def _rename_basic_resnet_weights()

in src/MaskRCNNDetection/maskrcnn_benchmark/utils/c2_model_loading.py [0:0]

• 29 lines of code
• 28 McCabe index (conditional complexity)

def _rename_basic_resnet_weights(layer_keys):
    layer_keys = [k.replace("_", ".") for k in layer_keys]
    layer_keys = [k.replace(".w", ".weight") for k in layer_keys]
    layer_keys = [k.replace(".bn", "_bn") for k in layer_keys]
    layer_keys = [k.replace(".b", ".bias") for k in layer_keys]
    layer_keys = [k.replace("_bn.s", "_bn.scale") for k in layer_keys]
    layer_keys = [k.replace(".biasranch", ".branch") for k in layer_keys]
    layer_keys = [k.replace("bbox.pred", "bbox_pred") for k in layer_keys]
    layer_keys = [k.replace("cls.score", "cls_score") for k in layer_keys]
    layer_keys = [k.replace("res.conv1_", "conv1_") for k in layer_keys]

    # RPN / Faster RCNN
    layer_keys = [k.replace(".biasbox", ".bbox") for k in layer_keys]
    layer_keys = [k.replace("conv.rpn", "rpn.conv") for k in layer_keys]
    layer_keys = [k.replace("rpn.bbox.pred", "rpn.bbox_pred") for k in layer_keys]
    layer_keys = [k.replace("rpn.cls.logits", "rpn.cls_logits") for k in layer_keys]

    # Affine-Channel -> BatchNorm enaming
    layer_keys = [k.replace("_bn.scale", "_bn.weight") for k in layer_keys]

    # Make torchvision-compatible
    layer_keys = [k.replace("conv1_bn.", "bn1.") for k in layer_keys]

    layer_keys = [k.replace("res2.", "layer1.") for k in layer_keys]
    layer_keys = [k.replace("res3.", "layer2.") for k in layer_keys]
    layer_keys = [k.replace("res4.", "layer3.") for k in layer_keys]
    layer_keys = [k.replace("res5.", "layer4.") for k in layer_keys]

    layer_keys = [k.replace(".branch2a.", ".conv1.") for k in layer_keys]
    layer_keys = [k.replace(".branch2a_bn.", ".bn1.") for k in layer_keys]
    layer_keys = [k.replace(".branch2b.", ".conv2.") for k in layer_keys]
    layer_keys = [k.replace(".branch2b_bn.", ".bn2.") for k in layer_keys]
    layer_keys = [k.replace(".branch2c.", ".conv3.") for k in layer_keys]
    layer_keys = [k.replace(".branch2c_bn.", ".bn3.") for k in layer_keys]

    layer_keys = [k.replace(".branch1.", ".downsample.0.") for k in layer_keys]
    layer_keys = [k.replace(".branch1_bn.", ".downsample.1.") for k in layer_keys]

    return layer_keys