bipolar_quant_op.md

BipolarQuant

Calculates the binary quantized values of one input data (Tensor) and produces one output data (Tensor). Additionally, takes one float as input, which define the scaling.

Version

This operator is not part of the ONNX standard and is not currently versioned.

Attributes

Inputs

X (differentiable) : tensor(float32)

input tensor to quantize

scale : float32

The scale factor

Outputs

Y (differentiable) : tensor(float32)

Output tensor

Examples

BipolarQuant

from onnx import helper
import numpy as np

# Define node settings and input
x = np.random.randn(100).astype(np.float32)*10.
scale = np.array(1.)

# Create node
node = helper.make_node(
    'BipolarQuant',
    domain='finn.custom_op.general',
    inputs=['x', 'scale'],
    outputs=['y'],
)

# Execute the same settings with the reference implementation (quant)
# See the sample implementation for more details on quant.
output_ref = binary_quant(x, scale)

# Execute node and compare
expect(node, inputs=[x, scale], outputs=[output_ref], name='test_binary_quant')

Sample Implementation

BipolarQuant

# SPDX-License-Identifier: Apache-2.0

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import numpy as np

def binary_quant(inp_tensor, scale):
    # Quantizing
    y_int = inp_tensor
    y_ones = np.ones(y_int.shape, dtype=y_int.dtype)
    y_int = np.where(y_int >= 0.0, y_ones, -y_ones)
    # Scaling
    out_tensor = y_int * scale

    return out_tensor