# 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 YOLOS checkpoints from the original repository. URL: https://github.com/hustvl/YOLOS""" import argparse import json from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) def get_yolos_config(yolos_name: str) -> YolosConfig: config = YolosConfig() # size of the architecture if "yolos_ti" in yolos_name: config.hidden_size = 192 config.intermediate_size = 768 config.num_hidden_layers = 12 config.num_attention_heads = 3 config.image_size = [800, 1333] config.use_mid_position_embeddings = False elif yolos_name == "yolos_s_dWr": config.hidden_size = 330 config.num_hidden_layers = 14 config.num_attention_heads = 6 config.intermediate_size = 1320 elif "yolos_s" in yolos_name: config.hidden_size = 384 config.intermediate_size = 1536 config.num_hidden_layers = 12 config.num_attention_heads = 6 elif "yolos_b" in yolos_name: config.image_size = [800, 1344] 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()} return config # we split up the matrix of each encoder layer into queries, keys and values def read_in_q_k_v(state_dict: dict, config: YolosConfig, base_model: bool = False): for i in range(config.num_hidden_layers): # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) in_proj_weight = state_dict.pop(f"blocks.{i}.attn.qkv.weight") in_proj_bias = state_dict.pop(f"blocks.{i}.attn.qkv.bias") # next, add query, keys and values (in that order) to the state dict state_dict[f"encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[: config.hidden_size, :] state_dict[f"encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[: config.hidden_size] state_dict[f"encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[ config.hidden_size : config.hidden_size * 2, : ] state_dict[f"encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[ config.hidden_size : config.hidden_size * 2 ] state_dict[f"encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[-config.hidden_size :, :] state_dict[f"encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-config.hidden_size :] def rename_key(name: str) -> str: if "backbone" in name: name = name.replace("backbone", "vit") if "cls_token" in name: name = name.replace("cls_token", "embeddings.cls_token") if "det_token" in name: name = name.replace("det_token", "embeddings.detection_tokens") if "mid_pos_embed" in name: name = name.replace("mid_pos_embed", "encoder.mid_position_embeddings") if "pos_embed" in name: name = name.replace("pos_embed", "embeddings.position_embeddings") if "patch_embed.proj" in name: name = name.replace("patch_embed.proj", "embeddings.patch_embeddings.projection") if "blocks" in name: name = name.replace("blocks", "encoder.layer") if "attn.proj" in name: name = name.replace("attn.proj", "attention.output.dense") if "attn" in name: name = name.replace("attn", "attention.self") if "norm1" in name: name = name.replace("norm1", "layernorm_before") if "norm2" in name: name = name.replace("norm2", "layernorm_after") if "mlp.fc1" in name: name = name.replace("mlp.fc1", "intermediate.dense") if "mlp.fc2" in name: name = name.replace("mlp.fc2", "output.dense") if "class_embed" in name: name = name.replace("class_embed", "class_labels_classifier") if "bbox_embed" in name: name = name.replace("bbox_embed", "bbox_predictor") if "vit.norm" in name: name = name.replace("vit.norm", "vit.layernorm") return name def convert_state_dict(orig_state_dict: dict, model: YolosForObjectDetection) -> dict: for key in orig_state_dict.copy().keys(): val = orig_state_dict.pop(key) if "qkv" in key: key_split = key.split(".") layer_num = int(key_split[2]) dim = model.vit.encoder.layer[layer_num].attention.attention.all_head_size if "weight" in key: orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.query.weight"] = val[:dim, :] orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.key.weight"] = val[ dim : dim * 2, : ] orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.value.weight"] = val[-dim:, :] else: orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.query.bias"] = val[:dim] orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.key.bias"] = val[dim : dim * 2] orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.value.bias"] = val[-dim:] else: orig_state_dict[rename_key(key)] = val return orig_state_dict # We will verify our results on an image of cute cats def prepare_img() -> torch.Tensor: url = "http://images.cocodataset.org/val2017/000000039769.jpg" im = Image.open(requests.get(url, stream=True).raw) return im @torch.no_grad() def convert_yolos_checkpoint( yolos_name: str, checkpoint_path: str, pytorch_dump_folder_path: str, push_to_hub: bool = False ): """ Copy/paste/tweak model's weights to our YOLOS structure. """ config = get_yolos_config(yolos_name) # load original state_dict state_dict = torch.load(checkpoint_path, map_location="cpu")["model"] # load 🤗 model model = YolosForObjectDetection(config) model.eval() new_state_dict = convert_state_dict(state_dict, model) model.load_state_dict(new_state_dict) # Check outputs on an image, prepared by YolosImageProcessor size = 800 if yolos_name != "yolos_ti" else 512 image_processor = YolosImageProcessor(format="coco_detection", size=size) encoding = image_processor(images=prepare_img(), return_tensors="pt") outputs = model(**encoding) logits, pred_boxes = outputs.logits, outputs.pred_boxes expected_slice_logits, expected_slice_boxes = None, None if yolos_name == "yolos_ti": expected_slice_logits = torch.tensor( [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]] ) expected_slice_boxes = torch.tensor( [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]] ) elif yolos_name == "yolos_s_200_pre": expected_slice_logits = torch.tensor( [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]] ) expected_slice_boxes = torch.tensor( [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]] ) elif yolos_name == "yolos_s_300_pre": expected_slice_logits = torch.tensor( [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]] ) expected_slice_boxes = torch.tensor( [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]] ) elif yolos_name == "yolos_s_dWr": expected_slice_logits = torch.tensor( [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]] ) expected_slice_boxes = torch.tensor( [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]] ) elif yolos_name == "yolos_base": expected_slice_logits = torch.tensor( [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]] ) expected_slice_boxes = torch.tensor( [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]] ) else: raise ValueError(f"Unknown yolos_name: {yolos_name}") assert torch.allclose(logits[0, :3, :3], expected_slice_logits, atol=1e-4) assert torch.allclose(pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4) Path(pytorch_dump_folder_path).mkdir(exist_ok=True) print(f"Saving model {yolos_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving image processor to {pytorch_dump_folder_path}") image_processor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: model_mapping = { "yolos_ti": "yolos-tiny", "yolos_s_200_pre": "yolos-small", "yolos_s_300_pre": "yolos-small-300", "yolos_s_dWr": "yolos-small-dwr", "yolos_base": "yolos-base", } print("Pushing to the hub...") model_name = model_mapping[yolos_name] image_processor.push_to_hub(model_name, organization="hustvl") model.push_to_hub(model_name, organization="hustvl") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--yolos_name", default="yolos_s_200_pre", type=str, help=( "Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre'," " 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'." ), ) parser.add_argument( "--checkpoint_path", default=None, type=str, help="Path to the original state dict (.pth file)." ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) 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_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)