layers_builder.py 文件源码

def build_pspnet(nb_classes, resnet_layers, input_shape, activation='softmax'):
    """Build PSPNet."""
    print("Building a PSPNet based on ResNet %i expecting inputs of shape %s predicting %i classes" % (resnet_layers, input_shape, nb_classes))

    inp = Input((input_shape[0], input_shape[1], 3))
    res = ResNet(inp, layers=resnet_layers)
    psp = build_pyramid_pooling_module(res, input_shape)

    x = Conv2D(512, (3, 3), strides=(1, 1), padding="same", name="conv5_4",
               use_bias=False)(psp)
    x = BN(name="conv5_4_bn")(x)
    x = Activation('relu')(x)
    x = Dropout(0.1)(x)

    x = Conv2D(nb_classes, (1, 1), strides=(1, 1), name="conv6")(x)
    x = Lambda(Interp, arguments={'shape': (input_shape[0], input_shape[1])})(x)
    x = Activation('softmax')(x)

    model = Model(inputs=inp, outputs=x)

    # Solver
    sgd = SGD(lr=learning_rate, momentum=0.9, nesterov=True)
    model.compile(optimizer=sgd,
                  loss='categorical_crossentropy',
                  metrics=['accuracy'])
    return model