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from __future__ import absolute_import, division, print_function, unicode_literals |
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import logging |
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import math |
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import random |
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import unittest |
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import torch |
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from fvcore.common.benchmark import benchmark |
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from detectron2.layers.rotated_boxes import pairwise_iou_rotated |
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from detectron2.structures.boxes import Boxes |
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from detectron2.structures.rotated_boxes import RotatedBoxes, pairwise_iou |
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from detectron2.utils.testing import reload_script_model |
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logger = logging.getLogger(__name__) |
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class TestRotatedBoxesLayer(unittest.TestCase): |
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def test_iou_0_dim_cpu(self): |
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boxes1 = torch.rand(0, 5, dtype=torch.float32) |
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boxes2 = torch.rand(10, 5, dtype=torch.float32) |
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expected_ious = torch.zeros(0, 10, dtype=torch.float32) |
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ious = pairwise_iou_rotated(boxes1, boxes2) |
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self.assertTrue(torch.allclose(ious, expected_ious)) |
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boxes1 = torch.rand(10, 5, dtype=torch.float32) |
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boxes2 = torch.rand(0, 5, dtype=torch.float32) |
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expected_ious = torch.zeros(10, 0, dtype=torch.float32) |
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ious = pairwise_iou_rotated(boxes1, boxes2) |
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self.assertTrue(torch.allclose(ious, expected_ious)) |
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@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") |
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def test_iou_0_dim_cuda(self): |
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boxes1 = torch.rand(0, 5, dtype=torch.float32) |
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boxes2 = torch.rand(10, 5, dtype=torch.float32) |
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expected_ious = torch.zeros(0, 10, dtype=torch.float32) |
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ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) |
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self.assertTrue(torch.allclose(ious_cuda.cpu(), expected_ious)) |
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boxes1 = torch.rand(10, 5, dtype=torch.float32) |
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boxes2 = torch.rand(0, 5, dtype=torch.float32) |
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expected_ious = torch.zeros(10, 0, dtype=torch.float32) |
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ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) |
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self.assertTrue(torch.allclose(ious_cuda.cpu(), expected_ious)) |
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def test_iou_half_overlap_cpu(self): |
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boxes1 = torch.tensor([[0.5, 0.5, 1.0, 1.0, 0.0]], dtype=torch.float32) |
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boxes2 = torch.tensor([[0.25, 0.5, 0.5, 1.0, 0.0]], dtype=torch.float32) |
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expected_ious = torch.tensor([[0.5]], dtype=torch.float32) |
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ious = pairwise_iou_rotated(boxes1, boxes2) |
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self.assertTrue(torch.allclose(ious, expected_ious)) |
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@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") |
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def test_iou_half_overlap_cuda(self): |
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boxes1 = torch.tensor([[0.5, 0.5, 1.0, 1.0, 0.0]], dtype=torch.float32) |
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boxes2 = torch.tensor([[0.25, 0.5, 0.5, 1.0, 0.0]], dtype=torch.float32) |
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expected_ious = torch.tensor([[0.5]], dtype=torch.float32) |
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ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) |
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self.assertTrue(torch.allclose(ious_cuda.cpu(), expected_ious)) |
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def test_iou_precision(self): |
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for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): |
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boxes1 = torch.tensor([[565, 565, 10, 10.0, 0]], dtype=torch.float32, device=device) |
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boxes2 = torch.tensor([[565, 565, 10, 8.3, 0]], dtype=torch.float32, device=device) |
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iou = 8.3 / 10.0 |
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expected_ious = torch.tensor([[iou]], dtype=torch.float32) |
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ious = pairwise_iou_rotated(boxes1, boxes2) |
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self.assertTrue(torch.allclose(ious.cpu(), expected_ious)) |
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@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") |
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def test_iou_too_many_boxes_cuda(self): |
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s1, s2 = 5, 1289035 |
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boxes1 = torch.zeros(s1, 5) |
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boxes2 = torch.zeros(s2, 5) |
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ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) |
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self.assertTupleEqual(tuple(ious_cuda.shape), (s1, s2)) |
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def test_iou_extreme(self): |
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for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): |
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boxes1 = torch.tensor([[160.0, 153.0, 230.0, 23.0, -37.0]], device=device) |
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boxes2 = torch.tensor( |
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[ |
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[ |
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-1.117407639806935e17, |
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1.3858420478349148e18, |
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1000.0000610351562, |
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1000.0000610351562, |
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1612.0, |
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] |
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], |
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device=device, |
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) |
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ious = pairwise_iou_rotated(boxes1, boxes2) |
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self.assertTrue(ious.min() >= 0, ious) |
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def test_iou_issue_2154(self): |
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for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): |
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boxes1 = torch.tensor( |
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[ |
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[ |
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296.6620178222656, |
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458.73883056640625, |
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23.515729904174805, |
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47.677001953125, |
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0.08795166015625, |
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] |
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], |
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device=device, |
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) |
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boxes2 = torch.tensor( |
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[[296.66201, 458.73882000000003, 23.51573, 47.67702, 0.087951]], |
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device=device, |
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) |
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ious = pairwise_iou_rotated(boxes1, boxes2) |
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expected_ious = torch.tensor([[1.0]], dtype=torch.float32) |
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self.assertTrue(torch.allclose(ious.cpu(), expected_ious)) |
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def test_iou_issue_2167(self): |
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for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): |
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boxes1 = torch.tensor( |
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[ |
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[ |
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2563.74462890625000000000, |
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1436.79016113281250000000, |
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2174.70336914062500000000, |
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214.09500122070312500000, |
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115.11834716796875000000, |
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] |
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], |
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device=device, |
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) |
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boxes2 = torch.tensor( |
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[ |
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[ |
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2563.74462890625000000000, |
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1436.79028320312500000000, |
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2174.70288085937500000000, |
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214.09495544433593750000, |
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115.11835479736328125000, |
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] |
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], |
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device=device, |
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) |
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ious = pairwise_iou_rotated(boxes1, boxes2) |
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expected_ious = torch.tensor([[1.0]], dtype=torch.float32) |
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self.assertTrue(torch.allclose(ious.cpu(), expected_ious)) |
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class TestRotatedBoxesStructure(unittest.TestCase): |
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def test_clip_area_0_degree(self): |
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for _ in range(50): |
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num_boxes = 100 |
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boxes_5d = torch.zeros(num_boxes, 5) |
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boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-100, 500) |
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boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-100, 500) |
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boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, 500) |
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boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, 500) |
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boxes_4d = torch.zeros(num_boxes, 4) |
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boxes_4d[:, 0] = boxes_5d[:, 0] - boxes_5d[:, 2] / 2.0 |
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boxes_4d[:, 1] = boxes_5d[:, 1] - boxes_5d[:, 3] / 2.0 |
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boxes_4d[:, 2] = boxes_5d[:, 0] + boxes_5d[:, 2] / 2.0 |
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boxes_4d[:, 3] = boxes_5d[:, 1] + boxes_5d[:, 3] / 2.0 |
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image_size = (500, 600) |
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test_boxes_4d = Boxes(boxes_4d) |
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test_boxes_5d = RotatedBoxes(boxes_5d) |
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areas_4d = test_boxes_4d.area() |
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areas_5d = test_boxes_5d.area() |
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self.assertTrue(torch.allclose(areas_4d, areas_5d, atol=1e-1, rtol=1e-5)) |
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test_boxes_4d.clip(image_size) |
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test_boxes_5d.clip(image_size) |
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areas_4d = test_boxes_4d.area() |
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areas_5d = test_boxes_5d.area() |
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self.assertTrue(torch.allclose(areas_4d, areas_5d, atol=1e-1, rtol=1e-5)) |
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def test_clip_area_arbitrary_angle(self): |
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num_boxes = 100 |
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boxes_5d = torch.zeros(num_boxes, 5) |
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boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-100, 500) |
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boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-100, 500) |
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boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, 500) |
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boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, 500) |
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boxes_5d[:, 4] = torch.FloatTensor(num_boxes).uniform_(-1800, 1800) |
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clip_angle_threshold = random.uniform(0, 180) |
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image_size = (500, 600) |
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test_boxes_5d = RotatedBoxes(boxes_5d) |
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areas_before = test_boxes_5d.area() |
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test_boxes_5d.clip(image_size, clip_angle_threshold) |
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areas_diff = test_boxes_5d.area() - areas_before |
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self.assertTrue(torch.all(areas_diff <= 0)) |
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self.assertTrue( |
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torch.all(torch.abs(boxes_5d[:, 4][torch.where(areas_diff < 0)]) < clip_angle_threshold) |
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) |
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def test_normalize_angles(self): |
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for _ in range(50): |
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num_boxes = 100 |
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boxes_5d = torch.zeros(num_boxes, 5) |
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boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-100, 500) |
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boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-100, 500) |
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boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, 500) |
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boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, 500) |
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boxes_5d[:, 4] = torch.FloatTensor(num_boxes).uniform_(-1800, 1800) |
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rotated_boxes = RotatedBoxes(boxes_5d) |
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normalized_boxes = rotated_boxes.clone() |
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normalized_boxes.normalize_angles() |
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self.assertTrue(torch.all(normalized_boxes.tensor[:, 4] >= -180)) |
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self.assertTrue(torch.all(normalized_boxes.tensor[:, 4] < 180)) |
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self.assertTrue(torch.allclose(boxes_5d[:, :4], normalized_boxes.tensor[:, :4])) |
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self.assertTrue( |
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torch.allclose( |
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torch.cos(boxes_5d[:, 4] * math.pi / 180), |
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torch.cos(normalized_boxes.tensor[:, 4] * math.pi / 180), |
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atol=1e-5, |
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) |
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) |
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self.assertTrue( |
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torch.allclose( |
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torch.sin(boxes_5d[:, 4] * math.pi / 180), |
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torch.sin(normalized_boxes.tensor[:, 4] * math.pi / 180), |
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atol=1e-5, |
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) |
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) |
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def test_pairwise_iou_0_degree(self): |
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for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): |
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boxes1 = torch.tensor( |
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[[0.5, 0.5, 1.0, 1.0, 0.0], [0.5, 0.5, 1.0, 1.0, 0.0]], |
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dtype=torch.float32, |
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device=device, |
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) |
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boxes2 = torch.tensor( |
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[ |
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[0.5, 0.5, 1.0, 1.0, 0.0], |
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[0.25, 0.5, 0.5, 1.0, 0.0], |
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[0.5, 0.25, 1.0, 0.5, 0.0], |
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[0.25, 0.25, 0.5, 0.5, 0.0], |
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[0.75, 0.75, 0.5, 0.5, 0.0], |
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[1.0, 1.0, 1.0, 1.0, 0.0], |
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], |
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dtype=torch.float32, |
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device=device, |
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) |
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expected_ious = torch.tensor( |
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[ |
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[1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], |
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[1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], |
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], |
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dtype=torch.float32, |
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device=device, |
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) |
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ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) |
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self.assertTrue(torch.allclose(ious, expected_ious)) |
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def test_pairwise_iou_45_degrees(self): |
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for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): |
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boxes1 = torch.tensor( |
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[ |
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[1, 1, math.sqrt(2), math.sqrt(2), 45], |
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[1, 1, 2 * math.sqrt(2), 2 * math.sqrt(2), -45], |
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], |
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dtype=torch.float32, |
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device=device, |
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) |
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boxes2 = torch.tensor([[1, 1, 2, 2, 0]], dtype=torch.float32, device=device) |
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expected_ious = torch.tensor([[0.5], [0.5]], dtype=torch.float32, device=device) |
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ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) |
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self.assertTrue(torch.allclose(ious, expected_ious)) |
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def test_pairwise_iou_orthogonal(self): |
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for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): |
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boxes1 = torch.tensor([[5, 5, 10, 6, 55]], dtype=torch.float32, device=device) |
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boxes2 = torch.tensor([[5, 5, 10, 6, -35]], dtype=torch.float32, device=device) |
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iou = (6.0 * 6.0) / (6.0 * 6.0 + 4.0 * 6.0 + 4.0 * 6.0) |
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expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device) |
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ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) |
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self.assertTrue(torch.allclose(ious, expected_ious)) |
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def test_pairwise_iou_large_close_boxes(self): |
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for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): |
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boxes1 = torch.tensor( |
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[[299.500000, 417.370422, 600.000000, 364.259186, 27.1828]], |
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dtype=torch.float32, |
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device=device, |
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) |
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boxes2 = torch.tensor( |
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[[299.500000, 417.370422, 600.000000, 364.259155, 27.1828]], |
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dtype=torch.float32, |
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device=device, |
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) |
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iou = 364.259155 / 364.259186 |
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expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device) |
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ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) |
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self.assertTrue(torch.allclose(ious, expected_ious)) |
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def test_pairwise_iou_many_boxes(self): |
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for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): |
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num_boxes1 = 100 |
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num_boxes2 = 200 |
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boxes1 = torch.stack( |
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[ |
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torch.tensor( |
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[5 + 20 * i, 5 + 20 * i, 10, 10, 0], |
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dtype=torch.float32, |
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device=device, |
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) |
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for i in range(num_boxes1) |
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] |
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) |
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boxes2 = torch.stack( |
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[ |
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torch.tensor( |
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[5 + 20 * i, 5 + 20 * i, 10, 1 + 9 * i / num_boxes2, 0], |
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dtype=torch.float32, |
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device=device, |
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) |
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for i in range(num_boxes2) |
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] |
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) |
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expected_ious = torch.zeros(num_boxes1, num_boxes2, dtype=torch.float32, device=device) |
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for i in range(min(num_boxes1, num_boxes2)): |
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expected_ious[i][i] = (1 + 9 * i / num_boxes2) / 10.0 |
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ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) |
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self.assertTrue(torch.allclose(ious, expected_ious)) |
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def test_pairwise_iou_issue1207_simplified(self): |
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for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): |
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boxes1 = torch.tensor([[3, 3, 8, 2, -45.0]], device=device) |
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boxes2 = torch.tensor([[6, 0, 8, 2, -45.0]], device=device) |
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iou = 0.0 |
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expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device) |
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ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) |
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self.assertTrue(torch.allclose(ious, expected_ious)) |
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def test_pairwise_iou_issue1207(self): |
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for device in ["cpu"] + (["cuda"] if torch.cuda.is_available() else []): |
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boxes1 = torch.tensor([[160.0, 153.0, 230.0, 23.0, -37.0]], device=device) |
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boxes2 = torch.tensor([[190.0, 127.0, 80.0, 21.0, -46.0]], device=device) |
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iou = 0.0 |
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expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device) |
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ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) |
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self.assertTrue(torch.allclose(ious, expected_ious)) |
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def test_empty_cat(self): |
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x = RotatedBoxes.cat([]) |
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self.assertTrue(x.tensor.shape, (0, 5)) |
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def test_scriptability(self): |
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def func(x): |
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boxes = RotatedBoxes(x) |
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test = boxes.to(torch.device("cpu")).tensor |
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return boxes.area(), test |
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f = torch.jit.script(func) |
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f = reload_script_model(f) |
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f(torch.rand((3, 5))) |
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data = torch.rand((3, 5)) |
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def func_cat(x: torch.Tensor): |
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boxes1 = RotatedBoxes(x) |
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boxes2 = RotatedBoxes(x) |
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boxes3 = boxes1.cat([boxes1, boxes2]) |
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return boxes3 |
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f = torch.jit.script(func_cat) |
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script_box = f(data) |
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self.assertTrue(torch.equal(torch.cat([data, data]), script_box.tensor)) |
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def benchmark_rotated_iou(): |
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num_boxes1 = 200 |
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num_boxes2 = 500 |
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boxes1 = torch.stack( |
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[ |
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torch.tensor([5 + 20 * i, 5 + 20 * i, 10, 10, 0], dtype=torch.float32) |
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for i in range(num_boxes1) |
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] |
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) |
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boxes2 = torch.stack( |
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[ |
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torch.tensor( |
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[5 + 20 * i, 5 + 20 * i, 10, 1 + 9 * i / num_boxes2, 0], |
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dtype=torch.float32, |
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) |
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for i in range(num_boxes2) |
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] |
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) |
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def func(dev, n=1): |
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b1 = boxes1.to(device=dev) |
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b2 = boxes2.to(device=dev) |
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def bench(): |
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for _ in range(n): |
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pairwise_iou_rotated(b1, b2) |
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if dev.type == "cuda": |
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torch.cuda.synchronize() |
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return bench |
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args = [{"dev": torch.device("cpu"), "n": 1}] |
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if torch.cuda.is_available(): |
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args.append({"dev": torch.device("cuda"), "n": 10}) |
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benchmark(func, "rotated_iou", args, warmup_iters=3) |
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if __name__ == "__main__": |
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unittest.main() |
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benchmark_rotated_iou() |
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