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- 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=(3, 224, 224)):
- """ 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)
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