Customizable Checkpoint Callbacks, Batch Shuffling and More

Customizable Checkpoint Callbacks, Batch Shuffling and More

Fixed download speed for image datasets.
lightly-magic can now be used with trainer.max_epochs=0.
Fixed the pytorch-lightning warning: "Passing a ModelCheckpoint instance to Trainer(checkpoint_callbacks=...) is deprecated since v1.1 and will no longer be supported in v1.3."

Customizable Checkpoint Callback

Checkpoint callbacks are now customizable (even from the command-line):

# save the 5 best models
lightly-train input_dir='data/' checkpoint_callback.save_top_k=5

# don't save the model of the last epoch
lightly-train input_dir='data/' checkpoint_callback.save_last=False

Batch Shuffling

Added batch shuffling to MoCo and SplitBatchNorm to simulate multi-gpu behaviour.

Image Resizing

Images can be resized before uploading them to the web-app:

# no resizing (default)
lightly-upload input_dir='data/' dataset_id='XYZ' token='123' resize=-1

# resize such that shortest edge of the image is 128
lightly-upload input_dir='data/' dataset_id='XYZ' token='123' resize=128

# resize images to (128, 128)
lightly-upload input_dir='data/' dataset_id='XYZ' token='123' resize=[128,128]

Models

• MoCo: Momentum Contrast for Unsupervised Visual Representation Learning, 2019
• SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020