def inference(self, box_cls, box_pred, mask_pred, image_sizes):
"""
Arguments:
box_cls (Tensor): tensor of shape (batch_size, num_queries, K).
The tensor predicts the classification probability for each query.
box_pred (Tensor): tensors of shape (batch_size, num_queries, 4).
The tensor predicts 4-vector (x,y,w,h) box
regression values for every queryx
image_sizes (List[torch.Size]): the input image sizes
Returns:
results (List[Instances]): a list of #images elements.
"""
assert len(box_cls) == len(image_sizes)
results = []
# For each box we assign the best class or the second best if the best on is `no_object`.
if self.use_focal_loss:
prob = box_cls.sigmoid()
# TODO make top-100 as an option for non-focal-loss as well
scores, topk_indexes = torch.topk(prob.view(box_cls.shape[0], -1),
100,
dim=1)
topk_boxes = topk_indexes // box_cls.shape[2]
labels = topk_indexes % box_cls.shape[2]
else:
scores, labels = F.softmax(box_cls, dim=-1)[:, :, :-1].max(-1)
for i, (
scores_per_image,
labels_per_image,
box_pred_per_image,
image_size,
) in enumerate(zip(scores, labels, box_pred, image_sizes)):
result = Instances(image_size)
boxes = box_cxcywh_to_xyxy(box_pred_per_image)
if self.use_focal_loss:
boxes = torch.gather(boxes, 0,
topk_boxes[i].unsqueeze(-1).repeat(1, 4))
result.pred_boxes = Boxes(boxes)
result.pred_boxes.scale(scale_x=image_size[1],
scale_y=image_size[0])
if self.mask_on:
mask = F.interpolate(
mask_pred[i].unsqueeze(0),
size=image_size,
mode="bilinear",
align_corners=False,
)
mask = mask[0].sigmoid() > 0.5
B, N, H, W = mask_pred.shape
mask = BitMasks(mask.cpu()).crop_and_resize(
result.pred_boxes.tensor.cpu(), 32)
result.pred_masks = mask.unsqueeze(1).to(mask_pred[0].device)
result.scores = scores_per_image
result.pred_classes = labels_per_image
results.append(result)
return results
def inference(self, box_cls, box_pred, mask_pred, image_sizes):
"""
Arguments:
box_cls (Tensor): tensor of shape (batch_size, num_queries, K).
The tensor predicts the classification probability for each query.
box_pred (Tensor): tensors of shape (batch_size, num_queries, 4).
The tensor predicts 4-vector (x,y,w,h) box
regression values for every queryx
image_sizes (List[torch.Size]): the input image sizes
Returns:
results (List[Instances]): a list of #images elements.
"""
assert len(box_cls) == len(image_sizes)
results = []
scores, labels = F.softmax(box_cls, axis=-1)[:, :, :-1].max(-1)
for i, (scores_per_image, labels_per_image, box_pred_per_image,
image_size) in enumerate(
zip(scores, labels, box_pred, image_sizes)):
result = Instances(image_size)
result.pred_boxes = Boxes(box_cxcywh_to_xyxy(box_pred_per_image))
result.pred_boxes.scale(scale_x=image_size[1], scale_y=\
image_size[0])
if self.mask_on:
mask = F.interpolate(mask_pred[i].unsqueeze(0), size=\
image_size, mode='bilinear', align_corners=False)
mask = mask[0].sigmoid() > 0.5
B, N, H, W = mask_pred.shape
mask = BitMasks(mask.cpu()).crop_and_resize(
result.pred_boxes.tensor.cpu(), 32)
result.pred_masks = mask.unsqueeze(1).to(mask_pred[0].device)
result.scores = scores_per_image
result.pred_classes = labels_per_image
results.append(result)
return results
