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Support pathlib.Path file paths when saving ONNX models #19727

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Merged
merged 3 commits into from
Apr 4, 2024
Merged

Support pathlib.Path file paths when saving ONNX models #19727

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7b4e7b1
Support pathlib.Path file paths when saving ONNX models
Apr 2, 2024
3297427
Merge branch 'master' into Fix-to_onnx-type-hint
awaelchli Apr 3, 2024
340c9fa
Merge and simplify .to_onnx file_path tests
Apr 3, 2024
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2 changes: 1 addition & 1 deletion src/lightning/pytorch/core/module.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1395,7 +1395,7 @@ def forward(self, x):
input_sample = self._on_before_batch_transfer(input_sample)
input_sample = self._apply_batch_transfer_handler(input_sample)

torch.onnx.export(self, input_sample, file_path, **kwargs)
torch.onnx.export(self, input_sample, str(file_path), **kwargs)
self.train(mode)

@torch.no_grad()
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15 changes: 15 additions & 0 deletions tests/tests_pytorch/models/test_onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@
# limitations under the License.
import operator
import os
from pathlib import Path
from unittest.mock import patch

import numpy as np
Expand Down Expand Up @@ -42,6 +43,20 @@ def test_model_saves_with_input_sample(tmp_path):
assert os.path.getsize(file_path) > 4e2


@RunIf(onnx=True)
def test_model_saves_with_pathlib_file_path(tmp_path):
"""Test that ONNX model saves with pathlib.Path file_path and size is greater than 3 MB."""
model = BoringModel()
trainer = Trainer(fast_dev_run=True)
trainer.fit(model)

file_path = Path(tmp_path) / "model.onnx"
input_sample = torch.randn((1, 32))
model.to_onnx(file_path, input_sample)
assert os.path.isfile(file_path)
assert os.path.getsize(file_path) > 4e2


@pytest.mark.parametrize(
"accelerator", [pytest.param("mps", marks=RunIf(mps=True)), pytest.param("gpu", marks=RunIf(min_cuda_gpus=True))]
)
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