GETTING_STARTED.md

Getting Started with AlphAction

The hyper-parameters of each experiment are controlled by a .yaml config file, which is located in the directory config_files. All of these configuration files assume that we are running on 8 GPUs. We need to create a symbolic link to the directory output, where the output (logs and checkpoints) will be saved. Besides, we recommend to create a directory models to place model weights. These can be done with following commands.

mkdir -p /path/to/output
ln -s /path/to/output data/output
mkdir -p /path/to/models
ln -s /path/to/models data/models

Training

Download pre-trained models from MODEL_ZOO.md. Then place pre-trained models in data/models directory with following structure:

models/
|_ pretrained_models/
|  |_ SlowFast-ResNet50-4x16.pth
|  |_ SlowFast-ResNet101-8x8.pth

To train on a single GPU, you only need to run following command. The argument --use-tfboard enables tensorboard to log training process. Because the config files assume that we are using 8 GPUs, the global batch size SOLVER.VIDEOS_PER_BATCH and TEST.VIDEOS_PER_BATCH can be too large for a single GPU. Therefore, in the following command, we modify the batch size and also adjust the learning rate and schedule length according to the linear scaling rule.

python train_net.py --config-file "path/to/config/file.yaml" \
--transfer --no-head --use-tfboard \
SOLVER.BASE_LR 0.000125 \
SOLVER.STEPS '(560000, 720000)' \
SOLVER.MAX_ITER 880000 \ 
SOLVER.VIDEOS_PER_BATCH 2 \
TEST.VIDEOS_PER_BATCH 2

We use the launch utility torch.distributed.launch to launch multiple processes for distributed training on multiple gpus. GPU_NUM should be replaced by the number of gpus to use. Hyper-parameters in the config file can still be modified in the way used in single-GPU training.

python -m torch.distributed.launch --nproc_per_node=GPU_NUM \
train_net.py --config-file "path/to/config/file.yaml" \
--transfer --no-head --use-tfboard

Inference

To do inference on multiple GPUs, you should run the following command. Note that our code first trys to load the last_checkpoint in the OUTPUT_DIR. If there is no such file in OUTPUT_DIR, it will then load the model from the path specified in MODEL.WEIGHT. To use MODEL.WEIGHT to do the inference, you need to ensure that there is no last_checkpoint in OUTPUT_DIR. You can download the model weights from MODEL_ZOO.md.

python -m torch.distributed.launch --nproc_per_node=GPU_NUM \
test_net.py --config-file "path/to/config/file.yaml" \
MODEL.WEIGHT "path/to/model/weight"