SparseRCNN/tests/modeling/test_roi_pooler.py

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import logging
import unittest
import torch

from detectron2.modeling.poolers import ROIPooler
from detectron2.structures import Boxes, RotatedBoxes

logger = logging.getLogger(__name__)


class TestROIPooler(unittest.TestCase):
    def _rand_boxes(self, num_boxes, x_max, y_max):
        coords = torch.rand(num_boxes, 4)
        coords[:, 0] *= x_max
        coords[:, 1] *= y_max
        coords[:, 2] *= x_max
        coords[:, 3] *= y_max
        boxes = torch.zeros(num_boxes, 4)
        boxes[:, 0] = torch.min(coords[:, 0], coords[:, 2])
        boxes[:, 1] = torch.min(coords[:, 1], coords[:, 3])
        boxes[:, 2] = torch.max(coords[:, 0], coords[:, 2])
        boxes[:, 3] = torch.max(coords[:, 1], coords[:, 3])
        return boxes

    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 = self._rand_boxes(
                num_boxes=N_rois, x_max=W * canonical_scale_factor, y_max=H * 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")


if __name__ == "__main__":
    unittest.main()