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Merge pull request #39131 from yongtang:32380-BatchNormalization-virtual_batch_size-None

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PiperOrigin-RevId: 309995310
Change-Id: I2d86a93834cabcc80f0f9a28d87985e9aa7b6c9c
This commit is contained in:
TensorFlower Gardener 2020-05-05 12:19:01 -07:00
commit 9dce54f00b
2 changed files with 15 additions and 2 deletions
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10
tensorflow/python/keras/layers/normalization.py
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@ -736,8 +736,14 @@ class BatchNormalizationBase(Layer):
if self.virtual_batch_size is not None: if self.virtual_batch_size is not None:
# Virtual batches (aka ghost batches) can be simulated by reshaping the # Virtual batches (aka ghost batches) can be simulated by reshaping the
# Tensor and reusing the existing batch norm implementation # Tensor and reusing the existing batch norm implementation
original_shape = [-1] + inputs.shape.as_list()[1:] original_shape = array_ops.shape(inputs)
expanded_shape = [self.virtual_batch_size, -1] + original_shape[1:] original_shape = array_ops.concat(
[constant_op.constant([-1]), original_shape[1:]], axis=0)
expanded_shape = array_ops.concat([
constant_op.constant([self.virtual_batch_size, -1]),
original_shape[1:]
],
axis=0)
# Will cause errors if virtual_batch_size does not divide the batch size # Will cause errors if virtual_batch_size does not divide the batch size
inputs = array_ops.reshape(inputs, expanded_shape) inputs = array_ops.reshape(inputs, expanded_shape)

7
tensorflow/python/keras/layers/normalization_test.py
View File

@ -354,6 +354,13 @@ class BatchNormalizationV2Test(keras_parameterized.TestCase):
# Updates should be tracked in a `wrap_function`. # Updates should be tracked in a `wrap_function`.
self.assertLen(layer.updates, 2) self.assertLen(layer.updates, 2)
@keras_parameterized.run_all_keras_modes
def test_basic_batchnorm_v2_none_shape_and_virtual_batch_size(self):
# Test case for GitHub issue for 32380
norm = normalization_v2.BatchNormalization(virtual_batch_size=8)
inp = keras.layers.Input(shape=(None, None, 3))
_ = norm(inp)
def _run_batchnorm_correctness_test(layer, dtype='float32', fused=False): def _run_batchnorm_correctness_test(layer, dtype='float32', fused=False):
model = keras.models.Sequential() model = keras.models.Sequential()