Accessing Tensor Sizes in PyTorch: .size or .shape, Which One to Choose?

Here's a breakdown of the key differences:

• Functionality:

  • .size is a method (function attached to an object) that returns a torch.Size object containing the size information.
  • .shape is an attribute (direct property of the object) that acts as an alias for .size(). It returns the same torch.Size object.

• Origin:

  • .size has been there since the beginning of PyTorch.

In essence, you can use either .size or .shape interchangeably. Here's an example:

import torch

# Create a tensor
tensor = torch.tensor([[1, 2, 3], [4, 5, 6]])

# Get the size using both methods
size_using_method = tensor.size()
size_using_attribute = tensor.shape

# They both print the same output: torch.Size([2, 3])
print(size_using_method)
print(size_using_attribute)

Choosing between .size and .shape:

• If you're familiar with NumPy and prefer consistency, use .shape.
• If you're working with older PyTorch code that primarily uses .size, there's no need to change it. Both methods are valid.

Remember, they both provide the same information about the tensor's dimensionality.

import torch

# Create a 2D tensor
my_tensor = torch.tensor([[1, 2, 3], [4, 5, 6]])

# Using the .size() method
print("Shape using .size():", my_tensor.size())

# Using the .shape attribute
print("Shape using .shape:", my_tensor.shape)

# Output:
# Shape using .size(): torch.Size([2, 3])
# Shape using .shape: torch.Size([2, 3])

This code creates a 2D tensor with two rows and three columns. Then, it uses both .size() and .shape to retrieve the shape information. As expected, both methods output the same result: torch.Size([2, 3]).

Here's another example demonstrating how to convert the torch.Size object to a regular Python list:

# Get the size as a torch.Size object
size_object = my_tensor.size()

# Convert the torch.Size object to a list
size_list = list(size_object)

print("Shape as a list:", size_list)

# Output:
# Shape as a list: [2, 3]

This code retrieves the shape using .size() and then converts it to a list using list(). This can be useful if you need to work with the dimensions as individual integers in your code.

1. Using Indexing:

While not the most efficient approach, you can indirectly determine the size of a tensor by indexing it completely. For example:

import torch

# Create a tensor
tensor = torch.tensor([[1, 2, 3], [4, 5, 6]])

# Accessing all elements indicates the full dimension
# (Not recommended for large tensors due to inefficiency)
first_dim_size = len(tensor)
second_dim_size = len(tensor[0])

print("Shape using indexing:", (first_dim_size, second_dim_size))

This code iterates through the entire tensor using indexing to get the number of elements in each dimension. It's important to note that this approach is less efficient than using .size or .shape, especially for large tensors.

1. Using NumPy (if applicable):

If you're already working with NumPy in your code and your tensor resides on the CPU (not GPU), you can convert the tensor to a NumPy array and then use the .shape attribute of NumPy arrays.

import torch
import numpy as np

# Create a tensor on CPU
tensor = torch.tensor([[1, 2, 3], [4, 5, 6]], device=torch.device('cpu'))

# Convert tensor to NumPy array (only for CPU tensors)
numpy_array = tensor.numpy()

# Get shape using NumPy
shape_using_numpy = numpy_array.shape

print("Shape using NumPy:", shape_using_numpy)

Keep in mind that this approach only works for CPU tensors. If your tensor is on the GPU, attempting to convert it to a NumPy array will result in an error.

In most cases, using .size or .shape is the recommended approach for getting the shape of a tensor in PyTorch due to their efficiency and directness. The workarounds mentioned above can be helpful in specific scenarios, but they are not general replacements.