使用Squeeze-Net 为基础网络的ssd（keras）

至于该网络优劣点还有待实验

# coding=utf-8
"""Keras implementation of SSD."""

import keras.backend as K
from keras.layers import Activation
from keras.layers import AtrousConvolution2D
from keras.layers import Conv2D
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import GlobalAveragePooling2D
from keras.layers import Input
from keras.layers import MaxPooling2D
from keras.layers import merge
from keras.layers import Reshape
from keras.layers import ZeroPadding2D
from keras.models import Model, Sequential

from ssd_layers import Normalize
from ssd_layers import PriorBox

from keras.models import Sequential
from keras.layers import Dense, Flatten, Dropout, Concatenate
from keras.layers.convolutional import Conv2D, MaxPooling2D
import numpy as np


def SqueezeNet(inputs, nb_classes=21):
    """ 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)
    """
    img_size = (inputs[0], inputs[1])
    input_img = (Input(shape=inputs))
    conv1 = Conv2D(
        64, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        strides=(1, 1), name='conv1', padding='same',
        data_format="channels_last")(input_img)
    # maxpool1
    maxpool1 = MaxPooling2D(
        pool_size=(2, 2), strides=(2, 2), name='maxpool1',
        data_format="channels_last")(conv1)

    # fire1
    fire1_squeeze = Conv2D(
        15, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        name='fire1_squeeze',
        data_format="channels_last")(maxpool1)
    fire1_expand1 = Conv2D(
        49, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire1_expand1',
        data_format="channels_last")(fire1_squeeze)
    fire1_expand2 = Conv2D(
        53, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire1_expand2',
        data_format="channels_last")(fire1_squeeze)
    merge1 = Concatenate(axis=3)([fire1_expand1, fire1_expand2])

    maxpool2 = MaxPooling2D(
        pool_size=(2, 2), strides=(2, 2), name='maxpool2',
        data_format="channels_last")(merge1)

    # fire2
    fire2_squeeze = Conv2D(
        15, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        name='fire2_squeeze',
        data_format="channels_last")(maxpool2)
    fire2_expand1 = Conv2D(
        54, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire2_expand1',
        data_format="channels_last")(fire2_squeeze)
    fire2_expand2 = Conv2D(
        52, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire2_expand2',
        data_format="channels_last")(fire2_squeeze)
    merge2 = Concatenate(axis=3)([fire2_expand1, fire2_expand2])

    # maxpool3
    maxpool3 = MaxPooling2D(
        pool_size=(3, 3), strides=(2, 2), padding='same', name='maxpool3',
        data_format="channels_last")(merge2)

    # fire3
    fire3_squeeze = Conv2D(
        29, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        name='fire3_squeeze',
        data_format="channels_last")(maxpool3)
    fire3_expand1 = Conv2D(
        92, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire3_expand1',
        data_format="channels_last")(fire3_squeeze)
    fire3_expand2 = Conv2D(
        94, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire3_expand2',
        data_format="channels_last")(fire3_squeeze)
    merge3 = Concatenate(axis=3)([fire3_expand1, fire3_expand2])

    # fire4
    fire4_squeeze = Conv2D(
        29, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        name='fire4_squeeze',
        data_format="channels_last")(merge3)
    fire4_expand1 = Conv2D(
        90, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        name='fire4_expand1',
        data_format="channels_last")(fire4_squeeze)
    fire4_expand2 = Conv2D(
        83, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire4_expand2',
        data_format="channels_last")(fire4_squeeze)
    merge4 = Concatenate(axis=3)([fire4_expand1, fire4_expand2])

    # maxpool4
    maxpool4 = MaxPooling2D(
        pool_size=(2, 2), strides=(2, 2), name='maxpool4', padding='same',
        data_format="channels_last")(merge4)

    # fire5
    fire5_squeeze = Conv2D(
        44, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        name='fire5_squeeze',
        data_format="channels_last")(maxpool4)
    fire5_expand1 = Conv2D(
        166, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire5_expand1',
        data_format="channels_last")(fire5_squeeze)
    fire5_expand2 = Conv2D(
        161, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire5_expand2',
        data_format="channels_last")(fire5_squeeze)
    merge5 = Concatenate(axis=3)([fire5_expand1, fire5_expand2])

    # fire6
    fire6_squeeze = Conv2D(
        45, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        name='fire6_squeeze',
        data_format="channels_last")(merge5)
    fire6_expand1 = Conv2D(
        155, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire6_expand1',
        data_format="channels_last")(fire6_squeeze)
    fire6_expand2 = Conv2D(
        146, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire6_expand2',
        data_format="channels_last")(fire6_squeeze)
    merge6 = Concatenate(axis=3)([fire6_expand1, fire6_expand2])

    # fire7
    fire7_squeeze = Conv2D(
        49, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        name='fire7_squeeze',
        data_format="channels_last")(merge6)
    fire7_expand1 = Conv2D(
        163, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire7_expand1',
        data_format="channels_last")(fire7_squeeze)
    fire7_expand2 = Conv2D(
        171, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire7_expand2',
        data_format="channels_last")(fire7_squeeze)
    merge7 = Concatenate(axis=3)([fire7_expand1, fire7_expand2])

    # fire8
    fire8_squeeze = Conv2D(
        25, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        name='fire8_squeeze',
        data_format="channels_last")(merge7)
    fire8_expand1 = Conv2D(
        29, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire8_expand1',
        data_format="channels_last")(fire8_squeeze)
    fire8_expand2 = Conv2D(
        54, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire8_expand2',
        data_format="channels_last")(fire8_squeeze)
    merge8 = Concatenate(axis=3)([fire8_expand1, fire8_expand2])

    # maxpool9
    maxpool9 = MaxPooling2D(
        pool_size=(3, 3), strides=(2, 2), padding='same', name='maxpool9',
        data_format="channels_last")(merge8)

    # fire9
    fire9_squeeze = Conv2D(
        37, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        name='fire9_squeeze',
        data_format="channels_last")(maxpool9)
    fire9_expand1 = Conv2D(
        45, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire9_expand1',
        data_format="channels_last")(fire9_squeeze)
    fire9_expand2 = Conv2D(
        56, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire9_expand2',
        data_format="channels_last")(fire9_squeeze)
    merge9 = Concatenate(axis=3)([fire9_expand1, fire9_expand2])

    # maxpool10
    maxpool10 = MaxPooling2D(
        pool_size=(2, 2), strides=(2, 2), name='maxpool10',
        data_format="channels_last")(merge9)

    # fire10
    fire10_squeeze = Conv2D(
        38, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire10_squeeze',
        data_format="channels_last")(maxpool10)
    fire10_expand1 = Conv2D(
        41, (1, 1), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire10_expand1',
        data_format="channels_last")(fire10_squeeze)
    fire10_expand2 = Conv2D(
        44, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='fire10_expand2',
        data_format="channels_last")(fire10_squeeze)
    merge10 = Concatenate(axis=3)([fire10_expand1, fire10_expand2])

    # cov12-1
    conv12_1 = Conv2D(
        51, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', strides=(2, 2), name='conv12_1',
        data_format='channels_last')(merge10)
    # padding_1
    padding_1 = ZeroPadding2D((1, 1), data_format='channels_last')(conv12_1)

    # conv12_2
    conv12_2 = Conv2D(
        46, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        name='conv12_2',
        data_format='channels_last')(padding_1)

    # conv13_1
    conv13_1 = Conv2D(
        55, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        padding='same', name='conv13_1',
        data_format='channels_last')(conv12_2)

    # padding_2
    # padding_2 = ZeroPadding2D((1,1),data_format='channels_last')(conv13_1)

    # conv13_2
    conv13_2 = Conv2D(
        85, (3, 3), activation='relu', kernel_initializer='glorot_uniform',
        name='conv13_2',
        data_format='channels_last')(conv13_1)

    # Prediction from Fire4
    num_priors = 3
    fire4_norm = Normalize(20, name='fire4_norm')(merge4)
    fire4_norm_mbox_loc = Conv2D(
        num_priors*4, (3, 3), name='fire4_norm_mbox_loc', padding='same',
        data_format='channels_last')(fire4_norm)
    fire4_mbox_norm_loc_flat = Flatten()(fire4_norm_mbox_loc)

    name = 'fire4_norm_mbox_conf'
    if nb_classes != 21:
        name += '_{}'.format(nb_classes)
    fire4_norm_mbox_conf = Conv2D(
        num_priors*nb_classes, (3, 3), name=name, padding='same',
        data_format='channels_last')(fire4_norm)
    fire4_norm_mbox_conf_flat = Flatten()(fire4_norm_mbox_conf)

    fire4_norm_mbox_priorbox = PriorBox(img_size, 30.0, aspect_ratios=[2],
                                        variances=[0.1, 0.1, 0.2, 0.2],
                                        name='fire4_norm_mbox_priorbox')(fire4_norm)
    fire4_priorbox_flatten = Flatten()(fire4_norm_mbox_priorbox)

    # Prediction from Fire8
    num_priors = 6
    fire8_mbox_loc = Conv2D(
        num_priors*4, (3, 3), name='fire8_mbox_loc', padding='same',
        data_format='channels_last')(merge8)
    fire8_mbox_loc_flat = Flatten()(fire8_mbox_loc)

    name = 'fire8_mbox_conf'
    if nb_classes != 21:
        name += '_{}'.format(nb_classes)
    fire8_mbox_conf = Conv2D(
        num_priors*nb_classes, (3, 3), name=name, padding='same',
        data_format='channels_last')(merge8)
    fire8_mbox_conf_flat = Flatten()(fire8_mbox_conf)

    fire8_mbox_priorbox = PriorBox(img_size, 60.0, max_size=114.0, aspect_ratios=[2, 3],
                                   variances=[0.1, 0.1, 0.2, 0.2],
                                   name='fire8_mbox_priorbox')(merge8)

    # Prediction from Fire9
    num_priors = 6
    fire9_mbox_loc = Conv2D(
        num_priors*4, (3, 3), name='fire9_mbox_loc', padding='same',
        data_format='channels_last')(merge9)
    fire9_mbox_loc_flat = Flatten()(fire9_mbox_loc)

    name = 'fire9_mbox_conf'
    if nb_classes != 21:
        name += '_{}'.format(nb_classes)
    fire9_mbox_conf = Conv2D(
        num_priors*nb_classes, (3, 3), name=name, padding='same',
        data_format='channels_last')(merge9)
    fire9_mbox_conf_flat = Flatten()(fire9_mbox_conf)

    fire9_mbox_priorbox = PriorBox(img_size, 114.0, max_size=168.0, aspect_ratios=[2, 3],
                                   variances=[0.1, 0.1, 0.2, 0.2],
                                   name='fire9_mbox_priorbox')(merge9)

    # Prediction from Fire10
    num_priors = 6
    fire10_mbox_loc = Conv2D(
        num_priors*4, (3, 3), name='fire10_mbox_loc', padding='same',
        data_format='channels_last')(merge10)
    fire10_mbox_loc_flat = Flatten()(fire10_mbox_loc)

    name = 'fire10_mbox_conf'
    if nb_classes != 21:
        name += '_{}'.format(nb_classes)
    fire10_mbox_conf = Conv2D(
        nb_classes*num_priors, (3, 3), name=name, padding='same',
        data_format='channels_last')(merge10)
    fire10_mbox_conf_flat = Flatten()(fire10_mbox_conf)

    fire10_mbox_priorbox = PriorBox(img_size, 168.0, max_size=222.0, aspect_ratios=[2, 3],
                                    variances=[0.1, 0.1, 0.2, 0.2],
                                    name='fire10_mbox_priorbox')(merge10)

    # Prediction from Conv12_2
    num_priors = 6
    conv12_maxpool = MaxPooling2D(pool_size=(1, 1), data_format="channels_last")(conv12_2)
    conv12_mbox_loc = Conv2D(
        num_priors*4, (3, 3), name='conv12_mbox_loc', padding='same',
        data_format='channels_last')(conv12_maxpool)
    conv12_mbox_loc_flat = Flatten()(conv12_mbox_loc)

    name = 'conv12_mbox_conf'
    if nb_classes != 21:
        name += '_{}'.format(nb_classes)
    conv12_mbox_conf = Conv2D(
        num_priors*nb_classes, (3, 3), name=name, padding='same',
        data_format='channels_last')(conv12_maxpool)
    conv12_mbox_conf_flat = Flatten()(conv12_mbox_conf)

    conv12_mbox_priorbox = PriorBox(img_size, 222.0, max_size=276.0, aspect_ratios=[2, 3],
                                    variances=[0.1, 0.1, 0.2, 0.2],
                                    name='conv12_mbox_priorbox')(conv12_maxpool)
    # pool6
    # pool6 = GlobalAveragePooling2D(name='pool6')(conv8_2)

    # Prediction from Conv13_2
    conv13_maxpool = MaxPooling2D(pool_size=(1, 1), data_format="channels_last")(conv13_2)
    num_priors = 6
    conv13_mbox_loc = Conv2D(
        num_priors*4, (3, 3), name='conv13_mbox_loc', padding='same',
        data_format='channels_last')(conv13_maxpool)
    conv13_mbox_loc_flat = Flatten()(conv13_mbox_loc)

    name = 'conv13_mbox_conf'
    if nb_classes != 21:
        name += '_{}'.format(nb_classes)
    conv13_mbox_conf = Conv2D(
        num_priors*nb_classes, (3, 3), name=name, padding='same',
        data_format='channels_last')(conv13_maxpool)
    conv13_mbox_conf_flat = Flatten()(conv13_mbox_conf)

    conv13_mbox_priorbox = PriorBox(img_size, 276.0, max_size=330.0, aspect_ratios=[2, 3],
                                    variances=[0.1, 0.1, 0.2, 0.2],
                                    name='conv13_mbox_priorbox')(conv13_maxpool)

    # Gather all predictions
    mbox_loc = Concatenate(axis=1)([fire4_mbox_norm_loc_flat,
                                    fire8_mbox_loc_flat,
                                    fire9_mbox_loc_flat,
                                    fire10_mbox_loc_flat,
                                    conv12_mbox_loc_flat,
                                    conv13_mbox_loc_flat])
    mbox_conf = Concatenate(axis=1)([fire4_norm_mbox_conf_flat,
                                     fire8_mbox_conf_flat,
                                     fire9_mbox_conf_flat,
                                     fire10_mbox_conf_flat,
                                     conv12_mbox_conf_flat,
                                     conv13_mbox_conf_flat])

    # fire4_mbox_priorbox_reshape = Reshape((-1,8),name = ' fire4_mbox_priorbox_reshape')(fire4_norm_mbox_priorbox)


    mbox_priorbox = Concatenate(axis=1)([fire4_norm_mbox_priorbox,
                                         fire8_mbox_priorbox,
                                         fire9_mbox_priorbox,
                                         fire10_mbox_priorbox,
                                         conv12_mbox_priorbox,
                                         conv13_mbox_priorbox])

    # dense = Dense(4096,activation='relu')(flatten_bbox)
    num_boxes = mbox_loc._keras_shape[-1] // 4
    if hasattr(mbox_loc, '_keras_shape'):
        num_boxes = mbox_loc._keras_shape[-1] // 4
    elif hasattr(mbox_loc, 'int_shape'):
        num_boxes = K.int_shape(mbox_loc)[-1] // 4

    mbox_loc_final = Reshape((num_boxes, 4), name='mbox_loc_final')(mbox_loc)
    mbox_conf_logits = Reshape((num_boxes, nb_classes), name='mbox_conf_logits')(mbox_conf)
    mbox_conf_final = Activation('softmax', name='mbox_conf_final')(mbox_conf_logits)

    predictions = Concatenate(axis=2, name='preditions')([mbox_loc_final, mbox_conf_final, mbox_priorbox])

    return Model(inputs=input_img, outputs=predictions)

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测试函数：

    model = SqueezeNet((300,300,3), 4)
    model.compile(loss='categorical_crossentropy',optimizer='sgd',metrics=['accuracy'])
    model.summary()