#!/usr/bin/env python
# ******************************************************************************
# Copyright 2022 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__ = ["QuantizedFlatten"]
import tensorflow as tf
import keras
import functools
import operator
from ..layers_base import (register_quantize_target, tensor_inputs, register_no_output_quantizer,
register_aligned_inputs)
from ..recorders import TensorRecorder
from ...tensors import FixedPoint
[docs]
@register_quantize_target(keras.layers.Flatten)
@register_no_output_quantizer
@register_aligned_inputs
@keras.saving.register_keras_serializable()
class QuantizedFlatten(keras.layers.Flatten):
"""A Flatten layer that operates on quantized inputs
"""
@tensor_inputs([FixedPoint])
def call(self, inputs):
if not inputs.per_tensor:
inputs, shift = inputs.expand(inputs.value_bits)
if getattr(self, 'input_shift', None) is None:
with tf.init_scope():
self.input_shift = TensorRecorder(name=self.name + "/input_shift")
self.input_shift(shift)
if tf.executing_eagerly():
flattened_shape = tf.constant([inputs.shape[0], -1])
else:
non_batch_dims = inputs.shape[1:]
last_dim = int(functools.reduce(operator.mul, non_batch_dims))
flattened_shape = tf.constant([-1, last_dim])
return tf.reshape(inputs, flattened_shape)