136 lines
4.6 KiB
Python
136 lines
4.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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from collections import OrderedDict
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import torch
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def convert_conv1(model_key, model_weight, state_dict, converted_names):
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if model_key.find('features.0.0') >= 0:
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new_key = model_key.replace('features.0.0', 'backbone.conv1.conv')
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else:
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new_key = model_key.replace('features.0.1', 'backbone.conv1.bn')
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state_dict[new_key] = model_weight
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converted_names.add(model_key)
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print(f'Convert {model_key} to {new_key}')
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def convert_conv5(model_key, model_weight, state_dict, converted_names):
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if model_key.find('features.18.0') >= 0:
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new_key = model_key.replace('features.18.0', 'backbone.conv2.conv')
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else:
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new_key = model_key.replace('features.18.1', 'backbone.conv2.bn')
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state_dict[new_key] = model_weight
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converted_names.add(model_key)
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print(f'Convert {model_key} to {new_key}')
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def convert_head(model_key, model_weight, state_dict, converted_names):
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new_key = model_key.replace('classifier.1', 'head.fc')
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state_dict[new_key] = model_weight
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converted_names.add(model_key)
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print(f'Convert {model_key} to {new_key}')
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def convert_block(model_key, model_weight, state_dict, converted_names):
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split_keys = model_key.split('.')
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layer_id = int(split_keys[1])
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new_layer_id = 0
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sub_id = 0
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if layer_id == 1:
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new_layer_id = 1
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sub_id = 0
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elif layer_id in range(2, 4):
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new_layer_id = 2
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sub_id = layer_id - 2
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elif layer_id in range(4, 7):
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new_layer_id = 3
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sub_id = layer_id - 4
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elif layer_id in range(7, 11):
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new_layer_id = 4
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sub_id = layer_id - 7
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elif layer_id in range(11, 14):
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new_layer_id = 5
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sub_id = layer_id - 11
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elif layer_id in range(14, 17):
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new_layer_id = 6
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sub_id = layer_id - 14
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elif layer_id == 17:
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new_layer_id = 7
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sub_id = 0
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new_key = model_key.replace(f'features.{layer_id}',
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f'backbone.layer{new_layer_id}.{sub_id}')
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if new_layer_id == 1:
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if new_key.find('conv.0.0') >= 0:
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new_key = new_key.replace('conv.0.0', 'conv.0.conv')
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elif new_key.find('conv.0.1') >= 0:
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new_key = new_key.replace('conv.0.1', 'conv.0.bn')
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elif new_key.find('conv.1') >= 0:
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new_key = new_key.replace('conv.1', 'conv.1.conv')
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elif new_key.find('conv.2') >= 0:
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new_key = new_key.replace('conv.2', 'conv.1.bn')
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else:
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raise ValueError(f'Unsupported conversion of key {model_key}')
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else:
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if new_key.find('conv.0.0') >= 0:
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new_key = new_key.replace('conv.0.0', 'conv.0.conv')
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elif new_key.find('conv.0.1') >= 0:
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new_key = new_key.replace('conv.0.1', 'conv.0.bn')
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elif new_key.find('conv.1.0') >= 0:
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new_key = new_key.replace('conv.1.0', 'conv.1.conv')
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elif new_key.find('conv.1.1') >= 0:
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new_key = new_key.replace('conv.1.1', 'conv.1.bn')
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elif new_key.find('conv.2') >= 0:
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new_key = new_key.replace('conv.2', 'conv.2.conv')
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elif new_key.find('conv.3') >= 0:
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new_key = new_key.replace('conv.3', 'conv.2.bn')
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else:
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raise ValueError(f'Unsupported conversion of key {model_key}')
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print(f'Convert {model_key} to {new_key}')
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state_dict[new_key] = model_weight
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converted_names.add(model_key)
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def convert(src, dst):
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"""Convert keys in torchvision pretrained MobileNetV2 models to mmpretrain
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style."""
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# load pytorch model
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blobs = torch.load(src, map_location='cpu')
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# convert to pytorch style
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state_dict = OrderedDict()
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converted_names = set()
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for key, weight in blobs.items():
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if 'features.0' in key:
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convert_conv1(key, weight, state_dict, converted_names)
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elif 'classifier' in key:
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convert_head(key, weight, state_dict, converted_names)
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elif 'features.18' in key:
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convert_conv5(key, weight, state_dict, converted_names)
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else:
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convert_block(key, weight, state_dict, converted_names)
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# check if all layers are converted
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for key in blobs:
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if key not in converted_names:
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print(f'not converted: {key}')
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# save checkpoint
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checkpoint = dict()
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checkpoint['state_dict'] = state_dict
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torch.save(checkpoint, dst)
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def main():
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parser = argparse.ArgumentParser(description='Convert model keys')
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parser.add_argument('src', help='src detectron model path')
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parser.add_argument('dst', help='save path')
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args = parser.parse_args()
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convert(args.src, args.dst)
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if __name__ == '__main__':
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main()
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