models.py 文件源码

def fcn16s_vggbase(input_shape=None, nb_class=None):
    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)
    pool4 = 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')(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')(x)
    x = Dropout(0.85)(x)
    x = Conv2D(filters=nb_class, kernel_size=(1, 1), strides=(1,1), kernel_initializer='he_normal', padding='valid', name='p5score')(x)
    x = Conv2DTranspose(filters=nb_class, kernel_size=(4,4), strides=(2,2), kernel_initializer='he_normal', padding='valid', name='p5upscore')(x)

    pool4 = Conv2D(filters=nb_class, kernel_size=(1,1), kernel_initializer='he_normal', padding='valid', name='pool4_score')(pool4)
    pool4_score = _crop(x, offset=(5,5), name='pool4_score2')(pool4)
    m = merge([pool4_score,x], mode='sum')
    upscore = Conv2DTranspose(filters=nb_class, kernel_size=(32,32), strides=(16,16), padding='valid', name='merged_score')(m)
    score = _crop(img_input, offset=(27,27), name='output_score')(upscore)

    weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5', VGG_WEIGHTS_PATH_NO_TOP, cache_subdir='models')
    mdl = Model(img_input, score, name='fcn16s')
    mdl.load_weights(weights_path, by_name=True)

    return mdl