"""Exports a YOLOv5 *.pt model to TorchScript, ONNX, CoreML formats Usage: $ python path/to/models/export.py --weights yolov5s.pt --img 640 --batch 1 """ import argparse import sys import time from pathlib import Path sys.path.append(Path(__file__).parent.parent.absolute().__str__()) # to run '$ python *.py' files in subdirectories import torch import torch.nn as nn from torch.utils.mobile_optimizer import optimize_for_mobile import models 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 if __name__ == '__main__': 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 size') # 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='optimize TorchScript for mobile') # TorchScript-only parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes') # ONNX-only parser.add_argument('--simplify', action='store_true', help='simplify ONNX model') # ONNX-only opt = parser.parse_args() opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand opt.include = [x.lower() for x in opt.include] print(opt) set_logging() t = time.time() # Load PyTorch model device = select_device(opt.device) model = attempt_load(opt.weights, map_location=device) # load FP32 model labels = model.names # Checks gs = int(max(model.stride)) # grid size (max stride) opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples assert not (opt.device.lower() == 'cpu' and opt.half), '--half only compatible with GPU export, i.e. use --device 0' # Input img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection # Update model if opt.half: img, model = img.half(), model.half() # to FP16 if opt.train: model.train() # training mode (no grid construction in Detect layer) for k, m in model.named_modules(): m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility if isinstance(m, models.common.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, models.yolo.Detect): m.inplace = opt.inplace m.onnx_dynamic = opt.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 {opt.weights} ({file_size(opt.weights):.1f} MB)") # TorchScript export ----------------------------------------------------------------------------------------------- if 'torchscript' in opt.include or 'coreml' in opt.include: prefix = colorstr('TorchScript:') try: print(f'\n{prefix} starting export with torch {torch.__version__}...') f = opt.weights.replace('.pt', '.torchscript.pt') # filename ts = torch.jit.trace(model, img, strict=False) (optimize_for_mobile(ts) if opt.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}') # ONNX export ------------------------------------------------------------------------------------------------------ if 'onnx' in opt.include: prefix = colorstr('ONNX:') try: import onnx print(f'{prefix} starting export with onnx {onnx.__version__}...') f = opt.weights.replace('.pt', '.onnx') # filename torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'], dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # size(1,3,640,640) 'output': {0: 'batch', 2: 'y', 3: 'x'}} if opt.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 opt.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=opt.dynamic, input_shapes={'images': list(img.shape)} if opt.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)') except Exception as e: print(f'{prefix} export failure: {e}') # CoreML export ---------------------------------------------------------------------------------------------------- if 'coreml' in opt.include: prefix = colorstr('CoreML:') try: import coremltools as ct print(f'{prefix} starting export with coremltools {ct.__version__}...') model = ct.convert(ts, inputs=[ct.ImageType('image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])]) f = opt.weights.replace('.pt', '.mlmodel') # filename model.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}') # Finish print(f'\nExport complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.')