153 lines
5.2 KiB
Python
153 lines
5.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os
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from tempfile import NamedTemporaryFile, TemporaryDirectory
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import mmcv
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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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import mmdeploy.backend.onnxruntime as ort_apis
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from mmdeploy.apis import build_task_processor
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from mmdeploy.codebase import import_codebase
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from mmdeploy.utils import Codebase, load_config
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from mmdeploy.utils.test import DummyModel, SwitchBackendWrapper
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try:
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import_codebase(Codebase.MMDET3D)
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except ImportError:
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pytest.skip(
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f'{Codebase.MMDET3D} is not installed.', allow_module_level=True)
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model_cfg_path = 'tests/test_codebase/test_mmdet3d/data/model_cfg.py'
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pcd_path = 'tests/test_codebase/test_mmdet3d/data/kitti/kitti_000008.bin'
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model_cfg = load_config(model_cfg_path)[0]
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deploy_cfg = mmcv.Config(
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dict(
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backend_config=dict(type='onnxruntime'),
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codebase_config=dict(type='mmdet3d', task='VoxelDetection'),
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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=['voxels', 'num_points', 'coors'],
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output_names=['scores', 'bbox_preds', 'dir_scores'])))
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onnx_file = NamedTemporaryFile(suffix='.onnx').name
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task_processor = build_task_processor(model_cfg, deploy_cfg, 'cpu')
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def test_build_pytorch_model():
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from mmdet3d.models import Base3DDetector
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model = task_processor.build_pytorch_model(None)
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assert isinstance(model, Base3DDetector)
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@pytest.fixture
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def backend_model():
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from mmdeploy.backend.onnxruntime import ORTWrapper
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ort_apis.__dict__.update({'ORTWrapper': ORTWrapper})
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wrapper = SwitchBackendWrapper(ORTWrapper)
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wrapper.set(
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outputs={
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'scores': torch.rand(1, 18, 32, 32),
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'bbox_preds': torch.rand(1, 42, 32, 32),
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'dir_scores': torch.rand(1, 12, 32, 32)
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})
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yield task_processor.build_backend_model([''])
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wrapper.recover()
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def test_build_backend_model(backend_model):
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from mmdeploy.codebase.mmdet3d.deploy.voxel_detection_model import \
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VoxelDetectionModel
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assert isinstance(backend_model, VoxelDetectionModel)
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@pytest.mark.parametrize('device', ['cpu', 'cuda:0'])
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def test_create_input(device):
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if device == 'cuda:0' and not torch.cuda.is_available():
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pytest.skip('cuda is not available')
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original_device = task_processor.device
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task_processor.device = device
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inputs = task_processor.create_input(pcd_path)
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assert len(inputs) == 2
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task_processor.device = original_device
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@pytest.mark.skipif(
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reason='Only support GPU test', condition=not torch.cuda.is_available())
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def test_run_inference(backend_model):
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task_processor.device = 'cuda:0'
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torch_model = task_processor.build_pytorch_model(None)
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input_dict, _ = task_processor.create_input(pcd_path)
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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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task_processor.device = 'cpu'
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@pytest.mark.skipif(
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reason='Only support GPU test', condition=not torch.cuda.is_available())
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def test_visualize():
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task_processor.device = 'cuda:0'
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input_dict, _ = task_processor.create_input(pcd_path)
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torch_model = task_processor.build_pytorch_model(None)
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results = task_processor.run_inference(torch_model, input_dict)
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with TemporaryDirectory() as dir:
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filename = dir + 'tmp.bin'
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task_processor.visualize(torch_model, pcd_path, results[0], filename,
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'test', False)
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assert os.path.exists(filename)
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task_processor.device = 'cpu'
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def test_build_dataset_and_dataloader():
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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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@pytest.mark.skipif(
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reason='Only support GPU test', condition=not torch.cuda.is_available())
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def test_single_gpu_test_and_evaluate():
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from mmcv.parallel import MMDataParallel
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task_processor.device = 'cuda:0'
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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, 5]), 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 = NamedTemporaryFile(suffix='.pkl').name
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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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task_processor.device = 'cpu'
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