MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
Platform (like ubuntu 16.04/win10): ubuntu 20.04
Python version: 3.6
Source framework with version (like Tensorflow 1.4.1 with GPU): keras 2.2.4, tensorflow-gpu 1.14
Destination framework with version (like CNTK 2.3 with GPU): pytorch 1.4.0
Pre-trained model path (webpath or webdisk path): model is a transfer learning resnet50
Running scripts: mmconvert -sf keras -df pytorch -in custom_resnet50.json -iw custom-resnet50-weights.h5 -om weights.pt
so the command outputs a file weights.py and weights.pt, and says something about a "b707ee71b00a45dd8d4c2fd686a7c643.npy" weights file being saved, but the weights file is not actually saved in the working directory and yet is necessary to actually load the pytorch model which is generated.
Thank you for your issues. I'm sorry for repling too late. I think if mmdnn reports no errors otherwise. Then weights file should be generated correctly. Maybe you should provide more details.
Platform (like ubuntu 16.04/win10): ubuntu 20.04 Python version: 3.6 Source framework with version (like Tensorflow 1.4.1 with GPU): keras 2.2.4, tensorflow-gpu 1.14 Destination framework with version (like CNTK 2.3 with GPU): pytorch 1.4.0 Pre-trained model path (webpath or webdisk path): model is a transfer learning resnet50 Running scripts: mmconvert -sf keras -df pytorch -in custom_resnet50.json -iw custom-resnet50-weights.h5 -om weights.pt
so the command outputs a file weights.py and weights.pt, and says something about a "b707ee71b00a45dd8d4c2fd686a7c643.npy" weights file being saved, but the weights file is not actually saved in the working directory and yet is necessary to actually load the pytorch model which is generated.