lib/models/resnet_video.py [110:155]:
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    use_temp_convs_set = [use_temp_convs_1, use_temp_convs_2, use_temp_convs_3, use_temp_convs_4, use_temp_convs_5]
    temp_strides_set   = [temp_strides_1, temp_strides_2, temp_strides_3, temp_strides_4, temp_strides_5]

    return use_temp_convs_set, temp_strides_set, pool_stride


def create_model(model, data, labels, split):
    group = cfg.RESNETS.NUM_GROUPS
    width_per_group = cfg.RESNETS.WIDTH_PER_GROUP
    batch_size = int(cfg.TRAIN.BATCH_SIZE / cfg.NUM_GPUS)

    logger.info(
        '--------------- ResNet-{} {}x{}d-{}, {} ---------------'.format(
            cfg.MODEL.DEPTH,
            group, width_per_group,
            cfg.RESNETS.TRANS_FUNC,
            cfg.DATASET))

    assert cfg.MODEL.DEPTH in BLOCK_CONFIG.keys(), \
        'Block config is not defined for specified model depth.'
    (n1, n2, n3, n4) = BLOCK_CONFIG[cfg.MODEL.DEPTH]

    res_block = resnet_helper._generic_residual_block_3d
    dim_inner = group * width_per_group

    use_temp_convs_set, temp_strides_set, pool_stride = obtain_arc(cfg.MODEL.VIDEO_ARC_CHOICE)
    print(use_temp_convs_set)
    print(temp_strides_set)


    conv_blob = model.ConvNd(
        data, 'conv1', 3, 64, [1 + use_temp_convs_set[0][0] * 2, 7, 7], strides=[temp_strides_set[0][0], 2, 2],
        pads=[use_temp_convs_set[0][0], 3, 3] * 2,
        weight_init=('MSRAFill', {}),
        bias_init=('ConstantFill', {'value': 0.}), no_bias=1
    )

    test_mode = False if split not in ['test', 'val'] else True
    if cfg.MODEL.USE_AFFINE is False:
        bn_blob = model.SpatialBN(
            conv_blob, 'res_conv1_bn', 64, epsilon=cfg.MODEL.BN_EPSILON,
            momentum=cfg.MODEL.BN_MOMENTUM, is_test=test_mode,
        )
    else:
        bn_blob = model.AffineNd(conv_blob, 'res_conv1_bn', 64)
    relu_blob = model.Relu(bn_blob, bn_blob)
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lib/models/resnet_video_org.py [110:155]:
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    use_temp_convs_set = [use_temp_convs_1, use_temp_convs_2, use_temp_convs_3, use_temp_convs_4, use_temp_convs_5]
    temp_strides_set   = [temp_strides_1, temp_strides_2, temp_strides_3, temp_strides_4, temp_strides_5]

    return use_temp_convs_set, temp_strides_set, pool_stride


def create_model(model, data, labels, split):
    group = cfg.RESNETS.NUM_GROUPS
    width_per_group = cfg.RESNETS.WIDTH_PER_GROUP
    batch_size = int(cfg.TRAIN.BATCH_SIZE / cfg.NUM_GPUS)

    logger.info(
        '--------------- ResNet-{} {}x{}d-{}, {} ---------------'.format(
            cfg.MODEL.DEPTH,
            group, width_per_group,
            cfg.RESNETS.TRANS_FUNC,
            cfg.DATASET))

    assert cfg.MODEL.DEPTH in BLOCK_CONFIG.keys(), \
        'Block config is not defined for specified model depth.'
    (n1, n2, n3, n4) = BLOCK_CONFIG[cfg.MODEL.DEPTH]

    res_block = resnet_helper._generic_residual_block_3d
    dim_inner = group * width_per_group

    use_temp_convs_set, temp_strides_set, pool_stride = obtain_arc(cfg.MODEL.VIDEO_ARC_CHOICE)
    print(use_temp_convs_set)
    print(temp_strides_set)


    conv_blob = model.ConvNd(
        data, 'conv1', 3, 64, [1 + use_temp_convs_set[0][0] * 2, 7, 7], strides=[temp_strides_set[0][0], 2, 2],
        pads=[use_temp_convs_set[0][0], 3, 3] * 2,
        weight_init=('MSRAFill', {}),
        bias_init=('ConstantFill', {'value': 0.}), no_bias=1
    )

    test_mode = False if split not in ['test', 'val'] else True
    if cfg.MODEL.USE_AFFINE is False:
        bn_blob = model.SpatialBN(
            conv_blob, 'res_conv1_bn', 64, epsilon=cfg.MODEL.BN_EPSILON,
            momentum=cfg.MODEL.BN_MOMENTUM, is_test=test_mode,
        )
    else:
        bn_blob = model.AffineNd(conv_blob, 'res_conv1_bn', 64)
    relu_blob = model.Relu(bn_blob, bn_blob)
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