mirror of
https://github.com/open-mmlab/mmdeploy.git
synced 2025-01-14 08:09:43 +08:00
d8e4a78636
* update test for mmcls and mmdet * update det3d mmedit mmocr mmpose mmrotate * update mmseg * bug fixing * refactor ops * rename variable * remove comment
161 lines
4.8 KiB
Python
161 lines
4.8 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os
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import mmcv
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import numpy as np
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import pytest
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import torch
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from torch.utils.data import DataLoader
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from torch.utils.data.dataset import Dataset
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from mmdeploy.apis import build_task_processor
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from mmdeploy.utils import load_config
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from mmdeploy.utils.test import DummyModel, SwitchBackendWrapper
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model_cfg_path = 'tests/test_codebase/test_mmrotate/data/model.py'
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@pytest.fixture(scope='module')
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def model_cfg():
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return load_config(model_cfg_path)[0]
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@pytest.fixture(scope='module')
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def deploy_cfg():
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return mmcv.Config(
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dict(
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backend_config=dict(type='onnxruntime'),
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codebase_config=dict(
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type='mmrotate',
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task='RotatedDetection',
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post_processing=dict(
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score_threshold=0.05,
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iou_threshold=0.1,
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pre_top_k=2000,
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keep_top_k=2000)),
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onnx_config=dict(
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type='onnx',
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export_params=True,
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keep_initializers_as_inputs=False,
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opset_version=11,
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input_shape=None,
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input_names=['input'],
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output_names=['dets', 'labels'])))
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@pytest.fixture(scope='module')
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def task_processor(model_cfg, deploy_cfg):
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return build_task_processor(model_cfg, deploy_cfg, 'cpu')
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img_shape = (32, 32)
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@pytest.fixture(scope='module')
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def img():
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return np.random.rand(*img_shape, 3)
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@pytest.fixture(scope='module')
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def torch_model(task_processor):
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return task_processor.init_pytorch_model(None)
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def test_init_pytorch_model(torch_model):
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from mmrotate.models import RotatedBaseDetector
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assert isinstance(torch_model, RotatedBaseDetector)
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@pytest.fixture(scope='module')
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def backend_model(task_processor):
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from mmdeploy.backend.onnxruntime import ORTWrapper
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with SwitchBackendWrapper(ORTWrapper) as wrapper:
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wrapper.set(outputs={
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'dets': torch.rand(1, 10, 6),
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'labels': torch.rand(1, 10)
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})
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yield task_processor.init_backend_model([''])
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def test_init_backend_model(backend_model):
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from mmdeploy.codebase.mmrotate.deploy.rotated_detection_model import \
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End2EndModel
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assert isinstance(backend_model, End2EndModel)
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@pytest.fixture(scope='module')
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def model_inputs(task_processor, img):
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return task_processor.create_input(img, input_shape=img_shape)
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@pytest.mark.parametrize('device', ['cpu'])
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def test_create_input(device, task_processor, model_inputs):
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original_device = task_processor.device
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task_processor.device = device
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assert len(model_inputs) == 2
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task_processor.device = original_device
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def test_run_inference(backend_model, task_processor, torch_model,
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model_inputs):
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input_dict, _ = model_inputs
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torch_results = task_processor.run_inference(torch_model, input_dict)
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backend_results = task_processor.run_inference(backend_model, input_dict)
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assert torch_results is not None
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assert backend_results is not None
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assert len(torch_results[0]) == len(backend_results[0])
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def test_visualize(backend_model, task_processor, model_inputs, img, tmp_path):
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input_dict, _ = model_inputs
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results = task_processor.run_inference(backend_model, input_dict)
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filename = str(tmp_path / 'tmp.jpg')
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task_processor.visualize(backend_model, img, results[0], filename, '')
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assert os.path.exists(filename)
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def test_get_partition_cfg(task_processor):
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with pytest.raises(NotImplementedError):
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_ = task_processor.get_partition_cfg(partition_type='')
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def test_build_dataset_and_dataloader(task_processor, model_cfg):
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dataset = task_processor.build_dataset(
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dataset_cfg=model_cfg, dataset_type='test')
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assert isinstance(dataset, Dataset), 'Failed to build dataset'
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dataloader = task_processor.build_dataloader(dataset, 1, 1)
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assert isinstance(dataloader, DataLoader), 'Failed to build dataloader'
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def test_single_gpu_test_and_evaluate(task_processor, model_cfg, tmp_path):
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from mmcv.parallel import MMDataParallel
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class DummyDataset(Dataset):
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def __getitem__(self, index):
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return 0
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def __len__(self):
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return 0
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def evaluate(self, *args, **kwargs):
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return 0
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def format_results(self, *args, **kwargs):
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return 0
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dataset = DummyDataset()
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# Prepare dataloader
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dataloader = DataLoader(dataset)
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# Prepare dummy model
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model = DummyModel(outputs=[torch.rand([1, 10, 6]), torch.rand([1, 10])])
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model = MMDataParallel(model, device_ids=[0])
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# Run test
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outputs = task_processor.single_gpu_test(model, dataloader)
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assert isinstance(outputs, list)
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output_file = str(tmp_path / 'tmp.pkl')
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task_processor.evaluate_outputs(
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model_cfg, outputs, dataset, 'bbox', out=output_file, format_only=True)
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