# Copyright (c) OpenMMLab. All rights reserved. import argparse import subprocess from collections import OrderedDict import torch from mmengine.runner import CheckpointLoader 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 'module' not in k: # NOTE: swin-b has no module prefix and swin-t has module prefix k = 'module.' + k if 'module.bbox_embed' in k: # NOTE: bbox_embed name is swin-b is different from swin-t k = k.replace('module.bbox_embed', 'module.transformer.decoder.bbox_embed') if 'module.backbone.0' in k: new_k = k.replace('module.backbone.0', '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) elif 'module.bert' in k: new_k = k.replace('module.bert', 'language_model.language_backbone.body.model') # new_k = k.replace('module.bert', 'bert') elif 'module.feat_map' in k: new_k = k.replace('module.feat_map', 'text_feat_map') elif 'module.input_proj' in k: new_k = k.replace('module.input_proj', 'neck.convs') if 'neck.convs.3' in new_k: # extra convs for 4th scale new_k = new_k.replace('neck.convs.3', 'neck.extra_convs.0') if '0.weight' in new_k: # 0.weight -> conv.weight new_k = new_k.replace('0.weight', 'conv.weight') if '0.bias' in new_k: # 0.bias -> conv.bias new_k = new_k.replace('0.bias', 'conv.bias') if '1.weight' in new_k: # 1.weight -> gn.weight new_k = new_k.replace('1.weight', 'gn.weight') if '1.bias' in new_k: # 1.bias -> gn.bias new_k = new_k.replace('1.bias', 'gn.bias') elif 'module.transformer.level_embed' in k: # module.transformer.level_embed -> level_embed new_k = k.replace('module.transformer.level_embed', 'level_embed') elif 'module.transformer.encoder' in k: # if '.layers' in k: new_k = k.replace('module.transformer.encoder', 'encoder') if 'norm1' in new_k: new_k = new_k.replace('norm1', 'norms.0') if 'norm2' in new_k: new_k = new_k.replace('norm2', 'norms.1') if 'norm3' in new_k: new_k = new_k.replace('norm3', 'norms.2') if 'linear1' in new_k: new_k = new_k.replace('linear1', 'ffn.layers.0.0') if 'linear2' in new_k: new_k = new_k.replace('linear2', 'ffn.layers.1') if 'text_layers' in new_k and 'self_attn' in new_k: new_k = new_k.replace('self_attn', 'self_attn.attn') elif 'module.transformer.enc_output' in k: if 'module.transformer.enc_output' in k and 'norm' not in k: new_k = k.replace('module.transformer.enc_output', 'memory_trans_fc') if 'module.transformer.enc_output_norm' in k: new_k = k.replace('module.transformer.enc_output_norm', 'memory_trans_norm') elif 'module.transformer.enc_out_bbox_embed.layers' in k: # ugly version if 'module.transformer.enc_out_bbox_embed.layers.0' in k: new_k = k.replace( 'module.transformer.enc_out_bbox_embed.layers.0', 'bbox_head.reg_branches.6.0') if 'module.transformer.enc_out_bbox_embed.layers.1' in k: new_k = k.replace( 'module.transformer.enc_out_bbox_embed.layers.1', 'bbox_head.reg_branches.6.2') if 'module.transformer.enc_out_bbox_embed.layers.2' in k: new_k = k.replace( 'module.transformer.enc_out_bbox_embed.layers.2', 'bbox_head.reg_branches.6.4') elif 'module.transformer.tgt_embed' in k: new_k = k.replace('module.transformer.tgt_embed', 'query_embedding') elif 'module.transformer.decoder' in k: new_k = k.replace('module.transformer.decoder', 'decoder') if 'norm1' in new_k: # norm1 in official GroundingDINO is the third norm in decoder new_k = new_k.replace('norm1', 'norms.2') if 'catext_norm' in new_k: # catext_norm in official GroundingDINO is the # second norm in decoder new_k = new_k.replace('catext_norm', 'norms.1') if 'norm2' in new_k: # norm2 in official GroundingDINO is the first norm in decoder new_k = new_k.replace('norm2', 'norms.0') if 'norm3' in new_k: new_k = new_k.replace('norm3', 'norms.3') if 'ca_text' in new_k: new_k = new_k.replace('ca_text', 'cross_attn_text') if 'in_proj_weight' in new_k: new_k = new_k.replace('in_proj_weight', 'attn.in_proj_weight') if 'in_proj_bias' in new_k: new_k = new_k.replace('in_proj_bias', 'attn.in_proj_bias') if 'out_proj.weight' in new_k: new_k = new_k.replace('out_proj.weight', 'attn.out_proj.weight') if 'out_proj.bias' in new_k: new_k = new_k.replace('out_proj.bias', 'attn.out_proj.bias') if 'linear1' in new_k: new_k = new_k.replace('linear1', 'ffn.layers.0.0') if 'linear2' in new_k: new_k = new_k.replace('linear2', 'ffn.layers.1') if 'self_attn' in new_k: new_k = new_k.replace('self_attn', 'self_attn.attn') if 'bbox_embed' in new_k: reg_layer_id = int(new_k.split('.')[2]) linear_id = int(new_k.split('.')[4]) weight_or_bias = new_k.split('.')[-1] new_k = 'bbox_head.reg_branches.' + \ str(reg_layer_id)+'.'+str(2*linear_id)+'.'+weight_or_bias else: print('skip:', k) continue new_ckpt[new_k] = new_v return new_ckpt def main(): parser = argparse.ArgumentParser( description='Convert keys to mmdet style.') parser.add_argument( 'src', default='groundingdino_swint_ogc.pth.pth', help='src model path or url') # The dst path must be a full path of the new checkpoint. parser.add_argument( 'dst', default='groundingdino_swint_ogc.pth_mmdet.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 = convert(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()