# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved from __future__ import absolute_import, division, print_function, unicode_literals import logging import math import random import unittest import torch from fvcore.common.benchmark import benchmark from detectron2.layers.rotated_boxes import pairwise_iou_rotated from detectron2.structures.boxes import Boxes from detectron2.structures.rotated_boxes import RotatedBoxes, pairwise_iou logger = logging.getLogger(__name__) class TestRotatedBoxesLayer(unittest.TestCase): def test_iou_0_dim_cpu(self): boxes1 = torch.rand(0, 5, dtype=torch.float32) boxes2 = torch.rand(10, 5, dtype=torch.float32) expected_ious = torch.zeros(0, 10, dtype=torch.float32) ious = pairwise_iou_rotated(boxes1, boxes2) self.assertTrue(torch.allclose(ious, expected_ious)) boxes1 = torch.rand(10, 5, dtype=torch.float32) boxes2 = torch.rand(0, 5, dtype=torch.float32) expected_ious = torch.zeros(10, 0, dtype=torch.float32) ious = pairwise_iou_rotated(boxes1, boxes2) self.assertTrue(torch.allclose(ious, expected_ious)) @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") def test_iou_0_dim_cuda(self): boxes1 = torch.rand(0, 5, dtype=torch.float32) boxes2 = torch.rand(10, 5, dtype=torch.float32) expected_ious = torch.zeros(0, 10, dtype=torch.float32) ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) self.assertTrue(torch.allclose(ious_cuda.cpu(), expected_ious)) boxes1 = torch.rand(10, 5, dtype=torch.float32) boxes2 = torch.rand(0, 5, dtype=torch.float32) expected_ious = torch.zeros(10, 0, dtype=torch.float32) ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) self.assertTrue(torch.allclose(ious_cuda.cpu(), expected_ious)) def test_iou_half_overlap_cpu(self): boxes1 = torch.tensor([[0.5, 0.5, 1.0, 1.0, 0.0]], dtype=torch.float32) boxes2 = torch.tensor([[0.25, 0.5, 0.5, 1.0, 0.0]], dtype=torch.float32) expected_ious = torch.tensor([[0.5]], dtype=torch.float32) ious = pairwise_iou_rotated(boxes1, boxes2) self.assertTrue(torch.allclose(ious, expected_ious)) @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") def test_iou_half_overlap_cuda(self): boxes1 = torch.tensor([[0.5, 0.5, 1.0, 1.0, 0.0]], dtype=torch.float32) boxes2 = torch.tensor([[0.25, 0.5, 0.5, 1.0, 0.0]], dtype=torch.float32) expected_ious = torch.tensor([[0.5]], dtype=torch.float32) ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) self.assertTrue(torch.allclose(ious_cuda.cpu(), expected_ious)) def test_iou_precision(self): for device in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []: boxes1 = torch.tensor([[565, 565, 10, 10.0, 0]], dtype=torch.float32, device=device) boxes2 = torch.tensor([[565, 565, 10, 8.3, 0]], dtype=torch.float32, device=device) iou = 8.3 / 10.0 expected_ious = torch.tensor([[iou]], dtype=torch.float32) ious = pairwise_iou_rotated(boxes1, boxes2) self.assertTrue(torch.allclose(ious.cpu(), expected_ious)) @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") def test_iou_too_many_boxes_cuda(self): s1, s2 = 5, 1289035 boxes1 = torch.zeros(s1, 5) boxes2 = torch.zeros(s2, 5) ious_cuda = pairwise_iou_rotated(boxes1.cuda(), boxes2.cuda()) self.assertTupleEqual(tuple(ious_cuda.shape), (s1, s2)) def test_iou_extreme(self): # Cause floating point issues in cuda kernels (#1266) for device in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []: boxes1 = torch.tensor([[160.0, 153.0, 230.0, 23.0, -37.0]], device=device) boxes2 = torch.tensor( [ [ -1.117407639806935e17, 1.3858420478349148e18, 1000.0000610351562, 1000.0000610351562, 1612.0, ] ], device=device, ) ious = pairwise_iou_rotated(boxes1, boxes2) self.assertTrue(ious.min() >= 0, ious) class TestRotatedBoxesStructure(unittest.TestCase): def test_clip_area_0_degree(self): for _ in range(50): num_boxes = 100 boxes_5d = torch.zeros(num_boxes, 5) boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-100, 500) boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-100, 500) boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, 500) boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, 500) # Convert from (x_ctr, y_ctr, w, h, 0) to (x1, y1, x2, y2) boxes_4d = torch.zeros(num_boxes, 4) boxes_4d[:, 0] = boxes_5d[:, 0] - boxes_5d[:, 2] / 2.0 boxes_4d[:, 1] = boxes_5d[:, 1] - boxes_5d[:, 3] / 2.0 boxes_4d[:, 2] = boxes_5d[:, 0] + boxes_5d[:, 2] / 2.0 boxes_4d[:, 3] = boxes_5d[:, 1] + boxes_5d[:, 3] / 2.0 image_size = (500, 600) test_boxes_4d = Boxes(boxes_4d) test_boxes_5d = RotatedBoxes(boxes_5d) # Before clip areas_4d = test_boxes_4d.area() areas_5d = test_boxes_5d.area() self.assertTrue(torch.allclose(areas_4d, areas_5d, atol=1e-1, rtol=1e-5)) # After clip test_boxes_4d.clip(image_size) test_boxes_5d.clip(image_size) areas_4d = test_boxes_4d.area() areas_5d = test_boxes_5d.area() self.assertTrue(torch.allclose(areas_4d, areas_5d, atol=1e-1, rtol=1e-5)) def test_clip_area_arbitrary_angle(self): num_boxes = 100 boxes_5d = torch.zeros(num_boxes, 5) boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-100, 500) boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-100, 500) boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, 500) boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, 500) boxes_5d[:, 4] = torch.FloatTensor(num_boxes).uniform_(-1800, 1800) clip_angle_threshold = random.uniform(0, 180) image_size = (500, 600) test_boxes_5d = RotatedBoxes(boxes_5d) # Before clip areas_before = test_boxes_5d.area() # After clip test_boxes_5d.clip(image_size, clip_angle_threshold) areas_diff = test_boxes_5d.area() - areas_before # the areas should only decrease after clipping self.assertTrue(torch.all(areas_diff <= 0)) # whenever the box is clipped (thus the area shrinks), # the angle for the box must be within the clip_angle_threshold # Note that the clip function will normalize the angle range # to be within (-180, 180] self.assertTrue( torch.all(torch.abs(boxes_5d[:, 4][torch.where(areas_diff < 0)]) < clip_angle_threshold) ) def test_normalize_angles(self): # torch.manual_seed(0) for _ in range(50): num_boxes = 100 boxes_5d = torch.zeros(num_boxes, 5) boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-100, 500) boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-100, 500) boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, 500) boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, 500) boxes_5d[:, 4] = torch.FloatTensor(num_boxes).uniform_(-1800, 1800) rotated_boxes = RotatedBoxes(boxes_5d) normalized_boxes = rotated_boxes.clone() normalized_boxes.normalize_angles() self.assertTrue(torch.all(normalized_boxes.tensor[:, 4] >= -180)) self.assertTrue(torch.all(normalized_boxes.tensor[:, 4] < 180)) # x, y, w, h should not change self.assertTrue(torch.allclose(boxes_5d[:, :4], normalized_boxes.tensor[:, :4])) # the cos/sin values of the angles should stay the same self.assertTrue( torch.allclose( torch.cos(boxes_5d[:, 4] * math.pi / 180), torch.cos(normalized_boxes.tensor[:, 4] * math.pi / 180), atol=1e-5, ) ) self.assertTrue( torch.allclose( torch.sin(boxes_5d[:, 4] * math.pi / 180), torch.sin(normalized_boxes.tensor[:, 4] * math.pi / 180), atol=1e-5, ) ) def test_pairwise_iou_0_degree(self): for device in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []: boxes1 = torch.tensor( [[0.5, 0.5, 1.0, 1.0, 0.0], [0.5, 0.5, 1.0, 1.0, 0.0]], dtype=torch.float32, device=device, ) boxes2 = torch.tensor( [ [0.5, 0.5, 1.0, 1.0, 0.0], [0.25, 0.5, 0.5, 1.0, 0.0], [0.5, 0.25, 1.0, 0.5, 0.0], [0.25, 0.25, 0.5, 0.5, 0.0], [0.75, 0.75, 0.5, 0.5, 0.0], [1.0, 1.0, 1.0, 1.0, 0.0], ], dtype=torch.float32, device=device, ) expected_ious = torch.tensor( [ [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], ], dtype=torch.float32, device=device, ) ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) self.assertTrue(torch.allclose(ious, expected_ious)) def test_pairwise_iou_45_degrees(self): for device in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []: boxes1 = torch.tensor( [ [1, 1, math.sqrt(2), math.sqrt(2), 45], [1, 1, 2 * math.sqrt(2), 2 * math.sqrt(2), -45], ], dtype=torch.float32, device=device, ) boxes2 = torch.tensor([[1, 1, 2, 2, 0]], dtype=torch.float32, device=device) expected_ious = torch.tensor([[0.5], [0.5]], dtype=torch.float32, device=device) ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) self.assertTrue(torch.allclose(ious, expected_ious)) def test_pairwise_iou_orthogonal(self): for device in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []: boxes1 = torch.tensor([[5, 5, 10, 6, 55]], dtype=torch.float32, device=device) boxes2 = torch.tensor([[5, 5, 10, 6, -35]], dtype=torch.float32, device=device) iou = (6.0 * 6.0) / (6.0 * 6.0 + 4.0 * 6.0 + 4.0 * 6.0) expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device) ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) self.assertTrue(torch.allclose(ious, expected_ious)) def test_pairwise_iou_large_close_boxes(self): for device in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []: boxes1 = torch.tensor( [[299.500000, 417.370422, 600.000000, 364.259186, 27.1828]], dtype=torch.float32, device=device, ) boxes2 = torch.tensor( [[299.500000, 417.370422, 600.000000, 364.259155, 27.1828]], dtype=torch.float32, device=device, ) iou = 364.259155 / 364.259186 expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device) ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) self.assertTrue(torch.allclose(ious, expected_ious)) def test_pairwise_iou_many_boxes(self): for device in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []: num_boxes1 = 100 num_boxes2 = 200 boxes1 = torch.stack( [ torch.tensor( [5 + 20 * i, 5 + 20 * i, 10, 10, 0], dtype=torch.float32, device=device ) for i in range(num_boxes1) ] ) boxes2 = torch.stack( [ torch.tensor( [5 + 20 * i, 5 + 20 * i, 10, 1 + 9 * i / num_boxes2, 0], dtype=torch.float32, device=device, ) for i in range(num_boxes2) ] ) expected_ious = torch.zeros(num_boxes1, num_boxes2, dtype=torch.float32, device=device) for i in range(min(num_boxes1, num_boxes2)): expected_ious[i][i] = (1 + 9 * i / num_boxes2) / 10.0 ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) self.assertTrue(torch.allclose(ious, expected_ious)) def test_pairwise_iou_issue1207_simplified(self): for device in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []: # Simplified test case of D2-issue-1207 boxes1 = torch.tensor([[3, 3, 8, 2, -45.0]], device=device) boxes2 = torch.tensor([[6, 0, 8, 2, -45.0]], device=device) iou = 0.0 expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device) ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) self.assertTrue(torch.allclose(ious, expected_ious)) def test_pairwise_iou_issue1207(self): for device in ["cpu"] + ["cuda"] if torch.cuda.is_available() else []: # The original test case in D2-issue-1207 boxes1 = torch.tensor([[160.0, 153.0, 230.0, 23.0, -37.0]], device=device) boxes2 = torch.tensor([[190.0, 127.0, 80.0, 21.0, -46.0]], device=device) iou = 0.0 expected_ious = torch.tensor([[iou]], dtype=torch.float32, device=device) ious = pairwise_iou(RotatedBoxes(boxes1), RotatedBoxes(boxes2)) self.assertTrue(torch.allclose(ious, expected_ious)) def test_empty_cat(self): x = RotatedBoxes.cat([]) self.assertTrue(x.tensor.shape, (0, 5)) def benchmark_rotated_iou(): num_boxes1 = 200 num_boxes2 = 500 boxes1 = torch.stack( [ torch.tensor([5 + 20 * i, 5 + 20 * i, 10, 10, 0], dtype=torch.float32) for i in range(num_boxes1) ] ) boxes2 = torch.stack( [ torch.tensor( [5 + 20 * i, 5 + 20 * i, 10, 1 + 9 * i / num_boxes2, 0], dtype=torch.float32 ) for i in range(num_boxes2) ] ) def func(dev, n=1): b1 = boxes1.to(device=dev) b2 = boxes2.to(device=dev) def bench(): for _ in range(n): pairwise_iou_rotated(b1, b2) if dev.type == "cuda": torch.cuda.synchronize() return bench # only run it once per timed loop, since it's slow args = [{"dev": torch.device("cpu"), "n": 1}] if torch.cuda.is_available(): args.append({"dev": torch.device("cuda"), "n": 10}) benchmark(func, "rotated_iou", args, warmup_iters=3) if __name__ == "__main__": unittest.main() benchmark_rotated_iou()