I am trying to use the DropBlock2D layer from KerasCV, version 0.9.0. However, I am only able to compile the layer into a model if I specify run_eagerly=True. This appears to be related to the fact that when forward pass from the non-eager run is attempted, a symbolic tensor is passed to the layer, which expects a concrete value instead. The Keras docs say that run_eagerly should reserved for debugging, so why is it necessary that I enable it here?

Interactive example (Google Colab)

I set up the model using the functional approach:

input = keras.layers.Input(shape=input_shape)
x = keras.layers.Conv2D(32, (1, 1))(input)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.ReLU()(x)
x = keras_cv.layers.DropBlock2D(rate=0.05, block_size=(14, 14))(x)
x = keras.layers.GlobalAveragePooling2D()(x)
output = keras.layers.Dense(num_classes)(x)

model = keras.Model(
    inputs=input,
    outputs=output,
)

Then I compile the model:

model.compile(
  loss=keras.losses.SparseCategoricalCrossentropy(),
  optimizer=keras.optimizers.Adam(learning_rate=1e-3),
  # Uncommenting below will make things work
  #run_eagerly=True 
)

Then I load data for training:

mnist_train, mnist_test = keras.datasets.fashion_mnist.load_data()
mnist_x_train, mnist_y_train = mnist_train

model.fit(
    mnist_x_train[0:20,:,:],
    mnist_y_train[0:20],
    epochs=2
)

It’s after the model.fit line where I get errors if run_eagerly is not True when I compile:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-28-15f407e6a023> in <cell line: 41>()
     39 mnist_x_train, mnist_y_train = mnist_train
     40 
---> 41 model.fit(
     42     mnist_x_train[0:20,:,:],
     43     mnist_y_train[0:20],

1 frames
/usr/local/lib/python3.10/dist-packages/keras_cv/src/layers/regularization/dropblock_2d.py in call(self, x, training)
    190         valid_block = ops.logical_and(
    191             ops.logical_and(
--> 192                 w_i >= int(dropblock_width // 2),
    193                 w_i < width - (dropblock_width - 1) // 2,
    194             ),

TypeError: Exception encountered when calling DropBlock2D.call().

int() argument must be a string, a bytes-like object or a real number, not 'SymbolicTensor'

Arguments received by DropBlock2D.call():
  • x=tf.Tensor(shape=(None, 28, 28, 32), dtype=float32)
  • training=True

I understand that the int cast won’t work on a symbolic tensor, but I am wondering if anything can be done about this short of filing a bug with KerasCV.