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  • python - What does `view ()` do in PyTorch? - Stack Overflow
    view() reshapes the tensor without copying memory, similar to numpy's reshape() Given a tensor a with 16 elements: import torch a = torch range(1, 16) To reshape this tensor to make it a 4 x 4 tensor, use: a = a view(4, 4) Now a will be a 4 x 4 tensor Note that after the reshape the total number of elements need to remain the same
  • What does `-1` of `view ()` mean in PyTorch? - Stack Overflow
    #%% """ Summary: view(-1, ) keeps the remaining dimensions as give and infers the -1 location such that it respects the original view of the tensor If it's only view(-1) then it only has 1 dimension given all the previous ones so it ends up flattening the tensor
  • Whats the difference between `reshape()` and `view()` in PyTorch?
    Returns a tensor with the same data and number of elements as input, but with the specified shape When possible, the returned tensor will be a view of input Otherwise, it will be a copy Contiguous inputs and inputs with compatible strides can be reshaped without copying, but you should not depend on the copying vs viewing behavior
  • python - `permute()` vs `view()` in PyTorch? - Stack Overflow
    tensor permute() permutes the order of the axes of a tensor tensor view() reshapes the tensor (analogous to numpy reshape) by reducing expanding the size of each dimension (if one increases, the others must decrease)
  • PyTorch: difference between reshape() and view() method
    A contiguous tensor is a tensor whose values are stored in a single, uninterrupted – thus, "contiguous" – piece of memory A non-contiguous tensor may have gaps in its memory layout Producing a view of a tensor means reinterpreting the arrangement of values in its memory Think of a piece of memory that stores 16 values: we can interpret
  • pytorch view tensor and reduce one dimension - Stack Overflow
    So I have a 4d tensor with shape [4,1,128,678] and I would like to view reshape it as [4,678,128] I have to do this for multiple tensors where the last shape value 678 is not always know and could be different, so [4,1,128,575] should also go to [4,575,128]
  • What is the difference between . flatten() and . view(-1) in PyTorch?
    The doc in your link says reshape(), reshape_as() and flatten() can return either a view or new tensor, user code shouldn’t rely on whether it’s view or not But view(-1) guarentees to return a view
  • What does . contiguous () do in PyTorch? - Stack Overflow
    narrow(), view(), expand() and transpose() For example: when you call transpose(), PyTorch doesn't generate a new tensor with a new layout, it just modifies meta information in the Tensor object so that the offset and stride describe the desired new shape In this example, the transposed tensor and original tensor share the same memory:
  • Configure PyCharm Debugger to Display Array Tensor Shape?
    When I'm debugging with PyCharm, I'd like for the debugger to display the shapes of my NumPy arrays Jax arrays PyTorch Tensors Instead, I see their values: Is there a way to configure PyCharm's





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