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Neural Network Tools: Converter and Analyzer. For caffe, pytorch, draknet and so on.
| Date | Stars |
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| 2026-07-31 | 356 |
| 2026-08-05 | 356 |
| 2026-08-06 | 356 |
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# Neural Network Tools: Converter, Constructor and Analyser 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). - [Converter](#Converter) - [Pytorch->Caffe](#Pytorch-to-Caffe) - [Analyser](#Analyser) - [Caffe Analyser](##Caffe-Analyser) - [Pytorch Analyser](##Pytorch-Analyser) - [Mxnet Analyser](##Mxnet-Analyser) - [Some useful functions](#Some-useful-functions) ### features 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](https://github.com/hahnyuan/nn_tools/tree/master/Caffe). 3. Analysing a model, get the operations number(ops) in every layers. ### requirements - Python2.7 or Python3.x - Each functions in this tools requires corresponding neural network python package (tensorflow pytorch and so on). # Converter ## Pytorch to Caffe The new version of pytorch_to_caffe supporting the newest version(from 0.2.0 to 1.0) of pytorch. NOTICE: The transfer output will be somewhat different with the original model, caused by implementation difference. - Supporting layers types: conv2d, transpose_conv2d, linear, max_pool2d, avg_pool2d, dropout, relu, prelu, threshold(only value=0),softmax, batch_norm, instance_norm - Supporting operations: torch.split, torch.cat - Supporting tensor Variable operations: var.view, var.mean, var.sum, var.contiguous, + (add), += (iadd), -(sub), -=(isub) \* (mul) *= (imul) / (div) - The not supporting operations will transferred to a Python layer in Caffe. You can implemented it by yourself. - Testify whether your transformed Caffe model is workable. See `tmp/testify_pytorch_to_caffe.py`. The supported above can transfer many kinds of nets. The tested network: - AlexNet(tested) - VGG(tested) - ResNet(tested) - Inception_V3(tested) - SqueezeNet(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. Note: You need `net.eval()` before converting the pytorch networks. Example: please see file `example/<alexnet/resnet/inception_v3>_pytorch_to_caffe.py`. ``` $python3 example/alexnet_pytorch_to_caffe.py Add blob blob0 : torch.Size([1, 3, 226, 226]) Processing Layer: features.0 Add blob conv_blob1 : torch.Size([1, 64, 55, 55]) Processing Layer: features.1 ... Transform Completed ``` If you have compiled Pycaffe and set the pycaffe path. You can run testify_pytorch_to_caffe to test whether the output of every Caffe layer is the same as the output in Pytorch. ``` $python3 example/testify_pytorch_to_caffe_example.py TEST layer features_0: PASS TEST layer features_1: PASS ... TEST output TEST output: PASS ``` # Analyser 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. ## Caffe Analyser Before you analyse your network, [Netscope](http://ethereon.github.io/netscope/#/editor) 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, channel, image_height, image_width. For example `python caffe_analyser.py resnet_18_deploy.prototxt analys_result.csv 1,3,224,224` ## Pytorch Analyser Supporting analyse the inheritors of torch.nn.Moudule class. Command：`pytorch_analyser.py [-h] [--out OUT] [--class_args ARGS] path name shape` - The path is the python file path which contaning your class. - The name is the class name or instance name in your python file. - The shape is the input shape of the network(split by
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:56802a8f1516ea67, topic:deep-learning, topic:pytorch