models.py 文件源码

def dilated_FCN_frontended(input_shape=None, weight_decay=None, nb_classes=40):

    img_input = Input(shape=input_shape)

    #x = ZeroPadding2D(padding=(100, 100), name='pad1')(img_input)
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
    x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same', name='block1_pool')(x)

    # Block 2
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='block2_pool')(x)

    # Block 3
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='block3_pool')(x)

    # Block 4
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)

    x = Conv2D(512, (3,3), dilation_rate=(2,2), activation='relu', name='block5_conv1')(x)
    x = Conv2D(512, (3,3), dilation_rate=(2,2), activation='relu', name='block5_conv2')(x)
    x = Conv2D(512, (3,3), dilation_rate=(2,2), activation='relu', name='block5_conv3')(x)

    x = Conv2D(4096, (3,3), kernel_initializer='he_normal', dilation_rate=(4,4), activation='relu', name='fc6')(x)
    x = Dropout(0.5, name='drop6')(x)
    x = Conv2D(4096, (1,1), kernel_initializer='he_normal', activation='relu', name='fc7')(x)
    x = Dropout(0.5, name='drop7')(x)
    x = Conv2D(nb_classes, (1,1), kernel_initializer='he_normal', activation='relu', name='fc_final')(x)


    #x = Conv2DTranspose(nb_classes, kernel_size=(64,64), strides=(32,32), padding='valid', name='upscore2')(x)    
    x = ZeroPadding2D(padding=(33,33))(x)
    x = Conv2D(2*nb_classes, (3,3), kernel_initializer='he_normal',activation='relu', name='dl_conv1')(x)
    x = Conv2D(2*nb_classes, (3,3), kernel_initializer='he_normal',activation='relu', name='dl_conv2')(x)
    x = Conv2D(4*nb_classes, (3,3), kernel_initializer='he_normal',dilation_rate=(2,2), activation='relu', name='dl_conv3')(x)
    x = Conv2D(8*nb_classes, (3,3), kernel_initializer='he_normal',dilation_rate=(4,4), activation='relu', name='dl_conv4')(x)
    x = Conv2D(16*nb_classes, (3,3), kernel_initializer='he_normal',dilation_rate=(8,8), activation='relu', name='dl_conv5')(x)
    x = Conv2D(32*nb_classes, (3,3), kernel_initializer='he_normal',dilation_rate=(16,16), activation='relu', name='dl_conv6')(x)
    x = Conv2D(32*nb_classes, (1,1), kernel_initializer='he_normal',name='dl_conv7')(x)
    x = Conv2D(1*nb_classes, (1,1), kernel_initializer='he_normal',name='dl_final')(x)
    x = Conv2DTranspose(nb_classes, kernel_initializer='he_normal', kernel_size=(64,64), strides=(8,8), padding='valid', name='upscore2')(x)
    x = CroppingLike2D(img_input, offset='centered', name='score')(x)
    #x = Cropping2D(cropping=((19,36), (19,29)), name='score')(x)


    mdl = Model(input=img_input, output=x, name='dilated_fcn')
    weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5', VGG_WEIGHTS_PATH_NO_TOP, cache_subdir='models')
    mdl.load_weights(weights_path, by_name=True)
    return mdl