# Copyright (c) OpenMMLab. All rights reserved.
import copy
import os
from tempfile import NamedTemporaryFile, TemporaryDirectory
from typing import Any
import mmcv
import numpy as np
import pytest
import torch
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Dataset
import mmdeploy.backend.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 DummyModel, SwitchBackendWrapper
try:
import_codebase(Codebase.MMDET)
except ImportError:
pytest.skip(f'{Codebase.MMDET} is not installed.', allow_module_level=True)
model_cfg_path = 'tests/test_codebase/test_mmdet/data/model.py'
model_cfg = load_config(model_cfg_path)[0]
deploy_cfg = mmcv.Config(
dict(
backend_config=dict(type='onnxruntime'),
codebase_config=dict(
type='mmdet',
task='ObjectDetection',
post_processing=dict(
score_threshold=0.05,
confidence_threshold=0.005, # for YOLOv3
iou_threshold=0.5,
max_output_boxes_per_class=200,
pre_top_k=5000,
keep_top_k=100,
background_label_id=-1,
)),
onnx_config=dict(
type='onnx',
export_params=True,
keep_initializers_as_inputs=False,
opset_version=11,
input_shape=None,
input_names=['input'],
output_names=['dets', 'labels'])))
onnx_file = NamedTemporaryFile(suffix='.onnx').name
task_processor = build_task_processor(model_cfg, deploy_cfg, 'cpu')
img_shape = (32, 32)
img = np.random.rand(*img_shape, 3)
@pytest.mark.parametrize('from_mmrazor', [True, False, '123', 0])
def test_init_pytorch_model(from_mmrazor: Any):
from mmdet.models import BaseDetector
if from_mmrazor is False:
_task_processor = task_processor
else:
_model_cfg_path = 'tests/test_codebase/test_mmdet/data/' \
'mmrazor_model.py'
_model_cfg = load_config(_model_cfg_path)[0]
_model_cfg.algorithm.architecture.model.type = 'mmdet.YOLOV3'
_model_cfg.algorithm.architecture.model.backbone.type = \
'mmcls.SearchableShuffleNetV2'
_deploy_cfg = copy.deepcopy(deploy_cfg)
_deploy_cfg.codebase_config['from_mmrazor'] = from_mmrazor
_task_processor = build_task_processor(_model_cfg, _deploy_cfg, 'cpu')
if not isinstance(from_mmrazor, bool):
with pytest.raises(
TypeError,
match='`from_mmrazor` attribute must be '
'boolean type! '
f'but got: {from_mmrazor}'):
_ = _task_processor.from_mmrazor
return
assert from_mmrazor == _task_processor.from_mmrazor
if from_mmrazor:
pytest.importorskip('mmrazor', reason='mmrazor is not installed.')
model = _task_processor.init_pytorch_model(None)
assert isinstance(model, BaseDetector)
@pytest.fixture
def backend_model():
from mmdeploy.backend.onnxruntime import ORTWrapper
ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
wrapper = SwitchBackendWrapper(ORTWrapper)
wrapper.set(outputs={
'dets': torch.rand(1, 10, 5),
'labels': torch.rand(1, 10)
})
yield task_processor.init_backend_model([''])
wrapper.recover()
def test_init_backend_model(backend_model):
from mmdeploy.codebase.mmdet.deploy.object_detection_model import \
End2EndModel
assert isinstance(backend_model, End2EndModel)
def test_can_postprocess_masks():
from mmdeploy.codebase.mmdet.deploy.object_detection_model import \
End2EndModel
num_dets = [0, 1, 5]
for num_det in num_dets:
det_bboxes = np.random.randn(num_det, 4)
det_masks = np.random.randn(num_det, 28, 28)
img_w, img_h = (30, 40)
masks = End2EndModel.postprocessing_masks(det_bboxes, det_masks, img_w,
img_h)
expected_shape = (num_det, img_h, img_w)
actual_shape = masks.shape
assert actual_shape == expected_shape, \
f'The expected shape of masks {expected_shape} ' \
f'did not match actual shape {actual_shape}.'
@pytest.mark.parametrize('device', ['cpu', 'cuda:0'])
def test_create_input(device):
if device == 'cuda:0' and not torch.cuda.is_available():
pytest.skip('cuda is not available')
original_device = task_processor.device
task_processor.device = device
inputs = task_processor.create_input(img, input_shape=img_shape)
assert len(inputs) == 2
task_processor.device = original_device
def test_run_inference(backend_model):
torch_model = task_processor.init_pytorch_model(None)
input_dict, _ = task_processor.create_input(img, input_shape=img_shape)
torch_results = task_processor.run_inference(torch_model, input_dict)
backend_results = task_processor.run_inference(backend_model, input_dict)
assert torch_results is not None
assert backend_results is not None
assert len(torch_results[0]) == len(backend_results[0])
def test_visualize(backend_model):
input_dict, _ = task_processor.create_input(img, input_shape=img_shape)
results = task_processor.run_inference(backend_model, input_dict)
with TemporaryDirectory() as dir:
filename = dir + 'tmp.jpg'
task_processor.visualize(backend_model, img, results[0], filename, '')
assert os.path.exists(filename)
@pytest.mark.parametrize('partition_type', ['single_stage', 'two_stage'])
# Currently only mmdet implements get_partition_cfg
def test_get_partition_cfg(partition_type):
from mmdeploy.codebase.mmdet.deploy.model_partition_cfg import \
MMDET_PARTITION_CFG
partition_cfg = task_processor.get_partition_cfg(
partition_type=partition_type)
assert partition_cfg == MMDET_PARTITION_CFG[partition_type]
def test_get_tensort_from_input():
input_data = {'img': [torch.ones(3, 4, 5)]}
inputs = task_processor.get_tensor_from_input(input_data)
assert torch.equal(inputs, torch.ones(3, 4, 5))
def test_build_dataset_and_dataloader():
dataset = task_processor.build_dataset(
dataset_cfg=model_cfg, dataset_type='test')
assert isinstance(dataset, Dataset), 'Failed to build dataset'
dataloader = task_processor.build_dataloader(dataset, 1, 1)
assert isinstance(dataloader, DataLoader), 'Failed to build dataloader'
def test_single_gpu_test_and_evaluate():
from mmcv.parallel import MMDataParallel
class DummyDataset(Dataset):
def __getitem__(self, index):
return 0
def __len__(self):
return 0
def evaluate(self, *args, **kwargs):
return 0
def format_results(self, *args, **kwargs):
return 0
dataset = DummyDataset()
# Prepare dataloader
dataloader = DataLoader(dataset)
# Prepare dummy model
model = DummyModel(outputs=[torch.rand([1, 10, 5]), torch.rand([1, 10])])
model = MMDataParallel(model, device_ids=[0])
# Run test
outputs = task_processor.single_gpu_test(model, dataloader)
assert isinstance(outputs, list)
output_file = NamedTemporaryFile(suffix='.pkl').name
task_processor.evaluate_outputs(
model_cfg, outputs, dataset, 'bbox', out=output_file, format_only=True)