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| import argparse | |
| import sys | |
| import time | |
| sys.path.append('./') # to run '$ python *.py' files in subdirectories | |
| import torch | |
| import torch.nn as nn | |
| import models | |
| from models.experimental import attempt_load | |
| from utils.activations import Hardswish, SiLU | |
| from utils.general import set_logging, check_img_size | |
| from utils.torch_utils import select_device | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--weights', type=str, default='./yolor-csp-c.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('--dynamic', action='store_true', help='dynamic ONNX axes') | |
| parser.add_argument('--grid', action='store_true', help='export Detect() layer grid') | |
| parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') | |
| opt = parser.parse_args() | |
| opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand | |
| 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 | |
| # Input | |
| img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection | |
| # Update model | |
| 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.forward = m.forward_export # assign forward (optional) | |
| model.model[-1].export = not opt.grid # set Detect() layer grid export | |
| y = model(img) # dry run | |
| # TorchScript export | |
| try: | |
| print('\nStarting TorchScript export with torch %s...' % torch.__version__) | |
| f = opt.weights.replace('.pt', '.torchscript.pt') # filename | |
| ts = torch.jit.trace(model, img, strict=False) | |
| ts.save(f) | |
| print('TorchScript export success, saved as %s' % f) | |
| except Exception as e: | |
| print('TorchScript export failure: %s' % e) | |
| # ONNX export | |
| try: | |
| import onnx | |
| print('\nStarting ONNX export with onnx %s...' % onnx.__version__) | |
| f = opt.weights.replace('.pt', '.onnx') # filename | |
| torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'], | |
| output_names=['classes', 'boxes'] if y is None else ['output'], | |
| 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 | |
| onnx_model = onnx.load(f) # load onnx model | |
| onnx.checker.check_model(onnx_model) # check onnx model | |
| # print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model | |
| print('ONNX export success, saved as %s' % f) | |
| except Exception as e: | |
| print('ONNX export failure: %s' % e) | |
| # CoreML export | |
| try: | |
| import coremltools as ct | |
| print('\nStarting CoreML export with coremltools %s...' % ct.__version__) | |
| # convert model from torchscript and apply pixel scaling as per detect.py | |
| model = ct.convert(ts, inputs=[ct.ImageType(name='image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])]) | |
| f = opt.weights.replace('.pt', '.mlmodel') # filename | |
| model.save(f) | |
| print('CoreML export success, saved as %s' % f) | |
| except Exception as e: | |
| print('CoreML export failure: %s' % e) | |
| # Finish | |
| print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t)) | |