module `onnxrt.ops_cpu.op_dequantize_linear`#

Short summary#

module mlprodict.onnxrt.ops_cpu.op_dequantize_linear

Runtime operator.

Classes#

class	truncated documentation
`DequantizeLinear`	DequantizeLinear ================ The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero …

Properties#

property	truncated documentation
`args_default`	Returns the list of arguments as well as the list of parameters with the default values (close to the signature). …
`args_default_modified`	Returns the list of modified parameters.
`args_mandatory`	Returns the list of optional arguments.
`args_optional`	Returns the list of optional arguments.
`atts_value`	Returns all parameters in a dictionary.

Methods#

method	truncated documentation
`__init__`
`_infer_shapes`
`_infer_sizes`
`_infer_types`
`_run`

Documentation#

Runtime operator.

source on GitHub

class mlprodict.onnxrt.ops_cpu.op_dequantize_linear.DequantizeLinear(onnx_node, desc=None, **options)#

Bases: mlprodict.onnxrt.ops_cpu._op.OpRun

The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full precision tensor. The dequantization formula is y = (x - x_zero_point) * x_scale. ‘x_scale’ and ‘x_zero_point’ must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization. ‘x_zero_point’ and ‘x’ must have same type. ‘x’ and ‘y’ must have same shape. In the case of dequantizing int32, there’s no zero point (zero point is supposed to be 0).

Attributes

axis: (Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). Default value is nameaxisi1typeINT (INT)

Inputs

Between 2 and 3 inputs.

x (heterogeneous)T: N-D quantized input tensor to be de-quantized.
x_scale (heterogeneous)tensor(float): Scale for input ‘x’. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization.
x_zero_point (optional, heterogeneous)T: Zero point for input ‘x’. Shape must match x_scale. It’s optional. Zero point is 0 when it’s not specified.

Outputs