Providing a tool for some fashion neural network frameworks.

The nn_tools is released under the MIT License (refer to the LICENSE file for details).

1. Converting a model between different frameworks.
2. Some convenient tools of manipulate caffemodel and prototxt quickly(like get or set weights of layers), see nn_tools.Caffe.
3. Analysing a model, get the operations number(ops) in every layers.

• Python2.7 or Python3.x
• Each functions in this tools requires corresponding neural network python package (tensorflow pytorch and so on).

The analyser can analyse all the model layers' [input_size, output_size, multiplication ops, addition ops, comparation ops, tot ops, weight size and so on] given a input tensor size, which is convenint for model deploy analyse.

Before you analyse your network, Netscope is recommended to visiualize your network.

Command：python caffe_analyser.py [-h] prototxt outdir shape

• The prototxt is the path of the prototxt file.
• The outdir is path to save the csv file.
• The shape is the input shape of the network(split by comma ,), in caffe image shape should be: batch_size, image_height, image_width, channel.

For example python caffe_analyser.py resnet_18_deploy.prototxt analys_result.csv 1,224,224,3

Supporting analyse the inheritors of torch.nn.Moudule class.

Command：pytorch_analyser.py [-h] [--out OUT] [--class_args ARGS] path class_name shape

• The path is the python file path which contaning your class.
• The class_name is the class name in your python file.
• The shape is the input shape of the network(split by comma ,), in pytorch image shape should be: batch_size, channel, image_height, image_width.
• The out (optinal) is path to save the csv file, default is '/tmp/pytorch_analyse.csv'.
• The class_args (optional) is the args to init the class in python file, default is empty.

For example python pytorch_analyser.py tmp/pytorch_analysis_test.py ResNet218 1,3,224,224

The new version of pytorch_to_caffe supporting the newest version(from 0.2.0 to 0.3.0) of pytorch. NOTICE: The old version DO NOT supporting the 0.3.0. NOTICE: The transfer output will be somewhat different with the original model, caused by implementation difference.

• Supporting layers types: conv2d, linear, max_pool2d, avg_pool2d, dropout, relu, threshold(only value=0), batch_norm
• Supporting operations: torch.split, torch.max, torch.cat
• Supporting tensor Variable operations: var.view, + (add), += (iadd), -(sub), -=(isub)

The supported above can transfer many kinds of nets, such as AlexNet(tested), VGG(tested), ResNet(tested), Inception_V3(tested).

The supported layers concluded the most popular layers and operations. The other layer types will be added soon, you can ask me to add them in issues.

Example: please see file example/alexnet_pytorch_to_caffe.py. Just Run python3 example/alexnet_pytorch_to_caffe.py