Experiments on ActivityNet, FCVID and Mini-Kinetics

Experiments on ActivityNet, FCVID and Mini-Kinetics

Requirements

• python 3.8
• pytorch 1.7.0
• torchvision 0.8.0
• hydra 1.1.0

Datasets

1. Please get the train/test split files for each dataset from Google Drive and put them in PATH_TO_DATASET.
2. Download videos from following links, or contact the corresponding authors for the access. Save them to PATH_TO_DATASET/videos

• ActivityNet-v1.3
• FCVID
• Mini-Kinetics. Please download Kinetics 400. For Mini-Kinetics used in our paper, you need to use the train/val splits file from AR-Net.

3. Extract frames using ops/video_jpg.py, the frames will be saved to PATH_TO_DATASET/frames. Minor modifications on file path are needed when extracting frames from different dataset.

Pre-trained Models on ActivityNet

Please download pre-trained weights and checkpoints from Google Drive.

• globalcnn.pth.tar: pre-trained weights for global CNN (MobileNet-v2).
• localcnn.pth.tar: pre-trained weights for local CNN (ResNet-50).
• 128checkpoint.pth.tar: checkpoint of stage 1 with patch size 128x128.
• 160checkpoint.pth.tar: checkpoint of stage 1 with patch size 160x160.
• 192checkpoint.pth.tar: checkpoint of stage 1 with patch size 192x192.
• 128s3_checkpoint.pth.tar: checkpoint to reproduce the result in paper with patch size 128x128.
• 160s3_checkpoint.pth.tar: checkpoint to reproduce the result in paper with patch size 160x160.
• 192s3_checkpoint.pth.tar: checkpoint to reproduce the result in paper with patch size 192x192.

Training

• Here we take training the model with patch size 128x128 on ActivityNet dataset for example.
• All logs and checkpoints will be saved in the directory: ./outputs/YYYY-MM-DD/HH-MM-SS
• Note that we store a set of default hyper-parameters in conf/default.yaml which can be overrided through command line. You can also use your own config files.
• Before training, please initialize Global CNN and Local CNN by fine-tuning the ImageNet pre-trained models in Pytorch using the following command:

for Global CNN:

CUDA_VISIBLE_DEVICES=0,1 python main_dist.py dataset=actnet data_dir=PATH_TO_DATASET train_stage=0 batch_size=64 workers=8 dropout=0.8 lr_type=cos backbone_lr=0.01 epochs=15 dist_url=tcp://127.0.0.1:8857 random_patch=true patch_size=128 glance_size=224 eval_freq=5 consensus=gru hidden_dim=1024 pretrain_glancer=true

for Local CNN:

CUDA_VISIBLE_DEVICES=0,1 python main_dist.py dataset=actnet data_dir=PATH_TO_DATASET train_stage=0 batch_size=64 workers=8 dropout=0.8 lr_type=cos backbone_lr=0.01 epochs=15 dist_url=tcp://127.0.0.1:8857 random_patch=true patch_size=128 glance_size=224 eval_freq=5 consensus=gru hidden_dim=1024 pretrain_glancer=false

• Training stage 1, pre-trained weights for Global CNN and Local CNN are required:

CUDA_VISIBLE_DEVICES=0,1 python main_dist.py dataset=actnet data_dir=PATH_TO_DATASET train_stage=1 batch_size=64 workers=8 dropout=0.8 lr_type=cos backbone_lr=0.0005 fc_lr=0.05 epochs=50 dist_url=tcp://127.0.0.1:8857 random_patch=true patch_size=128 glance_size=224 eval_freq=5 consensus=gru hidden_dim=1024 pretrained_glancer=PATH_TO_CHECKPOINTS pretrained_focuser=PATH_TO_CHECKPOINTS

• Training stage 2, a stage-1 checkpoint is required:

CUDA_VISIBLE_DEVICES=0 python main_dist.py dataset=actnet data_dir=PATH_TO_DATASET train_stage=2 batch_size=64 workers=8 dropout=0.8 lr_type=cos backbone_lr=0.0005 fc_lr=0.05 epochs=50 random_patch=false patch_size=128 glance_size=224 action_dim=49 eval_freq=5 consensus=gru hidden_dim=1024 resume=PATH_TO_CHECKPOINTS multiprocessing_distributed=false distributed=false

• Training stage 3, a stage-2 checkpoint is required:

CUDA_VISIBLE_DEVICES=0 python main_dist.py dataset=actnet data_dir=PATH_TO_DATASET train_stage=3 batch_size=64 workers=8 dropout=0.8 lr_type=cos backbone_lr=0.0005 fc_lr=0.005 epochs=10 random_patch=false patch_size=128 glance_size=224 action_dim=49 eval_freq=5 consensus=gru hidden_dim=1024 resume=PATH_TO_CHECKPOINTS multiprocessing_distributed=false distributed=false

Evaluate Pre-trained Models

• Here we take evaluating model with patch size 128x128 on ActivityNet for example.

CUDA_VISIBLE_DEVICES=0 python main_dist.py dataset=actnet data_dir=PATH_TO_DATASET train_stage=3 batch_size=64 workers=8 dropout=0.8 lr_type=cos backbone_lr=0.0005 fc_lr=0.005 epochs=10 random_patch=false patch_size=128 glance_size=224 action_dim=49 eval_freq=5 consensus=gru hidden_dim=1024 resume=PATH_TO_CHECKPOINTS multiprocessing_distributed=false distributed=false evaluate=true

Acknowledgement

We use the implementation of MobileNet-v2 and ResNet from Pytorch source code. We also borrow some codes for dataset preparation from AR-Net and PPO from here.