def efficientnet(width_coefficient=None, depth_coefficient=None, image_size=None,
                 dropout_rate=0.2, drop_connect_rate=0.2, num_classes=1000, include_top=True):
    """Create BlockArgs and GlobalParams for efficientnet model.

    Args:
        width_coefficient (float)
        depth_coefficient (float)
        image_size (int)
        dropout_rate (float)
        drop_connect_rate (float)
        num_classes (int)

        Meaning as the name suggests.

    Returns:
        blocks_args, global_params.
    """

    # Blocks args for the whole model(efficientnet-b0 by default)
    # It will be modified in the construction of EfficientNet Class according to model
    blocks_args = [
        'r1_k3_s11_e1_i32_o16_se0.25',
        'r2_k3_s22_e6_i16_o24_se0.25',
        'r2_k5_s22_e6_i24_o40_se0.25',
        'r3_k3_s22_e6_i40_o80_se0.25',
        'r3_k5_s11_e6_i80_o112_se0.25',
        'r4_k5_s22_e6_i112_o192_se0.25',
        'r1_k3_s11_e6_i192_o320_se0.25',
    ]
    blocks_args = BlockDecoder.decode(blocks_args)

    global_params = GlobalParams(
        width_coefficient=width_coefficient,
        depth_coefficient=depth_coefficient,
        image_size=image_size,
        dropout_rate=dropout_rate,

        num_classes=num_classes,
        batch_norm_momentum=0.99,
        batch_norm_epsilon=1e-3,
        drop_connect_rate=drop_connect_rate,
        depth_divisor=8,
        min_depth=None,
        include_top=include_top,
    )

    return blocks_args, global_params
Create BlockArgs and GlobalParams for efficientnet model.

    Args:
        width_coefficient (float)
        depth_coefficient (float)
        image_size (int)
        dropout_rate (float)
        drop_connect_rate (float)
        num_classes (int)

        Meaning as the name suggests.

    Returns:
        blocks_args, global_params.
    
efficientnet_pytorch/utils.py