train_loader.dataset.list_data

时间: 2023-09-24 18:12:34 浏览: 47
train_loader.dataset.list_data是一个属性，它表示train_loader的数据集中的list_data属性。根据提供的代码，list_data是数据集类的一个属性，它存储了数据集中的所有数据。在load_data方法中，每个说话人的信息被保存在uttrs列表中，并将uttrs列表保存在数据集的正确位置。因此，train_loader.dataset.list_data将返回数据集中的所有数据，包括每个说话人的信息。 #### 引用[.reference_title] - *1* *3* [【项目实战】AUTOVC 代码解析 —— data_loader.py](https://blog.csdn.net/weixin_42721167/article/details/114989866)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insert_down28v1,239^v3^insert_chatgpt"}} ] [.reference_item] - *2* [pytorch源码分析之torch.utils.data.Dataset类和torch.utils.data.DataLoader类](https://blog.csdn.net/qq_36653505/article/details/83351808)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insert_down28v1,239^v3^insert_chatgpt"}} ] [.reference_item] [ .reference_list ]

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train_loader.dataset是指train_loader中使用的数据集。在这个例子中，train_loader使用的是train_dataset，即训练集数据。所以train_loader.dataset指的是训练集数据。\[1\] #### 引用[.reference_title] - *1* *2*...

为每句代码做注释：flower_list = train_dataset.class_to_idx cla_dict = dict((val, key) for key, val in flower_list.items()) json_str = json.dumps(cla_dict, indent=4) with open('class_indices.json', 'w') as json_file: json_file.write(json_str) batch_size = 16 nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) print('Using {} dataloader workers every process'.format(nw)) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=0) validate_dataset = datasets.ImageFolder(root=os.path.join(image_path, "val"), transform=data_transform["val"]) val_num = len(validate_dataset) validate_loader = torch.utils.data.DataLoader(validate_dataset, batch_size=batch_size, shuffle=False, num_workers=0) print("using {} images for training, {} images for validation.".format(train_num, val_num))

train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=0) # 创建验证集dataset和dataloader validate_dataset = datasets.ImageFolder(root=os.path.join...

Traceback (most recent call last): File "D:\DBNet.pytorch-master\tools\train.py", line 78, in <module> main(config) File "D:\DBNet.pytorch-master\tools\train.py", line 38, in main train_loader = get_dataloader(config['dataset']['train'], config['distributed']) File "D:\DBNet.pytorch-master\data_loader\__init__.py", line 84, in get_dataloader _dataset = get_dataset(data_path=data_path, module_name=dataset_name, transform=img_transfroms, dataset_args=dataset_args) File "D:\DBNet.pytorch-master\data_loader\__init__.py", line 24, in get_dataset **dataset_args) File "D:\DBNet.pytorch-master\data_loader\dataset.py", line 17, in __init__ super().__init__(data_path, img_mode, pre_processes, filter_keys, ignore_tags, transform) File "D:\DBNet.pytorch-master\base\base_dataset.py", line 18, in __init__ assert item in self.data_list[0], 'data_list from load_data must contains {}'.format(item_keys) IndexError: list index out of range

具体来说，在你的代码中，第17行的__init__方法中的data_list是一个空列表，而你尝试访问它的第一个元素时发生了索引超出范围的错误。 要解决这个问题，你需要确保在使用data_list之前，它已经被正确地填充了...

num_epochs = 10 batch_size = 8 dataset = DataSet(np.array(x_train), list(y_train)) train_size = int(len(x_train) * 0.7) test_size = len(y_train) - train_size train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size]) train_loader = Data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True) test_loader = Data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True, drop_last=True) 这段代码里的训练超参数有哪些

这段代码中的训练超参数包括： ...- train_loader：训练数据集的数据加载器，用于将数据分批次加载到模型中进行训练。 - test_loader：测试数据集的数据加载器，用于将数据分批次加载到模型中进行测试。

No. of samples: 803 Traceback (most recent call last): File "D:/Github/test/C3D-LSTM--PyTorch-master/train_c3d_lstm.py", line 215, in <module> main() File "D:/Github/test/C3D-LSTM--PyTorch-master/train_c3d_lstm.py", line 189, in main train_phase(train_dataloader, optimizer, criterion, epoch) File "D:/Github/test/C3D-LSTM--PyTorch-master/train_c3d_lstm.py", line 86, in train_phase for data in train_dataloader: File "D:\miniconda3\envs\test01\lib\site-packages\torch\utils\data\dataloader.py", line 521, in __next__ data = self._next_data() File "D:\miniconda3\envs\test01\lib\site-packages\torch\utils\data\dataloader.py", line 561, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "D:\miniconda3\envs\test01\lib\site-packages\torch\utils\data\_utils\fetch.py", line 49, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "D:\miniconda3\envs\test01\lib\site-packages\torch\utils\data\_utils\fetch.py", line 49, in data = [self.dataset[idx] for idx in possibly_batched_index] File "D:\Github\test\C3D-LSTM--PyTorch-master\data_loader.py", line 84, in __getitem__ images[i] = load_image_train(image_list[i], hori_flip, transform) IndexError: list index out of range

根据错误堆栈信息，问题出现在data_loader.py文件的第84行，具体是在__getitem__方法中的images[i] = load_image_train(image_list[i], hori_flip, transform)这行代码。 这个错误通常表示你正在尝试访问一个...

帮我看看这段代码报错原因： Traceback (most recent call last): File "/home/bder73002/hpy/ConvNextV2_Demo/train+.py", line 274, in <module> train_loss, train_acc = train(model_ft, DEVICE, train_loader, optimizer, epoch,model_ema) File "/home/bder73002/hpy/ConvNextV2_Demo/train+.py", line 48, in train loss = torch.nan_to_num(criterion_train(output, targets)) # 计算loss File "/home/bder73002/anaconda3/envs/python3.9.2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/home/bder73002/hpy/ConvNextV2_Demo/models/losses.py", line 38, in forward index.scatter_(1, target.data.view(-1, 1).type(torch.LongTensor), 1) RuntimeError: Expected index [128, 1] to be smaller than self [16, 8] apart from dimension 1 部分代码如下：cls_num_list = np.zeros(classes) for , label in train_loader.dataset: cls_num_list[label] += 1 criterion_train = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, s=30) class LDAMLoss(nn.Module): def __init__(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).__init__() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s self.weight = weight def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) # index.scatter_(1, target.data.view(-1, 1), 1) index.scatter_(1, target.data.view(-1, 1).type(torch.LongTensor), 1) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(0,1)) batch_m = batch_m.view((-1, 1)) x_m = x - batch_m output = torch.where(index, x_m, x) return F.cross_entropy(self.s*output, target, weight=self.weight)

这个错误是由于 index 张量的大小与 output 张量的...num_classes = len(train_loader.dataset.classes) criterion_train = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, s=30, num_classes=num_classes)

# 定义数据集 train_data = torchvision.datasets.ImageFolder(os.path.join(data_dir, 'train'), transform=transform_train) val_data = torchvision.datasets.ImageFolder(os.path.join(data_dir, 'val'), transform=transform_val) # 定义数据加载器 train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, shuffle=True, num_workers=4) val_loader = torch.utils.data.DataLoader(val_data, batch_size=32, shuffle=False, num_workers=4)，好像并没有按照一定的比例你分割数据集

train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, shuffle=True, num_workers=4) val_loader = torch.utils.data.DataLoader(val_data, batch_size=32, shuffle=False, num_workers=4) ...

train_dataset = ImageFolder(args.dataset, split="train", transform=train_transforms)

train_dataset = ImageFolder(args.dataset, split="train", transform=train_transforms)是用于创建训练数据集的代码。它使用了torchvision库中的ImageFolder类，该类可以方便地加载图像数据集。其中args.dataset是...

帮我看看这段代码报错原因：Traceback (most recent call last): File "/home/bder73002/hpy/ConvNextV2_Demo/train+.py", line 272, in <module> train_loss, train_acc = train(model_ft, DEVICE, train_loader, optimizer, epoch,model_ema) File "/home/bder73002/hpy/ConvNextV2_Demo/train+.py", line 48, in train loss = torch.nan_to_num(criterion_train(output, targets)) # 计算loss File "/home/bder73002/anaconda3/envs/python3.9.2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/home/bder73002/hpy/ConvNextV2_Demo/models/losses.py", line 37, in forward index.scatter_(1, target.data.view(-1, 1), 1) IndexError: scatter_(): Expected dtype int64 for index. 部分代码如下：cls_num_list = np.zeros(classes) for _, label in train_loader.dataset: cls_num_list[label] += 1 criterion_train = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, s=30) class LDAMLoss(nn.Module): def __init__(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).__init__() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s self.weight = weight def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index.scatter_(1, target.data.view(-1, 1), 1) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(0,1)) batch_m = batch_m.view((-1, 1)) x_m = x - batch_m output = torch.where(index, x_m, x) return F.cross_entropy(self.s*output, target, weight=self.weight)

根据报错信息，可以看出问题出在这一行代码上：index.scatter_(1, target.data.view(-1, 1), 1)，报错信息中指出 scatter_() 函数期望得到的是 int64 类型的索引，但是目前传入的 target.data.view(-1, 1) ...

test_loss = 0.0 class_correct = list(0. for i in range(2)) class_total = list(0. for i in range(2)) model.eval() # iterate over test data for data, target in test_loader: # move tensors to GPU if CUDA is available if train_on_gpu: data, target = data.cuda(), target.cuda() # forward pass: compute predicted outputs by passing inputs to the model output = model(data) # calculate the batch loss loss = criterion(output, target) # update test loss test_loss += loss.item()*data.size(0) # convert output probabilities to predicted class _, pred = torch.max(output, 1) # compare predictions to true label correct_tensor = pred.eq(target.data.view_as(pred)) correct = np.squeeze(correct_tensor.numpy()) if not train_on_gpu else np.squeeze(correct_tensor.cpu().numpy()) # calculate test accuracy for each object class for i in range(batch_size): label = target.data[i] class_correct[label] += correct[i].item() class_total[label] += 1 # average test loss test_loss = test_loss/len(test_loader.dataset) print('Test Loss: {:.6f}\n'.format(test_loss))哪里有问题

1. 你需要确保 test_loader 是正确加载测试数据的。 2. model.eval() 用于将模型设置为评估模式，这会影响到一些层的行为，例如 BatchNorm 和 Dropout。 3. 确保 criterion 是正确的损失函数。 4. 如果你...

dataset = CocoDetection(root=r'D:\file\study\data\COCO2017\train2017', annFile=r'D:\file\study\data\COCO2017\annotations\instances_train2017.json', transforms=transforms.Compose([transforms.ToTensor()])) # 定义训练集和测试集的比例 train_ratio = 0.8 test_ratio = 0.2 # 计算训练集和测试集的数据数量 num_data = len(dataset) num_train_data = int(num_data * train_ratio) num_test_data = num_data - num_train_data # 使用random_split函数将数据集划分为训练集和测试集 train_dataset, test_dataset = random_split(dataset, [num_train_data, num_test_data]) # 打印训练集和测试集的数据数量 print(f"Number of training data: {len(train_dataset)}") print(f"Number of test data: {len(test_dataset)}") train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True, num_workers=0) test_loader = DataLoader(test_dataset, batch_size=8, shuffle=True, num_workers=0) # define the optimizer and the learning rate scheduler params = [p for p in model.parameters() if p.requires_grad] optimizer = torch.optim.SGD(params, lr=0.005, momentum=0.9, weight_decay=0.0005) lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1) # train the model for 10 epochs num_epochs = 10 for epoch in range(num_epochs): # 将模型设置为训练模式 model.train() # 初始化训练损失的累计值 train_loss = 0.0 # 构建一个迭代器，用于遍历数据集 for i, images, targets in train_loader: print(images) print(targets) # 将数据转移到设备上 images = list(image.to(device) for image in images) targets = [{k: v.to(device) for k, v in t.items()} for t in targets]上述代码报错：TypeError: call() takes 2 positional arguments but 3 were given

这个错误是因为在你的数据读取过程中，train_loader返回了三个值，包括索引、图像和目标，但是你在迭代器中只使用了后两个值。为了解决这个问题，你可以修改迭代器的定义，将索引也加入到迭代器中，如下所示： ...

train the model for 10 epochs num_epochs = 10 for epoch in range(num_epochs): # train for one epoch, printing every 10 iterations torchvision.models.detection.train_one_epoch(model, optimizer, data_loader, device=torch.device('cuda')) # update the learning rate lr_scheduler.step() # evaluate on the test dataset every epoch torchvision.models.detection.evaluate(model, data_loader, device=torch.device('cuda'))详细写出上述代码中的train_one_epoch和evaluate函数，给出代码注释

def train_one_epoch(model, optimizer, data_loader, device): """ 训练模型一个epoch :param model: 目标检测模型 :param optimizer: 优化器 :param data_loader: 数据加载器 :param device: 设备（CPU或GPU...

pytorch部分代码如下：class LDAMLoss(nn.Module): def init(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).init() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s if weight is not None: weight = torch.FloatTensor(weight).cuda() self.weight = weight self.cls_num_list = cls_num_list def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(0,1)) batch_m = batch_m.view((-1, 1)) # size=(batch_size, 1) (-1,1) x_m = x - batch_m output = torch.where(index, x_m, x) if self.weight is not None: output = output * self.weight[None, :] logit = output * self.s return F.cross_entropy(logit, target, weight=self.weight) train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True,drop_last=True) test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=True) cls_num_list = np.zeros(classes) for , label in train_loader.dataset: cls_num_list[label] += 1 criterion_train = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, s=30) criterion_val = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, s=30) mixup_fn = Mixup( mixup_alpha=0.8, cutmix_alpha=1.0, cutmix_minmax=None, prob=0.1, switch_prob=0.5, mode='batch', label_smoothing=0.1, num_classes=classes) for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device, non_blocking=True), Variable(target).to(device,non_blocking=True) # 3、将数据输入mixup_fn生成mixup数据 samples, targets = mixup_fn(data, target) targets = torch.tensor(targets).to(torch.long) # 4、将上一步生成的数据输入model，输出预测结果，再计算loss output = model(samples) # 5、梯度清零（将loss关于weight的导数变成0） optimizer.zero_grad() # 6、若使用混合精度 if use_amp: with torch.cuda.amp.autocast(): # 开启混合精度 loss = torch.nan_to_num(criterion_train(output, targets)) # 计算loss scaler.scale(loss).backward() # 梯度放大 torch.nn.utils.clip_grad_norm(model.parameters(), CLIP_GRAD) # 梯度裁剪，防止梯度爆炸 scaler.step(optimizer) # 更新下一次迭代的scaler scaler.update() # 否则，直接反向传播求梯度 else: loss = criterion_train(output, targets) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD) optimizer.step() 报错：RuntimeError: Expected index [112, 1] to be smaller than self [16, 7] apart from dimension 1

这个报错是因为在计算batch_m时，index_float的维度是反过来的，应该将index_...batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(1,0)) ... 你可以尝试修改后再运行看看是否还会出现报错。

train_loss.append(mean_loss/len(trainloader))

The code appends the calculated mean loss divided by the length of the training data loader to the "train_loss" list. This list likely stores the training loss values for each epoch of the model. ...

pytorch代码如下：class LDAMLoss(nn.Module): def init(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).init() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s if weight is not None: weight = torch.FloatTensor(weight).cuda() self.weight = weight self.cls_num_list = cls_num_list def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(1,0)) # 0,1 batch_m = batch_m.view((-1, 1)) # size=(batch_size, 1) (-1,1) x_m = x - batch_m output = torch.where(index, x_m, x) if self.weight is not None: output = output * self.weight[None, :] logit = output * self.s return F.cross_entropy(logit, target, weight=self.weight) classes=7, cls_num_list = np.zeros(classes) for , label in train_loader.dataset: cls_num_list[label] += 1 criterion_train = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, s=30) criterion_val = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, s=30) for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device, non_blocking=True), Variable(target).to(device,non_blocking=True) # 3、将数据输入mixup_fn生成mixup数据 samples, targets = mixup_fn(data, target) targets = torch.tensor(targets).to(torch.long) # 4、将上一步生成的数据输入model，输出预测结果，再计算loss output = model(samples) # 5、梯度清零（将loss关于weight的导数变成0） optimizer.zero_grad() # 6、若使用混合精度 if use_amp: with torch.cuda.amp.autocast(): # 开启混合精度 loss = torch.nan_to_num(criterion_train(output, targets)) # 计算loss scaler.scale(loss).backward() # 梯度放大 torch.nn.utils.clip_grad_norm(model.parameters(), CLIP_GRAD) # 梯度裁剪，防止梯度爆炸 scaler.step(optimizer) # 更新下一次迭代的scaler scaler.update() 报错：File "/home/adminis/hpy/ConvNextV2_Demo/models/losses.py", line 53, in forward return F.cross_entropy(logit, target, weight=self.weight) File "/home/adminis/anaconda3/envs/wln/lib/python3.9/site-packages/torch/nn/functional.py", line 2824, in cross_entropy return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index) RuntimeError: multi-target not supported at /pytorch/aten/src/THCUNN/generic/ClassNLLCriterion.cu:15

batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(1,0)) # 0,1 batch_m = batch_m.view((-1, 1)) # size=(batch_size, 1) x_m = x - batch_m output = torch.where(index, x_m, x) if self...

检查一下：import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset from sklearn.metrics import roc_auc_score # 定义神经网络模型 class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.fc1 = nn.Linear(10, 64) self.fc2 = nn.Linear(64, 32) self.fc3 = nn.Linear(32, 1) self.sigmoid = nn.Sigmoid() def forward(self, x): x = self.fc1(x) x = nn.functional.relu(x) x = self.fc2(x) x = nn.functional.relu(x) x = self.fc3(x) x = self.sigmoid(x) return x # 加载数据集 data = torch.load('data.pt') x_train, y_train, x_test, y_test = data train_dataset = TensorDataset(x_train, y_train) train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) test_dataset = TensorDataset(x_test, y_test) test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False) # 定义损失函数和优化器 criterion = nn.BCELoss() optimizer = optim.Adam(net.parameters(), lr=0.01) # 训练模型 net = Net() for epoch in range(10): running_loss = 0.0 for i, data in enumerate(train_loader): inputs, labels = data optimizer.zero_grad() outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() # 在测试集上计算AUC y_pred = [] y_true = [] with torch.no_grad(): for data in test_loader: inputs, labels = data outputs = net(inputs) y_pred += outputs.tolist() y_true += labels.tolist() auc = roc_auc_score(y_true, y_pred) print('Epoch %d, loss: %.3f, test AUC: %.3f' % (epoch + 1, running_loss / len(train_loader), auc))

data = torch.load('data.pt') x_train, y_train, x_test, y_test = data train_dataset = TensorDataset(x_train, y_train) train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) test_dataset ...

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