agnostic-regression for bbox

[zimenglan-sysu-512]

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[zimenglan-sysu-512]

relative to #111

[fmassa]

This is awesome, thanks a lot!

I started writing some comments, but then I realized that I needed to double think a few things. I'll try to get to reviewing it fully by the end of the week.

Also, did you launched trainings with class-agnostic box regression (to compare against Detectron)?

[zimenglan-sysu-512]

hi @fmassa
i run e2e_mask_rcnn_R_50_FPN_1x with class-agnostic regression and ohem( but set POSITIVE_FRACTION to be 0.25) which i forgot to turn off (sorry for that), and get the results:

bbox
Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.380
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.593
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.414
Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.214
Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.410
Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.501
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.313
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.492
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.516
Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.324
Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.550
Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.661

mask
Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.343
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.558
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.363
Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151
Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.367
Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.510
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.294
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.447
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.467
Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.264
Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.502
Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.626

it seems ohem helps a little. (i also train the same setting except class-agnostic regression, it got 37.9/34.4). i think the class-agnostic regression works

[fmassa]

This looks good to me, thanks a lot!

I have only one question, but I think this can be merged already.
I'd just like to discuss a bit about it before merging.

[fmassa]

this is how Detectron implements it: by using 0 for background and 1 for the other classes.

But I'm starting to wonder if this is actually necessary here: given that we index out all negative examples via sampled_pos_inds_subset, we don't backprop through the background elements anyway.
Given that, I think we could have only num_bbox_reg_classes = 1, and the rest of the code should work as well.

@rbgirshick what do you think? Am I missing something here?

[fmassa]

After a second thought, we still use the +1 for classes for the box regression, so in order to maintain consistency I'll keep the same here.

Thanks a lot!

[zimenglan-sysu-512]

hi @fmassa
after merging from master branch, i re-run the experiment e2e_mask_rcnn_R_50_FPN_1x without ohem, and get 37.6&34.1.

[fmassa]

Hi @zimenglan-sysu-512 ,

Thanks for the information, those numbers look good I think, thanks!

[xxradon]

@zimenglan-sysu-512 I noticed you had implement OHEM on maskrcnn-benchmark,can you share your code or give us some suggestions to implement.Thank you very much.

[zimenglan-sysu-512]

hi @xxradon
here is my implementation in these lines:

        # https://pytorch.org/docs/stable/_modules/torch/nn/functional.html#cross_entropy
        reduction = "mean" # default for 'F.cross_entropy'
        if self.reduction == "none":
            reduction = self.reduction
        classification_loss = F.cross_entropy(
            class_logits, labels, reduction=reduction
        )

        # 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)

        reduction = self.reduction
        box_loss = smooth_l1_loss(
            box_regression[sampled_pos_inds_subset[:, None], map_inds],
            regression_targets[sampled_pos_inds_subset],
            reduction=reduction,
            beta=1,
        )
        if self.reduction in ['sum',]:
            box_loss = box_loss / labels.numel()

        if self.reduction == 'none':
            num_props = classification_loss.size()[0]
            box_loss_paste = torch.zeros((num_props,), device=device)
            box_loss_paste[sampled_pos_inds_subset] = box_loss
            box_loss = box_loss_paste

        return classification_loss, box_loss

u should modify the corresponding smooth_l1_loss to return the losses of all rois.

and then in box_head.py to sum the loss_classifier and loss_box_reg like this:

def ohem_for_bbox_loss(ohem_batch_size, loss_classifier, loss_box_reg):
    '''select the top-K highest-loss proposals'''
    ohem_batch_size = min(
        ohem_batch_size, loss_classifier.size()[0]
    )
    cls_box_loss = loss_classifier.detach() + loss_box_reg.detach()
    ohem_idxs = torch.topk(cls_box_loss, ohem_batch_size)[1]
    loss_classifier = loss_classifier[ohem_idxs]
    loss_box_reg = loss_box_reg[ohem_idxs]
    loss_classifier = loss_classifier.mean()
    loss_box_reg = loss_box_reg.mean()
    return loss_classifier, loss_box_reg

hope it can help u.
good luck.

[zimenglan-sysu-512]

related to #439 and #630

[xxradon]

@zimenglan-sysu-512 Thank you very much,it's very helpful.