Quantize_per_channel

Usage

torch_quantize_per_channel(self, scales, zero_points, axis, dtype)

Arguments

self: (Tensor) float tensor to quantize
scales: (Tensor) float 1D tensor of scales to use, size should match input.size(axis)
zero_points: (int) integer 1D tensor of offset to use, size should match input.size(axis)
axis: (int) dimension on which apply per-channel quantization
dtype: (torch.dtype) the desired data type of returned tensor. Has to be one of the quantized dtypes: torch_quint8, torch.qint8, torch.qint32

quantize_per_channel(input, scales, zero_points, axis, dtype) -> Tensor

Converts a float tensor to per-channel quantized tensor with given scales and zero points.

Examples

if (torch_is_installed()) {
x = torch_tensor(matrix(c(-1.0, 0.0, 1.0, 2.0), ncol = 2, byrow = TRUE))
torch_quantize_per_channel(x, torch_tensor(c(0.1, 0.01)), 
                           torch_tensor(c(10L, 0L)), 0, torch_quint8())
torch_quantize_per_channel(x, torch_tensor(c(0.1, 0.01)), 
                           torch_tensor(c(10L, 0L)), 0, torch_quint8())$int_repr()
}
#> torch_tensor
#>    0   10
#>  100  200
#> [ CPUByteType{2,2} ]