inference_i3d_enn.py

# model settings
model = dict(
    type='Recognizer3D',
    backbone=dict(
        type='ResNet3d',
        pretrained2d=True,
        pretrained='torchvision://resnet50',
        depth=50,
        conv_cfg=dict(type='Conv3d'),
        norm_eval=False,
        inflate=((1, 1, 1), (1, 0, 1, 0), (1, 0, 1, 0, 1, 0), (0, 1, 0)),
        zero_init_residual=False),
    cls_head=dict(
        type='I3DHead',
        loss_cls=dict(type='EvidenceLoss',
                      num_classes=101,
                      evidence='exp',
                      loss_type='log',
                      annealing_method='exp'),
        num_classes=101,
        in_channels=2048,
        spatial_type='avg',
        dropout_ratio=0.5,
        init_std=0.01))
# model training and testing settings
evidence='exp'  # only used for EDL
test_cfg = dict(average_clips='score')
# dataset settings
dataset_type = 'VideoDataset'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
test_pipeline = [
    dict(type='OpenCVInit', num_threads=1),
    dict(
        type='SampleFrames',
        clip_len=32,
        frame_interval=2,
        num_clips=1,
        test_mode=True),
    dict(type='OpenCVDecode'),
    dict(type='Resize', scale=(-1, 256)),
    dict(type='ThreeCrop', crop_size=256),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='FormatShape', input_format='NCTHW'),
    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
    dict(type='ToTensor', keys=['imgs'])
]
data = dict(
    videos_per_gpu=8,
    workers_per_gpu=4,
    test=dict(
        type=dataset_type,
        ann_file=None,
        data_prefix=None,
        pipeline=test_pipeline))