mirror of https://github.com/RE-OWOD/RE-OWOD
358 lines
15 KiB
Python
358 lines
15 KiB
Python
# 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()
|