# Copyright (c) OpenMMLab. All rights reserved. import argparse from collections import OrderedDict import torch convert_dict = { # stem 'model.0': 'backbone.stem.0', 'model.1': 'backbone.stem.1', 'model.2': 'backbone.stem.2', # stage1 # ConvModule 'model.3': 'backbone.stage1.0', # ELANBlock expand_channel_2x 'model.4': 'backbone.stage1.1.short_conv', 'model.5': 'backbone.stage1.1.main_conv', 'model.6': 'backbone.stage1.1.blocks.0.0', 'model.7': 'backbone.stage1.1.blocks.0.1', 'model.8': 'backbone.stage1.1.blocks.1.0', 'model.9': 'backbone.stage1.1.blocks.1.1', 'model.11': 'backbone.stage1.1.final_conv', # stage2 # MaxPoolBlock reduce_channel_2x 'model.13': 'backbone.stage2.0.maxpool_branches.1', 'model.14': 'backbone.stage2.0.stride_conv_branches.0', 'model.15': 'backbone.stage2.0.stride_conv_branches.1', # ELANBlock expand_channel_2x 'model.17': 'backbone.stage2.1.short_conv', 'model.18': 'backbone.stage2.1.main_conv', 'model.19': 'backbone.stage2.1.blocks.0.0', 'model.20': 'backbone.stage2.1.blocks.0.1', 'model.21': 'backbone.stage2.1.blocks.1.0', 'model.22': 'backbone.stage2.1.blocks.1.1', 'model.24': 'backbone.stage2.1.final_conv', # stage3 # MaxPoolBlock reduce_channel_2x 'model.26': 'backbone.stage3.0.maxpool_branches.1', 'model.27': 'backbone.stage3.0.stride_conv_branches.0', 'model.28': 'backbone.stage3.0.stride_conv_branches.1', # ELANBlock expand_channel_2x 'model.30': 'backbone.stage3.1.short_conv', 'model.31': 'backbone.stage3.1.main_conv', 'model.32': 'backbone.stage3.1.blocks.0.0', 'model.33': 'backbone.stage3.1.blocks.0.1', 'model.34': 'backbone.stage3.1.blocks.1.0', 'model.35': 'backbone.stage3.1.blocks.1.1', 'model.37': 'backbone.stage3.1.final_conv', # stage4 # MaxPoolBlock reduce_channel_2x 'model.39': 'backbone.stage4.0.maxpool_branches.1', 'model.40': 'backbone.stage4.0.stride_conv_branches.0', 'model.41': 'backbone.stage4.0.stride_conv_branches.1', # ELANBlock no_change_channel 'model.43': 'backbone.stage4.1.short_conv', 'model.44': 'backbone.stage4.1.main_conv', 'model.45': 'backbone.stage4.1.blocks.0.0', 'model.46': 'backbone.stage4.1.blocks.0.1', 'model.47': 'backbone.stage4.1.blocks.1.0', 'model.48': 'backbone.stage4.1.blocks.1.1', 'model.50': 'backbone.stage4.1.final_conv', # neck SPPCSPBlock 'model.51.cv1': 'neck.reduce_layers.2.main_layers.0', 'model.51.cv3': 'neck.reduce_layers.2.main_layers.1', 'model.51.cv4': 'neck.reduce_layers.2.main_layers.2', 'model.51.cv5': 'neck.reduce_layers.2.fuse_layers.0', 'model.51.cv6': 'neck.reduce_layers.2.fuse_layers.1', 'model.51.cv2': 'neck.reduce_layers.2.short_layers', 'model.51.cv7': 'neck.reduce_layers.2.final_conv', # neck 'model.52': 'neck.upsample_layers.0.0', 'model.54': 'neck.reduce_layers.1', # neck ELANBlock reduce_channel_2x 'model.56': 'neck.top_down_layers.0.short_conv', 'model.57': 'neck.top_down_layers.0.main_conv', 'model.58': 'neck.top_down_layers.0.blocks.0', 'model.59': 'neck.top_down_layers.0.blocks.1', 'model.60': 'neck.top_down_layers.0.blocks.2', 'model.61': 'neck.top_down_layers.0.blocks.3', 'model.63': 'neck.top_down_layers.0.final_conv', 'model.64': 'neck.upsample_layers.1.0', 'model.66': 'neck.reduce_layers.0', # neck ELANBlock reduce_channel_2x 'model.68': 'neck.top_down_layers.1.short_conv', 'model.69': 'neck.top_down_layers.1.main_conv', 'model.70': 'neck.top_down_layers.1.blocks.0', 'model.71': 'neck.top_down_layers.1.blocks.1', 'model.72': 'neck.top_down_layers.1.blocks.2', 'model.73': 'neck.top_down_layers.1.blocks.3', 'model.75': 'neck.top_down_layers.1.final_conv', # neck MaxPoolBlock no_change_channel 'model.77': 'neck.downsample_layers.0.maxpool_branches.1', 'model.78': 'neck.downsample_layers.0.stride_conv_branches.0', 'model.79': 'neck.downsample_layers.0.stride_conv_branches.1', # neck ELANBlock reduce_channel_2x 'model.81': 'neck.bottom_up_layers.0.short_conv', 'model.82': 'neck.bottom_up_layers.0.main_conv', 'model.83': 'neck.bottom_up_layers.0.blocks.0', 'model.84': 'neck.bottom_up_layers.0.blocks.1', 'model.85': 'neck.bottom_up_layers.0.blocks.2', 'model.86': 'neck.bottom_up_layers.0.blocks.3', 'model.88': 'neck.bottom_up_layers.0.final_conv', # neck MaxPoolBlock no_change_channel 'model.90': 'neck.downsample_layers.1.maxpool_branches.1', 'model.91': 'neck.downsample_layers.1.stride_conv_branches.0', 'model.92': 'neck.downsample_layers.1.stride_conv_branches.1', # neck ELANBlock reduce_channel_2x 'model.94': 'neck.bottom_up_layers.1.short_conv', 'model.95': 'neck.bottom_up_layers.1.main_conv', 'model.96': 'neck.bottom_up_layers.1.blocks.0', 'model.97': 'neck.bottom_up_layers.1.blocks.1', 'model.98': 'neck.bottom_up_layers.1.blocks.2', 'model.99': 'neck.bottom_up_layers.1.blocks.3', 'model.101': 'neck.bottom_up_layers.1.final_conv', # RepVGGBlock 'model.102.rbr_dense.0': 'neck.out_layers.0.rbr_dense.conv', 'model.102.rbr_dense.1': 'neck.out_layers.0.rbr_dense.bn', 'model.102.rbr_1x1.0': 'neck.out_layers.0.rbr_1x1.conv', 'model.102.rbr_1x1.1': 'neck.out_layers.0.rbr_1x1.bn', 'model.103.rbr_dense.0': 'neck.out_layers.1.rbr_dense.conv', 'model.103.rbr_dense.1': 'neck.out_layers.1.rbr_dense.bn', 'model.103.rbr_1x1.0': 'neck.out_layers.1.rbr_1x1.conv', 'model.103.rbr_1x1.1': 'neck.out_layers.1.rbr_1x1.bn', 'model.104.rbr_dense.0': 'neck.out_layers.2.rbr_dense.conv', 'model.104.rbr_dense.1': 'neck.out_layers.2.rbr_dense.bn', 'model.104.rbr_1x1.0': 'neck.out_layers.2.rbr_1x1.conv', 'model.104.rbr_1x1.1': 'neck.out_layers.2.rbr_1x1.bn', # head 'model.105.m': 'bbox_head.head_module.convs_pred' } def convert(src, dst): """Convert keys in detectron pretrained YOLOv7 models to mmyolo style.""" try: yolov7_model = torch.load(src)['model'].float() blobs = yolov7_model.state_dict() except ModuleNotFoundError: raise RuntimeError( 'This script must be placed under the WongKinYiu/yolov7 repo,' ' because loading the official pretrained model need' ' `model.py` to build model.') state_dict = OrderedDict() for key, weight in blobs.items(): if key.find('anchors') >= 0 or key.find('anchor_grid') >= 0: continue num, module = key.split('.')[1:3] if int(num) < 102 and int(num) != 51: prefix = f'model.{num}' new_key = key.replace(prefix, convert_dict[prefix]) state_dict[new_key] = weight print(f'Convert {key} to {new_key}') elif int(num) < 105 and int(num) != 51: strs_key = key.split('.')[:4] new_key = key.replace('.'.join(strs_key), convert_dict['.'.join(strs_key)]) state_dict[new_key] = weight print(f'Convert {key} to {new_key}') else: strs_key = key.split('.')[:3] new_key = key.replace('.'.join(strs_key), convert_dict['.'.join(strs_key)]) state_dict[new_key] = weight print(f'Convert {key} to {new_key}') # save checkpoint checkpoint = dict() checkpoint['state_dict'] = state_dict torch.save(checkpoint, dst) # Note: This script must be placed under the yolov7 repo to run. def main(): parser = argparse.ArgumentParser(description='Convert model keys') parser.add_argument( '--src', default='yolov7.pt', help='src yolov7 model path') parser.add_argument('--dst', default='mm_yolov7l.pt', help='save path') args = parser.parse_args() convert(args.src, args.dst) if __name__ == '__main__': main()