def test_image_pooler(self):
H, W = 8, 6
image = torch.zeros(3, H, W)
# xyxy
boxes = torch.Tensor([[2, 3, 5, 7]])
image[0, 3:7, 2:5] = 1
image[1, 3:7, 2:5] = 2
image[2, 3:7, 2:5] = 4
img_pooler = image_pooler.ImagePooler(resize_short=6,
resize_max=12).eval()
pooled_img, pooled_box, transforms = img_pooler(image, boxes)
# check pooled images
self.assertEqual(pooled_img.shape, torch.Size([3, 8, 6]))
self.assertArrayEqual(torch.unique(pooled_img[0, :, :]), [1])
self.assertArrayEqual(torch.unique(pooled_img[1, :, :]), [2])
self.assertArrayEqual(torch.unique(pooled_img[2, :, :]), [4])
# check pooled boxes, in xyxy format
self.assertArrayEqual(pooled_box, [[0, 0, 6, 8]])
# inverse of transforms
trans_inv = transforms.inverse()
# inverse of boxes, xyxy
inversed_box = trans_inv.apply_box(pooled_box)
self.assertArrayEqual(inversed_box, boxes)
pooled_sub_box = np.array([[2, 2, 4, 6]])
inversed_sub_box = trans_inv.apply_box(pooled_sub_box)
self.assertArrayEqual(inversed_sub_box, [[3, 4, 4, 6]])
def test_image_pooler_scale_box_large_crop_scale_and_resize(self):
""" Crop bbox that is scaled, resize the cropped box """
H, W = 398, 224
all_boxes = Boxes(
torch.Tensor([[50, 40, 100, 80], [150, 60, 200, 120]]))
image = rh.get_batched_inputs(1, (H, W), (H, W), all_boxes)[0]["image"]
boxes = bu.get_box_union(all_boxes)
img_pooler = image_pooler.ImagePooler(
resize_type="resize_shortest",
resize_short=48,
resize_max=180,
box_scale_factor=1.2,
).eval()
pooled_img, pooled_box, transforms = img_pooler(image, boxes.tensor)
self.assertEqual(pooled_img.shape, torch.Size([3, 48, 90]))
# bbox with scaling in the original space
orig_crop_box = transforms.inverse().apply_box(
[0, 0, pooled_img.shape[2], pooled_img.shape[1]])
self.assertArrayEqual(orig_crop_box, [[35, 32, 215, 128]])
# bbox without scaling in the original space
orig_boxes = transforms.inverse().apply_box(pooled_box)
self.assertArrayEqual(orig_boxes, boxes.tensor)
sub_boxes = rh.get_detected_instances_from_image([{
"image": pooled_img
}])[0].pred_boxes
# [[7.5, 4, 32.5, 24], [57.5, 14, 82.5, 44]]
self.assertArrayEqual(sub_boxes.tensor,
[[7, 4, 33, 24], [57, 14, 83, 44]])
def test_image_pooler_scale_box_large_crop_only(self):
""" Crop bbox """
H, W = 398, 224
all_boxes = Boxes(
torch.Tensor([[50, 40, 100, 80], [150, 60, 200, 120]]))
image = rh.get_batched_inputs(1, (H, W), (H, W), all_boxes)[0]["image"]
boxes = bu.get_box_union(all_boxes)
self.assertArrayEqual(boxes.tensor, [[50, 40, 200, 120]])
img_pooler = image_pooler.ImagePooler(resize_type=None,
box_scale_factor=1.0).eval()
pooled_img, pooled_box, transforms = img_pooler(image, boxes.tensor)
self.assertEqual(pooled_img.shape, torch.Size([3, 80, 150]))
sub_boxes = rh.get_detected_instances_from_image([{
"image": pooled_img
}])[0].pred_boxes
self.assertArrayEqual(sub_boxes.tensor,
[[0, 0, 50, 40], [100, 20, 150, 80]])
def test_image_pooler_scale_box(self):
H, W = 8, 6
image = torch.zeros(3, H, W)
# xyxy
boxes = torch.Tensor([[2, 3, 5, 7]])
image[0, 3:7, 2:5] = 1
image[1, 3:7, 2:5] = 2
image[2, 3:7, 2:5] = 4
img_pooler = image_pooler.ImagePooler(resize_type=None,
box_scale_factor=4.0).eval()
pooled_img, pooled_box, transforms = img_pooler(image, boxes)
# check pooled images
self.assertEqual(pooled_img.shape, torch.Size([3, 8, 6]))
self.assertArrayEqual(pooled_img, image)
# check pooled boxes, in xyxy format, the box before scaling
self.assertArrayEqual(pooled_box, [[2, 3, 5, 7]])
def test_image_pooler_scale_box_large_crop_and_scale(self):
""" Crop bbox that is scaled """
H, W = 398, 224
all_boxes = Boxes(
torch.Tensor([[50, 40, 100, 80], [150, 60, 200, 120]]))
image = rh.get_batched_inputs(1, (H, W), (H, W), all_boxes)[0]["image"]
boxes = bu.get_box_union(all_boxes)
img_pooler = image_pooler.ImagePooler(resize_type=None,
box_scale_factor=1.2).eval()
pooled_img, pooled_box, transforms = img_pooler(image, boxes.tensor)
self.assertEqual(pooled_img.shape, torch.Size([3, 96, 180]))
# bbox with scaling in the original space
orig_crop_box = transforms.inverse().apply_box(
[0, 0, pooled_img.shape[2], pooled_img.shape[1]])
self.assertArrayEqual(orig_crop_box, [[35, 32, 215, 128]])
sub_boxes = rh.get_detected_instances_from_image([{
"image": pooled_img
}])[0].pred_boxes
# gt_offset_xy = (50 - 35 = 15, 40 - 32 = 8)
self.assertArrayEqual(sub_boxes.tensor,
[[15, 8, 65, 48], [115, 28, 165, 88]])
