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- # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
- """
- Export a PyTorch model to TorchScript, ONNX, CoreML formats
- Usage:
- $ python path/to/export.py --weights yolov5s.pt --img 640 --batch 1
- """
- import argparse
- 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__).absolute()
- sys.path.append(FILE.parents[0].as_posix()) # add yolov5/ to path
- from models.common import Conv
- from models.yolo import Detect
- from models.experimental import attempt_load
- from utils.activations import Hardswish, SiLU
- from utils.general import colorstr, check_img_size, check_requirements, file_size, set_logging
- from utils.torch_utils import select_device
- def export_torchscript(model, img, file, optimize):
- # TorchScript model export
- prefix = colorstr('TorchScript:')
- try:
- print(f'\n{prefix} starting export with torch {torch.__version__}...')
- f = file.with_suffix('.torchscript.pt')
- ts = torch.jit.trace(model, img, 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)')
- return ts
- except Exception as e:
- print(f'{prefix} export failure: {e}')
- def export_onnx(model, img, file, opset, train, dynamic, simplify):
- # ONNX model export
- prefix = colorstr('ONNX:')
- 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, img, 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(img.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, img, file):
- # CoreML model export
- prefix = colorstr('CoreML:')
- 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, img, strict=False) # TorchScript model
- model = ct.convert(ts, inputs=[ct.ImageType('image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
- 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}')
- def run(weights='./yolov5s.pt', # weights path
- img_size=(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
- dynamic=False, # ONNX: dynamic axes
- simplify=False, # ONNX: simplify model
- opset=12, # ONNX: opset version
- ):
- t = time.time()
- include = [x.lower() for x in include]
- img_size *= 2 if len(img_size) == 1 else 1 # expand
- file = Path(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) # load FP32 model
- names = model.names
- # Input
- gs = int(max(model.stride)) # grid size (max stride)
- img_size = [check_img_size(x, gs) for x in img_size] # verify img_size are gs-multiples
- img = torch.zeros(batch_size, 3, *img_size).to(device) # image size(1,3,320,192) iDetection
- # Update model
- if half:
- img, model = img.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.Hardswish):
- m.act = Hardswish()
- elif 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(img) # dry runs
- print(f"\n{colorstr('PyTorch:')} starting from {weights} ({file_size(weights):.1f} MB)")
- # Exports
- if 'torchscript' in include:
- export_torchscript(model, img, file, optimize)
- if 'onnx' in include:
- export_onnx(model, img, file, opset, train, dynamic, simplify)
- if 'coreml' in include:
- export_coreml(model, img, 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('--weights', type=str, default='./yolov5s.pt', help='weights path')
- parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image (height, width)')
- 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('--include', nargs='+', default=['torchscript', 'onnx', 'coreml'], help='include formats')
- 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('--dynamic', action='store_true', help='ONNX: 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')
- 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)
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