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.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.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.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.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.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.reshape

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

torch.set_printoptions

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