mmdeploy/tests/test_apis/test_torch2onnx.py

104 lines
3.0 KiB
Python

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