def get_configuration()

in cvnets/models/classification/config/resnet.py [0:0]

• 109 lines of code
• 5 McCabe index (conditional complexity)

def get_configuration(opts) -> Dict:
    depth = getattr(opts, "model.classification.resnet.depth", 50)
    resnet_config = dict()
    
    if depth == 18:
        resnet_config["layer2"] = {
            "num_blocks": 2,
            "mid_channels": 64,
            "block_type": "basic",
            "stride": 1
        }
        resnet_config["layer3"] = {
            "num_blocks": 2,
            "mid_channels": 128,
            "block_type": "basic",
            "stride": 2
        }
        resnet_config["layer4"] = {
            "num_blocks": 2,
            "mid_channels": 256,
            "block_type": "basic",
            "stride": 2
        }
        resnet_config["layer5"] = {
            "num_blocks": 2,
            "mid_channels": 512,
            "block_type": "basic",
            "stride": 2
        }
    elif depth == 34:
        resnet_config["layer2"] = {
            "num_blocks": 3,
            "mid_channels": 64,
            "block_type": "basic",
            "stride": 1
        }
        resnet_config["layer3"] = {
            "num_blocks": 4,
            "mid_channels": 128,
            "block_type": "basic",
            "stride": 2
        }
        resnet_config["layer4"] = {
            "num_blocks": 6,
            "mid_channels": 256,
            "block_type": "basic",
            "stride": 2
        }
        resnet_config["layer5"] = {
            "num_blocks": 3,
            "mid_channels": 512,
            "block_type": "basic",
            "stride": 2
        }
    elif depth == 50:
        resnet_config["layer2"] = {
            "num_blocks": 3,
            "mid_channels": 64,
            "block_type": "bottleneck",
            "stride": 1
        }
        resnet_config["layer3"] = {
            "num_blocks": 4,
            "mid_channels": 128,
            "block_type": "bottleneck",
            "stride": 2
        }
        resnet_config["layer4"] = {
            "num_blocks": 6,
            "mid_channels": 256,
            "block_type": "bottleneck",
            "stride": 2
        }
        resnet_config["layer5"] = {
            "num_blocks": 3,
            "mid_channels": 512,
            "block_type": "bottleneck",
            "stride": 2
        }
    elif depth == 101:
        resnet_config["layer2"] = {
            "num_blocks": 3,
            "mid_channels": 64,
            "block_type": "bottleneck",
            "stride": 1
        }
        resnet_config["layer3"] = {
            "num_blocks": 4,
            "mid_channels": 128,
            "block_type": "bottleneck",
            "stride": 2
        }
        resnet_config["layer4"] = {
            "num_blocks": 23,
            "mid_channels": 256,
            "block_type": "bottleneck",
            "stride": 2
        }
        resnet_config["layer5"] = {
            "num_blocks": 3,
            "mid_channels": 512,
            "block_type": "bottleneck",
            "stride": 2
        }
    else:
        logger.error(
            "ResNet models are supported with depths of 18, 34, 50 and 101. Please specify depth using "
            "--model.classification.resnet.depth flag. Got: {}".format(depth)
        )
    return resnet_config