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
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
|
- # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
- """
- Export a YOLOv5 PyTorch model to TorchScript, ONNX, CoreML, TensorFlow (saved_model, pb, TFLite, TF.js,) formats
- TensorFlow exports authored by https://github.com/zldrobit
- Usage:
- $ python path/to/export.py --weights yolov5s.pt --include torchscript onnx coreml saved_model pb tflite tfjs
- Inference:
- $ python path/to/detect.py --weights yolov5s.pt
- yolov5s.onnx (must export with --dynamic)
- yolov5s_saved_model
- yolov5s.pb
- yolov5s.tflite
- TensorFlow.js:
- $ # Edit yolov5s_web_model/model.json to sort Identity* in ascending order
- $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example
- $ npm install
- $ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model
- $ npm start
- """
- import argparse
- import subprocess
- import sys
- import time
- from pathlib import Path
- import torch
- import torch.nn as nn
- from torch.utils.mobile_optimizer import optimize_for_mobile
- FILE = Path(__file__).resolve()
- ROOT = FILE.parents[0] # yolov5/ dir
- if str(ROOT) not in sys.path:
- sys.path.append(str(ROOT)) # add ROOT to PATH
- from models.common import Conv
- from models.experimental import attempt_load
- from models.yolo import Detect
- from utils.activations import SiLU
- from utils.datasets import LoadImages
- from utils.general import colorstr, check_dataset, check_img_size, check_requirements, file_size, set_logging, url2file
- from utils.torch_utils import select_device
- def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
- # YOLOv5 TorchScript model export
- try:
- print(f'\n{prefix} starting export with torch {torch.__version__}...')
- f = file.with_suffix('.torchscript.pt')
- ts = torch.jit.trace(model, im, strict=False)
- (optimize_for_mobile(ts) if optimize else ts).save(f)
- print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
- except Exception as e:
- print(f'{prefix} export failure: {e}')
- def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorstr('ONNX:')):
- # YOLOv5 ONNX export
- try:
- check_requirements(('onnx',))
- import onnx
- print(f'\n{prefix} starting export with onnx {onnx.__version__}...')
- f = file.with_suffix('.onnx')
- torch.onnx.export(model, im, f, verbose=False, opset_version=opset,
- training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL,
- do_constant_folding=not train,
- input_names=['images'],
- output_names=['output'],
- dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # shape(1,3,640,640)
- 'output': {0: 'batch', 1: 'anchors'} # shape(1,25200,85)
- } if dynamic else None)
- # Checks
- model_onnx = onnx.load(f) # load onnx model
- onnx.checker.check_model(model_onnx) # check onnx model
- # print(onnx.helper.printable_graph(model_onnx.graph)) # print
- # Simplify
- if simplify:
- try:
- check_requirements(('onnx-simplifier',))
- import onnxsim
- print(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
- model_onnx, check = onnxsim.simplify(
- model_onnx,
- dynamic_input_shape=dynamic,
- input_shapes={'images': list(im.shape)} if dynamic else None)
- assert check, 'assert check failed'
- onnx.save(model_onnx, f)
- except Exception as e:
- print(f'{prefix} simplifier failure: {e}')
- print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
- print(f"{prefix} run --dynamic ONNX model inference with: 'python detect.py --weights {f}'")
- except Exception as e:
- print(f'{prefix} export failure: {e}')
- def export_coreml(model, im, file, prefix=colorstr('CoreML:')):
- # YOLOv5 CoreML export
- ct_model = None
- try:
- check_requirements(('coremltools',))
- import coremltools as ct
- print(f'\n{prefix} starting export with coremltools {ct.__version__}...')
- f = file.with_suffix('.mlmodel')
- model.train() # CoreML exports should be placed in model.train() mode
- ts = torch.jit.trace(model, im, strict=False) # TorchScript model
- ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255.0, bias=[0, 0, 0])])
- ct_model.save(f)
- print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
- except Exception as e:
- print(f'\n{prefix} export failure: {e}')
- return ct_model
- def export_saved_model(model, im, file, dynamic,
- tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45,
- conf_thres=0.25, prefix=colorstr('TensorFlow saved_model:')):
- # YOLOv5 TensorFlow saved_model export
- keras_model = None
- try:
- import tensorflow as tf
- from tensorflow import keras
- from models.tf import TFModel, TFDetect
- print(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
- f = str(file).replace('.pt', '_saved_model')
- batch_size, ch, *imgsz = list(im.shape) # BCHW
- tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
- im = tf.zeros((batch_size, *imgsz, 3)) # BHWC order for TensorFlow
- y = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
- inputs = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
- outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
- keras_model = keras.Model(inputs=inputs, outputs=outputs)
- keras_model.trainable = False
- keras_model.summary()
- keras_model.save(f, save_format='tf')
- print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
- except Exception as e:
- print(f'\n{prefix} export failure: {e}')
- return keras_model
- def export_pb(keras_model, im, file, prefix=colorstr('TensorFlow GraphDef:')):
- # YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
- try:
- import tensorflow as tf
- from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
- print(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
- f = file.with_suffix('.pb')
- m = tf.function(lambda x: keras_model(x)) # full model
- m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))
- frozen_func = convert_variables_to_constants_v2(m)
- frozen_func.graph.as_graph_def()
- tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False)
- print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
- except Exception as e:
- print(f'\n{prefix} export failure: {e}')
- def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('TensorFlow Lite:')):
- # YOLOv5 TensorFlow Lite export
- try:
- import tensorflow as tf
- from models.tf import representative_dataset_gen
- print(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
- batch_size, ch, *imgsz = list(im.shape) # BCHW
- f = str(file).replace('.pt', '-fp16.tflite')
- converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
- converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
- converter.target_spec.supported_types = [tf.float16]
- converter.optimizations = [tf.lite.Optimize.DEFAULT]
- if int8:
- dataset = LoadImages(check_dataset(data)['train'], img_size=imgsz, auto=False) # representative data
- converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib)
- converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
- converter.target_spec.supported_types = []
- converter.inference_input_type = tf.uint8 # or tf.int8
- converter.inference_output_type = tf.uint8 # or tf.int8
- converter.experimental_new_quantizer = False
- f = str(file).replace('.pt', '-int8.tflite')
- tflite_model = converter.convert()
- open(f, "wb").write(tflite_model)
- print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
- except Exception as e:
- print(f'\n{prefix} export failure: {e}')
- def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')):
- # YOLOv5 TensorFlow.js export
- try:
- check_requirements(('tensorflowjs',))
- import tensorflowjs as tfjs
- print(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
- f = str(file).replace('.pt', '_web_model') # js dir
- f_pb = file.with_suffix('.pb') # *.pb path
- cmd = f"tensorflowjs_converter --input_format=tf_frozen_model " \
- f"--output_node_names='Identity,Identity_1,Identity_2,Identity_3' {f_pb} {f}"
- subprocess.run(cmd, shell=True)
- print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
- except Exception as e:
- print(f'\n{prefix} export failure: {e}')
- @torch.no_grad()
- def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
- weights=ROOT / 'yolov5s.pt', # weights path
- imgsz=(640, 640), # image (height, width)
- batch_size=1, # batch size
- device='cpu', # cuda device, i.e. 0 or 0,1,2,3 or cpu
- include=('torchscript', 'onnx', 'coreml'), # include formats
- half=False, # FP16 half-precision export
- inplace=False, # set YOLOv5 Detect() inplace=True
- train=False, # model.train() mode
- optimize=False, # TorchScript: optimize for mobile
- int8=False, # CoreML/TF INT8 quantization
- dynamic=False, # ONNX/TF: dynamic axes
- simplify=False, # ONNX: simplify model
- opset=12, # ONNX: opset version
- ):
- t = time.time()
- include = [x.lower() for x in include]
- tf_exports = list(x in include for x in ('saved_model', 'pb', 'tflite', 'tfjs')) # TensorFlow exports
- imgsz *= 2 if len(imgsz) == 1 else 1 # expand
- file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights)
- # Load PyTorch model
- device = select_device(device)
- assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0'
- model = attempt_load(weights, map_location=device, inplace=True, fuse=True) # load FP32 model
- nc, names = model.nc, model.names # number of classes, class names
- # Input
- gs = int(max(model.stride)) # grid size (max stride)
- imgsz = [check_img_size(x, gs) for x in imgsz] # verify img_size are gs-multiples
- im = torch.zeros(batch_size, 3, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection
- # Update model
- if half:
- im, model = im.half(), model.half() # to FP16
- model.train() if train else model.eval() # training mode = no Detect() layer grid construction
- for k, m in model.named_modules():
- if isinstance(m, Conv): # assign export-friendly activations
- if isinstance(m.act, nn.SiLU):
- m.act = SiLU()
- elif isinstance(m, Detect):
- m.inplace = inplace
- m.onnx_dynamic = dynamic
- # m.forward = m.forward_export # assign forward (optional)
- for _ in range(2):
- y = model(im) # dry runs
- print(f"\n{colorstr('PyTorch:')} starting from {file} ({file_size(file):.1f} MB)")
- # Exports
- if 'torchscript' in include:
- export_torchscript(model, im, file, optimize)
- if 'onnx' in include:
- export_onnx(model, im, file, opset, train, dynamic, simplify)
- if 'coreml' in include:
- export_coreml(model, im, file)
- # TensorFlow Exports
- if any(tf_exports):
- pb, tflite, tfjs = tf_exports[1:]
- assert not (tflite and tfjs), 'TFLite and TF.js models must be exported separately, please pass only one type.'
- model = export_saved_model(model, im, file, dynamic, tf_nms=tfjs, agnostic_nms=tfjs) # keras model
- if pb or tfjs: # pb prerequisite to tfjs
- export_pb(model, im, file)
- if tflite:
- export_tflite(model, im, file, int8=int8, data=data, ncalib=100)
- if tfjs:
- export_tfjs(model, im, file)
- # Finish
- print(f'\nExport complete ({time.time() - t:.2f}s)'
- f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
- f'\nVisualize with https://netron.app')
- def parse_opt():
- parser = argparse.ArgumentParser()
- parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
- parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path')
- parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640, 640], help='image (h, w)')
- parser.add_argument('--batch-size', type=int, default=1, help='batch size')
- parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
- parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True')
- parser.add_argument('--train', action='store_true', help='model.train() mode')
- parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
- parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization')
- parser.add_argument('--dynamic', action='store_true', help='ONNX/TF: dynamic axes')
- parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
- parser.add_argument('--opset', type=int, default=13, help='ONNX: opset version')
- parser.add_argument('--include', nargs='+',
- default=['torchscript', 'onnx'],
- help='available formats are (torchscript, onnx, coreml, saved_model, pb, tflite, tfjs)')
- opt = parser.parse_args()
- return opt
- def main(opt):
- set_logging()
- print(colorstr('export: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
- run(**vars(opt))
- if __name__ == "__main__":
- opt = parse_opt()
- main(opt)
|