This is the code for our CVPR 2019 paper Incremental Object Learning from Contiguous Views
The code generate data using CRIB can be found at the CRIB Data Generator repository
- Python 3.5+
- Pytorch 1.0.0
- For other requirements:
pip install numpy torchvision opencv-python tqdm Cython - Running the script for plotting results as a graph also needs matplotlib
pip install matplotlib- For building the color jittering C++ module (Cython required - build using development package of python), run the following commands:
cd utils/color_jitter
python setup.py build_ext --inplace
cd ../..This is a stripped down version of the code and does not include the entire CRIB (Continual Recognition Inspired by Babies) data generator. For running experiments using this code, please download CRIB-Toys data from the following link
The main program has a separate train and test process. Both can run simultaneously using 1 GPU provided that batch_size + test_batch_size images can fit on GPU memory. By default, the train and test processes use the first and second GPU devices visible, unless the '--one_gpu' flag is used, in which case both use the first device visible.
usage: main_incr_toys.py [-h] [--outfile OUTFILE] [--save_all]
[--save_all_dir SAVE_ALL_DIR] [--resume]
[--resume_outfile RESUME_OUTFILE] [--init_lr INIT_LR]
[--init_lr_ft INIT_LR_FT] [--num_epoch NUM_EPOCH]
[--num_epoch_ft NUM_EPOCH_FT] [--lrd LRD] [--wd WD]
[--batch_size BATCH_SIZE] [--llr_freq LLR_FREQ]
[--batch_size_test BATCH_SIZE_TEST] [--lexp_len LEXP_LEN]
[--size_test SIZE_TEST] [--num_exemplars NUM_EXEMPLARS]
[--img_size IMG_SIZE] [--rendered_img_size RENDERED_IMG_SIZE]
[--total_classes TOTAL_CLASSES] [--num_iters NUM_ITERS]
[--algo ALGO] [--no_dist] [--pt] [--ncm] [--diff_order]
[--no_jitter] [--h_ch H_CH] [--s_ch S_CH] [--l_ch L_CH]
[--test_freq TEST_FREQ] [--num_workers NUM_WORKERS] [--one_gpu]
Incremental learning
optional arguments:
-h, --help show this help message and exit
--outfile OUTFILE Output file name (should have .csv extension)
--save_all Option to save models after each test_freq number of
learning exposures
--save_all_dir SAVE_ALL_DIR
Directory to store all models in
--resume Resume training from checkpoint at outfile
--resume_outfile RESUME_OUTFILE
Output file name after resuming
--init_lr INIT_LR initial learning rate
--init_lr_ft INIT_LR_FT
Init learning rate for balanced finetuning (for E2E)
--num_epoch NUM_EPOCH
Number of epochs
--num_epoch_ft NUM_EPOCH_FT
Number of epochs for balanced finetuning (for E2E)
--lrd LRD Learning rate decrease factor
--wd WD Weight decay for SGD
--batch_size BATCH_SIZE
Mini batch size for training
--llr_freq LLR_FREQ Learning rate lowering frequency for SGD (for E2E)
--batch_size_test BATCH_SIZE_TEST
Mini batch size for testing
--lexp_len LEXP_LEN Number of frames in Learning Exposure
--size_test SIZE_TEST
Number of test images per object
--num_exemplars NUM_EXEMPLARS
number of exemplars
--img_size IMG_SIZE Size of images input to the network
--rendered_img_size RENDERED_IMG_SIZE
Size of rendered images
--total_classes TOTAL_CLASSES
Total number of classes
--num_iters NUM_ITERS
Total number of learning exposures (currently only
integer multiples of args.total_classes each class
seen equal number of times)
--algo ALGO Algorithm to run. Options : icarl, e2e, lwf
--no_dist Option to switch off distillation loss
--pt Option to start from an ImageNet pretrained model
--ncm Use nearest class mean classification (for E2E)
--diff_order Use a random order of classes introduced
--no_jitter Option for no color jittering (for iCaRL)
--h_ch H_CH Color jittering : max hue change
--s_ch S_CH Color jittering : max saturation change
--l_ch L_CH Color jittering : max lightness change
--test_freq TEST_FREQ
Number of iterations of training after which a test is
done/model saved
--num_workers NUM_WORKERS
Maximum number of threads spawned at anystage of
execution
--one_gpu Option to run multiprocessing on 1 GPU
Following is an example command to run an incremental learning experiment on CRIB-Toys followed by the command to run a script for plotting results
python main_incr_toys.py --num_exemplars 600 --num_epoch 20 --total_classes 50 --num_iters 500 --algo icarl --pt --no_dist --batch_size 100 --diff_order --outfile results/icarl_pt_nd_50obj_10exp.csv
python plot_results.py -f results/icarl_pt_nd_50obj_10exp.csvIf you use this code, please cite our work :
@InProceedings{Stojanov_2019_CVPR,
author = {Stojanov, Stefan and Mishra, Samarth and Anh Thai, Ngoc and Dhanda, Nikhil and Humayun, Ahmad and Yu, Chen and Smith, Linda B. and Rehg, James M.},
title = {Incremental Object Learning From Contiguous Views},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
We used donlee90's Pytorch implementation of iCaRL as a starting point for our implementation.
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