# 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 Deformable DETR checkpoints.""" import argparse import json from pathlib import Path import requests import torch from huggingface_hub import cached_download, hf_hub_url from PIL import Image from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) def rename_key(orig_key): if "backbone.0.body" in orig_key: orig_key = orig_key.replace("backbone.0.body", "backbone.conv_encoder.model") if "transformer" in orig_key: orig_key = orig_key.replace("transformer.", "") if "norm1" in orig_key: if "encoder" in orig_key: orig_key = orig_key.replace("norm1", "self_attn_layer_norm") else: orig_key = orig_key.replace("norm1", "encoder_attn_layer_norm") if "norm2" in orig_key: if "encoder" in orig_key: orig_key = orig_key.replace("norm2", "final_layer_norm") else: orig_key = orig_key.replace("norm2", "self_attn_layer_norm") if "norm3" in orig_key: orig_key = orig_key.replace("norm3", "final_layer_norm") if "linear1" in orig_key: orig_key = orig_key.replace("linear1", "fc1") if "linear2" in orig_key: orig_key = orig_key.replace("linear2", "fc2") if "query_embed" in orig_key: orig_key = orig_key.replace("query_embed", "query_position_embeddings") if "cross_attn" in orig_key: orig_key = orig_key.replace("cross_attn", "encoder_attn") return orig_key def read_in_q_k_v(state_dict): # transformer decoder self-attention layers for i in range(6): # read in weights + bias of input projection layer of self-attention in_proj_weight = state_dict.pop(f"decoder.layers.{i}.self_attn.in_proj_weight") in_proj_bias = state_dict.pop(f"decoder.layers.{i}.self_attn.in_proj_bias") # next, add query, keys and values (in that order) to the state dict state_dict[f"decoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :] state_dict[f"decoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256] state_dict[f"decoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :] state_dict[f"decoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512] state_dict[f"decoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :] state_dict[f"decoder.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_deformable_detr_checkpoint( checkpoint_path, single_scale, dilation, with_box_refine, two_stage, pytorch_dump_folder_path, push_to_hub, ): """ Copy/paste/tweak model's weights to our Deformable DETR structure. """ # load default config config = DeformableDetrConfig() # set config attributes if single_scale: config.num_feature_levels = 1 config.dilation = dilation config.with_box_refine = with_box_refine config.two_stage = two_stage # set labels config.num_labels = 91 repo_id = "huggingface/label-files" filename = "coco-detection-id2label.json" id2label = json.load(open(cached_download(hf_hub_url(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 image_processor = DeformableDetrImageProcessor(format="coco_detection") # prepare image img = prepare_img() encoding = image_processor(images=img, return_tensors="pt") pixel_values = encoding["pixel_values"] logger.info("Converting model...") # load original state dict state_dict = torch.load(checkpoint_path, map_location="cpu")["model"] # rename keys for key in state_dict.copy().keys(): val = state_dict.pop(key) state_dict[rename_key(key)] = val # query, key and value matrices need special treatment read_in_q_k_v(state_dict) # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them prefix = "model." for key in state_dict.copy().keys(): if not key.startswith("class_embed") and not key.startswith("bbox_embed"): val = state_dict.pop(key) state_dict[prefix + key] = val # finally, create HuggingFace model and load state dict model = DeformableDetrForObjectDetection(config) model.load_state_dict(state_dict) model.eval() device = "cuda" if torch.cuda.is_available() else "cpu" model.to(device) # verify our conversion outputs = model(pixel_values.to(device)) expected_logits = torch.tensor( [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]] ) expected_boxes = torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]) if single_scale: expected_logits = torch.tensor( [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]] ) expected_boxes = torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]) if single_scale and dilation: expected_logits = torch.tensor( [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]] ) expected_boxes = torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]) if with_box_refine: expected_logits = torch.tensor( [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]] ) expected_boxes = torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]) if with_box_refine and two_stage: expected_logits = torch.tensor( [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]] ) expected_boxes = torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]) print("Logits:", outputs.logits[0, :3, :3]) assert torch.allclose(outputs.logits[0, :3, :3], expected_logits.to(device), atol=1e-4) assert torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes.to(device), atol=1e-4) print("Everything ok!") # 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) # Push to hub if push_to_hub: model_name = "deformable-detr" model_name += "-single-scale" if single_scale else "" model_name += "-dc5" if dilation else "" model_name += "-with-box-refine" if with_box_refine else "" model_name += "-two-stage" if two_stage else "" print("Pushing model to hub...") model.push_to_hub(repo_path_or_name=model_name, organization="nielsr", commit_message="Add model") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--checkpoint_path", type=str, default="/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth", help="Path to Pytorch checkpoint (.pth file) you'd like to convert.", ) parser.add_argument("--single_scale", action="store_true", help="Whether to set config.num_features_levels = 1.") parser.add_argument("--dilation", action="store_true", help="Whether to set config.dilation=True.") parser.add_argument("--with_box_refine", action="store_true", help="Whether to set config.with_box_refine=True.") parser.add_argument("--two_stage", action="store_true", help="Whether to set config.two_stage=True.") parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the folder to output PyTorch model.", ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." ) args = parser.parse_args() convert_deformable_detr_checkpoint( args.checkpoint_path, args.single_scale, args.dilation, args.with_box_refine, args.two_stage, args.pytorch_dump_folder_path, args.push_to_hub, )