| """ ONNX export script |
| |
| Export PyTorch models as ONNX graphs. |
| |
| This export script originally started as an adaptation of code snippets found at |
| https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html |
| |
| The default parameters work with PyTorch 1.6 and ONNX 1.7 and produce an optimal ONNX graph |
| for hosting in the ONNX runtime (see onnx_validate.py). To export an ONNX model compatible |
| with caffe2 (see caffe2_benchmark.py and caffe2_validate.py), the --keep-init and --aten-fallback |
| flags are currently required. |
| |
| Older versions of PyTorch/ONNX (tested PyTorch 1.4, ONNX 1.5) do not need extra flags for |
| caffe2 compatibility, but they produce a model that isn't as fast running on ONNX runtime. |
| |
| Most new release of PyTorch and ONNX cause some sort of breakage in the export / usage of ONNX models. |
| Please do your research and search ONNX and PyTorch issue tracker before asking me. Thanks. |
| |
| Copyright 2020 Ross Wightman |
| """ |
| import argparse |
|
|
| import timm |
| from timm.utils.model import reparameterize_model |
| from timm.utils.onnx import onnx_export |
|
|
| parser = argparse.ArgumentParser(description='PyTorch ImageNet Validation') |
| parser.add_argument('output', metavar='ONNX_FILE', |
| help='output model filename') |
| parser.add_argument('--model', '-m', metavar='MODEL', default='mobilenetv3_large_100', |
| help='model architecture (default: mobilenetv3_large_100)') |
| parser.add_argument('--opset', type=int, default=None, |
| help='ONNX opset to use (default: 10)') |
| parser.add_argument('--keep-init', action='store_true', default=False, |
| help='Keep initializers as input. Needed for Caffe2 compatible export in newer PyTorch/ONNX.') |
| parser.add_argument('--aten-fallback', action='store_true', default=False, |
| help='Fallback to ATEN ops. Helps fix AdaptiveAvgPool issue with Caffe2 in newer PyTorch/ONNX.') |
| parser.add_argument('--dynamic-size', action='store_true', default=False, |
| help='Export model width dynamic width/height. Not recommended for "tf" models with SAME padding.') |
| parser.add_argument('--check-forward', action='store_true', default=False, |
| help='Do a full check of torch vs onnx forward after export.') |
| parser.add_argument('-b', '--batch-size', default=1, type=int, |
| metavar='N', help='mini-batch size (default: 1)') |
| parser.add_argument('--img-size', default=None, type=int, |
| metavar='N', help='Input image dimension, uses model default if empty') |
| parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN', |
| help='Override mean pixel value of dataset') |
| parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD', |
| help='Override std deviation of of dataset') |
| parser.add_argument('--num-classes', type=int, default=1000, |
| help='Number classes in dataset') |
| parser.add_argument('--checkpoint', default='', type=str, metavar='PATH', |
| help='path to checkpoint (default: none)') |
| parser.add_argument('--reparam', default=False, action='store_true', |
| help='Reparameterize model') |
| parser.add_argument('--training', default=False, action='store_true', |
| help='Export in training mode (default is eval)') |
| parser.add_argument('--verbose', default=False, action='store_true', |
| help='Extra stdout output') |
| parser.add_argument('--dynamo', default=False, action='store_true', |
| help='Use torch dynamo export.') |
|
|
| def main(): |
| args = parser.parse_args() |
|
|
| args.pretrained = True |
| if args.checkpoint: |
| args.pretrained = False |
|
|
| print("==> Creating PyTorch {} model".format(args.model)) |
| |
| |
| model = timm.create_model( |
| args.model, |
| num_classes=args.num_classes, |
| in_chans=3, |
| pretrained=args.pretrained, |
| checkpoint_path=args.checkpoint, |
| exportable=True, |
| ) |
|
|
| if args.reparam: |
| model = reparameterize_model(model) |
|
|
| onnx_export( |
| model, |
| args.output, |
| opset=args.opset, |
| dynamic_size=args.dynamic_size, |
| aten_fallback=args.aten_fallback, |
| keep_initializers=args.keep_init, |
| check_forward=args.check_forward, |
| training=args.training, |
| verbose=args.verbose, |
| use_dynamo=args.dynamo, |
| input_size=(3, args.img_size, args.img_size), |
| batch_size=args.batch_size, |
| ) |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
