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# Copyright (c) Meta Platforms, Inc. and affiliates
from re import L
import torch
from torch.nn import functional as F
from typing import List, Tuple
from fvcore.nn import giou_loss, smooth_l1_loss
from detectron2.utils.events import get_event_storage
from detectron2.layers import cat, cross_entropy, nonzero_tuple, batched_nms
from detectron2.structures import Instances, Boxes
from detectron2.modeling.roi_heads.fast_rcnn import (
FastRCNNOutputLayers, _log_classification_stats
)
from cubercnn.modeling.proposal_generator.rpn import matched_pairwise_iou
def fast_rcnn_inference(
boxes: List[torch.Tensor],
scores: List[torch.Tensor],
image_shapes: List[Tuple[int, int]],
score_thresh: float,
nms_thresh: float,
topk_per_image: int,
):
"""
Call `fast_rcnn_inference_single_image` for all images.
Args:
boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agnostic
boxes for each image. Element i has shape (Ri, K * 4) if doing
class-specific regression, or (Ri, 4) if doing class-agnostic
regression, where Ri is the number of predicted objects for image i.
This is compatible with the output of :meth:`FastRCNNOutputLayers.predict_boxes`.
scores (list[Tensor]): A list of Tensors of predicted class scores for each image.
Element i has shape (Ri, K + 1), where Ri is the number of predicted objects
for image i. Compatible with the output of :meth:`FastRCNNOutputLayers.predict_probs`.
image_shapes (list[tuple]): A list of (width, height) tuples for each image in the batch.
score_thresh (float): Only return detections with a confidence score exceeding this
threshold.
nms_thresh (float): The threshold to use for box non-maximum suppression. Value in [0, 1].
topk_per_image (int): The number of top scoring detections to return. Set < 0 to return
all detections.
Returns:
instances: (list[Instances]): A list of N instances, one for each image in the batch,
that stores the topk most confidence detections.
kept_indices: (list[Tensor]): A list of 1D tensor of length of N, each element indicates
the corresponding boxes/scores index in [0, Ri) from the input, for image i.
"""
result_per_image = [
fast_rcnn_inference_single_image(
boxes_per_image, scores_per_image, image_shape, score_thresh, nms_thresh, topk_per_image
)
for scores_per_image, boxes_per_image, image_shape in zip(scores, boxes, image_shapes)
]
return [x[0] for x in result_per_image], [x[1] for x in result_per_image]
def fast_rcnn_inference_single_image(
boxes,
scores,
image_shape: Tuple[int, int],
score_thresh: float,
nms_thresh: float,
topk_per_image: int,
):
"""
Single-image inference. Return bounding-box detection results by thresholding
on scores and applying non-maximum suppression (NMS).
Args:
Same as `fast_rcnn_inference`, but with boxes, scores, and image shapes
per image.
Returns:
Same as `fast_rcnn_inference`, but for only one image.
"""
valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
if not valid_mask.all():
boxes = boxes[valid_mask]
scores = scores[valid_mask]
scores = scores[:, :-1]
num_bbox_reg_classes = boxes.shape[1] // 4
# Convert to Boxes to use the `clip` function ...
boxes = Boxes(boxes.reshape(-1, 4))
boxes.clip(image_shape)
boxes = boxes.tensor.view(-1, num_bbox_reg_classes, 4) # R x C x 4
# 1. Filter results based on detection scores. It can make NMS more efficient
# by filtering out low-confidence detections.
filter_mask = scores > score_thresh # R x K
# R' x 2. First column contains indices of the R predictions;
# Second column contains indices of classes.
filter_inds = filter_mask.nonzero()
if num_bbox_reg_classes == 1:
boxes = boxes[filter_inds[:, 0], 0]
else:
boxes = boxes[filter_mask]
scores_full = scores[filter_inds[:, 0]]
scores = scores[filter_mask]
# 2. Apply NMS for each class independently.
keep = batched_nms(boxes, scores, filter_inds[:, 1], nms_thresh)
if topk_per_image >= 0:
keep = keep[:topk_per_image]
boxes, scores, filter_inds, scores_full = boxes[keep], scores[keep], filter_inds[keep], scores_full[keep]
result = Instances(image_shape)
result.pred_boxes = Boxes(boxes)
result.scores = scores
result.scores_full = scores_full
result.pred_classes = filter_inds[:, 1]
return result, filter_inds[:, 0]
class FastRCNNOutputs(FastRCNNOutputLayers):
def inference(self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances]):
"""
Args:
predictions: return values of :meth:`forward()`.
proposals (list[Instances]): proposals that match the features that were
used to compute predictions. The ``proposal_boxes`` field is expected.
Returns:
list[Instances]: same as `fast_rcnn_inference`.
list[Tensor]: same as `fast_rcnn_inference`.
"""
boxes = self.predict_boxes(predictions, proposals)
scores = self.predict_probs(predictions, proposals)
image_shapes = [x.image_size for x in proposals]
return fast_rcnn_inference(
boxes,
scores,
image_shapes,
self.test_score_thresh,
self.test_nms_thresh,
self.test_topk_per_image,
)
def losses(self, predictions, proposals):
"""
Args:
predictions: return values of :meth:`forward()`.
proposals (list[Instances]): proposals that match the features that were used
to compute predictions. The fields ``proposal_boxes``, ``gt_boxes``,
``gt_classes`` are expected.
Returns:
Dict[str, Tensor]: dict of losses
"""
scores, proposal_deltas = predictions
# parse classification outputs
gt_classes = (
cat([p.gt_classes for p in proposals], dim=0) if len(proposals) else torch.empty(0)
)
# parse box regression outputs
if len(proposals):
proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0) # Nx4
assert not proposal_boxes.requires_grad, "Proposals should not require gradients!"
# If "gt_boxes" does not exist, the proposals must be all negative and
# should not be included in regression loss computation.
# Here we just use proposal_boxes as an arbitrary placeholder because its
# value won't be used in self.box_reg_loss().
gt_boxes = cat(
[(p.gt_boxes if p.has("gt_boxes") else p.proposal_boxes).tensor for p in proposals],
dim=0,
)
else:
proposal_boxes = gt_boxes = torch.empty((0, 4), device=proposal_deltas.device)
normalize_factor = max(gt_classes.numel(), 1.0)
'''
Standard Faster R-CNN losses
'''
_log_classification_stats(scores, gt_classes)
loss_cls = cross_entropy(scores, gt_classes, reduction="mean")
loss_box_reg = self.box_reg_loss(proposal_boxes, gt_boxes, proposal_deltas, gt_classes, reduction="none")
loss_box_reg = (loss_box_reg).sum() / normalize_factor
losses = {
"BoxHead/loss_cls": loss_cls,
"BoxHead/loss_box_reg": loss_box_reg,
}
return {k: v * self.loss_weight.get(k, 1.0) for k, v in losses.items()}
def box_reg_loss(self, proposal_boxes, gt_boxes, pred_deltas, gt_classes, reduction='mean'):
"""
Args:
All boxes are tensors with the same shape Rx(4 or 5).
gt_classes is a long tensor of shape R, the gt class label of each proposal.
R shall be the number of proposals.
"""
box_dim = proposal_boxes.shape[1] # 4 or 5
# Regression loss is only computed for foreground proposals (those matched to a GT)
fg_inds = nonzero_tuple((gt_classes >= 0) & (gt_classes < self.num_classes))[0]
if pred_deltas.shape[1] == box_dim: # cls-agnostic regression
fg_pred_deltas = pred_deltas[fg_inds]
else:
fg_pred_deltas = pred_deltas.view(-1, self.num_classes, box_dim)[
fg_inds, gt_classes[fg_inds]
]
if reduction == 'mean':
if self.box_reg_loss_type == "smooth_l1":
gt_pred_deltas = self.box2box_transform.get_deltas(
proposal_boxes[fg_inds],
gt_boxes[fg_inds],
)
loss_box_reg = smooth_l1_loss(
fg_pred_deltas, gt_pred_deltas, self.smooth_l1_beta, reduction="sum"
)
elif self.box_reg_loss_type == "giou":
fg_pred_boxes = self.box2box_transform.apply_deltas(
fg_pred_deltas, proposal_boxes[fg_inds]
)
loss_box_reg = giou_loss(fg_pred_boxes, gt_boxes[fg_inds], reduction="sum")
else:
raise ValueError(f"Invalid bbox reg loss type '{self.box_reg_loss_type}'")
# The reg loss is normalized using the total number of regions (R), not the number
# of foreground regions even though the box regression loss is only defined on
# foreground regions. Why? Because doing so gives equal training influence to
# each foreground example. To see how, consider two different minibatches:
# (1) Contains a single foreground region
# (2) Contains 100 foreground regions
# If we normalize by the number of foreground regions, the single example in
# minibatch (1) will be given 100 times as much influence as each foreground
# example in minibatch (2). Normalizing by the total number of regions, R,
# means that the single example in minibatch (1) and each of the 100 examples
# in minibatch (2) are given equal influence.
return loss_box_reg / max(gt_classes.numel(), 1.0) # return 0 if empty
elif reduction == 'none':
if self.box_reg_loss_type == "smooth_l1":
gt_pred_deltas = self.box2box_transform.get_deltas(
proposal_boxes[fg_inds],
gt_boxes[fg_inds],
)
loss_box_reg = smooth_l1_loss(
fg_pred_deltas, gt_pred_deltas, self.smooth_l1_beta, reduction="none"
)
else:
raise ValueError(f"Invalid bbox reg loss type '{self.box_reg_loss_type}'")
# return non-reduced type
return loss_box_reg
else:
raise ValueError(f"Invalid bbox reg reduction type '{reduction}'")
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