122 lines
3.8 KiB
Python
122 lines
3.8 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import os
|
|
import tempfile
|
|
from tempfile import NamedTemporaryFile
|
|
|
|
import mmcv
|
|
import numpy as np
|
|
import pytest
|
|
import torch
|
|
|
|
import mmdeploy.apis.onnxruntime as ort_apis
|
|
from mmdeploy.apis import build_task_processor
|
|
from mmdeploy.codebase import import_codebase
|
|
from mmdeploy.utils import Codebase, load_config
|
|
from mmdeploy.utils.test import SwitchBackendWrapper
|
|
|
|
import_codebase(Codebase.MMEDIT)
|
|
|
|
model_cfg = 'tests/test_codebase/test_mmedit/data/model.py'
|
|
model_cfg = load_config(model_cfg)[0]
|
|
deploy_cfg = mmcv.Config(
|
|
dict(
|
|
backend_config=dict(type='onnxruntime'),
|
|
codebase_config=dict(type='mmedit', task='SuperResolution'),
|
|
onnx_config=dict(
|
|
type='onnx',
|
|
export_params=True,
|
|
keep_initializers_as_inputs=False,
|
|
opset_version=11,
|
|
input_shape=None,
|
|
input_names=['input'],
|
|
output_names=['output'])))
|
|
input_img = np.random.rand(32, 32, 3)
|
|
img_shape = [32, 32]
|
|
input = {'lq': input_img}
|
|
onnx_file = NamedTemporaryFile(suffix='.onnx').name
|
|
task_processor = build_task_processor(model_cfg, deploy_cfg, 'cpu')
|
|
|
|
|
|
def test_init_pytorch_model():
|
|
torch_model = task_processor.init_pytorch_model(None)
|
|
assert torch_model is not None
|
|
|
|
|
|
@pytest.fixture
|
|
def backend_model():
|
|
from mmdeploy.backend.onnxruntime import ORTWrapper
|
|
ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
|
|
wrapper = SwitchBackendWrapper(ORTWrapper)
|
|
wrapper.set(outputs={
|
|
'output': torch.rand(3, 50, 50),
|
|
})
|
|
|
|
yield task_processor.init_backend_model([''])
|
|
|
|
wrapper.recover()
|
|
|
|
|
|
def test_init_backend_model(backend_model):
|
|
assert backend_model is not None
|
|
|
|
|
|
def test_create_input():
|
|
inputs = task_processor.create_input(input_img, img_shape=img_shape)
|
|
assert inputs is not None
|
|
|
|
|
|
def test_visualize(backend_model):
|
|
result = task_processor.run_inference(backend_model, input)
|
|
with tempfile.TemporaryDirectory() as dir:
|
|
filename = dir + 'tmp.jpg'
|
|
task_processor.visualize(backend_model, input_img, result[0], filename,
|
|
'onnxruntime')
|
|
assert os.path.exists(filename)
|
|
|
|
|
|
def test_run_inference(backend_model):
|
|
results = task_processor.run_inference(backend_model, input)
|
|
assert results is not None
|
|
|
|
|
|
def test_get_tensor_from_input():
|
|
assert type(task_processor.get_tensor_from_input(input)) is not dict
|
|
|
|
|
|
def test_get_partition_cfg():
|
|
with pytest.raises(NotImplementedError):
|
|
task_processor.get_partition_cfg(None)
|
|
|
|
|
|
def test_build_dataset():
|
|
data = dict(
|
|
test={
|
|
'type': 'SRFolderDataset',
|
|
'lq_folder': 'tests/test_codebase/test_mmedit/data/imgs',
|
|
'gt_folder': 'tests/test_codebase/test_mmedit/data/imgs',
|
|
'scale': 1,
|
|
'filename_tmpl': '{}',
|
|
'pipeline': [
|
|
{
|
|
'type': 'LoadImageFromFile'
|
|
},
|
|
]
|
|
})
|
|
dataset_cfg = mmcv.Config(dict(data=data))
|
|
dataset = task_processor.build_dataset(
|
|
dataset_cfg=dataset_cfg, dataset_type='test')
|
|
assert dataset is not None, 'Failed to build dataset'
|
|
dataloader = task_processor.build_dataloader(dataset, 1, 1)
|
|
assert dataloader is not None, 'Failed to build dataloader'
|
|
|
|
|
|
def test_single_gpu_test(backend_model):
|
|
from mmcv.parallel import MMDataParallel
|
|
dataset = task_processor.build_dataset(model_cfg, dataset_type='test')
|
|
assert dataset is not None, 'Failed to build dataset'
|
|
dataloader = task_processor.build_dataloader(dataset, 1, 1)
|
|
assert dataloader is not None, 'Failed to build dataloader'
|
|
backend_model = MMDataParallel(backend_model, device_ids=[0])
|
|
outputs = task_processor.single_gpu_test(backend_model, dataloader)
|
|
assert outputs is not None, 'Failed to test model'
|