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Running
on
Zero
| # 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 | |
| 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() | |