# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert Conditional DETR checkpoints.""" import argparse import json from collections import OrderedDict from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import ( ConditionalDetrConfig, ConditionalDetrForObjectDetection, ConditionalDetrForSegmentation, ConditionalDetrImageProcessor, ) from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) # here we list all keys to be renamed (original name on the left, our name on the right) rename_keys = [] for i in range(6): # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms rename_keys.append( (f"transformer.encoder.layers.{i}.self_attn.out_proj.weight", f"encoder.layers.{i}.self_attn.out_proj.weight") ) rename_keys.append( (f"transformer.encoder.layers.{i}.self_attn.out_proj.bias", f"encoder.layers.{i}.self_attn.out_proj.bias") ) rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"encoder.layers.{i}.fc1.weight")) rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"encoder.layers.{i}.fc1.bias")) rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"encoder.layers.{i}.fc2.weight")) rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"encoder.layers.{i}.fc2.bias")) rename_keys.append( (f"transformer.encoder.layers.{i}.norm1.weight", f"encoder.layers.{i}.self_attn_layer_norm.weight") ) rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"encoder.layers.{i}.self_attn_layer_norm.bias")) rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"encoder.layers.{i}.final_layer_norm.weight")) rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"encoder.layers.{i}.final_layer_norm.bias")) # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms rename_keys.append( (f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"decoder.layers.{i}.self_attn.out_proj.weight") ) rename_keys.append( (f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"decoder.layers.{i}.self_attn.out_proj.bias") ) rename_keys.append( ( f"transformer.decoder.layers.{i}.cross_attn.out_proj.weight", f"decoder.layers.{i}.encoder_attn.out_proj.weight", ) ) rename_keys.append( ( f"transformer.decoder.layers.{i}.cross_attn.out_proj.bias", f"decoder.layers.{i}.encoder_attn.out_proj.bias", ) ) rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"decoder.layers.{i}.fc1.weight")) rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"decoder.layers.{i}.fc1.bias")) rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"decoder.layers.{i}.fc2.weight")) rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"decoder.layers.{i}.fc2.bias")) rename_keys.append( (f"transformer.decoder.layers.{i}.norm1.weight", f"decoder.layers.{i}.self_attn_layer_norm.weight") ) rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"decoder.layers.{i}.self_attn_layer_norm.bias")) rename_keys.append( (f"transformer.decoder.layers.{i}.norm2.weight", f"decoder.layers.{i}.encoder_attn_layer_norm.weight") ) rename_keys.append( (f"transformer.decoder.layers.{i}.norm2.bias", f"decoder.layers.{i}.encoder_attn_layer_norm.bias") ) rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"decoder.layers.{i}.final_layer_norm.weight")) rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"decoder.layers.{i}.final_layer_norm.bias")) # q, k, v projections in self/cross-attention in decoder for conditional DETR rename_keys.append( (f"transformer.decoder.layers.{i}.sa_qcontent_proj.weight", f"decoder.layers.{i}.sa_qcontent_proj.weight") ) rename_keys.append( (f"transformer.decoder.layers.{i}.sa_kcontent_proj.weight", f"decoder.layers.{i}.sa_kcontent_proj.weight") ) rename_keys.append( (f"transformer.decoder.layers.{i}.sa_qpos_proj.weight", f"decoder.layers.{i}.sa_qpos_proj.weight") ) rename_keys.append( (f"transformer.decoder.layers.{i}.sa_kpos_proj.weight", f"decoder.layers.{i}.sa_kpos_proj.weight") ) rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.weight", f"decoder.layers.{i}.sa_v_proj.weight")) rename_keys.append( (f"transformer.decoder.layers.{i}.ca_qcontent_proj.weight", f"decoder.layers.{i}.ca_qcontent_proj.weight") ) # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight")) rename_keys.append( (f"transformer.decoder.layers.{i}.ca_kcontent_proj.weight", f"decoder.layers.{i}.ca_kcontent_proj.weight") ) rename_keys.append( (f"transformer.decoder.layers.{i}.ca_kpos_proj.weight", f"decoder.layers.{i}.ca_kpos_proj.weight") ) rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.weight", f"decoder.layers.{i}.ca_v_proj.weight")) rename_keys.append( (f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight", f"decoder.layers.{i}.ca_qpos_sine_proj.weight") ) rename_keys.append( (f"transformer.decoder.layers.{i}.sa_qcontent_proj.bias", f"decoder.layers.{i}.sa_qcontent_proj.bias") ) rename_keys.append( (f"transformer.decoder.layers.{i}.sa_kcontent_proj.bias", f"decoder.layers.{i}.sa_kcontent_proj.bias") ) rename_keys.append((f"transformer.decoder.layers.{i}.sa_qpos_proj.bias", f"decoder.layers.{i}.sa_qpos_proj.bias")) rename_keys.append((f"transformer.decoder.layers.{i}.sa_kpos_proj.bias", f"decoder.layers.{i}.sa_kpos_proj.bias")) rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.bias", f"decoder.layers.{i}.sa_v_proj.bias")) rename_keys.append( (f"transformer.decoder.layers.{i}.ca_qcontent_proj.bias", f"decoder.layers.{i}.ca_qcontent_proj.bias") ) # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias")) rename_keys.append( (f"transformer.decoder.layers.{i}.ca_kcontent_proj.bias", f"decoder.layers.{i}.ca_kcontent_proj.bias") ) rename_keys.append((f"transformer.decoder.layers.{i}.ca_kpos_proj.bias", f"decoder.layers.{i}.ca_kpos_proj.bias")) rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.bias", f"decoder.layers.{i}.ca_v_proj.bias")) rename_keys.append( (f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias", f"decoder.layers.{i}.ca_qpos_sine_proj.bias") ) # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads # for conditional DETR, also convert reference point head and query scale MLP rename_keys.extend( [ ("input_proj.weight", "input_projection.weight"), ("input_proj.bias", "input_projection.bias"), ("query_embed.weight", "query_position_embeddings.weight"), ("transformer.decoder.norm.weight", "decoder.layernorm.weight"), ("transformer.decoder.norm.bias", "decoder.layernorm.bias"), ("class_embed.weight", "class_labels_classifier.weight"), ("class_embed.bias", "class_labels_classifier.bias"), ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"), ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"), ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"), ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"), ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"), ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"), ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"), ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"), ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"), ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"), ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"), ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"), ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"), ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"), ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"), ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"), ] ) def rename_key(state_dict, old, new): val = state_dict.pop(old) state_dict[new] = val def rename_backbone_keys(state_dict): new_state_dict = OrderedDict() for key, value in state_dict.items(): if "backbone.0.body" in key: new_key = key.replace("backbone.0.body", "backbone.conv_encoder.model") new_state_dict[new_key] = value else: new_state_dict[key] = value return new_state_dict def read_in_q_k_v(state_dict, is_panoptic=False): prefix = "" if is_panoptic: prefix = "conditional_detr." # first: transformer encoder for i in range(6): # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias) in_proj_weight = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight") in_proj_bias = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias") # next, add query, keys and values (in that order) to the state dict state_dict[f"encoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :] state_dict[f"encoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256] state_dict[f"encoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :] state_dict[f"encoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512] state_dict[f"encoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :] state_dict[f"encoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:] # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" im = Image.open(requests.get(url, stream=True).raw) return im @torch.no_grad() def convert_conditional_detr_checkpoint(model_name, pytorch_dump_folder_path): """ Copy/paste/tweak model's weights to our CONDITIONAL_DETR structure. """ # load default config config = ConditionalDetrConfig() # set backbone and dilation attributes if "resnet101" in model_name: config.backbone = "resnet101" if "dc5" in model_name: config.dilation = True is_panoptic = "panoptic" in model_name if is_panoptic: config.num_labels = 250 else: config.num_labels = 91 repo_id = "huggingface/label-files" filename = "coco-detection-id2label.json" id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} config.id2label = id2label config.label2id = {v: k for k, v in id2label.items()} # load image processor format = "coco_panoptic" if is_panoptic else "coco_detection" image_processor = ConditionalDetrImageProcessor(format=format) # prepare image img = prepare_img() encoding = image_processor(images=img, return_tensors="pt") pixel_values = encoding["pixel_values"] logger.info(f"Converting model {model_name}...") # load original model from torch hub conditional_detr = torch.hub.load("DeppMeng/ConditionalDETR", model_name, pretrained=True).eval() state_dict = conditional_detr.state_dict() # rename keys for src, dest in rename_keys: if is_panoptic: src = "conditional_detr." + src rename_key(state_dict, src, dest) state_dict = rename_backbone_keys(state_dict) # query, key and value matrices need special treatment read_in_q_k_v(state_dict, is_panoptic=is_panoptic) # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them prefix = "conditional_detr.model." if is_panoptic else "model." for key in state_dict.copy().keys(): if is_panoptic: if ( key.startswith("conditional_detr") and not key.startswith("class_labels_classifier") and not key.startswith("bbox_predictor") ): val = state_dict.pop(key) state_dict["conditional_detr.model" + key[4:]] = val elif "class_labels_classifier" in key or "bbox_predictor" in key: val = state_dict.pop(key) state_dict["conditional_detr." + key] = val elif key.startswith("bbox_attention") or key.startswith("mask_head"): continue else: val = state_dict.pop(key) state_dict[prefix + key] = val else: if not key.startswith("class_labels_classifier") and not key.startswith("bbox_predictor"): val = state_dict.pop(key) state_dict[prefix + key] = val # finally, create HuggingFace model and load state dict model = ConditionalDetrForSegmentation(config) if is_panoptic else ConditionalDetrForObjectDetection(config) model.load_state_dict(state_dict) model.eval() model.push_to_hub(repo_id=model_name, organization="DepuMeng", commit_message="Add model") # verify our conversion original_outputs = conditional_detr(pixel_values) outputs = model(pixel_values) assert torch.allclose(outputs.logits, original_outputs["pred_logits"], atol=1e-4) assert torch.allclose(outputs.pred_boxes, original_outputs["pred_boxes"], atol=1e-4) if is_panoptic: assert torch.allclose(outputs.pred_masks, original_outputs["pred_masks"], atol=1e-4) # Save model and image processor logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}...") Path(pytorch_dump_folder_path).mkdir(exist_ok=True) model.save_pretrained(pytorch_dump_folder_path) image_processor.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--model_name", default="conditional_detr_resnet50", type=str, help="Name of the CONDITIONAL_DETR model you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model." ) args = parser.parse_args() convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)