CapProNet with backbone of ResNet in TensorFlow

This is the offical implementation of paper CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces with backbone of Resnet model for ImageNet database. The backbone network has been borrowd from Tensorflow offical implementation of resnet here. See the following papers for more detailes about the backbone architecture:

[1] Deep Residual Learning for Image Recognition by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Dec 2015.

[2] Identity Mappings in Deep Residual Networks by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Jul 2016.

In code v1 refers to the resnet defined in [1], while v2 correspondingly refers to [2]. The principle difference between the two versions is that v1 applies batch normalization and activation after convolution, while v2 applies batch normalization, then activation, and finally convolution. A schematic comparison is presented in Figure 1 (left) of [2].

Please proceed according to which dataset you would like to train/evaluate on:

Setup

We used Tensorflow version 1.8 and Python 2.7 for our experiments. First make sure you've added the models folder to your Python path; otherwise you may encounter an error like ImportError: No module named official.resnet.

ImageNet

Setup

To begin, you will need to download the ImageNet dataset and convert it to TFRecord format. Follow along with the Inception guide in order to prepare the dataset.

Once your dataset is ready, you can begin training the model as follows:

change your directory to ./official/resnet
python caps_imagenet_main.py --data_dir=/path/to/imagenet

The model will begin training and will automatically evaluate itself on the validation data roughly once per epoch.

Note that there are a number of other options you can specify, including --model_dir to choose where to store the model and --resnet_size to choose the model size (options include ResNet-18 through ResNet-200). See resnet.py for the full list of options.

Compute Devices

Training is accomplished using the DistributionStrategies API. (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/distribute/README.md)

The appropriate distribution strategy is chosen based on the --num_gpus flag. By default this flag is one if TensorFlow is compiled with CUDA, and zero otherwise.

num_gpus:

• 0: Use OneDeviceStrategy and train on CPU.
• 1: Use OneDeviceStrategy and train on GPU.
• 2+: Use MirroredStrategy (data parallelism) to distribute a batch between devices.