my_fastrcnn_loss_with_focal_loss.py

import torch.nn.functional as F
from torch import nn

class FocalLoss(nn.Module):
    
    def __init__(self, weight=None,
                 gamma=2.5, reduction='mean'):
        nn.Module.__init__(self)
        self.weight=weight
        self.gamma = gamma
        self.reduction = reduction
        
    def forward(self, input_tensor, target_tensor):
        log_prob = F.log_softmax(input_tensor, dim=-1)
        prob = torch.exp(log_prob)
        return F.nll_loss(
            ((1 - prob) ** self.gamma) * log_prob, 
            target_tensor, 
            weight=self.weight,
            reduction = self.reduction
        )

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.
    Args:
        class_logits (Tensor)
        box_regression (Tensor)
        labels (list[BoxList])
        regression_targets (Tensor)
    Returns:
        classification_loss (Tensor)
        box_loss (Tensor)
    """

    labels = torch.cat(labels, dim=0)
    regression_targets = torch.cat(regression_targets, dim=0)

    #この部分をfocal_lossへ変更する
    #classification_loss = F.cross_entropy(class_logits, labels)
    focal=FocalLoss()
    classification_loss = focal(class_logits, labels)
    #変更はここまで

    # 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.where(labels > 0)[0]
    labels_pos = labels[sampled_pos_inds_subset]
    N, num_classes = class_logits.shape
    box_regression = box_regression.reshape(N, box_regression.size(-1) // 4, 4)

    box_loss = F.smooth_l1_loss(
        box_regression[sampled_pos_inds_subset, labels_pos],
        regression_targets[sampled_pos_inds_subset],
        beta=1 / 9,
        reduction='sum',
    )
    box_loss = box_loss / labels.numel()

    return classification_loss, box_loss