# PytorchConverter

**Repository Path**: int66/PytorchConverter

## Basic Information

- **Project Name**: PytorchConverter
- **Description**: Pytorch model to caffe & ncnn
- **Primary Language**: Unknown
- **License**: BSD-2-Clause
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-24
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Pytorch Converter
Pytorch model to Caffe & [ncnn](https://github.com/Tencent/ncnn)

## Model Examples
  - SqueezeNet from torchvision
  - DenseNet from torchvision
  - [ResNet50](https://drive.google.com/file/d/0B5B31rlbCRZfcS1rY3BtVWhDREk/view?usp=sharing) (with ceiling_mode=True)
  - MobileNet
  - AnimeGAN pretrained model from author (https://github.com/jayleicn/animeGAN)
  - SSD-like object detection net(for ncnn)
  - UNet (no pretrained model yet, just default initialization)
        
## Attentions
  - **Mind the difference on ceil_mode of pooling layer among Pytorch and Caffe, ncnn**
    - You can convert Pytorch models with all pooling layer's ceil_mode=True.
    - Or compile a custom version of Caffe/ncnn with floor() replaced by ceil() in pooling layer inference.

  - **Python Errors: Use Pytorch 0.2.0 Only to Convert Your Model**
    - Higher version of pytorch 0.3.0, 0.3.1, 0.4.0 seemingly have blocked third party model conversion.
    - Please note that you can still TRAIN your model on pytorch 0.3.0~0.4.0. The converter running on 0.2.0 could still load higher version models correctly.

  - **Other Python packages requirements:**
    - to Caffe: numpy, protobuf (to gen caffe proto)
    - to ncnn: numpy
    - for testing Caffe result: pycaffe, cv2

  - **Model Loading Error**
    - Use compatible model saving & loading method, e.g.    

      ```
      # Saving, notice the difference on DataParallel
      net_for_saving = net.module if use_nn_DataParallel else net
      torch.save(net_for_saving.state_dict(), path)
      
      # Loading
      net.load_state_dict(torch.load(path, map_location=lambda storge, loc: storage))
      ```