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Add 'with EventStorage()' context #5446

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8 changes: 4 additions & 4 deletions detectron2/modeling/proposal_generator/rpn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@
from detectron2.config import configurable
from detectron2.layers import Conv2d, ShapeSpec, cat
from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou
from detectron2.utils.events import get_event_storage
from detectron2.utils.events import EventStorage
from detectron2.utils.memory import retry_if_cuda_oom
from detectron2.utils.registry import Registry

Expand Down Expand Up @@ -398,9 +398,9 @@ def losses(
pos_mask = gt_labels == 1
num_pos_anchors = pos_mask.sum().item()
num_neg_anchors = (gt_labels == 0).sum().item()
storage = get_event_storage()
storage.put_scalar("rpn/num_pos_anchors", num_pos_anchors / num_images)
storage.put_scalar("rpn/num_neg_anchors", num_neg_anchors / num_images)
with EventStorage() as storage:
storage.put_scalar("rpn/num_pos_anchors", num_pos_anchors / num_images)
storage.put_scalar("rpn/num_neg_anchors", num_neg_anchors / num_images)

localization_loss = _dense_box_regression_loss(
anchors,
Expand Down
12 changes: 6 additions & 6 deletions detectron2/modeling/roi_heads/fast_rcnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
from detectron2.layers import ShapeSpec, batched_nms, cat, cross_entropy, nonzero_tuple
from detectron2.modeling.box_regression import Box2BoxTransform, _dense_box_regression_loss
from detectron2.structures import Boxes, Instances
from detectron2.utils.events import get_event_storage
from detectron2.utils.events import EventStorage

__all__ = ["fast_rcnn_inference", "FastRCNNOutputLayers"]

Expand Down Expand Up @@ -108,11 +108,11 @@ def _log_classification_stats(pred_logits, gt_classes, prefix="fast_rcnn"):
num_accurate = (pred_classes == gt_classes).nonzero().numel()
fg_num_accurate = (fg_pred_classes == fg_gt_classes).nonzero().numel()

storage = get_event_storage()
storage.put_scalar(f"{prefix}/cls_accuracy", num_accurate / num_instances)
if num_fg > 0:
storage.put_scalar(f"{prefix}/fg_cls_accuracy", fg_num_accurate / num_fg)
storage.put_scalar(f"{prefix}/false_negative", num_false_negative / num_fg)
with EventStorage() as storage:
storage.put_scalar(f"{prefix}/cls_accuracy", num_accurate / num_instances)
if num_fg > 0:
storage.put_scalar(f"{prefix}/fg_cls_accuracy", fg_num_accurate / num_fg)
storage.put_scalar(f"{prefix}/false_negative", num_false_negative / num_fg)


def fast_rcnn_inference_single_image(
Expand Down
12 changes: 6 additions & 6 deletions detectron2/modeling/roi_heads/roi_heads.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@
from detectron2.config import configurable
from detectron2.layers import ShapeSpec, nonzero_tuple
from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou
from detectron2.utils.events import get_event_storage
from detectron2.utils.events import EventStorage
from detectron2.utils.registry import Registry

from ..backbone.resnet import BottleneckBlock, ResNet
Expand Down Expand Up @@ -115,8 +115,8 @@ def select_proposals_with_visible_keypoints(proposals: List[Instances]) -> List[
all_num_fg.append(selection_idxs.numel())
ret.append(proposals_per_image[selection_idxs])

storage = get_event_storage()
storage.put_scalar("keypoint_head/num_fg_samples", np.mean(all_num_fg))
with EventStorage() as storage:
storage.put_scalar("keypoint_head/num_fg_samples", np.mean(all_num_fg))
return ret


Expand Down Expand Up @@ -295,9 +295,9 @@ def label_and_sample_proposals(
proposals_with_gt.append(proposals_per_image)

# Log the number of fg/bg samples that are selected for training ROI heads
storage = get_event_storage()
storage.put_scalar("roi_head/num_fg_samples", np.mean(num_fg_samples))
storage.put_scalar("roi_head/num_bg_samples", np.mean(num_bg_samples))
with EventStorage() as storage:
storage.put_scalar("roi_head/num_fg_samples", np.mean(num_fg_samples))
storage.put_scalar("roi_head/num_bg_samples", np.mean(num_bg_samples))

return proposals_with_gt

Expand Down