Samples here are illustrative. For a runnable walkthrough, see examples.
Let's take a look at the one from PySlowFast (partial):
DATA:
NUM_FRAMES: 8
SAMPLING_RATE: 8
TRAIN_JITTER_SCALES: [256, 320]
TRAIN_CROP_SIZE: 224
TEST_CROP_SIZE: 256
INPUT_CHANNEL_NUM: [3]
RESNET:
ZERO_INIT_FINAL_BN: True
WIDTH_PER_GROUP: 64
NUM_GROUPS: 1
DEPTH: 50
TRANS_FUNC: bottleneck_transform
STRIDE_1X1: False
NUM_BLOCK_TEMP_KERNEL: [[3], [4], [6], [3]]
NONLOCAL:
LOCATION: [[[]], [[]], [[]], [[]]]
GROUP: [[1], [1], [1], [1]]
INSTANTIATION: softmax
This looks intuitive at first glance, but it has many problems.
First, it is impossible to change the dataloader style to different sampling / augmentation strategy.
Second, some parameters need to be hard-coded and memorised.
Third, adding a new model architecture requires re-designing of the entire config system.
Therefore, PyVideoAI provides Python config structure that enables intuitive full customisation!
It's not that complicated if you know what to do. You only need to change 3 config files (dataset_configs, model_configs, exp_configs).
Most of the dataset_configs and model_configs settings are optional, and what matters is exp_configs. However, let's not hardcode everything, but rather split the configs.
From exp_configs, you can access to dataset_cfg and model_cfg environment.
For example (simplified),
dataset_configs/hmdb.py
num_classes = 51
task = 'singlelabel_classification'
# ...model_configs/i3d_resnet50.py
# Define your own model by modifying the function body!
from model import ResNetModel
def load_model(num_classes, input_frame_length, crop_size, input_channel_num):
model = ResNetModel('i3d', depth=50, output=num_classes, ...)
return model
# ...exp_configs/hmdb/i3d_resnet50-testrun.py
batch_size = 4 # per process (per GPU)
def optimiser(params):
return torch.optim.SGD(params, lr = 0.001, momentum = 0.9, weight_decay = 5e-4)
def scheduler(optimiser, iters_per_epoch, last_epoch=-1):
return torch.optim.lr_scheduler.StepLR(optimiser, step_size = 50 * iters_per_epoch, gamma = 0.1, last_epoch=last_epoch) # Here, last_epoch means last iteration.
# ...To run the above example,
CUDA_VISIBLE_DEVICES=0,1,2,3 python tools/run_train.py -D hmdb -M i3d_resnet50 -E testrun --local_world_size 4 -e 200To resume, add -l -1 argument.