Source code for akida_models.utk_face.model_vgg

#!/usr/bin/env python
# ******************************************************************************
# Copyright 2020 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
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"""
VGG model definition for UTKFace regression.
"""

from tensorflow.keras import Model
from tensorflow.keras.layers import Dropout, Flatten, Input
from tensorflow.keras.utils import get_file

# CNN2SNN imports
from cnn2snn import load_quantized_model, quantize

from ..layer_blocks import conv_block, dense_block

BASE_WEIGHT_PATH = 'http://data.brainchip.com/models/vgg/'


[docs]def vgg_utk_face(input_shape=(32, 32, 3), weight_quantization=0, activ_quantization=0, input_weight_quantization=None): """Instantiates a VGG-like model for the regression example on age estimation using UTKFace dataset. Args: input_shape (tuple): input shape tuple of the model weight_quantization (int): sets all weights in the model to have a particular quantization bitwidth except for the weights in the first layer. * '0' implements floating point 32-bit weights. * '2' through '8' implements n-bit weights where n is from 2-8 bits. activ_quantization (int): sets all activations in the model to have a particular activation quantization bitwidth. * '0' implements floating point 32-bit activations. * '1' through '8' implements n-bit weights where n is from 1-8 bits. input_weight_quantization (int): sets weight quantization in the first layer. Defaults to weight_quantization value. * 'None' implements the same bitwidth as the other weights. * '0' implements floating point 32-bit weights. * '2' through '8' implements n-bit weights where n is from 2-8 bits. Returns: tf.keras.Model: a quantized Keras model for VGG/UTKFace """ # Overrides input weight quantization if None if input_weight_quantization is None: input_weight_quantization = weight_quantization img_input = Input(shape=input_shape) x = conv_block(img_input, filters=32, kernel_size=(3, 3), name='conv_0', use_bias=False, add_batchnorm=True, add_activation=True) x = conv_block(x, filters=32, kernel_size=(3, 3), name='conv_1', padding='same', pooling='max', pool_size=2, use_bias=False, add_batchnorm=True, add_activation=True) x = Dropout(0.3)(x) x = conv_block(x, filters=64, kernel_size=(3, 3), padding='same', name='conv_2', use_bias=False, add_batchnorm=True, add_activation=True) x = conv_block(x, filters=64, kernel_size=(3, 3), padding='same', name='conv_3', pooling='max', pool_size=2, use_bias=False, add_batchnorm=True, add_activation=True) x = Dropout(0.3)(x) x = conv_block(x, filters=84, kernel_size=(3, 3), padding='same', name='conv_4', use_bias=False, add_batchnorm=True, add_activation=True) x = Dropout(0.3)(x) x = Flatten()(x) x = dense_block(x, units=64, name='dense_1', use_bias=False, add_batchnorm=True, add_activation=True) x = dense_block(x, units=1, name='dense_2', add_activation=False) model = Model(img_input, x, name='vgg_utk_face') if ((weight_quantization != 0) or (activ_quantization != 0) or (input_weight_quantization != 0)): return quantize(model, weight_quantization, activ_quantization, input_weight_quantization) return model
[docs]def vgg_utk_face_pretrained(): """ Helper method to retrieve a `vgg_utk_face` model that was trained on UTK Face dataset. Returns: tf.keras.Model: a Keras Model instance. """ model_name = 'vgg_utk_face_iq8_wq2_aq2.h5' file_hash = 'c5f7c722c19dd1f2f9743acd8d315b0ef51adb1f2dd42138b1e34c5f81782b96' model_path = get_file(fname=model_name, origin=BASE_WEIGHT_PATH + model_name, file_hash=file_hash, cache_subdir='models') return load_quantized_model(model_path)