Source code for akida_models.cifar10.model_vgg

#!/usr/bin/env python
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# Copyright 2020 Brainchip Holdings Ltd.
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# 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
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#    http://www.apache.org/licenses/LICENSE-2.0
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"""
VGG model definition for CIFAR10 classification.
"""

from tensorflow.keras.layers import Input, Flatten
from tensorflow.keras import Model
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_cifar10(input_shape=(32, 32, 3), classes=10, weight_quantization=0, activ_quantization=0, input_weight_quantization=None): """Instantiates a vgg-like model for the "Cifar-10" example. This model is based on the vgg architecture, mainly with fewer layers. The weights and activations are quantized such that it can be converted into an Akida model. This architecture is inspired by vgg. Args: input_shape (tuple): input shape tuple of the model classes (int): number of classes to classify images into 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. * '2' through '8' implements n-bit weights where n is from 2-8 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/cifar10 """ # 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=128, kernel_size=(3, 3), name='conv_0', padding='same', use_bias=False, add_batchnorm=True, add_activation=True) x = conv_block(x, filters=128, kernel_size=(3, 3), name='conv_1', padding='same', use_bias=False, pooling='max', add_batchnorm=True, add_activation=True) x = conv_block(x, filters=256, kernel_size=(3, 3), name='conv_2', padding='same', use_bias=False, add_batchnorm=True, add_activation=True) x = conv_block(x, filters=256, kernel_size=(3, 3), name='conv_3', padding='same', use_bias=False, pooling='max', add_batchnorm=True, add_activation=True) x = conv_block(x, filters=512, name='conv_4', kernel_size=(3, 3), padding='same', use_bias=False, add_batchnorm=True, add_activation=True) x = conv_block(x, filters=512, kernel_size=(3, 3), name='conv_5', padding='same', use_bias=False, pooling='max', add_batchnorm=True, add_activation=True) x = conv_block(x, filters=1024, kernel_size=(1, 1), name='conv_6', padding='same', use_bias=False, add_batchnorm=True, add_activation=True) x = Flatten()(x) x = dense_block(x, units=1024, name='dense_7', use_bias=False, add_batchnorm=True, add_activation=True) x = dense_block(x, units=classes, name='dense_8', use_bias=False, add_batchnorm=False, add_activation=False) model = Model(img_input, x, name='vgg_cifar10') 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_cifar10_pretrained(): """ Helper method to retrieve a `vgg_cifar10` model that was trained on CIFAR10 dataset. Returns: tf.keras.Model: a Keras Model instance. """ model_name = 'vgg_cifar10_iq2_wq2_aq2.h5' file_hash = '9f94ba8efc30877bc086cb77c22f220b26384f9c98cdae9e2cfb6c9e22a97033' 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)