mmdeploy/tests/test_apis/test_onnx2ncnn.py

68 lines
1.7 KiB
Python

# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
import pytest
import torch
import torch.nn as nn
from mmdeploy.backend.ncnn.onnx2ncnn import get_output_model_file
from mmdeploy.utils import Backend
from mmdeploy.utils.test import backend_checker
onnx_file = tempfile.NamedTemporaryFile(suffix='.onnx').name
test_img = torch.rand([1, 3, 8, 8])
@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()
def generate_onnx_file(model):
with torch.no_grad():
dynamic_axes = {
'input': {
0: 'batch',
2: 'width',
3: 'height'
},
'output': {
0: 'batch'
}
}
torch.onnx.export(
model,
test_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)
@backend_checker(Backend.NCNN)
def test_onnx2ncnn():
from mmdeploy.apis.ncnn import onnx2ncnn
model = test_model
generate_onnx_file(model)
work_dir, _ = osp.split(onnx_file)
save_param, save_bin = get_output_model_file(onnx_file, work_dir=work_dir)
onnx2ncnn(onnx_file, save_param, save_bin)
assert osp.exists(work_dir)
assert osp.exists(save_param)
assert osp.exists(save_bin)