File size: 27,513 Bytes
0c2c19f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
import argparse
import json
import os
import platform
import subprocess
import sys
import time
import warnings
from pathlib import Path

import pandas as pd
import torch
import yaml
from torch.utils.mobile_optimizer import optimize_for_mobile

FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
if platform.system() != 'Windows':
    ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

from models.experimental import attempt_load
from models.yolo import Detect
from utils.datasets import LoadImages
from utils.general import (LOGGER, check_dataset, check_img_size, check_requirements, check_version, colorstr,
                           file_size, print_args, url2file)
from utils.torch_utils import select_device


def export_formats():
    # YOLOv5 export formats
    x = [
        ['PyTorch', '-', '.pt', True, True],
        ['TorchScript', 'torchscript', '.torchscript', True, True],
        ['ONNX', 'onnx', '.onnx', True, True],
        ['OpenVINO', 'openvino', '_openvino_model', True, False],
        ['TensorRT', 'engine', '.engine', False, True],
        ['CoreML', 'coreml', '.mlmodel', True, False],
        ['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True],
        ['TensorFlow GraphDef', 'pb', '.pb', True, True],
        ['TensorFlow Lite', 'tflite', '.tflite', True, False],
        ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],
        ['TensorFlow.js', 'tfjs', '_web_model', False, False],]
    return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])


def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
    # YOLOv5 TorchScript model export
    try:
        LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
        f = file.with_suffix('.torchscript')

        ts = torch.jit.trace(model, im, strict=False)
        d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names}
        extra_files = {'config.txt': json.dumps(d)}  # torch._C.ExtraFilesMap()
        if optimize:  # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
            optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files)
        else:
            ts.save(str(f), _extra_files=extra_files)

        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        LOGGER.info(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

        LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
        f = file.with_suffix('.onnx')

        torch.onnx.export(
            model.cpu() if dynamic else model,  # --dynamic only compatible with cpu
            im.cpu() if dynamic else 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

        # Metadata
        d = {'stride': int(max(model.stride)), 'names': model.names}
        for k, v in d.items():
            meta = model_onnx.metadata_props.add()
            meta.key, meta.value = k, str(v)
        onnx.save(model_onnx, f)

        # Simplify
        if simplify:
            try:
                check_requirements(('onnx-simplifier',))
                import onnxsim

                LOGGER.info(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:
                LOGGER.info(f'{prefix} simplifier failure: {e}')
        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        LOGGER.info(f'{prefix} export failure: {e}')


def export_openvino(model, file, half, prefix=colorstr('OpenVINO:')):
    # YOLOv5 OpenVINO export
    try:
        check_requirements(('openvino-dev',))  # requires openvino-dev: https://pypi.org/project/openvino-dev/
        import openvino.inference_engine as ie

        LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
        f = str(file).replace('.pt', f'_openvino_model{os.sep}')

        cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} --data_type {'FP16' if half else 'FP32'}"
        subprocess.check_output(cmd.split())  # export
        with open(Path(f) / file.with_suffix('.yaml').name, 'w') as g:
            yaml.dump({'stride': int(max(model.stride)), 'names': model.names}, g)  # add metadata.yaml

        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')


def export_coreml(model, im, file, int8, half, prefix=colorstr('CoreML:')):
    # YOLOv5 CoreML export
    try:
        check_requirements(('coremltools',))
        import coremltools as ct

        LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
        f = file.with_suffix('.mlmodel')

        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, bias=[0, 0, 0])])
        bits, mode = (8, 'kmeans_lut') if int8 else (16, 'linear') if half else (32, None)
        if bits < 32:
            if platform.system() == 'Darwin':  # quantization only supported on macOS
                with warnings.catch_warnings():
                    warnings.filterwarnings("ignore", category=DeprecationWarning)  # suppress numpy==1.20 float warning
                    ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
            else:
                print(f'{prefix} quantization only supported on macOS, skipping...')
        ct_model.save(f)

        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return ct_model, f
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')
        return None, None


def export_engine(model, im, file, train, half, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
    # YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt
    try:
        assert im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `python export.py --device 0`'
        try:
            import tensorrt as trt
        except Exception:
            if platform.system() == 'Linux':
                check_requirements(('nvidia-tensorrt',), cmds=('-U --index-url https://pypi.ngc.nvidia.com',))
            import tensorrt as trt

        if trt.__version__[0] == '7':  # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012
            grid = model.model[-1].anchor_grid
            model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid]
            export_onnx(model, im, file, 12, train, False, simplify)  # opset 12
            model.model[-1].anchor_grid = grid
        else:  # TensorRT >= 8
            check_version(trt.__version__, '8.0.0', hard=True)  # require tensorrt>=8.0.0
            export_onnx(model, im, file, 13, train, False, simplify)  # opset 13
        onnx = file.with_suffix('.onnx')

        LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
        assert onnx.exists(), f'failed to export ONNX file: {onnx}'
        f = file.with_suffix('.engine')  # TensorRT engine file
        logger = trt.Logger(trt.Logger.INFO)
        if verbose:
            logger.min_severity = trt.Logger.Severity.VERBOSE

        builder = trt.Builder(logger)
        config = builder.create_builder_config()
        config.max_workspace_size = workspace * 1 << 30
        # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30)  # fix TRT 8.4 deprecation notice

        flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
        network = builder.create_network(flag)
        parser = trt.OnnxParser(network, logger)
        if not parser.parse_from_file(str(onnx)):
            raise RuntimeError(f'failed to load ONNX file: {onnx}')

        inputs = [network.get_input(i) for i in range(network.num_inputs)]
        outputs = [network.get_output(i) for i in range(network.num_outputs)]
        LOGGER.info(f'{prefix} Network Description:')
        for inp in inputs:
            LOGGER.info(f'{prefix}\tinput "{inp.name}" with shape {inp.shape} and dtype {inp.dtype}')
        for out in outputs:
            LOGGER.info(f'{prefix}\toutput "{out.name}" with shape {out.shape} and dtype {out.dtype}')

        LOGGER.info(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine in {f}')
        if builder.platform_has_fast_fp16 and half:
            config.set_flag(trt.BuilderFlag.FP16)
        with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
            t.write(engine.serialize())
        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')


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,
                       keras=False,
                       prefix=colorstr('TensorFlow SavedModel:')):
    # YOLOv5 TensorFlow SavedModel export
    try:
        import tensorflow as tf
        from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2

        from models.tf import TFDetect, TFModel

        LOGGER.info(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, ch))  # BHWC order for TensorFlow
        _ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
        inputs = tf.keras.Input(shape=(*imgsz, ch), 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 = tf.keras.Model(inputs=inputs, outputs=outputs)
        keras_model.trainable = False
        keras_model.summary()
        if keras:
            keras_model.save(f, save_format='tf')
        else:
            spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)
            m = tf.function(lambda x: keras_model(x))  # full model
            m = m.get_concrete_function(spec)
            frozen_func = convert_variables_to_constants_v2(m)
            tfm = tf.Module()
            tfm.__call__ = tf.function(lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x)[0], [spec])
            tfm.__call__(im)
            tf.saved_model.save(tfm,
                                f,
                                options=tf.saved_model.SaveOptions(experimental_custom_gradients=False)
                                if check_version(tf.__version__, '2.6') else tf.saved_model.SaveOptions())
        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return keras_model, f
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')
        return None, None


def export_pb(keras_model, 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

        LOGGER.info(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)

        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')


def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
    # YOLOv5 TensorFlow Lite export
    try:
        import tensorflow as tf

        LOGGER.info(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:
            from models.tf import representative_dataset_gen
            dataset = LoadImages(check_dataset(data)['train'], img_size=imgsz, auto=False)  # representative data
            converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100)
            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 = True
            f = str(file).replace('.pt', '-int8.tflite')
        if nms or agnostic_nms:
            converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS)

        tflite_model = converter.convert()
        open(f, "wb").write(tflite_model)
        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')


def export_edgetpu(file, prefix=colorstr('Edge TPU:')):
    # YOLOv5 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/
    try:
        cmd = 'edgetpu_compiler --version'
        help_url = 'https://coral.ai/docs/edgetpu/compiler/'
        assert platform.system() == 'Linux', f'export only supported on Linux. See {help_url}'
        if subprocess.run(f'{cmd} >/dev/null', shell=True).returncode != 0:
            LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}')
            sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0  # sudo installed on system
            for c in (
                    'curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -',
                    'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
                    'sudo apt-get update', 'sudo apt-get install edgetpu-compiler'):
                subprocess.run(c if sudo else c.replace('sudo ', ''), shell=True, check=True)
        ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]

        LOGGER.info(f'\n{prefix} starting export with Edge TPU compiler {ver}...')
        f = str(file).replace('.pt', '-int8_edgetpu.tflite')  # Edge TPU model
        f_tfl = str(file).replace('.pt', '-int8.tflite')  # TFLite model

        cmd = f"edgetpu_compiler -s -o {file.parent} {f_tfl}"
        subprocess.run(cmd.split(), check=True)

        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')


def export_tfjs(file, prefix=colorstr('TensorFlow.js:')):
    # YOLOv5 TensorFlow.js export
    try:
        check_requirements(('tensorflowjs',))
        import re

        import tensorflowjs as tfjs

        LOGGER.info(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
        f_json = f'{f}/model.json'  # *.json 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.split())

        with open(f_json) as j:
            json = j.read()
        with open(f_json, 'w') as j:  # sort JSON Identity_* in ascending order
            subst = re.sub(
                r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
                r'"Identity.?.?": {"name": "Identity.?.?"}, '
                r'"Identity.?.?": {"name": "Identity.?.?"}, '
                r'"Identity.?.?": {"name": "Identity.?.?"}}}', r'{"outputs": {"Identity": {"name": "Identity"}, '
                r'"Identity_1": {"name": "Identity_1"}, '
                r'"Identity_2": {"name": "Identity_2"}, '
                r'"Identity_3": {"name": "Identity_3"}}}', json)
            j.write(subst)

        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        LOGGER.info(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'),  # include formats
        half=False,  # FP16 half-precision export
        inplace=False,  # set YOLOv5 Detect() inplace=True
        train=False,  # model.train() mode
        keras=False,  # use Keras
        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
        verbose=False,  # TensorRT: verbose log
        workspace=4,  # TensorRT: workspace size (GB)
        nms=False,  # TF: add NMS to model
        agnostic_nms=False,  # TF: add agnostic NMS to model
        topk_per_class=100,  # TF.js NMS: topk per class to keep
        topk_all=100,  # TF.js NMS: topk for all classes to keep
        iou_thres=0.45,  # TF.js NMS: IoU threshold
        conf_thres=0.25,  # TF.js NMS: confidence threshold
):
    t = time.time()
    include = [x.lower() for x in include]  # to lowercase
    fmts = tuple(export_formats()['Argument'][1:])  # --include arguments
    flags = [x in include for x in fmts]
    assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {fmts}'
    jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = flags  # export booleans
    file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights)  # PyTorch weights

    # Load PyTorch model
    device = select_device(device)
    if half:
        assert device.type != 'cpu' or coreml, '--half only compatible with GPU export, i.e. use --device 0'
        assert not dynamic, '--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both'
    model = attempt_load(weights, device=device, inplace=True, fuse=True)  # load FP32 model
    nc, names = model.nc, model.names  # number of classes, class names

    # Checks
    imgsz *= 2 if len(imgsz) == 1 else 1  # expand
    assert nc == len(names), f'Model class count {nc} != len(names) {len(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
    model.train() if train else model.eval()  # training mode = no Detect() layer grid construction
    for k, m in model.named_modules():
        if isinstance(m, Detect):
            m.inplace = inplace
            m.onnx_dynamic = dynamic
            m.export = True

    for _ in range(2):
        y = model(im)  # dry runs
    if half and not coreml:
        im, model = im.half(), model.half()  # to FP16
    shape = tuple(y[0].shape)  # model output shape
    LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")

    # Exports
    f = [''] * 10  # exported filenames
    warnings.filterwarnings(action='ignore', category=torch.jit.TracerWarning)  # suppress TracerWarning
    if jit:
        f[0] = export_torchscript(model, im, file, optimize)
    if engine:  # TensorRT required before ONNX
        f[1] = export_engine(model, im, file, train, half, simplify, workspace, verbose)
    if onnx or xml:  # OpenVINO requires ONNX
        f[2] = export_onnx(model, im, file, opset, train, dynamic, simplify)
    if xml:  # OpenVINO
        f[3] = export_openvino(model, file, half)
    if coreml:
        _, f[4] = export_coreml(model, im, file, int8, half)

    # TensorFlow Exports
    if any((saved_model, pb, tflite, edgetpu, tfjs)):
        if int8 or edgetpu:  # TFLite --int8 bug https://github.com/ultralytics/yolov5/issues/5707
            check_requirements(('flatbuffers==1.12',))  # required before `import tensorflow`
        assert not tflite or not tfjs, 'TFLite and TF.js models must be exported separately, please pass only one type.'
        model, f[5] = export_saved_model(model.cpu(),
                                         im,
                                         file,
                                         dynamic,
                                         tf_nms=nms or agnostic_nms or tfjs,
                                         agnostic_nms=agnostic_nms or tfjs,
                                         topk_per_class=topk_per_class,
                                         topk_all=topk_all,
                                         iou_thres=iou_thres,
                                         conf_thres=conf_thres,
                                         keras=keras)
        if pb or tfjs:  # pb prerequisite to tfjs
            f[6] = export_pb(model, file)
        if tflite or edgetpu:
            f[7] = export_tflite(model, im, file, int8=int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms)
        if edgetpu:
            f[8] = export_edgetpu(file)
        if tfjs:
            f[9] = export_tfjs(file)

    # Finish
    f = [str(x) for x in f if x]  # filter out '' and None
    if any(f):
        h = '--half' if half else ''  # --half FP16 inference arg
        LOGGER.info(f'\nExport complete ({time.time() - t:.2f}s)'
                    f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
                    f"\nDetect:          python detect.py --weights {f[-1]} {h}"
                    f"\nValidate:        python val.py --weights {f[-1]} {h}"
                    f"\nPyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}')"
                    f"\nVisualize:       https://netron.app")
    return f  # return list of exported files/dirs


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', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)')
    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('--keras', action='store_true', help='TF: use Keras')
    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=12, help='ONNX: opset version')
    parser.add_argument('--verbose', action='store_true', help='TensorRT: verbose log')
    parser.add_argument('--workspace', type=int, default=4, help='TensorRT: workspace size (GB)')
    parser.add_argument('--nms', action='store_true', help='TF: add NMS to model')
    parser.add_argument('--agnostic-nms', action='store_true', help='TF: add agnostic NMS to model')
    parser.add_argument('--topk-per-class', type=int, default=100, help='TF.js NMS: topk per class to keep')
    parser.add_argument('--topk-all', type=int, default=100, help='TF.js NMS: topk for all classes to keep')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='TF.js NMS: IoU threshold')
    parser.add_argument('--conf-thres', type=float, default=0.25, help='TF.js NMS: confidence threshold')
    parser.add_argument('--include',
                        nargs='+',
                        default=['torchscript', 'onnx'],
                        help='torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs')
    opt = parser.parse_args()
    print_args(vars(opt))
    return opt


def main(opt):
    for opt.weights in (opt.weights if isinstance(opt.weights, list) else [opt.weights]):
        run(**vars(opt))


if __name__ == "__main__":
    opt = parse_opt()
    main(opt)