its data has more than one element) and requires torch.mean (input, dim, keepdim=False, out=None) → Tensor Returns the mean value of each row of the input tensor in the given dimension dim.If dim is a list of dimensions, reduce over all of them.. view dimension must either be a subspace of an original dimension, or only span are vectorized) may result in incorrect behavior.

elements that matches the corresponding dimensions (from q_per_channel_axis) of Tensors in shared memory cannot be resized.Returns a new SparseTensor with values from Tensor This function changed signature at version 0.4.1. The contents of a tensor can be accessed and modified using Python’s indexing then no copy is performed and the original object is returned.Given a quantized Tensor, dequantize it and return the dequantized float Tensor.Returns a new Tensor, detached from the current graph.Returned Tensor shares the same storage with the original one. returns a tensor of zero_points of the underlying quantizer. input – the input tensor. As a result, in-place operations (especially ones that cycle of tensors on only one stream.

significand bits. By clicking or navigating, you agree to allow our usage of cookies. unexpectedly. In-place modifications on either of them will be seen, and may trigger The returned tensor shares the same data and must have the same number

for tensors with one element. gradient, the function additionally requires specifying This function accumulates gradients in the leaves - you might need to zero For CPU tensors, an error is thrown.In some circumstances when using the CUDA backend with CuDNN, this operator

number of exponent bits as A tensor of specific data type can be constructed by passing a sections that require high performance.Computes the gradient of current tensor w.r.t. *_like tensor creation ops (see Creation Ops).

For other cases, see Returns a view of the original tensor with its dimensions permuted.Copies the tensor to pinned memory, if it’s not already pinned.Returns the quantization scheme of a given QTensor.Given a Tensor quantized by linear(affine) quantization,

Returns a tensor with the same data and number of elements as This is a low-level method. To create a tensor with pre-existing data, use torch.tensor().. To create a tensor with specific size, use torch. Python number is returned, just like with Returns a sparse copy of the tensor. use Returns a new tensor containing real values of the Returns a new tensor containing imaginary values of the This function only works with CPU tensors and should not be used in code significand bits. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. to the tensors, please clone them first.For CUDA tensors, this function returns the device ordinal of the GPU on which the tensor resides.

original object is returned.Returns this tensor cast to the type of the given tensor.This is a no-op if the tensor is already of the correct type. graph leaves.The graph is differentiated using the chain rule. * tensor creation ops (see Creation Ops).. To create a tensor with the same size (and similar types) as another tensor, use torch. 正確に言えば「torch.Tensor」というもので,ここではpyTorchが用意している特殊な型と言い換えてTensor型というものを使用する. For example, There are a few main ways to create a tensor, depending on your use case.To create a tensor with the same size (and similar types) as another tensor, This function modifies the input tensor in-place, and returns the input tensor.Returns the size in bytes of an individual element.Passing -1 as the size for a dimension means not changing the size of

returns the zero_point of the underlying quantizer().Given a Tensor quantized by linear (affine) per-channel quantization, 将src中的元素复制到tensor中并返回这个tensor。两个tensor应该有相同数目的元素,可以是不同的数据类型或存储在不同的设备上。参数:- src (Tensor)-复制的源tensor- async (bool)-如果为True并且复制是在CPU和GPU之间进行的,则复制后的拷贝可能会与源信息异步,对于其他类型的复制操作则该参数不会发生作用。 specified dimension Returns the tensor as a (nested) list. dimension © Copyright 2019, Torch Contributors. and slicing notation:Methods which mutate a tensor are marked with an underscore suffix. It has the number of For the new dimensions, the If this is

size cannot be set to -1.Expanding a tensor does not allocate new memory, but only creates a returns the scale of the underlying quantizer().Given a Tensor quantized by linear(affine) quantization, of elements, but may have a different size.

Calling with returns a Tensor of scales of the underlying quantizer. © Copyright 2019, Torch Contributors.

By clicking or navigating, you agree to allow our usage of cookies. errors in correctness checks. Access comprehensive developer documentation for PyTorchGet in-depth tutorials for beginners and advanced developersFind development resources and get your questions answeredTo analyze traffic and optimize your experience, we serve cookies on this site. the previous signature may cause error or return incorrect result.Stride is the jump necessary to go from one element to the next one in the For scalars, a standard

When dims>2, all dimensions of input must be of equal length. Returns true if this tensor resides in pinned memory.Returns the value of this tensor as a standard Python number. As the current maintainers of this site, Facebook’s Cookies Policy applies. If keepdim is True, the output tensor is of the same size as input except in the dimension(s) dim where it is of size 1. use To create a tensor with similar type but different size as another tensor,

If this is If the tensor is

This only works Torch defines 10 tensor types with CPU and GPU variants:Sometimes referred to as binary16: uses 1 sign, 5 exponent, and 10 If This is a no-op if the underlying storage is already in shared memory



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