Add virtual_batch support

1 files changed, 39 insertions, 0 deletions

diff --git a/lib/virtual_batch.py b/lib/virtual_batch.py
new file mode 100644
index 0000000..dab0419
--- /dev/null
+++ b/lib/virtual_batch.py
@@ -0,0 +1,39 @@
+import tensorflow as tf
+from tensorflow.keras import backend as K
+from tensorflow.keras.layers import Layer
+from lib.virtual_batchnorm_impl import VBN
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.keras.engine.base_layer import InputSpec
+from tensorflow.python.keras import initializers
+
+class VirtualBatchNormalization(Layer):
+    def __init__(self,
+               momentum=0.99,
+               center=True,
+               scale=True,
+               beta_initializer='zeros',
+               gamma_initializer='ones',
+               beta_regularizer=None,
+               gamma_regularizer=None,
+               **kwargs):
+
+        self.beta_initializer = initializers.get(beta_initializer)
+        self.gamma_initializer = initializers.get(gamma_initializer)
+ 
+        super(VirtualBatchNormalization, self).__init__(**kwargs)
+
+    def build(self, input_shape):
+        input_shape = tensor_shape.TensorShape(input_shape)
+        if not input_shape.ndims:
+          raise ValueError('Input has undefined rank:', input_shape)
+        ndims = len(input_shape)
+        self.input_spec = InputSpec(ndim=ndims)
+        #super(VirtualBatchNormalization, self).build(input_shape)  # Be sure to call this at the end
+
+    def call(self, x):
+        outputs = VBN(x, gamma_initializer=self.gamma_initializer, beta_initializer=self.beta_initializer)(x)
+        outputs.set_shape(x.get_shape())
+        return outputs
+
+    def compute_output_shape(self, input_shape):
+        return input_shape