Tensorflow-T3D

Tensorflow implementation for 'Temporal 3D ConvNets'(t3d)

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Author	yfxc
E-mail	1512165940@qq.com
Tensorflow	1.10+（DO NOT SUPPORT 2.0）

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Introduction

'T3D' model can be applied to action recognition.Paper Url :https://arxiv.org/abs/1711.08200
Here is the tensorflow implementation with tf.slim to make code leaner. Different from the authors' original implementation.I convert traditional 3D convolution(eg. tf.nn.conv3d ) to P3D('pseudo-3d') convolution which can greatly reduce the number of parameters.
(P3D details:http://openaccess.thecvf.com/content_ICCV_2017/papers/Qiu_Learning_Spatio-Temporal_Representation_ICCV_2017_paper.pdf )

Preparing your own dataset.

Suppose you are about to use UCF dataset.Firstly converting videos to images is necessary. To do this,you could run codes like follows:(Suppose UCF-101 dataset is in the same directory as the code-files.)

• ./process_video2image.sh UCF101
• And next step,you should get the 'train.list' and 'test.list' which you would afterwards fetch from for training data and testing data individually:(number ‘5’ indicates that one-fifth of all data is testing data.)
• ./process_gettxt.sh UCF101 5

Note that:Due to the fact that Relative Path of the video clips exist in 'train.list' and 'test.list', So you must make sure that 'DataGenerator.py' and UCF-101 are in the same directory! or modify the codes by yourself.

Train or Eval model

After getting your own data and setting preference parameters in 'settings.py',You can run python train.py --txt='./train.list'(input parameter 'txt' has default value:'./train.list',in this way you can just run python train.py) to train model. You can also train and test model in 'tf-p3d-train_eval.ipynb' with jupyter notebook.

Others

• Changing the properties for data augmentation in 'DataAugmenter.py'

Warning

• DO NOT use tf.contrib.layers.batch_norm() or slim.batch_norm() which may lead to wrong answers when testing.Using tf.layers.batch_normalization(training=...) instead.