Source code for akida_models.imagenet.model_vgg

#!/usr/bin/env python
# ******************************************************************************
# Copyright 2021 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.
# ******************************************************************************
"""
VGG model definition for ImageNet classification.
"""

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

from cnn2snn import quantize, load_quantized_model

from ..layer_blocks import conv_block, dense_block

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


[docs]def vgg_imagenet(input_shape=(224, 224, 3), classes=1000, include_top=True, pooling=None, weight_quantization=0, activ_quantization=0, input_weight_quantization=None): """Instantiates a VGG11 architecture with reduced number of filters in convolutional layers (i.e. a quarter of the filters of the original implementation of https://arxiv.org/pdf/1409.1556.pdf). Args: input_shape (tuple, optional): input shape tuple. Defaults to (224, 224, 3). classes (int, optional): optional number of classes to classify images into. Defaults to 1000. include_top (bool, optional): whether to include the classification layers at the top of the model. Defaults to True. pooling (str, optional): Optional pooling mode for feature extraction when `include_top` is `False`. Defaults to None. * `None` means that the output of the model will be the 4D tensor output of the last convolutional block. * `avg` means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor. weight_quantization (int, optional): sets all weights in the model to have a particular quantization bitwidth except for the weights in the first layer. Defaults to 0. * '0' implements floating point 32-bit weights. * '2' through '8' implements n-bit weights where n is from 2-8 bits. activ_quantization (int, optional): sets all activations in the model to have a particular activation quantization bitwidth. Defaults to 0. * '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, optional): sets weight quantization in the first layer. Defaults to weight_quantization value. * '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 Keras model for VGG/ImageNet """ # check if overrides have been provided and override if input_weight_quantization is None: input_weight_quantization = weight_quantization # Define weight regularization weight_regularizer = regularizers.l2(4e-5) img_input = Input(shape=input_shape) # Block 1 x = conv_block(img_input, filters=16, name='block1_conv1', kernel_size=(3, 3), padding='same', kernel_regularizer=weight_regularizer, add_batchnorm=True, add_activation=True, pooling='max', pool_size=(2, 2)) # Block 2 x = conv_block(x, filters=32, name='block2_conv1', kernel_size=(3, 3), padding='same', kernel_regularizer=weight_regularizer, add_batchnorm=True, add_activation=True, pooling='max', pool_size=(2, 2)) # Block 3 x = conv_block(x, filters=64, name='block3_conv1', kernel_size=(3, 3), padding='same', kernel_regularizer=weight_regularizer, add_batchnorm=True, add_activation=True) x = conv_block(x, filters=64, name='block3_conv2', kernel_size=(3, 3), padding='same', kernel_regularizer=weight_regularizer, add_batchnorm=True, add_activation=True, pooling='max', pool_size=(2, 2)) # Block 4 x = conv_block(x, filters=128, name='block4_conv1', kernel_size=(3, 3), padding='same', kernel_regularizer=weight_regularizer, add_batchnorm=True, add_activation=True) x = conv_block(x, filters=128, name='block4_conv2', kernel_size=(3, 3), padding='same', kernel_regularizer=weight_regularizer, add_batchnorm=True, add_activation=True, pooling='max', pool_size=(2, 2)) # Block 5 x = conv_block(x, filters=128, name='block5_conv1', kernel_size=(3, 3), padding='same', kernel_regularizer=weight_regularizer, add_batchnorm=True, add_activation=True) layer_pooling = 'global_avg' if pooling == 'avg' else 'max' x = conv_block(x, filters=128, name='block5_conv2', kernel_size=(3, 3), padding='same', kernel_regularizer=weight_regularizer, add_batchnorm=True, add_activation=True, pooling=layer_pooling, pool_size=(2, 2)) if include_top: # Classification block x = Flatten(name='flatten')(x) x = dense_block(x, units=4096, name='fc1', add_batchnorm=True, add_activation=True) x = Dropout(0.5, name='dropout_1')(x) x = dense_block(x, units=4096, name='fc2', add_batchnorm=True, add_activation=True) x = Dropout(0.5, name='dropout_2')(x) x = dense_block(x, units=classes, name='predictions', add_batchnorm=False, add_activation=False) act_function = 'softmax' if classes > 1 else 'sigmoid' x = Activation(act_function, name=f'act_{act_function}')(x) # Create model model = Model(img_input, x, name='vgg11_%s_%s' % (input_shape[0], classes)) 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_imagenet_pretrained(): """ Helper method to retrieve a `vgg_imagenet` model that was trained on ImageNet dataset. Returns: tf.keras.Model: a Keras Model instance. """ model_name = 'vgg11_imagenet_224_iq8_wq4_aq4.h5' file_hash = 'e585de7ff31ff4e521b193d4eccd3c00896f9149dacea7ac17af8c0f9d361fb3' 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)
[docs]def vgg_melanoma_pretrained(): """ Helper method to retrieve a `vgg_imagenet` model that was trained on SIIM-ISIC Melanoma Classification dataset. Returns: tf.keras.Model: a Keras Model instance. """ model_name = 'vgg11_melanoma_iq8_wq4_aq4.h5' file_hash = 'e44274cf287529ebb70290bd21b4fc83979929c4ac1247390a6f1ba040ddf260' 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)