yolov5 loss.py 代码详解

yolov5 loss.py 代码详解

yolov5 loss.py 是 YOLOv5 模型中的一个关键文件，主要负责计算模型的损失函数。下面是该文件的代码详解：

1. 导入必要的库

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

2. 定义损失函数类

class YOLOv5Loss(nn.Module):
    def __init__(self, anchors, strides, iou_threshold, num_classes, img_size):
        super(YOLOv5Loss, self).__init__()
        self.anchors = anchors
        self.strides = strides
        self.iou_threshold = iou_threshold
        self.num_classes = num_classes
        self.img_size = img_size

该类继承自 nn.Module，包含了一些必要的参数，如 anchors，strides，iou_threshold，num_classes 和 img_size。

3. 定义计算损失函数的方法

def forward(self, x, targets=None):
    bs, _, ny, nx = x.shape  # batch size, channels, grid size
    na = self.anchors.shape[]  # number of anchors
    stride = self.img_size / max(ny, nx)  # compute stride
    yolo_out, grid = [], []
    for i in range(3):
        yolo_out.append(x[i].view(bs, na, self.num_classes + 5, ny, nx).permute(, 1, 3, 4, 2).contiguous())
        grid.append(torch.meshgrid(torch.arange(ny), torch.arange(nx)))
        ny, nx = ny // 2, nx // 2
    loss, nGT, nCorrect, mask = , , , torch.zeros(bs, na, ny, nx)
    for i in range(3):
        y, g = yolo_out[i], grid[i]
        y[..., :2] = (y[..., :2].sigmoid() + g) * stride  # xy
        y[..., 2:4] = y[..., 2:4].exp() * self.anchors[i].to(x.device)  # wh
        y[..., :4] *= mask.unsqueeze(-1).to(x.device)
        y[..., 4:] = y[..., 4:].sigmoid()
        if targets is not None:
            na_t, _, _, _, _ = targets.shape
            t = targets[..., 2:6] * stride
            gxy = g.unsqueeze().unsqueeze(-1).to(x.device)
            gi, gj = gxy[..., ], gxy[..., 1]
            b = t[..., :4]
            iou = box_iou(b, y[..., :4])  # iou
            iou_max, _ = iou.max(2)
            # Match targets to anchors
            a = torch.arange(na_t).view(-1, 1).repeat(1, na)
            t = targets[a, iou_max >= self.iou_threshold]  # select targets
            # Compute losses
            if len(t):
                # xy loss
                xy = y[..., :2] - gxy
                xy_loss = (torch.abs(xy) - .5).pow(2) * mask.unsqueeze(-1).to(x.device)
                # wh loss
                wh = torch.log(y[..., 2:4] / self.anchors[i].to(x.device) + 1e-16)
                wh_loss = F.huber_loss(wh, t[..., 2:4], reduction='none') * mask.unsqueeze(-1).to(x.device)
                # class loss
                tcls = t[..., ].long()
                tcls_onehot = torch.zeros_like(y[..., 5:])
                tcls_onehot[torch.arange(len(t)), tcls] = 1
                cls_loss = F.binary_cross_entropy(y[..., 5:], tcls_onehot, reduction='none') * mask.unsqueeze(-1).to(x.device)
                # objectness loss
                obj_loss = F.binary_cross_entropy(y[..., 4:5], iou_max.unsqueeze(-1), reduction='none') * mask.to(x.device)
                # total loss
                loss += (xy_loss + wh_loss + cls_loss + obj_loss).sum()
                nGT += len(t)
                nCorrect += (iou_max >= self.iou_threshold).sum().item()
        mask = torch.zeros(bs, na, ny, nx)
        if targets is not None:
            t = targets[..., 2:6] * stride
            gi, gj = g[..., ], g[..., 1]
            a = targets[..., 1].long()
            mask[torch.arange(bs), a, gj, gi] = 1
    return loss, nGT, nCorrect

该方法接受输入 x 和 targets，其中 x 是模型的输出，targets 是真实标签。该方法首先根据输入 x 的形状计算出 batch size，channels，grid size 和 number of anchors 等参数，然后根据这些参数计算出 stride 和 grid。接着，该方法将输入 x 分成三个部分，每个部分都包含了 na 个 anchors 和 self.num_classes + 5 个通道。然后，该方法将每个部分的输出转换成合适的形状，并计算出每个 anchor 的中心点坐标和宽高。接着，该方法根据 targets 计算出损失函数，包括 xy loss，wh loss，class loss 和 objectness loss。最后，该方法返回损失函数的值，以及 nGT 和 nCorrect。

4. 定义计算 box iou 的方法

def box_iou(box1, box2):
    """
    Returns the IoU of two bounding boxes
    """
    b1_x1, b1_y1, b1_x2, b1_y2 = box1[..., ], box1[..., 1], box1[..., 2], box1[..., 3]
    b2_x1, b2_y1, b2_x2, b2_y2 = box2[..., ], box2[..., 1], box2[..., 2], box2[..., 3]
    inter_rect_x1 = torch.max(b1_x1, b2_x1)
    inter_rect_y1 = torch.max(b1_y1, b2_y1)
    inter_rect_x2 = torch.min(b1_x2, b2_x2)
    inter_rect_y2 = torch.min(b1_y2, b2_y2)
    inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=) * torch.clamp(inter_rect_y2 - inter_rect_y1 + 1, min=)
    b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)
    b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)
    iou = inter_area / (b1_area + b2_area - inter_area + 1e-16)
    return iou

该方法接受两个参数 box1 和 box2，分别表示两个 bounding box 的坐标。该方法首先计算出两个 bounding box 的交集和并集，然后计算出它们的 IoU。

以上就是 yolov5 loss.py 代码的详解。