from tensorflow.keras.layers import Layer
import tensorflow as tf
[docs]class SparseConv3D(Layer):
"""
Sparse implementation of the Convolutional layer. If used in a model,
must be saved and loaded via pickle
"""
def __init__(
self,
filters,
bias,
padding='same',
strides=1,
activation=None,
**kwargs
):
"""
Parameters
----------
filters : tf.Tensor
The convolutional filters
bias : tf.Tensor
the bias tensor
padding : str, int, or tuple of int (default 'same')
The padding to use
strides : int or tuple of int (default 1)
The strides to use
activation : None, str, or keras activation function (default None)
The activation function to use
"""
super().__init__(**kwargs)
self.w = tf.sparse.from_dense(filters)
self.b = tf.sparse.from_dense(bias)
self.padding = padding
self.strides = strides
self.activation = tf.keras.activations.get(activation)
[docs] def build(self, input_shape):
"""
Build the layer in preparation to be trained or called. Should not be called directly,
but rather is called when the layer is added to a model
"""
pass
[docs] def call(self, inputs):
"""
This is where the layer's logic lives and is called upon inputs
Parameters
----------
inputs : TensorFlow Tensor or Tensor-like
The inputs to the layer
Returns
-------
outputs : TensorFlow Tensor
The outputs of the layer's logic
"""
conv_output = tf.nn.convolution(
inputs,
tf.sparse.to_dense(self.w),
padding=self.padding.upper() if isinstance(
self.padding, str) else self.padding,
strides=self.strides,
data_format='NDHWC'
)
conv_output = conv_output + tf.sparse.to_dense(self.b)
return self.activation(conv_output)
[docs] def get_config(self):
config = super().get_config().copy()
config.update(
{
'padding': self.padding,
'strides': self.strides,
'activation': tf.keras.activations.serialize(self.activation)
}
)
return config
[docs] @classmethod
def from_layer(cls, layer):
"""
Create a layer from an instance of another layer
"""
weights = layer.get_weights()
w = weights[0]
b = weights[1]
padding = layer.padding
strides = layer.strides
activation = layer.activation
return cls(
w,
b,
padding,
strides,
activation
)
[docs] @classmethod
def from_config(cls, config):
return cls(**config)