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# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import unittest
import torch
from detectron2.modeling.poolers import ROIPooler
from detectron2.structures import Boxes, RotatedBoxes
from detectron2.utils.testing import random_boxes
logger = logging.getLogger(__name__)
class TestROIPooler(unittest.TestCase):
def _test_roialignv2_roialignrotated_match(self, device):
pooler_resolution = 14
canonical_level = 4
canonical_scale_factor = 2 ** canonical_level
pooler_scales = (1.0 / canonical_scale_factor,)
sampling_ratio = 0
N, C, H, W = 2, 4, 10, 8
N_rois = 10
std = 11
mean = 0
feature = (torch.rand(N, C, H, W) - 0.5) * 2 * std + mean
features = [feature.to(device)]
rois = []
rois_rotated = []
for _ in range(N):
boxes = random_boxes(N_rois, W * canonical_scale_factor)
rotated_boxes = torch.zeros(N_rois, 5)
rotated_boxes[:, 0] = (boxes[:, 0] + boxes[:, 2]) / 2.0
rotated_boxes[:, 1] = (boxes[:, 1] + boxes[:, 3]) / 2.0
rotated_boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
rotated_boxes[:, 3] = boxes[:, 3] - boxes[:, 1]
rois.append(Boxes(boxes).to(device))
rois_rotated.append(RotatedBoxes(rotated_boxes).to(device))
roialignv2_pooler = ROIPooler(
output_size=pooler_resolution,
scales=pooler_scales,
sampling_ratio=sampling_ratio,
pooler_type="ROIAlignV2",
)
roialignv2_out = roialignv2_pooler(features, rois)
roialignrotated_pooler = ROIPooler(
output_size=pooler_resolution,
scales=pooler_scales,
sampling_ratio=sampling_ratio,
pooler_type="ROIAlignRotated",
)
roialignrotated_out = roialignrotated_pooler(features, rois_rotated)
self.assertTrue(torch.allclose(roialignv2_out, roialignrotated_out, atol=1e-4))
def test_roialignv2_roialignrotated_match_cpu(self):
self._test_roialignv2_roialignrotated_match(device="cpu")
@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
def test_roialignv2_roialignrotated_match_cuda(self):
self._test_roialignv2_roialignrotated_match(device="cuda")
def _test_scriptability(self, device):
pooler_resolution = 14
canonical_level = 4
canonical_scale_factor = 2 ** canonical_level
pooler_scales = (1.0 / canonical_scale_factor,)
sampling_ratio = 0
N, C, H, W = 2, 4, 10, 8
N_rois = 10
std = 11
mean = 0
feature = (torch.rand(N, C, H, W) - 0.5) * 2 * std + mean
features = [feature.to(device)]
rois = []
for _ in range(N):
boxes = random_boxes(N_rois, W * canonical_scale_factor)
rois.append(Boxes(boxes).to(device))
roialignv2_pooler = ROIPooler(
output_size=pooler_resolution,
scales=pooler_scales,
sampling_ratio=sampling_ratio,
pooler_type="ROIAlignV2",
)
roialignv2_out = roialignv2_pooler(features, rois)
scripted_roialignv2_out = torch.jit.script(roialignv2_pooler)(features, rois)
self.assertTrue(torch.equal(roialignv2_out, scripted_roialignv2_out))
def test_scriptability_cpu(self):
self._test_scriptability(device="cpu")
@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
def test_scriptability_gpu(self):
self._test_scriptability(device="cuda")
def test_no_images(self):
N, C, H, W = 0, 32, 32, 32
feature = torch.rand(N, C, H, W) - 0.5
features = [feature]
pooler = ROIPooler(
output_size=14, scales=(1.0,), sampling_ratio=0.0, pooler_type="ROIAlignV2"
)
output = pooler.forward(features, [])
self.assertEqual(output.shape, (0, C, 14, 14))
def test_roi_pooler_tracing(self):
class Model(torch.nn.Module):
def __init__(self, roi):
super(Model, self).__init__()
self.roi = roi
def forward(self, x, boxes):
return self.roi(x, [Boxes(boxes)])
pooler_resolution = 14
canonical_level = 4
canonical_scale_factor = 2 ** canonical_level
pooler_scales = (1.0 / canonical_scale_factor, 0.5 / canonical_scale_factor)
sampling_ratio = 0
N, C, H, W = 1, 4, 10, 8
N_rois = 10
std = 11
mean = 0
feature = (torch.rand(N, C, H, W) - 0.5) * 2 * std + mean
feature = [feature, feature]
rois = random_boxes(N_rois, W * canonical_scale_factor)
# Add one larger box so that this level has only one box.
# This may trigger the bug https://github.com/pytorch/pytorch/issues/49852
# that we shall workaround.
rois = torch.cat([rois, torch.tensor([[0, 0, 448, 448]])])
model = Model(
ROIPooler(
output_size=pooler_resolution,
scales=pooler_scales,
sampling_ratio=sampling_ratio,
pooler_type="ROIAlign",
)
)
with torch.no_grad():
func = torch.jit.trace(model, (feature, rois))
o = func(feature, rois)
self.assertEqual(o.shape, (11, 4, 14, 14))
o = func(feature, rois[:5])
self.assertEqual(o.shape, (5, 4, 14, 14))
o = func(feature, random_boxes(20, W * canonical_scale_factor))
self.assertEqual(o.shape, (20, 4, 14, 14))
if __name__ == "__main__":
unittest.main()
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