JigsawPuzzlePytorch
Pytorch implementation of the paper "Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles" by Mehdi Noroozi GitHub
Partially tested Performances Coming Soon
Dependencies
- Tested with Python 2.7
- Pytorch v0.3
- Tensorflow is used for logging. Remove the Logger all scripts if tensorflow is missing
Train the JigsawPuzzleSolver
Setup Loader
Two DataLoader are provided:
- ImageLoader: per each iteration it loads data in image format (jpg,png ,...)
- Dataset/JigsawImageLoader.py uses PyTorch DataLoader and iterator
- Dataset/ImageDataLoader.py custom implementation.
The default loader is JigsawImageLoader.py. ImageDataLoader.py is slightly faster when using single core.
The images can be preprocessed using produce_small_data.py which resize the image to 256, keeping the aspect ratio, and crops a patch of size 255x255 in the center.
Run Training
Fill the path information in run_jigsaw_training.sh. IMAGENET_FOLD needs to point to the folder containing ILSVRC2012_img_train.
./run_jigsaw_training.sh [GPU_ID]
or call the python script
python JigsawTrain.py [*path_to_imagenet*] --checkpoint [*path_checkpoints_and_logs*] --gpu [*GPU_ID*] --batch [*batch_size*]
By default the network uses 1000 permutations with maximum hamming distance selected using select_permutations.py.
To change the file name loaded for the permutations, open the file JigsawLoader.py and change the permutation file in the method retrive_permutations
Details:
- The input of the network should be 64x64, but I need to resize to 75x75, otherwise the output of conv5 is 2x2 instead of 3x3 like the official architecture
- Jigsaw trained using the approach of the paper: SGD, LRN layers, 70 epochs
- Implemented shortcuts: spatial jittering, normalize each patch indipendently, color jittering, 30% black&white image
- The LRN layer crushes with a PyTorch version older than 0.3
ToDo
- TensorboardX
- LMDB DataLoader