wehub-resource-sync
64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
# Copyright 2024 MIT Han Lab
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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import os
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import warnings
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from typing import Tuple
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import torch
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def export_onnx(
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model: torch.nn.Module,
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input_shape: Tuple[int],
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export_path: str,
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opset: int,
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export_dtype: torch.dtype,
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export_device: torch.device,
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) -> None:
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model.eval()
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dummy_input = {"x": torch.randn(input_shape, dtype=export_dtype, device=export_device)}
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dynamic_axes = {
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"x": {0: "batch_size"},
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}
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# _ = model(**dummy_input)
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output_names = ["image_embeddings"]
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export_dir = os.path.dirname(export_path)
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if not os.path.exists(export_dir):
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os.makedirs(export_dir)
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
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warnings.filterwarnings("ignore", category=UserWarning)
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print(f"Exporting onnx model to {export_path}...")
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with open(export_path, "wb") as f:
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torch.onnx.export(
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model,
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tuple(dummy_input.values()),
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f,
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export_params=True,
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verbose=False,
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opset_version=opset,
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do_constant_folding=True,
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input_names=list(dummy_input.keys()),
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output_names=output_names,
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dynamic_axes=dynamic_axes,
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)
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