def test_paste_mask_scriptable(self):
scripted_f = torch.jit.script(paste_masks_in_image)
N = 10
masks = torch.rand(N, 28, 28)
boxes = Boxes(random_boxes(N, 100)).tensor
image_shape = (150, 150)
out = paste_masks_in_image(masks, boxes, image_shape)
scripted_out = scripted_f(masks, boxes, image_shape)
self.assertTrue(torch.equal(out, scripted_out))
def process_annotation(self, ann, mask_side_len=28):
# Parse annotation data
img_info = self.coco.loadImgs(ids=[ann["image_id"]])[0]
height, width = img_info["height"], img_info["width"]
gt_polygons = [
np.array(p, dtype=np.float64) for p in ann["segmentation"]
]
gt_bbox = BoxMode.convert(ann["bbox"], BoxMode.XYWH_ABS,
BoxMode.XYXY_ABS)
gt_bit_mask = polygons_to_bitmask(gt_polygons, height, width)
# Run rasterize ..
torch_gt_bbox = torch.tensor(gt_bbox).to(dtype=torch.float32).reshape(
-1, 4)
box_bitmasks = {
"polygon":
PolygonMasks([gt_polygons
]).crop_and_resize(torch_gt_bbox, mask_side_len)[0],
"gridsample":
rasterize_polygons_with_grid_sample(gt_bit_mask, gt_bbox,
mask_side_len),
"roialign":
BitMasks(torch.from_numpy(
gt_bit_mask[None, :, :])).crop_and_resize(
torch_gt_bbox, mask_side_len)[0],
}
# Run paste ..
results = defaultdict(dict)
for k, box_bitmask in box_bitmasks.items():
padded_bitmask, scale = pad_masks(box_bitmask[None, :, :], 1)
scaled_boxes = scale_boxes(torch_gt_bbox, scale)
r = results[k]
r["old"] = paste_mask_in_image_old(padded_bitmask[0],
scaled_boxes[0],
height,
width,
threshold=0.5)
r["aligned"] = paste_masks_in_image(box_bitmask[None, :, :],
Boxes(torch_gt_bbox),
(height, width))[0]
table = []
for rasterize_method, r in results.items():
for paste_method, mask in r.items():
mask = np.asarray(mask)
iou = iou_between_full_image_bit_masks(
gt_bit_mask.astype("uint8"), mask)
table.append((rasterize_method, paste_method, iou))
return table
def bench():
for _ in range(n):
paste_masks_in_image(m, b, image_shape)
if device.type == "cuda":
torch.cuda.synchronize()