def __call__(self, mask_logits):
"""
Arguments:
mask_logits (Tensor)
Return:
mask_loss (Tensor): scalar tensor containing the loss
If we use maskiou head, we will return extra feature for maskiou head.
"""
labels = [
proposals_per_img.get_field("labels")
for proposals_per_img in self.positive_proposals
]
mask_targets = [
proposals_per_img.get_field("mask_targets")
for proposals_per_img in self.positive_proposals
]
labels = cat(labels, dim=0)
mask_targets = cat(mask_targets, dim=0)
positive_inds = torch.nonzero(labels > 0).squeeze(1)
labels_pos = labels[positive_inds]
# torch.mean (in binary_cross_entropy_with_logits) doesn't
# accept empty tensors, so handle it separately
if mask_targets.numel() == 0:
return mask_logits.sum() * 0
mask_loss = F.binary_cross_entropy_with_logits(
mask_logits[positive_inds, labels_pos], mask_targets)
mask_loss *= cfg.MRCNN.LOSS_WEIGHT
return mask_loss
def __call__(self, keypoint_logits):
heatmaps = []
valid = []
for proposals_per_image in self.positive_proposals:
kp = proposals_per_image.get_field("keypoints_target")
heatmaps_per_image, valid_per_image = project_keypoints_to_heatmap(
kp, proposals_per_image, self.resolution)
heatmaps.append(heatmaps_per_image.view(-1))
valid.append(valid_per_image.view(-1))
keypoint_targets = cat(heatmaps, dim=0)
valid = cat(valid, dim=0).to(dtype=torch.uint8)
valid = torch.nonzero(valid).squeeze(1)
# torch.mean (in binary_cross_entropy_with_logits) does'nt
# accept empty tensors, so handle it sepaartely
if keypoint_targets.numel() == 0 or len(valid) == 0:
return keypoint_logits.sum() * 0
N, K, H, W = keypoint_logits.shape
keypoint_logits = keypoint_logits.view(N * K, H * W)
keypoint_loss = F.cross_entropy(keypoint_logits[valid],
keypoint_targets[valid])
keypoint_loss *= cfg.KRCNN.LOSS_WEIGHT
return keypoint_loss
def convert_to_roi_format(self, boxes):
concat_boxes = cat([b.bbox for b in boxes], dim=0)
device, dtype = concat_boxes.device, concat_boxes.dtype
ids = cat(
[
torch.full((len(b), 1), i, dtype=dtype, device=device)
for i, b in enumerate(boxes)
],
dim=0,
)
rois = torch.cat([ids, concat_boxes], dim=1)
return rois
def __call__(self, class_logits, box_regression):
"""
Computes the loss for Faster R-CNN.
This requires that the subsample method has been called beforehand.
Arguments:
class_logits (list[Tensor])
box_regression (list[Tensor])
Returns:
classification_loss (Tensor)
box_loss (Tensor)
"""
loss_dict = {}
if not hasattr(self, "_proposals"):
raise RuntimeError("subsample needs to be called before")
proposals = self._proposals
labels = cat([proposal.get_field("labels") for proposal in proposals], dim=0)
assert class_logits[0] is not None or box_regression[0] is not None, 'Fast R-CNN should keep 1 branch at least'
if class_logits[0] is not None:
class_logits = cat(class_logits, dim=0)
classification_loss = F.cross_entropy(class_logits, labels)
loss_dict["loss_classifier"] = classification_loss
if box_regression[0] is not None:
box_regression = cat(box_regression, dim=0)
device = box_regression.device
regression_targets = cat([proposal.get_field("regression_targets") for proposal in proposals], dim=0)
# get indices that correspond to the regression targets for
# the corresponding ground truth labels, to be used with
# advanced indexing
sampled_pos_inds_subset = torch.nonzero(labels > 0).squeeze(1)
labels_pos = labels[sampled_pos_inds_subset]
if self.cls_agnostic_bbox_reg:
map_inds = torch.tensor([4, 5, 6, 7], device=device)
else:
map_inds = 4 * labels_pos[:, None] + torch.tensor([0, 1, 2, 3], device=device)
box_loss = smooth_l1_loss(
box_regression[sampled_pos_inds_subset[:, None], map_inds],
regression_targets[sampled_pos_inds_subset],
size_average=False,
beta=cfg.FAST_RCNN.SMOOTH_L1_BETA,
)
box_loss = box_loss / labels.numel()
loss_dict["loss_box_reg"] = box_loss
return loss_dict
def __call__(self, parsing_logits):
parsing_targets = [proposals_per_img.get_field("parsing_targets") for proposals_per_img in self.positive_proposals]
parsing_targets = cat(parsing_targets, dim=0)
if parsing_targets.numel() == 0:
if not self.parsingiou_on:
return parsing_logits.sum() * 0
else:
return parsing_logits.sum() * 0, None
if self.parsingiou_on:
# TODO: use tensor for speeding up
pred_parsings_np = parsing_logits.detach().argmax(dim=1).cpu().numpy()
parsing_targets_np = parsing_targets.cpu().numpy()
N = parsing_targets_np.shape[0]
parsingiou_targets = np.zeros(N, dtype=np.float)
for _ in range(N):
parsing_iou = cal_one_mean_iou(parsing_targets_np[_], pred_parsings_np[_], cfg.PRCNN.NUM_PARSING)
parsingiou_targets[_] = np.nanmean(parsing_iou)
parsingiou_targets = torch.from_numpy(parsingiou_targets).to(parsing_targets.device, dtype=torch.float)
parsing_loss = F.cross_entropy(
parsing_logits, parsing_targets, reduction="mean"
)
parsing_loss *= cfg.PRCNN.LOSS_WEIGHT
if not self.parsingiou_on:
return parsing_loss
else:
return parsing_loss, parsingiou_targets
def __call__(self, semantic_pred, targets):
labels = self.semseg_batch_resize(targets)
labels = cat([label for label in labels], dim=0).long()
assert len(labels.shape) == 3
loss_semseg = F.cross_entropy(semantic_pred,
labels,
ignore_index=self.ignore_label)
loss_semseg *= self.loss_weight
return loss_semseg
def __call__(self, boxlists):
"""
Arguments:
boxlists (list[BoxList])
"""
# Compute level ids
s = torch.sqrt(cat([boxlist.area() for boxlist in boxlists]))
# Eqn.(1) in FPN paper
target_lvls = torch.floor(self.lvl0 + torch.log2(s / self.s0 + self.eps))
target_lvls = torch.clamp(target_lvls, min=self.k_min, max=self.k_max)
return target_lvls.to(torch.int64) - self.k_min
def __call__(self, parsing_logits):
parsing_targets = [
proposals_per_img.get_field("parsing_targets")
for proposals_per_img in self.positive_proposals
]
parsing_targets = cat(parsing_targets, dim=0)
if parsing_targets.numel() == 0:
return parsing_logits.sum() * 0
parsing_loss = F.cross_entropy(parsing_logits,
parsing_targets,
reduction="mean")
parsing_loss *= cfg.PRCNN.LOSS_WEIGHT
return parsing_loss
def __call__(self, logits):
targets = [
proposals_per_img.get_field("targets")
for proposals_per_img in self.positive_proposals
]
targets = cat(targets, dim=0).float()
if targets.numel() == 0:
return logits['fused'].sum() * 0
loss_fused = self.loss_weight * F.binary_cross_entropy_with_logits(
logits['fused'], targets)
loss_unfused = self.loss_weight * F.binary_cross_entropy_with_logits(
logits['unfused'], targets)
loss = loss_fused + loss_unfused
return loss
def __call__(self, mask_logits):
"""
Arguments:
mask_logits (Tensor)
Return:
mask_loss (Tensor): scalar tensor containing the loss
If we use maskiou head, we will return extra feature for maskiou head.
"""
labels = [
proposals_per_img.get_field("labels")
for proposals_per_img in self.positive_proposals
]
mask_targets = [
proposals_per_img.get_field("mask_targets")
for proposals_per_img in self.positive_proposals
]
if self.maskiou_on:
mask_ratios = [
proposals_per_img.get_field("mask_ratios")
for proposals_per_img in self.positive_proposals
]
labels = cat(labels, dim=0)
mask_targets = cat(mask_targets, dim=0)
positive_inds = torch.nonzero(labels > 0).squeeze(1)
labels_pos = labels[positive_inds]
# torch.mean (in binary_cross_entropy_with_logits) doesn't
# accept empty tensors, so handle it separately
if mask_targets.numel() == 0:
if not self.maskiou_on:
return mask_logits.sum() * 0
else:
selected_index = torch.arange(mask_logits.shape[0],
device=labels.device)
selected_mask = mask_logits[selected_index, labels]
mask_num, mask_h, mask_w = selected_mask.shape
selected_mask = selected_mask.reshape(mask_num, 1, mask_h,
mask_w)
return mask_logits.sum() * 0, selected_mask, labels, None
if self.maskiou_on:
mask_ratios = cat(mask_ratios, dim=0)
value_eps = 1e-10 * torch.ones(mask_targets.shape[0],
device=labels.device)
mask_ratios = torch.max(mask_ratios, value_eps)
pred_masks = mask_logits[positive_inds, labels_pos]
pred_masks[:] = pred_masks > 0.5
mask_targets_full_area = mask_targets.sum(dim=[1, 2]) / mask_ratios
mask_ovr = pred_masks * mask_targets
mask_ovr_area = mask_ovr.sum(dim=[1, 2])
mask_union_area = pred_masks.sum(
dim=[1, 2]) + mask_targets_full_area - mask_ovr_area
value_1 = torch.ones(pred_masks.shape[0], device=labels.device)
value_0 = torch.zeros(pred_masks.shape[0], device=labels.device)
mask_union_area = torch.max(mask_union_area, value_1)
mask_ovr_area = torch.max(mask_ovr_area, value_0)
maskiou_targets = mask_ovr_area / mask_union_area
mask_loss = F.binary_cross_entropy_with_logits(
mask_logits[positive_inds, labels_pos], mask_targets)
mask_loss *= cfg.MRCNN.LOSS_WEIGHT
if not self.maskiou_on:
return mask_loss
else:
selected_index = torch.arange(mask_logits.shape[0],
device=labels.device)
selected_mask = mask_logits[selected_index, labels]
mask_num, mask_h, mask_w = selected_mask.shape
selected_mask = selected_mask.reshape(mask_num, 1, mask_h, mask_w)
selected_mask = selected_mask.sigmoid()
return mask_loss, selected_mask, labels, maskiou_targets
