#!/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
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"""
VGG model definition for UTKFace regression.
"""
from keras import Model
from keras.layers import Dropout, Flatten, Input, Rescaling
from cnn2snn import load_quantized_model, quantize
from ..layer_blocks import conv_block, dense_block
from ..utils import fetch_file
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,
input_scaling=(127, -1)):
"""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.
input_scaling (tuple, optional): scale factor and offset to apply to
inputs. Defaults to (127, -1). Note that following Akida convention,
the scale factor is an integer used as a divider.
Returns:
keras.Model: a 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, name="input")
if input_scaling is None:
x = img_input
else:
scale, offset = input_scaling
x = Rescaling(1. / scale, offset, name="rescaling")(img_input)
x = conv_block(x,
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, name="dropout_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, name="dropout_4")(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, name="dropout_5")(x)
x = Flatten(name="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:
keras.Model: a Keras Model instance.
"""
model_name = 'vgg_utk_face_iq8_wq2_aq2.h5'
file_hash = 'e341d2d5e4655846ddc7aceff0d4e324cbfbcca16f3cfefc65e7b0863e4a23a3'
model_path = fetch_file(BASE_WEIGHT_PATH + model_name,
fname=model_name,
file_hash=file_hash,
cache_subdir='models')
return load_quantized_model(model_path)