train_dataset= VOCData(root='./VOCdevkit/') train_loader= DataLoader(train_dataset, batch_size=4, shuffle=True) net= Unet(21) optimizer= optim.SGD(net.parameters(), lr=0.001, momentum=0.9) criterion=nn.CrossEntropyLoss() net.to(device) net.train() num_epochs= 100forepochinrange(num_e...
train() # 设置模型为训练模式 for images, masks in data_loader: optimizer.zero_grad() # 清零梯度 outputs = model(images) # 前向传播 loss = criterion(outputs, masks) # 计算损失 loss.backward() # 反向传播 optimizer.step() # 更新权重 print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {...
(train_loader()): image, label = data pred = model(image) # 预测结果 loss = dice_loss(pred, label) # 计算损失函数值 if batch_id % LOG_GAP == 0: # 定期输出训练结果 print("Epoch:%2d,Batch:%3d,Loss:%.5f" % (ep, batch_id, loss)) ep_loss_list.append(loss.item()) ...
(train_loader()): image, label = data pred = model(image) # 预测结果 loss = F.cross_entropy(pred, label, axis=1) # 计算损失函数值 if batch_id % LOG_GAP == 0: # 定期输出训练结果 print("Epoch:%d,Batch:%3d,Loss:%.5f" % (ep, batch_id, loss)) loss_arr.append(loss.item()...
class ISBI_Loader(Dataset): def __init__(self, data_path): # 初始化函数,读取所有data_path下的图片 self.data_path = data_path # glob.glob(pathname)返回所有匹配的文件路径列表,字符串前面加r是让字符串不转义,这里获取”data_path/image/*.png下所有文件“ ...
importtorchimportcv2importosimportglobfromtorch.utils.dataimportDatasetimportrandomclassISBI_Loader(Dataset):def__init__(self,data_path):# 初始化函数,读取所有data_path下的图片self.data_path=data_pathself.imgs_path=glob.glob(os.path.join(data_path,'image/*.png'))defaugment(self,image,flipCode)...
(val_set, batch_size=4, shuffle=True, num_workers=2) #以batch为每次读取数据量迭代读取数据集 #注意tensor的格式为b,w,h,c 而pytorch要求的格式为b,c,w,h,要通过inputs = np.transpose(inputs, (0,3,1,2))转换 for i, data in enumerate(train_loader, 0): names, inputs, labels = data...
model = UNet().to('cuda') criterion = nn.MSELoss() optimizer = optim.Adam(model.parameters(), lr=0.001) def train_model(model, train_loader, criterion, optimizer, num_epochs=10): for epoch in range(num_epochs): model.train() running_loss = 0.0 for noisy_images, clean_images in tr...
The input images and target masks should be in thedata/imgsanddata/masksfolders respectively (note that theimgsandmasksfolder should not contain any sub-folder or any other files, due to the greedy data-loader). For Carvana, images are RGB and masks are black and white. ...
for step, (features, labels) in enumerate(loader, 1): # 梯度清零 model.optimizer.zero_grad() # 正向传播求损失 predictions = model(features) loss = model.loss_func(predictions, labels) metric = accuracy(predictions, labels) # 反向传播求梯度 ...