sunnymarkliu / pytorch_misc

Code snippets created for the PyTorch discussion board

PyTorch misc

Collection of code snippets I've written for the PyTorch discussion board.

All scripts were testes using the PyTorch 1.0 preview and torchvision 0.2.1.

Additional libraries, e.g. numpy or pandas, are used in a few scripts.

Some scripts might be a good starter to create a tutorial.

Overview

• accumulate_gradients - Comparison of accumulated gradients/losses to vanilla batch update.
• adaptive_batchnorm- Adaptive BN implementation using two additional parameters: out = a * x + b * bn(x).
• adaptive_pooling_torchvision - Example of using adaptive pooling layers in pretrained models to use different spatial input shapes.
• batch_norm_manual - Comparison of PyTorch BatchNorm layers and a manual calculation.
• change_crop_in_dataset - Change the image crop size on the fly using a Dataset.
• channel_to_patches - Permute image data so that channel values of each pixel are flattened to an image patch around the pixel.
• conv_rnn - Combines a 3DCNN with an RNN; uses windowed frames as inputs.
• csv_chunk_read - Provide data chunks from continuous .csv file.
• densenet_forwardhook - Use forward hooks to get intermediate activations from densenet121. Uses separate modules to process these activations further.
• edge_weighting_segmentation - Apply weighting to edges for a segmentation task.
• image_rotation_with_matrix - Rotate an image given an angle using 1.) a nested loop and 2.) a rotation matrix and mesh grid.
• LocallyConnected2d - Implementation of a locally connected 2d layer.
• mnist_autoencoder - Simple autoencoder for MNIST data. Includes visualizations of output images, intermediate activations and conv kernels.
• mnist_permuted - MNIST training using permuted pixel locations.
• model_sharding_data_parallel - Model sharding with DataParallel using 2 pairs of 2 GPUs.
• momentum_update_nograd - Script to see how parameters are updated when an optimizer is used with momentum/running estimates, even if gradients are zero.
• shared_array - Script to demonstrate the usage of shared arrays using multiple workers.
• shared_dict - Script to demonstrate the usage of shared dicts using multiple workers.
• unet_demo - Simple UNet demo.
• weighted_sampling - Usage of WeightedRandomSampler using an imbalanced dataset with class imbalance 99 to 1.

Feedback is very welcome!