在 keras 层包装 python 可调用

Question

In keras / tensorflow it is often quite simple to describe layers directly as functions that map their input to an output, like so:

def resnet_block(x, kernel_size):
   ch = x.shape[-1]
   out = Conv2D(ch, kernel_size, strides = (1,1), padding='same', activation='relu')(x)
   out = Conv2D(ch, kernel_size, strides = (1,1), padding='same', activation='relu')(out)
   out = Add()([x,out])
   return out

whereas subclassing Layer to get something like

r = ResNetBlock(kernel_size=(3,3))
y = r(x)

is a little more cumbersome (or even a lot more cumbersome for more complex examples).

Since keras seems perfectly happy to construct the underlying weights of its layers when they're being called for the first time, I was wondering if it was possible to just wrap functions such as the one above and let keras figure things out once there are inputs, ie I would like it to look like this:

r = FunctionWrapperLayer(lambda x:resnet_block(x, kernel_size=(3,3)))
y = r(x)

I've made an attempt at implementing FunctionWrapperLayer , which looks as follows:

class FunctionWrapperLayer(Layer):
    def __init__(self, fn):
        super(FunctionWrapperLayer, self).__init__()
        self.fn = fn
    
    def build(self, input_shape):
        shape = input_shape[1:]
        inputs = Input(shape)
        outputs = self.fn(inputs)
        self.model = Model(inputs=inputs, outputs=outputs)
        self.model.compile()
        
    def call(self, x):
        return self.model(x)

This looks like it might work, however I've run into some bizarre issues whenever I use activations, eg with

def bad(x):
    out = tf.keras.activations.sigmoid(x)
    out = Conv2D(1, (1,1), strides=(1,1), padding='same')(out)
    return out
x = tf.constant(tf.reshape(tf.range(48,dtype=tf.float32),[1,4,-1,1])
w = FunctionWrapperLayer(bad)
w(x)

I get the following error

FailedPreconditionError:  Error while reading resource variable _AnonymousVar34 from Container: localhost. This could mean that the variable was uninitialized. Not found: Resource localhost/_AnonymousVar34/class tensorflow::Var does not exist.
     [[node conv2d_6/BiasAdd/ReadVariableOp (defined at <ipython-input-33-fc380d9255c5>:12) ]] [Op:__inference_keras_scratch_graph_353]

What this suggests to me is that there is something inherently wrong with initializing models like that in the build method. Maybe someone has a better idea as to what might be going on there or how else to get the functionality I would like.

Update: As mentioned by jr15, the above does work when the function involved only uses keras layers. However, the following ALSO works, which has me a little puzzled:

i = Input(x.shape[1:])
o = bad(i)
model = Model(inputs=i, outputs=o)
model(x)

Incidentally, model.submodules yields

(<tensorflow.python.keras.engine.input_layer.InputLayer at 0x219d80c77c0>,
 <tensorflow.python.keras.engine.base_layer.TensorFlowOpLayer at 0x219d7afc820>,
 <tensorflow.python.keras.layers.convolutional.Conv2D at 0x219d7deafa0>)

meaning the activation is automatically turned into a "TensorFlowOpLayer" when doing it like that.

Another update: Looking at the original error message, it seems like the activation isn't the only culprit. If I remove the convolution and use the wrapper everything works as well and again I find a "TensorFlowOpLayer" when inspecting the submodules.

Answer 1

You solution actually works! The trouble you're running into is that tf.keras.activations.sigmoid is not a Layer, but a plain Tensorflow function. To make it work, use keras.layers.Activation("sigmoid")(x) instead. For the more general case, where you want to use some Tensorflow function as a layer, you can wrap it in a Lambda layer like so:

out = keras.layers.Lambda(lambda x: tf.some_function(x))(out)

See the docs for more info: https://keras.io/api/layers/core_layers/lambda/

Answer 2

With Tensorflow 2.4 it apparently just works now. The submodules now show a "TFOpLambda" layer.

To anybody interested, here is some slightly improved wrapper code that also accommodates multi-input models:

class FunctionWrapperLayer(Layer):
    def __init__(self, fn):
        super(FunctionWrapperLayer, self).__init__()
        self.fn = fn
        
    def build(self, input_shapes):
        super(FunctionWrapperLayer, self).build(input_shapes)
        if type(input_shapes) is list:
            inputs = [Input(shape[1:]) for shape in input_shapes]
        else:
            inputs = Input(input_shapes[1:])
        outputs = self.fn(inputs)
        self.fn_model = Model(inputs=inputs, outputs=outputs)
        self.fn_model.compile()
        
    def call(self, x):
        return self.fn_model(x)