# mmdet to groundingdino import argparse from collections import OrderedDict import torch from mmengine.runner import CheckpointLoader # convert the functions from mmdet to groundingdino def correct_unfold_reduction_order(x): out_channel, in_channel = x.shape x = x.reshape(out_channel, in_channel // 4, 4).transpose(1, 2) x = x[:, [0, 2, 1, 3], :] x = x.reshape(out_channel, in_channel) return x def correct_unfold_norm_order(x): in_channel = x.shape[0] x = x.reshape(in_channel // 4, 4).transpose(0, 1) x = x[[0, 2, 1, 3], :] x = x.reshape(in_channel) return x def convert(ckpt): """Inverse mapping of checkpoint parameters to their original names.""" # Create a dictionary to hold the reversed checkpoint new_ckpt = OrderedDict() for k, v in list(ckpt.items()): new_v = v # Start with the original value # Inverse rules based on the convert function (from specific to general) if k.startswith('decoder'): new_k = k.replace('decoder', 'transformer.decoder') if 'norms.2' in new_k: new_k = new_k.replace('norms.2', 'norm1') if 'norms.1' in new_k: new_k = new_k.replace('norms.1', 'catext_norm') if 'norms.0' in new_k: new_k = new_k.replace('norms.0', 'norm2') if 'norms.3' in new_k: new_k = new_k.replace('norms.3', 'norm3') if 'cross_attn_text' in new_k: new_k = new_k.replace('cross_attn_text', 'ca_text') new_k = new_k.replace('attn.in_proj_weight', 'in_proj_weight') new_k = new_k.replace('attn.in_proj_bias', 'in_proj_bias') new_k = new_k.replace('attn.out_proj.weight', 'out_proj.weight') new_k = new_k.replace('attn.out_proj.bias', 'out_proj.bias') if 'ffn.layers.0.0' in new_k: new_k = new_k.replace('ffn.layers.0.0', 'linear1') if 'ffn.layers.1' in new_k: new_k = new_k.replace('ffn.layers.1', 'linear2') if 'self_attn.attn' in new_k: new_k = new_k.replace('self_attn.attn', 'self_attn') new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict ######################################################################### # encoder部分最后的reg_layer_id是6，和decoder区分开来 elif k.startswith('bbox_head.reg_branches.6'): if k.startswith('bbox_head.reg_branches.6.0'): new_k = k.replace('bbox_head.reg_branches.6.0', 'transformer.enc_out_bbox_embed.layers.0') if k.startswith('bbox_head.reg_branches.6.2'): new_k = k.replace('bbox_head.reg_branches.6.2', 'transformer.enc_out_bbox_embed.layers.1') if k.startswith('bbox_head.reg_branches.6.4'): new_k = k.replace('bbox_head.reg_branches.6.4', 'transformer.enc_out_bbox_embed.layers.2') new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict ######################################################################### elif k.startswith('query_embedding'): new_k = k.replace('query_embedding', 'transformer.tgt_embed') new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict ######################################################################### elif k.startswith('bbox_head.reg_branches'): # mmdet直接省略了参数名的一部分，需要查看groundingdino的checkpoint # groundingdino有两部分参数值是一致的 # 分别是bbox_embed和transformer.decoder.embed # 所以mmdet直接将两部分参数进行了“合并” reg_layer_id = int(k.split('.')[2]) linear_id = int(k.split('.')[3]) weight_or_bias = k.split('.')[-1] new_k1 = 'transformer.decoder.bbox_embed.' + \ str(reg_layer_id) + '.layers.' + str(linear_id // 2) + '.' + weight_or_bias new_k2 = 'bbox_embed.' + \ str(reg_layer_id) + '.layers.' + str(linear_id // 2) + '.' + weight_or_bias new_ckpt[new_k1] = new_v # Add the key and value to the original checkpoint dict new_ckpt[new_k2] = new_v # Add the key and value to the original checkpoint dict ######################################################################### elif k.startswith('bbox_head.cls_branches.6'): # mmdet在contrastive_embed中添加了bias项 # 但是decoder应该是0~5，所以6应该是采取两阶段微调后对应的enc_out.class_embed new_k = 'transformer.enc_out_class_embed.bias' new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict ######################################################################### elif k.startswith('bbox_head.cls_branches'): # mmdet在contrastive_embed中添加了bias项 new_k1 = 'transformer.decoder.class_embed.' + k[-6:] new_k2 = 'class_embed.' + k[-6:] new_ckpt[new_k1] = new_v # Add the key and value to the original checkpoint dict new_ckpt[new_k2] = new_v # Add the key and value to the original checkpoint dict ######################################################################### elif k.startswith('memory_trans_'): if k.startswith('memory_trans_fc'): new_k = k.replace('memory_trans_fc', 'transformer.enc_output') elif k.startswith('memory_trans_norm'): new_k = k.replace('memory_trans_norm', 'transformer.enc_output_norm') new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict ######################################################################### elif k.startswith('encoder'): new_k = k.replace('encoder', 'transformer.encoder') new_k = new_k.replace('norms.0', 'norm1') new_k = new_k.replace('norms.1', 'norm2') new_k = new_k.replace('norms.2', 'norm3') new_k = new_k.replace('ffn.layers.0.0', 'linear1') new_k = new_k.replace('ffn.layers.1', 'linear2') if 'text_layers' in new_k: new_k = new_k.replace('self_attn.attn', 'self_attn') new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict ######################################################################### elif k.startswith('level_embed'): new_k = k.replace('level_embed', 'transformer.level_embed') new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict ######################################################################### elif k.startswith('neck.convs'): new_k = k.replace('neck.convs', 'input_proj') new_k = new_k.replace('neck.extra_convs.0', 'neck.convs.3') new_k = new_k.replace('conv.weight', '0.weight') new_k = new_k.replace('conv.bias', '0.bias') new_k = new_k.replace('gn.weight', '1.weight') new_k = new_k.replace('gn.bias', '1.bias') new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict ######################################################################### elif 'neck.extra_convs.0' in k: new_k = k.replace('neck.extra_convs.0', 'neck.convs.4') new_k = new_k.replace('neck.convs', 'input_proj') new_k = new_k.replace('conv.weight', '0.weight') new_k = new_k.replace('conv.bias', '0.bias') new_k = new_k.replace('gn.weight', '1.weight') new_k = new_k.replace('gn.bias', '1.bias') new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict ######################################################################### elif k.startswith('text_feat_map'): new_k = k.replace('text_feat_map', 'feat_map') new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict ######################################################################### elif k.startswith('language_model.language_backbone.body.model'): new_k = k.replace('language_model.language_backbone.body.model', 'bert') new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict ######################################################################### elif k.startswith('backbone'): new_k = k.replace('backbone', 'backbone.0') if 'patch_embed.projection' in new_k: new_k = new_k.replace('patch_embed.projection', 'patch_embed.proj') elif 'drop_after_pos' in new_k: new_k = new_k.replace('drop_after_pos', 'pos_drop') if 'stages' in new_k: new_k = new_k.replace('stages', 'layers') if 'ffn.layers.0.0' in new_k: new_k = new_k.replace('ffn.layers.0.0', 'mlp.fc1') elif 'ffn.layers.1' in new_k: new_k = new_k.replace('ffn.layers.1', 'mlp.fc2') elif 'attn.w_msa' in new_k: new_k = new_k.replace('attn.w_msa', 'attn') 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) new_ckpt[new_k] = new_v # Add the key and value to the original checkpoint dict ######################################################################### else: print('skip:', k) continue # if 'transformer.decoder.bbox_embed' in new_k: # new_k = new_k.replace('transformer.decoder.bbox_embed', 'bbox_embed') # if new_k.startswith('module.'): # new_k = new_k.replace('module.', '') return new_ckpt def main(): parser = argparse.ArgumentParser( description='Convert keys to GroundingDINO style.') parser.add_argument( 'src', nargs='?', default='grounding_dino_swin-l_pretrain_all-56d69e78.pth', help='src model path or url') # The dst path must be a full path of the new checkpoint. parser.add_argument( 'dst', nargs='?', default='mmdet_swinl.pth_groundingdino.pth', help='save path') args = parser.parse_args() checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu') # mmdet中是state_dict而不是model if 'state_dict' in checkpoint: state_dict = checkpoint['state_dict'] else: state_dict = checkpoint weight = convert(state_dict) torch.save(weight, args.dst) # sha = subprocess.check_output(['sha256sum', args.dst]).decode() # sha = calculate_sha256(args.dst) # final_file = args.dst.replace('.pth', '') + '-{}.pth'.format(sha[:8]) # subprocess.Popen(['mv', args.dst, final_file]) print(f'Done!!, save to {args.dst}') if __name__ == '__main__': main() # skip: dn_query_generator.label_embedding.weight