com.microsoft - QuantizeLinear#

QuantizeLinear - 1 (com.microsoft)#

Version

This version of the operator has been available since version 1 of domain com.microsoft.

Summary

The linear quantization operator. It consumes a full precision data, a scale, a zero point to compute the low precision / quantized tensor. The quantization formula is y = saturate ((x / y_scale) + y_zero_point).For saturation, it saturates to [0, 255] if it’s uint8, or [-128, 127] if it’s int8. For (x / y_scale), it’s rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. Scale and zero point must have same shape. They must be either scalar (per tensor) or 1-D tensor (per ‘axis’).

Attributes

  • axis: The axis along which same quantization parameters are applied. It’s optional.If it’s not specified, it means per-tensor quantization and input ‘x_scale’ and ‘x_zero_point’ must be scalars.If it’s specified, it means per ‘axis’ quantization and input ‘x_scale’ and ‘x_zero_point’ must be 1-D tensors. Default value is ?.

Inputs

  • x (heterogeneous) - T1: N-D full precision Input tensor to be quantized.

  • y_scale (heterogeneous) - T1: Scale for doing quantization to get ‘y’. It could be a scalar or a 1-D tensor,which means a per-tensor or per-axis quantization. If it’s a 1-D tensor, its number of elements should be equal to the dimension value of ‘axis’ dimension of input ‘x’.

  • y_zero_point (heterogeneous) - T2: Zero point for doing quantization to get ‘y’. It could be a scalar or a 1-D tensor, which means a per-tensoror per-axis quantization. If it’s a 1-D tensor, its number of elements should be equal to the dimension value of ‘axis’ dimension of input ‘x’.

Outputs

  • y (heterogeneous) - T2: N-D quantized output tensor. It has same shape as input ‘x’.

Examples

default

node = onnx.helper.make_node('QuantizeLinear',
                             inputs=['x', 'y_scale', 'y_zero_point'],
                             outputs=['y'],)

x = np.array([0, 2, 3, 1000, -254, -1000]).astype(np.float32)
y_scale = np.float32(2)
y_zero_point = np.uint8(128)
y = np.array([128, 129, 130, 255, 1, 0]).astype(np.uint8)

expect(node, inputs=[x, y_scale, y_zero_point], outputs=[y],
       name='test_quantizelinear')

_axis

node = onnx.helper.make_node('QuantizeLinear',
                             inputs=['x', 'y_scale', 'y_zero_point'],
                             outputs=['y'],)

x = np.array([[[[-162, 10],
                [-100, 232],
                [-20, -50]],

               [[-76, 0],
                [0, 252],
                [32, -44]],

               [[245, -485],
                [-960, -270],
                [-375, -470]], ], ], dtype=np.float32)
y_scale = np.array([2, 4, 5], dtype=np.float32)
y_zero_point = np.array([84, 24, 196], dtype=np.uint8)
y = (x / y_scale.reshape(1, 3, 1, 1) + y_zero_point.reshape(1, 3, 1, 1)).astype(np.uint8)

expect(node, inputs=[x, y_scale, y_zero_point], outputs=[y],
       name='test_quantizelinear_axis')