130 lines
4.1 KiB
Python
130 lines
4.1 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import os
|
|
import os.path as osp
|
|
import tempfile
|
|
|
|
import mmcv
|
|
import numpy as np
|
|
import pytest
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
from mmdeploy.utils import Backend
|
|
from mmdeploy.utils.test import backend_checker
|
|
|
|
|
|
@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
|
|
|
|
|
|
def generate_onnx_file(model, export_img, onnx_file):
|
|
with torch.no_grad():
|
|
dynamic_axes = {
|
|
'input': {
|
|
0: 'batch',
|
|
2: 'width',
|
|
3: 'height'
|
|
},
|
|
'output': {
|
|
0: 'batch'
|
|
}
|
|
}
|
|
torch.onnx.export(
|
|
model,
|
|
export_img,
|
|
onnx_file,
|
|
output_names=['output'],
|
|
input_names=['input'],
|
|
keep_initializers_as_inputs=True,
|
|
do_constant_folding=True,
|
|
verbose=False,
|
|
opset_version=11,
|
|
dynamic_axes=dynamic_axes)
|
|
assert osp.exists(onnx_file)
|
|
|
|
|
|
def get_outputs(pytorch_model, openvino_model_path, input):
|
|
output_pytorch = pytorch_model(input).numpy()
|
|
|
|
from mmdeploy.backend.openvino import OpenVINOWrapper
|
|
openvino_model = OpenVINOWrapper(openvino_model_path)
|
|
openvino_output = openvino_model({'input': input})['output']
|
|
|
|
return output_pytorch, openvino_output
|
|
|
|
|
|
@backend_checker(Backend.OPENVINO)
|
|
def test_onnx2openvino():
|
|
from mmdeploy.apis.openvino import get_output_model_file, onnx2openvino
|
|
pytorch_model = TestModel().eval()
|
|
export_img = torch.rand([1, 3, 8, 8])
|
|
onnx_file = tempfile.NamedTemporaryFile(suffix='.onnx').name
|
|
generate_onnx_file(pytorch_model, export_img, onnx_file)
|
|
|
|
input_info = {'input': export_img.shape}
|
|
output_names = ['output']
|
|
openvino_dir = tempfile.TemporaryDirectory().name
|
|
onnx2openvino(input_info, output_names, onnx_file, openvino_dir)
|
|
openvino_model_path = get_output_model_file(onnx_file, openvino_dir)
|
|
assert osp.exists(openvino_model_path), \
|
|
'The file (.xml) for OpenVINO IR has not been created.'
|
|
|
|
test_img = torch.rand([1, 3, 16, 16])
|
|
output_pytorch, openvino_output = get_outputs(pytorch_model,
|
|
openvino_model_path,
|
|
test_img)
|
|
assert np.allclose(output_pytorch, openvino_output), \
|
|
'OpenVINO and PyTorch outputs are not the same.'
|
|
|
|
|
|
@backend_checker(Backend.OPENVINO)
|
|
def test_can_not_run_onnx2openvino_without_mo():
|
|
current_environ = dict(os.environ)
|
|
os.environ.clear()
|
|
|
|
is_error = False
|
|
try:
|
|
from mmdeploy.apis.openvino import onnx2openvino
|
|
onnx2openvino({}, ['output'], 'tmp.onnx', '/tmp')
|
|
except RuntimeError:
|
|
is_error = True
|
|
|
|
os.environ.update(current_environ)
|
|
assert is_error, \
|
|
'The onnx2openvino script was launched without checking for MO.'
|
|
|
|
|
|
@backend_checker(Backend.OPENVINO)
|
|
def test_get_input_shape_from_cfg():
|
|
from mmdeploy.apis.openvino import get_input_shape_from_cfg
|
|
|
|
# Test with default value
|
|
deploy_cfg = mmcv.Config()
|
|
model_cfg = mmcv.Config()
|
|
input_shape = get_input_shape_from_cfg(deploy_cfg, model_cfg)
|
|
assert input_shape == [1, 3, 800, 1344], \
|
|
'The function returned a different default shape.'
|
|
|
|
# Test with model_cfg that contains the required data.
|
|
height, width = 800, 1200
|
|
model_cfg = mmcv.Config(
|
|
{'test_pipeline': [{}, {
|
|
'img_scale': (width, height)
|
|
}]})
|
|
input_shape = get_input_shape_from_cfg(deploy_cfg, model_cfg)
|
|
assert input_shape == [1, 3, height, width], \
|
|
'The shape in the model_cfg does not match the output shape.'
|
|
|
|
# Test with deploy_cfg that contains the required data.
|
|
height, width = 600, 1000
|
|
deploy_cfg = mmcv.Config({'onnx_config': {'input_shape': (width, height)}})
|
|
input_shape = get_input_shape_from_cfg(deploy_cfg, model_cfg)
|
|
assert input_shape == [1, 3, height, width], \
|
|
'The shape in the deploy_cfg does not match the output shape.'
|