104 lines
3.1 KiB
Python
104 lines
3.1 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 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 mmengine import Config
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from mmdeploy.apis.onnx import export
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from mmdeploy.utils.config_utils import (get_backend, get_dynamic_axes,
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get_onnx_config)
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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 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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output_prefix = osp.splitext(onnx_file)[0]
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context_info = dict(cfg=deploy_cfg)
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backend = get_backend(deploy_cfg).value
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onnx_cfg = get_onnx_config(deploy_cfg)
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opset_version = onnx_cfg.get('opset_version', 11)
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input_names = onnx_cfg['input_names']
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output_names = onnx_cfg['output_names']
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axis_names = input_names + output_names
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dynamic_axes = get_dynamic_axes(deploy_cfg, axis_names)
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verbose = not onnx_cfg.get('strip_doc_string', True) or onnx_cfg.get(
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'verbose', False)
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keep_initializers_as_inputs = onnx_cfg.get('keep_initializers_as_inputs',
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True)
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export(
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test_model,
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test_img,
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context_info=context_info,
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output_path_prefix=output_prefix,
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backend=backend,
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input_names=input_names,
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output_names=output_names,
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opset_version=opset_version,
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dynamic_axes=dynamic_axes,
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verbose=verbose,
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keep_initializers_as_inputs=keep_initializers_as_inputs)
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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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