2e23d38ce7
Inputs_size (array_ops.size()) used to determine whether to use optional_get_next() API code path defaults to using int32 dtype. If input size is big enough this can lead to integer overflow and cause model to diverge. Correct usage will be to use inputs.get_shape()[0] to get the batch size -- instead of using array_ops.size() which returns the number of elements in inputs tensor which can be arbitrarily large. PiperOrigin-RevId: 305823718 Change-Id: Idc5660d80406fe233b162b73330c6fce4d5357b4
212 lines
8.2 KiB
Python
212 lines
8.2 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Test DistributionStrategy in the zero batch case."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from absl.testing import parameterized
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import numpy as np
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.distribute import combinations
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from tensorflow.python.distribute import strategy_combinations
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from tensorflow.python.eager import backprop
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from tensorflow.python.eager import def_function
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.layers import normalization
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.ops.losses import losses
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from tensorflow.python.platform import test
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from tensorflow.python.training import gradient_descent
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class NormalizationTest(test.TestCase, parameterized.TestCase):
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@combinations.generate(
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combinations.combine(
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distribution=[
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strategy_combinations.one_device_strategy,
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],
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mode=["graph"],
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fused=[True, False]))
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def testBNWithZeroBatchInputGraph(self, distribution, fused):
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distribution.extended.experimental_enable_get_next_as_optional = True
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with distribution.scope(), self.cached_session() as sess:
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bn_list = []
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inputs = np.random.random((0, 4, 4, 3)) + 100
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targets = np.random.random((0, 4, 4, 3))
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inputs_placeholder = array_ops.placeholder(
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dtype=dtypes.float32, shape=[None, 4, 4, 3])
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targets_placeholder = array_ops.placeholder(
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dtype=dtypes.float32, shape=[None, 4, 4, 3])
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def step_fn(is_training, inputs, targets=None):
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bn = normalization.BatchNormalization(
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axis=3, epsilon=1e-3, momentum=0.9, fused=fused)
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bn_list.append(bn)
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outputs = bn.apply(inputs, training=is_training)
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if not is_training:
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return outputs
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loss = losses.mean_squared_error(targets, outputs)
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optimizer = gradient_descent.GradientDescentOptimizer(0.01)
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train_op = optimizer.minimize(loss)
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with ops.control_dependencies([train_op]):
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return array_ops.identity(loss)
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train_op = distribution.extended.call_for_each_replica(
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step_fn, args=(True, inputs_placeholder, targets_placeholder))
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predict_op = distribution.extended.call_for_each_replica(
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step_fn, args=(False, inputs_placeholder))
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bn = bn_list[0]
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self.evaluate(variables.global_variables_initializer())
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# Check for initial statistics and weights.
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moving_mean, moving_var = self.evaluate(
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[bn.moving_mean, bn.moving_variance])
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self.assertAllEqual([0, 0, 0], moving_mean)
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self.assertAllEqual([1, 1, 1], moving_var)
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np_gamma, np_beta = self.evaluate([bn.gamma, bn.beta])
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self.assertAllEqual([1, 1, 1], np_gamma)
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self.assertAllEqual([0, 0, 0], np_beta)
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for _ in range(100):
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np_output, _, _ = sess.run([train_op] + bn.updates, {
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inputs_placeholder: inputs,
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targets_placeholder: targets
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})
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self.assertEqual(0.0, np_output)
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# Verify that the statistics and weights are not changed after training.
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moving_mean, moving_var = self.evaluate(
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[bn.moving_mean, bn.moving_variance])
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self.assertAllEqual([0, 0, 0], moving_mean)
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self.assertAllEqual([1, 1, 1], moving_var)
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np_gamma, np_beta = self.evaluate([bn.gamma, bn.beta])
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self.assertAllEqual([1, 1, 1], np_gamma)
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self.assertAllEqual([0, 0, 0], np_beta)
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# Test inference.
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np_output = sess.run(predict_op, {inputs_placeholder: inputs})
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self.assertEqual([], np_output.tolist())
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@combinations.generate(
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combinations.combine(
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distribution=[
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strategy_combinations.one_device_strategy,
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],
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mode=["eager"],
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fused=[True, False]))
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def testBNWithZeroBatchInput(self, distribution, fused):
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distribution.extended.experimental_enable_get_next_as_optional = True
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with distribution.scope():
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inputs = np.random.random((0, 4, 4, 3)).astype(np.float32) + 100
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targets = np.random.random((0, 4, 4, 3)).astype(np.float32)
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bn = normalization.BatchNormalization(
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axis=3, epsilon=1e-3, momentum=0.9, fused=fused)
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optimizer = gradient_descent.GradientDescentOptimizer(0.01)
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@def_function.function
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def train_step():
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def step_fn(inputs, targets):
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with backprop.GradientTape() as tape:
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outputs = bn.apply(inputs, training=True)
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loss = losses.mean_squared_error(targets, outputs)
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grads = tape.gradient(loss, bn.variables)
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optimizer.apply_gradients(zip(grads, bn.variables))
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return loss
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return distribution.run(step_fn, args=(inputs, targets))
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for _ in range(100):
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np_output = train_step().numpy()
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self.assertEqual(0.0, np_output)
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# Verify that the statistics and weights are not changed after training.
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self.assertAllEqual([0, 0, 0], bn.moving_mean.numpy())
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self.assertAllEqual([1, 1, 1], bn.moving_variance.numpy())
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self.assertAllEqual([1, 1, 1], bn.gamma.numpy())
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self.assertAllEqual([0, 0, 0], bn.beta.numpy())
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@def_function.function
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def test_step():
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def step_fn(inputs):
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outputs = bn.apply(inputs, training=False)
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return outputs
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return distribution.run(step_fn, args=(inputs,))
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# Test inference.
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self.assertAllEqual(np.zeros(shape=(0, 4, 4, 3), dtype=np.float32),
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test_step().numpy())
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@combinations.generate(
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combinations.combine(
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distribution=[
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strategy_combinations.one_device_strategy,
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],
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mode=["eager"],
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fused=[True, False]))
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def testBNWithDynamicBatchInputEager(self, distribution, fused):
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distribution.extended.experimental_enable_get_next_as_optional = True
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with distribution.scope():
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# Explicitly create dataset with drop_remainder=False.
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# This would make batch size unknown.
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inputs = np.random.random((11, 4, 4, 3)).astype(np.float32) + 100
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targets = np.random.random((11, 4, 4, 3)).astype(np.float32)
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dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets)).batch(
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10, drop_remainder=False).repeat()
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dataset_iterator = iter(
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distribution.experimental_distribute_dataset(dataset))
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bn = normalization.BatchNormalization(
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axis=-1, epsilon=1e-3, momentum=0.9, fused=fused)
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optimizer = gradient_descent.GradientDescentOptimizer(0.01)
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@def_function.function
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def train_step(iterator):
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def step_fn(inputs):
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features, targets = inputs
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with backprop.GradientTape() as tape:
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outputs = bn(features, training=True)
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loss = losses.mean_squared_error(targets, outputs)
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grads = tape.gradient(loss, bn.variables)
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optimizer.apply_gradients(zip(grads, bn.variables))
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return loss
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return distribution.run(step_fn, args=(next(iterator),))
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for _ in range(100):
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train_step(dataset_iterator).numpy()
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# Verify that the statistics and weights are updated.
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self.assertNotAllEqual(np.ndarray([0, 0, 0]), bn.moving_mean.numpy())
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self.assertNotAllEqual(np.ndarray([1, 1, 1]), bn.moving_variance.numpy())
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self.assertNotAllEqual(np.ndarray([1, 1, 1]), bn.gamma.numpy())
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self.assertNotAllEqual(np.ndarray([0, 0, 0]), bn.beta.numpy())
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if __name__ == "__main__":
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test.main()
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