transform-Unet代码

我无法回答这个问题，但是我可以提供一些关于Transform-Unet的信息：Transform-Unet是一种特殊的卷积神经网络，用于图像分割，它使用U-Net架构来实现。它使用深度可分离卷积，以及一种特殊的多尺度上采样模块，以提高分割精度。

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torch 实现unet 医学分割代码

以下是使用PyTorch实现UNet医学图像分割的示例代码：

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

class DoubleConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(DoubleConv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        return self.conv(x)

class UNet(nn.Module):
    def __init__(self, in_channels=1, out_channels=1, features=[64, 128, 256, 512]):
        super(UNet, self).__init__()
        self.ups = nn.ModuleList()
        self.downs = nn.ModuleList()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)

        # 定义编码器
        for feature in features:
            self.downs.append(DoubleConv(in_channels, feature))
            in_channels = feature

        # 定义解码器
        for feature in reversed(features):
            self.ups.append(nn.ConvTranspose2d(feature*2, feature, kernel_size=2, stride=2))
            self.ups.append(DoubleConv(feature*2, feature))

        self.bottleneck = DoubleConv(features[-1], features[-1]*2)
        self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)

    def forward(self, x):
        skip_connections = []

        # 编码器
        for down in self.downs:
            x = down(x)
            skip_connections.append(x)
            x = self.pool(x)

        # 中间层
        x = self.bottleneck(x)

        # 解码器
        skip_connections = skip_connections[::-1]
        for idx in range(0, len(self.ups), 2):
            x = self.ups[idx](x)
            skip_connection = skip_connections[idx//2]

            if x.shape != skip_connection.shape:
                x = transforms.CenterCrop([skip_connection.shape[2], skip_connection.shape[3]])(x)

            x = torch.cat((skip_connection, x), dim=1)
            x = self.ups[idx+1](x)

        return self.final_conv(x)

# 加载数据集
train_transforms = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor()
])

train_dataset = ImageFolder("data/train", transform=train_transforms)
train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)

# 定义模型
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = UNet().to(device)

# 定义损失函数和优化器
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(model.parameters(), lr=1e-4)

# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
    for data, target in train_loader:
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()

# 测试模型
test_transforms = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor()
])

test_dataset = ImageFolder("data/test", transform=test_transforms)
test_loader = DataLoader(test_dataset, batch_size=4, shuffle=False)

with torch.no_grad():
    for data, target in test_loader:
        data, target = data.to(device), target.to(device)
        output = model(data)
        binary_output = torch.sigmoid(output)
        # 计算预测结果的准确率、召回率、F1值等指标

以上是使用PyTorch实现UNet医学图像分割的代码示例，其中包含了数据预处理、模型定义、损失函数和优化器的定义、模型训练和测试等步骤。需要根据实际情况进行修改和调整。

用python unet 写语义分割代码

下面是一个使用 Python 和 Unet 来实现语义分割的示例代码：

import numpy as np
import os
import skimage.io as io
import skimage.transform as trans
import numpy as np
from keras.models import *
from keras.layers import *
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras import backend as keras

def unet(pretrained_weights = None,input_size = (256,256,1)):
    inputs = Input(input_size)
    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
    conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

    conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
    conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
    drop5 = Dropout(0.5)(conv5)

    up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
    merge6 = concatenate([drop4,up6], axis = 3)
    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same',