SqueezeAndExcite2D layer
SqueezeAndExcite2D class
keras_cv.layers.SqueezeAndExcite2D(
filters,
bottleneck_filters=None,
squeeze_activation="relu",
excite_activation="sigmoid",
**kwargs
)
Implements Squeeze and Excite block as in Squeeze-and-Excitation Networks. This layer tries to use a content aware mechanism to assign channel-wise weights adaptively. It first squeezes the feature maps into a single value using global average pooling, which are then fed into two Conv1D layers, which act like fully-connected layers. The first layer reduces the dimensionality of the feature maps, and second layer restores it to its original value.
The resultant values are the adaptive weights for each channel. These weights are then multiplied with the original inputs to scale the outputs based on their individual weightages.
Arguments
- filters: Number of input and output filters. The number of input and output filters is same.
- bottleneck_filters: (Optional) Number of bottleneck filters. Defaults
to
0.25 * filters - squeeze_activation: (Optional) String, callable (or
keras.layers.Layer) or keras.activations.Activation instance
denoting activation to be applied after squeeze convolution.
Defaults to
relu. - excite_activation: (Optional) String, callable (or
keras.layers.Layer) or keras.activations.Activation instance
denoting activation to be applied after excite convolution.
Defaults to
sigmoid.
Example
# (...)
input = tf.ones((1, 5, 5, 16), dtype=tf.float32)
x = keras.layers.Conv2D(16, (3, 3))(input)
output = keras_cv.layers.SqueezeAndExciteBlock(16)(x)
# (...)