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# Copyright (c) Facebook, Inc. and its affiliates.
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))
# instances.gt_densepose = None cannot happen because instances attributes need a length
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)))
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