如何以干净，高效的方式在pytorch中获得迷你批次？

Question

我试图做一个简单的事情，用火炬训练一个带有随机梯度下降（SGD）的线性模型：

import numpy as np

import torch
from torch.autograd import Variable

import pdb

def get_batch2(X,Y,M,dtype):
    X,Y = X.data.numpy(), Y.data.numpy()
    N = len(Y)
    valid_indices = np.array( range(N) )
    batch_indices = np.random.choice(valid_indices,size=M,replace=False)
    batch_xs = torch.FloatTensor(X[batch_indices,:]).type(dtype)
    batch_ys = torch.FloatTensor(Y[batch_indices]).type(dtype)
    return Variable(batch_xs, requires_grad=False), Variable(batch_ys, requires_grad=False)

def poly_kernel_matrix( x,D ):
    N = len(x)
    Kern = np.zeros( (N,D+1) )
    for n in range(N):
        for d in range(D+1):
            Kern[n,d] = x[n]**d;
    return Kern

## data params
N=5 # data set size
Degree=4 # number dimensions/features
D_sgd = Degree+1
##
x_true = np.linspace(0,1,N) # the real data points
y = np.sin(2*np.pi*x_true)
y.shape = (N,1)
## TORCH
dtype = torch.FloatTensor
# dtype = torch.cuda.FloatTensor # Uncomment this to run on GPU
X_mdl = poly_kernel_matrix( x_true,Degree )
X_mdl = Variable(torch.FloatTensor(X_mdl).type(dtype), requires_grad=False)
y = Variable(torch.FloatTensor(y).type(dtype), requires_grad=False)
## SGD mdl
w_init = torch.zeros(D_sgd,1).type(dtype)
W = Variable(w_init, requires_grad=True)
M = 5 # mini-batch size
eta = 0.1 # step size
for i in range(500):
    batch_xs, batch_ys = get_batch2(X_mdl,y,M,dtype)
    # Forward pass: compute predicted y using operations on Variables
    y_pred = batch_xs.mm(W)
    # Compute and print loss using operations on Variables. Now loss is a Variable of shape (1,) and loss.data is a Tensor of shape (1,); loss.data[0] is a scalar value holding the loss.
    loss = (1/N)*(y_pred - batch_ys).pow(2).sum()
    # Use autograd to compute the backward pass. Now w will have gradients
    loss.backward()
    # Update weights using gradient descent; w1.data are Tensors,
    # w.grad are Variables and w.grad.data are Tensors.
    W.data -= eta * W.grad.data
    # Manually zero the gradients after updating weights
    W.grad.data.zero_()

#
c_sgd = W.data.numpy()
X_mdl = X_mdl.data.numpy()
y = y.data.numpy()
#
Xc_pinv = np.dot(X_mdl,c_sgd)
print('J(c_sgd) = ', (1/N)*(np.linalg.norm(y-Xc_pinv)**2) )
print('loss = ',loss.data[0])

代码运行良好，尽管我的get_batch2方法似乎真的很简单/天真，但可能是因为我是pytorch的新手，但我还没有找到一个讨论如何检索数据批处理的好地方。我通过他们的教程（http://pytorch.org/tutorials/beginner/pytorch_with_examples.html）和数据集（http://pytorch.org/tutorials/beginner/data_loading_tutorial.html）没有运气。这些教程似乎都假设一个人已经在开始时已经拥有批量和批量大小，然后继续使用该数据进行训练而不更改它（具体来看http://pytorch.org/tutorials/beginner/pytorch_with_examples.html#pytorch-variables-and-autograd）。

所以我的问题是我真的需要将我的数据变回numpy以便我可以获取它的一些随机样本然后将其变回pytorch with Variable以便能够在内存中训练吗？火炬有没有办法获得迷你批次？

我看了一些火炬提供的功能，但没有运气：

#pdb.set_trace()
#valid_indices = torch.arange(0,N).numpy()
#valid_indices = np.array( range(N) )
#batch_indices = np.random.choice(valid_indices,size=M,replace=False)
#indices = torch.LongTensor(batch_indices)
#batch_xs, batch_ys = torch.index_select(X_mdl, 0, indices), torch.index_select(y, 0, indices)
#batch_xs,batch_ys = torch.index_select(X_mdl, 0, indices), torch.index_select(y, 0, indices)

即使我提供的代码工作正常，我担心它不是一个有效的实现，如果我使用GPU会有相当大的进一步减速（因为我猜它把东西放在内存中然后取出它们回到他们认为GPU就像傻了一样。）

我根据建议使用torch.index_select()的答案实施了一个新的：

def get_batch2(X,Y,M):
    '''
    get batch for pytorch model
    '''
    # TODO fix and make it nicer, there is pytorch forum question
    #X,Y = X.data.numpy(), Y.data.numpy()
    X,Y = X, Y
    N = X.size()[0]
    batch_indices = torch.LongTensor( np.random.randint(0,N+1,size=M) )
    pdb.set_trace()
    batch_xs = torch.index_select(X,0,batch_indices)
    batch_ys = torch.index_select(Y,0,batch_indices)
    return Variable(batch_xs, requires_grad=False), Variable(batch_ys, requires_grad=False)

然而，这似乎有问题，因为如果X,Y不是变量，它就不起作用......这真的很奇怪。我把它添加到了pytorch论坛：https://discuss.pytorch.org/t/how-to-get-mini-batches-in-pytorch-in-a-clean-and-efficient-way/10322

现在我正在努力解决的问题是让这项工作适用于gpu。我最新的版本：

def get_batch2(X,Y,M,dtype):
    '''
    get batch for pytorch model
    '''
    # TODO fix and make it nicer, there is pytorch forum question
    #X,Y = X.data.numpy(), Y.data.numpy()
    X,Y = X, Y
    N = X.size()[0]
    if dtype ==  torch.cuda.FloatTensor:
        batch_indices = torch.cuda.LongTensor( np.random.randint(0,N,size=M) )# without replacement
    else:
        batch_indices = torch.LongTensor( np.random.randint(0,N,size=M) ).type(dtype)  # without replacement
    pdb.set_trace()
    batch_xs = torch.index_select(X,0,batch_indices)
    batch_ys = torch.index_select(Y,0,batch_indices)
    return Variable(batch_xs, requires_grad=False), Variable(batch_ys, requires_grad=False)

错误：

RuntimeError: tried to construct a tensor from a int sequence, but found an item of type numpy.int64 at index (0)

我不明白，我真的必须这样做：

ints = [ random.randint(0,N) for i i range(M)]

得到整数？

如果数据可以是变量，那也是理想的。似乎torch.index_select不适用于Variable类型数据。

这个整数列表仍然没有用：

TypeError: torch.addmm received an invalid combination of arguments - got (int, torch.cuda.FloatTensor, int, torch.cuda.FloatTensor, torch.FloatTensor, out=torch.cuda.FloatTensor), but expected one of:
 * (torch.cuda.FloatTensor source, torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2, *, torch.cuda.FloatTensor out)
 * (torch.cuda.FloatTensor source, torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2, *, torch.cuda.FloatTensor out)
 * (float beta, torch.cuda.FloatTensor source, torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2, *, torch.cuda.FloatTensor out)
 * (torch.cuda.FloatTensor source, float alpha, torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2, *, torch.cuda.FloatTensor out)
 * (float beta, torch.cuda.FloatTensor source, torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2, *, torch.cuda.FloatTensor out)
 * (torch.cuda.FloatTensor source, float alpha, torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2, *, torch.cuda.FloatTensor out)
 * (float beta, torch.cuda.FloatTensor source, float alpha, torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2, *, torch.cuda.FloatTensor out)
      didn't match because some of the arguments have invalid types: (int, torch.cuda.FloatTensor, int, torch.cuda.FloatTensor, torch.FloatTensor, out=torch.cuda.FloatTensor)
 * (float beta, torch.cuda.FloatTensor source, float alpha, torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2, *, torch.cuda.FloatTensor out)
      didn't match because some of the arguments have invalid types: (int, torch.cuda.FloatTensor, int, torch.cuda.FloatTensor, torch.FloatTensor, out=torch.cuda.FloatTensor)

Answer 1

使用数据加载器。

数据集

首先定义数据集。您可以在torchvision.datasets中使用包数据集，也可以使用遵循Imagenet结构的ImageFolder数据集类。

trainset=torchvision.datasets.ImageFolder(root='/path/to/your/data/trn', transform=generic_transform)
testset=torchvision.datasets.ImageFolder(root='/path/to/your/data/val', transform=generic_transform)

变换

变换对于动态预处理加载的数据非常有用。如果您使用的是图片，则必须使用ToTensor()转换将加载的图片从PIL转换为torch.tensor。可以将更多变换打包到合成变换中，如下所示。

generic_transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.ToPILImage(),
    #transforms.CenterCrop(size=128),
    transforms.Lambda(lambda x: myimresize(x, (128, 128))),
    transforms.ToTensor(),
    transforms.Normalize((0., 0., 0.), (6, 6, 6))
])

数据加载器

然后定义一个数据加载器，在训练时准备下一批。您可以设置数据加载的线程数。

trainloader=torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True, num_workers=8)
testloader=torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False, num_workers=8)

对于培训，您只需枚举数据加载器。

  for i, data in enumerate(trainloader, 0):
    inputs, labels = data    
    inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())
    # continue training...

NumPy Stuff

是。您必须使用torch.tensor方法将numpy转换为.numpy()才能使用它。如果您正在使用CUDA，则必须先使用.cpu()方法将数据从GPU下载到CPU，然后再调用.numpy()。就个人而言，来自MATLAB背景，我更喜欢用火炬张量进行大部分工作，然后将数据转换为numpy仅用于可视化。另外请记住，火炬以通道优先模式存储数据，而numpy和PIL使用channel-last。这意味着您需要使用np.rollaxis将通道轴移动到最后一个。示例代码如下。

np.rollaxis(make_grid(mynet.ftrextractor(inputs).data, nrow=8, padding=1).cpu().numpy(), 0, 3)

登录

我发现可视化特征图的最佳方法是使用张量板。代码位于yunjey/pytorch-tutorial。

Answer 2

不确定你要做什么。 W.r.t.批处理你不必转换为numpy。你可以使用index_select()，例如：

for epoch in range(500):
    k=0
    loss = 0
    while k < X_mdl.size(0):

        random_batch = [0]*5
        for i in range(k,k+M):
            random_batch[i] = np.random.choice(N-1)
        random_batch = torch.LongTensor(random_batch)
        batch_xs = X_mdl.index_select(0, random_batch)
        batch_ys = y.index_select(0, random_batch)

        # Forward pass: compute predicted y using operations on Variables
        y_pred = batch_xs.mul(W)
        # etc..

其他代码也必须改变。

我的猜测，你想创建一个连接你的X张量和Y张量的get_batch函数。类似的东西：

def make_batch(list_of_tensors):
    X, y = list_of_tensors[0]
    # may need to unsqueeze X and y to get right dimensions
    for i, (sample, label) in enumerate(list_of_tensors[1:]):
        X = torch.cat((X, sample), dim=0)
        y = torch.cat((y, label), dim=0)
    return X, y

然后在训练期间选择，例如max_batch_size = 32，通过切片的例子。

for epoch:
  X, y = make_batch(list_of_tensors)
  X = Variable(X, requires_grad=False)
  y = Variable(y, requires_grad=False)

  k = 0   
   while k < X.size(0):
     inputs = X[k:k+max_batch_size,:]
     labels = y[k:k+max_batch_size,:]
     # some computation
     k+= max_batch_size

Answer 3

创建一个类，该类是torch.utils.data.Dataset的子类，并将其传递给torch.utils.data.Dataloader。以下是我的项目的示例。

class CandidateDataset(Dataset):
    def __init__(self, x, y):
        self.len = x.shape[0]
        if torch.cuda.is_available():
            device = 'cuda'
        else:
            device = 'cpu'
        self.x_data = torch.as_tensor(x, device=device, dtype=torch.float)
        self.y_data = torch.as_tensor(y, device=device, dtype=torch.long)

    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index]

    def __len__(self):
        return self.len

def fit(self, candidate_count):
        feature_matrix = np.empty(shape=(candidate_count, 600))
        target_matrix = np.empty(shape=(candidate_count, 1))
        fill_matrices(feature_matrix, target_matrix)
        candidate_ds = CandidateDataset(feature_matrix, target_matrix)
        train_loader = DataLoader(dataset = candidate_ds, batch_size = self.BATCH_SIZE, shuffle = True)
        for epoch in range(self.N_EPOCHS):
            print('starting epoch ' + str(epoch))
            for batch_idx, (inputs, labels) in enumerate(train_loader):
                print('starting batch ' + str(batch_idx) + ' epoch ' + str(epoch))
                inputs, labels = Variable(inputs), Variable(labels)
                self.optimizer.zero_grad()
                inputs = inputs.view(1, inputs.size()[0], 600)
                # init hidden with number of rows in input
                y_pred = self.model(inputs, self.model.initHidden(inputs.size()[1]))
                labels.squeeze_()
                # labels should be tensor with batch_size rows. Column the index of the class (0 or 1)
                loss = self.loss_f(y_pred, labels)
                loss.backward()
                self.optimizer.step()
                print('done batch ' + str(batch_idx) + ' epoch ' + str(epoch))

Answer 4

您可以使用torch.utils.data

假设您已经从目录中加载了数据，并且在训练和测试numpy数组中，您可以继承torch.utils.data.Dataset类来创建数据集对象

class MyDataset(Dataset):
    def __init__(self, x, y):
        super(MyDataset, self).__init__()
        assert x.shape[0] == y.shape[0] # assuming shape[0] = dataset size
        self.x = x
        self.y = y


    def __len__(self):
        return self.y.shape[0]

    def __getitem__(self, index):
        return self.x[index], self.y[index]

然后，创建您的数据集对象

traindata = MyDataset(train_x, train_y)

最后，使用DataLoader创建迷你批次

trainloader = torch.utils.data.DataLoader(traindata, batch_size=64, shuffle=True)

Answer 5

一种替代方法可能是使用pd.DataFrame.sample

train = pd.read_csv(TrainSetPath)
test = pd.read_csv(TestSetPath)

# use df.sample() to shuffle the data frame 
train = train.sample(frac=1)
test = test.sample(frac=1)

for i in range(epochs):
        for j in range(batch_per_epoch):
            train_batch = train.sample(n=BatchSize, axis='index',replace=True)
            y_train = train_batch['Target']
            X_train = train_batch.drop(['Target'], axis=1)
            
            # convert data frames to tensors and send them to GPU (if used)
            X_train = torch.tensor(np.mat(X_train)).float().to(device)
            y_train = torch.tensor(np.mat(y_train)).float().to(device)