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Fix GroupNormalization #611

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merged 2 commits into from
Dec 2, 2019
Merged

Fix GroupNormalization #611

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fd6d95f
Fix GroupNormalization
DawyD Oct 21, 2019
5256abd
Lint
seanpmorgan Dec 2, 2019
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13 changes: 7 additions & 6 deletions tensorflow_addons/layers/normalizations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -161,19 +161,20 @@ def _reshape_into_groups(self, inputs, input_shape, tensor_input_shape):

group_shape = [tensor_input_shape[i] for i in range(len(input_shape))]
group_shape[self.axis] = input_shape[self.axis] // self.groups
group_shape.insert(1, self.groups)
group_shape.insert(self.axis, self.groups)
group_shape = tf.stack(group_shape)
reshaped_inputs = tf.reshape(inputs, group_shape)
return reshaped_inputs, group_shape

def _apply_normalization(self, reshaped_inputs, input_shape):

group_shape = tf.keras.backend.int_shape(reshaped_inputs)
group_reduction_axes = list(range(len(group_shape)))
# Remember the ordering of the tensor is [batch, group , steps]. Jump
# the first 2 to calculate the variance and the mean
group_reduction_axes = list(range(1, len(group_shape)))
axis = -2 if self.axis == -1 else self.axis - 1
group_reduction_axes.pop(axis)

mean, variance = tf.nn.moments(
reshaped_inputs, group_reduction_axes[2:], keepdims=True)
reshaped_inputs, group_reduction_axes, keepdims=True)

gamma, beta = self._get_reshaped_weights(input_shape)
normalized_inputs = tf.nn.batch_normalization(
Expand Down Expand Up @@ -269,7 +270,7 @@ def _add_beta_weight(self, input_shape):
def _create_broadcast_shape(self, input_shape):
broadcast_shape = [1] * len(input_shape)
broadcast_shape[self.axis] = input_shape[self.axis] // self.groups
broadcast_shape.insert(1, self.groups)
broadcast_shape.insert(self.axis, self.groups)
return broadcast_shape


Expand Down
16 changes: 8 additions & 8 deletions tensorflow_addons/layers/normalizations_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,7 +53,7 @@ def run_reshape_test(axis, group, input_shape, expected_shape):
self.assertEqual(int(group_shape[i]), expected_shape[i])

input_shape = (10, 10, 10)
expected_shape = [10, 5, 10, 2]
expected_shape = [10, 10, 5, 2]
run_reshape_test(2, 5, input_shape, expected_shape)

input_shape = (10, 10, 10)
Expand Down Expand Up @@ -108,18 +108,18 @@ def _test_specific_layer(self, inputs, axis, groups, center, scale):
np_inputs = inputs.numpy()
reshaped_dims = list(np_inputs.shape)
reshaped_dims[axis] = reshaped_dims[axis] // groups
reshaped_dims.insert(1, groups)
reshaped_dims.insert(axis, groups)
reshaped_inputs = np.reshape(np_inputs, tuple(reshaped_dims))

group_reduction_axes = list(range(1, len(reshaped_dims)))
axis = -2 if axis == -1 else axis - 1
group_reduction_axes.pop(axis)

# Calculate mean and variance
mean = np.mean(
reshaped_inputs,
axis=tuple(range(2, len(reshaped_dims))),
keepdims=True)
reshaped_inputs, axis=tuple(group_reduction_axes), keepdims=True)
variance = np.var(
reshaped_inputs,
axis=tuple(range(2, len(reshaped_dims))),
keepdims=True)
reshaped_inputs, axis=tuple(group_reduction_axes), keepdims=True)

# Get gamma and beta initalized by layer
gamma, beta = layer._get_reshaped_weights(input_shape)
Expand Down