PyTorch: How to get the shape of a Tensor as a list of int

PyTorch: How to get the shape of a Tensor as a list of int

In PyTorch, you can get the shape of a tensor as a list of integers using the .size() or .shape attribute of the tensor, along with the .tolist() method. Here's how you can do it:

import torch # Create a sample tensor tensor = torch.tensor([[1, 2, 3], [4, 5, 6]]) # Get the shape of the tensor as a list of integers shape_list = tensor.size().tolist() # or tensor.shape.tolist() print(shape_list) # Output: [2, 3]

In this example, the .size() or .shape attribute of the tensor returns a tuple representing the shape of the tensor. The .tolist() method converts this tuple to a list of integers.

You can use the same approach for tensors of different dimensions as well. Just remember that the shape will be represented as a list of integers, where each integer corresponds to the size of the tensor along that dimension.

Examples

  1. "How to get the shape of a PyTorch tensor?"

    • This query explores obtaining the shape of a PyTorch tensor.
    • Explanation: You can get the shape of a tensor using the .shape attribute.
    • import torch tensor = torch.randn(3, 4, 5) # Example tensor with shape (3, 4, 5) tensor_shape = tensor.shape # Get the shape of the tensor print("Tensor shape:", tensor_shape)

  2. "How to convert the shape of a PyTorch tensor into a list of ints?"

    • This query explores converting the tensor's shape into a list.
    • Explanation: The .shape attribute returns a torch.Size object, which can be converted to a list.
    • import torch tensor = torch.randn(3, 4, 5) shape_as_list = list(tensor.shape) # Convert the shape to a list of integers print("Shape as list:", shape_as_list)

  3. "How to get the number of dimensions in a PyTorch tensor?"

    • This query explores retrieving the number of dimensions (rank) in a tensor.
    • Explanation: The number of dimensions can be obtained from the length of the tensor's shape.
    • import torch tensor = torch.randn(3, 4, 5) num_dimensions = len(tensor.shape) # Get the number of dimensions print("Number of dimensions:", num_dimensions)

  4. "How to check if a PyTorch tensor has a specific shape?"

    • This query explores how to verify a tensor's shape.
    • Explanation: You can compare the shape of a tensor to a known list of expected dimensions.
    • import torch tensor = torch.randn(3, 4, 5) expected_shape = [3, 4, 5] # Check if the tensor has the expected shape if list(tensor.shape) == expected_shape: print("Tensor has the expected shape.") else: print("Tensor does not have the expected shape.")

  5. "How to reshape a PyTorch tensor?"

    • This query explores reshaping a tensor to a different shape.
    • Explanation: You can use .view() or .reshape() to change a tensor's shape.
    • import torch tensor = torch.randn(3, 4, 5) # Reshape to a different shape reshaped_tensor = tensor.view(-1) # Flatten to 1D reshaped_shape = list(reshaped_tensor.shape) # Get new shape print("Reshaped shape:", reshaped_shape)

  6. "How to change the shape of a tensor to a specific 2D shape in PyTorch?"

    • This query explores changing a tensor's shape to a specified 2D format.
    • Explanation: You can reshape tensors to specific dimensions.
    • import torch tensor = torch.randn(3, 4, 5) # Change to a 2D shape (e.g., 3x20) new_shape = (3, 20) reshaped_tensor = tensor.view(*new_shape) # Use '*' to unpack the shape tuple reshaped_shape = list(reshaped_tensor.shape) print("New 2D shape:", reshaped_shape)

  7. "How to get the batch size from a tensor in PyTorch?"

    • This query explores extracting the batch size from a tensor's shape.
    • Explanation: The first dimension often represents the batch size.
    • import torch tensor = torch.randn(32, 3, 64, 64) # Example tensor with batch size 32 batch_size = tensor.shape[0] # First dimension is the batch size print("Batch size:", batch_size)

  8. "How to get the height and width of a PyTorch tensor?"

    • This query explores extracting specific dimensions from a tensor.
    • Explanation: Tensors representing images typically have height and width dimensions.
    • import torch tensor = torch.randn(32, 3, 64, 64) # Example tensor with height and width height = tensor.shape[2] # Third dimension is height width = tensor.shape[3] # Fourth dimension is width print("Height:", height, "Width:", width)

  9. "How to handle dynamic shapes in PyTorch tensors?"

    • This query explores managing tensors with varying shapes during operations.
    • Explanation: You can adjust operations to handle dynamic shapes.
    • import torch tensor1 = torch.randn(3, 4, 5) tensor2 = torch.randn(2, 3, 4) # Different shape # Using broadcasting to align shapes for addition tensor1_resized = tensor1[:, :2, :3] # Reshape to match result_tensor = tensor1_resized + tensor2 # Addition with broadcasting result_shape = list(result_tensor.shape) print("Resultant shape:", result_shape)

  10. "How to calculate the total number of elements in a PyTorch tensor?"

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