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Fix onnx export #2475

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Merged
merged 2 commits into from
Apr 15, 2025
Merged

Fix onnx export #2475

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b1d09e2
Fix #2472, torch.onnx.export_ (with return output) finally removed :(
rwightman Apr 15, 2025
990f618
Remove torch_out from onnx export, no point without the export_ fn
rwightman Apr 15, 2025
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12 changes: 11 additions & 1 deletion onnx_export.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,8 @@
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('--input-size', default=None, nargs=3, type=int, metavar='N',
help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), 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',
Expand Down Expand Up @@ -82,6 +84,14 @@ def main():
if args.reparam:
model = reparameterize_model(model)

if args.input_size is not None:
assert len(args.input_size) == 3, 'input-size should be N H W (channels, height, width)'
input_size = args.input_size
elif args.img_size is not None:
input_size = (3, args.img_size, args.img_size)
else:
input_size = None

onnx_export(
model,
args.output,
Expand All @@ -93,7 +103,7 @@ def main():
training=args.training,
verbose=args.verbose,
use_dynamo=args.dynamo,
input_size=(3, args.img_size, args.img_size),
input_size=input_size,
batch_size=args.batch_size,
)

Expand Down
11 changes: 3 additions & 8 deletions timm/utils/onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,7 +43,7 @@ def onnx_export(

if example_input is None:
if not input_size:
assert hasattr(model, 'default_cfg')
assert hasattr(model, 'default_cfg'), 'Cannot file model default config, input size must be provided'
input_size = model.default_cfg.get('input_size')
example_input = torch.randn((batch_size,) + input_size, requires_grad=training)

Expand Down Expand Up @@ -78,9 +78,8 @@ def onnx_export(
export_options=export_options,
)
export_output.save(output_file)
torch_out = None
else:
torch_out = torch.onnx._export(
torch.onnx.export(
model,
example_input,
output_file,
Expand All @@ -101,9 +100,5 @@ def onnx_export(
if check_forward and not training:
import numpy as np
onnx_out = onnx_forward(output_file, example_input)
if torch_out is not None:
np.testing.assert_almost_equal(torch_out.numpy(), onnx_out, decimal=3)
np.testing.assert_almost_equal(original_out.numpy(), torch_out.numpy(), decimal=5)
else:
np.testing.assert_almost_equal(original_out.numpy(), onnx_out, decimal=3)
np.testing.assert_almost_equal(original_out.numpy(), onnx_out, decimal=3)