YOLOv3：基于 PyTorch 的目标检测模型实现
引言
YOLOv3（You Only Look Once）是一种流行的单阶段目标检测算法，它能够直接在输入图像上预测边界框和类别概率。YOLOv3 的优势在于其高效性和准确性，使其在实时目标检测任务中表现出色。本文将详细介绍如何使用 PyTorch 实现 YOLOv3 模型，并提供完整的代码实现。
1. YOLOv3 简介
YOLOv3 是 YOLO 系列算法的第三个版本，它在前两个版本的基础上进行了改进，提高了检测的准确性和速度。YOLOv3 的主要特点包括：
- 单阶段检测：YOLOv3 直接在输入图像上预测边界框和类别概率，无需生成候选框。
- 多尺度检测：YOLOv3 使用三个不同尺度的特征图进行检测，能够检测不同大小的目标。
- 高效率：YOLOv3 的设计使其能够在实时应用中高效运行。
2. 环境准备
在开始实现之前，确保你已经安装了以下必要的依赖库：
pip install torch numpy matplotlib
3. 代码实现
3.1 导入必要的库
from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from utils.parse_config import * # 用于解析配置文件
from utils.utils import build_targets, to_cpu, non_max_suppression # 用于目标构建和后处理
import matplotlib.pyplot as plt
import matplotlib.patches as patches
3.2 构建模块列表
create_modules 函数根据配置文件构建网络层：
def create_modules(module_defs):
"""
Constructs module list of layer blocks from module configuration in module_defs
"""
hyperparams = module_defs.pop(0) # 获取超参数
output_filters = [int(hyperparams["channels"])] # 输出特征图的个数，也是卷积核的个数
module_list = nn.ModuleList() # 用于存储网络层的 ModuleList
for module_i, module_def in enumerate(module_defs):
modules = nn.Sequential() # 用于线性堆叠网络层
if module_def["type"] == "convolutional":
# 获取卷积层的参数
bn = int(module_def["batch_normalize"])
filters = int(module_def["filters"]) # 卷积核的个数
kernel_size = int(module_def["size"])
pad = (kernel_size - 1) // 2
# 添加卷积层
modules.add_module(
f"conv_{module_i}", # 卷积层名称
nn.Conv2d(
in_channels=output_filters[-1], # 输入特征图的数量
out_channels=filters, # 输出特征图的数量
kernel_size=kernel_size, # 卷积核的大小
stride=int(module_def["stride"]), # 卷积核滑动的步长
padding=pad, # 填充的层数
bias=not bn, # 是否添加偏置项
),
)
if bn:
# 添加批量归一化层
modules.add_module(f"batch_norm_{module_i}", nn.BatchNorm2d(filters, momentum=0.9))
if module_def["activation"] == "leaky":
# 添加 LeakyReLU 激活函数
modules.add_module(f"leaky_{module_i}", nn.LeakyReLU(0.1))
elif module_def["type"] == "maxpool":
# 获取最大池化层的参数
kernel_size = int(module_def["size"])
stride = int(module_def["stride"])
if kernel_size == 2 and stride == 1:
# 添加零填充层
modules.add_module(f"_debug_padding_{module_i}", nn.ZeroPad2d((0, 1, 0, 1)))
# 添加最大池化层
maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride, padding=int((kernel_size - 1) // 2))
modules.add_module(f"maxpool_{module_i}", maxpool)
elif module_def["type"] == "upsample":
# 获取上采样层的参数
upsample = Upsample(scale_factor=int(module_def["stride"]), mode="nearest")
modules.add_module(f"upsample_{module_i}", upsample)
elif module_def["type"] == "route":
# 获取路由层的参数
layers = [int(x) for x in module_def["layers"].split(",")]
filters = sum([output_filters[1:][i] for i in layers])
modules.add_module(f"route_{module_i}", EmptyLayer()) # 添加空层
elif module_def["type"] == "shortcut":
# 获取残差层的参数
filters = output_filters[1:][int(module_def["from"])]
modules.add_module(f"shortcut_{module_i}", EmptyLayer()) # 添加空层
elif module_def["type"] == "yolo":
# 获取 YOLO 层的参数
anchor_idxs = [int(x) for x in module_def["mask"].split(",")]
anchors = [int(x) for x in module_def["anchors"].split(",")]
anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
anchors = [anchors[i] for i in anchor_idxs]
num_classes = int(module_def["classes"])
img_size = int(hyperparams["height"])
# 定义检测层
yolo_layer = YOLOLayer(anchors, num_classes, img_size)
modules.add_module(f"yolo_{module_i}", yolo_layer)
# 将当前模块添加到模块列表中
module_list.append(modules)
output_filters.append(filters) # 保存每一层的卷积核个数
return hyperparams, module_list
3.3 上采样层
Upsample 类实现上采样操作：
class Upsample(nn.Module):
""" nn.Upsample is deprecated """
def __init__(self, scale_factor, mode="nearest"):
super(Upsample, self).__init__()
self.scale_factor = scale_factor # 上采样比例
self.mode = mode # 上采样模式
def forward(self, x):
# 使用 PyTorch 的 interpolate 函数进行上采样
x = F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
return x
3.4 空层
EmptyLayer 类用于占位，例如在 route 和 shortcut 层中：
class EmptyLayer(nn.Module):
"""Placeholder for 'route' and 'shortcut' layers"""
def __init__(self):
super(EmptyLayer, self).__init__()
3.5 YOLO 检测层
YOLOLayer 类负责预测边界框、置信度和类别概率：
class YOLOLayer(nn.Module):
"""Detection layer"""
def __init__(self, anchors, num_classes, img_dim=416):
super(YOLOLayer, self).__init__()
self.anchors = anchors # 锚框
self.num_anchors = len(anchors) # 锚框数量
self.num_classes = num_classes # 类别数量
self.ignore_thres = 0.5 # 忽略阈值
self.mse_loss = nn.MSELoss() # 均方误差损失
self.bce_loss = nn.BCELoss() # 二元交叉熵损失
self.obj_scale = 1 # 有目标的损失权重
self.noobj_scale = 100 # 无目标的损失权重
self.metrics = {} # 用于存储评估指标
self.img_dim = img_dim # 输入图像尺寸
self.grid_size = 0 # 网格大小
def compute_grid_offsets(self, grid_size, cuda=True):
"""计算网格偏移量"""
self.grid_size = grid_size
g = self.grid_size
FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
self.stride = self.img_dim / self.grid_size # 每个网格的像素大小
# 计算每个网格的偏移量
self.grid_x = torch.arange(g).repeat(g, 1).view([1, 1, g, g]).type(FloatTensor)
self.grid_y = torch.arange(g).repeat(g, 1).t().view([1, 1, g, g]).type(FloatTensor)
self.scaled_anchors = FloatTensor([(a_w / self.stride, a_h / self.stride) for a_w, a_h in self.anchors])
self.anchor_w = self.scaled_anchors[:, 0:1].view((1, self.num_anchors, 1, 1))
self.anchor_h = self.scaled_anchors[:, 1:2].view((1, self.num_anchors, 1, 1))
def forward(self, x, targets=None, img_dim=None):
"""前向传播"""
FloatTensor = torch.cuda.FloatTensor if x.is_cuda else torch.FloatTensor
LongTensor = torch.cuda.LongTensor if x.is_cuda else torch.LongTensor
ByteTensor = torch.cuda.ByteTensor if x.is_cuda else torch.ByteTensor
self.img_dim = img_dim
num_samples = x.size(0)
grid_size = x.size(2)
# 重塑预测张量
prediction = (
x.view(num_samples, self.num_anchors, self.num_classes + 5, grid_size, grid_size)
.permute(0, 1, 3, 4, 2)
.contiguous()
)
# 提取预测结果
x = torch.sigmoid(prediction[..., 0]) # 中心点 x
y = torch.sigmoid(prediction[..., 1]) # 中心点 y
w = prediction[..., 2] # 宽度
h = prediction[..., 3] # 高度
pred_conf = torch.sigmoid(prediction[..., 4]) # 置信度
pred_cls = torch.sigmoid(prediction[..., 5:]) # 类别预测
# 如果网格大小不匹配，重新计算偏移量
if grid_size != self.grid_size:
self.compute_grid_offsets(grid_size, cuda=x.is_cuda)
# 添加偏移量并缩放锚框
pred_boxes = FloatTensor(prediction[..., :4].shape)
pred_boxes[..., 0] = x.data + self.grid_x
pred_boxes[..., 1] = y.data + self.grid_y
pred_boxes[..., 2] = torch.exp(w.data) * self.anchor_w
pred_boxes[..., 3] = torch.exp(h.data) * self.anchor_h
# 拼接最终输出
output = torch.cat(
(
pred_boxes.view(num_samples, -1, 4) * self.stride,
pred_conf.view(num_samples, -1, 1),
pred_cls.view(num_samples, -1, self.num_classes),
),
-1,
)
if targets is None:
return output, 0
else:
# 构建目标张量
iou_scores, class_mask, obj_mask, noobj_mask, tx, ty, tw, th, tcls, tconf = build_targets(
pred_boxes=pred_boxes,
pred_cls=pred_cls,
target=targets,
anchors=self.scaled_anchors,
ignore_thres=self.ignore_thres,
)
# 计算损失
loss_x = self.mse_loss(x[obj_mask], tx[obj_mask])
loss_y = self.mse_loss(y[obj_mask], ty[obj_mask])
loss_w = self.mse_loss(w[obj_mask], tw[obj_mask])
loss_h = self.mse_loss(h[obj_mask], th[obj_mask])
loss_conf_obj = self.bce_loss(pred_conf[obj_mask], tconf[obj_mask])
loss_conf_noobj = self.bce_loss(pred_conf[noobj_mask], tconf[noobj_mask])
loss_conf = self.obj_scale * loss_conf_obj + self.noobj_scale * loss_conf_noobj
loss_cls = self.bce_loss(pred_cls[obj_mask], tcls[obj_mask])
total_loss = loss_x + loss_y + loss_w + loss_h + loss_conf + loss_cls
# 计算评估指标
cls_acc = 100 * class_mask[obj_mask].mean()
conf_obj = pred_conf[obj_mask].mean()
conf_noobj = pred_conf[noobj_mask].mean()
conf50 = (pred_conf > 0.5).float()
iou50 = (iou_scores > 0.5).float()
iou75 = (iou_scores > 0.75).float()
detected_mask = conf50 * class_mask * tconf
precision = torch.sum(iou50 * detected_mask) / (conf50.sum() + 1e-16)
recall50 = torch.sum(iou50 * detected_mask) / (obj_mask.sum() + 1e-16)
recall75 = torch.sum(iou75 * detected_mask) / (obj_mask.sum() + 1e-16)
self.metrics = {
"loss": to_cpu(total_loss).item(),
"x": to_cpu(loss_x).item(),
"y": to_cpu(loss_y).item(),
"w": to_cpu(loss_w).item(),
"h": to_cpu(loss_h).item(),
"conf": to_cpu(loss_conf).item(),
"cls": to_cpu(loss_cls).item(),
"cls_acc": to_cpu(cls_acc).item(),
"recall50": to_cpu(recall50).item(),
"recall75": to_cpu(recall75).item(),
"precision": to_cpu(precision).item(),
"conf_obj": to_cpu(conf_obj).item(),
"conf_noobj": to_cpu(conf_noobj).item(),
"grid_size": grid_size,
}
return output, total_loss
3.6 YOLOv3 模型
Darknet 类是 YOLOv3 模型的主体，负责加载配置文件、构建网络、前向传播、加载和保存权重：
class Darknet(nn.Module):
"""YOLOv3 object detection model"""
def __init__(self, config_path, img_size=416):
super(Dark