torch.Tensor — PyTorch 1.10.1 documentation
https://pytorch.org/docs/stable/tensorstorch.ByteTensor. /. 1. Sometimes referred to as binary16: uses 1 sign, 5 exponent, and 10 significand bits. Useful when precision is important at the expense of range. 2. Sometimes referred to as Brain Floating Point: uses 1 sign, 8 exponent, and 7 significand bits. Useful when range is important, since it has the same number of exponent bits ...
How do I create a scale matrix for rescaling a PyTorch tensor ...
stackoverflow.com › questions › 64407726Oct 17, 2020 · def get_scale_mat(m, device, dtype): scale_mat = torch.tensor([[m, 0., 0.], [0., m, 0.]]) return scale_mat def scale_tensor(x, scale): assert scale > 0 scale_matrix = get_scale_mat(scale, x.device, x.dtype)[None, ...].repeat(x.shape[0],1,1) grid = F.affine_grid(scale_matrix, x.size()) x = F.grid_sample(x, grid) return x
torch.quantize_per_tensor — PyTorch 1.10.1 documentation
pytorch.org › torchtorch.quantize_per_tensor. Converts a float tensor to a quantized tensor with given scale and zero point. dtype ( torch.dtype) – the desired data type of returned tensor. Has to be one of the quantized dtypes: torch.quint8, torch.qint8, torch.qint32. A newly quantized tensor or list of quantized tensors.
Pytorch Tensor scaling - PyTorch Forums
discuss.pytorch.org › t › pytorch-tensor-scalingFeb 28, 2019 · You can easily clone the sklearn behavior using this small script: x = torch.randn (10, 5) * 10 scaler = StandardScaler () arr_norm = scaler.fit_transform (x.numpy ()) # PyTorch impl m = x.mean (0, keepdim=True) s = x.std (0, unbiased=False, keepdim=True) x -= m x /= s torch.allclose (x, torch.from_numpy (arr_norm)) Alternatively, you could of course just use the sklearn scaler directly, as torch.numpy () and torch.from_numpy () return arrays which share the underlying data, and are thus ...
torch.Tensor.to — PyTorch 1.10.1 documentation
pytorch.org › docs › stabletorch.Tensor.to. Performs Tensor dtype and/or device conversion. A torch.dtype and torch.device are inferred from the arguments of self.to (*args, **kwargs). If the self Tensor already has the correct torch.dtype and torch.device, then self is returned. Otherwise, the returned tensor is a copy of self with the desired torch.dtype and torch.device.
torch.Tensor — PyTorch 1.10.1 documentation
pytorch.org › docs › stabletorch.ByteTensor. /. 1. Sometimes referred to as binary16: uses 1 sign, 5 exponent, and 10 significand bits. Useful when precision is important at the expense of range. 2. Sometimes referred to as Brain Floating Point: uses 1 sign, 8 exponent, and 7 significand bits. Useful when range is important, since it has the same number of exponent bits ...