-
Notifications
You must be signed in to change notification settings - Fork 25.2k
/
convert_yolos_to_pytorch.py
260 lines (224 loc) 路 10.7 KB
/
convert_yolos_to_pytorch.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
# 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 torch
from PIL import Image
import requests
from huggingface_hub import hf_hub_download
from transformers import DetrFeatureExtractor, YolosConfig, YolosForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
def get_yolos_config(yolos_name):
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 = "datasets/huggingface/label-files"
filename = "coco-detection-id2label.json"
id2label = json.load(open(hf_hub_download(repo_id, filename), "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, config, base_model=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):
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.det_token")
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, model):
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():
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, checkpoint_path, pytorch_dump_folder_path, push_to_hub=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 馃 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 DetrFeatureExtractor
feature_extractor = DetrFeatureExtractor(format="coco_detection")
encoding = feature_extractor(images=prepare_img(), return_tensors="pt")
pixel_values = encoding["pixel_values"]
outputs = model(pixel_values)
logits, pred_boxes = outputs.logits, outputs.pred_boxes
print("Actual logits:", logits[0, :3, :3])
print("Actual boxes:", pred_boxes[0, :3, :3])
expected_slice_logits, expected_slice_boxes = None, None
if yolos_name == "yolos_ti":
expected_slice_logits = torch.tensor(
[[-43.3948, -14.6000, -19.4968], [-27.2291, -7.9266, -10.7180], [-44.8437, -22.2187, -34.0474]]
)
expected_slice_boxes = torch.tensor(
[[0.4408, 0.0750, 0.8582], [0.5009, 0.4857, 1.0000], [0.1724, 0.2022, 0.2394]]
)
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()
expected_slice_boxes = torch.tensor()
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 feature extractor to {pytorch_dump_folder_path}")
feature_extractor.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]
feature_extractor.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.")
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 馃 hub."
)
args = parser.parse_args()
convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)