +for line in f: + ens = line.strip().split(' ') + category = " ".join(ens[0:-1]) + label = int(ens[-1]) + category_label_map[category] = label +f.close() + + +def generate_pkl(entry): + mode = entry[4] + category = entry[0].strip('"') + category_dir = category + video_path = os.path.join( + './', + entry[1] + "_%06d" % int(entry[2]) + "_%06d" % int(entry[3]) + ".mp4") + video_path = os.path.join(source_dir, category_dir, video_path) + label = category_label_map[category] + + vid = './' + video_path.split('/')[-1].split('.')[0] + if os.path.exists(video_path): + if not os.path.exists(vid): + os.makedirs(vid) + os.system('ffmpeg -i ' + video_path + ' -q 0 ' + vid + '/%06d.jpg') + else: + print("File not exists {}".format(video_path)) + return + + images = sorted(glob.glob(vid + '/*.jpg')) + ims = [] + for img in images: + f = open(img, 'rb') + ims.append(f.read()) + f.close() + + output_pkl = vid + ".pkl" + output_pkl = os.path.join(target_dir, output_pkl) + f = open(output_pkl, 'wb') + pickle.dump((vid, label, ims), f, protocol=2) + f.close() + + os.system('rm -rf %s' % vid) + + +pool = Pool(processes=int(sys.argv[4])) +pool.map(generate_pkl, all_video_entries) +pool.close() +pool.join() diff --git a/tsm/images/temporal_shift.png b/tsm/images/temporal_shift.png new file mode 100644 index 0000000000000000000000000000000000000000..7679c4459d2b0ee37134b99fe1e8177b1a69f8b0 GIT binary patch literal 170430 zcmb4q1yq#n+AfVWNH@waKzOi*fPkd>01f^L-PemY z1O#kS8yOkZ7cw$*sxFR}Hue?>2nt~d+UPp!{UjOsY7btp1U^u}XrX`JIPv|dwCMDM z7jIP`GocWRRpu}0sScEG$%ZLc}Ni$D3opfkP?8Ik6)fk`C@{t+;Su=*2qXSb2 z`hj04zy)X}UHt6B^y8f-Ve&g7J^(*cNE}c2YD3>UDmwi2Rj0M<%)x^;Mj?4#-Vg%& 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