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transformers_4_35_0
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/deformable_detr
/convert_deformable_detr_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 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 | |
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, | |
) | |