Train/Test pytorch network.

You may only need two scripts from this repo: deepnet_training.py, deepnet_test.py. The rest are used as an example and hope to make this repo reusable. No special dependencies are required.

• The train/test code are defined in train.py and test.py, they are project dependent. You would like to rewrite part of them to meet your requirements.

• Train loop is defined in deepnet_training.py, You can keep them as it is and just simply copy it to different projects.

• In order to load the pre-trained model, see the code in deepnet_test.py. Most of time, I simply copy it to the project folder.

• net.py, the network structure can be defined here, or you can create a new file to define different modules.
• train.py train the network. The datasets, network to use can be declared here. It will write the output to the --output folder.
• test.py loads the pre-trained network, do the forward pass, use some callbacks to make the code clean.
• dataset.py defined the pytorch dataset for Dataloader. You can define different datasets and combine them together.
• callbacks_training.py, after each minibatch/epoch the callback will be called. This is very practical that you can manipulate/visualize the network input/output without change other scripts. Do the same in a callbacks_test.py. Three functions are pre-defined:
  • plot loss curves, in the mean while save them as .csv file for further processing.
  • plot gradient curves, the gradient hook layer is defined in net.py.
  • visualize some samples during training.
• torchsummary.py, updated to show the size of the feature map.
• deepnet_train/test.py is used to make the training loops or test forwards happen. Most boring code in many pytorch projects, just copy/paste them to the new project. Tested in pytorch 0.4&1.0.

Clean the dirty code. Update GradientHook Gradient Record won't work for pytroch>=1.0, please remove these parts. It's easy to be fixed, you can choose to use gradient_hooks.

Thanks to MathGaron https://github.com/MathGaron/pytorch_toolbox