#!/usr/bin/env python
# ******************************************************************************
# Copyright 2023 Brainchip Holdings Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ******************************************************************************
__all__ = ["QuantizedRescaling"]
import tensorflow as tf
import keras
from .layers_base import register_quantize_target, register_no_output_quantizer, tensor_inputs
from ..tensors import QFloat
[docs]@register_quantize_target(keras.layers.Rescaling)
@register_no_output_quantizer
@tf.keras.utils.register_keras_serializable()
class QuantizedRescaling(keras.layers.Rescaling):
"""A layer that multiplies integer inputs by a scale
This is a simplified version of the keras Rescaling layer:
- it only supports a scalar scale,
- it only supports zero offsets.
This layer assumes the inputs are 8-bit integer: it simply wraps them into
an 8-bit per-tensor QFloat with the specified scale.
Args:
scale (float): a scalar scale.
"""
def __init__(self, scale, **kwargs):
super().__init__(scale, **kwargs)
if tf.rank(self.scale) > 0:
raise ValueError("QuantizedRescaling only accepts scalar scale.")
if tf.reduce_any(self.offset != 0):
raise ValueError("QuantizedRescaling only accepts zero offset.")
@tensor_inputs([tf.Tensor])
def call(self, inputs):
# Wrap them into a QFloat with the specified scale
return QFloat(inputs, self.scale)