def test(model, path): model.eval() mean_loss = [] for s in ['val', 'test']: image_root = '{}/{}'.format(path, s) gt_root = '{}/{}'.format(path, s) test_loader = test_dataset(image_root, gt_root) dice_bank = [] iou_bank = [] loss_bank = [] acc_bank = [] for i in range(test_loader.size): image, gt = test_loader.load_data() image = image.cuda() with torch.no_grad(): _, _, res = model(image) loss = structure_loss(res, torch.tensor(gt).unsqueeze(0).unsqueeze(0).cuda()) res = res.sigmoid().data.cpu().numpy().squeeze() gt = 1*(gt>0.5) res = 1*(res > 0.5) dice = mean_dice_np(gt, res) iou = mean_iou_np(gt, res) acc = np.sum(res == gt) / (res.shape[0]*res.shape[1]) loss_bank.append(loss.item()) dice_bank.append(dice) iou_bank.append(iou) acc_bank.append(acc) print('{} Loss: {:.4f}, Dice: {:.4f}, IoU: {:.4f}, Acc: {:.4f}'. format(s, np.mean(loss_bank), np.mean(dice_bank), np.mean(iou_bank), np.mean(acc_bank))) mean_loss.append(np.mean(loss_bank)) return mean_loss[0] 中文解释每一句

这段代码是用于测试一个图像分割模型的性能。首先，模型被设置为评估模式，接着对于验证集和测试集分别进行测试。数据集的路径被设置为image_root和gt_root。然后，使用test_dataset函数加载测试集的数据。接下来，对于每一个样本，图像和标注被加载并送入模型进行预测。计算预测结果和标注之间的结构损失并将结果转换为numpy数组。计算Dice, IoU和准确率等性能度量，并将其存储在相应的bank中。最后，输出测试集上的平均损失，Dice, IoU和准确率等性能度量。整个测试过程是在无梯度下进行的。函数的返回值是验证集上的平均损失。

相关问题

用pytorch基于深度残差对图片能见度检测的代码

以下是基于PyTorch实现的深度残差网络用于图像能见度检测的代码。

首先，我们需要导入必要的库和模块：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from PIL import Image
import numpy as np
import os

接下来，我们需要定义一个数据集类，以便读取并处理数据：

class VisibilityDataset(Dataset):
    def __init__(self, image_dir, label_dir, transform=None):
        self.image_dir = image_dir
        self.label_dir = label_dir
        self.transform = transform
        self.image_files = os.listdir(image_dir)

    def __len__(self):
        return len(self.image_files)

    def __getitem__(self, idx):
        image_path = os.path.join(self.image_dir, self.image_files[idx])
        label_path = os.path.join(self.label_dir, self.image_files[idx][:-4] + '_visibility.png')
        image = Image.open(image_path).convert('RGB')
        label = Image.open(label_path).convert('L')
        if self.transform:
            image = self.transform(image)
        return image, label

然后，我们需要定义一个深度残差网络模型：

class ResNet(nn.Module):
    def __init__(self):
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.layer1 = self._make_layer(64, 2)
        self.layer2 = self._make_layer(128, 2, stride=2)
        self.layer3 = self._make_layer(256, 2, stride=2)
        self.layer4 = self._make_layer(512, 2, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, 1)

    def _make_layer(self, planes, blocks, stride=1):
        layers = []
        layers.append(nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False))
        layers.append(nn.BatchNorm2d(planes))
        layers.append(nn.ReLU(inplace=True))
        for i in range(1, blocks):
            layers.append(nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False))
            layers.append(nn.BatchNorm2d(planes))
            layers.append(nn.ReLU(inplace=True))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

接下来，我们需要设置一些超参数：

batch_size = 32
learning_rate = 0.001
num_epochs = 10

然后，我们需要定义数据预处理：

transform = transforms.Compose([
    transforms.Resize((256,256)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])

接下来，我们可以初始化数据集并分割数据集：

data_dir = './data'
train_image_dir = os.path.join(data_dir, 'train_images')
train_label_dir = os.path.join(data_dir, 'train_labels')
test_image_dir = os.path.join(data_dir, 'test_images')
test_label_dir = os.path.join(data_dir, 'test_labels')

train_dataset = VisibilityDataset(train_image_dir, train_label_dir, transform=transform)
test_dataset = VisibilityDataset(test_image_dir, test_label_dir, transform=transform)

train_size = int(0.8 * len(train_dataset))
val_size = len(train_dataset) - train_size
train_dataset, val_dataset = torch.utils.data.random_split(train_dataset, [train_size, val_size])

接下来，我们可以初始化数据加载器：

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

然后，我们可以初始化模型、损失函数和优化器：

model = ResNet()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

接下来，我们可以训练模型并在验证集上进行评估：

for epoch in range(num_epochs):
    model.train()
    running_loss = 0.0
    for i, (images, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels.float())
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 10 == 9:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 10))
            running_loss = 0.0

    model.eval()
    val_loss = 0.0
    with torch.no_grad():
        for images, labels in val_loader:
            outputs = model(images)
            loss = criterion(outputs, labels.float())
            val_loss += loss.item()
    print('Epoch %d: validation loss: %.3f' % (epoch + 1, val_loss / len(val_loader)))

print('Finished training')

最后，我们可以在测试集上进行评估：

model.eval()
test_loss = 0.0
with torch.no_grad():
    for images, labels in test_loader:
        outputs = model(images)
        loss = criterion(outputs, labels.float())
        test_loss += loss.item()
print('Test loss: %.3f' % (test_loss / len(test_loader)))

这就是基于PyTorch实现的深度残差网络用于图像能见度检测的代码。

python编程4个函数。dataset函数中写两个加载数据集的类。一个用于加载训练集和验证集，对coco数据集的单张自然图像进行预处理，返回tensor形式的自然图像。另一个用于加载测试集，对TNO数据集的红外与可见光图像进行预处理，返回tensor形式红外与可见光图像。net函数中构建出此融合方法的网络,在初始化函数中定义各种神经网络操作,并编写encoder、fusion和decoder函数，分别表示编码器、融合策略和解码器。train函数需要实例化训练网络、dataset和dataloader,设置优化器与训练轮数等参数。在训练中对训练集进行多轮训练，每轮训练结束后对验证集进行验证，记录每轮的平均训练损失和平均验证损失，并将模型保存到pth文件夹中。test函数需要实例化测试集、加载模型参数，融合测试集中的红外与可见光图像，并保存到result文件夹中。

很高兴为您提供代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms
from torch.utils.data import Dataset, DataLoader

# 定义加载数据集的类
class COCODataset(Dataset):
    def __init__(self, data_dir, transform=None):
        self.data_dir = data_dir
        self.transform = transform

    def __len__(self):
        return len(os.listdir(self.data_dir))

    def __getitem__(self, idx):
        img_path = os.path.join(self.data_dir, f"{idx}.jpg")
        img = Image.open(img_path).convert("RGB")

        if self.transform:
            img = self.transform(img)

        return img

class TNODataset(Dataset):
    def __init__(self, data_dir, transform=None):
        self.data_dir = data_dir
        self.transform = transform

    def __len__(self):
        return len(os.listdir(self.data_dir))

    def __getitem__(self, idx):
        ir_path = os.path.join(self.data_dir, f"{idx}_ir.jpg")
        vis_path = os.path.join(self.data_dir, f"{idx}_vis.jpg")

        ir_img = Image.open(ir_path).convert("RGB")
        vis_img = Image.open(vis_path).convert("RGB")

        if self.transform:
            ir_img = self.transform(ir_img)
            vis_img = self.transform(vis_img)

        return ir_img, vis_img

# 定义网络结构
class FusionNet(nn.Module):
    def __init__(self):
        super(FusionNet, self).__init__()

        self.encoder = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),

            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),

            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),

            nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )

        self.fusion = nn.Sequential(
            nn.Conv2d(1024, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),

            nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),

            nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
        )

        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),

            nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),

            nn.ConvTranspose2d(32, 3, kernel_size=4, stride=2, padding=1),
            nn.Tanh(),
        )

    def forward(self, ir, vis):
        ir_enc = self.encoder(ir)
        vis_enc = self.encoder(vis)
        enc = torch.cat((ir_enc, vis_enc), dim=1)

        fusion = self.fusion(enc)

        dec = self.decoder(fusion)

        return dec

# 定义训练函数
def train(model, train_loader, val_loader, criterion, optimizer, num_epochs, device):
    best_loss = float('inf')

    for epoch in range(num_epochs):
        train_loss = 0.0
        val_loss = 0.0

        model.train() # 将模型设为训练模式
        for i, data in enumerate(train_loader):
            ir, vis = data
            ir = ir.to(device)
            vis = vis.to(device)

            optimizer.zero_grad()

            outputs = model(ir, vis)

            loss = criterion(outputs, ir)

            loss.backward()

            optimizer.step()

            train_loss += loss.item()

        model.eval() # 将模型设为验证模式
        with torch.no_grad():
            for i, data in enumerate(val_loader):
                ir, vis = data
                ir = ir.to(device)
                vis = vis.to(device)

                outputs = model(ir, vis)

                loss = criterion(outputs, ir)

                val_loss += loss.item()

        train_loss /= len(train_loader)
        val_loss /= len(val_loader)

        print(f"Epoch {epoch+1} - Train Loss: {train_loss:.4f} - Val Loss: {val_loss:.4f}")

        if val_loss < best_loss:
            best_loss = val_loss
            torch.save(model.state_dict(), "pth/model.pth")

# 定义测试函数
def test(model, test_loader, device):
    model.load_state_dict(torch.load("pth/model.pth"))

    model.eval() # 将模型设为测试模式
    with torch.no_grad():
        for i, data in enumerate(test_loader):
            ir, vis = data
            ir = ir.to(device)
            vis = vis.to(device)

            outputs = model(ir, vis)

            img = torch.cat((ir, vis, outputs), dim=0)
            img = transforms.ToPILImage()(img.cpu())
            img.save(f"result/{i}.jpg")

# 数据预处理
transform_train = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

transform_test = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# 实例化数据集和数据加载器
train_val_dataset = COCODataset("data/train_val", transform_train)
test_dataset = TNODataset("data/test", transform_test)

train_dataset, val_dataset = torch.utils.data.random_split(train_val_dataset, [80000, 20000])

train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)
test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False)

# 实例化模型和优化器
model = FusionNet().to(device)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
train(model, train_loader, val_loader, criterion, optimizer, num_epochs=10, device=device)

# 测试模型
test(model, test_loader, device=device)