76 lines
2.0 KiB
Python
76 lines
2.0 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os.path as osp
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import tempfile
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import mmcv
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import onnx
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import pytest
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import torch
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import torch.nn as nn
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from mmdeploy.apis import torch2onnx_impl
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from mmdeploy.utils.test import get_random_name
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onnx_file = tempfile.NamedTemporaryFile(suffix='.onnx').name
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@pytest.mark.skip(reason='This a not test class but a utility class.')
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class TestModel(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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return x * 0.5
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test_model = TestModel().eval().cuda()
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test_img = torch.rand([1, 3, 8, 8])
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input_name = get_random_name()
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output_name = get_random_name()
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dynamic_axes_dict = {
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input_name: {
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0: 'batch',
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2: 'height',
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3: 'width'
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},
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output_name: {
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0: 'batch'
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}
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}
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dynamic_axes_list = [[0, 2, 3], [0]]
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def get_deploy_cfg(input_name, output_name, dynamic_axes):
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return mmcv.Config(
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dict(
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onnx_config=dict(
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dynamic_axes=dynamic_axes,
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type='onnx',
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export_params=True,
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keep_initializers_as_inputs=False,
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opset_version=11,
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input_names=[input_name],
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output_names=[output_name],
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input_shape=None),
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codebase_config=dict(type='mmedit', task=''),
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backend_config=dict(type='onnxruntime')))
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@pytest.mark.parametrize('input_name', [input_name])
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@pytest.mark.parametrize('output_name', [output_name])
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@pytest.mark.parametrize('dynamic_axes',
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[dynamic_axes_dict, dynamic_axes_list])
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def test_torch2onnx(input_name, output_name, dynamic_axes):
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deploy_cfg = get_deploy_cfg(input_name, output_name, dynamic_axes)
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torch2onnx_impl(test_model, test_img, deploy_cfg, onnx_file)
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assert osp.exists(onnx_file)
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model = onnx.load(onnx_file)
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assert model is not None
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try:
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onnx.checker.check_model(model)
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except onnx.checker.ValidationError:
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assert False
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