Weights_Keras_2_Pytorch

最近想在Pytorch项目里使用一下谷歌的NIMA，但是发现没有预训练好的pytorch权重，于是整理了一下将Keras预训练权重转为Pytorch的代码，目前是支持Keras的Conv2D, Dense, DepthwiseConv2D, BatchNormlization的转换。需要注意的是在Pytorch模型内需要给每一层命名为与Keras每一层相同的名字，才能对应转换。

I recently wanted to use Google's NIMA in the Pytorch project, but found that there were no pre-trained pytorch weights, so I organized the code to convert Keras pre-trained weights to Pytorch, which currently supports Keras' Conv2D, Dense, DepthwiseConv2D, BatchNormlization conversion. Note that you need to name each layer within the Pytorch model with the same name as each layer of Keras in order to correspond to the conversion.

文件介绍：

weights_keras2pytorch.py 是Keras预训练权重转为Pytorch的代码；

weights_keras2pytorch.py is the code for converting Keras pre-trained weights to Pytorch.

model_keras_NIMA.py 是谷歌NR-IQA NIMA的Keras版模型代码；

model_keras_NIMA.py is the Keras version of the Google NR-IQA NIMA model code.

model_pytorch_NIMA.py 是NIMA的Pytorch版模型代码；

model_pytorch_NIMA.py is the Pytorch version of the model code for NIMA.

mobilenet_weights.h5 是用mobilenet实现NIMA的预训练权重；

mobilenet_weights.h5 is a pre-trained weights for implementing NIMA with mobilenet.

NIMA_pytorch_model.pth 是用转换代码转换出来的权重；

NIMA_pytorch_model.pth is the weights converted with the conversion code.

Requirements：

h5py==3.1.0

Keras

keras==2.6.0 Keras-Preprocessing==1.1.2

Tensorflow

tensorboard==2.7.0 tensorboard-data-server==0.6.1 tensorboard-plugin-wit==1.8.0 tensorflow-estimator==2.7.0 tensorflow-gpu==2.6.0

Pytorch

torch==1.10.0 torchaudio==0.10.0 torchvision==0.11.1

上述只是我用的环境，并不是一定需要这么高的版本。

The above is just the environment I use, and does not necessarily require such a high version.