英文:
Apply Torchvision Transforms to a numpy array
问题
我编写了以下代码：
transform = transforms.Compose([
transforms.ToPILImage(),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
train_dataset = torch.utils.data.TensorDataset(torch.from_numpy(X_train).float(), torch.from_numpy(y_train).float())
val_dataset = torch.utils.data.TensorDataset(torch.from_numpy(X_val).float(), torch.from_numpy(y_val).float())
# 定义数据加载器
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=32, shuffle=False, num_workers=4)
我想要将这个变换组应用到我的数据集（X_train和X_val），它们都是NumPy数组。如何应用变换来增强我的数据集并进行归一化？我应该在模型训练之前还是在模型训练期间应用它们？
英文:
I wrote the following code:
transform = transforms.Compose([
transforms.ToPILImage(),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
train_dataset = torch.utils.data.TensorDataset(torch.from_numpy(X_train).float(), torch.from_numpy(y_train).float())
val_dataset = torch.utils.data.TensorDataset(torch.from_numpy(X_val).float(), torch.from_numpy(y_val).float())
# Define the dataloaders
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=32, shuffle=False, num_workers=4)
I would like to apply the transform compose to my dataset (X_train and X_val) which are both numpy array. How can I apply transform to augment my dataset and normalize it.
Should I apply it before the model training or during model training?
答案1
得分: 1
你可以简单地将转换器添加到你的训练流程中：
train_transformed = transform(torch.from_numpy(X_train).float())
val_transformed = transform(torch.from_numpy(X_val).float())
train_dataset = torch.utils.data.TensorDataset(train_transformed, torch.from_numpy(y_train).float())
val_dataset = torch.utils.data.TensorDataset(val_transformed, torch.from_numpy(y_val).float())
这样转换就会在数据加载过程中应用，即在训练之前。
英文:
You can simply add the transformers to your training pipeline:
train_transformed = transform(torch.from_numpy(X_train).float())
val_transformed = transform(torch.from_numpy(X_val).float())
train_dataset = torch.utils.data.TensorDataset(train_transformed, torch.from_numpy(y_train).float())
val_dataset = torch.utils.data.TensorDataset(val_transformed, torch.from_numpy(y_val).float())
This way the transform is applied during data loading which is before training.
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