models.py 文件源码

def fcn_vggbase(input_shape=(None,None,3)):

    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')(x)
    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 = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='block4_pool')(x)

    # Block 5
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), padding='same',name='block5_pool')(x)

    x = Conv2D(filters=4096, kernel_size=(7, 7), W_regularizer=l2(0.00005), activation='relu', padding='valid', name='fc6_lsun')(x)
    x = Dropout(0.85)(x)
    x = Conv2D(filters=4096, kernel_size=(1, 1), W_regularizer=l2(0.00005), activation='relu', padding='valid', name='fc7_lsun')(x)
    x = Dropout(0.85)(x)
    x = Conv2D(filters=5, kernel_size=(1, 1), strides=(1,1), kernel_initializer='he_normal', padding='valid', name='lsun_score')(x)

    x = Conv2DTranspose(filters=5, kernel_initializer='he_normal', kernel_size=(64, 64), strides=(32, 32), padding='valid',use_bias=False, name='lsun_upscore2')(x)
    output = _crop(img_input,offset=(32,32), name='score')(x)

    model = Model(img_input, output)
    weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5', VGG_WEIGHTS_PATH_NO_TOP, cache_subdir='models')
    model.load_weights(weights_path, by_name=True)

    return model