pytorch手提包图像分割

针对手提包图像分割任务，可以使用FCN（Fully Convolutional Networks）模型进行训练和预测。下面是一个简单的pytorch实现步骤：

1.准备数据集，将训练数据和标签分别放到bag_data和bag_data_mask文件夹下。

2.定义FCN模型，可以使用pytorch内置的预训练模型，如VGG16等，也可以自己定义模型。

3.定义损失函数，这里可以使用交叉熵损失函数。

4.定义优化器，这里可以使用Adam优化器。

5.进行训练，将训练数据输入模型，计算损失函数并反向传播更新模型参数。

6.进行预测，将测试数据输入模型，得到预测结果。

下面是一个简单的代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import ImageFolder
from torchvision.models import vgg16

# 定义FCN模型
class FCN(nn.Module):
    def __init__(self):
        super(FCN, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, 3, padding=100),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, stride=2, ceil_mode=True),
            nn.Conv2d(64, 128, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, stride=2, ceil_mode=True),
            nn.Conv2d(128, 256, 3, padding=1),            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, stride=2, ceil_mode=True),
            nn.Conv2d(256, 512, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, stride=2, ceil_mode=True),
            nn.Conv2d(512, 512, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, stride=2, ceil_mode=True),
        )
        self.classifier = nn.Sequential(
            nn.Conv2d(512, 4096, 7),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Conv2d(4096, 4096, 1),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Conv2d(4096, 2, 1),
            nn.ConvTranspose2d(2, 2, 64, stride=32, bias=False),
        )

    def forward(self, x):
        x = self.features(x)
        x = self.classifier(x)
        return x

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 加载数据集
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
])
train_dataset = ImageFolder('bag_data', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)

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

# 进行预测
test_dataset = ImageFolder('bag_data', transform=transform)
test_loader = DataLoader(test_dataset, batch_size=4, shuffle=False)
model.eval()
with torch.no_grad():
    for data in test_loader:
        inputs, labels = data
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        print(predicted)