I’m trying to update the proposal losses function of MaskRCNN to increase the recall. I’m trying to do this by adding a positive weight to the BCE function
How I create my proposal losses function:
CLASS_WEIGHTS = torch.tensor([50])
def compute_loss(
objectness: Tensor, pred_bbox_deltas: Tensor, labels: List[Tensor], regression_targets: List[Tensor]
) -> Tuple[Tensor, Tensor]:
"""
Args:
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 = model.rpn.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 = F.smooth_l1_loss(
pred_bbox_deltas[sampled_pos_inds],
regression_targets[sampled_pos_inds],
beta=1 / 9,
reduction="sum",
) / (sampled_inds.numel())
objectness_loss = F.binary_cross_entropy_with_logits(objectness[sampled_inds], labels[sampled_inds],
pos_weight=CLASS_WEIGHTS # USE CLASS WEIGHT HERE
)
return objectness_loss, box_loss
Then how I set the model to use this proposal losses function:
model = maskrcnn_resnet50_fpn(weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT)
model.rpn.compute_loss = compute_loss
When I train the model now:
- the
compute_lossfunction is called, as expected - the loss increases significantly (e.g. before it was 1, now it is like 50), as expected
- BUT the recall and precision stay around the same (e.g. recall stagnates around 0.55 after training for several epochs)
Why is this the case? How do I get the recall to improve (i.e. how do I generate more proposals)?
FYI: I already tried setting the score threshold to 0, this didn’t do anything either…