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