def compute_loss(self, objectness, pred_bbox_deltas, labels, regression_targets):
sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels)
sampled_pos_inds = torch.where(torch.cat(sampled_pos_inds, dim=0))[0]
sampled_neg_inds = torch.where(torch.cat(sampled_neg_inds, dim=0))[0]
sampled_inds = torch.cat([sampled_pos_inds, sampled_neg_inds], dim=0)
objectness = objectness.flatten()
labels = torch.cat(labels, dim=0)
regression_targets = torch.cat(regression_targets, dim=0)
box_loss = det_utils.smooth_l1_loss(pred_bbox_deltas[sampled_pos_inds], regression_targets[sampled_pos_inds],
beta = 1/9, size_average = False) / (sampled_inds.numel())
objectness_loss = F.binary_cross_entropy_with_logits(objectness[sampled_inds], labels[sampled_inds])
return objectness_loss, box_loss
def fastrcnn_loss(class_logits, box_regression, labels, regression_targets): # type: (Tensor, Tensor, List[Tensor], List[Tensor]) -> Tuple[Tensor, Tensor] """ Computes the loss for Faster R-CNN. Arguments: class_logits (Tensor) box_regression (Tensor) labels (list[BoxList]) regression_targets (Tensor) Returns: classification_loss (Tensor) box_loss (Tensor) """ labels = torch.cat(labels, dim=0) regression_targets = torch.cat(regression_targets, dim=0) classification_loss = F.cross_entropy(class_logits, labels) # 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] N, num_classes = class_logits.shape box_regression = box_regression.reshape(N, -1, 4) box_loss = det_utils.smooth_l1_loss( box_regression[sampled_pos_inds_subset, labels_pos], regression_targets[sampled_pos_inds_subset], beta=1 / 9, size_average=False, ) box_loss = box_loss / labels.numel() return classification_loss, box_loss
def compute_loss(self, objectness, pred_bbox_deltas, labels,
regression_targets):
# type: (Tensor, Tensor, List[Tensor], List[Tensor]) -> Tuple[Tensor, Tensor]
"""
Arguments:
objectness (Tensor)
pred_bbox_deltas (Tensor)
labels (List[Tensor])
regression_targets (List[Tensor])
Returns:
objectness_loss (Tensor)
box_loss (Tensor)
"""
sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels)
sampled_pos_inds = torch.where(torch.cat(sampled_pos_inds, dim=0))[0]
sampled_neg_inds = torch.where(torch.cat(sampled_neg_inds, dim=0))[0]
sampled_inds = torch.cat([sampled_pos_inds, sampled_neg_inds], dim=0)
objectness = objectness.flatten()
labels = torch.cat(labels, dim=0)
regression_targets = torch.cat(regression_targets, dim=0)
box_loss = det_utils.smooth_l1_loss(
pred_bbox_deltas[sampled_pos_inds],
regression_targets[sampled_pos_inds],
beta=1 / 9,
size_average=False,
) / (sampled_inds.numel())
objectness_loss = F.binary_cross_entropy_with_logits(
objectness[sampled_inds], labels[sampled_inds])
return objectness_loss, box_loss
