# 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', 'onnx-simplifier')) 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: 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: 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=12, 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)