SqueezeNet/SqueezeNet.py

import h5py
from keras.models import Model
from keras.layers import Input, Activation, Concatenate
from keras.layers import Flatten, Dropout
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers import GlobalAveragePooling2D


def SqueezeNet(nb_classes, inputs):
    """ Keras Implementation of SqueezeNet(arXiv 1602.07360)
    @param nb_classes: total number of final categories
    Arguments:
    inputs -- shape of the input images (channel, cols, rows)
    """

    input_img = Input(shape=inputs)
    conv1 = Convolution2D(96, (7, 7), activation='relu', kernel_initializer='glorot_uniform',
        strides=(2, 2), padding='same', name='conv1',
        data_format="channels_first")(input_img)

    maxpool1 = MaxPooling2D(
        pool_size=(3, 3), strides=(2, 2), name='maxpool1',
        data_format="channels_first")(conv1)
    
    fire2_squeeze = Convolution2D(
        16, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire2_squeeze',
        data_format="channels_first")(maxpool1)
    
    fire2_expand1 = Convolution2D(
        64, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire2_expand1',
        data_format="channels_first")(fire2_squeeze)
    
    fire2_expand2 = Convolution2D(
        64, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire2_expand2',
        data_format="channels_first")(fire2_squeeze)
    merge2 = Concatenate(axis=1)([fire2_expand1, fire2_expand2])

    fire3_squeeze = Convolution2D(
        16, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire3_squeeze',
        data_format="channels_first")(merge2)
    fire3_expand1 = Convolution2D(
        64, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire3_expand1',
        data_format="channels_first")(fire3_squeeze)
    fire3_expand2 = Convolution2D(
        64, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire3_expand2',
        data_format="channels_first")(fire3_squeeze)
    
    merge3 = Concatenate(axis=1)([fire3_expand1, fire3_expand2])

    fire4_squeeze = Convolution2D(
        32, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire4_squeeze',
        data_format="channels_first")(merge3)
    
    fire4_expand1 = Convolution2D(
        128, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire4_expand1',
        data_format="channels_first")(fire4_squeeze)
    
    fire4_expand2 = Convolution2D(
        128, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire4_expand2',
        data_format="channels_first")(fire4_squeeze)
    
    merge4 = Concatenate(axis=1)([fire4_expand1, fire4_expand2])
    
    maxpool4 = MaxPooling2D(
        pool_size=(3, 3), strides=(2, 2), name='maxpool4',
        data_format="channels_first")(merge4)

    fire5_squeeze = Convolution2D(
        32, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire5_squeeze',
        data_format="channels_first")(maxpool4)
    
    fire5_expand1 = Convolution2D(
        128, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire5_expand1',
        data_format="channels_first")(fire5_squeeze)
    fire5_expand2 = Convolution2D(
        128, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire5_expand2',
        data_format="channels_first")(fire5_squeeze)
    merge5 = Concatenate(axis=1)([fire5_expand1, fire5_expand2])

    fire6_squeeze = Convolution2D(
        48, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire6_squeeze',
        data_format="channels_first")(merge5)
    
    fire6_expand1 = Convolution2D(
        192, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire6_expand1',
        data_format="channels_first")(fire6_squeeze)
    
    fire6_expand2 = Convolution2D(
        192, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire6_expand2',
        data_format="channels_first")(fire6_squeeze)
    
    merge6 = Concatenate(axis=1)([fire6_expand1, fire6_expand2])

    fire7_squeeze = Convolution2D(
        48, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire7_squeeze',
        data_format="channels_first")(merge6)
    
    fire7_expand1 = Convolution2D(
        192, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire7_expand1',
        data_format="channels_first")(fire7_squeeze)
    
    fire7_expand2 = Convolution2D(
        192, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire7_expand2',
        data_format="channels_first")(fire7_squeeze)
    
    merge7 = Concatenate(axis=1)([fire7_expand1, fire7_expand2])

    fire8_squeeze = Convolution2D(
        64, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire8_squeeze',
        data_format="channels_first")(merge7)
    
    fire8_expand1 = Convolution2D(
        256, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire8_expand1',
        data_format="channels_first")(fire8_squeeze)
    
    fire8_expand2 = Convolution2D(
        256, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire8_expand2',
        data_format="channels_first")(fire8_squeeze)
    
    merge8 = Concatenate(axis=1)([fire8_expand1, fire8_expand2])

    maxpool8 = MaxPooling2D(
        pool_size=(3, 3), strides=(2, 2), name='maxpool8',
        data_format="channels_first")(merge8)
    
    fire9_squeeze = Convolution2D(
        64, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire9_squeeze',
        data_format="channels_first")(maxpool8)
    
    fire9_expand1 = Convolution2D(
        256, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire9_expand1',
        data_format="channels_first")(fire9_squeeze)
    
    fire9_expand2 = Convolution2D(
        256, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire9_expand2',
        data_format="channels_first")(fire9_squeeze)
    
    merge9 = Concatenate(axis=1)([fire9_expand1, fire9_expand2])

    fire9_dropout = Dropout(0.5, name='fire9_dropout')(merge9)
    
    conv10 = Convolution2D(
        nb_classes, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='valid', name='conv10',
        data_format="channels_first")(fire9_dropout)

    global_avgpool10 = GlobalAveragePooling2D(data_format='channels_first')(conv10)
    softmax = Activation("softmax", name='softmax')(global_avgpool10)

    return Model(inputs=input_img, outputs=softmax)