# Copyright (c) OpenMMLab. All rights reserved. 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()