209 lines
7.3 KiB
Python
209 lines
7.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import copy
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import os
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from tempfile import NamedTemporaryFile, TemporaryDirectory
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from typing import Any
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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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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.MMDET)
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except ImportError:
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pytest.skip(f'{Codebase.MMDET} is not installed.', allow_module_level=True)
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model_cfg_path = 'tests/test_codebase/test_mmdet/data/model.py'
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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(
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type='mmdet',
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task='ObjectDetection',
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post_processing=dict(
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score_threshold=0.05,
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confidence_threshold=0.005, # for YOLOv3
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iou_threshold=0.5,
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max_output_boxes_per_class=200,
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pre_top_k=5000,
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keep_top_k=100,
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background_label_id=-1,
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)),
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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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onnx_file = NamedTemporaryFile(suffix='.onnx').name
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task_processor = build_task_processor(model_cfg, deploy_cfg, 'cpu')
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img_shape = (32, 32)
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img = np.random.rand(*img_shape, 3)
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@pytest.mark.parametrize('from_mmrazor', [True, False, '123', 0])
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def test_init_pytorch_model(from_mmrazor: Any):
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from mmdet.models import BaseDetector
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if from_mmrazor is False:
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_task_processor = task_processor
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else:
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_model_cfg_path = 'tests/test_codebase/test_mmdet/data/' \
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'mmrazor_model.py'
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_model_cfg = load_config(_model_cfg_path)[0]
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_model_cfg.algorithm.architecture.model.type = 'mmdet.YOLOV3'
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_model_cfg.algorithm.architecture.model.backbone.type = \
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'mmcls.SearchableShuffleNetV2'
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_deploy_cfg = copy.deepcopy(deploy_cfg)
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_deploy_cfg.codebase_config['from_mmrazor'] = from_mmrazor
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_task_processor = build_task_processor(_model_cfg, _deploy_cfg, 'cpu')
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if not isinstance(from_mmrazor, bool):
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with pytest.raises(
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TypeError,
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match='`from_mmrazor` attribute must be '
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'boolean type! '
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f'but got: {from_mmrazor}'):
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_ = _task_processor.from_mmrazor
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return
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assert from_mmrazor == _task_processor.from_mmrazor
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if from_mmrazor:
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pytest.importorskip('mmrazor', reason='mmrazor is not installed.')
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model = _task_processor.init_pytorch_model(None)
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assert isinstance(model, BaseDetector)
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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(outputs={
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'dets': torch.rand(1, 10, 5),
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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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wrapper.recover()
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def test_init_backend_model(backend_model):
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from mmdeploy.codebase.mmdet.deploy.object_detection_model import \
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End2EndModel
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assert isinstance(backend_model, End2EndModel)
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def test_can_postprocess_masks():
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from mmdeploy.codebase.mmdet.deploy.object_detection_model import \
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End2EndModel
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num_dets = [0, 1, 5]
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for num_det in num_dets:
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det_bboxes = np.random.randn(num_det, 4)
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det_masks = np.random.randn(num_det, 28, 28)
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img_w, img_h = (30, 40)
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masks = End2EndModel.postprocessing_masks(det_bboxes, det_masks, img_w,
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img_h)
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expected_shape = (num_det, img_h, img_w)
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actual_shape = masks.shape
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assert actual_shape == expected_shape, \
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f'The expected shape of masks {expected_shape} ' \
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f'did not match actual shape {actual_shape}.'
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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(img, input_shape=img_shape)
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assert len(inputs) == 2
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task_processor.device = original_device
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def test_run_inference(backend_model):
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torch_model = task_processor.init_pytorch_model(None)
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input_dict, _ = task_processor.create_input(img, input_shape=img_shape)
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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):
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input_dict, _ = task_processor.create_input(img, input_shape=img_shape)
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results = task_processor.run_inference(backend_model, input_dict)
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with TemporaryDirectory() as dir:
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filename = dir + '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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@pytest.mark.parametrize('partition_type', ['single_stage', 'two_stage'])
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# Currently only mmdet implements get_partition_cfg
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def test_get_partition_cfg(partition_type):
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from mmdeploy.codebase.mmdet.deploy.model_partition_cfg import \
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MMDET_PARTITION_CFG
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partition_cfg = task_processor.get_partition_cfg(
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partition_type=partition_type)
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assert partition_cfg == MMDET_PARTITION_CFG[partition_type]
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def test_get_tensort_from_input():
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input_data = {'img': [torch.ones(3, 4, 5)]}
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inputs = task_processor.get_tensor_from_input(input_data)
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assert torch.equal(inputs, torch.ones(3, 4, 5))
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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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def test_single_gpu_test_and_evaluate():
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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, 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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