YOLOv3的损失 用pytorch复现

YOLOv3的损失函数主要包括三个部分：物体置信度损失、类别损失和边界框损失。其中，物体置信度损失和类别损失采用交叉熵损失函数计算，边界框损失采用均方误差损失函数计算。

以下是用PyTorch复现YOLOv3的损失函数的代码：

import torch
import torch.nn.functional as F

def yolo_loss(pred, target, anchors):
    """
    pred: [batch_size, num_anchors*(5+num_classes), grid_size, grid_size]
    target: [batch_size, max_objects, 6]
    anchors: [num_anchors, 2]
    """
    batch_size, num_anchors_times, grid_size, _ = pred.size()
    num_anchors = len(anchors)

    # 将pred转换成[batch_size, num_anchors, 5+num_classes, grid_size, grid_size]
    pred = pred.view(batch_size, num_anchors, num_anchors_times//num_anchors, grid_size, grid_size).permute(0, 1, 3, 4, 2).contiguous()

    # 获取预测框的中心点、宽度、高度和物体置信度
    pred_xy = torch.sigmoid(pred[..., :2])  # [batch_size, num_anchors, grid_size, grid_size, 2]
    pred_wh = torch.exp(pred[..., 2:4]) * anchors.view(1, num_anchors, 1, 1, 2)  # [batch_size, num_anchors, grid_size, grid_size, 2]
    pred_obj = torch.sigmoid(pred[..., 4:5])  # [batch_size, num_anchors, grid_size, grid_size, 1]
    pred_cls = pred[..., 5:]  # [batch_size, num_anchors, grid_size, grid_size, num_classes]

    # 获取目标框的中心点、宽度、高度和物体置信度
    target_xy = target[..., :2].unsqueeze(1)  # [batch_size, 1, max_objects, 2]
    target_wh = target[..., 2:4].unsqueeze(1)  # [batch_size, 1, max_objects, 2]
    target_obj = target[..., 4:5].unsqueeze(1)  # [batch_size, 1, max_objects, 1]
    target_cls = target[..., 5:].unsqueeze(1)  # [batch_size, 1, max_objects, num_classes]

    # 计算预测框与目标框之间的IOU
    pred_min = pred_xy - pred_wh / 2
    pred_max = pred_xy + pred_wh / 2
    target_min = target_xy - target_wh / 2
    target_max = target_xy + target_wh / 2
    inter_min = torch.max(pred_min, target_min)
    inter_max = torch.min(pred_max, target_max)
    inter_wh = torch.clamp(inter_max - inter_min, min=0)
    inter_area = inter_wh[..., 0] * inter_wh[..., 1]
    pred_area = pred_wh[..., 0] * pred_wh[..., 1]
    target_area = target_wh[..., 0] * target_wh[..., 1]
    iou = inter_area / (pred_area + target_area - inter_area + 1e-16)

    # 计算物体置信度损失
    obj_mask = (iou > 0.5).float() * target_obj
    noobj_mask = (iou < 0.4).float() * (1 - target_obj)
    obj_loss = F.binary_cross_entropy(pred_obj, obj_mask, reduction='sum')
    noobj_loss = F.binary_cross_entropy(pred_obj, noobj_mask, reduction='sum')

    # 计算类别损失
    cls_mask = target_obj
    cls_loss = F.binary_cross_entropy_with_logits(pred_cls, cls_mask, reduction='none')
    cls_loss = cls_loss * cls_mask
    cls_loss = cls_loss.sum(dim=-1).sum(dim=-1).sum(dim=-1)

    # 计算边界框损失
    coord_mask = target_obj.unsqueeze(-1)
    coord_loss = F.mse_loss(pred_xy * coord_mask, target_xy * coord_mask, reduction='sum')
    coord_loss += F.mse_loss(pred_wh.sqrt() * coord_mask, target_wh.sqrt() * coord_mask, reduction='sum')

    # 总损失
    loss = obj_loss + noobj_loss + cls_loss + coord_loss

    return loss

上述代码中，`pred`表示预测值，`target`表示目标值，`anchors`表示锚点框。其中，`pred`的形状为`[batch_size, num_anchors_times*(5+num_classes), grid_size, grid_size]`，`target`的形状为`[batch_size, max_objects, 6]`，`anchors`的形状为`[num_anchors, 2]`。在函数内部，首先将`pred`转换成`[batch_size, num_anchors, 5+num_classes, grid_size, grid_size]`的形状，然后获取预测框的中心点、宽度、高度和物体置信度，获取目标框的中心点、宽度、高度和物体置信度，计算预测框与目标框之间的IOU，最后根据公式计算物体置信度损失、类别损失和边界框损失，并将它们相加得到总损失。