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): 预测类别概率信息，shape=[num_anchors, num_classes]
box_regression (Tensor): 预测边目标界框回归信息
labels (list[BoxList]): 真实类别信息
regression_targets (Tensor): 真实目标边界框信息
Returns:
classification_loss (Tensor)
box_loss (Tensor)
"""
labels = torch.cat(labels, dim=0)
regression_targets = torch.cat(regression_targets, dim=0)
# 计算类别损失信息
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
# 返回标签类别大于0的索引
# sampled_pos_inds_subset = torch.nonzero(torch.gt(labels, 0)).squeeze(1)
sampled_pos_inds_subset = torch.where(torch.gt(labels, 0))[0]
# 返回标签类别大于0位置的类别信息
labels_pos = labels[sampled_pos_inds_subset]
# shape=[num_proposal, num_classes]
N, num_classes = class_logits.shape
box_regression = box_regression.reshape(N, -1, 4)
# 计算边界框损失信息
box_loss = det_utils.smooth_l1_loss(
# 获取指定索引proposal的指定类别box信息
box_regression[sampled_pos_inds_subset, labels_pos],
regression_targets[sampled_pos_inds_subset],
beta=1 / 9,
size_average=False,
) / 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]
"""
计算RPN损失，包括类别损失（前景与背景），bbox regression损失
Arguments:
objectness (Tensor)：预测的前景概率
pred_bbox_deltas (Tensor)：预测的bbox regression
labels (List[Tensor])：真实的标签 1, 0, -1（batch中每一张图片的labels对应List的一个元素中）
regression_targets (List[Tensor])：真实的bbox regression
Returns:
objectness_loss (Tensor) : 类别损失
box_loss (Tensor)：边界框回归损失
"""
# 按照给定的batch_size_per_image, positive_fraction选择正负样本
sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels)
# 将一个batch中的所有正负样本List(Tensor)分别拼接在一起，并获取非零位置的索引
# sampled_pos_inds = torch.nonzero(torch.cat(sampled_pos_inds, dim=0)).squeeze(1)
sampled_pos_inds = torch.where(torch.cat(sampled_pos_inds, dim=0))[0]
# sampled_neg_inds = torch.nonzero(torch.cat(sampled_neg_inds, dim=0)).squeeze(1)
sampled_neg_inds = torch.where(torch.cat(sampled_neg_inds, dim=0))[0]
# 将所有正负样本索引拼接在一起
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)
# 计算边界框回归损失
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())
# 计算目标预测概率损失
objectness_loss = F.binary_cross_entropy_with_logits(
objectness[sampled_inds], labels[sampled_inds]
)
return objectness_loss, box_loss
