stgcn_mc3.py

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model = dict(
    type='MCSTGCNRunner',
    backbone=dict(
        type='STGCN',
        in_channels = 3,
        graph_args = dict(layout = 'coco', strategy='spatial'),
        edge_importance_weighting = True,
        dropout=0.5,
    ),
    head=dict(
        type='STGCNMCHead',
        mc_params = dict(pose = dict(in_channels=256, num_class=4),
                         action = dict(in_channels=256, num_class=9))        
    ),
    device='cuda:0'
)
 
loss = dict(
    CrossEntropyLoss = dict(
        weight = 1.0,
        weights = [1.0, 2.0],
        pred_keys = ['pose', 'action'],
        gt_keys = ['label_pose', 'label_action'],
        data_num = dict(pose = [98, 517, 11, 35], action = [100, 100, 100, 24, 100, 34, 3, 100, 100])
    )
)
 
data_loader = dict(
    type = 'ActionDatasetLoader',
    train_data_folder = '/media/safemotion/HDD5/pjm_test/action_mc_train/1st/train',
    test_data_folder = '/media/safemotion/HDD5/pjm_test/action_mc_train/1st/val',
    use_normalize = True,
)
 
train = dict(
    num_workers = 8,
    init_lr = 0.1,
    batch_size = 32,
    epochs = 3000,
    optimizer = 'SGD',
    optimizer_args = dict(momentum=0.9, nesterov=True, weight_decay=0.0001),
    adjust_lr_epoch = [500, 1000, 1500, 2000],
    adjust_lr_rate = [0.1]*4,    
    val_interval = 1,
    
    pretrained = None,#'/media/safemotion/HDD5/pjm_test/action_train_test2/9.pth',
    save_root = '/media/safemotion/HDD5/pjm_test/action_train_mc_test3',
 
)
 
test = dict(
    pretrained = '/media/safemotion/HDD5/pjm_test/action_train_mc_test3/',
    save_root = '/media/safemotion/HDD5/pjm_test/action_train_mc_test3',
)