| |
|
|
| import unittest |
| import torch |
|
|
| from detectron2.structures import Boxes, BoxMode, Instances |
|
|
| from densepose.modeling.losses.utils import ChartBasedAnnotationsAccumulator |
| from densepose.structures import DensePoseDataRelative, DensePoseList |
|
|
| image_shape = (100, 100) |
| instances = Instances(image_shape) |
| n_instances = 3 |
| instances.proposal_boxes = Boxes(torch.rand(n_instances, 4)) |
| instances.gt_boxes = Boxes(torch.rand(n_instances, 4)) |
|
|
|
|
| |
| class TestChartBasedAnnotationsAccumulator(unittest.TestCase): |
| def test_chart_based_annotations_accumulator_no_gt_densepose(self): |
| accumulator = ChartBasedAnnotationsAccumulator() |
| accumulator.accumulate(instances) |
| expected_values = {"nxt_bbox_with_dp_index": 0, "nxt_bbox_index": n_instances} |
| for key in accumulator.__dict__: |
| self.assertEqual(getattr(accumulator, key), expected_values.get(key, [])) |
|
|
| def test_chart_based_annotations_accumulator_gt_densepose_none(self): |
| instances.gt_densepose = [None] * n_instances |
| accumulator = ChartBasedAnnotationsAccumulator() |
| accumulator.accumulate(instances) |
| expected_values = {"nxt_bbox_with_dp_index": 0, "nxt_bbox_index": n_instances} |
| for key in accumulator.__dict__: |
| self.assertEqual(getattr(accumulator, key), expected_values.get(key, [])) |
|
|
| def test_chart_based_annotations_accumulator_gt_densepose(self): |
| data_relative_keys = [ |
| DensePoseDataRelative.X_KEY, |
| DensePoseDataRelative.Y_KEY, |
| DensePoseDataRelative.I_KEY, |
| DensePoseDataRelative.U_KEY, |
| DensePoseDataRelative.V_KEY, |
| DensePoseDataRelative.S_KEY, |
| ] |
| annotations = [DensePoseDataRelative({k: [0] for k in data_relative_keys})] * n_instances |
| instances.gt_densepose = DensePoseList(annotations, instances.gt_boxes, image_shape) |
| accumulator = ChartBasedAnnotationsAccumulator() |
| accumulator.accumulate(instances) |
| bbox_xywh_est = BoxMode.convert( |
| instances.proposal_boxes.tensor.clone(), BoxMode.XYXY_ABS, BoxMode.XYWH_ABS |
| ) |
| bbox_xywh_gt = BoxMode.convert( |
| instances.gt_boxes.tensor.clone(), BoxMode.XYXY_ABS, BoxMode.XYWH_ABS |
| ) |
| expected_values = { |
| "s_gt": [ |
| torch.zeros((3, DensePoseDataRelative.MASK_SIZE, DensePoseDataRelative.MASK_SIZE)) |
| ] |
| * n_instances, |
| "bbox_xywh_est": bbox_xywh_est.split(1), |
| "bbox_xywh_gt": bbox_xywh_gt.split(1), |
| "point_bbox_with_dp_indices": [torch.tensor([i]) for i in range(n_instances)], |
| "point_bbox_indices": [torch.tensor([i]) for i in range(n_instances)], |
| "bbox_indices": list(range(n_instances)), |
| "nxt_bbox_with_dp_index": n_instances, |
| "nxt_bbox_index": n_instances, |
| } |
| default_value = [torch.tensor([0])] * 3 |
| for key in accumulator.__dict__: |
| to_test = getattr(accumulator, key) |
| gt_value = expected_values.get(key, default_value) |
| if key in ["nxt_bbox_with_dp_index", "nxt_bbox_index"]: |
| self.assertEqual(to_test, gt_value) |
| elif key == "bbox_indices": |
| self.assertListEqual(to_test, gt_value) |
| else: |
| self.assertTrue(torch.allclose(torch.stack(to_test), torch.stack(gt_value))) |
|
|
