654 lines
31 KiB
Python
654 lines
31 KiB
Python
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
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Format | `export.py --include` | Model
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--- | --- | ---
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PyTorch | - | yolov5s.pt
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TorchScript | `torchscript` | yolov5s.torchscript
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ONNX | `onnx` | yolov5s.onnx
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OpenVINO | `openvino` | yolov5s_openvino_model/
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TensorRT | `engine` | yolov5s.engine
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CoreML | `coreml` | yolov5s.mlmodel
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TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/
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TensorFlow GraphDef | `pb` | yolov5s.pb
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TensorFlow Lite | `tflite` | yolov5s.tflite
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TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite
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TensorFlow.js | `tfjs` | yolov5s_web_model/
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PaddlePaddle | `paddle` | yolov5s_paddle_model/
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Requirements:
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$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU
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$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU
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Usage:
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$ python export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ...
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Inference:
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$ python detect.py --weights yolov5s.pt # PyTorch
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yolov5s.torchscript # TorchScript
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yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov5s_openvino_model # OpenVINO
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yolov5s.engine # TensorRT
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yolov5s.mlmodel # CoreML (macOS-only)
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yolov5s_saved_model # TensorFlow SavedModel
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yolov5s.pb # TensorFlow GraphDef
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yolov5s.tflite # TensorFlow Lite
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yolov5s_edgetpu.tflite # TensorFlow Edge TPU
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yolov5s_paddle_model # PaddlePaddle
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TensorFlow.js:
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$ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example
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$ npm install
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$ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model
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$ npm start
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"""
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import argparse
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import contextlib
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import json
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import os
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import platform
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import re
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import subprocess
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import sys
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import time
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import warnings
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from pathlib import Path
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import pandas as pd
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import torch
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from torch.utils.mobile_optimizer import optimize_for_mobile
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[0] # YOLOv5 root directory
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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if platform.system() != 'Windows':
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from models.experimental import attempt_load
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from models.yolo import ClassificationModel, Detect, DetectionModel, SegmentationModel
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from utils.dataloaders import LoadImages
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from utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_version,
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check_yaml, colorstr, file_size, get_default_args, print_args, url2file, yaml_save)
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from utils.torch_utils import select_device, smart_inference_mode
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MACOS = platform.system() == 'Darwin' # macOS environment
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def export_formats():
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# YOLOv5 export formats
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x = [
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['PyTorch', '-', '.pt', True, True],
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['TorchScript', 'torchscript', '.torchscript', True, True],
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['ONNX', 'onnx', '.onnx', True, True],
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['OpenVINO', 'openvino', '_openvino_model', True, False],
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['TensorRT', 'engine', '.engine', False, True],
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['CoreML', 'coreml', '.mlmodel', True, False],
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['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True],
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['TensorFlow GraphDef', 'pb', '.pb', True, True],
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['TensorFlow Lite', 'tflite', '.tflite', True, False],
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['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],
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['TensorFlow.js', 'tfjs', '_web_model', False, False],
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['PaddlePaddle', 'paddle', '_paddle_model', True, True],]
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return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
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def try_export(inner_func):
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# YOLOv5 export decorator, i..e @try_export
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inner_args = get_default_args(inner_func)
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def outer_func(*args, **kwargs):
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prefix = inner_args['prefix']
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try:
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with Profile() as dt:
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f, model = inner_func(*args, **kwargs)
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LOGGER.info(f'{prefix} export success ✅ {dt.t:.1f}s, saved as {f} ({file_size(f):.1f} MB)')
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return f, model
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except Exception as e:
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LOGGER.info(f'{prefix} export failure ❌ {dt.t:.1f}s: {e}')
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return None, None
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return outer_func
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@try_export
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def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
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# YOLOv5 TorchScript model export
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LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
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f = file.with_suffix('.torchscript')
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ts = torch.jit.trace(model, im, strict=False)
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d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names}
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extra_files = {'config.txt': json.dumps(d)} # torch._C.ExtraFilesMap()
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if optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
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optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files)
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else:
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ts.save(str(f), _extra_files=extra_files)
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return f, None
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@try_export
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def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX:')):
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# YOLOv5 ONNX export
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check_requirements('onnx>=1.12.0')
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import onnx
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LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
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f = file.with_suffix('.onnx')
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output_names = ['output0', 'output1'] if isinstance(model, SegmentationModel) else ['output0']
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if dynamic:
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dynamic = {'images': {0: 'batch', 2: 'height', 3: 'width'}} # shape(1,3,640,640)
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if isinstance(model, SegmentationModel):
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dynamic['output0'] = {0: 'batch', 1: 'anchors'} # shape(1,25200,85)
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dynamic['output1'] = {0: 'batch', 2: 'mask_height', 3: 'mask_width'} # shape(1,32,160,160)
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elif isinstance(model, DetectionModel):
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dynamic['output0'] = {0: 'batch', 1: 'anchors'} # shape(1,25200,85)
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torch.onnx.export(
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model.cpu() if dynamic else model, # --dynamic only compatible with cpu
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im.cpu() if dynamic else im,
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f,
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verbose=False,
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opset_version=opset,
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do_constant_folding=True, # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False
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input_names=['images'],
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output_names=output_names,
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dynamic_axes=dynamic or None)
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# Checks
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model_onnx = onnx.load(f) # load onnx model
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onnx.checker.check_model(model_onnx) # check onnx model
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# Metadata
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d = {'stride': int(max(model.stride)), 'names': model.names}
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for k, v in d.items():
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meta = model_onnx.metadata_props.add()
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meta.key, meta.value = k, str(v)
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onnx.save(model_onnx, f)
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# Simplify
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if simplify:
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try:
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cuda = torch.cuda.is_available()
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check_requirements(('onnxruntime-gpu' if cuda else 'onnxruntime', 'onnx-simplifier>=0.4.1'))
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import onnxsim
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LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
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model_onnx, check = onnxsim.simplify(model_onnx)
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assert check, 'assert check failed'
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onnx.save(model_onnx, f)
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except Exception as e:
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LOGGER.info(f'{prefix} simplifier failure: {e}')
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return f, model_onnx
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@try_export
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def export_openvino(file, metadata, half, prefix=colorstr('OpenVINO:')):
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# YOLOv5 OpenVINO export
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check_requirements('openvino-dev') # requires openvino-dev: https://pypi.org/project/openvino-dev/
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import openvino.inference_engine as ie
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LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
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f = str(file).replace('.pt', f'_openvino_model{os.sep}')
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cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} --data_type {'FP16' if half else 'FP32'}"
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subprocess.run(cmd.split(), check=True, env=os.environ) # export
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yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata) # add metadata.yaml
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return f, None
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@try_export
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def export_paddle(model, im, file, metadata, prefix=colorstr('PaddlePaddle:')):
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# YOLOv5 Paddle export
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check_requirements(('paddlepaddle', 'x2paddle'))
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import x2paddle
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from x2paddle.convert import pytorch2paddle
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LOGGER.info(f'\n{prefix} starting export with X2Paddle {x2paddle.__version__}...')
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f = str(file).replace('.pt', f'_paddle_model{os.sep}')
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pytorch2paddle(module=model, save_dir=f, jit_type='trace', input_examples=[im]) # export
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yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata) # add metadata.yaml
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return f, None
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@try_export
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def export_coreml(model, im, file, int8, half, prefix=colorstr('CoreML:')):
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# YOLOv5 CoreML export
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check_requirements('coremltools')
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import coremltools as ct
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LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
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f = file.with_suffix('.mlmodel')
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ts = torch.jit.trace(model, im, strict=False) # TorchScript model
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ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
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bits, mode = (8, 'kmeans_lut') if int8 else (16, 'linear') if half else (32, None)
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if bits < 32:
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if MACOS: # quantization only supported on macOS
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore", category=DeprecationWarning) # suppress numpy==1.20 float warning
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ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
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else:
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print(f'{prefix} quantization only supported on macOS, skipping...')
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ct_model.save(f)
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return f, ct_model
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@try_export
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def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
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# YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt
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assert im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `python export.py --device 0`'
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try:
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import tensorrt as trt
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except Exception:
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if platform.system() == 'Linux':
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check_requirements('nvidia-tensorrt', cmds='-U --index-url https://pypi.ngc.nvidia.com')
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import tensorrt as trt
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if trt.__version__[0] == '7': # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012
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grid = model.model[-1].anchor_grid
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model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid]
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export_onnx(model, im, file, 12, dynamic, simplify) # opset 12
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model.model[-1].anchor_grid = grid
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else: # TensorRT >= 8
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check_version(trt.__version__, '8.0.0', hard=True) # require tensorrt>=8.0.0
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export_onnx(model, im, file, 12, dynamic, simplify) # opset 12
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onnx = file.with_suffix('.onnx')
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LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
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assert onnx.exists(), f'failed to export ONNX file: {onnx}'
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f = file.with_suffix('.engine') # TensorRT engine file
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logger = trt.Logger(trt.Logger.INFO)
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if verbose:
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logger.min_severity = trt.Logger.Severity.VERBOSE
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builder = trt.Builder(logger)
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config = builder.create_builder_config()
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config.max_workspace_size = workspace * 1 << 30
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# config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30) # fix TRT 8.4 deprecation notice
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flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
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network = builder.create_network(flag)
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parser = trt.OnnxParser(network, logger)
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if not parser.parse_from_file(str(onnx)):
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raise RuntimeError(f'failed to load ONNX file: {onnx}')
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inputs = [network.get_input(i) for i in range(network.num_inputs)]
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outputs = [network.get_output(i) for i in range(network.num_outputs)]
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for inp in inputs:
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LOGGER.info(f'{prefix} input "{inp.name}" with shape{inp.shape} {inp.dtype}')
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for out in outputs:
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LOGGER.info(f'{prefix} output "{out.name}" with shape{out.shape} {out.dtype}')
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if dynamic:
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if im.shape[0] <= 1:
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LOGGER.warning(f"{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument")
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profile = builder.create_optimization_profile()
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for inp in inputs:
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profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape)
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config.add_optimization_profile(profile)
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LOGGER.info(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine as {f}')
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if builder.platform_has_fast_fp16 and half:
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config.set_flag(trt.BuilderFlag.FP16)
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with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
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t.write(engine.serialize())
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return f, None
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@try_export
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def export_saved_model(model,
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im,
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file,
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dynamic,
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tf_nms=False,
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agnostic_nms=False,
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topk_per_class=100,
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topk_all=100,
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iou_thres=0.45,
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conf_thres=0.25,
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keras=False,
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prefix=colorstr('TensorFlow SavedModel:')):
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# YOLOv5 TensorFlow SavedModel export
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try:
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import tensorflow as tf
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except Exception:
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check_requirements(f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}")
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import tensorflow as tf
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from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
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from models.tf import TFModel
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LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
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f = str(file).replace('.pt', '_saved_model')
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batch_size, ch, *imgsz = list(im.shape) # BCHW
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tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
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im = tf.zeros((batch_size, *imgsz, ch)) # BHWC order for TensorFlow
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_ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
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inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if dynamic else batch_size)
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outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
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keras_model = tf.keras.Model(inputs=inputs, outputs=outputs)
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keras_model.trainable = False
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keras_model.summary()
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if keras:
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keras_model.save(f, save_format='tf')
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else:
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spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)
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m = tf.function(lambda x: keras_model(x)) # full model
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m = m.get_concrete_function(spec)
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frozen_func = convert_variables_to_constants_v2(m)
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tfm = tf.Module()
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tfm.__call__ = tf.function(lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x), [spec])
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tfm.__call__(im)
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tf.saved_model.save(tfm,
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f,
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options=tf.saved_model.SaveOptions(experimental_custom_gradients=False) if check_version(
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tf.__version__, '2.6') else tf.saved_model.SaveOptions())
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return f, keras_model
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@try_export
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def export_pb(keras_model, file, prefix=colorstr('TensorFlow GraphDef:')):
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# YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
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import tensorflow as tf
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from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
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LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
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f = file.with_suffix('.pb')
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m = tf.function(lambda x: keras_model(x)) # full model
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m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))
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frozen_func = convert_variables_to_constants_v2(m)
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frozen_func.graph.as_graph_def()
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tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False)
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return f, None
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@try_export
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def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
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# YOLOv5 TensorFlow Lite export
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import tensorflow as tf
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LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
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batch_size, ch, *imgsz = list(im.shape) # BCHW
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f = str(file).replace('.pt', '-fp16.tflite')
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converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
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converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
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converter.target_spec.supported_types = [tf.float16]
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converter.optimizations = [tf.lite.Optimize.DEFAULT]
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if int8:
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from models.tf import representative_dataset_gen
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dataset = LoadImages(check_dataset(check_yaml(data))['train'], img_size=imgsz, auto=False)
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converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100)
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converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
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converter.target_spec.supported_types = []
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converter.inference_input_type = tf.uint8 # or tf.int8
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converter.inference_output_type = tf.uint8 # or tf.int8
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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)
|
|
return f, None
|
|
|
|
|
|
@try_export
|
|
def export_edgetpu(file, prefix=colorstr('Edge TPU:')):
|
|
# YOLOv5 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/
|
|
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 -d -k 10 --out_dir {file.parent} {f_tfl}"
|
|
subprocess.run(cmd.split(), check=True)
|
|
return f, None
|
|
|
|
|
|
@try_export
|
|
def export_tfjs(file, prefix=colorstr('TensorFlow.js:')):
|
|
# YOLOv5 TensorFlow.js export
|
|
check_requirements('tensorflowjs')
|
|
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())
|
|
|
|
json = Path(f_json).read_text()
|
|
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)
|
|
return f, None
|
|
|
|
|
|
def add_tflite_metadata(file, metadata, num_outputs):
|
|
# Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata
|
|
with contextlib.suppress(ImportError):
|
|
# check_requirements('tflite_support')
|
|
from tflite_support import flatbuffers
|
|
from tflite_support import metadata as _metadata
|
|
from tflite_support import metadata_schema_py_generated as _metadata_fb
|
|
|
|
tmp_file = Path('/tmp/meta.txt')
|
|
with open(tmp_file, 'w') as meta_f:
|
|
meta_f.write(str(metadata))
|
|
|
|
model_meta = _metadata_fb.ModelMetadataT()
|
|
label_file = _metadata_fb.AssociatedFileT()
|
|
label_file.name = tmp_file.name
|
|
model_meta.associatedFiles = [label_file]
|
|
|
|
subgraph = _metadata_fb.SubGraphMetadataT()
|
|
subgraph.inputTensorMetadata = [_metadata_fb.TensorMetadataT()]
|
|
subgraph.outputTensorMetadata = [_metadata_fb.TensorMetadataT()] * num_outputs
|
|
model_meta.subgraphMetadata = [subgraph]
|
|
|
|
b = flatbuffers.Builder(0)
|
|
b.Finish(model_meta.Pack(b), _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
|
|
metadata_buf = b.Output()
|
|
|
|
populator = _metadata.MetadataPopulator.with_model_file(file)
|
|
populator.load_metadata_buffer(metadata_buf)
|
|
populator.load_associated_files([str(tmp_file)])
|
|
populator.populate()
|
|
tmp_file.unlink()
|
|
|
|
|
|
@smart_inference_mode()
|
|
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
|
|
keras=False, # use Keras
|
|
optimize=False, # TorchScript: optimize for mobile
|
|
int8=False, # CoreML/TF INT8 quantization
|
|
dynamic=False, # ONNX/TF/TensorRT: 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, paddle = 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
|
|
|
|
# Checks
|
|
imgsz *= 2 if len(imgsz) == 1 else 1 # expand
|
|
if optimize:
|
|
assert device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'
|
|
|
|
# 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.eval()
|
|
for k, m in model.named_modules():
|
|
if isinstance(m, Detect):
|
|
m.inplace = inplace
|
|
m.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] if isinstance(y, tuple) else y).shape) # model output shape
|
|
metadata = {'stride': int(max(model.stride)), 'names': model.names} # model metadata
|
|
LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")
|
|
|
|
# Exports
|
|
f = [''] * len(fmts) # exported filenames
|
|
warnings.filterwarnings(action='ignore', category=torch.jit.TracerWarning) # suppress TracerWarning
|
|
if jit: # TorchScript
|
|
f[0], _ = export_torchscript(model, im, file, optimize)
|
|
if engine: # TensorRT required before ONNX
|
|
f[1], _ = export_engine(model, im, file, half, dynamic, simplify, workspace, verbose)
|
|
if onnx or xml: # OpenVINO requires ONNX
|
|
f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify)
|
|
if xml: # OpenVINO
|
|
f[3], _ = export_openvino(file, metadata, half)
|
|
if coreml: # CoreML
|
|
f[4], _ = export_coreml(model, im, file, int8, half)
|
|
if any((saved_model, pb, tflite, edgetpu, tfjs)): # TensorFlow formats
|
|
assert not tflite or not tfjs, 'TFLite and TF.js models must be exported separately, please pass only one type.'
|
|
assert not isinstance(model, ClassificationModel), 'ClassificationModel export to TF formats not yet supported.'
|
|
f[5], s_model = 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(s_model, file)
|
|
if tflite or edgetpu:
|
|
f[7], _ = export_tflite(s_model, im, file, int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms)
|
|
if edgetpu:
|
|
f[8], _ = export_edgetpu(file)
|
|
add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs))
|
|
if tfjs:
|
|
f[9], _ = export_tfjs(file)
|
|
if paddle: # PaddlePaddle
|
|
f[10], _ = export_paddle(model, im, file, metadata)
|
|
|
|
# Finish
|
|
f = [str(x) for x in f if x] # filter out '' and None
|
|
if any(f):
|
|
cls, det, seg = (isinstance(model, x) for x in (ClassificationModel, DetectionModel, SegmentationModel)) # type
|
|
det &= not seg # segmentation models inherit from SegmentationModel(DetectionModel)
|
|
dir = Path('segment' if seg else 'classify' if cls else '')
|
|
h = '--half' if half else '' # --half FP16 inference arg
|
|
s = "# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference" if cls else \
|
|
"# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference" if seg else ''
|
|
LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)'
|
|
f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
|
|
f"\nDetect: python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}"
|
|
f"\nValidate: python {dir / 'val.py'} --weights {f[-1]} {h}"
|
|
f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}') {s}"
|
|
f"\nVisualize: https://netron.app")
|
|
return f # return list of exported files/dirs
|
|
|
|
|
|
def parse_opt(known=False):
|
|
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('--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/TensorRT: dynamic axes')
|
|
parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
|
|
parser.add_argument('--opset', type=int, default=17, 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'],
|
|
help='torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle')
|
|
opt = parser.parse_known_args()[0] if known else 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)
|