How to Resize Tensors for Deep Learning in PyTorch

Tensors are a fundamental data structure used in deep learning frameworks like PyTorch. They can represent multidimensional arrays and matrices, making them useful for computations during model training and inference.

However, we often need to manipulate the size and shape of tensors for various operations. Resizing a tensor correctly is crucial for ensuring dimensional consistency across layers of a neural network.

In this comprehensive guide, we will explore the ins and outs of resizing tensors in PyTorch.

Overview of Tensors in PyTorch

Let‘s first briefly understand what tensors are in PyTorch:

• Tensors are multi-dimensional arrays containing elements at each index. For example, a 2D tensor can represent a matrix or image.
• Tensors store numeric data types like 32-bit floats and integers. This allows mathematical operations on GPUs and TPUs.
• The torch.Tensor() constructor creates tensors in PyTorch. We can initialize from a Python list or NumPy array.
• Tensors have attributes like shape, dtype and device (CPU/GPU). These describe the tensor‘s configuration.
• PyTorch has useful functions like torch.zeros(), torch.ones(), etc to initialize tensors.

Here is an example tensor with shape 3×3 (3 rows and 3 columns):

import torch

tensor = torch.Tensor([[1, 2, 3], 
                       [4, 5, 6],
                       [7, 8, 9]])

print(tensor)
print(tensor.shape)

Output:

 1  2  3
 4  5  6 
 7  8  9
[torch.FloatTensor of size 3x3]

This covers the basics – tensors are core components in PyTorch models. Next, let‘s learn how to resize tensors.

Why Resize Tensors?

There are several reasons you may need to resize tensors in PyTorch:

• Match dimensions – When feeding tensors from one layer to the next in a neural network, the dimensions must match. For example, the input channels of a conv layer must match input tensor.

• Add or remove samples – You may want to add or remove samples (rows) from a batch of data. For instance, evaluate on a smaller subset.

• Debugging/testing – Reshaping to a smaller size can help debug or test code on dummy data. Full-sized tensors consume substantial memory.

• Visualization – antes may need reshaping to standard sizes for visualization like 1xHxW or HxWxC.

• Hardware constraints – Sometimes tensor sizes need reducing to meet GPU/TPU memory limits during training.

The key reasons are maintaining dimensional consistency across layers and meeting hardware constraints. Robust PyTorch code should handle tensor resizing correctly.

Methods to Resize Tensors in PyTorch

PyTorch provides several methods to resize existing tensors to new dimensionalities:

1. tensor.view() – View/reshape a tensor‘s size and dimensions
2. tensor.reshape() – Resize tensor to specified dimensions
3. tensor.squeeze() – Remove single-dimensional entries from shape
4. tensor.unsqueeze() – Add single-dimensional entries to shape
5. torch.flatten() – Reduce tensor to 1D
6. tensor.expand() – Expand tensor dimensions to match PASS shape

Let‘s look at each of these methods for resizing tensors in more detail:

1. tensor.view()

The tensor.view() method is used to reshape a tensor to new dimensions but keeps the same underlying data. This returns a new tensor object with the resized shape.

import torch

x = torch.rand(2, 3, 4) 
print(x.shape)
print(x.numel()) # total elements

y = x.view(2, 12) # reshape to 2 rows, 12 columns 
print(y.shape)
print(y.numel()) # elements still same 

Here we have reshaped the 2x3x4 tensor to 2×12 while keeping total elements same.

Some key properties of view():

• Total number of elements cannot change
• Contiguous tensors can be reshaped more flexibly
• Returns view or shallow copy avoiding new memory allocation

By just changing the conceptual shape, view() is an efficient way to resize tensors for operations.

2. tensor.reshape()

The tensor.reshape() method serves the same purpose as view() but offers some extras:

• Can create non-contiguous output tensors
• Lets specifying one tensor dimension as -1 to infer size
• Handles corner cases without errors

For example:

x = torch.rand(3, 2, 4)
y = x.reshape(3, -1) # infer dimension 2
z = x.reshape(3, 8) # same as above
print(y.shape) # torch.Size([3, 8])  

a = torch.rand(2, 3, 2, 2) 
b = a.reshape(-1, 2, 2) # batch dimension inferred

So reshape() handles unknown sizes and can produce non-contiguous outputs if needed.

3. tensor.squeeze()

The tensor.squeeze() method removes dimensions of size 1 from a tensor shape.

For example, converting 4d tensor to 2d by squeezing length-1 dims:

import torch

x = torch.rand(1, 2, 1, 4)  
print(x.shape) 

y = x.squeeze()
print(y.shape) # torch.Size([2, 4]) - removes 1-dims

Common uses cases of squeeze():

• Remove batch dimension of size 1 for certain operations
• Handle legacy dimension conventions of shape (1xCxHxW)

Note that squeeze() returns a view tensor without extra memory allocation.

4. tensor.unsqueeze()

Opposite to squeezing, we can unsqueeze a tensor to add length-1 dimensions using tensor.unsqueeze().

For example:

x = torch.tensor([1, 2, 3]) # 1D vector
y = x.unsqueeze(0)   # adds batch dimension 
print(y.shape) # torch.Size([1, 3]) 

z = x.unsqueeze(-1) 
print(z.shape) # torch.Size([3, 1]) - adds last dim

unsqueeze() is useful when handling upgrades between vector, matrix and tensor operations.

5. torch.flatten()

The torch.flatten() function flattens a tensor to 1 dimension:

x = torch.rand(32, 3, 28, 28)
y = torch.flatten(x)
print(y.shape) # torch.Size([32, 3 * 28 * 28)) 

This is used to reduce tensors to vectors before dense layers. But does not handle batch dimension.

A common pattern is:

x = torch.rand(32, 3, 28, 28)
x = x.view(32, -1) # resize to batch and vectors
x = model(x) # pass through dense layer 

6. tensor.expand()

The tensor.expand() methods expands a tensor‘s dimensions to match a given shape by repeating elements.

For example:

x = torch.tensor([[1], [2], [3]])
y = x.expand(3, 4) # pass shape as tuple
print(y.shape) # torch.Size([3, 4])

# Each column in row will have elements repeated 
print(y)  

tensor([[1, 1, 1, 1],
        [2, 2, 2, 2],
        [3, 3, 3, 3]])

This allows expanding smaller tensors to align dims for certain broadcasting operations.

These cover all the PyTorch methods to resize existing tensors by transforming dimensions and sizes.

When to Resize Tensors?

Now we know the methods, but when should we resize tensors during model development and inference?

Here are some best practices on points in code where tensor resizing occurs:

1. After creating datasets and dataloader – Resize input samples like images to standard sizes for model.

2. Enter and exit fully connected layers – Flatten to vectors and reshape back.

3. Across dimension changing layers – For example, before and after convolutions with different parameters.

4. Split or concatenate layer outputs – Reshape accordingly to stack tensors.

5. Building custom layers and models – Reshape tensors between custom layer operations.

6. Transfer models to deployment servers – Resize for production environment constraints.

7. Generate batch inputs for inference – Standardize dimensions and format.

So primarily around layer transitions, data preprocessing, and model deployment.

Additionally, always check tensor shapes before and after operations for bugs. Where possible catch size mismatches early by assert statements or exceptions.

Now let‘s look at implementing an end-to-end CNN with strategic tensor resizing at each stage.

Building a CNN with Tensor Reshaping

To put our knowledge into practice, let us build a simple CNN for MNIST digit classification that applies tensor reshaping at crucial points:

1. Import Libraries and Data

We import PyTorch and download the MNIST dataset:

import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms

# Device configuration
device = torch.device(‘cuda‘ if torch.cuda.is_available() else ‘cpu‘)

# MNIST dataset 
train_dataset = torchvision.datasets.MNIST(root=‘../../data/‘,
                                           train=True, 
                                           transform=transforms.ToTensor(),
                                           download=True)

test_dataset = torchvision.datasets.MNIST(root=‘../../data/‘,
                                          train=False, 
                                          transform=transforms.ToTensor())

This gives us standard 28×28 grayscale images.

2. Create Data Loaders

We define data loaders for convenient minibatch loading:

# Data loader
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=100, 
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=100, 
                                          shuffle=False)

Batch size is set at 100 here with shuffling enabled for training data.

3. Define Convolutional Model

Next, we define a CNN with 2 convolution layers and 2 dense layers for classification:

# Convolutional neural network 
class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()

        # Input channels = 1, output channels = 18
        self.cnn1 = nn.Conv2d(in_channels=1, out_channels=18, kernel_size=3, stride=1, padding=1)
        self.relu1 = nn.ReLU()

        # Input channels = 18, output channels = 32
        self.cnn2 = nn.Conv2d(in_channels=18, out_channels=32, kernel_size=3, stride=1, padding=1)
        self.relu2 = nn.ReLU()

        # Fully connected layer 
        self.fc1 = nn.Linear(32*28*28, 256) 
        self.fc2 = nn.Linear(256, 10)

    # Forward pass
    def forward(self, x):
        out = self.cnn1(x)
        out = self.relu1(out)  
        out = self.cnn2(out)
        out = self.relu2(out)

        # Resize before fully connected layers 
        out = out.view(out.size(0), -1)

        out = self.fc1(out)
        out = self.fc2(out)  
        return out

We fix paddings to retain 28×28 image sizes after convolutions. Before passing to dense layers, the tensor is reshaped to batch size and feature vectors using out.view().

This handles transition from convolutional filters to fully connected classification.

4. Initialize Model, Optimizer, Loss

We setup model components and training functions:

model = ConvNet().to(device)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)  

Cross-entropy loss and Adam optimizer are standard for classification.

5. Train and Evaluate Model

Finally, we train the CNN end-to-end on MNIST data:

# Train the model
total_step = len(train_loader)
for epoch in range(10):
    for i, (images, labels) in enumerate(train_loader):  
        # Load images as tensors with reshape
        images = images.to(device).reshape(-1, 1, 28, 28)

        # Forward pass
        outputs = model(images)
        loss = criterion(outputs, labels.to(device))

        # Backward and optimize  
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    print (‘Epoch [{}/{}], Loss: {:.4f}‘ 
           .format(epoch+1, 10, loss.item()))

# Test the model      
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = images.to(device).reshape(-1, 1, 28, 28)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)

        total += labels.size(0)
        correct += (predicted == labels.to(device)).sum().item()

    print(‘Accuracy of the model on the test images: {} %‘.format(100 * correct / total))

We reshape input images to add a channel dimension before passing to CNN.

After 10 epochs of training, we get ~98% test accuracy – not bad for a basic CNN!

This end-to-end example illustrates how to strategically resize tensors across layer transitions and dataloader outputs for optimal performance.

Tensor Resizing Tips and Tricks

Here are some handy tips while resizing tensors in your PyTorch code:

• Use asserts to catch size mismatches early in development
• Set paddings in CNNs to retain sizes across layers
• Always reshape before flattening or after unflattening tensors
• Resize batches to smaller sizes for debugging or testing
• View is faster than reshape if possible due to memory
• Prefer squeezing dimensions over removing manually
• Adjust dimensions order based on framework conventions
• Add checks before production deployment to catch issues

And some common pitfalls to avoid:

• Not handling batch dimension correctly
• Attempting to resize tensor on incorrect device
• Using untrained object methods like expand/squeeze
• Introducing unwanted bottlebeck dimensions
• Flattening before batch normalization layers

Get these right to save hours of headache!

Conclusion

There we have it – a comprehensive guide to resizing tensors in PyTorch covering:

• Reasons for tensor reshaping in models
• Methods like view, reshape, flatten, squeeze, etc
• Strategic points to resize tensors in CNNs and other models
• Example CNN implementation that resizes tensors across layers
• Tips and tricks for tensor manipulation

The key ideas are maintaining dimensional consistency across layers, fitting hardware constraints, implementing based on framework conventions and verifying with asserts and checks.

With this you should be ready to wield PyTorch tensors like a samurai sword for your next deep learning project!