torch.from_numpy

uint8 , and numpy.bool . Warning. Writing to a tensor created from a read-only NumPy array is not supported and will result in undefined behavior.

torch.as_tensor

If data is a NumPy array (an ndarray) with the same dtype and device then a tensor is constructed using torch.from_numpy() . See also.

torch.Tensor.numpy

Returns the tensor as a NumPy ndarray . If force is False (the default), the conversion is performed only if the tensor is on the CPU, does not require grad ...

torch.asarray

When obj is a tensor, NumPy array, or DLPack capsule the returned tensor will, by default, not require a gradient, have the same datatype as obj , be on the ...

torch.nan_to_num

torch.nan_to_num. torch.nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None) → Tensor. Replaces NaN , positive infinity, and negative infinity ...

torch.tensor_split

This function is based on NumPy's numpy.array_split() . Parameters: input (Tensor) – the tensor to split. indices_or_sections (Tensor, int or list or tuple ...

torch.reshape

Returns a tensor with the same data and number of elements as input , but with the specified shape.

torch.flatten

Unlike NumPy's flatten, which always copies input's data, this function may return the original object, a view, or copy. If no dimensions are flattened, ...

torch.meshgrid

meshgrid(*tensors) currently has the same behavior as calling numpy.meshgrid(*arrays, indexing='ij') . In the future torch.meshgrid will transition to indexing= ...

torch.set_printoptions

threshold – Total number of array elements which trigger summarization rather than full repr (default = 1000).