mirror of
https://github.com/open-mmlab/mmdeploy.git
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2afbb9945e
* wip * update twostage detector support * fix unit test * sdk wip * comment * refactor export info * fix * support roi trans * update rotate.yml * clear model.py, support torch1.13
114 lines
3.6 KiB
Python
114 lines
3.6 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 numpy as np
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import pytest
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import torch
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from mmengine import Config
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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 SwitchBackendWrapper
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try:
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import_codebase(Codebase.MMROTATE)
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except ImportError:
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pytest.skip(
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f'{Codebase.MMROTATE} is not installed.', allow_module_level=True)
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model_cfg_path = 'tests/test_codebase/test_mmrotate/data/model.py'
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model_cfg = load_config(model_cfg_path)[0]
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deploy_cfg = 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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onnx_file = NamedTemporaryFile(suffix='.onnx').name
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task_processor = None
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img_shape = (32, 32)
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img = np.random.rand(*img_shape, 3)
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@pytest.fixture(autouse=True)
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def init_task_processor():
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global task_processor
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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 mmdet.models import BaseDetector
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model = task_processor.build_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, 6),
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'labels': torch.randint(1, 10, (1, 10))
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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.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.mark.parametrize('device', ['cpu'])
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def test_create_input(device):
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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_visualize(backend_model):
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input_dict, _ = task_processor.create_input(img, input_shape=img_shape)
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results = backend_model.test_step(input_dict)[0]
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with TemporaryDirectory() as dir:
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filename = dir + 'tmp.jpg'
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task_processor.visualize(img, results, filename, 'window')
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assert os.path.exists(filename)
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def test_get_partition_cfg():
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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():
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dataset = task_processor.build_dataset(
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dataset_cfg=model_cfg.test_dataloader.dataset)
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assert isinstance(dataset, Dataset), 'Failed to build dataset'
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dataloader_cfg = task_processor.model_cfg.test_dataloader
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dataloader = task_processor.build_dataloader(dataloader_cfg)
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assert isinstance(dataloader, DataLoader), 'Failed to build dataloader'
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