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| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import argparse | |
| import subprocess | |
| from collections import OrderedDict | |
| import torch | |
| from mmengine.runner import CheckpointLoader | |
| convert_dict_fpn = { | |
| 'backbone.fpn_lateral3': 'neck.lateral_convs.0.conv', | |
| 'backbone.fpn_lateral4': 'neck.lateral_convs.1.conv', | |
| 'backbone.fpn_lateral5': 'neck.lateral_convs.2.conv', | |
| 'backbone.fpn_output3': 'neck.fpn_convs.0.conv', | |
| 'backbone.fpn_output4': 'neck.fpn_convs.1.conv', | |
| 'backbone.fpn_output5': 'neck.fpn_convs.2.conv', | |
| 'backbone.top_block.p6': 'neck.fpn_convs.3.conv', | |
| 'backbone.top_block.p7': 'neck.fpn_convs.4.conv', | |
| } | |
| convert_dict_rpn = { | |
| 'proposal_generator.centernet_head.bbox_tower.0': | |
| 'rpn_head.reg_convs.0.conv', | |
| 'proposal_generator.centernet_head.bbox_tower.1': | |
| 'rpn_head.reg_convs.0.gn', | |
| 'proposal_generator.centernet_head.bbox_tower.3': | |
| 'rpn_head.reg_convs.1.conv', | |
| 'proposal_generator.centernet_head.bbox_tower.4': | |
| 'rpn_head.reg_convs.1.gn', | |
| 'proposal_generator.centernet_head.bbox_tower.6': | |
| 'rpn_head.reg_convs.2.conv', | |
| 'proposal_generator.centernet_head.bbox_tower.7': | |
| 'rpn_head.reg_convs.2.gn', | |
| 'proposal_generator.centernet_head.bbox_tower.9': | |
| 'rpn_head.reg_convs.3.conv', | |
| 'proposal_generator.centernet_head.bbox_tower.10': | |
| 'rpn_head.reg_convs.3.gn', | |
| 'proposal_generator.centernet_head.bbox_pred': 'rpn_head.conv_reg', | |
| 'proposal_generator.centernet_head.scales.0.scale': | |
| 'rpn_head.scales.0.scale', | |
| 'proposal_generator.centernet_head.scales.1.scale': | |
| 'rpn_head.scales.1.scale', | |
| 'proposal_generator.centernet_head.scales.2.scale': | |
| 'rpn_head.scales.2.scale', | |
| 'proposal_generator.centernet_head.scales.3.scale': | |
| 'rpn_head.scales.3.scale', | |
| 'proposal_generator.centernet_head.scales.4.scale': | |
| 'rpn_head.scales.4.scale', | |
| 'proposal_generator.centernet_head.agn_hm': 'rpn_head.conv_cls', | |
| } | |
| convert_dict_roi = { | |
| 'roi_heads.box_head.0.fc1': 'roi_head.bbox_head.0.shared_fcs.0', | |
| 'roi_heads.box_head.0.fc2': 'roi_head.bbox_head.0.shared_fcs.1', | |
| 'roi_heads.box_head.1.fc1': 'roi_head.bbox_head.1.shared_fcs.0', | |
| 'roi_heads.box_head.1.fc2': 'roi_head.bbox_head.1.shared_fcs.1', | |
| 'roi_heads.box_head.2.fc1': 'roi_head.bbox_head.2.shared_fcs.0', | |
| 'roi_heads.box_head.2.fc2': 'roi_head.bbox_head.2.shared_fcs.1', | |
| 'roi_heads.box_predictor.0.freq_weight': | |
| 'roi_head.bbox_head.0.freq_weight', | |
| 'roi_heads.box_predictor.0.cls_score.zs_weight': | |
| 'roi_head.bbox_head.0.fc_cls.zs_weight', | |
| 'roi_heads.box_predictor.0.cls_score.linear': | |
| 'roi_head.bbox_head.0.fc_cls.linear', | |
| 'roi_heads.box_predictor.0.bbox_pred.0': 'roi_head.bbox_head.0.fc_reg.0', | |
| 'roi_heads.box_predictor.0.bbox_pred.2': 'roi_head.bbox_head.0.fc_reg.2', | |
| 'roi_heads.box_predictor.1.freq_weight': | |
| 'roi_head.bbox_head.1.freq_weight', | |
| 'roi_heads.box_predictor.1.cls_score.zs_weight': | |
| 'roi_head.bbox_head.1.fc_cls.zs_weight', | |
| 'roi_heads.box_predictor.1.cls_score.linear': | |
| 'roi_head.bbox_head.1.fc_cls.linear', | |
| 'roi_heads.box_predictor.1.bbox_pred.0': 'roi_head.bbox_head.1.fc_reg.0', | |
| 'roi_heads.box_predictor.1.bbox_pred.2': 'roi_head.bbox_head.1.fc_reg.2', | |
| 'roi_heads.box_predictor.2.freq_weight': | |
| 'roi_head.bbox_head.2.freq_weight', | |
| 'roi_heads.box_predictor.2.cls_score.zs_weight': | |
| 'roi_head.bbox_head.2.fc_cls.zs_weight', | |
| 'roi_heads.box_predictor.2.cls_score.linear': | |
| 'roi_head.bbox_head.2.fc_cls.linear', | |
| 'roi_heads.box_predictor.2.bbox_pred.0': 'roi_head.bbox_head.2.fc_reg.0', | |
| 'roi_heads.box_predictor.2.bbox_pred.2': 'roi_head.bbox_head.2.fc_reg.2', | |
| 'roi_heads.mask_head.mask_fcn1': 'roi_head.mask_head.convs.0.conv', | |
| 'roi_heads.mask_head.mask_fcn2': 'roi_head.mask_head.convs.1.conv', | |
| 'roi_heads.mask_head.mask_fcn3': 'roi_head.mask_head.convs.2.conv', | |
| 'roi_heads.mask_head.mask_fcn4': 'roi_head.mask_head.convs.3.conv', | |
| 'roi_heads.mask_head.deconv': 'roi_head.mask_head.upsample', | |
| 'roi_heads.mask_head.predictor': 'roi_head.mask_head.conv_logits', | |
| } | |
| def correct_unfold_reduction_order(x): | |
| out_channel, in_channel = x.shape | |
| x = x.reshape(out_channel, 4, in_channel // 4) | |
| x = x[:, [0, 2, 1, 3], :].transpose(1, 2).reshape(out_channel, in_channel) | |
| return x | |
| def correct_unfold_norm_order(x): | |
| in_channel = x.shape[0] | |
| x = x.reshape(4, in_channel // 4) | |
| x = x[[0, 2, 1, 3], :].transpose(0, 1).reshape(in_channel) | |
| return x | |
| def convert(ckpt): | |
| new_ckpt = OrderedDict() | |
| for k, v in list(ckpt.items()): | |
| new_v = v | |
| if 'backbone.bottom_up' in k: | |
| new_k = k.replace('backbone.bottom_up', 'backbone') | |
| # for Transformer backbone | |
| if 'patch_embed.proj' in new_k: | |
| new_k = new_k.replace('patch_embed.proj', | |
| 'patch_embed.projection') | |
| elif 'pos_drop' in new_k: | |
| new_k = new_k.replace('pos_drop', 'drop_after_pos') | |
| if 'layers' in new_k: | |
| new_k = new_k.replace('layers', 'stages') | |
| if 'mlp.fc1' in new_k: | |
| new_k = new_k.replace('mlp.fc1', 'ffn.layers.0.0') | |
| elif 'mlp.fc2' in new_k: | |
| new_k = new_k.replace('mlp.fc2', 'ffn.layers.1') | |
| elif 'attn' in new_k: | |
| new_k = new_k.replace('attn', 'attn.w_msa') | |
| if 'downsample' in k: | |
| if 'reduction.' in k: | |
| new_v = correct_unfold_reduction_order(v) | |
| elif 'norm.' in k: | |
| new_v = correct_unfold_norm_order(v) | |
| # for resnet | |
| if 'base.' in k: | |
| new_k = new_k.replace('base.', '') | |
| elif 'backbone.fpn' in k or 'backbone.top_block' in k: | |
| old_k = k.replace('.weight', '') | |
| old_k = old_k.replace('.bias', '') | |
| new_k = k.replace(old_k, convert_dict_fpn[old_k]) | |
| elif 'proposal_generator' in k: | |
| old_k = k.replace('.weight', '') | |
| old_k = old_k.replace('.bias', '') | |
| new_k = k.replace(old_k, convert_dict_rpn[old_k]) | |
| elif 'roi_heads' in k: | |
| old_k = k.replace('.weight', '') | |
| old_k = old_k.replace('.bias', '') | |
| new_k = k.replace(old_k, convert_dict_roi[old_k]) | |
| else: | |
| print('skip:', k) | |
| continue | |
| new_ckpt[new_k] = new_v | |
| return new_ckpt | |
| def main(): | |
| parser = argparse.ArgumentParser( | |
| description='Convert keys in pretrained eva ' | |
| 'models to mmpretrain style.') | |
| parser.add_argument( | |
| '--src', | |
| default='Detic_LbaseI_CLIP_SwinB_896b32_4x_ft4x_max-size.pth', | |
| help='src model path or url') | |
| # The dst path must be a full path of the new checkpoint. | |
| parser.add_argument( | |
| '--dst', | |
| default='detic_centernet2_swin-b_fpn_4x_lvis-base_in21k-lvis.pth', | |
| help='save path') | |
| args = parser.parse_args() | |
| checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu') | |
| if 'model' in checkpoint: | |
| state_dict = checkpoint['model'] | |
| else: | |
| state_dict = checkpoint | |
| weight = {} | |
| new_state_dict = convert(state_dict) | |
| if 'backbone.fc.weight' in new_state_dict.keys(): | |
| del [new_state_dict['backbone.fc.weight']] | |
| if 'backbone.fc.bias' in new_state_dict.keys(): | |
| del [new_state_dict['backbone.fc.bias']] | |
| weight['state_dict'] = new_state_dict | |
| torch.save(weight, args.dst) | |
| sha = subprocess.check_output(['sha256sum', args.dst]).decode() | |
| final_file = args.dst.replace('.pth', '') + '-{}.pth'.format(sha[:8]) | |
| subprocess.Popen(['mv', args.dst, final_file]) | |
| print(f'Done!!, save to {final_file}') | |
| if __name__ == '__main__': | |
| main() | |