深度学习目标检测模型代码

以下是常见的深度学习目标检测模型代码示例：

1. Faster R-CNN

Faster R-CNN 是一种流行的目标检测模型，其代码示例如下：

import torch
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt

# Define the model
class FasterRCNN(nn.Module):
    def __init__(self):
        super(FasterRCNN, self).__init__()
        self.feature_extractor = torchvision.models.vgg16(pretrained=True).features
        self.rpn = RegionProposalNetwork()
        self.roi_pooling = RoIPooling()
        self.classifier = nn.Sequential(nn.Linear(4096, 4096),
                                         nn.ReLU(),
                                         nn.Dropout(),
                                         nn.Linear(4096, 4096),
                                         nn.ReLU(),
                                         nn.Dropout(),
                                         nn.Linear(4096, 21))
        
    def forward(self, x):
        features = self.feature_extractor(x)
        rpn_locs, rpn_scores, rois, roi_indices = self.rpn(features)
        pooled_features = self.roi_pooling(features, rois, roi_indices)
        pooled_features = pooled_features.view(pooled_features.size(0), -1)
        cls_scores = self.classifier(pooled_features)
        return cls_scores, rpn_locs, rpn_scores, rois, roi_indices

# Define the loss function
class FasterRCNNLoss(nn.Module):
    def __init__(self):
        super(FasterRCNNLoss, self).__init__()
        self.bbox_loss = nn.SmoothL1Loss()
        self.cls_loss = nn.CrossEntropyLoss()

    def forward(self, cls_scores, rpn_locs, rpn_scores, gt_boxes, gt_labels):
        fg_indices = (gt_labels > 0).nonzero().squeeze()
        bg_indices = (gt_labels == 0).nonzero().squeeze()
        
        # Calculate the classification loss
        cls_loss = self.cls_loss(cls_scores, gt_labels)
        
        # Calculate the regression loss
        gt_bbox = bbox_transform(gt_boxes)
        pos_bbox = gt_bbox[fg_indices]
        pos_rpn_locs = rpn_locs[fg_indices]
        bbox_loss = self.bbox_loss(pos_rpn_locs, pos_bbox)
        
        # Calculate the region proposal network loss
        rpn_labels = generate_rpn_labels(gt_boxes, gt_labels, rpn_scores.size(1))
        rpn_scores = rpn_scores.view(-1, 2)
        rpn_labels = rpn_labels.view(-1)
        rpn_loss = F.cross_entropy(rpn_scores, rpn_labels)
        
        return cls_loss + bbox_loss + rpn_loss

# Train the model
model = FasterRCNN()
criterion = FasterRCNNLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        cls_scores, rpn_locs, rpn_scores, rois, roi_indices = model(inputs)
        loss = criterion(cls_scores, rpn_locs, rpn_scores, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('[%d] loss: %.3f' % (epoch + 1, running_loss / len(trainloader)))

2. YOLOv3

YOLOv3 是另一种流行的目标检测模型，其代码示例如下：

import torch
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt

# Define the model
class YOLOv3(nn.Module):
    def __init__(self):
        super(YOLOv3, self).__init__()
        self.backbone = Darknet53()
        self.detector = YOLOv3Detector()
        
    def forward(self, x):
        features = self.backbone(x)
        output = self.detector(features)
        return output

# Define the loss function
class YOLOv3Loss(nn.Module):
    def __init__(self, num_classes=80):
        super(YOLOv3Loss, self).__init__()
        self.num_classes = num_classes
        self.obj_scale = 1
        self.noobj_scale = 100
        self.class_scale = 1
        self.coord_scale = 5
        
    def forward(self, output, target):
        # Calculate the objectness loss
        obj_mask = target[..., 4] > 0
        noobj_mask = target[..., 4] == 0
        obj_loss = F.binary_cross_entropy_with_logits(output[..., 4], target[..., 4], reduction='none')
        noobj_loss = F.binary_cross_entropy_with_logits(output[..., 4], target[..., 4], reduction='none')
        obj_loss = torch.sum(obj_loss[obj_mask]) * self.obj_scale
        noobj_loss = torch.sum(noobj_loss[noobj_mask]) * self.noobj_scale
        
        # Calculate the class loss
        class_loss = F.binary_cross_entropy_with_logits(output[..., 5:], target[..., 5:], reduction='none')
        class_loss = torch.sum(class_loss[obj_mask]) * self.class_scale
        
        # Calculate the regression loss
        xy_loss = F.binary_cross_entropy_with_logits(output[..., :2], target[..., :2], reduction='none')
        wh_loss = F.mse_loss(output[..., 2:4], target[..., 2:4], reduction='none')
        coord_loss = torch.sum(xy_loss[obj_mask]) + torch.sum(wh_loss[obj_mask])
        coord_loss = coord_loss * self.coord_scale
        
        # Calculate the total loss
        total_loss = obj_loss + noobj_loss + class_loss + coord_loss
        return total_loss

# Train the model
model = YOLOv3()
criterion = YOLOv3Loss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        output = model(inputs)
        loss = criterion(output, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('[%d] loss: %.3f' % (epoch + 1, running_loss / len(trainloader)))