| | """ 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() |
| |
|
