models.py 文件源码

def dilat_fets(input_shape=None, classes=40):

    model_in = Input(shape=input_shape)
    h = Convolution2D(64, 3, 3, activation='relu', name='conv1_1')(model_in)
    h = Convolution2D(64, 3, 3, activation='relu', name='conv1_2')(h)
    h = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool1')(h)

    h = Convolution2D(128, 3, 3, activation='relu', name='conv2_1')(h)
    h = Convolution2D(128, 3, 3, activation='relu', name='conv2_2')(h)
    h = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool2')(h)

    h = Convolution2D(256, 3, 3, activation='relu', name='conv3_1')(h)
    h = Convolution2D(256, 3, 3, activation='relu', name='conv3_2')(h)
    h = Convolution2D(256, 3, 3, activation='relu', name='conv3_3')(h)
    h = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool3')(h)

    h = Convolution2D(512, 3, 3, activation='relu', name='conv4_1')(h)
    h = Convolution2D(512, 3, 3, activation='relu', name='conv4_2')(h)
    h = Convolution2D(512, 3, 3, activation='relu', name='conv4_3')(h)

    h = AtrousConvolution2D(512, 3, 3, dilation_rate=(2, 2), activation='relu', name='conv5_1')(h)
    h = AtrousConvolution2D(512, 3, 3, dilation_rate=(2, 2), activation='relu', name='conv5_2')(h)
    h = AtrousConvolution2D(512, 3, 3, dilation_rate=(2, 2), activation='relu', name='conv5_3')(h)

    h = AtrousConvolution2D(4096, 7, 7, dilation_rate=(4, 4), activation='relu', name='fc6')(h)
    h = Dropout(0.5, name='drop6')(h)
    h = Convolution2D(4096, 1, 1, activation='relu', name='fc7')(h)
    h = Dropout(0.5, name='drop7')(h)
    h = Convolution2D(classes, 1, 1, activation='relu', name='fc-final')(h)

    h = ZeroPadding2D(padding=(33, 33))(h)

    h = Convolution2D(2 * classes, 3, 3, activation='relu', name='ct_conv1_1')(h)
    h = Convolution2D(2 * classes, 3, 3, activation='relu', name='ct_conv1_2')(h)
    h = AtrousConvolution2D(4 * classes, 3, 3, dilation_rate=(2, 2), activation='relu', name='ct_conv2_1')(h)
    h = AtrousConvolution2D(8 * classes, 3, 3, dilation_rate=(4, 4), activation='relu', name='ct_conv3_1')(h)
    h = AtrousConvolution2D(16 * classes, 3, 3, dilation_rate=(8, 8), activation='relu', name='ct_conv4_1')(h)
    h = AtrousConvolution2D(32 * classes, 3, 3, dilation_rate=(16, 16), activation='relu', name='ct_conv5_1')(h)
    h = Convolution2D(32 * classes, 3, 3, activation='relu', name='ct_fc1')(h)
    h = Convolution2D(classes, 1, 1, name='ct_final')(h)


    model = Model(input=model_in, output=logits, name='dilation_voc12')
    return model