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Grounded-Segment-Anything
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transformers_4_35_0
/models
/conditional_detr
/convert_conditional_detr_original_pytorch_checkpoint_to_pytorch.py
| # 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 | |
| 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) | |