今天我们将学习如何准备计算机视觉中最重要的网络之一：U-Net。如果你没有代码和数据集也没关系，可以分别通过下面两个链接进行访问：
代码：
Kaggle的MRI分割数据集：
主要步骤：
-
数据集的探索
-
数据集和Dataloader类的创建
-
架构的创建
-
检查损失（DICE和二元交叉熵）
-
结果
数据集的探索
我们得到了一组（255 x 255）的MRI扫描的2D图像，以及它们相应的必须将每个像素分类为0（健康）或1（肿瘤）。
这里有一些例子：
第一行：肿瘤，第二行：健康主题
数据集和Dataloader类
这是涉及神经网络的每个项目中都会找到的一步。
数据集类
ruby
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
class BrainMriDataset(Dataset):
def __init__(self, df, transforms):
# df contains the paths to all files
self.df = df
# transforms is the set of data augmentation operations we use
self.transforms = transforms
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
image = cv2.imread(self.df.iloc[idx, 1])
mask = cv2.imread(self.df.iloc[idx, 2], 0)
augmented = self.transforms(image=image,
mask=mask)
image = augmented['image'] # Dimension (3, 255, 255)
mask = augmented['mask'] # Dimension (255, 255)
# We notice that the image has one more dimension (3 color channels), so we have to one one "artificial" dimension to the mask to match it
mask = np.expand_dims(mask, axis=0) # Dimension (1, 255, 255)
return image, mask数据加载器
既然我们已经创建了Dataset类来重新整形张量，我们首先需要定义训练集（用于训练模型），验证集（用于监控训练并避免过拟合），以及测试集，最终评估模型在未见数据上的性能。
makefile
# Split df into train_df and val_df
train_df, val_df = train_test_split(df, stratify=df.diagnosis, test_size=0.1)
train_df = train_df.reset_index(drop=True)
val_df = val_df.reset_index(drop=True)
# Split train_df into train_df and test_df
train_df, test_df = train_test_split(train_df, stratify=train_df.diagnosis, test_size=0.15)
train_df = train_df.reset_index(drop=True)
train_dataset = BrainMriDataset(train_df, transforms=transforms)
train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_dataset = BrainMriDataset(val_df, transforms=transforms)
val_dataloader = DataLoader(val_dataset, batch_size=32, shuffle=False)
test_dataset = BrainMriDataset(test_df, transforms=transforms)
test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False)U-Net架构
U-Net架构是用于图像分割任务的强大模型，是卷积神经网络（CNN）的一种类型，其名称来自其U形状的结构。U-Net最初由Olaf Ronneberger等人在2015年的论文中首次开发，标题为"U-Net:用于生物医学图像分割的卷积网络"。
其结构涉及编码（降采样）路径和解码（上采样）路径。U-Net至今仍然是一个非常成功的模型，其成功来自两个主要因素：
-
对称结构（U形状）
-
前向连接（图片上的灰色箭头）
前向连接的主要思想是，随着我们在层中越来越深入，我们会失去有关原始图像的一些信息。然而，我们的任务是对图像进行分割，我们需要精确的图像来对每个像素进行分类。这就是为什么我们在对称解码器层的每一层中重新注入图像的原因。以下是通过Pytorch实现的代码：
ruby
train_dataset = BrainMriDataset(train_df, transforms=transforms)
train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_dataset = BrainMriDataset(val_df, transforms=transforms)
val_dataloader = DataLoader(val_dataset, batch_size=32, shuffle=False)
test_dataset = BrainMriDataset(test_df, transforms=transforms)
test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False)
class UNet(nn.Module):
def __init__(self):
super().__init__()
# Define convolutional layers
# These are used in the "down" path of the U-Net,
# where the image is successively downsampled
self.conv_down1 = double_conv(3, 64)
self.conv_down2 = double_conv(64, 128)
self.conv_down3 = double_conv(128, 256)
self.conv_down4 = double_conv(256, 512)
# Define max pooling layer for downsampling
self.maxpool = nn.MaxPool2d(2)
# Define upsampling layer
self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
# Define convolutional layers
# These are used in the "up" path of the U-Net,
# where the image is successively upsampled
self.conv_up3 = double_conv(256 + 512, 256)
self.conv_up2 = double_conv(128 + 256, 128)
self.conv_up1 = double_conv(128 + 64, 64)
# Define final convolution to output correct number of classes
# 1 because there are only two classes (tumor or not tumor)
self.last_conv = nn.Conv2d(64, 1, kernel_size=1)
def forward(self, x):
# Forward pass through the network
# Down path
conv1 = self.conv_down1(x)
x = self.maxpool(conv1)
conv2 = self.conv_down2(x)
x = self.maxpool(conv2)
conv3 = self.conv_down3(x)
x = self.maxpool(conv3)
x = self.conv_down4(x)
# Up path
x = self.upsample(x)
x = torch.cat([x, conv3], dim=1)
x = self.conv_up3(x)
x = self.upsample(x)
x = torch.cat([x, conv2], dim=1)
x = self.conv_up2(x)
x = self.upsample(x)
x = torch.cat([x, conv1], dim=1)
x = self.conv_up1(x)
# Final output
out = self.last_conv(x)
out = torch.sigmoid(out)
return out损失和评估标准
与每个神经网络一样，都有一个目标函数，一种损失，我们通过梯度下降最小化它。我们还引入了评估标准，它帮助我们训练模型（如果它在连续的3个时期中没有改善，那么我们停止训练，因为模型正在过拟合）。从这一段中有两个主要要点：
-
损失函数是两个损失函数的组合（DICE损失，二元交叉熵）
-
评估函数是DICE分数，不要与DICE损失混淆
DICE损失：
DICE损失
备注：我们添加了一个平滑参数（epsilon）以避免除以零。
二元交叉熵损失：
BCE
于是，我们的总损失是：
让我们一起实现它：
properties
def dice_coef_loss(inputs, target):
smooth = 1.0
intersection = 2.0 * ((target * inputs).sum()) + smooth
union = target.sum() + inputs.sum() + smooth
return 1 - (intersection / union)
def bce_dice_loss(inputs, target):
inputs = inputs.float()
target = target.float()
dicescore = dice_coef_loss(inputs, target)
bcescore = nn.BCELoss()
bceloss = bcescore(inputs, target)
return bceloss + dicescore评估标准（Dice系数）：
我们使用的评估函数是DICE分数。它在0到1之间，1是最好的。
Dice分数的图示
其数学实现如下：
python
def dice_coef_metric(inputs, target):
intersection = 2.0 * (target * inputs).sum()
union = target.sum() + inputs.sum()
if target.sum() == 0 and inputs.sum() == 0:
return 1.0
return intersection / union训练循环
properties
def train_model(model_name, model, train_loader, val_loader, train_loss, optimizer, lr_scheduler, num_epochs):
print(model_name)
loss_history = []
train_history = []
val_history = []
for epoch in range(num_epochs):
model.train() # Enter train mode
# We store the training loss and dice scores
losses = []
train_iou = []
if lr_scheduler:
warmup_factor = 1.0 / 100
warmup_iters = min(100, len(train_loader) - 1)
lr_scheduler = warmup_lr_scheduler(optimizer, warmup_iters, warmup_factor)
# Add tqdm to the loop (to visualize progress)
for i_step, (data, target) in enumerate(tqdm(train_loader, desc=f"Training epoch {epoch+1}/{num_epochs}")):
data = data.to(device)
target = target.to(device)
outputs = model(data)
out_cut = np.copy(outputs.data.cpu().numpy())
# If the score is less than a threshold (0.5), the prediction is 0, otherwise its 1
out_cut[np.nonzero(out_cut < 0.5)] = 0.0
out_cut[np.nonzero(out_cut >= 0.5)] = 1.0
train_dice = dice_coef_metric(out_cut, target.data.cpu().numpy())
loss = train_loss(outputs, target)
losses.append(loss.item())
train_iou.append(train_dice)
# Reset the gradients
optimizer.zero_grad()
# Perform backpropagation to compute gradients
loss.backward()
# Update the parameters with the computed gradients
optimizer.step()
if lr_scheduler:
lr_scheduler.step()
val_mean_iou = compute_iou(model, val_loader)
loss_history.append(np.array(losses).mean())
train_history.append(np.array(train_iou).mean())
val_history.append(val_mean_iou)
print("Epoch [%d]" % (epoch))
print("Mean loss on train:", np.array(losses).mean(),
"\nMean DICE on train:", np.array(train_iou).mean(),
"\nMean DICE on validation:", val_mean_iou)
return loss_history, train_history, val_history结果
让我们在一个带有肿瘤的主题上评估我们的模型：
结果看起来相当不错！我们可以看到模型明显学到了关于图像结构的一些有用信息。然而，它可能可以更好地细化分割，这可以通过我们将很快讨论的更先进的技术来实现。U-Net至今仍然广泛使用，但有一个著名的模型达到了最先进的性能，称为nn-UNet。
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