OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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83 lines
2.5 KiB
83 lines
2.5 KiB
# Copyright (c) OpenMMLab. All rights reserved. |
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"""Tests for async interface.""" |
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import asyncio |
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import os |
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import sys |
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import asynctest |
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import mmcv |
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import torch |
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from mmdet.apis import async_inference_detector, init_detector |
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if sys.version_info >= (3, 7): |
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from mmdet.utils.contextmanagers import concurrent |
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class AsyncTestCase(asynctest.TestCase): |
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use_default_loop = False |
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forbid_get_event_loop = True |
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TEST_TIMEOUT = int(os.getenv('ASYNCIO_TEST_TIMEOUT', '30')) |
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def _run_test_method(self, method): |
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result = method() |
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if asyncio.iscoroutine(result): |
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self.loop.run_until_complete( |
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asyncio.wait_for(result, timeout=self.TEST_TIMEOUT)) |
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class MaskRCNNDetector: |
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def __init__(self, |
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model_config, |
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checkpoint=None, |
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streamqueue_size=3, |
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device='cuda:0'): |
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self.streamqueue_size = streamqueue_size |
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self.device = device |
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# build the model and load checkpoint |
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self.model = init_detector( |
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model_config, checkpoint=None, device=self.device) |
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self.streamqueue = None |
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async def init(self): |
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self.streamqueue = asyncio.Queue() |
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for _ in range(self.streamqueue_size): |
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stream = torch.cuda.Stream(device=self.device) |
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self.streamqueue.put_nowait(stream) |
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if sys.version_info >= (3, 7): |
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async def apredict(self, img): |
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if isinstance(img, str): |
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img = mmcv.imread(img) |
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async with concurrent(self.streamqueue): |
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result = await async_inference_detector(self.model, img) |
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return result |
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class AsyncInferenceTestCase(AsyncTestCase): |
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if sys.version_info >= (3, 7): |
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async def test_simple_inference(self): |
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if not torch.cuda.is_available(): |
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import pytest |
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pytest.skip('test requires GPU and torch+cuda') |
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ori_grad_enabled = torch.is_grad_enabled() |
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root_dir = os.path.dirname(os.path.dirname(__name__)) |
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model_config = os.path.join( |
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root_dir, 'configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py') |
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detector = MaskRCNNDetector(model_config) |
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await detector.init() |
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img_path = os.path.join(root_dir, 'demo/demo.jpg') |
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bboxes, _ = await detector.apredict(img_path) |
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self.assertTrue(bboxes) |
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# asy inference detector will hack grad_enabled, |
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# so restore here to avoid it to influence other tests |
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torch.set_grad_enabled(ori_grad_enabled)
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