mirror of https://github.com/open-mmlab/mmcv.git
144 lines
5.4 KiB
Python
144 lines
5.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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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 mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE, IS_NPU_AVAILABLE
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class TestNmsRotated:
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@pytest.mark.parametrize('device', [
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pytest.param(
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'npu',
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marks=pytest.mark.skipif(
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not IS_NPU_AVAILABLE, reason='requires NPU support')),
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pytest.param(
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'cuda',
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marks=pytest.mark.skipif(
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not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
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pytest.param(
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'mlu',
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marks=pytest.mark.skipif(
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not IS_MLU_AVAILABLE, reason='requires MLU support'))
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])
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def test_ml_nms_rotated(self, device):
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from mmcv.ops import nms_rotated
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np_boxes = np.array(
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[[6.0, 3.0, 8.0, 7.0, 0.5, 0.7], [3.0, 6.0, 9.0, 11.0, 0.6, 0.8],
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[3.0, 7.0, 10.0, 12.0, 0.3, 0.5], [1.0, 4.0, 13.0, 7.0, 0.6, 0.9]
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],
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dtype=np.float32)
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np_labels = np.array([1, 0, 1, 0], dtype=np.float32)
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np_expect_dets = np.array(
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[[1.0, 4.0, 13.0, 7.0, 0.6], [3.0, 6.0, 9.0, 11.0, 0.6],
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[6.0, 3.0, 8.0, 7.0, 0.5]],
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dtype=np.float32)
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np_expect_keep_inds = np.array([3, 1, 0], dtype=np.int64)
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boxes = torch.from_numpy(np_boxes).to(device)
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labels = torch.from_numpy(np_labels).to(device)
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# test cw angle definition
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dets, keep_inds = nms_rotated(boxes[:, :5], boxes[:, -1], 0.5, labels)
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assert np.allclose(dets.cpu().numpy()[:, :5], np_expect_dets)
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assert np.allclose(keep_inds.cpu().numpy(), np_expect_keep_inds)
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# test ccw angle definition
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boxes[..., -2] *= -1
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dets, keep_inds = nms_rotated(
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boxes[:, :5], boxes[:, -1], 0.5, labels, clockwise=False)
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dets[..., -2] *= -1
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assert np.allclose(dets.cpu().numpy()[:, :5], np_expect_dets)
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assert np.allclose(keep_inds.cpu().numpy(), np_expect_keep_inds)
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@pytest.mark.parametrize('device', [
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pytest.param(
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'npu',
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marks=pytest.mark.skipif(
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not IS_NPU_AVAILABLE, reason='requires NPU support')),
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pytest.param(
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'cuda',
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marks=pytest.mark.skipif(
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not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
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pytest.param(
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'mlu',
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marks=pytest.mark.skipif(
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not IS_MLU_AVAILABLE, reason='requires MLU support'))
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])
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def test_nms_rotated(self, device):
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from mmcv.ops import nms_rotated
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np_boxes = np.array(
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[[6.0, 3.0, 8.0, 7.0, 0.5, 0.7], [3.0, 6.0, 9.0, 11.0, 0.6, 0.8],
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[3.0, 7.0, 10.0, 12.0, 0.3, 0.5], [1.0, 4.0, 13.0, 7.0, 0.6, 0.9]
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],
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dtype=np.float32)
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np_expect_dets = np.array(
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[[1.0, 4.0, 13.0, 7.0, 0.6], [3.0, 6.0, 9.0, 11.0, 0.6],
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[6.0, 3.0, 8.0, 7.0, 0.5]],
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dtype=np.float32)
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np_expect_keep_inds = np.array([3, 1, 0], dtype=np.int64)
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boxes = torch.from_numpy(np_boxes).to(device)
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# test cw angle definition
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dets, keep_inds = nms_rotated(boxes[:, :5], boxes[:, -1], 0.5)
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assert np.allclose(dets.cpu().numpy()[:, :5], np_expect_dets)
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assert np.allclose(keep_inds.cpu().numpy(), np_expect_keep_inds)
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# test ccw angle definition
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boxes[..., -2] *= -1
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dets, keep_inds = nms_rotated(
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boxes[:, :5], boxes[:, -1], 0.5, clockwise=False)
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dets[..., -2] *= -1
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assert np.allclose(dets.cpu().numpy()[:, :5], np_expect_dets)
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assert np.allclose(keep_inds.cpu().numpy(), np_expect_keep_inds)
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def test_batched_nms(self):
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# test batched_nms with nms_rotated
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from mmcv.ops import batched_nms
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np_boxes = np.array(
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[[6.0, 3.0, 8.0, 7.0, 0.5, 0.7], [3.0, 6.0, 9.0, 11.0, 0.6, 0.8],
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[3.0, 7.0, 10.0, 12.0, 0.3, 0.5], [1.0, 4.0, 13.0, 7.0, 0.6, 0.9]
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],
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dtype=np.float32)
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np_labels = np.array([1, 0, 1, 0], dtype=np.float32)
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np_expect_agnostic_dets = np.array(
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[[1.0, 4.0, 13.0, 7.0, 0.6], [3.0, 6.0, 9.0, 11.0, 0.6],
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[6.0, 3.0, 8.0, 7.0, 0.5]],
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dtype=np.float32)
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np_expect_agnostic_keep_inds = np.array([3, 1, 0], dtype=np.int64)
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np_expect_dets = np.array(
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[[1.0, 4.0, 13.0, 7.0, 0.6], [3.0, 6.0, 9.0, 11.0, 0.6],
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[6.0, 3.0, 8.0, 7.0, 0.5], [3.0, 7.0, 10.0, 12.0, 0.3]],
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dtype=np.float32)
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np_expect_keep_inds = np.array([3, 1, 0, 2], dtype=np.int64)
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nms_cfg = dict(type='nms_rotated', iou_threshold=0.5)
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# test class_agnostic is True
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boxes, keep = batched_nms(
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torch.from_numpy(np_boxes[:, :5]),
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torch.from_numpy(np_boxes[:, -1]),
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torch.from_numpy(np_labels),
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nms_cfg,
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class_agnostic=True)
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assert np.allclose(boxes.cpu().numpy()[:, :5], np_expect_agnostic_dets)
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assert np.allclose(keep.cpu().numpy(), np_expect_agnostic_keep_inds)
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# test class_agnostic is False
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boxes, keep = batched_nms(
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torch.from_numpy(np_boxes[:, :5]),
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torch.from_numpy(np_boxes[:, -1]),
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torch.from_numpy(np_labels),
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nms_cfg,
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class_agnostic=False)
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assert np.allclose(boxes.cpu().numpy()[:, :5], np_expect_dets)
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assert np.allclose(keep.cpu().numpy(), np_expect_keep_inds)
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