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# =============================================================================
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy
from tensorflow.contrib.periodic_resample import periodic_resample
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors_impl
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import variables
from tensorflow.python.platform import googletest
class PeriodicResampleTest(test_util.TensorFlowTestCase):
def testPeriodicResampleBasic2D(self):
input_tensor = numpy.arange(12).reshape((3, 4))
desired_shape = numpy.array([6, None])
output_tensor = input_tensor.reshape((6, 2))
with self.cached_session():
variables.global_variables_initializer().run()
result = periodic_resample(input_tensor, desired_shape).eval()
self.assertAllEqual(result, output_tensor)
def testPeriodicResampleTruncatedBasic2D(self):
input_tensor = numpy.arange(12).reshape((3, 4))
desired_shape = numpy.array([5, None])
output_tensor = input_tensor.reshape((6, 2))[:-1]
with self.cached_session():
variables.global_variables_initializer().run()
result = periodic_resample(input_tensor, desired_shape).eval()
self.assertAllEqual(result, output_tensor)
def testPeriodicResampleBasic3D(self):
input_tensor = numpy.arange(2 * 2 * 4).reshape((2, 2, 4))
desired_shape = numpy.array([4, 4, None])
output_tensor = numpy.array([[[0], [2], [4], [6]], [[1], [3], [5], [7]],
[[8], [10], [12], [14]], [[9], [11], [13],
[15]]])
# NOTE: output_tensor != input_tensor.reshape((4, 4, -1))
with self.cached_session():
variables.global_variables_initializer().run()
result = periodic_resample(input_tensor, desired_shape).eval()
# input_tensor[0, 0, 0] == result[0, 0, 0]
# input_tensor[0, 0, 1] == result[1, 0, 0]
# input_tensor[0, 0, 2] == result[0, 1, 0]
# input_tensor[0, 0, 3] == result[1, 1, 0]
self.assertAllEqual(result, output_tensor)
def testPeriodicResampleBasic4D(self):
input_tensor = numpy.arange(2 * 2 * 2 * 8).reshape((2, 2, 2, 8))
desired_shape = numpy.array([4, 4, 4, None])
output_tensor = numpy.array(
[[[[0], [4], [8], [12]], [[2], [6], [10], [14]],
[[16], [20], [24], [28]], [[18], [22], [26], [30]]],
[[[1], [5], [9], [13]], [[3], [7], [11], [15]], [[17], [21], [25],
[29]],
[[19], [23], [27],
[31]]], [[[32], [36], [40], [44]], [[34], [38], [42], [46]],
[[48], [52], [56], [60]], [[50], [54], [58], [62]]],
[[[33], [37], [41], [45]], [[35], [39], [43], [47]],
[[49], [53], [57], [61]], [[51], [55], [59], [63]]]])
# NOTE: output_tensor != input_tensor.reshape((4, 4, 4, -1))
with self.cached_session():
variables.global_variables_initializer().run()
result = periodic_resample(input_tensor, desired_shape).eval()
self.assertAllEqual(result, output_tensor)
def testPeriodicResampleErrors(self):
input_tensor = numpy.zeros(shape=[1, 2, 2, 4])
with self.cached_session():
with self.assertRaisesWithPredicateMatch(
errors_impl.InvalidArgumentError,
'Dimension 3 input tensor has size 4, desired shape has size 1'):
periodic_resample(input_tensor, [None, 4, 4, 1]).eval()
with self.assertRaisesWithPredicateMatch(
errors_impl.InvalidArgumentError,
'4, to be the same as the length of the desired shape, 3'):
periodic_resample(input_tensor, [None, 4, 4]).eval()
def testPeriodicResampleGradient(self):
desired_shape = numpy.array([4, 4, None])
result_shape = (4, 4, 1)
input_shape = (2, 2, 4)
with self.cached_session() as sess:
x = array_ops.placeholder(dtypes.float32, shape=input_shape)
output = periodic_resample(x, desired_shape)
error = gradient_checker.compute_gradient_error(
x, input_shape, output, result_shape)
self.assertLess(error, 1e-4)
def testPeriodicResampleShapeInference(self):
with self.cached_session() as sess:
# Case 1: output shape can be fully inferreed.
x = array_ops.placeholder(dtypes.float32, shape=(2, 2, 4))
output = periodic_resample(x, [4, 4, None])
self.assertEqual(output.shape, [4, 4, 1])
# Case 2: output shape can not be inferred - report desired shape.
x = array_ops.placeholder(dtypes.float32, shape=(2, 2, None))
output = periodic_resample(x, [4, 4, None])
self.assertTrue(output.shape.is_compatible_with([4, 4, None]))
self.assertEqual(output.shape[2].value, None)
if __name__ == '__main__':
googletest.main()
|
tensorflow-master
|
tensorflow/contrib/periodic_resample/python/kernel_tests/periodic_resample_op_test.py
|
# =============================================================================
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# pylint: disable=unused-import
from tensorflow.contrib.periodic_resample.python.ops import gen_periodic_resample_op
from tensorflow.contrib.periodic_resample.python.ops.gen_periodic_resample_op import periodic_resample, periodic_resample_op_grad
from tensorflow.contrib.util import loader
from tensorflow.python.framework import ops
from tensorflow.python.platform import resource_loader
# pylint: enable=unused-import
_periodic_resample_op = loader.load_op_library(
resource_loader.get_path_to_datafile('_periodic_resample_op.so'))
@ops.RegisterGradient("PeriodicResample")
def _periodic_resample_grad_cc(op, grad):
return periodic_resample_op_grad(
grad, op.inputs[0].shape, op.get_attr('shape'))
|
tensorflow-master
|
tensorflow/contrib/periodic_resample/python/ops/periodic_resample_op.py
|
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for the deprecated summary ops in tf.contrib.deprecated."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import constant_op
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import logging_ops
from tensorflow.python.platform import test
class DeprecatedSummariesTest(test.TestCase):
def testScalarSummary(self):
with self.cached_session():
c = constant_op.constant(3)
s = logging_ops.scalar_summary('tag', c)
self.assertEqual(s.op.type, u'ScalarSummary')
def testHistogramSummary(self):
with self.cached_session():
c = constant_op.constant(3)
s = logging_ops.histogram_summary('tag', c)
self.assertEqual(s.op.type, u'HistogramSummary')
def testImageSummary(self):
with self.cached_session():
i = array_ops.ones((5, 4, 4, 3))
s = logging_ops.image_summary('tag', i)
self.assertEqual(s.op.type, u'ImageSummary')
def testAudioSummary(self):
with self.cached_session():
c = constant_op.constant(3.0)
s = logging_ops.audio_summary('tag', c, sample_rate=8000)
self.assertEqual(s.op.type, u'AudioSummaryV2')
def testMergeSummary(self):
with self.cached_session():
c = constant_op.constant(3)
a = logging_ops.scalar_summary('a', c)
b = logging_ops.scalar_summary('b', c)
s = logging_ops.merge_summary([a, b])
self.assertEqual(s.op.type, u'MergeSummary')
if __name__ == '__main__':
test.main()
|
tensorflow-master
|
tensorflow/contrib/deprecated/summaries_test.py
|
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Non-core alias for the deprecated tf.X_summary ops.
For TensorFlow 1.0, we have reorganized the TensorFlow summary ops into a
submodule, and made some semantic tweaks. The first thing to note is that we
moved the APIs around as follows:
```python
tf.scalar_summary -> tf.compat.v1.summary.scalar
tf.histogram_summary -> tf.compat.v1.summary.histogram
tf.audio_summary -> tf.compat.v1.summary.audio
tf.image_summary -> tf.compat.v1.summary.image
tf.merge_summary -> tf.compat.v1.summary.merge
tf.merge_all_summaries -> tf.compat.v1.summary.merge_all
```
We think this API is cleaner and will improve long-term discoverability and
clarity of the TensorFlow API. But we also took the opportunity to make an
important change to how summary "tags" work. The "tag" of a summary is the
string that is associated with the output data, i.e. the key for organizing the
generated protobufs.
Previously, the tag was allowed to be any unique string; it had no relation
to the summary op generating it, and no relation to the TensorFlow name system.
This behavior made it very difficult to write reusable that would add
summary ops to the graph. If you had a function to add summary ops, you would
need to pass in a `tf.name_scope`, manually, to that function to create
deduplicated tags. Otherwise your program would fail with a runtime error due
to tag collision.
The new summary APIs under `tf.summary` throw away the "tag" as an independent
concept; instead, the first argument is the node name. So summary tags now
automatically inherit the surrounding `tf.name_scope`, and automatically
are deduplicated if there is a conflict. Now however, the only allowed
characters are alphanumerics, underscores, and forward slashes. To make
migration easier, the new APIs automatically convert illegal characters to
underscores.
Just as an example, consider the following "before" and "after" code snippets:
```python
# Before
def add_activation_summaries(v, scope):
tf.scalar_summary("%s/fraction_of_zero" % scope, tf.nn.fraction_of_zero(v))
tf.histogram_summary("%s/activations" % scope, v)
# After
def add_activation_summaries(v):
tf.compat.v1.summary.scalar("fraction_of_zero", tf.nn.fraction_of_zero(v))
tf.compat.v1.summary.histogram("activations", v)
```
Now, so long as the add_activation_summaries function is called from within the
right `tf.name_scope`, the behavior is the same.
Because this change does modify the behavior and could break tests, we can't
automatically migrate usage to the new APIs. That is why we are making the old
APIs temporarily available here at `tf.contrib.deprecated`.
In addition to the name change described above, there are two further changes
to the new summary ops:
- the "max_images" argument for `tf.image_summary` was renamed to "max_outputs
for `tf.compat.v1.summary.image`
- `tf.scalar_summary` accepted arbitrary tensors of tags and values. But
`tf.compat.v1.summary.scalar` requires a single scalar name and scalar value.
In most
cases, you can create `tf.compat.v1.summary.scalar` in a loop to get the same
behavior
As before, TensorBoard groups charts by the top-level `tf.name_scope` which may
be inconvenient, for in the new summary ops, the summary will inherit that
`tf.name_scope` without user control. We plan to add more grouping mechanisms
to TensorBoard, so it will be possible to specify the TensorBoard group for
each summary via the summary API.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# pylint: disable=unused-import
from tensorflow.python.ops.logging_ops import audio_summary
from tensorflow.python.ops.logging_ops import histogram_summary
from tensorflow.python.ops.logging_ops import image_summary
from tensorflow.python.ops.logging_ops import merge_all_summaries
from tensorflow.python.ops.logging_ops import merge_summary
from tensorflow.python.ops.logging_ops import scalar_summary
from tensorflow.python.util.all_util import remove_undocumented
# pylint: enable=unused-import,line-too-long
_allowed_symbols = [
'audio_summary', 'histogram_summary', 'image_summary',
'merge_all_summaries', 'merge_summary', 'scalar_summary'
]
remove_undocumented(__name__, _allowed_symbols)
|
tensorflow-master
|
tensorflow/contrib/deprecated/__init__.py
|
tensorflow-master
|
tensorflow/examples/__init__.py
|
|
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Example of Estimator for Iris plant dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from sklearn import datasets
from sklearn import metrics
from sklearn import model_selection
import tensorflow as tf
X_FEATURE = 'x' # Name of the input feature.
def my_model(features, labels, mode):
"""DNN with three hidden layers, and dropout of 0.1 probability."""
# Create three fully connected layers respectively of size 10, 20, and 10 with
# each layer having a dropout probability of 0.1.
net = features[X_FEATURE]
for units in [10, 20, 10]:
net = tf.layers.dense(net, units=units, activation=tf.nn.relu)
net = tf.layers.dropout(net, rate=0.1)
# Compute logits (1 per class).
logits = tf.layers.dense(net, 3, activation=None)
# Compute predictions.
predicted_classes = tf.argmax(logits, 1)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
'class': predicted_classes,
'prob': tf.nn.softmax(logits)
}
return tf.estimator.EstimatorSpec(mode, predictions=predictions)
# Compute loss.
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
# Create training op.
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
# Compute evaluation metrics.
eval_metric_ops = {
'accuracy': tf.metrics.accuracy(
labels=labels, predictions=predicted_classes)
}
return tf.estimator.EstimatorSpec(
mode, loss=loss, eval_metric_ops=eval_metric_ops)
def main(unused_argv):
iris = datasets.load_iris()
x_train, x_test, y_train, y_test = model_selection.train_test_split(
iris.data, iris.target, test_size=0.2, random_state=42)
classifier = tf.estimator.Estimator(model_fn=my_model)
# Train.
train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
classifier.train(input_fn=train_input_fn, steps=1000)
# Predict.
test_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
predictions = classifier.predict(input_fn=test_input_fn)
y_predicted = np.array(list(p['class'] for p in predictions))
y_predicted = y_predicted.reshape(np.array(y_test).shape)
# Score with sklearn.
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy (sklearn): {0:f}'.format(score))
# Score with tensorflow.
scores = classifier.evaluate(input_fn=test_input_fn)
print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
if __name__ == '__main__':
tf.app.run()
|
tensorflow-master
|
tensorflow/examples/learn/iris_custom_model.py
|
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Example of DNNClassifier for Iris plant dataset, with exponential decay."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from sklearn import datasets
from sklearn import metrics
from sklearn import model_selection
import tensorflow as tf
X_FEATURE = 'x' # Name of the input feature.
def my_model(features, labels, mode):
"""DNN with three hidden layers."""
# Create three fully connected layers respectively of size 10, 20, and 10.
net = features[X_FEATURE]
for units in [10, 20, 10]:
net = tf.layers.dense(net, units=units, activation=tf.nn.relu)
# Compute logits (1 per class).
logits = tf.layers.dense(net, 3, activation=None)
# Compute predictions.
predicted_classes = tf.argmax(logits, 1)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
'class': predicted_classes,
'prob': tf.nn.softmax(logits)
}
return tf.estimator.EstimatorSpec(mode, predictions=predictions)
# Compute loss.
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
# Create training op with exponentially decaying learning rate.
if mode == tf.estimator.ModeKeys.TRAIN:
global_step = tf.train.get_global_step()
learning_rate = tf.train.exponential_decay(
learning_rate=0.1, global_step=global_step,
decay_steps=100, decay_rate=0.001)
optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss, global_step=global_step)
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
# Compute evaluation metrics.
eval_metric_ops = {
'accuracy': tf.metrics.accuracy(
labels=labels, predictions=predicted_classes)
}
return tf.estimator.EstimatorSpec(
mode, loss=loss, eval_metric_ops=eval_metric_ops)
def main(unused_argv):
iris = datasets.load_iris()
x_train, x_test, y_train, y_test = model_selection.train_test_split(
iris.data, iris.target, test_size=0.2, random_state=42)
classifier = tf.estimator.Estimator(model_fn=my_model)
# Train.
train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
classifier.train(input_fn=train_input_fn, steps=1000)
# Predict.
test_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
predictions = classifier.predict(input_fn=test_input_fn)
y_predicted = np.array(list(p['class'] for p in predictions))
y_predicted = y_predicted.reshape(np.array(y_test).shape)
# Score with sklearn.
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy (sklearn): {0:f}'.format(score))
# Score with tensorflow.
scores = classifier.evaluate(input_fn=test_input_fn)
print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
if __name__ == '__main__':
tf.app.run()
|
tensorflow-master
|
tensorflow/examples/learn/iris_custom_decay_dnn.py
|
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Export an RNN cell in SavedModel format."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import app
from absl import flags
import numpy as np
import tensorflow.compat.v2 as tf
FLAGS = flags.FLAGS
flags.DEFINE_string("export_dir", None, "Directory to export SavedModel.")
def main(argv):
del argv
root = tf.train.Checkpoint()
# Create a cell and attach to our trackable.
root.rnn_cell = tf.keras.layers.LSTMCell(units=10, recurrent_initializer=None)
# Wrap the rnn_cell.__call__ function and assign to next_state.
root.next_state = tf.function(root.rnn_cell.__call__, autograph=False)
# Wrap the rnn_cell.get_initial_function using a decorator and assign to an
# attribute with the same name.
@tf.function(input_signature=[tf.TensorSpec([None, None], tf.float32)])
def get_initial_state(tensor):
return root.rnn_cell.get_initial_state(tensor, None, None)
root.get_initial_state = get_initial_state
# Construct an initial_state, then call next_state explicitly to trigger a
# trace for serialization (we need an explicit call, because next_state has
# not been annotated with an input_signature).
initial_state = root.get_initial_state(
tf.constant(np.random.uniform(size=[3, 10]).astype(np.float32)))
root.next_state(
tf.constant(np.random.uniform(size=[3, 19]).astype(np.float32)),
initial_state)
tf.saved_model.save(root, FLAGS.export_dir)
if __name__ == "__main__":
app.run(main)
|
tensorflow-master
|
tensorflow/examples/saved_model/integration_tests/export_rnn_cell.py
|
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Utility to write SavedModel integration tests.
SavedModel testing requires isolation between the process that creates and
consumes it. This file helps doing that by relaunching the same binary that
calls `assertCommandSucceeded` with an environment flag indicating what source
file to execute. That binary must start by calling `MaybeRunScriptInstead`.
This allows to wire this into existing building systems without having to depend
on data dependencies. And as so allow to keep a fixed binary size and allows
interop with GPU tests.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import importlib
import os
import subprocess
import sys
from absl import app
import tensorflow.compat.v2 as tf
from tensorflow.python.platform import tf_logging as logging
class TestCase(tf.test.TestCase):
"""Base class to write SavedModel integration tests."""
def assertCommandSucceeded(self, script_name, **flags):
"""Runs an integration test script with given flags."""
run_script = sys.argv[0]
if run_script.endswith(".py"):
command_parts = [sys.executable, run_script]
else:
command_parts = [run_script]
for flag_key, flag_value in flags.items():
command_parts.append("--%s=%s" % (flag_key, flag_value))
env = dict(TF2_BEHAVIOR="enabled", SCRIPT_NAME=script_name)
logging.info("Running: %s with environment flags %s" % (command_parts, env))
subprocess.check_call(command_parts, env=dict(os.environ, **env))
def MaybeRunScriptInstead():
if "SCRIPT_NAME" in os.environ:
# Append current path to import path and execute `SCRIPT_NAME` main.
sys.path.extend([os.path.dirname(__file__)])
module_name = os.environ["SCRIPT_NAME"]
retval = app.run(importlib.import_module(module_name).main)
sys.exit(retval)
|
tensorflow-master
|
tensorflow/examples/saved_model/integration_tests/integration_scripts.py
|
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Deploys a SavedModel with an MNIST classifier to TFLite."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import app
from absl import flags
import numpy as np
import tensorflow.compat.v2 as tf
from tensorflow.examples.saved_model.integration_tests import mnist_util
FLAGS = flags.FLAGS
flags.DEFINE_string(
'saved_model_dir', None,
'Directory of the SavedModel to deploy.')
flags.DEFINE_bool(
'use_fashion_mnist', False,
'Use Fashion MNIST (products) instead of the real MNIST (digits).')
flags.DEFINE_bool(
'fast_test_mode', False,
'Limit amount of test data for running in unit tests.')
flags.DEFINE_string(
'tflite_output_file', None,
'The filename of the .tflite model file to write (optional).')
def main(argv):
del argv
# First convert the SavedModel in a pristine environment.
converter = tf.lite.TFLiteConverter.from_saved_model(FLAGS.saved_model_dir)
lite_model_content = converter.convert()
# Here is how you can save it for actual deployment.
if FLAGS.tflite_output_file:
with open(FLAGS.tflite_output_file, 'wb') as outfile:
outfile.write(lite_model_content)
# For testing, the TFLite model can be executed like this.
interpreter = tf.lite.Interpreter(model_content=lite_model_content)
def lite_model(images):
interpreter.allocate_tensors()
interpreter.set_tensor(interpreter.get_input_details()[0]['index'], images)
interpreter.invoke()
return interpreter.get_tensor(interpreter.get_output_details()[0]['index'])
# Load the SavedModel again for use as a test baseline.
imported = tf.saved_model.load(FLAGS.saved_model_dir)
def tf_model(images):
output_dict = imported.signatures['serving_default'](tf.constant(images))
logits, = output_dict.values() # Unpack single value.
return logits
# Compare model outputs on the test inputs.
(_, _), (x_test, _) = mnist_util.load_reshaped_data(
use_fashion_mnist=FLAGS.use_fashion_mnist,
fake_tiny_data=FLAGS.fast_test_mode)
for i, x in enumerate(x_test):
x = x[None, ...] # Make batch of size 1.
y_lite = lite_model(x)
y_tf = tf_model(x)
# This numpy primitive uses plain `raise` and works outside tf.TestCase.
# Model outputs are probabilities that sum to 1, so atol makes sense here.
np.testing.assert_allclose(y_lite, y_tf, rtol=0, atol=1e-5,
err_msg='Mismatch at test example %d' % i)
if __name__ == '__main__':
app.run(main)
|
tensorflow-master
|
tensorflow/examples/saved_model/integration_tests/deploy_mnist_cnn.py
|
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Exports a convolutional feature extractor for MNIST in SavedModel format.
The feature extractor is a convolutional neural network plus a hidden layer
that gets trained as part of an MNIST classifier and then written to a
SavedModel (without the classification layer). From there, use_mnist_cnn.py
picks it up for transfer learning.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import app
from absl import flags
import tensorflow.compat.v2 as tf
from tensorflow.examples.saved_model.integration_tests import mnist_util
from tensorflow.python.util import tf_decorator
from tensorflow.python.util import tf_inspect
FLAGS = flags.FLAGS
flags.DEFINE_string(
'export_dir', None,
'Directory of exported SavedModel.')
flags.DEFINE_integer(
'epochs', 10,
'Number of epochs to train.')
flags.DEFINE_bool(
'fast_test_mode', False,
'Shortcut training for running in unit tests.')
flags.DEFINE_bool(
'export_print_hparams', False,
'If true, the exported function will print its effective hparams.')
def make_feature_extractor(l2_strength, dropout_rate):
"""Returns a Keras Model to compute a feature vector from MNIST images."""
regularizer = lambda: tf.keras.regularizers.l2(l2_strength)
net = inp = tf.keras.Input(mnist_util.INPUT_SHAPE)
net = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', name='conv1',
kernel_regularizer=regularizer())(net)
net = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', name='conv2',
kernel_regularizer=regularizer())(net)
net = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), name='pool1')(net)
net = tf.keras.layers.Dropout(dropout_rate, name='dropout1')(net)
net = tf.keras.layers.Flatten(name='flatten')(net)
net = tf.keras.layers.Dense(10, activation='relu', name='dense1',
kernel_regularizer=regularizer())(net)
return tf.keras.Model(inputs=inp, outputs=net)
def set_feature_extractor_hparams(model, dropout_rate):
model.get_layer('dropout1').rate = dropout_rate
def make_classifier(feature_extractor, l2_strength, dropout_rate=0.5):
"""Returns a Keras Model to classify MNIST using feature_extractor."""
regularizer = lambda: tf.keras.regularizers.l2(l2_strength)
net = inp = tf.keras.Input(mnist_util.INPUT_SHAPE)
net = feature_extractor(net)
net = tf.keras.layers.Dropout(dropout_rate)(net)
net = tf.keras.layers.Dense(mnist_util.NUM_CLASSES, activation='softmax',
kernel_regularizer=regularizer())(net)
return tf.keras.Model(inputs=inp, outputs=net)
def wrap_keras_model_for_export(model, batch_input_shape,
set_hparams, default_hparams):
"""Wraps `model` for saving and loading as SavedModel."""
# The primary input to the module is a Tensor with a batch of images.
# Here we determine its spec.
inputs_spec = tf.TensorSpec(shape=batch_input_shape, dtype=tf.float32)
# The module also accepts certain hparams as optional Tensor inputs.
# Here, we cut all the relevant slices from `default_hparams`
# (and don't worry if anyone accidentally modifies it later).
if default_hparams is None: default_hparams = {}
hparam_keys = list(default_hparams.keys())
hparam_defaults = tuple(default_hparams.values())
hparams_spec = {name: tf.TensorSpec.from_tensor(tf.constant(value))
for name, value in default_hparams.items()}
# The goal is to save a function with this argspec...
argspec = tf_inspect.FullArgSpec(
args=(['inputs', 'training'] + hparam_keys),
defaults=((False,) + hparam_defaults),
varargs=None, varkw=None,
kwonlyargs=[], kwonlydefaults=None,
annotations={})
# ...and this behavior:
def call_fn(inputs, training, *args):
if FLAGS.export_print_hparams:
args = [tf.keras.backend.print_tensor(args[i], 'training=%s and %s='
% (training, hparam_keys[i]))
for i in range(len(args))]
kwargs = dict(zip(hparam_keys, args))
if kwargs: set_hparams(model, **kwargs)
return model(inputs, training=training)
# We cannot spell out `args` in def statement for call_fn, but since
# tf.function uses tf_inspect, we can use tf_decorator to wrap it with
# the desired argspec.
def wrapped(*args, **kwargs): # TODO(arnoegw): Can we use call_fn itself?
return call_fn(*args, **kwargs)
traced_call_fn = tf.function(autograph=False)(
tf_decorator.make_decorator(call_fn, wrapped, decorator_argspec=argspec))
# Now we need to trigger traces for all supported combinations of the
# non-Tensor-value inputs.
for training in (True, False):
traced_call_fn.get_concrete_function(inputs_spec, training, **hparams_spec)
# Finally, we assemble the object for tf.saved_model.save().
obj = tf.train.Checkpoint()
obj.__call__ = traced_call_fn
obj.trainable_variables = model.trainable_variables
obj.variables = model.trainable_variables + model.non_trainable_variables
# Make tf.functions for the regularization terms of the loss.
obj.regularization_losses = [_get_traced_loss(model, i)
for i in range(len(model.losses))]
return obj
def _get_traced_loss(model, i):
"""Returns tf.function for model.losses[i] with a trace for zero args.
The intended usage is
[_get_traced_loss(model, i) for i in range(len(model.losses))]
This is better than
[tf.function(lambda: model.losses[i], input_signature=[]) for i ...]
because it avoids capturing a loop index in a lambda, and removes any
chance of deferring the trace.
Args:
model: a Keras Model.
i: an integer between from 0 up to but to len(model.losses).
"""
f = tf.function(lambda: model.losses[i])
_ = f.get_concrete_function()
return f
def main(argv):
del argv
# Build a complete classifier model using a feature extractor.
default_hparams = dict(dropout_rate=0.25)
l2_strength = 0.01 # Not a hparam for inputs -> outputs.
feature_extractor = make_feature_extractor(l2_strength=l2_strength,
**default_hparams)
classifier = make_classifier(feature_extractor, l2_strength=l2_strength)
# Train the complete model.
(x_train, y_train), (x_test, y_test) = mnist_util.load_reshaped_data(
fake_tiny_data=FLAGS.fast_test_mode)
classifier.compile(loss=tf.keras.losses.categorical_crossentropy,
optimizer=tf.keras.optimizers.SGD(),
metrics=['accuracy'])
classifier.fit(x_train, y_train,
batch_size=128,
epochs=FLAGS.epochs,
verbose=1,
validation_data=(x_test, y_test))
# Save the feature extractor to a framework-agnostic SavedModel for reuse.
# Note that the feature_extractor object has not been compiled or fitted,
# so it does not contain an optimizer and related state.
exportable = wrap_keras_model_for_export(feature_extractor,
(None,) + mnist_util.INPUT_SHAPE,
set_feature_extractor_hparams,
default_hparams)
tf.saved_model.save(exportable, FLAGS.export_dir)
if __name__ == '__main__':
app.run(main)
|
tensorflow-master
|
tensorflow/examples/saved_model/integration_tests/export_mnist_cnn.py
|
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Utils related to tf.distribute.strategy."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from tensorflow.python.distribute import strategy_combinations
_strategies = [
strategy_combinations.one_device_strategy,
strategy_combinations.mirrored_strategy_with_one_cpu,
strategy_combinations.mirrored_strategy_with_one_gpu,
strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
strategy_combinations.mirrored_strategy_with_two_gpus,
]
named_strategies = collections.OrderedDict(
[(None, None)] + [(str(s), s) for s in _strategies])
class MaybeDistributionScope(object):
"""Provides a context allowing no distribution strategy."""
@staticmethod
def from_name(name):
return MaybeDistributionScope(named_strategies[name].strategy if name
else None)
def __init__(self, distribution):
self._distribution = distribution
self._scope = None
def __enter__(self):
if self._distribution:
self._scope = self._distribution.scope()
self._scope.__enter__()
def __exit__(self, exc_type, value, traceback):
if self._distribution:
self._scope.__exit__(exc_type, value, traceback)
self._scope = None
|
tensorflow-master
|
tensorflow/examples/saved_model/integration_tests/distribution_strategy_utils.py
|
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Load and use RNN model stored as a SavedModel."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import app
from absl import flags
import tensorflow.compat.v2 as tf
FLAGS = flags.FLAGS
flags.DEFINE_string("model_dir", None, "Directory to load SavedModel from.")
def main(argv):
del argv
sentences = [
"<S> sentence <E>", "<S> second sentence <E>", "<S> third sentence<E>"
]
model = tf.saved_model.load(FLAGS.model_dir)
model.train(tf.constant(sentences))
decoded = model.decode_greedy(
sequence_length=10, first_word=tf.constant("<S>"))
_ = [d.numpy() for d in decoded]
if __name__ == "__main__":
app.run(main)
|
tensorflow-master
|
tensorflow/examples/saved_model/integration_tests/use_text_rnn_model.py
|
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Load and use text embedding module in a Dataset map function."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import app
from absl import flags
import numpy as np
import tensorflow.compat.v2 as tf
FLAGS = flags.FLAGS
flags.DEFINE_string("model_dir", None, "Directory to load SavedModel from.")
def train():
"""Build a Keras model and train with mock data."""
module = tf.saved_model.load(FLAGS.model_dir)
def _map_fn(features, labels):
features = tf.expand_dims(features, 0)
features = module(features)
features = tf.squeeze(features, 0)
return features, labels
features = np.array(["my first sentence", "my second sentence"])
labels = np.array([1, 0])
dataset = tf.data.Dataset.from_tensor_slices((features, labels)).map(_map_fn)
# Create the sequential keras model.
l = tf.keras.layers
model = tf.keras.Sequential()
model.add(l.Dense(10, activation="relu"))
model.add(l.Dense(1, activation="sigmoid"))
model.compile(
optimizer="adam",
loss="binary_crossentropy",
metrics=["accuracy"])
model.fit_generator(generator=dataset.batch(10), epochs=5)
def main(argv):
del argv
train()
if __name__ == "__main__":
tf.enable_v2_behavior()
app.run(main)
|
tensorflow-master
|
tensorflow/examples/saved_model/integration_tests/use_text_embedding_in_dataset.py
|
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Load and use an RNN cell stored as a SavedModel."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import app
from absl import flags
import numpy as np
import tensorflow.compat.v2 as tf
FLAGS = flags.FLAGS
flags.DEFINE_string("model_dir", None, "Directory to load SavedModel from.")
def main(argv):
del argv
cell = tf.saved_model.load(FLAGS.model_dir)
initial_state = cell.get_initial_state(
tf.constant(np.random.uniform(size=[3, 10]).astype(np.float32)))
cell.next_state(
tf.constant(np.random.uniform(size=[3, 19]).astype(np.float32)),
initial_state)
if __name__ == "__main__":
app.run(main)
|
tensorflow-master
|
tensorflow/examples/saved_model/integration_tests/use_rnn_cell.py
|
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""SavedModel integration tests."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from absl.testing import parameterized
import tensorflow.compat.v2 as tf
from tensorflow.examples.saved_model.integration_tests import distribution_strategy_utils as ds_utils
from tensorflow.examples.saved_model.integration_tests import integration_scripts as scripts
from tensorflow.python.distribute import combinations
class SavedModelTest(scripts.TestCase, parameterized.TestCase):
def __init__(self, method_name="runTest", has_extra_deps=False):
super(SavedModelTest, self).__init__(method_name)
self.has_extra_deps = has_extra_deps
def skipIfMissingExtraDeps(self):
"""Skip test if it requires extra dependencies.
b/132234211: The extra dependencies are not available in all environments
that run the tests, e.g. "tensorflow_hub" is not available from tests
within "tensorflow" alone. Those tests are instead run by another
internal test target.
"""
if not self.has_extra_deps:
self.skipTest("Missing extra dependencies")
def test_text_rnn(self):
export_dir = self.get_temp_dir()
self.assertCommandSucceeded("export_text_rnn_model", export_dir=export_dir)
self.assertCommandSucceeded("use_text_rnn_model", model_dir=export_dir)
def test_rnn_cell(self):
export_dir = self.get_temp_dir()
self.assertCommandSucceeded("export_rnn_cell", export_dir=export_dir)
self.assertCommandSucceeded("use_rnn_cell", model_dir=export_dir)
def test_text_embedding_in_sequential_keras(self):
self.skipIfMissingExtraDeps()
export_dir = self.get_temp_dir()
self.assertCommandSucceeded(
"export_simple_text_embedding", export_dir=export_dir)
self.assertCommandSucceeded(
"use_model_in_sequential_keras", model_dir=export_dir)
def test_text_embedding_in_dataset(self):
if tf.test.is_gpu_available():
self.skipTest("b/132156097 - fails if there is a gpu available")
export_dir = self.get_temp_dir()
self.assertCommandSucceeded(
"export_simple_text_embedding", export_dir=export_dir)
self.assertCommandSucceeded(
"use_text_embedding_in_dataset", model_dir=export_dir)
TEST_MNIST_CNN_GENERATE_KWARGS = dict(
combinations=combinations.combine(
named_strategy=list(ds_utils.named_strategies.values()),
retrain_flag_value=["true", "false"],
regularization_loss_multiplier=[None, 2]), # Test for b/134528831.
test_combinations=[combinations.NamedGPUCombination()])
@combinations.generate(**TEST_MNIST_CNN_GENERATE_KWARGS)
def test_mnist_cnn(self, named_strategy, retrain_flag_value,
regularization_loss_multiplier):
self.skipIfMissingExtraDeps()
fast_test_mode = True
temp_dir = self.get_temp_dir()
feature_extrator_dir = os.path.join(temp_dir, "mnist_feature_extractor")
# TODO(b/135043074): remove this if-else.
if named_strategy is None:
full_model_dir = os.path.join(temp_dir, "full_model")
else:
full_model_dir = None
self.assertCommandSucceeded(
"export_mnist_cnn",
fast_test_mode=fast_test_mode,
export_dir=feature_extrator_dir)
use_kwargs = dict(fast_test_mode=fast_test_mode,
input_saved_model_dir=feature_extrator_dir,
retrain=retrain_flag_value)
if full_model_dir is not None:
use_kwargs["output_saved_model_dir"] = full_model_dir
if named_strategy:
use_kwargs["strategy"] = str(named_strategy)
if regularization_loss_multiplier is not None:
use_kwargs[
"regularization_loss_multiplier"] = regularization_loss_multiplier
self.assertCommandSucceeded("use_mnist_cnn", **use_kwargs)
if full_model_dir is not None:
self.assertCommandSucceeded(
"deploy_mnist_cnn",
fast_test_mode=fast_test_mode,
saved_model_dir=full_model_dir)
if __name__ == "__main__":
scripts.MaybeRunScriptInstead()
tf.test.main()
|
tensorflow-master
|
tensorflow/examples/saved_model/integration_tests/saved_model_test.py
|
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Text RNN model stored as a SavedModel."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import app
from absl import flags
import tensorflow.compat.v2 as tf
FLAGS = flags.FLAGS
flags.DEFINE_string("export_dir", None, "Directory to export SavedModel.")
class TextRnnModel(tf.train.Checkpoint):
"""Text RNN model.
A full generative text RNN model that can train and decode sentences from a
starting word.
"""
def __init__(self, vocab, emb_dim, buckets, state_size):
super(TextRnnModel, self).__init__()
self._buckets = buckets
self._lstm_cell = tf.keras.layers.LSTMCell(units=state_size)
self._rnn_layer = tf.keras.layers.RNN(
self._lstm_cell, return_sequences=True)
self._embeddings = tf.Variable(tf.random.uniform(shape=[buckets, emb_dim]))
self._logit_layer = tf.keras.layers.Dense(buckets)
self._set_up_vocab(vocab)
def _tokenize(self, sentences):
# Perform a minimalistic text preprocessing by removing punctuation and
# splitting on spaces.
normalized_sentences = tf.strings.regex_replace(
input=sentences, pattern=r"\pP", rewrite="")
sparse_tokens = tf.strings.split(normalized_sentences, " ").to_sparse()
# Deal with a corner case: there is one empty sentence.
sparse_tokens, _ = tf.sparse.fill_empty_rows(sparse_tokens, tf.constant(""))
# Deal with a corner case: all sentences are empty.
sparse_tokens = tf.sparse.reset_shape(sparse_tokens)
return (sparse_tokens.indices, sparse_tokens.values,
sparse_tokens.dense_shape)
def _set_up_vocab(self, vocab_tokens):
# TODO(vbardiovsky): Currently there is no real vocabulary, because
# saved_model serialization does not support trackable resources. Add a real
# vocabulary when it does.
vocab_list = ["UNK"] * self._buckets
for vocab_token in vocab_tokens:
index = self._words_to_indices(vocab_token).numpy()
vocab_list[index] = vocab_token
# This is a variable representing an inverse index.
self._vocab_tensor = tf.Variable(vocab_list)
def _indices_to_words(self, indices):
return tf.gather(self._vocab_tensor, indices)
def _words_to_indices(self, words):
return tf.strings.to_hash_bucket(words, self._buckets)
@tf.function(input_signature=[tf.TensorSpec([None], tf.dtypes.string)])
def train(self, sentences):
token_ids, token_values, token_dense_shape = self._tokenize(sentences)
tokens_sparse = tf.sparse.SparseTensor(
indices=token_ids, values=token_values, dense_shape=token_dense_shape)
tokens = tf.sparse.to_dense(tokens_sparse, default_value="")
sparse_lookup_ids = tf.sparse.SparseTensor(
indices=tokens_sparse.indices,
values=self._words_to_indices(tokens_sparse.values),
dense_shape=tokens_sparse.dense_shape)
lookup_ids = tf.sparse.to_dense(sparse_lookup_ids, default_value=0)
# Targets are the next word for each word of the sentence.
tokens_ids_seq = lookup_ids[:, 0:-1]
tokens_ids_target = lookup_ids[:, 1:]
tokens_prefix = tokens[:, 0:-1]
# Mask determining which positions we care about for a loss: all positions
# that have a valid non-terminal token.
mask = tf.logical_and(
tf.logical_not(tf.equal(tokens_prefix, "")),
tf.logical_not(tf.equal(tokens_prefix, "<E>")))
input_mask = tf.cast(mask, tf.int32)
with tf.GradientTape() as t:
sentence_embeddings = tf.nn.embedding_lookup(self._embeddings,
tokens_ids_seq)
lstm_initial_state = self._lstm_cell.get_initial_state(
sentence_embeddings)
lstm_output = self._rnn_layer(
inputs=sentence_embeddings, initial_state=lstm_initial_state)
# Stack LSTM outputs into a batch instead of a 2D array.
lstm_output = tf.reshape(lstm_output, [-1, self._lstm_cell.output_size])
logits = self._logit_layer(lstm_output)
targets = tf.reshape(tokens_ids_target, [-1])
weights = tf.cast(tf.reshape(input_mask, [-1]), tf.float32)
losses = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=targets, logits=logits)
# Final loss is the mean loss for all token losses.
final_loss = tf.math.divide(
tf.reduce_sum(tf.multiply(losses, weights)),
tf.reduce_sum(weights),
name="final_loss")
watched = t.watched_variables()
gradients = t.gradient(final_loss, watched)
for w, g in zip(watched, gradients):
w.assign_sub(g)
return final_loss
@tf.function
def decode_greedy(self, sequence_length, first_word):
initial_state = self._lstm_cell.get_initial_state(
dtype=tf.float32, batch_size=1)
sequence = [first_word]
current_word = first_word
current_id = tf.expand_dims(self._words_to_indices(current_word), 0)
current_state = initial_state
for _ in range(sequence_length):
token_embeddings = tf.nn.embedding_lookup(self._embeddings, current_id)
lstm_outputs, current_state = self._lstm_cell(token_embeddings,
current_state)
lstm_outputs = tf.reshape(lstm_outputs, [-1, self._lstm_cell.output_size])
logits = self._logit_layer(lstm_outputs)
softmax = tf.nn.softmax(logits)
next_ids = tf.math.argmax(softmax, axis=1)
next_words = self._indices_to_words(next_ids)[0]
current_id = next_ids
current_word = next_words
sequence.append(current_word)
return sequence
def main(argv):
del argv
sentences = ["<S> hello there <E>", "<S> how are you doing today <E>"]
vocab = [
"<S>", "<E>", "hello", "there", "how", "are", "you", "doing", "today"
]
module = TextRnnModel(vocab=vocab, emb_dim=10, buckets=100, state_size=128)
for _ in range(100):
_ = module.train(tf.constant(sentences))
# We have to call this function explicitly if we want it exported, because it
# has no input_signature in the @tf.function decorator.
decoded = module.decode_greedy(
sequence_length=10, first_word=tf.constant("<S>"))
_ = [d.numpy() for d in decoded]
tf.saved_model.save(module, FLAGS.export_dir)
if __name__ == "__main__":
app.run(main)
|
tensorflow-master
|
tensorflow/examples/saved_model/integration_tests/export_text_rnn_model.py
|
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Load and use text embedding module in sequential Keras."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import app
from absl import flags
import numpy as np
import tensorflow.compat.v2 as tf
import tensorflow_hub as hub
FLAGS = flags.FLAGS
flags.DEFINE_string("model_dir", None, "Directory to load SavedModel from.")
def train(fine_tuning):
"""Build a Keras model and train with mock data."""
features = np.array(["my first sentence", "my second sentence"])
labels = np.array([1, 0])
dataset = tf.data.Dataset.from_tensor_slices((features, labels))
module = tf.saved_model.load(FLAGS.model_dir)
# Create the sequential keras model.
l = tf.keras.layers
model = tf.keras.Sequential()
model.add(l.Reshape((), batch_input_shape=[None, 1], dtype=tf.string))
# TODO(b/124219898): output_shape should be optional.
model.add(hub.KerasLayer(module, output_shape=[10], trainable=fine_tuning))
model.add(l.Dense(100, activation="relu"))
model.add(l.Dense(50, activation="relu"))
model.add(l.Dense(1, activation="sigmoid"))
model.compile(
optimizer="adam",
loss="binary_crossentropy",
metrics=["accuracy"],
# TODO(b/124446120): Remove after fixed.
run_eagerly=True)
model.fit_generator(generator=dataset.batch(1), epochs=5)
def main(argv):
del argv
train(fine_tuning=False)
train(fine_tuning=True)
if __name__ == "__main__":
app.run(main)
|
tensorflow-master
|
tensorflow/examples/saved_model/integration_tests/use_model_in_sequential_keras.py
|
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Imports a convolutional feature extractor for MNIST in SavedModel format.
This program picks up the SavedModel written by export_mnist_cnn.py and
uses the feature extractor contained in it to do classification on either
classic MNIST (digits) or Fashion MNIST (thumbnails of apparel). Optionally,
it trains the feature extractor further as part of the new classifier.
As expected, that makes training slower but does not help much for the
original training dataset but helps a lot for transfer to the other dataset.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import app
from absl import flags
import tensorflow.compat.v2 as tf
import tensorflow_hub as hub
from tensorflow.examples.saved_model.integration_tests import distribution_strategy_utils as ds_utils
from tensorflow.examples.saved_model.integration_tests import mnist_util
FLAGS = flags.FLAGS
flags.DEFINE_string(
'input_saved_model_dir', None,
'Directory of the reusable SavedModel that is imported into this program.')
flags.DEFINE_integer(
'epochs', 5,
'Number of epochs to train.')
flags.DEFINE_bool(
'retrain', False,
'If set, the imported SavedModel is trained further.')
flags.DEFINE_float(
'dropout_rate', None,
'If set, dropout rate passed to the SavedModel.')
flags.DEFINE_float(
'regularization_loss_multiplier', None,
'If set, multiplier for the regularization losses in the SavedModel.')
flags.DEFINE_bool(
'use_fashion_mnist', False,
'Use Fashion MNIST (products) instead of the real MNIST (digits). '
'With this, --retrain gains a lot.')
flags.DEFINE_bool(
'fast_test_mode', False,
'Shortcut training for running in unit tests.')
flags.DEFINE_string(
'output_saved_model_dir', None,
'Directory of the SavedModel that was exported for reuse.')
flags.DEFINE_string('strategy', None,
'Name of the distribution strategy to use.')
def make_feature_extractor(saved_model_path, trainable,
regularization_loss_multiplier):
"""Load a pre-trained feature extractor and wrap it for use in Keras."""
if regularization_loss_multiplier is not None:
# TODO(b/63257857): Scaling regularization losses requires manual loading
# and modification of the SavedModel
obj = tf.saved_model.load(saved_model_path)
def _scale_one_loss(l): # Separate def avoids lambda capture of loop var.
f = tf.function(lambda: tf.multiply(regularization_loss_multiplier, l()))
_ = f.get_concrete_function()
return f
obj.regularization_losses = [_scale_one_loss(l)
for l in obj.regularization_losses]
# The modified object is then passed to hub.KerasLayer instead of the
# string handle. That prevents it from saving a Keras config (b/134528831).
handle = obj
else:
# If possible, we exercise the more common case of passing a string handle
# such that hub.KerasLayer can save a Keras config (b/134528831).
handle = saved_model_path
arguments = {}
if FLAGS.dropout_rate is not None:
arguments['dropout_rate'] = FLAGS.dropout_rate
return hub.KerasLayer(handle, trainable=trainable, arguments=arguments)
def make_classifier(feature_extractor, l2_strength=0.01, dropout_rate=0.5):
"""Returns a Keras Model to classify MNIST using feature_extractor."""
regularizer = lambda: tf.keras.regularizers.l2(l2_strength)
net = inp = tf.keras.Input(mnist_util.INPUT_SHAPE)
net = feature_extractor(net)
if dropout_rate:
net = tf.keras.layers.Dropout(dropout_rate)(net)
net = tf.keras.layers.Dense(mnist_util.NUM_CLASSES, activation='softmax',
kernel_regularizer=regularizer())(net)
return tf.keras.Model(inputs=inp, outputs=net)
def main(argv):
del argv
with ds_utils.MaybeDistributionScope.from_name(FLAGS.strategy):
feature_extractor = make_feature_extractor(
FLAGS.input_saved_model_dir,
FLAGS.retrain,
FLAGS.regularization_loss_multiplier)
model = make_classifier(feature_extractor)
model.compile(loss=tf.keras.losses.categorical_crossentropy,
optimizer=tf.keras.optimizers.SGD(),
metrics=['accuracy'])
# Train the classifier (possibly on a different dataset).
(x_train, y_train), (x_test, y_test) = mnist_util.load_reshaped_data(
use_fashion_mnist=FLAGS.use_fashion_mnist,
fake_tiny_data=FLAGS.fast_test_mode)
print('Training on %s with %d trainable and %d untrainable variables.' %
('Fashion MNIST' if FLAGS.use_fashion_mnist else 'MNIST',
len(model.trainable_variables), len(model.non_trainable_variables)))
model.fit(x_train, y_train,
batch_size=128,
epochs=FLAGS.epochs,
verbose=1,
validation_data=(x_test, y_test))
if FLAGS.output_saved_model_dir:
tf.saved_model.save(model, FLAGS.output_saved_model_dir)
if __name__ == '__main__':
app.run(main)
|
tensorflow-master
|
tensorflow/examples/saved_model/integration_tests/use_mnist_cnn.py
|
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Text embedding model stored as a SavedModel."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tempfile
from absl import app
from absl import flags
import tensorflow.compat.v2 as tf
# TODO(vbardiovsky): remove these when symbols are public.
from tensorflow.python.ops import lookup_ops
from tensorflow.python.training.tracking import tracking
FLAGS = flags.FLAGS
flags.DEFINE_string("export_dir", None, "Directory to export SavedModel.")
def write_vocabulary_file(vocabulary):
"""Write temporary vocab file for module construction."""
tmpdir = tempfile.mkdtemp()
vocabulary_file = os.path.join(tmpdir, "tokens.txt")
with tf.io.gfile.GFile(vocabulary_file, "w") as f:
for entry in vocabulary:
f.write(entry + "\n")
return vocabulary_file
class TextEmbeddingModel(tf.train.Checkpoint):
"""Text embedding model.
A text embeddings model that takes a sentences on input and outputs the
sentence embedding.
"""
def __init__(self, vocabulary, emb_dim, oov_buckets):
super(TextEmbeddingModel, self).__init__()
self._oov_buckets = oov_buckets
self._vocabulary_file = tracking.TrackableAsset(
write_vocabulary_file(vocabulary))
self._total_size = len(vocabulary) + oov_buckets
self._table = lookup_ops.index_table_from_file(
vocabulary_file=self._vocabulary_file,
num_oov_buckets=self._oov_buckets,
hasher_spec=lookup_ops.FastHashSpec)
self.embeddings = tf.Variable(
tf.random.uniform(shape=[self._total_size, emb_dim]))
self.variables = [self.embeddings]
self.trainable_variables = self.variables
def _tokenize(self, sentences):
# Perform a minimalistic text preprocessing by removing punctuation and
# splitting on spaces.
normalized_sentences = tf.strings.regex_replace(
input=sentences, pattern=r"\pP", rewrite="")
normalized_sentences = tf.reshape(normalized_sentences, [-1])
sparse_tokens = tf.strings.split(normalized_sentences, " ").to_sparse()
# Deal with a corner case: there is one empty sentence.
sparse_tokens, _ = tf.sparse.fill_empty_rows(sparse_tokens, tf.constant(""))
# Deal with a corner case: all sentences are empty.
sparse_tokens = tf.sparse.reset_shape(sparse_tokens)
sparse_token_ids = self._table.lookup(sparse_tokens.values)
return (sparse_tokens.indices, sparse_token_ids, sparse_tokens.dense_shape)
@tf.function(input_signature=[tf.TensorSpec([None], tf.dtypes.string)])
def __call__(self, sentences):
token_ids, token_values, token_dense_shape = self._tokenize(sentences)
return tf.nn.safe_embedding_lookup_sparse(
embedding_weights=self.embeddings,
sparse_ids=tf.SparseTensor(token_ids, token_values, token_dense_shape),
sparse_weights=None,
combiner="sqrtn")
def main(argv):
del argv
vocabulary = ["cat", "is", "on", "the", "mat"]
module = TextEmbeddingModel(vocabulary=vocabulary, emb_dim=10, oov_buckets=10)
tf.saved_model.save(module, FLAGS.export_dir)
if __name__ == "__main__":
app.run(main)
|
tensorflow-master
|
tensorflow/examples/saved_model/integration_tests/export_simple_text_embedding.py
|
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Convenience wrapper around Keras' MNIST and Fashion MNIST data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.compat.v2 as tf
INPUT_SHAPE = (28, 28, 1)
NUM_CLASSES = 10
def _load_random_data(num_train_and_test):
return ((np.random.randint(0, 256, (num, 28, 28), dtype=np.uint8),
np.random.randint(0, 10, (num,), dtype=np.int64))
for num in num_train_and_test)
def load_reshaped_data(use_fashion_mnist=False, fake_tiny_data=False):
"""Returns MNIST or Fashion MNIST or fake train and test data."""
load = ((lambda: _load_random_data([16, 128])) if fake_tiny_data else
tf.keras.datasets.fashion_mnist.load_data if use_fashion_mnist else
tf.keras.datasets.mnist.load_data)
(x_train, y_train), (x_test, y_test) = load()
return ((_prepare_image(x_train), _prepare_label(y_train)),
(_prepare_image(x_test), _prepare_label(y_test)))
def _prepare_image(x):
"""Converts images to [n,h,w,c] format in range [0,1]."""
return x[..., None].astype('float32') / 255.
def _prepare_label(y):
"""Conerts labels to one-hot encoding."""
return tf.keras.utils.to_categorical(y, NUM_CLASSES)
|
tensorflow-master
|
tensorflow/examples/saved_model/integration_tests/mnist_util.py
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""ZeroOut op Python library."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import tensorflow as tf
_zero_out_module = tf.load_op_library(
os.path.join(tf.resource_loader.get_data_files_path(),
'zero_out_op_kernel_3.so'))
zero_out = _zero_out_module.zero_out
|
tensorflow-master
|
tensorflow/examples/adding_an_op/zero_out_op_3.py
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Test for version 1 of the zero_out op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import tensorflow as tf
from tensorflow.examples.adding_an_op import zero_out_op_1
from tensorflow.python.framework import test_util
class ZeroOut1Test(tf.test.TestCase):
@test_util.run_deprecated_v1
def test(self):
with self.cached_session():
result = zero_out_op_1.zero_out([5, 4, 3, 2, 1])
self.assertAllEqual(result.eval(), [5, 0, 0, 0, 0])
def testLoadTwice(self):
zero_out_loaded_again = tf.load_op_library(os.path.join(
tf.resource_loader.get_data_files_path(), 'zero_out_op_kernel_1.so'))
self.assertEqual(zero_out_loaded_again, zero_out_op_1._zero_out_module)
if __name__ == '__main__':
tf.test.main()
|
tensorflow-master
|
tensorflow/examples/adding_an_op/zero_out_1_test.py
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""ZeroOut ops Python library."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import tensorflow as tf
_zero_out_module = tf.load_op_library(
os.path.join(tf.resource_loader.get_data_files_path(),
'zero_out_op_kernel_2.so'))
zero_out = _zero_out_module.zero_out
zero_out2 = _zero_out_module.zero_out2
zero_out3 = _zero_out_module.zero_out3
|
tensorflow-master
|
tensorflow/examples/adding_an_op/zero_out_op_2.py
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Test for version 3 of the zero_out op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.examples.adding_an_op import zero_out_op_3
from tensorflow.python.framework import test_util
class ZeroOut3Test(tf.test.TestCase):
@test_util.run_deprecated_v1
def test(self):
with self.cached_session():
result = zero_out_op_3.zero_out([5, 4, 3, 2, 1])
self.assertAllEqual(result.eval(), [5, 0, 0, 0, 0])
@test_util.run_deprecated_v1
def testAttr(self):
with self.cached_session():
result = zero_out_op_3.zero_out([5, 4, 3, 2, 1], preserve_index=3)
self.assertAllEqual(result.eval(), [0, 0, 0, 2, 0])
@test_util.run_deprecated_v1
def testNegative(self):
with self.cached_session():
result = zero_out_op_3.zero_out([5, 4, 3, 2, 1], preserve_index=-1)
with self.assertRaisesOpError("Need preserve_index >= 0, got -1"):
self.evaluate(result)
@test_util.run_deprecated_v1
def testLarge(self):
with self.cached_session():
result = zero_out_op_3.zero_out([5, 4, 3, 2, 1], preserve_index=17)
with self.assertRaisesOpError("preserve_index out of range"):
self.evaluate(result)
if __name__ == "__main__":
tf.test.main()
|
tensorflow-master
|
tensorflow/examples/adding_an_op/zero_out_3_test.py
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Test for version 2 of the zero_out op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.examples.adding_an_op import zero_out_grad_2 # pylint: disable=unused-import
from tensorflow.examples.adding_an_op import zero_out_op_2
from tensorflow.python.framework import test_util
class ZeroOut2Test(tf.test.TestCase):
@test_util.run_deprecated_v1
def test(self):
with self.cached_session():
result = zero_out_op_2.zero_out([5, 4, 3, 2, 1])
self.assertAllEqual(result.eval(), [5, 0, 0, 0, 0])
@test_util.run_deprecated_v1
def test_2d(self):
with self.cached_session():
result = zero_out_op_2.zero_out([[6, 5, 4], [3, 2, 1]])
self.assertAllEqual(result.eval(), [[6, 0, 0], [0, 0, 0]])
@test_util.run_deprecated_v1
def test_grad(self):
with self.cached_session():
shape = (5,)
x = tf.constant([5, 4, 3, 2, 1], dtype=tf.float32)
y = zero_out_op_2.zero_out(x)
err = tf.test.compute_gradient_error(x, shape, y, shape)
self.assertLess(err, 1e-4)
@test_util.run_deprecated_v1
def test_grad_2d(self):
with self.cached_session():
shape = (2, 3)
x = tf.constant([[6, 5, 4], [3, 2, 1]], dtype=tf.float32)
y = zero_out_op_2.zero_out(x)
err = tf.test.compute_gradient_error(x, shape, y, shape)
self.assertLess(err, 1e-4)
if __name__ == '__main__':
tf.test.main()
|
tensorflow-master
|
tensorflow/examples/adding_an_op/zero_out_2_test.py
|
tensorflow-master
|
tensorflow/examples/adding_an_op/__init__.py
|
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Cuda op Python library."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import tensorflow as tf
if tf.test.is_built_with_cuda():
_cuda_op_module = tf.load_op_library(os.path.join(
tf.resource_loader.get_data_files_path(), 'cuda_op_kernel.so'))
add_one = _cuda_op_module.add_one
|
tensorflow-master
|
tensorflow/examples/adding_an_op/cuda_op.py
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""The gradient of the tutorial zero_out op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import sparse_ops
@ops.RegisterGradient("ZeroOut")
def _zero_out_grad(op, grad):
"""The gradients for `zero_out`.
Args:
op: The `zero_out` `Operation` that we are differentiating, which we can use
to find the inputs and outputs of the original op.
grad: Gradient with respect to the output of the `zero_out` op.
Returns:
Gradients with respect to the input of `zero_out`.
"""
to_zero = op.inputs[0]
shape = array_ops.shape(to_zero)
index = array_ops.zeros_like(shape)
first_grad = array_ops.reshape(grad, [-1])[0]
to_zero_grad = sparse_ops.sparse_to_dense([index], shape, first_grad, 0)
return [to_zero_grad] # List of one Tensor, since we have one input
|
tensorflow-master
|
tensorflow/examples/adding_an_op/zero_out_grad_2.py
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Test that user ops can be used as expected."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.python.framework import test_util
class FactTest(tf.test.TestCase):
@test_util.run_deprecated_v1
def test(self):
with self.cached_session():
print(tf.user_ops.my_fact().eval())
if __name__ == '__main__':
tf.test.main()
|
tensorflow-master
|
tensorflow/examples/adding_an_op/fact_test.py
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""ZeroOut op Python library."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import tensorflow as tf
_zero_out_module = tf.load_op_library(
os.path.join(tf.resource_loader.get_data_files_path(),
'zero_out_op_kernel_1.so'))
zero_out = _zero_out_module.zero_out
|
tensorflow-master
|
tensorflow/examples/adding_an_op/zero_out_op_1.py
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Test for version 1 of the zero_out op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.examples.adding_an_op import cuda_op
class AddOneTest(tf.test.TestCase):
def test(self):
if tf.test.is_built_with_cuda():
result = cuda_op.add_one([5, 4, 3, 2, 1])
self.assertAllEqual(result, [6, 5, 4, 3, 2])
if __name__ == '__main__':
tf.test.main()
|
tensorflow-master
|
tensorflow/examples/adding_an_op/cuda_op_test.py
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""MNIST model training with TensorFlow eager execution.
See:
https://research.googleblog.com/2017/10/eager-execution-imperative-define-by.html
This program demonstrates training, export, and inference of a convolutional
neural network model with eager execution enabled.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
from absl import app
from absl import flags
import numpy as np
import tensorflow as tf
tfe = tf.contrib.eager
flags.DEFINE_integer(
name='log_interval',
default=10,
help='batches between logging training status')
flags.DEFINE_float(name='learning_rate', default=0.01, help='Learning rate.')
flags.DEFINE_float(
name='momentum', short_name='m', default=0.5, help='SGD momentum.')
flags.DEFINE_integer(
name='batch_size',
default=100,
help='Batch size to use during training / eval')
flags.DEFINE_integer(
name='train_epochs', default=10, help='Number of epochs to train')
flags.DEFINE_string(
name='model_dir',
default='/tmp/tensorflow/mnist',
help='Where to save checkpoints, tensorboard summaries, etc.')
flags.DEFINE_bool(
name='clean',
default=False,
help='Whether to clear model directory before training')
FLAGS = flags.FLAGS
def create_model():
"""Model to recognize digits in the MNIST dataset.
Network structure is equivalent to:
https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/examples/tutorials/mnist/mnist_deep.py
and
https://github.com/tensorflow/models/blob/master/tutorials/image/mnist/convolutional.py
But uses the tf.keras API.
Returns:
A tf.keras.Model.
"""
# Assumes data_format == 'channel_last'.
# See https://www.tensorflow.org/performance/performance_guide#data_formats
input_shape = [28, 28, 1]
l = tf.keras.layers
max_pool = l.MaxPooling2D((2, 2), (2, 2), padding='same')
# The model consists of a sequential chain of layers, so tf.keras.Sequential
# (a subclass of tf.keras.Model) makes for a compact description.
model = tf.keras.Sequential(
[
l.Reshape(
target_shape=input_shape,
input_shape=(28 * 28,)),
l.Conv2D(2, 5, padding='same', activation=tf.nn.relu),
max_pool,
l.Conv2D(4, 5, padding='same', activation=tf.nn.relu),
max_pool,
l.Flatten(),
l.Dense(32, activation=tf.nn.relu),
l.Dropout(0.4),
l.Dense(10)
])
# TODO(brianklee): Remove when @kaftan makes this happen by default.
# TODO(brianklee): remove `autograph=True` when kwarg default is flipped.
model.call = tfe.function(model.call, autograph=True)
# Needs to have input_signature specified in order to be exported
# since model.predict() is never called before saved_model.export()
# TODO(brianklee): Update with input signature, depending on how the impl of
# saved_model.restore() pans out.
model.predict = tfe.function(model.predict, autograph=True)
# ,input_signature=(tensor_spec.TensorSpec(shape=[28, 28, None], dtype=tf.float32),) # pylint: disable=line-too-long
return model
def mnist_datasets():
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
# Numpy defaults to dtype=float64; TF defaults to float32. Stick with float32.
x_train, x_test = x_train / np.float32(255), x_test / np.float32(255)
y_train, y_test = y_train.astype(np.int64), y_test.astype(np.int64)
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test))
return train_dataset, test_dataset
def loss(logits, labels):
return tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=labels))
def compute_accuracy(logits, labels):
predictions = tf.argmax(logits, axis=1, output_type=tf.int64)
labels = tf.cast(labels, tf.int64)
return tf.reduce_mean(
tf.cast(tf.equal(predictions, labels), dtype=tf.float32))
# TODO(brianklee): Enable @tf.function on the training loop when zip, enumerate
# are supported by autograph.
def train(model, optimizer, dataset, step_counter, log_interval=None,
num_steps=None):
"""Trains model on `dataset` using `optimizer`."""
start = time.time()
for (batch, (images, labels)) in enumerate(dataset):
if num_steps is not None and batch > num_steps:
break
with tf.contrib.summary.record_summaries_every_n_global_steps(
10, global_step=step_counter):
# Record the operations used to compute the loss given the input,
# so that the gradient of the loss with respect to the variables
# can be computed.
with tf.GradientTape() as tape:
logits = model(images, training=True)
loss_value = loss(logits, labels)
tf.contrib.summary.scalar('loss', loss_value)
tf.contrib.summary.scalar('accuracy', compute_accuracy(logits, labels))
grads = tape.gradient(loss_value, model.variables)
optimizer.apply_gradients(
zip(grads, model.variables), global_step=step_counter)
if log_interval and batch % log_interval == 0:
rate = log_interval / (time.time() - start)
print('Step #%d\tLoss: %.6f (%d steps/sec)' % (batch, loss_value, rate))
start = time.time()
def test(model, dataset):
"""Perform an evaluation of `model` on the examples from `dataset`."""
avg_loss = tfe.metrics.Mean('loss', dtype=tf.float32)
accuracy = tfe.metrics.Accuracy('accuracy', dtype=tf.float32)
for (images, labels) in dataset:
logits = model(images, training=False)
avg_loss(loss(logits, labels))
accuracy(
tf.argmax(logits, axis=1, output_type=tf.int64),
tf.cast(labels, tf.int64))
print('Test set: Average loss: %.4f, Accuracy: %4f%%\n' %
(avg_loss.result(), 100 * accuracy.result()))
with tf.contrib.summary.always_record_summaries():
tf.contrib.summary.scalar('loss', avg_loss.result())
tf.contrib.summary.scalar('accuracy', accuracy.result())
def train_and_export(flags_obj):
"""Run MNIST training and eval loop in eager mode.
Args:
flags_obj: An object containing parsed flag values.
"""
# Load the datasets
train_ds, test_ds = mnist_datasets()
train_ds = train_ds.shuffle(60000).batch(flags_obj.batch_size)
test_ds = test_ds.batch(flags_obj.batch_size)
# Create the model and optimizer
model = create_model()
optimizer = tf.train.MomentumOptimizer(
flags_obj.learning_rate, flags_obj.momentum)
# See summaries with `tensorboard --logdir=<model_dir>`
train_dir = os.path.join(flags_obj.model_dir, 'summaries', 'train')
test_dir = os.path.join(flags_obj.model_dir, 'summaries', 'eval')
summary_writer = tf.contrib.summary.create_file_writer(
train_dir, flush_millis=10000)
test_summary_writer = tf.contrib.summary.create_file_writer(
test_dir, flush_millis=10000, name='test')
# Create and restore checkpoint (if one exists on the path)
checkpoint_dir = os.path.join(flags_obj.model_dir, 'checkpoints')
checkpoint_prefix = os.path.join(checkpoint_dir, 'ckpt')
step_counter = tf.train.get_or_create_global_step()
checkpoint = tf.train.Checkpoint(
model=model, optimizer=optimizer, step_counter=step_counter)
# Restore variables on creation if a checkpoint exists.
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
# Train and evaluate for a set number of epochs.
for _ in range(flags_obj.train_epochs):
start = time.time()
with summary_writer.as_default():
train(model, optimizer, train_ds, step_counter,
flags_obj.log_interval, num_steps=1)
end = time.time()
print('\nTrain time for epoch #%d (%d total steps): %f' %
(checkpoint.save_counter.numpy() + 1,
step_counter.numpy(),
end - start))
with test_summary_writer.as_default():
test(model, test_ds)
checkpoint.save(checkpoint_prefix)
# TODO(brianklee): Enable this functionality after @allenl implements this.
# export_path = os.path.join(flags_obj.model_dir, 'export')
# tf.saved_model.save(export_path, model)
def import_and_eval(flags_obj):
export_path = os.path.join(flags_obj.model_dir, 'export')
model = tf.saved_model.restore(export_path)
_, (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_test = x_test / np.float32(255)
y_predict = model(x_test)
accuracy = compute_accuracy(y_predict, y_test)
print('Model accuracy: {:0.2f}%'.format(accuracy.numpy() * 100))
def apply_clean(flags_obj):
if flags_obj.clean and tf.gfile.Exists(flags_obj.model_dir):
tf.logging.info('--clean flag set. Removing existing model dir: {}'.format(
flags_obj.model_dir))
tf.gfile.DeleteRecursively(flags_obj.model_dir)
def main(_):
apply_clean(flags.FLAGS)
train_and_export(flags.FLAGS)
# TODO(brianklee): Enable this functionality after @allenl implements this.
# import_and_eval(flags.FLAGS)
if __name__ == '__main__':
app.run(main)
|
tensorflow-master
|
tensorflow/examples/tf2_showcase/mnist.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import numpy as np
import tensorflow as tf
def load_graph(model_file):
graph = tf.Graph()
graph_def = tf.GraphDef()
with open(model_file, "rb") as f:
graph_def.ParseFromString(f.read())
with graph.as_default():
tf.import_graph_def(graph_def)
return graph
def read_tensor_from_image_file(file_name,
input_height=299,
input_width=299,
input_mean=0,
input_std=255):
input_name = "file_reader"
output_name = "normalized"
file_reader = tf.read_file(file_name, input_name)
if file_name.endswith(".png"):
image_reader = tf.image.decode_png(
file_reader, channels=3, name="png_reader")
elif file_name.endswith(".gif"):
image_reader = tf.squeeze(
tf.image.decode_gif(file_reader, name="gif_reader"))
elif file_name.endswith(".bmp"):
image_reader = tf.image.decode_bmp(file_reader, name="bmp_reader")
else:
image_reader = tf.image.decode_jpeg(
file_reader, channels=3, name="jpeg_reader")
float_caster = tf.cast(image_reader, tf.float32)
dims_expander = tf.expand_dims(float_caster, 0)
resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
sess = tf.Session()
result = sess.run(normalized)
return result
def load_labels(label_file):
label = []
proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
for l in proto_as_ascii_lines:
label.append(l.rstrip())
return label
if __name__ == "__main__":
file_name = "tensorflow/examples/label_image/data/grace_hopper.jpg"
model_file = \
"tensorflow/examples/label_image/data/inception_v3_2016_08_28_frozen.pb"
label_file = "tensorflow/examples/label_image/data/imagenet_slim_labels.txt"
input_height = 299
input_width = 299
input_mean = 0
input_std = 255
input_layer = "input"
output_layer = "InceptionV3/Predictions/Reshape_1"
parser = argparse.ArgumentParser()
parser.add_argument("--image", help="image to be processed")
parser.add_argument("--graph", help="graph/model to be executed")
parser.add_argument("--labels", help="name of file containing labels")
parser.add_argument("--input_height", type=int, help="input height")
parser.add_argument("--input_width", type=int, help="input width")
parser.add_argument("--input_mean", type=int, help="input mean")
parser.add_argument("--input_std", type=int, help="input std")
parser.add_argument("--input_layer", help="name of input layer")
parser.add_argument("--output_layer", help="name of output layer")
args = parser.parse_args()
if args.graph:
model_file = args.graph
if args.image:
file_name = args.image
if args.labels:
label_file = args.labels
if args.input_height:
input_height = args.input_height
if args.input_width:
input_width = args.input_width
if args.input_mean:
input_mean = args.input_mean
if args.input_std:
input_std = args.input_std
if args.input_layer:
input_layer = args.input_layer
if args.output_layer:
output_layer = args.output_layer
graph = load_graph(model_file)
t = read_tensor_from_image_file(
file_name,
input_height=input_height,
input_width=input_width,
input_mean=input_mean,
input_std=input_std)
input_name = "import/" + input_layer
output_name = "import/" + output_layer
input_operation = graph.get_operation_by_name(input_name)
output_operation = graph.get_operation_by_name(output_name)
with tf.Session(graph=graph) as sess:
results = sess.run(output_operation.outputs[0], {
input_operation.outputs[0]: t
})
results = np.squeeze(results)
top_k = results.argsort()[-5:][::-1]
labels = load_labels(label_file)
for i in top_k:
print(labels[i], results[i])
|
tensorflow-master
|
tensorflow/examples/label_image/label_image.py
|
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A collection of "getting started" examples."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
|
tensorflow-master
|
tensorflow/examples/get_started/__init__.py
|
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Regression using the DNNRegressor Estimator."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import imports85 # pylint: disable=g-bad-import-order
STEPS = 1000
PRICE_NORM_FACTOR = 1000
def my_dnn_regression_fn(features, labels, mode, params):
"""A model function implementing DNN regression for a custom Estimator."""
# Extract the input into a dense layer, according to the feature_columns.
top = tf.feature_column.input_layer(features, params["feature_columns"])
# Iterate over the "hidden_units" list of layer sizes, default is [20].
for units in params.get("hidden_units", [20]):
# Add a hidden layer, densely connected on top of the previous layer.
top = tf.layers.dense(inputs=top, units=units, activation=tf.nn.relu)
# Connect a linear output layer on top.
output_layer = tf.layers.dense(inputs=top, units=1)
# Reshape the output layer to a 1-dim Tensor to return predictions
predictions = tf.squeeze(output_layer, 1)
if mode == tf.estimator.ModeKeys.PREDICT:
# In `PREDICT` mode we only need to return predictions.
return tf.estimator.EstimatorSpec(
mode=mode, predictions={"price": predictions})
# Calculate loss using mean squared error
average_loss = tf.losses.mean_squared_error(labels, predictions)
# Pre-made estimators use the total_loss instead of the average,
# so report total_loss for compatibility.
batch_size = tf.shape(labels)[0]
total_loss = tf.to_float(batch_size) * average_loss
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = params.get("optimizer", tf.train.AdamOptimizer)
optimizer = optimizer(params.get("learning_rate", None))
train_op = optimizer.minimize(
loss=average_loss, global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(
mode=mode, loss=total_loss, train_op=train_op)
# In evaluation mode we will calculate evaluation metrics.
assert mode == tf.estimator.ModeKeys.EVAL
# Calculate root mean squared error
rmse = tf.metrics.root_mean_squared_error(labels, predictions)
# Add the rmse to the collection of evaluation metrics.
eval_metrics = {"rmse": rmse}
return tf.estimator.EstimatorSpec(
mode=mode,
# Report sum of error for compatibility with pre-made estimators
loss=total_loss,
eval_metric_ops=eval_metrics)
def main(argv):
"""Builds, trains, and evaluates the model."""
assert len(argv) == 1
(train, test) = imports85.dataset()
# Switch the labels to units of thousands for better convergence.
def normalize_price(features, labels):
return features, labels / PRICE_NORM_FACTOR
train = train.map(normalize_price)
test = test.map(normalize_price)
# Build the training input_fn.
def input_train():
return (
# Shuffling with a buffer larger than the data set ensures
# that the examples are well mixed.
train.shuffle(1000).batch(128)
# Repeat forever
.repeat())
# Build the validation input_fn.
def input_test():
return test.shuffle(1000).batch(128)
# The first way assigns a unique weight to each category. To do this you must
# specify the category's vocabulary (values outside this specification will
# receive a weight of zero). Here we specify the vocabulary using a list of
# options. The vocabulary can also be specified with a vocabulary file (using
# `categorical_column_with_vocabulary_file`). For features covering a
# range of positive integers use `categorical_column_with_identity`.
body_style_vocab = ["hardtop", "wagon", "sedan", "hatchback", "convertible"]
body_style = tf.feature_column.categorical_column_with_vocabulary_list(
key="body-style", vocabulary_list=body_style_vocab)
make = tf.feature_column.categorical_column_with_hash_bucket(
key="make", hash_bucket_size=50)
feature_columns = [
tf.feature_column.numeric_column(key="curb-weight"),
tf.feature_column.numeric_column(key="highway-mpg"),
# Since this is a DNN model, convert categorical columns from sparse
# to dense.
# Wrap them in an `indicator_column` to create a
# one-hot vector from the input.
tf.feature_column.indicator_column(body_style),
# Or use an `embedding_column` to create a trainable vector for each
# index.
tf.feature_column.embedding_column(make, dimension=3),
]
# Build a custom Estimator, using the model_fn.
# `params` is passed through to the `model_fn`.
model = tf.estimator.Estimator(
model_fn=my_dnn_regression_fn,
params={
"feature_columns": feature_columns,
"learning_rate": 0.001,
"optimizer": tf.train.AdamOptimizer,
"hidden_units": [20, 20]
})
# Train the model.
model.train(input_fn=input_train, steps=STEPS)
# Evaluate how the model performs on data it has not yet seen.
eval_result = model.evaluate(input_fn=input_test)
# Print the Root Mean Square Error (RMSE).
print("\n" + 80 * "*")
print("\nRMS error for the test set: ${:.0f}"
.format(PRICE_NORM_FACTOR * eval_result["rmse"]))
print()
if __name__ == "__main__":
# The Estimator periodically generates "INFO" logs; make these logs visible.
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run(main=main)
|
tensorflow-master
|
tensorflow/examples/get_started/regression/custom_regression.py
|
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Regression using the DNNRegressor Estimator."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import imports85 # pylint: disable=g-bad-import-order
STEPS = 5000
PRICE_NORM_FACTOR = 1000
def main(argv):
"""Builds, trains, and evaluates the model."""
assert len(argv) == 1
(train, test) = imports85.dataset()
# Switch the labels to units of thousands for better convergence.
def normalize_price(features, labels):
return features, labels / PRICE_NORM_FACTOR
train = train.map(normalize_price)
test = test.map(normalize_price)
# Build the training input_fn.
def input_train():
return (
# Shuffling with a buffer larger than the data set ensures
# that the examples are well mixed.
train.shuffle(1000).batch(128)
# Repeat forever
.repeat())
# Build the validation input_fn.
def input_test():
return test.shuffle(1000).batch(128)
# The first way assigns a unique weight to each category. To do this you must
# specify the category's vocabulary (values outside this specification will
# receive a weight of zero). Here we specify the vocabulary using a list of
# options. The vocabulary can also be specified with a vocabulary file (using
# `categorical_column_with_vocabulary_file`). For features covering a
# range of positive integers use `categorical_column_with_identity`.
body_style_vocab = ["hardtop", "wagon", "sedan", "hatchback", "convertible"]
body_style = tf.feature_column.categorical_column_with_vocabulary_list(
key="body-style", vocabulary_list=body_style_vocab)
make = tf.feature_column.categorical_column_with_hash_bucket(
key="make", hash_bucket_size=50)
feature_columns = [
tf.feature_column.numeric_column(key="curb-weight"),
tf.feature_column.numeric_column(key="highway-mpg"),
# Since this is a DNN model, convert categorical columns from sparse
# to dense.
# Wrap them in an `indicator_column` to create a
# one-hot vector from the input.
tf.feature_column.indicator_column(body_style),
# Or use an `embedding_column` to create a trainable vector for each
# index.
tf.feature_column.embedding_column(make, dimension=3),
]
# Build a DNNRegressor, with 2x20-unit hidden layers, with the feature columns
# defined above as input.
model = tf.estimator.DNNRegressor(
hidden_units=[20, 20], feature_columns=feature_columns)
# Train the model.
model.train(input_fn=input_train, steps=STEPS)
# Evaluate how the model performs on data it has not yet seen.
eval_result = model.evaluate(input_fn=input_test)
# The evaluation returns a Python dictionary. The "average_loss" key holds the
# Mean Squared Error (MSE).
average_loss = eval_result["average_loss"]
# Convert MSE to Root Mean Square Error (RMSE).
print("\n" + 80 * "*")
print("\nRMS error for the test set: ${:.0f}"
.format(PRICE_NORM_FACTOR * average_loss**0.5))
print()
if __name__ == "__main__":
# The Estimator periodically generates "INFO" logs; make these logs visible.
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run(main=main)
|
tensorflow-master
|
tensorflow/examples/get_started/regression/dnn_regression.py
|
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A collection of regression examples using `Estimators`."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
|
tensorflow-master
|
tensorflow/examples/get_started/regression/__init__.py
|
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Linear regression with categorical features."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import imports85 # pylint: disable=g-bad-import-order
STEPS = 1000
PRICE_NORM_FACTOR = 1000
def main(argv):
"""Builds, trains, and evaluates the model."""
assert len(argv) == 1
(train, test) = imports85.dataset()
# Switch the labels to units of thousands for better convergence.
def normalize_price(features, labels):
return features, labels / PRICE_NORM_FACTOR
train = train.map(normalize_price)
test = test.map(normalize_price)
# Build the training input_fn.
def input_train():
return (
# Shuffling with a buffer larger than the data set ensures
# that the examples are well mixed.
train.shuffle(1000).batch(128)
# Repeat forever
.repeat())
# Build the validation input_fn.
def input_test():
return test.shuffle(1000).batch(128)
# The following code demonstrates two of the ways that `feature_columns` can
# be used to build a model with categorical inputs.
# The first way assigns a unique weight to each category. To do this, you must
# specify the category's vocabulary (values outside this specification will
# receive a weight of zero).
# Alternatively, you can define the vocabulary in a file (by calling
# `categorical_column_with_vocabulary_file`) or as a range of positive
# integers (by calling `categorical_column_with_identity`)
body_style_vocab = ["hardtop", "wagon", "sedan", "hatchback", "convertible"]
body_style_column = tf.feature_column.categorical_column_with_vocabulary_list(
key="body-style", vocabulary_list=body_style_vocab)
# The second way, appropriate for an unspecified vocabulary, is to create a
# hashed column. It will create a fixed length list of weights, and
# automatically assign each input category to a weight. Due to the
# pseudo-randomness of the process, some weights may be shared between
# categories, while others will remain unused.
make_column = tf.feature_column.categorical_column_with_hash_bucket(
key="make", hash_bucket_size=50)
feature_columns = [
# This model uses the same two numeric features as `linear_regressor.py`
tf.feature_column.numeric_column(key="curb-weight"),
tf.feature_column.numeric_column(key="highway-mpg"),
# This model adds two categorical colums that will adjust the price based
# on "make" and "body-style".
body_style_column,
make_column,
]
# Build the Estimator.
model = tf.estimator.LinearRegressor(feature_columns=feature_columns)
# Train the model.
# By default, the Estimators log output every 100 steps.
model.train(input_fn=input_train, steps=STEPS)
# Evaluate how the model performs on data it has not yet seen.
eval_result = model.evaluate(input_fn=input_test)
# The evaluation returns a Python dictionary. The "average_loss" key holds the
# Mean Squared Error (MSE).
average_loss = eval_result["average_loss"]
# Convert MSE to Root Mean Square Error (RMSE).
print("\n" + 80 * "*")
print("\nRMS error for the test set: ${:.0f}"
.format(PRICE_NORM_FACTOR * average_loss**0.5))
print()
if __name__ == "__main__":
# The Estimator periodically generates "INFO" logs; make these logs visible.
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run(main=main)
|
tensorflow-master
|
tensorflow/examples/get_started/regression/linear_regression_categorical.py
|
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A simple smoke test that runs these examples for 1 training iteration."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import pandas as pd
from six.moves import StringIO
import tensorflow.examples.get_started.regression.imports85 as imports85
sys.modules["imports85"] = imports85
# pylint: disable=g-bad-import-order,g-import-not-at-top
import tensorflow.data as data
import tensorflow.examples.get_started.regression.dnn_regression as dnn_regression
import tensorflow.examples.get_started.regression.linear_regression_categorical as linear_regression_categorical
import tensorflow.examples.get_started.regression.custom_regression as custom_regression
from tensorflow.python.platform import googletest
from tensorflow.python.platform import test
# pylint: disable=g-bad-import-order,g-import-not-at-top
# pylint: disable=line-too-long
FOUR_LINES = "\n".join([
"1,?,alfa-romero,gas,std,two,hatchback,rwd,front,94.50,171.20,65.50,52.40,2823,ohcv,six,152,mpfi,2.68,3.47,9.00,154,5000,19,26,16500",
"2,164,audi,gas,std,four,sedan,fwd,front,99.80,176.60,66.20,54.30,2337,ohc,four,109,mpfi,3.19,3.40,10.00,102,5500,24,30,13950",
"2,164,audi,gas,std,four,sedan,4wd,front,99.40,176.60,66.40,54.30,2824,ohc,five,136,mpfi,3.19,3.40,8.00,115,5500,18,22,17450",
"2,?,audi,gas,std,two,sedan,fwd,front,99.80,177.30,66.30,53.10,2507,ohc,five,136,mpfi,3.19,3.40,8.50,110,5500,19,25,15250",
])
# pylint: enable=line-too-long
def four_lines_dataframe():
text = StringIO(FOUR_LINES)
return pd.read_csv(
text, names=imports85.types.keys(), dtype=imports85.types, na_values="?")
def four_lines_dataset(*args, **kwargs):
del args, kwargs
return data.Dataset.from_tensor_slices(FOUR_LINES.split("\n"))
class RegressionTest(googletest.TestCase):
"""Test the regression examples in this directory."""
@test.mock.patch.dict(data.__dict__, {"TextLineDataset": four_lines_dataset})
@test.mock.patch.dict(imports85.__dict__, {"_get_imports85": (lambda: None)})
@test.mock.patch.dict(linear_regression_categorical.__dict__, {"STEPS": 1})
def test_linear_regression_categorical(self):
linear_regression_categorical.main([""])
@test.mock.patch.dict(data.__dict__, {"TextLineDataset": four_lines_dataset})
@test.mock.patch.dict(imports85.__dict__, {"_get_imports85": (lambda: None)})
@test.mock.patch.dict(dnn_regression.__dict__, {"STEPS": 1})
def test_dnn_regression(self):
dnn_regression.main([""])
@test.mock.patch.dict(data.__dict__, {"TextLineDataset": four_lines_dataset})
@test.mock.patch.dict(imports85.__dict__, {"_get_imports85": (lambda: None)})
@test.mock.patch.dict(custom_regression.__dict__, {"STEPS": 1})
def test_custom_regression(self):
custom_regression.main([""])
if __name__ == "__main__":
googletest.main()
|
tensorflow-master
|
tensorflow/examples/get_started/regression/test.py
|
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A dataset loader for imports85.data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
import tensorflow as tf
try:
import pandas as pd # pylint: disable=g-import-not-at-top
except ImportError:
pass
URL = "https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data"
# Order is important for the csv-readers, so we use an OrderedDict here.
defaults = collections.OrderedDict([
("symboling", [0]),
("normalized-losses", [0.0]),
("make", [""]),
("fuel-type", [""]),
("aspiration", [""]),
("num-of-doors", [""]),
("body-style", [""]),
("drive-wheels", [""]),
("engine-location", [""]),
("wheel-base", [0.0]),
("length", [0.0]),
("width", [0.0]),
("height", [0.0]),
("curb-weight", [0.0]),
("engine-type", [""]),
("num-of-cylinders", [""]),
("engine-size", [0.0]),
("fuel-system", [""]),
("bore", [0.0]),
("stroke", [0.0]),
("compression-ratio", [0.0]),
("horsepower", [0.0]),
("peak-rpm", [0.0]),
("city-mpg", [0.0]),
("highway-mpg", [0.0]),
("price", [0.0])
]) # pyformat: disable
types = collections.OrderedDict((key, type(value[0]))
for key, value in defaults.items())
def _get_imports85():
path = tf.contrib.keras.utils.get_file(URL.split("/")[-1], URL)
return path
def dataset(y_name="price", train_fraction=0.7):
"""Load the imports85 data as a (train,test) pair of `Dataset`.
Each dataset generates (features_dict, label) pairs.
Args:
y_name: The name of the column to use as the label.
train_fraction: A float, the fraction of data to use for training. The
remainder will be used for evaluation.
Returns:
A (train,test) pair of `Datasets`
"""
# Download and cache the data
path = _get_imports85()
# Define how the lines of the file should be parsed
def decode_line(line):
"""Convert a csv line into a (features_dict,label) pair."""
# Decode the line to a tuple of items based on the types of
# csv_header.values().
items = tf.decode_csv(line, list(defaults.values()))
# Convert the keys and items to a dict.
pairs = zip(defaults.keys(), items)
features_dict = dict(pairs)
# Remove the label from the features_dict
label = features_dict.pop(y_name)
return features_dict, label
def has_no_question_marks(line):
"""Returns True if the line of text has no question marks."""
# split the line into an array of characters
chars = tf.string_split(line[tf.newaxis], "").values
# for each character check if it is a question mark
is_question = tf.equal(chars, "?")
any_question = tf.reduce_any(is_question)
no_question = ~any_question
return no_question
def in_training_set(line):
"""Returns a boolean tensor, true if the line is in the training set."""
# If you randomly split the dataset you won't get the same split in both
# sessions if you stop and restart training later. Also a simple
# random split won't work with a dataset that's too big to `.cache()` as
# we are doing here.
num_buckets = 1000000
bucket_id = tf.string_to_hash_bucket_fast(line, num_buckets)
# Use the hash bucket id as a random number that's deterministic per example
return bucket_id < int(train_fraction * num_buckets)
def in_test_set(line):
"""Returns a boolean tensor, true if the line is in the training set."""
# Items not in the training set are in the test set.
# This line must use `~` instead of `not` because `not` only works on python
# booleans but we are dealing with symbolic tensors.
return ~in_training_set(line)
base_dataset = (
tf.data
# Get the lines from the file.
.TextLineDataset(path)
# drop lines with question marks.
.filter(has_no_question_marks))
train = (base_dataset
# Take only the training-set lines.
.filter(in_training_set)
# Decode each line into a (features_dict, label) pair.
.map(decode_line)
# Cache data so you only decode the file once.
.cache())
# Do the same for the test-set.
test = (base_dataset.filter(in_test_set).cache().map(decode_line))
return train, test
def raw_dataframe():
"""Load the imports85 data as a pd.DataFrame."""
# Download and cache the data
path = _get_imports85()
# Load it into a pandas dataframe
df = pd.read_csv(path, names=types.keys(), dtype=types, na_values="?")
return df
def load_data(y_name="price", train_fraction=0.7, seed=None):
"""Get the imports85 data set.
A description of the data is available at:
https://archive.ics.uci.edu/ml/datasets/automobile
The data itself can be found at:
https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data
Args:
y_name: the column to return as the label.
train_fraction: the fraction of the dataset to use for training.
seed: The random seed to use when shuffling the data. `None` generates a
unique shuffle every run.
Returns:
a pair of pairs where the first pair is the training data, and the second
is the test data:
`(x_train, y_train), (x_test, y_test) = get_imports85_dataset(...)`
`x` contains a pandas DataFrame of features, while `y` contains the label
array.
"""
# Load the raw data columns.
data = raw_dataframe()
# Delete rows with unknowns
data = data.dropna()
# Shuffle the data
np.random.seed(seed)
# Split the data into train/test subsets.
x_train = data.sample(frac=train_fraction, random_state=seed)
x_test = data.drop(x_train.index)
# Extract the label from the features dataframe.
y_train = x_train.pop(y_name)
y_test = x_test.pop(y_name)
return (x_train, y_train), (x_test, y_test)
|
tensorflow-master
|
tensorflow/examples/get_started/regression/imports85.py
|
tensorflow-master
|
tensorflow/examples/android/__init__.py
|
|
tensorflow-master
|
tensorflow/examples/android/jni/__init__.py
|
|
tensorflow-master
|
tensorflow/examples/tutorials/__init__.py
|
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an 'AS IS' BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A simple MNIST classifier which displays summaries in TensorBoard.
This is an unimpressive MNIST model, but it is a good example of using
tf.name_scope to make a graph legible in the TensorBoard graph explorer, and of
naming summary tags so that they are grouped meaningfully in TensorBoard.
It demonstrates the functionality of every TensorBoard dashboard.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import sys
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
FLAGS = None
def train():
# Import data
mnist = input_data.read_data_sets(FLAGS.data_dir,
fake_data=FLAGS.fake_data)
sess = tf.InteractiveSession()
# Create a multilayer model.
# Input placeholders
with tf.name_scope('input'):
x = tf.placeholder(tf.float32, [None, 784], name='x-input')
y_ = tf.placeholder(tf.int64, [None], name='y-input')
with tf.name_scope('input_reshape'):
image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
tf.summary.image('input', image_shaped_input, 10)
# We can't initialize these variables to 0 - the network will get stuck.
def weight_variable(shape):
"""Create a weight variable with appropriate initialization."""
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
"""Create a bias variable with appropriate initialization."""
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def variable_summaries(var):
"""Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
with tf.name_scope('summaries'):
mean = tf.reduce_mean(var)
tf.summary.scalar('mean', mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
tf.summary.scalar('stddev', stddev)
tf.summary.scalar('max', tf.reduce_max(var))
tf.summary.scalar('min', tf.reduce_min(var))
tf.summary.histogram('histogram', var)
def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
"""Reusable code for making a simple neural net layer.
It does a matrix multiply, bias add, and then uses ReLU to nonlinearize.
It also sets up name scoping so that the resultant graph is easy to read,
and adds a number of summary ops.
"""
# Adding a name scope ensures logical grouping of the layers in the graph.
with tf.name_scope(layer_name):
# This Variable will hold the state of the weights for the layer
with tf.name_scope('weights'):
weights = weight_variable([input_dim, output_dim])
variable_summaries(weights)
with tf.name_scope('biases'):
biases = bias_variable([output_dim])
variable_summaries(biases)
with tf.name_scope('Wx_plus_b'):
preactivate = tf.matmul(input_tensor, weights) + biases
tf.summary.histogram('pre_activations', preactivate)
activations = act(preactivate, name='activation')
tf.summary.histogram('activations', activations)
return activations
hidden1 = nn_layer(x, 784, 500, 'layer1')
with tf.name_scope('dropout'):
keep_prob = tf.placeholder(tf.float32)
tf.summary.scalar('dropout_keep_probability', keep_prob)
dropped = tf.nn.dropout(hidden1, keep_prob)
# Do not apply softmax activation yet, see below.
y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)
with tf.name_scope('cross_entropy'):
# The raw formulation of cross-entropy,
#
# tf.reduce_mean(-tf.reduce_sum(y_ * tf.math.log(tf.softmax(y)),
# reduction_indices=[1]))
#
# can be numerically unstable.
#
# So here we use tf.compat.v1.losses.sparse_softmax_cross_entropy on the
# raw logit outputs of the nn_layer above, and then average across
# the batch.
with tf.name_scope('total'):
cross_entropy = tf.losses.sparse_softmax_cross_entropy(
labels=y_, logits=y)
tf.summary.scalar('cross_entropy', cross_entropy)
with tf.name_scope('train'):
train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(
cross_entropy)
with tf.name_scope('accuracy'):
with tf.name_scope('correct_prediction'):
correct_prediction = tf.equal(tf.argmax(y, 1), y_)
with tf.name_scope('accuracy'):
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', accuracy)
# Merge all the summaries and write them out to
# /tmp/tensorflow/mnist/logs/mnist_with_summaries (by default)
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)
test_writer = tf.summary.FileWriter(FLAGS.log_dir + '/test')
tf.global_variables_initializer().run()
# Train the model, and also write summaries.
# Every 10th step, measure test-set accuracy, and write test summaries
# All other steps, run train_step on training data, & add training summaries
def feed_dict(train):
"""Make a TensorFlow feed_dict: maps data onto Tensor placeholders."""
if train or FLAGS.fake_data:
xs, ys = mnist.train.next_batch(100, fake_data=FLAGS.fake_data)
k = FLAGS.dropout
else:
xs, ys = mnist.test.images, mnist.test.labels
k = 1.0
return {x: xs, y_: ys, keep_prob: k}
for i in range(FLAGS.max_steps):
if i % 10 == 0: # Record summaries and test-set accuracy
summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))
test_writer.add_summary(summary, i)
print('Accuracy at step %s: %s' % (i, acc))
else: # Record train set summaries, and train
if i % 100 == 99: # Record execution stats
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
summary, _ = sess.run([merged, train_step],
feed_dict=feed_dict(True),
options=run_options,
run_metadata=run_metadata)
train_writer.add_run_metadata(run_metadata, 'step%03d' % i)
train_writer.add_summary(summary, i)
print('Adding run metadata for', i)
else: # Record a summary
summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
train_writer.add_summary(summary, i)
train_writer.close()
test_writer.close()
def main(_):
if tf.gfile.Exists(FLAGS.log_dir):
tf.gfile.DeleteRecursively(FLAGS.log_dir)
tf.gfile.MakeDirs(FLAGS.log_dir)
with tf.Graph().as_default():
train()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--fake_data', nargs='?', const=True, type=bool,
default=False,
help='If true, uses fake data for unit testing.')
parser.add_argument('--max_steps', type=int, default=1000,
help='Number of steps to run trainer.')
parser.add_argument('--learning_rate', type=float, default=0.001,
help='Initial learning rate')
parser.add_argument('--dropout', type=float, default=0.9,
help='Keep probability for training dropout.')
parser.add_argument(
'--data_dir',
type=str,
default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
'tensorflow/mnist/input_data'),
help='Directory for storing input data')
parser.add_argument(
'--log_dir',
type=str,
default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
'tensorflow/mnist/logs/mnist_with_summaries'),
help='Summaries log directory')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
|
tensorflow-master
|
tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Imports mnist tutorial libraries used by tutorial examples."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.examples.tutorials.mnist import mnist
|
tensorflow-master
|
tensorflow/examples/tutorials/mnist/__init__.py
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Simple MNIST classifier example with JIT XLA and timelines.
Note: Please see further comments in the BUILD file to invoke XLA.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.python.client import timeline
FLAGS = None
def main(_):
# Import data
mnist = input_data.read_data_sets(FLAGS.data_dir)
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x, w) + b
# Define loss and optimizer
y_ = tf.placeholder(tf.int64, [None])
# The raw formulation of cross-entropy,
#
# tf.reduce_mean(-tf.reduce_sum(y_ * tf.math.log(tf.nn.softmax(y)),
# reduction_indices=[1]))
#
# can be numerically unstable.
#
# So here we use tf.compat.v1.losses.sparse_softmax_cross_entropy on the raw
# logit outputs of 'y', and then average across the batch.
cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y)
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
config = tf.ConfigProto()
jit_level = 0
if FLAGS.xla:
# Turns on XLA JIT compilation.
jit_level = tf.OptimizerOptions.ON_1
config.graph_options.optimizer_options.global_jit_level = jit_level
run_metadata = tf.RunMetadata()
sess = tf.Session(config=config)
tf.global_variables_initializer().run(session=sess)
# Train
train_loops = 1000
for i in range(train_loops):
batch_xs, batch_ys = mnist.train.next_batch(100)
# Create a timeline for the last loop and export to json to view with
# chrome://tracing/.
if i == train_loops - 1:
sess.run(train_step,
feed_dict={x: batch_xs,
y_: batch_ys},
options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),
run_metadata=run_metadata)
trace = timeline.Timeline(step_stats=run_metadata.step_stats)
with open('/tmp/timeline.ctf.json', 'w') as trace_file:
trace_file.write(trace.generate_chrome_trace_format())
else:
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
# Test trained model
correct_prediction = tf.equal(tf.argmax(y, 1), y_)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy,
feed_dict={x: mnist.test.images,
y_: mnist.test.labels}))
sess.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--data_dir',
type=str,
default='/tmp/tensorflow/mnist/input_data',
help='Directory for storing input data')
parser.add_argument(
'--xla', type=bool, default=True, help='Turn xla via JIT on')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
|
tensorflow-master
|
tensorflow/examples/tutorials/mnist/mnist_softmax_xla.py
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Trains and Evaluates the MNIST network using a feed dictionary."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# pylint: disable=missing-docstring
import argparse
import os
import sys
import time
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.examples.tutorials.mnist import mnist
# Basic model parameters as external flags.
FLAGS = None
def placeholder_inputs(batch_size):
"""Generate placeholder variables to represent the input tensors.
These placeholders are used as inputs by the rest of the model building
code and will be fed from the downloaded data in the .run() loop, below.
Args:
batch_size: The batch size will be baked into both placeholders.
Returns:
images_placeholder: Images placeholder.
labels_placeholder: Labels placeholder.
"""
# Note that the shapes of the placeholders match the shapes of the full
# image and label tensors, except the first dimension is now batch_size
# rather than the full size of the train or test data sets.
images_placeholder = tf.placeholder(tf.float32, shape=(batch_size,
mnist.IMAGE_PIXELS))
labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size))
return images_placeholder, labels_placeholder
def fill_feed_dict(data_set, images_pl, labels_pl):
"""Fills the feed_dict for training the given step.
A feed_dict takes the form of:
feed_dict = {
<placeholder>: <tensor of values to be passed for placeholder>,
....
}
Args:
data_set: The set of images and labels, from input_data.read_data_sets()
images_pl: The images placeholder, from placeholder_inputs().
labels_pl: The labels placeholder, from placeholder_inputs().
Returns:
feed_dict: The feed dictionary mapping from placeholders to values.
"""
# Create the feed_dict for the placeholders filled with the next
# `batch size` examples.
images_feed, labels_feed = data_set.next_batch(FLAGS.batch_size,
FLAGS.fake_data)
feed_dict = {
images_pl: images_feed,
labels_pl: labels_feed,
}
return feed_dict
def do_eval(sess,
eval_correct,
images_placeholder,
labels_placeholder,
data_set):
"""Runs one evaluation against the full epoch of data.
Args:
sess: The session in which the model has been trained.
eval_correct: The Tensor that returns the number of correct predictions.
images_placeholder: The images placeholder.
labels_placeholder: The labels placeholder.
data_set: The set of images and labels to evaluate, from
input_data.read_data_sets().
"""
# And run one epoch of eval.
true_count = 0 # Counts the number of correct predictions.
steps_per_epoch = data_set.num_examples // FLAGS.batch_size
num_examples = steps_per_epoch * FLAGS.batch_size
for step in xrange(steps_per_epoch):
feed_dict = fill_feed_dict(data_set,
images_placeholder,
labels_placeholder)
true_count += sess.run(eval_correct, feed_dict=feed_dict)
precision = float(true_count) / num_examples
print('Num examples: %d Num correct: %d Precision @ 1: %0.04f' %
(num_examples, true_count, precision))
def run_training():
"""Train MNIST for a number of steps."""
# Get the sets of images and labels for training, validation, and
# test on MNIST.
data_sets = input_data.read_data_sets(FLAGS.input_data_dir, FLAGS.fake_data)
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
# Generate placeholders for the images and labels.
images_placeholder, labels_placeholder = placeholder_inputs(
FLAGS.batch_size)
# Build a Graph that computes predictions from the inference model.
logits = mnist.inference(images_placeholder,
FLAGS.hidden1,
FLAGS.hidden2)
# Add to the Graph the Ops for loss calculation.
loss = mnist.loss(logits, labels_placeholder)
# Add to the Graph the Ops that calculate and apply gradients.
train_op = mnist.training(loss, FLAGS.learning_rate)
# Add the Op to compare the logits to the labels during evaluation.
eval_correct = mnist.evaluation(logits, labels_placeholder)
# Build the summary Tensor based on the TF collection of Summaries.
summary = tf.summary.merge_all()
# Add the variable initializer Op.
init = tf.global_variables_initializer()
# Create a saver for writing training checkpoints.
saver = tf.train.Saver()
# Create a session for running Ops on the Graph.
sess = tf.Session()
# Instantiate a SummaryWriter to output summaries and the Graph.
summary_writer = tf.summary.FileWriter(FLAGS.log_dir, sess.graph)
# And then after everything is built:
# Run the Op to initialize the variables.
sess.run(init)
# Start the training loop.
for step in xrange(FLAGS.max_steps):
start_time = time.time()
# Fill a feed dictionary with the actual set of images and labels
# for this particular training step.
feed_dict = fill_feed_dict(data_sets.train,
images_placeholder,
labels_placeholder)
# Run one step of the model. The return values are the activations
# from the `train_op` (which is discarded) and the `loss` Op. To
# inspect the values of your Ops or variables, you may include them
# in the list passed to sess.run() and the value tensors will be
# returned in the tuple from the call.
_, loss_value = sess.run([train_op, loss],
feed_dict=feed_dict)
duration = time.time() - start_time
# Write the summaries and print an overview fairly often.
if step % 100 == 0:
# Print status to stdout.
print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, duration))
# Update the events file.
summary_str = sess.run(summary, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
summary_writer.flush()
# Save a checkpoint and evaluate the model periodically.
if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_file = os.path.join(FLAGS.log_dir, 'model.ckpt')
saver.save(sess, checkpoint_file, global_step=step)
# Evaluate against the training set.
print('Training Data Eval:')
do_eval(sess,
eval_correct,
images_placeholder,
labels_placeholder,
data_sets.train)
# Evaluate against the validation set.
print('Validation Data Eval:')
do_eval(sess,
eval_correct,
images_placeholder,
labels_placeholder,
data_sets.validation)
# Evaluate against the test set.
print('Test Data Eval:')
do_eval(sess,
eval_correct,
images_placeholder,
labels_placeholder,
data_sets.test)
def main(_):
if tf.gfile.Exists(FLAGS.log_dir):
tf.gfile.DeleteRecursively(FLAGS.log_dir)
tf.gfile.MakeDirs(FLAGS.log_dir)
run_training()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--learning_rate',
type=float,
default=0.01,
help='Initial learning rate.'
)
parser.add_argument(
'--max_steps',
type=int,
default=2000,
help='Number of steps to run trainer.'
)
parser.add_argument(
'--hidden1',
type=int,
default=128,
help='Number of units in hidden layer 1.'
)
parser.add_argument(
'--hidden2',
type=int,
default=32,
help='Number of units in hidden layer 2.'
)
parser.add_argument(
'--batch_size',
type=int,
default=100,
help='Batch size. Must divide evenly into the dataset sizes.'
)
parser.add_argument(
'--input_data_dir',
type=str,
default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
'tensorflow/mnist/input_data'),
help='Directory to put the input data.'
)
parser.add_argument(
'--log_dir',
type=str,
default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
'tensorflow/mnist/logs/fully_connected_feed'),
help='Directory to put the log data.'
)
parser.add_argument(
'--fake_data',
default=False,
help='If true, uses fake data for unit testing.',
action='store_true'
)
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
|
tensorflow-master
|
tensorflow/examples/tutorials/mnist/fully_connected_feed.py
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functions for downloading and reading MNIST data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# pylint: disable=unused-import
import gzip
import os
import tempfile
import numpy
from six.moves import urllib
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets
# pylint: enable=unused-import
|
tensorflow-master
|
tensorflow/examples/tutorials/mnist/input_data.py
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Builds the MNIST network.
Implements the inference/loss/training pattern for model building.
1. inference() - Builds the model as far as required for running the network
forward to make predictions.
2. loss() - Adds to the inference model the layers required to generate loss.
3. training() - Adds to the loss model the Ops required to generate and
apply gradients.
This file is used by the various "fully_connected_*.py" files and not meant to
be run.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import tensorflow as tf
# The MNIST dataset has 10 classes, representing the digits 0 through 9.
NUM_CLASSES = 10
# The MNIST images are always 28x28 pixels.
IMAGE_SIZE = 28
IMAGE_PIXELS = IMAGE_SIZE * IMAGE_SIZE
def inference(images, hidden1_units, hidden2_units):
"""Build the MNIST model up to where it may be used for inference.
Args:
images: Images placeholder, from inputs().
hidden1_units: Size of the first hidden layer.
hidden2_units: Size of the second hidden layer.
Returns:
softmax_linear: Output tensor with the computed logits.
"""
# Hidden 1
with tf.name_scope('hidden1'):
weights = tf.Variable(
tf.truncated_normal([IMAGE_PIXELS, hidden1_units],
stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))),
name='weights')
biases = tf.Variable(tf.zeros([hidden1_units]),
name='biases')
hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
# Hidden 2
with tf.name_scope('hidden2'):
weights = tf.Variable(
tf.truncated_normal([hidden1_units, hidden2_units],
stddev=1.0 / math.sqrt(float(hidden1_units))),
name='weights')
biases = tf.Variable(tf.zeros([hidden2_units]),
name='biases')
hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
# Linear
with tf.name_scope('softmax_linear'):
weights = tf.Variable(
tf.truncated_normal([hidden2_units, NUM_CLASSES],
stddev=1.0 / math.sqrt(float(hidden2_units))),
name='weights')
biases = tf.Variable(tf.zeros([NUM_CLASSES]),
name='biases')
logits = tf.matmul(hidden2, weights) + biases
return logits
def loss(logits, labels):
"""Calculates the loss from the logits and the labels.
Args:
logits: Logits tensor, float - [batch_size, NUM_CLASSES].
labels: Labels tensor, int32 - [batch_size].
Returns:
loss: Loss tensor of type float.
"""
labels = tf.to_int64(labels)
return tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
def training(loss, learning_rate):
"""Sets up the training Ops.
Creates a summarizer to track the loss over time in TensorBoard.
Creates an optimizer and applies the gradients to all trainable variables.
The Op returned by this function is what must be passed to the
`sess.run()` call to cause the model to train.
Args:
loss: Loss tensor, from loss().
learning_rate: The learning rate to use for gradient descent.
Returns:
train_op: The Op for training.
"""
# Add a scalar summary for the snapshot loss.
tf.summary.scalar('loss', loss)
# Create the gradient descent optimizer with the given learning rate.
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
# Create a variable to track the global step.
global_step = tf.Variable(0, name='global_step', trainable=False)
# Use the optimizer to apply the gradients that minimize the loss
# (and also increment the global step counter) as a single training step.
train_op = optimizer.minimize(loss, global_step=global_step)
return train_op
def evaluation(logits, labels):
"""Evaluate the quality of the logits at predicting the label.
Args:
logits: Logits tensor, float - [batch_size, NUM_CLASSES].
labels: Labels tensor, int32 - [batch_size], with values in the
range [0, NUM_CLASSES).
Returns:
A scalar int32 tensor with the number of examples (out of batch_size)
that were predicted correctly.
"""
# For a classifier model, we can use the in_top_k Op.
# It returns a bool tensor with shape [batch_size] that is true for
# the examples where the label is in the top k (here k=1)
# of all logits for that example.
correct = tf.nn.in_top_k(logits, labels, 1)
# Return the number of true entries.
return tf.reduce_sum(tf.cast(correct, tf.int32))
|
tensorflow-master
|
tensorflow/examples/tutorials/mnist/mnist.py
|
tensorflow-master
|
tensorflow/examples/tutorials/word2vec/__init__.py
|
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Basic word2vec example."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import collections
import math
import os
import random
import sys
from tempfile import gettempdir
import zipfile
import numpy as np
from six.moves import urllib
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector
data_index = 0
def word2vec_basic(log_dir):
"""Example of building, training and visualizing a word2vec model."""
# Create the directory for TensorBoard variables if there is not.
if not os.path.exists(log_dir):
os.makedirs(log_dir)
# Step 1: Download the data.
url = 'http://mattmahoney.net/dc/'
# pylint: disable=redefined-outer-name
def maybe_download(filename, expected_bytes):
"""Download a file if not present, and make sure it's the right size."""
local_filename = os.path.join(gettempdir(), filename)
if not os.path.exists(local_filename):
local_filename, _ = urllib.request.urlretrieve(url + filename,
local_filename)
statinfo = os.stat(local_filename)
if statinfo.st_size == expected_bytes:
print('Found and verified', filename)
else:
print(statinfo.st_size)
raise Exception('Failed to verify ' + local_filename +
'. Can you get to it with a browser?')
return local_filename
filename = maybe_download('text8.zip', 31344016)
# Read the data into a list of strings.
def read_data(filename):
"""Extract the first file enclosed in a zip file as a list of words."""
with zipfile.ZipFile(filename) as f:
data = tf.compat.as_str(f.read(f.namelist()[0])).split()
return data
vocabulary = read_data(filename)
print('Data size', len(vocabulary))
# Step 2: Build the dictionary and replace rare words with UNK token.
vocabulary_size = 50000
def build_dataset(words, n_words):
"""Process raw inputs into a dataset."""
count = [['UNK', -1]]
count.extend(collections.Counter(words).most_common(n_words - 1))
dictionary = {}
for word, _ in count:
dictionary[word] = len(dictionary)
data = []
unk_count = 0
for word in words:
index = dictionary.get(word, 0)
if index == 0: # dictionary['UNK']
unk_count += 1
data.append(index)
count[0][1] = unk_count
reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
return data, count, dictionary, reversed_dictionary
# Filling 4 global variables:
# data - list of codes (integers from 0 to vocabulary_size-1).
# This is the original text but words are replaced by their codes
# count - map of words(strings) to count of occurrences
# dictionary - map of words(strings) to their codes(integers)
# reverse_dictionary - maps codes(integers) to words(strings)
data, count, unused_dictionary, reverse_dictionary = build_dataset(
vocabulary, vocabulary_size)
del vocabulary # Hint to reduce memory.
print('Most common words (+UNK)', count[:5])
print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])
# Step 3: Function to generate a training batch for the skip-gram model.
def generate_batch(batch_size, num_skips, skip_window):
global data_index
assert batch_size % num_skips == 0
assert num_skips <= 2 * skip_window
batch = np.ndarray(shape=(batch_size), dtype=np.int32)
labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
span = 2 * skip_window + 1 # [ skip_window target skip_window ]
buffer = collections.deque(maxlen=span) # pylint: disable=redefined-builtin
if data_index + span > len(data):
data_index = 0
buffer.extend(data[data_index:data_index + span])
data_index += span
for i in range(batch_size // num_skips):
context_words = [w for w in range(span) if w != skip_window]
words_to_use = random.sample(context_words, num_skips)
for j, context_word in enumerate(words_to_use):
batch[i * num_skips + j] = buffer[skip_window]
labels[i * num_skips + j, 0] = buffer[context_word]
if data_index == len(data):
buffer.extend(data[0:span])
data_index = span
else:
buffer.append(data[data_index])
data_index += 1
# Backtrack a little bit to avoid skipping words in the end of a batch
data_index = (data_index + len(data) - span) % len(data)
return batch, labels
batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)
for i in range(8):
print(batch[i], reverse_dictionary[batch[i]], '->', labels[i, 0],
reverse_dictionary[labels[i, 0]])
# Step 4: Build and train a skip-gram model.
batch_size = 128
embedding_size = 128 # Dimension of the embedding vector.
skip_window = 1 # How many words to consider left and right.
num_skips = 2 # How many times to reuse an input to generate a label.
num_sampled = 64 # Number of negative examples to sample.
# We pick a random validation set to sample nearest neighbors. Here we limit
# the validation samples to the words that have a low numeric ID, which by
# construction are also the most frequent. These 3 variables are used only for
# displaying model accuracy, they don't affect calculation.
valid_size = 16 # Random set of words to evaluate similarity on.
valid_window = 100 # Only pick dev samples in the head of the distribution.
valid_examples = np.random.choice(valid_window, valid_size, replace=False)
graph = tf.Graph()
with graph.as_default():
# Input data.
with tf.name_scope('inputs'):
train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
valid_dataset = tf.constant(valid_examples, dtype=tf.int32)
# Ops and variables pinned to the CPU because of missing GPU implementation
with tf.device('/cpu:0'):
# Look up embeddings for inputs.
with tf.name_scope('embeddings'):
embeddings = tf.Variable(
tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
embed = tf.nn.embedding_lookup(embeddings, train_inputs)
# Construct the variables for the NCE loss
with tf.name_scope('weights'):
nce_weights = tf.Variable(
tf.truncated_normal([vocabulary_size, embedding_size],
stddev=1.0 / math.sqrt(embedding_size)))
with tf.name_scope('biases'):
nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
# Compute the average NCE loss for the batch.
# tf.nce_loss automatically draws a new sample of the negative labels each
# time we evaluate the loss.
# Explanation of the meaning of NCE loss:
# http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
with tf.name_scope('loss'):
loss = tf.reduce_mean(
tf.nn.nce_loss(
weights=nce_weights,
biases=nce_biases,
labels=train_labels,
inputs=embed,
num_sampled=num_sampled,
num_classes=vocabulary_size))
# Add the loss value as a scalar to summary.
tf.summary.scalar('loss', loss)
# Construct the SGD optimizer using a learning rate of 1.0.
with tf.name_scope('optimizer'):
optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)
# Compute the cosine similarity between minibatch examples and all
# embeddings.
norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keepdims=True))
normalized_embeddings = embeddings / norm
valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings,
valid_dataset)
similarity = tf.matmul(
valid_embeddings, normalized_embeddings, transpose_b=True)
# Merge all summaries.
merged = tf.summary.merge_all()
# Add variable initializer.
init = tf.global_variables_initializer()
# Create a saver.
saver = tf.train.Saver()
# Step 5: Begin training.
num_steps = 100001
with tf.Session(graph=graph) as session:
# Open a writer to write summaries.
writer = tf.summary.FileWriter(log_dir, session.graph)
# We must initialize all variables before we use them.
init.run()
print('Initialized')
average_loss = 0
for step in xrange(num_steps):
batch_inputs, batch_labels = generate_batch(batch_size, num_skips,
skip_window)
feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}
# Define metadata variable.
run_metadata = tf.RunMetadata()
# We perform one update step by evaluating the optimizer op (including it
# in the list of returned values for session.run()
# Also, evaluate the merged op to get all summaries from the returned
# "summary" variable. Feed metadata variable to session for visualizing
# the graph in TensorBoard.
_, summary, loss_val = session.run([optimizer, merged, loss],
feed_dict=feed_dict,
run_metadata=run_metadata)
average_loss += loss_val
# Add returned summaries to writer in each step.
writer.add_summary(summary, step)
# Add metadata to visualize the graph for the last run.
if step == (num_steps - 1):
writer.add_run_metadata(run_metadata, 'step%d' % step)
if step % 2000 == 0:
if step > 0:
average_loss /= 2000
# The average loss is an estimate of the loss over the last 2000
# batches.
print('Average loss at step ', step, ': ', average_loss)
average_loss = 0
# Note that this is expensive (~20% slowdown if computed every 500 steps)
if step % 10000 == 0:
sim = similarity.eval()
for i in xrange(valid_size):
valid_word = reverse_dictionary[valid_examples[i]]
top_k = 8 # number of nearest neighbors
nearest = (-sim[i, :]).argsort()[1:top_k + 1]
log_str = 'Nearest to %s:' % valid_word
for k in xrange(top_k):
close_word = reverse_dictionary[nearest[k]]
log_str = '%s %s,' % (log_str, close_word)
print(log_str)
final_embeddings = normalized_embeddings.eval()
# Write corresponding labels for the embeddings.
with open(log_dir + '/metadata.tsv', 'w') as f:
for i in xrange(vocabulary_size):
f.write(reverse_dictionary[i] + '\n')
# Save the model for checkpoints.
saver.save(session, os.path.join(log_dir, 'model.ckpt'))
# Create a configuration for visualizing embeddings with the labels in
# TensorBoard.
config = projector.ProjectorConfig()
embedding_conf = config.embeddings.add()
embedding_conf.tensor_name = embeddings.name
embedding_conf.metadata_path = os.path.join(log_dir, 'metadata.tsv')
projector.visualize_embeddings(writer, config)
writer.close()
# Step 6: Visualize the embeddings.
# pylint: disable=missing-docstring
# Function to draw visualization of distance between embeddings.
def plot_with_labels(low_dim_embs, labels, filename):
assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'
plt.figure(figsize=(18, 18)) # in inches
for i, label in enumerate(labels):
x, y = low_dim_embs[i, :]
plt.scatter(x, y)
plt.annotate(
label,
xy=(x, y),
xytext=(5, 2),
textcoords='offset points',
ha='right',
va='bottom')
plt.savefig(filename)
try:
# pylint: disable=g-import-not-at-top
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
tsne = TSNE(
perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')
plot_only = 500
low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])
labels = [reverse_dictionary[i] for i in xrange(plot_only)]
plot_with_labels(low_dim_embs, labels, os.path.join(gettempdir(),
'tsne.png'))
except ImportError as ex:
print('Please install sklearn, matplotlib, and scipy to show embeddings.')
print(ex)
# All functionality is run after tf.compat.v1.app.run() (b/122547914). This
# could be split up but the methods are laid sequentially with their usage for
# clarity.
def main(unused_argv):
# Give a folder path as an argument with '--log_dir' to save
# TensorBoard summaries. Default is a log folder in current directory.
current_path = os.path.dirname(os.path.realpath(sys.argv[0]))
parser = argparse.ArgumentParser()
parser.add_argument(
'--log_dir',
type=str,
default=os.path.join(current_path, 'log'),
help='The log directory for TensorBoard summaries.')
flags, unused_flags = parser.parse_known_args()
word2vec_basic(flags.log_dir)
if __name__ == '__main__':
tf.app.run()
|
tensorflow-master
|
tensorflow/examples/tutorials/word2vec/word2vec_basic.py
|
tensorflow-master
|
tensorflow/examples/tutorials/layers/__init__.py
|
|
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convolutional Neural Network Estimator for MNIST, built with tf.layers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.INFO)
def cnn_model_fn(features, labels, mode):
"""Model function for CNN."""
# Input Layer
# Reshape X to 4-D tensor: [batch_size, width, height, channels]
# MNIST images are 28x28 pixels, and have one color channel
input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
# Convolutional Layer #1
# Computes 32 features using a 5x5 filter with ReLU activation.
# Padding is added to preserve width and height.
# Input Tensor Shape: [batch_size, 28, 28, 1]
# Output Tensor Shape: [batch_size, 28, 28, 32]
conv1 = tf.layers.conv2d(
inputs=input_layer,
filters=32,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
# Pooling Layer #1
# First max pooling layer with a 2x2 filter and stride of 2
# Input Tensor Shape: [batch_size, 28, 28, 32]
# Output Tensor Shape: [batch_size, 14, 14, 32]
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
# Convolutional Layer #2
# Computes 64 features using a 5x5 filter.
# Padding is added to preserve width and height.
# Input Tensor Shape: [batch_size, 14, 14, 32]
# Output Tensor Shape: [batch_size, 14, 14, 64]
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=64,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
# Pooling Layer #2
# Second max pooling layer with a 2x2 filter and stride of 2
# Input Tensor Shape: [batch_size, 14, 14, 64]
# Output Tensor Shape: [batch_size, 7, 7, 64]
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
# Flatten tensor into a batch of vectors
# Input Tensor Shape: [batch_size, 7, 7, 64]
# Output Tensor Shape: [batch_size, 7 * 7 * 64]
pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
# Dense Layer
# Densely connected layer with 1024 neurons
# Input Tensor Shape: [batch_size, 7 * 7 * 64]
# Output Tensor Shape: [batch_size, 1024]
dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
# Add dropout operation; 0.6 probability that element will be kept
dropout = tf.layers.dropout(
inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
# Logits layer
# Input Tensor Shape: [batch_size, 1024]
# Output Tensor Shape: [batch_size, 10]
logits = tf.layers.dense(inputs=dropout, units=10)
predictions = {
# Generate predictions (for PREDICT and EVAL mode)
"classes": tf.argmax(input=logits, axis=1),
# Add `softmax_tensor` to the graph. It is used for PREDICT and by the
# `logging_hook`.
"probabilities": tf.nn.softmax(logits, name="softmax_tensor")
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
# Calculate Loss (for both TRAIN and EVAL modes)
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
# Configure the Training Op (for TRAIN mode)
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(
loss=loss,
global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
# Add evaluation metrics (for EVAL mode)
eval_metric_ops = {
"accuracy": tf.metrics.accuracy(
labels=labels, predictions=predictions["classes"])}
return tf.estimator.EstimatorSpec(
mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
def main(unused_argv):
# Load training and eval data
mnist = tf.contrib.learn.datasets.load_dataset("mnist")
train_data = mnist.train.images # Returns np.array
train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
eval_data = mnist.test.images # Returns np.array
eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)
# Create the Estimator
mnist_classifier = tf.estimator.Estimator(
model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")
# Set up logging for predictions
# Log the values in the "Softmax" tensor with label "probabilities"
tensors_to_log = {"probabilities": "softmax_tensor"}
logging_hook = tf.train.LoggingTensorHook(
tensors=tensors_to_log, every_n_iter=50)
# Train the model
train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
x={"x": train_data},
y=train_labels,
batch_size=100,
num_epochs=None,
shuffle=True)
mnist_classifier.train(
input_fn=train_input_fn,
steps=20000,
hooks=[logging_hook])
# Evaluate the model and print results
eval_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
x={"x": eval_data}, y=eval_labels, num_epochs=1, shuffle=False)
eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
print(eval_results)
if __name__ == "__main__":
tf.app.run()
|
tensorflow-master
|
tensorflow/examples/tutorials/layers/cnn_mnist.py
|
tensorflow-master
|
tensorflow/examples/tutorials/input_fn/__init__.py
|
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for speech commands models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.examples.speech_commands import models
from tensorflow.python.framework import test_util
from tensorflow.python.platform import test
class ModelsTest(test.TestCase):
def _modelSettings(self):
return models.prepare_model_settings(
label_count=10,
sample_rate=16000,
clip_duration_ms=1000,
window_size_ms=20,
window_stride_ms=10,
feature_bin_count=40,
preprocess="mfcc")
def testPrepareModelSettings(self):
self.assertIsNotNone(
models.prepare_model_settings(
label_count=10,
sample_rate=16000,
clip_duration_ms=1000,
window_size_ms=20,
window_stride_ms=10,
feature_bin_count=40,
preprocess="mfcc"))
@test_util.run_deprecated_v1
def testCreateModelConvTraining(self):
model_settings = self._modelSettings()
with self.cached_session() as sess:
fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]])
logits, dropout_prob = models.create_model(fingerprint_input,
model_settings, "conv", True)
self.assertIsNotNone(logits)
self.assertIsNotNone(dropout_prob)
self.assertIsNotNone(sess.graph.get_tensor_by_name(logits.name))
self.assertIsNotNone(sess.graph.get_tensor_by_name(dropout_prob.name))
@test_util.run_deprecated_v1
def testCreateModelConvInference(self):
model_settings = self._modelSettings()
with self.cached_session() as sess:
fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]])
logits = models.create_model(fingerprint_input, model_settings, "conv",
False)
self.assertIsNotNone(logits)
self.assertIsNotNone(sess.graph.get_tensor_by_name(logits.name))
@test_util.run_deprecated_v1
def testCreateModelLowLatencyConvTraining(self):
model_settings = self._modelSettings()
with self.cached_session() as sess:
fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]])
logits, dropout_prob = models.create_model(
fingerprint_input, model_settings, "low_latency_conv", True)
self.assertIsNotNone(logits)
self.assertIsNotNone(dropout_prob)
self.assertIsNotNone(sess.graph.get_tensor_by_name(logits.name))
self.assertIsNotNone(sess.graph.get_tensor_by_name(dropout_prob.name))
@test_util.run_deprecated_v1
def testCreateModelFullyConnectedTraining(self):
model_settings = self._modelSettings()
with self.cached_session() as sess:
fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]])
logits, dropout_prob = models.create_model(
fingerprint_input, model_settings, "single_fc", True)
self.assertIsNotNone(logits)
self.assertIsNotNone(dropout_prob)
self.assertIsNotNone(sess.graph.get_tensor_by_name(logits.name))
self.assertIsNotNone(sess.graph.get_tensor_by_name(dropout_prob.name))
def testCreateModelBadArchitecture(self):
model_settings = self._modelSettings()
with self.cached_session():
fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]])
with self.assertRaises(Exception) as e:
models.create_model(fingerprint_input, model_settings,
"bad_architecture", True)
self.assertTrue("not recognized" in str(e.exception))
@test_util.run_deprecated_v1
def testCreateModelTinyConvTraining(self):
model_settings = self._modelSettings()
with self.cached_session() as sess:
fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]])
logits, dropout_prob = models.create_model(
fingerprint_input, model_settings, "tiny_conv", True)
self.assertIsNotNone(logits)
self.assertIsNotNone(dropout_prob)
self.assertIsNotNone(sess.graph.get_tensor_by_name(logits.name))
self.assertIsNotNone(sess.graph.get_tensor_by_name(dropout_prob.name))
if __name__ == "__main__":
test.main()
|
tensorflow-master
|
tensorflow/examples/speech_commands/models_test.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Model definitions for simple speech recognition.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import tensorflow as tf
def _next_power_of_two(x):
"""Calculates the smallest enclosing power of two for an input.
Args:
x: Positive float or integer number.
Returns:
Next largest power of two integer.
"""
return 1 if x == 0 else 2**(int(x) - 1).bit_length()
def prepare_model_settings(label_count, sample_rate, clip_duration_ms,
window_size_ms, window_stride_ms, feature_bin_count,
preprocess):
"""Calculates common settings needed for all models.
Args:
label_count: How many classes are to be recognized.
sample_rate: Number of audio samples per second.
clip_duration_ms: Length of each audio clip to be analyzed.
window_size_ms: Duration of frequency analysis window.
window_stride_ms: How far to move in time between frequency windows.
feature_bin_count: Number of frequency bins to use for analysis.
preprocess: How the spectrogram is processed to produce features.
Returns:
Dictionary containing common settings.
Raises:
ValueError: If the preprocessing mode isn't recognized.
"""
desired_samples = int(sample_rate * clip_duration_ms / 1000)
window_size_samples = int(sample_rate * window_size_ms / 1000)
window_stride_samples = int(sample_rate * window_stride_ms / 1000)
length_minus_window = (desired_samples - window_size_samples)
if length_minus_window < 0:
spectrogram_length = 0
else:
spectrogram_length = 1 + int(length_minus_window / window_stride_samples)
if preprocess == 'average':
fft_bin_count = 1 + (_next_power_of_two(window_size_samples) / 2)
average_window_width = int(math.floor(fft_bin_count / feature_bin_count))
fingerprint_width = int(math.ceil(fft_bin_count / average_window_width))
elif preprocess == 'mfcc':
average_window_width = -1
fingerprint_width = feature_bin_count
else:
raise ValueError('Unknown preprocess mode "%s" (should be "mfcc" or'
' "average")' % (preprocess))
fingerprint_size = fingerprint_width * spectrogram_length
return {
'desired_samples': desired_samples,
'window_size_samples': window_size_samples,
'window_stride_samples': window_stride_samples,
'spectrogram_length': spectrogram_length,
'fingerprint_width': fingerprint_width,
'fingerprint_size': fingerprint_size,
'label_count': label_count,
'sample_rate': sample_rate,
'preprocess': preprocess,
'average_window_width': average_window_width,
}
def create_model(fingerprint_input, model_settings, model_architecture,
is_training, runtime_settings=None):
"""Builds a model of the requested architecture compatible with the settings.
There are many possible ways of deriving predictions from a spectrogram
input, so this function provides an abstract interface for creating different
kinds of models in a black-box way. You need to pass in a TensorFlow node as
the 'fingerprint' input, and this should output a batch of 1D features that
describe the audio. Typically this will be derived from a spectrogram that's
been run through an MFCC, but in theory it can be any feature vector of the
size specified in model_settings['fingerprint_size'].
The function will build the graph it needs in the current TensorFlow graph,
and return the tensorflow output that will contain the 'logits' input to the
softmax prediction process. If training flag is on, it will also return a
placeholder node that can be used to control the dropout amount.
See the implementations below for the possible model architectures that can be
requested.
Args:
fingerprint_input: TensorFlow node that will output audio feature vectors.
model_settings: Dictionary of information about the model.
model_architecture: String specifying which kind of model to create.
is_training: Whether the model is going to be used for training.
runtime_settings: Dictionary of information about the runtime.
Returns:
TensorFlow node outputting logits results, and optionally a dropout
placeholder.
Raises:
Exception: If the architecture type isn't recognized.
"""
if model_architecture == 'single_fc':
return create_single_fc_model(fingerprint_input, model_settings,
is_training)
elif model_architecture == 'conv':
return create_conv_model(fingerprint_input, model_settings, is_training)
elif model_architecture == 'low_latency_conv':
return create_low_latency_conv_model(fingerprint_input, model_settings,
is_training)
elif model_architecture == 'low_latency_svdf':
return create_low_latency_svdf_model(fingerprint_input, model_settings,
is_training, runtime_settings)
elif model_architecture == 'tiny_conv':
return create_tiny_conv_model(fingerprint_input, model_settings,
is_training)
else:
raise Exception('model_architecture argument "' + model_architecture +
'" not recognized, should be one of "single_fc", "conv",' +
' "low_latency_conv, "low_latency_svdf",' +
' or "tiny_conv"')
def load_variables_from_checkpoint(sess, start_checkpoint):
"""Utility function to centralize checkpoint restoration.
Args:
sess: TensorFlow session.
start_checkpoint: Path to saved checkpoint on disk.
"""
saver = tf.train.Saver(tf.global_variables())
saver.restore(sess, start_checkpoint)
def create_single_fc_model(fingerprint_input, model_settings, is_training):
"""Builds a model with a single hidden fully-connected layer.
This is a very simple model with just one matmul and bias layer. As you'd
expect, it doesn't produce very accurate results, but it is very fast and
simple, so it's useful for sanity testing.
Here's the layout of the graph:
(fingerprint_input)
v
[MatMul]<-(weights)
v
[BiasAdd]<-(bias)
v
Args:
fingerprint_input: TensorFlow node that will output audio feature vectors.
model_settings: Dictionary of information about the model.
is_training: Whether the model is going to be used for training.
Returns:
TensorFlow node outputting logits results, and optionally a dropout
placeholder.
"""
if is_training:
dropout_prob = tf.placeholder(tf.float32, name='dropout_prob')
fingerprint_size = model_settings['fingerprint_size']
label_count = model_settings['label_count']
weights = tf.get_variable(
name='weights',
initializer=tf.truncated_normal_initializer(stddev=0.001),
shape=[fingerprint_size, label_count])
bias = tf.get_variable(
name='bias', initializer=tf.zeros_initializer, shape=[label_count])
logits = tf.matmul(fingerprint_input, weights) + bias
if is_training:
return logits, dropout_prob
else:
return logits
def create_conv_model(fingerprint_input, model_settings, is_training):
"""Builds a standard convolutional model.
This is roughly the network labeled as 'cnn-trad-fpool3' in the
'Convolutional Neural Networks for Small-footprint Keyword Spotting' paper:
http://www.isca-speech.org/archive/interspeech_2015/papers/i15_1478.pdf
Here's the layout of the graph:
(fingerprint_input)
v
[Conv2D]<-(weights)
v
[BiasAdd]<-(bias)
v
[Relu]
v
[MaxPool]
v
[Conv2D]<-(weights)
v
[BiasAdd]<-(bias)
v
[Relu]
v
[MaxPool]
v
[MatMul]<-(weights)
v
[BiasAdd]<-(bias)
v
This produces fairly good quality results, but can involve a large number of
weight parameters and computations. For a cheaper alternative from the same
paper with slightly less accuracy, see 'low_latency_conv' below.
During training, dropout nodes are introduced after each relu, controlled by a
placeholder.
Args:
fingerprint_input: TensorFlow node that will output audio feature vectors.
model_settings: Dictionary of information about the model.
is_training: Whether the model is going to be used for training.
Returns:
TensorFlow node outputting logits results, and optionally a dropout
placeholder.
"""
if is_training:
dropout_prob = tf.placeholder(tf.float32, name='dropout_prob')
input_frequency_size = model_settings['fingerprint_width']
input_time_size = model_settings['spectrogram_length']
fingerprint_4d = tf.reshape(fingerprint_input,
[-1, input_time_size, input_frequency_size, 1])
first_filter_width = 8
first_filter_height = 20
first_filter_count = 64
first_weights = tf.get_variable(
name='first_weights',
initializer=tf.truncated_normal_initializer(stddev=0.01),
shape=[first_filter_height, first_filter_width, 1, first_filter_count])
first_bias = tf.get_variable(
name='first_bias',
initializer=tf.zeros_initializer,
shape=[first_filter_count])
first_conv = tf.nn.conv2d(fingerprint_4d, first_weights, [1, 1, 1, 1],
'SAME') + first_bias
first_relu = tf.nn.relu(first_conv)
if is_training:
first_dropout = tf.nn.dropout(first_relu, dropout_prob)
else:
first_dropout = first_relu
max_pool = tf.nn.max_pool(first_dropout, [1, 2, 2, 1], [1, 2, 2, 1], 'SAME')
second_filter_width = 4
second_filter_height = 10
second_filter_count = 64
second_weights = tf.get_variable(
name='second_weights',
initializer=tf.truncated_normal_initializer(stddev=0.01),
shape=[
second_filter_height, second_filter_width, first_filter_count,
second_filter_count
])
second_bias = tf.get_variable(
name='second_bias',
initializer=tf.zeros_initializer,
shape=[second_filter_count])
second_conv = tf.nn.conv2d(max_pool, second_weights, [1, 1, 1, 1],
'SAME') + second_bias
second_relu = tf.nn.relu(second_conv)
if is_training:
second_dropout = tf.nn.dropout(second_relu, dropout_prob)
else:
second_dropout = second_relu
second_conv_shape = second_dropout.get_shape()
second_conv_output_width = second_conv_shape[2]
second_conv_output_height = second_conv_shape[1]
second_conv_element_count = int(
second_conv_output_width * second_conv_output_height *
second_filter_count)
flattened_second_conv = tf.reshape(second_dropout,
[-1, second_conv_element_count])
label_count = model_settings['label_count']
final_fc_weights = tf.get_variable(
name='final_fc_weights',
initializer=tf.truncated_normal_initializer(stddev=0.01),
shape=[second_conv_element_count, label_count])
final_fc_bias = tf.get_variable(
name='final_fc_bias',
initializer=tf.zeros_initializer,
shape=[label_count])
final_fc = tf.matmul(flattened_second_conv, final_fc_weights) + final_fc_bias
if is_training:
return final_fc, dropout_prob
else:
return final_fc
def create_low_latency_conv_model(fingerprint_input, model_settings,
is_training):
"""Builds a convolutional model with low compute requirements.
This is roughly the network labeled as 'cnn-one-fstride4' in the
'Convolutional Neural Networks for Small-footprint Keyword Spotting' paper:
http://www.isca-speech.org/archive/interspeech_2015/papers/i15_1478.pdf
Here's the layout of the graph:
(fingerprint_input)
v
[Conv2D]<-(weights)
v
[BiasAdd]<-(bias)
v
[Relu]
v
[MatMul]<-(weights)
v
[BiasAdd]<-(bias)
v
[MatMul]<-(weights)
v
[BiasAdd]<-(bias)
v
[MatMul]<-(weights)
v
[BiasAdd]<-(bias)
v
This produces slightly lower quality results than the 'conv' model, but needs
fewer weight parameters and computations.
During training, dropout nodes are introduced after the relu, controlled by a
placeholder.
Args:
fingerprint_input: TensorFlow node that will output audio feature vectors.
model_settings: Dictionary of information about the model.
is_training: Whether the model is going to be used for training.
Returns:
TensorFlow node outputting logits results, and optionally a dropout
placeholder.
"""
if is_training:
dropout_prob = tf.placeholder(tf.float32, name='dropout_prob')
input_frequency_size = model_settings['fingerprint_width']
input_time_size = model_settings['spectrogram_length']
fingerprint_4d = tf.reshape(fingerprint_input,
[-1, input_time_size, input_frequency_size, 1])
first_filter_width = 8
first_filter_height = input_time_size
first_filter_count = 186
first_filter_stride_x = 1
first_filter_stride_y = 1
first_weights = tf.get_variable(
name='first_weights',
initializer=tf.truncated_normal_initializer(stddev=0.01),
shape=[first_filter_height, first_filter_width, 1, first_filter_count])
first_bias = tf.get_variable(
name='first_bias',
initializer=tf.zeros_initializer,
shape=[first_filter_count])
first_conv = tf.nn.conv2d(fingerprint_4d, first_weights, [
1, first_filter_stride_y, first_filter_stride_x, 1
], 'VALID') + first_bias
first_relu = tf.nn.relu(first_conv)
if is_training:
first_dropout = tf.nn.dropout(first_relu, dropout_prob)
else:
first_dropout = first_relu
first_conv_output_width = math.floor(
(input_frequency_size - first_filter_width + first_filter_stride_x) /
first_filter_stride_x)
first_conv_output_height = math.floor(
(input_time_size - first_filter_height + first_filter_stride_y) /
first_filter_stride_y)
first_conv_element_count = int(
first_conv_output_width * first_conv_output_height * first_filter_count)
flattened_first_conv = tf.reshape(first_dropout,
[-1, first_conv_element_count])
first_fc_output_channels = 128
first_fc_weights = tf.get_variable(
name='first_fc_weights',
initializer=tf.truncated_normal_initializer(stddev=0.01),
shape=[first_conv_element_count, first_fc_output_channels])
first_fc_bias = tf.get_variable(
name='first_fc_bias',
initializer=tf.zeros_initializer,
shape=[first_fc_output_channels])
first_fc = tf.matmul(flattened_first_conv, first_fc_weights) + first_fc_bias
if is_training:
second_fc_input = tf.nn.dropout(first_fc, dropout_prob)
else:
second_fc_input = first_fc
second_fc_output_channels = 128
second_fc_weights = tf.get_variable(
name='second_fc_weights',
initializer=tf.truncated_normal_initializer(stddev=0.01),
shape=[first_fc_output_channels, second_fc_output_channels])
second_fc_bias = tf.get_variable(
name='second_fc_bias',
initializer=tf.zeros_initializer,
shape=[second_fc_output_channels])
second_fc = tf.matmul(second_fc_input, second_fc_weights) + second_fc_bias
if is_training:
final_fc_input = tf.nn.dropout(second_fc, dropout_prob)
else:
final_fc_input = second_fc
label_count = model_settings['label_count']
final_fc_weights = tf.get_variable(
name='final_fc_weights',
initializer=tf.truncated_normal_initializer(stddev=0.01),
shape=[second_fc_output_channels, label_count])
final_fc_bias = tf.get_variable(
name='final_fc_bias',
initializer=tf.zeros_initializer,
shape=[label_count])
final_fc = tf.matmul(final_fc_input, final_fc_weights) + final_fc_bias
if is_training:
return final_fc, dropout_prob
else:
return final_fc
def create_low_latency_svdf_model(fingerprint_input, model_settings,
is_training, runtime_settings):
"""Builds an SVDF model with low compute requirements.
This is based in the topology presented in the 'Compressing Deep Neural
Networks using a Rank-Constrained Topology' paper:
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43813.pdf
Here's the layout of the graph:
(fingerprint_input)
v
[SVDF]<-(weights)
v
[BiasAdd]<-(bias)
v
[Relu]
v
[MatMul]<-(weights)
v
[BiasAdd]<-(bias)
v
[MatMul]<-(weights)
v
[BiasAdd]<-(bias)
v
[MatMul]<-(weights)
v
[BiasAdd]<-(bias)
v
This model produces lower recognition accuracy than the 'conv' model above,
but requires fewer weight parameters and, significantly fewer computations.
During training, dropout nodes are introduced after the relu, controlled by a
placeholder.
Args:
fingerprint_input: TensorFlow node that will output audio feature vectors.
The node is expected to produce a 2D Tensor of shape:
[batch, model_settings['fingerprint_width'] *
model_settings['spectrogram_length']]
with the features corresponding to the same time slot arranged contiguously,
and the oldest slot at index [:, 0], and newest at [:, -1].
model_settings: Dictionary of information about the model.
is_training: Whether the model is going to be used for training.
runtime_settings: Dictionary of information about the runtime.
Returns:
TensorFlow node outputting logits results, and optionally a dropout
placeholder.
Raises:
ValueError: If the inputs tensor is incorrectly shaped.
"""
if is_training:
dropout_prob = tf.placeholder(tf.float32, name='dropout_prob')
input_frequency_size = model_settings['fingerprint_width']
input_time_size = model_settings['spectrogram_length']
# Validation.
input_shape = fingerprint_input.get_shape()
if len(input_shape) != 2:
raise ValueError('Inputs to `SVDF` should have rank == 2.')
if input_shape[-1].value is None:
raise ValueError('The last dimension of the inputs to `SVDF` '
'should be defined. Found `None`.')
if input_shape[-1].value % input_frequency_size != 0:
raise ValueError('Inputs feature dimension %d must be a multiple of '
'frame size %d', fingerprint_input.shape[-1].value,
input_frequency_size)
# Set number of units (i.e. nodes) and rank.
rank = 2
num_units = 1280
# Number of filters: pairs of feature and time filters.
num_filters = rank * num_units
# Create the runtime memory: [num_filters, batch, input_time_size]
batch = 1
memory = tf.get_variable(
initializer=tf.zeros_initializer,
shape=[num_filters, batch, input_time_size],
trainable=False,
name='runtime-memory')
first_time_flag = tf.get_variable(
name="first_time_flag",
dtype=tf.int32,
initializer=1)
# Determine the number of new frames in the input, such that we only operate
# on those. For training we do not use the memory, and thus use all frames
# provided in the input.
# new_fingerprint_input: [batch, num_new_frames*input_frequency_size]
if is_training:
num_new_frames = input_time_size
else:
window_stride_ms = int(model_settings['window_stride_samples'] * 1000 /
model_settings['sample_rate'])
num_new_frames = tf.cond(
tf.equal(first_time_flag, 1),
lambda: input_time_size,
lambda: int(runtime_settings['clip_stride_ms'] / window_stride_ms))
first_time_flag = 0
new_fingerprint_input = fingerprint_input[
:, -num_new_frames*input_frequency_size:]
# Expand to add input channels dimension.
new_fingerprint_input = tf.expand_dims(new_fingerprint_input, 2)
# Create the frequency filters.
weights_frequency = tf.get_variable(
name='weights_frequency',
initializer=tf.truncated_normal_initializer(stddev=0.01),
shape=[input_frequency_size, num_filters])
# Expand to add input channels dimensions.
# weights_frequency: [input_frequency_size, 1, num_filters]
weights_frequency = tf.expand_dims(weights_frequency, 1)
# Convolve the 1D feature filters sliding over the time dimension.
# activations_time: [batch, num_new_frames, num_filters]
activations_time = tf.nn.conv1d(
new_fingerprint_input, weights_frequency, input_frequency_size, 'VALID')
# Rearrange such that we can perform the batched matmul.
# activations_time: [num_filters, batch, num_new_frames]
activations_time = tf.transpose(activations_time, perm=[2, 0, 1])
# Runtime memory optimization.
if not is_training:
# We need to drop the activations corresponding to the oldest frames, and
# then add those corresponding to the new frames.
new_memory = memory[:, :, num_new_frames:]
new_memory = tf.concat([new_memory, activations_time], 2)
tf.assign(memory, new_memory)
activations_time = new_memory
# Create the time filters.
weights_time = tf.get_variable(
name='weights_time',
initializer=tf.truncated_normal_initializer(stddev=0.01),
shape=[num_filters, input_time_size])
# Apply the time filter on the outputs of the feature filters.
# weights_time: [num_filters, input_time_size, 1]
# outputs: [num_filters, batch, 1]
weights_time = tf.expand_dims(weights_time, 2)
outputs = tf.matmul(activations_time, weights_time)
# Split num_units and rank into separate dimensions (the remaining
# dimension is the input_shape[0] -i.e. batch size). This also squeezes
# the last dimension, since it's not used.
# [num_filters, batch, 1] => [num_units, rank, batch]
outputs = tf.reshape(outputs, [num_units, rank, -1])
# Sum the rank outputs per unit => [num_units, batch].
units_output = tf.reduce_sum(outputs, axis=1)
# Transpose to shape [batch, num_units]
units_output = tf.transpose(units_output)
# Appy bias.
bias = tf.get_variable(
name='bias', initializer=tf.zeros_initializer, shape=[num_units])
first_bias = tf.nn.bias_add(units_output, bias)
# Relu.
first_relu = tf.nn.relu(first_bias)
if is_training:
first_dropout = tf.nn.dropout(first_relu, dropout_prob)
else:
first_dropout = first_relu
first_fc_output_channels = 256
first_fc_weights = tf.get_variable(
name='first_fc_weights',
initializer=tf.truncated_normal_initializer(stddev=0.01),
shape=[num_units, first_fc_output_channels])
first_fc_bias = tf.get_variable(
name='first_fc_bias',
initializer=tf.zeros_initializer,
shape=[first_fc_output_channels])
first_fc = tf.matmul(first_dropout, first_fc_weights) + first_fc_bias
if is_training:
second_fc_input = tf.nn.dropout(first_fc, dropout_prob)
else:
second_fc_input = first_fc
second_fc_output_channels = 256
second_fc_weights = tf.get_variable(
name='second_fc_weights',
initializer=tf.truncated_normal_initializer(stddev=0.01),
shape=[first_fc_output_channels, second_fc_output_channels])
second_fc_bias = tf.get_variable(
name='second_fc_bias',
initializer=tf.zeros_initializer,
shape=[second_fc_output_channels])
second_fc = tf.matmul(second_fc_input, second_fc_weights) + second_fc_bias
if is_training:
final_fc_input = tf.nn.dropout(second_fc, dropout_prob)
else:
final_fc_input = second_fc
label_count = model_settings['label_count']
final_fc_weights = tf.get_variable(
name='final_fc_weights',
initializer=tf.truncated_normal_initializer(stddev=0.01),
shape=[second_fc_output_channels, label_count])
final_fc_bias = tf.get_variable(
name='final_fc_bias',
initializer=tf.zeros_initializer,
shape=[label_count])
final_fc = tf.matmul(final_fc_input, final_fc_weights) + final_fc_bias
if is_training:
return final_fc, dropout_prob
else:
return final_fc
def create_tiny_conv_model(fingerprint_input, model_settings, is_training):
"""Builds a convolutional model aimed at microcontrollers.
Devices like DSPs and microcontrollers can have very small amounts of
memory and limited processing power. This model is designed to use less
than 20KB of working RAM, and fit within 32KB of read-only (flash) memory.
Here's the layout of the graph:
(fingerprint_input)
v
[Conv2D]<-(weights)
v
[BiasAdd]<-(bias)
v
[Relu]
v
[MatMul]<-(weights)
v
[BiasAdd]<-(bias)
v
This doesn't produce particularly accurate results, but it's designed to be
used as the first stage of a pipeline, running on a low-energy piece of
hardware that can always be on, and then wake higher-power chips when a
possible utterance has been found, so that more accurate analysis can be done.
During training, a dropout node is introduced after the relu, controlled by a
placeholder.
Args:
fingerprint_input: TensorFlow node that will output audio feature vectors.
model_settings: Dictionary of information about the model.
is_training: Whether the model is going to be used for training.
Returns:
TensorFlow node outputting logits results, and optionally a dropout
placeholder.
"""
if is_training:
dropout_prob = tf.placeholder(tf.float32, name='dropout_prob')
input_frequency_size = model_settings['fingerprint_width']
input_time_size = model_settings['spectrogram_length']
fingerprint_4d = tf.reshape(fingerprint_input,
[-1, input_time_size, input_frequency_size, 1])
first_filter_width = 8
first_filter_height = 10
first_filter_count = 8
first_weights = tf.get_variable(
name='first_weights',
initializer=tf.truncated_normal_initializer(stddev=0.01),
shape=[first_filter_height, first_filter_width, 1, first_filter_count])
first_bias = tf.get_variable(
name='first_bias',
initializer=tf.zeros_initializer,
shape=[first_filter_count])
first_conv_stride_x = 2
first_conv_stride_y = 2
first_conv = tf.nn.conv2d(fingerprint_4d, first_weights,
[1, first_conv_stride_y, first_conv_stride_x, 1],
'SAME') + first_bias
first_relu = tf.nn.relu(first_conv)
if is_training:
first_dropout = tf.nn.dropout(first_relu, dropout_prob)
else:
first_dropout = first_relu
first_dropout_shape = first_dropout.get_shape()
first_dropout_output_width = first_dropout_shape[2]
first_dropout_output_height = first_dropout_shape[1]
first_dropout_element_count = int(
first_dropout_output_width * first_dropout_output_height *
first_filter_count)
flattened_first_dropout = tf.reshape(first_dropout,
[-1, first_dropout_element_count])
label_count = model_settings['label_count']
final_fc_weights = tf.get_variable(
name='final_fc_weights',
initializer=tf.truncated_normal_initializer(stddev=0.01),
shape=[first_dropout_element_count, label_count])
final_fc_bias = tf.get_variable(
name='final_fc_bias',
initializer=tf.zeros_initializer,
shape=[label_count])
final_fc = (
tf.matmul(flattened_first_dropout, final_fc_weights) + final_fc_bias)
if is_training:
return final_fc, dropout_prob
else:
return final_fc
|
tensorflow-master
|
tensorflow/examples/speech_commands/models.py
|
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for data input for speech commands."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow as tf
from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio
from tensorflow.examples.speech_commands import train
from tensorflow.python.framework import test_util
from tensorflow.python.platform import gfile
from tensorflow.python.platform import test
# Used to convert a dictionary into an object, for mocking parsed flags.
class DictStruct(object):
def __init__(self, **entries):
self.__dict__.update(entries)
class TrainTest(test.TestCase):
def _getWavData(self):
with self.cached_session():
sample_data = tf.zeros([32000, 2])
wav_encoder = contrib_audio.encode_wav(sample_data, 16000)
wav_data = self.evaluate(wav_encoder)
return wav_data
def _saveTestWavFile(self, filename, wav_data):
with open(filename, 'wb') as f:
f.write(wav_data)
def _saveWavFolders(self, root_dir, labels, how_many):
wav_data = self._getWavData()
for label in labels:
dir_name = os.path.join(root_dir, label)
os.mkdir(dir_name)
for i in range(how_many):
file_path = os.path.join(dir_name, 'some_audio_%d.wav' % i)
self._saveTestWavFile(file_path, wav_data)
def _prepareDummyTrainingData(self):
tmp_dir = self.get_temp_dir()
wav_dir = os.path.join(tmp_dir, 'wavs')
os.mkdir(wav_dir)
self._saveWavFolders(wav_dir, ['a', 'b', 'c'], 100)
background_dir = os.path.join(wav_dir, '_background_noise_')
os.mkdir(background_dir)
wav_data = self._getWavData()
for i in range(10):
file_path = os.path.join(background_dir, 'background_audio_%d.wav' % i)
self._saveTestWavFile(file_path, wav_data)
return wav_dir
def _getDefaultFlags(self):
flags = {
'data_url': '',
'data_dir': self._prepareDummyTrainingData(),
'wanted_words': 'a,b,c',
'sample_rate': 16000,
'clip_duration_ms': 1000,
'window_size_ms': 30,
'window_stride_ms': 20,
'feature_bin_count': 40,
'preprocess': 'mfcc',
'silence_percentage': 25,
'unknown_percentage': 25,
'validation_percentage': 10,
'testing_percentage': 10,
'summaries_dir': os.path.join(self.get_temp_dir(), 'summaries'),
'train_dir': os.path.join(self.get_temp_dir(), 'train'),
'time_shift_ms': 100,
'how_many_training_steps': '2',
'learning_rate': '0.01',
'quantize': False,
'model_architecture': 'conv',
'check_nans': False,
'start_checkpoint': '',
'batch_size': 1,
'background_volume': 0.25,
'background_frequency': 0.8,
'eval_step_interval': 1,
'save_step_interval': 1,
}
return DictStruct(**flags)
@test_util.run_deprecated_v1
def testTrain(self):
train.FLAGS = self._getDefaultFlags()
train.main('')
self.assertTrue(
gfile.Exists(
os.path.join(train.FLAGS.train_dir,
train.FLAGS.model_architecture + '.pbtxt')))
self.assertTrue(
gfile.Exists(
os.path.join(train.FLAGS.train_dir,
train.FLAGS.model_architecture + '_labels.txt')))
self.assertTrue(
gfile.Exists(
os.path.join(train.FLAGS.train_dir,
train.FLAGS.model_architecture + '.ckpt-1.meta')))
@test_util.run_deprecated_v1
def testQuantizedTrain(self):
train.FLAGS = self._getDefaultFlags()
train.FLAGS.quantize = True
train.FLAGS.model_architecture = 'tiny_conv'
train.main('')
self.assertTrue(
gfile.Exists(
os.path.join(train.FLAGS.train_dir,
train.FLAGS.model_architecture + '.pbtxt')))
self.assertTrue(
gfile.Exists(
os.path.join(train.FLAGS.train_dir,
train.FLAGS.model_architecture + '_labels.txt')))
self.assertTrue(
gfile.Exists(
os.path.join(train.FLAGS.train_dir,
train.FLAGS.model_architecture + '.ckpt-1.meta')))
if __name__ == '__main__':
test.main()
|
tensorflow-master
|
tensorflow/examples/speech_commands/train_test.py
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for data input for speech commands."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow as tf
from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio
from tensorflow.examples.speech_commands import wav_to_features
from tensorflow.python.framework import test_util
from tensorflow.python.platform import test
class WavToFeaturesTest(test.TestCase):
def _getWavData(self):
with self.cached_session() as sess:
sample_data = tf.zeros([32000, 2])
wav_encoder = contrib_audio.encode_wav(sample_data, 16000)
wav_data = self.evaluate(wav_encoder)
return wav_data
def _saveTestWavFile(self, filename, wav_data):
with open(filename, "wb") as f:
f.write(wav_data)
def _saveWavFolders(self, root_dir, labels, how_many):
wav_data = self._getWavData()
for label in labels:
dir_name = os.path.join(root_dir, label)
os.mkdir(dir_name)
for i in range(how_many):
file_path = os.path.join(dir_name, "some_audio_%d.wav" % i)
self._saveTestWavFile(file_path, wav_data)
@test_util.run_deprecated_v1
def testWavToFeatures(self):
tmp_dir = self.get_temp_dir()
wav_dir = os.path.join(tmp_dir, "wavs")
os.mkdir(wav_dir)
self._saveWavFolders(wav_dir, ["a", "b", "c"], 100)
input_file_path = os.path.join(tmp_dir, "input.wav")
output_file_path = os.path.join(tmp_dir, "output.c")
wav_data = self._getWavData()
self._saveTestWavFile(input_file_path, wav_data)
wav_to_features.wav_to_features(16000, 1000, 10, 10, 40, True, "average",
input_file_path, output_file_path)
with open(output_file_path, "rb") as f:
content = f.read()
self.assertTrue(b"const unsigned char g_input_data" in content)
if __name__ == "__main__":
test.main()
|
tensorflow-master
|
tensorflow/examples/speech_commands/wav_to_features_test.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for test file generation for speech commands."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.examples.speech_commands import generate_streaming_test_wav
from tensorflow.python.platform import test
class GenerateStreamingTestWavTest(test.TestCase):
def testMixInAudioSample(self):
track_data = np.zeros([10000])
sample_data = np.ones([1000])
generate_streaming_test_wav.mix_in_audio_sample(
track_data, 2000, sample_data, 0, 1000, 1.0, 100, 100)
self.assertNear(1.0, track_data[2500], 0.0001)
self.assertNear(0.0, track_data[3500], 0.0001)
if __name__ == "__main__":
test.main()
|
tensorflow-master
|
tensorflow/examples/speech_commands/generate_streaming_test_wav_test.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Runs a trained audio graph against WAVE files and reports the results.
The model, labels and .wav files specified in the arguments will be loaded, and
then the predictions from running the model against the audio data will be
printed to the console. This is a useful script for sanity checking trained
models, and as an example of how to use an audio model from Python.
Here's an example of running it:
python tensorflow/examples/speech_commands/label_wav_dir.py \
--graph=/tmp/my_frozen_graph.pb \
--labels=/tmp/speech_commands_train/conv_labels.txt \
--wav_dir=/tmp/speech_dataset/left
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import glob
import sys
import tensorflow as tf
# pylint: disable=unused-import
from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio
# pylint: enable=unused-import
FLAGS = None
def load_graph(filename):
"""Unpersists graph from file as default graph."""
with tf.gfile.GFile(filename, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
def load_labels(filename):
"""Read in labels, one label per line."""
return [line.rstrip() for line in tf.gfile.GFile(filename)]
def run_graph(wav_dir, labels, input_layer_name, output_layer_name,
num_top_predictions):
"""Runs the audio data through the graph and prints predictions."""
with tf.Session() as sess:
# Feed the audio data as input to the graph.
# predictions will contain a two-dimensional array, where one
# dimension represents the input image count, and the other has
# predictions per class
for wav_path in glob.glob(wav_dir + '/*.wav'):
if not wav_path or not tf.gfile.Exists(wav_path):
tf.logging.fatal('Audio file does not exist %s', wav_path)
with open(wav_path, 'rb') as wav_file:
wav_data = wav_file.read()
softmax_tensor = sess.graph.get_tensor_by_name(output_layer_name)
predictions, = sess.run(softmax_tensor, {input_layer_name: wav_data})
# Sort to show labels in order of confidence
print('\n%s' % (wav_path.split('/')[-1]))
top_k = predictions.argsort()[-num_top_predictions:][::-1]
for node_id in top_k:
human_string = labels[node_id]
score = predictions[node_id]
print('%s (score = %.5f)' % (human_string, score))
return 0
def label_wav(wav_dir, labels, graph, input_name, output_name, how_many_labels):
"""Loads the model and labels, and runs the inference to print predictions."""
if not labels or not tf.gfile.Exists(labels):
tf.logging.fatal('Labels file does not exist %s', labels)
if not graph or not tf.gfile.Exists(graph):
tf.logging.fatal('Graph file does not exist %s', graph)
labels_list = load_labels(labels)
# load graph, which is stored in the default session
load_graph(graph)
run_graph(wav_dir, labels_list, input_name, output_name, how_many_labels)
def main(_):
"""Entry point for script, converts flags to arguments."""
label_wav(FLAGS.wav_dir, FLAGS.labels, FLAGS.graph, FLAGS.input_name,
FLAGS.output_name, FLAGS.how_many_labels)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--wav_dir', type=str, default='', help='Audio file to be identified.')
parser.add_argument(
'--graph', type=str, default='', help='Model to use for identification.')
parser.add_argument(
'--labels', type=str, default='', help='Path to file containing labels.')
parser.add_argument(
'--input_name',
type=str,
default='wav_data:0',
help='Name of WAVE data input node in model.')
parser.add_argument(
'--output_name',
type=str,
default='labels_softmax:0',
help='Name of node outputting a prediction in the model.')
parser.add_argument(
'--how_many_labels',
type=int,
default=3,
help='Number of results to show.')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
|
tensorflow-master
|
tensorflow/examples/speech_commands/label_wav_dir.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Converts WAV audio files into input features for neural networks.
The models used in this example take in two-dimensional spectrograms as the
input to their neural network portions. For testing and porting purposes it's
useful to be able to generate these spectrograms outside of the full model, so
that on-device implementations using their own FFT and streaming code can be
tested against the version used in training for example. The output is as a
C source file, so it can be easily linked into an embedded test application.
To use this, run:
bazel run tensorflow/examples/speech_commands:wav_to_features -- \
--input_wav=my.wav --output_c_file=my_wav_data.c
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os.path
import sys
import tensorflow as tf
import input_data
import models
from tensorflow.python.platform import gfile
FLAGS = None
def wav_to_features(sample_rate, clip_duration_ms, window_size_ms,
window_stride_ms, feature_bin_count, quantize, preprocess,
input_wav, output_c_file):
"""Converts an audio file into its corresponding feature map.
Args:
sample_rate: Expected sample rate of the wavs.
clip_duration_ms: Expected duration in milliseconds of the wavs.
window_size_ms: How long each spectrogram timeslice is.
window_stride_ms: How far to move in time between spectogram timeslices.
feature_bin_count: How many bins to use for the feature fingerprint.
quantize: Whether to train the model for eight-bit deployment.
preprocess: Spectrogram processing mode. Can be "mfcc" or "average".
input_wav: Path to the audio WAV file to read.
output_c_file: Where to save the generated C source file.
"""
# Start a new TensorFlow session.
sess = tf.InteractiveSession()
model_settings = models.prepare_model_settings(
0, sample_rate, clip_duration_ms, window_size_ms, window_stride_ms,
feature_bin_count, preprocess)
audio_processor = input_data.AudioProcessor(None, None, 0, 0, '', 0, 0,
model_settings, None)
results = audio_processor.get_features_for_wav(input_wav, model_settings,
sess)
features = results[0]
variable_base = os.path.splitext(os.path.basename(input_wav).lower())[0]
# Save a C source file containing the feature data as an array.
with gfile.GFile(output_c_file, 'w') as f:
f.write('/* File automatically created by\n')
f.write(' * tensorflow/examples/speech_commands/wav_to_features.py \\\n')
f.write(' * --sample_rate=%d \\\n' % sample_rate)
f.write(' * --clip_duration_ms=%d \\\n' % clip_duration_ms)
f.write(' * --window_size_ms=%d \\\n' % window_size_ms)
f.write(' * --window_stride_ms=%d \\\n' % window_stride_ms)
f.write(' * --feature_bin_count=%d \\\n' % feature_bin_count)
if quantize:
f.write(' * --quantize \\\n')
f.write(' * --preprocess="%s" \\\n' % preprocess)
f.write(' * --input_wav="%s" \\\n' % input_wav)
f.write(' * --output_c_file="%s" \\\n' % output_c_file)
f.write(' */\n\n')
f.write('const int g_%s_width = %d;\n' % (variable_base, features.shape[2]))
f.write(
'const int g_%s_height = %d;\n' % (variable_base, features.shape[1]))
if quantize:
features_min, features_max = input_data.get_features_range(model_settings)
f.write('const unsigned char g_%s_data[] = {' % variable_base)
i = 0
for value in features.flatten():
quantized_value = int(
round(
(255 * (value - features_min)) / (features_max - features_min)))
if quantized_value < 0:
quantized_value = 0
if quantized_value > 255:
quantized_value = 255
if i == 0:
f.write('\n ')
f.write('%d, ' % quantized_value)
i = (i + 1) % 10
else:
f.write('const float g_%s_data[] = {\n' % variable_base)
i = 0
for value in features.flatten():
if i == 0:
f.write('\n ')
f.write(' ,%f' % value)
i = (i + 1) % 10
f.write('\n};\n')
def main(_):
# We want to see all the logging messages.
tf.logging.set_verbosity(tf.logging.INFO)
wav_to_features(FLAGS.sample_rate, FLAGS.clip_duration_ms,
FLAGS.window_size_ms, FLAGS.window_stride_ms,
FLAGS.feature_bin_count, FLAGS.quantize, FLAGS.preprocess,
FLAGS.input_wav, FLAGS.output_c_file)
tf.logging.info('Wrote to "%s"' % (FLAGS.output_c_file))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--sample_rate',
type=int,
default=16000,
help='Expected sample rate of the wavs',)
parser.add_argument(
'--clip_duration_ms',
type=int,
default=1000,
help='Expected duration in milliseconds of the wavs',)
parser.add_argument(
'--window_size_ms',
type=float,
default=30.0,
help='How long each spectrogram timeslice is.',)
parser.add_argument(
'--window_stride_ms',
type=float,
default=10.0,
help='How far to move in time between spectogram timeslices.',)
parser.add_argument(
'--feature_bin_count',
type=int,
default=40,
help='How many bins to use for the MFCC fingerprint',
)
parser.add_argument(
'--quantize',
type=bool,
default=False,
help='Whether to train the model for eight-bit deployment')
parser.add_argument(
'--preprocess',
type=str,
default='mfcc',
help='Spectrogram processing mode. Can be "mfcc" or "average"')
parser.add_argument(
'--input_wav',
type=str,
default=None,
help='Path to the audio WAV file to read')
parser.add_argument(
'--output_c_file',
type=str,
default=None,
help='Where to save the generated C source file containing the features')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
|
tensorflow-master
|
tensorflow/examples/speech_commands/wav_to_features.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for WAVE file labeling tool."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow as tf
from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio
from tensorflow.examples.speech_commands import label_wav
from tensorflow.python.platform import test
class LabelWavTest(test.TestCase):
def _getWavData(self):
with self.cached_session() as sess:
sample_data = tf.zeros([1000, 2])
wav_encoder = contrib_audio.encode_wav(sample_data, 16000)
wav_data = self.evaluate(wav_encoder)
return wav_data
def _saveTestWavFile(self, filename, wav_data):
with open(filename, "wb") as f:
f.write(wav_data)
def testLabelWav(self):
tmp_dir = self.get_temp_dir()
wav_data = self._getWavData()
wav_filename = os.path.join(tmp_dir, "wav_file.wav")
self._saveTestWavFile(wav_filename, wav_data)
input_name = "test_input"
output_name = "test_output"
graph_filename = os.path.join(tmp_dir, "test_graph.pb")
with tf.Session() as sess:
tf.placeholder(tf.string, name=input_name)
tf.zeros([1, 3], name=output_name)
with open(graph_filename, "wb") as f:
f.write(sess.graph.as_graph_def().SerializeToString())
labels_filename = os.path.join(tmp_dir, "test_labels.txt")
with open(labels_filename, "w") as f:
f.write("a\nb\nc\n")
label_wav.label_wav(wav_filename, labels_filename, graph_filename,
input_name + ":0", output_name + ":0", 3)
if __name__ == "__main__":
test.main()
|
tensorflow-master
|
tensorflow/examples/speech_commands/label_wav_test.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Runs a trained audio graph against a WAVE file and reports the results.
The model, labels and .wav file specified in the arguments will be loaded, and
then the predictions from running the model against the audio data will be
printed to the console. This is a useful script for sanity checking trained
models, and as an example of how to use an audio model from Python.
Here's an example of running it:
python tensorflow/examples/speech_commands/label_wav.py \
--graph=/tmp/my_frozen_graph.pb \
--labels=/tmp/speech_commands_train/conv_labels.txt \
--wav=/tmp/speech_dataset/left/a5d485dc_nohash_0.wav
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
import tensorflow as tf
# pylint: disable=unused-import
from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio
# pylint: enable=unused-import
FLAGS = None
def load_graph(filename):
"""Unpersists graph from file as default graph."""
with tf.gfile.GFile(filename, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
def load_labels(filename):
"""Read in labels, one label per line."""
return [line.rstrip() for line in tf.gfile.GFile(filename)]
def run_graph(wav_data, labels, input_layer_name, output_layer_name,
num_top_predictions):
"""Runs the audio data through the graph and prints predictions."""
with tf.Session() as sess:
# Feed the audio data as input to the graph.
# predictions will contain a two-dimensional array, where one
# dimension represents the input image count, and the other has
# predictions per class
softmax_tensor = sess.graph.get_tensor_by_name(output_layer_name)
predictions, = sess.run(softmax_tensor, {input_layer_name: wav_data})
# Sort to show labels in order of confidence
top_k = predictions.argsort()[-num_top_predictions:][::-1]
for node_id in top_k:
human_string = labels[node_id]
score = predictions[node_id]
print('%s (score = %.5f)' % (human_string, score))
return 0
def label_wav(wav, labels, graph, input_name, output_name, how_many_labels):
"""Loads the model and labels, and runs the inference to print predictions."""
if not wav or not tf.gfile.Exists(wav):
tf.logging.fatal('Audio file does not exist %s', wav)
if not labels or not tf.gfile.Exists(labels):
tf.logging.fatal('Labels file does not exist %s', labels)
if not graph or not tf.gfile.Exists(graph):
tf.logging.fatal('Graph file does not exist %s', graph)
labels_list = load_labels(labels)
# load graph, which is stored in the default session
load_graph(graph)
with open(wav, 'rb') as wav_file:
wav_data = wav_file.read()
run_graph(wav_data, labels_list, input_name, output_name, how_many_labels)
def main(_):
"""Entry point for script, converts flags to arguments."""
label_wav(FLAGS.wav, FLAGS.labels, FLAGS.graph, FLAGS.input_name,
FLAGS.output_name, FLAGS.how_many_labels)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--wav', type=str, default='', help='Audio file to be identified.')
parser.add_argument(
'--graph', type=str, default='', help='Model to use for identification.')
parser.add_argument(
'--labels', type=str, default='', help='Path to file containing labels.')
parser.add_argument(
'--input_name',
type=str,
default='wav_data:0',
help='Name of WAVE data input node in model.')
parser.add_argument(
'--output_name',
type=str,
default='labels_softmax:0',
help='Name of node outputting a prediction in the model.')
parser.add_argument(
'--how_many_labels',
type=int,
default=3,
help='Number of results to show.')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
|
tensorflow-master
|
tensorflow/examples/speech_commands/label_wav.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for data input for speech commands."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import tensorflow as tf
from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio
from tensorflow.examples.speech_commands import input_data
from tensorflow.examples.speech_commands import models
from tensorflow.python.framework import test_util
from tensorflow.python.platform import test
class InputDataTest(test.TestCase):
def _getWavData(self):
with self.cached_session() as sess:
sample_data = tf.zeros([32000, 2])
wav_encoder = contrib_audio.encode_wav(sample_data, 16000)
wav_data = self.evaluate(wav_encoder)
return wav_data
def _saveTestWavFile(self, filename, wav_data):
with open(filename, "wb") as f:
f.write(wav_data)
def _saveWavFolders(self, root_dir, labels, how_many):
wav_data = self._getWavData()
for label in labels:
dir_name = os.path.join(root_dir, label)
os.mkdir(dir_name)
for i in range(how_many):
file_path = os.path.join(dir_name, "some_audio_%d.wav" % i)
self._saveTestWavFile(file_path, wav_data)
def _model_settings(self):
return {
"desired_samples": 160,
"fingerprint_size": 40,
"label_count": 4,
"window_size_samples": 100,
"window_stride_samples": 100,
"fingerprint_width": 40,
"preprocess": "mfcc",
}
def _runGetDataTest(self, preprocess, window_length_ms):
tmp_dir = self.get_temp_dir()
wav_dir = os.path.join(tmp_dir, "wavs")
os.mkdir(wav_dir)
self._saveWavFolders(wav_dir, ["a", "b", "c"], 100)
background_dir = os.path.join(wav_dir, "_background_noise_")
os.mkdir(background_dir)
wav_data = self._getWavData()
for i in range(10):
file_path = os.path.join(background_dir, "background_audio_%d.wav" % i)
self._saveTestWavFile(file_path, wav_data)
model_settings = models.prepare_model_settings(
4, 16000, 1000, window_length_ms, 20, 40, preprocess)
with self.cached_session() as sess:
audio_processor = input_data.AudioProcessor(
"", wav_dir, 10, 10, ["a", "b"], 10, 10, model_settings, tmp_dir)
result_data, result_labels = audio_processor.get_data(
10, 0, model_settings, 0.3, 0.1, 100, "training", sess)
self.assertEqual(10, len(result_data))
self.assertEqual(10, len(result_labels))
def testPrepareWordsList(self):
words_list = ["a", "b"]
self.assertGreater(
len(input_data.prepare_words_list(words_list)), len(words_list))
def testWhichSet(self):
self.assertEqual(
input_data.which_set("foo.wav", 10, 10),
input_data.which_set("foo.wav", 10, 10))
self.assertEqual(
input_data.which_set("foo_nohash_0.wav", 10, 10),
input_data.which_set("foo_nohash_1.wav", 10, 10))
@test_util.run_deprecated_v1
def testPrepareDataIndex(self):
tmp_dir = self.get_temp_dir()
self._saveWavFolders(tmp_dir, ["a", "b", "c"], 100)
audio_processor = input_data.AudioProcessor("", tmp_dir, 10, 10,
["a", "b"], 10, 10,
self._model_settings(), tmp_dir)
self.assertLess(0, audio_processor.set_size("training"))
self.assertTrue("training" in audio_processor.data_index)
self.assertTrue("validation" in audio_processor.data_index)
self.assertTrue("testing" in audio_processor.data_index)
self.assertEquals(input_data.UNKNOWN_WORD_INDEX,
audio_processor.word_to_index["c"])
def testPrepareDataIndexEmpty(self):
tmp_dir = self.get_temp_dir()
self._saveWavFolders(tmp_dir, ["a", "b", "c"], 0)
with self.assertRaises(Exception) as e:
_ = input_data.AudioProcessor("", tmp_dir, 10, 10, ["a", "b"], 10, 10,
self._model_settings(), tmp_dir)
self.assertTrue("No .wavs found" in str(e.exception))
def testPrepareDataIndexMissing(self):
tmp_dir = self.get_temp_dir()
self._saveWavFolders(tmp_dir, ["a", "b", "c"], 100)
with self.assertRaises(Exception) as e:
_ = input_data.AudioProcessor("", tmp_dir, 10, 10, ["a", "b", "d"], 10,
10, self._model_settings(), tmp_dir)
self.assertTrue("Expected to find" in str(e.exception))
@test_util.run_deprecated_v1
def testPrepareBackgroundData(self):
tmp_dir = self.get_temp_dir()
background_dir = os.path.join(tmp_dir, "_background_noise_")
os.mkdir(background_dir)
wav_data = self._getWavData()
for i in range(10):
file_path = os.path.join(background_dir, "background_audio_%d.wav" % i)
self._saveTestWavFile(file_path, wav_data)
self._saveWavFolders(tmp_dir, ["a", "b", "c"], 100)
audio_processor = input_data.AudioProcessor("", tmp_dir, 10, 10,
["a", "b"], 10, 10,
self._model_settings(), tmp_dir)
self.assertEqual(10, len(audio_processor.background_data))
def testLoadWavFile(self):
tmp_dir = self.get_temp_dir()
file_path = os.path.join(tmp_dir, "load_test.wav")
wav_data = self._getWavData()
self._saveTestWavFile(file_path, wav_data)
sample_data = input_data.load_wav_file(file_path)
self.assertIsNotNone(sample_data)
def testSaveWavFile(self):
tmp_dir = self.get_temp_dir()
file_path = os.path.join(tmp_dir, "load_test.wav")
save_data = np.zeros([16000, 1])
input_data.save_wav_file(file_path, save_data, 16000)
loaded_data = input_data.load_wav_file(file_path)
self.assertIsNotNone(loaded_data)
self.assertEqual(16000, len(loaded_data))
@test_util.run_deprecated_v1
def testPrepareProcessingGraph(self):
tmp_dir = self.get_temp_dir()
wav_dir = os.path.join(tmp_dir, "wavs")
os.mkdir(wav_dir)
self._saveWavFolders(wav_dir, ["a", "b", "c"], 100)
background_dir = os.path.join(wav_dir, "_background_noise_")
os.mkdir(background_dir)
wav_data = self._getWavData()
for i in range(10):
file_path = os.path.join(background_dir, "background_audio_%d.wav" % i)
self._saveTestWavFile(file_path, wav_data)
model_settings = {
"desired_samples": 160,
"fingerprint_size": 40,
"label_count": 4,
"window_size_samples": 100,
"window_stride_samples": 100,
"fingerprint_width": 40,
"preprocess": "mfcc",
}
audio_processor = input_data.AudioProcessor("", wav_dir, 10, 10, ["a", "b"],
10, 10, model_settings, tmp_dir)
self.assertIsNotNone(audio_processor.wav_filename_placeholder_)
self.assertIsNotNone(audio_processor.foreground_volume_placeholder_)
self.assertIsNotNone(audio_processor.time_shift_padding_placeholder_)
self.assertIsNotNone(audio_processor.time_shift_offset_placeholder_)
self.assertIsNotNone(audio_processor.background_data_placeholder_)
self.assertIsNotNone(audio_processor.background_volume_placeholder_)
self.assertIsNotNone(audio_processor.output_)
@test_util.run_deprecated_v1
def testGetDataAverage(self):
self._runGetDataTest("average", 10)
@test_util.run_deprecated_v1
def testGetDataAverageLongWindow(self):
self._runGetDataTest("average", 30)
@test_util.run_deprecated_v1
def testGetDataMfcc(self):
self._runGetDataTest("mfcc", 30)
@test_util.run_deprecated_v1
def testGetUnprocessedData(self):
tmp_dir = self.get_temp_dir()
wav_dir = os.path.join(tmp_dir, "wavs")
os.mkdir(wav_dir)
self._saveWavFolders(wav_dir, ["a", "b", "c"], 100)
model_settings = {
"desired_samples": 160,
"fingerprint_size": 40,
"label_count": 4,
"window_size_samples": 100,
"window_stride_samples": 100,
"fingerprint_width": 40,
"preprocess": "mfcc",
}
audio_processor = input_data.AudioProcessor("", wav_dir, 10, 10, ["a", "b"],
10, 10, model_settings, tmp_dir)
result_data, result_labels = audio_processor.get_unprocessed_data(
10, model_settings, "training")
self.assertEqual(10, len(result_data))
self.assertEqual(10, len(result_labels))
@test_util.run_deprecated_v1
def testGetFeaturesForWav(self):
tmp_dir = self.get_temp_dir()
wav_dir = os.path.join(tmp_dir, "wavs")
os.mkdir(wav_dir)
self._saveWavFolders(wav_dir, ["a", "b", "c"], 1)
desired_samples = 1600
model_settings = {
"desired_samples": desired_samples,
"fingerprint_size": 40,
"label_count": 4,
"window_size_samples": 100,
"window_stride_samples": 100,
"fingerprint_width": 40,
"average_window_width": 6,
"preprocess": "average",
}
with self.cached_session() as sess:
audio_processor = input_data.AudioProcessor(
"", wav_dir, 10, 10, ["a", "b"], 10, 10, model_settings, tmp_dir)
sample_data = np.zeros([desired_samples, 1])
for i in range(desired_samples):
phase = i % 4
if phase == 0:
sample_data[i, 0] = 0
elif phase == 1:
sample_data[i, 0] = -1
elif phase == 2:
sample_data[i, 0] = 0
elif phase == 3:
sample_data[i, 0] = 1
test_wav_path = os.path.join(tmp_dir, "test_wav.wav")
input_data.save_wav_file(test_wav_path, sample_data, 16000)
results = audio_processor.get_features_for_wav(test_wav_path,
model_settings, sess)
spectrogram = results[0]
self.assertEqual(1, spectrogram.shape[0])
self.assertEqual(16, spectrogram.shape[1])
self.assertEqual(11, spectrogram.shape[2])
self.assertNear(0, spectrogram[0, 0, 0], 0.1)
self.assertNear(200, spectrogram[0, 0, 5], 0.1)
def testGetFeaturesRange(self):
model_settings = {
"preprocess": "average",
}
features_min, _ = input_data.get_features_range(model_settings)
self.assertNear(0.0, features_min, 1e-5)
def testGetMfccFeaturesRange(self):
model_settings = {
"preprocess": "mfcc",
}
features_min, features_max = input_data.get_features_range(model_settings)
self.assertLess(features_min, features_max)
if __name__ == "__main__":
test.main()
|
tensorflow-master
|
tensorflow/examples/speech_commands/input_data_test.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Simple speech recognition to spot a limited number of keywords.
This is a self-contained example script that will train a very basic audio
recognition model in TensorFlow. It downloads the necessary training data and
runs with reasonable defaults to train within a few hours even only using a CPU.
For more information, please see
https://www.tensorflow.org/tutorials/audio_recognition.
It is intended as an introduction to using neural networks for audio
recognition, and is not a full speech recognition system. For more advanced
speech systems, I recommend looking into Kaldi. This network uses a keyword
detection style to spot discrete words from a small vocabulary, consisting of
"yes", "no", "up", "down", "left", "right", "on", "off", "stop", and "go".
To run the training process, use:
bazel run tensorflow/examples/speech_commands:train
This will write out checkpoints to /tmp/speech_commands_train/, and will
download over 1GB of open source training data, so you'll need enough free space
and a good internet connection. The default data is a collection of thousands of
one-second .wav files, each containing one spoken word. This data set is
collected from https://aiyprojects.withgoogle.com/open_speech_recording, please
consider contributing to help improve this and other models!
As training progresses, it will print out its accuracy metrics, which should
rise above 90% by the end. Once it's complete, you can run the freeze script to
get a binary GraphDef that you can easily deploy on mobile applications.
If you want to train on your own data, you'll need to create .wavs with your
recordings, all at a consistent length, and then arrange them into subfolders
organized by label. For example, here's a possible file structure:
my_wavs >
up >
audio_0.wav
audio_1.wav
down >
audio_2.wav
audio_3.wav
other>
audio_4.wav
audio_5.wav
You'll also need to tell the script what labels to look for, using the
`--wanted_words` argument. In this case, 'up,down' might be what you want, and
the audio in the 'other' folder would be used to train an 'unknown' category.
To pull this all together, you'd run:
bazel run tensorflow/examples/speech_commands:train -- \
--data_dir=my_wavs --wanted_words=up,down
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os.path
import sys
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
import input_data
import models
from tensorflow.python.platform import gfile
FLAGS = None
def main(_):
# We want to see all the logging messages for this tutorial.
tf.logging.set_verbosity(tf.logging.INFO)
# Start a new TensorFlow session.
sess = tf.InteractiveSession()
# Begin by making sure we have the training data we need. If you already have
# training data of your own, use `--data_url= ` on the command line to avoid
# downloading.
model_settings = models.prepare_model_settings(
len(input_data.prepare_words_list(FLAGS.wanted_words.split(','))),
FLAGS.sample_rate, FLAGS.clip_duration_ms, FLAGS.window_size_ms,
FLAGS.window_stride_ms, FLAGS.feature_bin_count, FLAGS.preprocess)
audio_processor = input_data.AudioProcessor(
FLAGS.data_url, FLAGS.data_dir,
FLAGS.silence_percentage, FLAGS.unknown_percentage,
FLAGS.wanted_words.split(','), FLAGS.validation_percentage,
FLAGS.testing_percentage, model_settings, FLAGS.summaries_dir)
fingerprint_size = model_settings['fingerprint_size']
label_count = model_settings['label_count']
time_shift_samples = int((FLAGS.time_shift_ms * FLAGS.sample_rate) / 1000)
# Figure out the learning rates for each training phase. Since it's often
# effective to have high learning rates at the start of training, followed by
# lower levels towards the end, the number of steps and learning rates can be
# specified as comma-separated lists to define the rate at each stage. For
# example --how_many_training_steps=10000,3000 --learning_rate=0.001,0.0001
# will run 13,000 training loops in total, with a rate of 0.001 for the first
# 10,000, and 0.0001 for the final 3,000.
training_steps_list = list(map(int, FLAGS.how_many_training_steps.split(',')))
learning_rates_list = list(map(float, FLAGS.learning_rate.split(',')))
if len(training_steps_list) != len(learning_rates_list):
raise Exception(
'--how_many_training_steps and --learning_rate must be equal length '
'lists, but are %d and %d long instead' % (len(training_steps_list),
len(learning_rates_list)))
input_placeholder = tf.placeholder(
tf.float32, [None, fingerprint_size], name='fingerprint_input')
if FLAGS.quantize:
fingerprint_min, fingerprint_max = input_data.get_features_range(
model_settings)
fingerprint_input = tf.fake_quant_with_min_max_args(
input_placeholder, fingerprint_min, fingerprint_max)
else:
fingerprint_input = input_placeholder
logits, dropout_prob = models.create_model(
fingerprint_input,
model_settings,
FLAGS.model_architecture,
is_training=True)
# Define loss and optimizer
ground_truth_input = tf.placeholder(
tf.int64, [None], name='groundtruth_input')
# Optionally we can add runtime checks to spot when NaNs or other symptoms of
# numerical errors start occurring during training.
control_dependencies = []
if FLAGS.check_nans:
checks = tf.add_check_numerics_ops()
control_dependencies = [checks]
# Create the back propagation and training evaluation machinery in the graph.
with tf.name_scope('cross_entropy'):
cross_entropy_mean = tf.losses.sparse_softmax_cross_entropy(
labels=ground_truth_input, logits=logits)
if FLAGS.quantize:
tf.contrib.quantize.create_training_graph(quant_delay=0)
with tf.name_scope('train'), tf.control_dependencies(control_dependencies):
learning_rate_input = tf.placeholder(
tf.float32, [], name='learning_rate_input')
train_step = tf.train.GradientDescentOptimizer(
learning_rate_input).minimize(cross_entropy_mean)
predicted_indices = tf.argmax(logits, 1)
correct_prediction = tf.equal(predicted_indices, ground_truth_input)
confusion_matrix = tf.confusion_matrix(
ground_truth_input, predicted_indices, num_classes=label_count)
evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.get_default_graph().name_scope('eval'):
tf.summary.scalar('cross_entropy', cross_entropy_mean)
tf.summary.scalar('accuracy', evaluation_step)
global_step = tf.train.get_or_create_global_step()
increment_global_step = tf.assign(global_step, global_step + 1)
saver = tf.train.Saver(tf.global_variables())
# Merge all the summaries and write them out to /tmp/retrain_logs (by default)
merged_summaries = tf.summary.merge_all(scope='eval')
train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train',
sess.graph)
validation_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/validation')
tf.global_variables_initializer().run()
start_step = 1
if FLAGS.start_checkpoint:
models.load_variables_from_checkpoint(sess, FLAGS.start_checkpoint)
start_step = global_step.eval(session=sess)
tf.logging.info('Training from step: %d ', start_step)
# Save graph.pbtxt.
tf.train.write_graph(sess.graph_def, FLAGS.train_dir,
FLAGS.model_architecture + '.pbtxt')
# Save list of words.
with gfile.GFile(
os.path.join(FLAGS.train_dir, FLAGS.model_architecture + '_labels.txt'),
'w') as f:
f.write('\n'.join(audio_processor.words_list))
# Training loop.
training_steps_max = np.sum(training_steps_list)
for training_step in xrange(start_step, training_steps_max + 1):
# Figure out what the current learning rate is.
training_steps_sum = 0
for i in range(len(training_steps_list)):
training_steps_sum += training_steps_list[i]
if training_step <= training_steps_sum:
learning_rate_value = learning_rates_list[i]
break
# Pull the audio samples we'll use for training.
train_fingerprints, train_ground_truth = audio_processor.get_data(
FLAGS.batch_size, 0, model_settings, FLAGS.background_frequency,
FLAGS.background_volume, time_shift_samples, 'training', sess)
# Run the graph with this batch of training data.
train_summary, train_accuracy, cross_entropy_value, _, _ = sess.run(
[
merged_summaries,
evaluation_step,
cross_entropy_mean,
train_step,
increment_global_step,
],
feed_dict={
fingerprint_input: train_fingerprints,
ground_truth_input: train_ground_truth,
learning_rate_input: learning_rate_value,
dropout_prob: 0.5
})
train_writer.add_summary(train_summary, training_step)
tf.logging.info('Step #%d: rate %f, accuracy %.1f%%, cross entropy %f' %
(training_step, learning_rate_value, train_accuracy * 100,
cross_entropy_value))
is_last_step = (training_step == training_steps_max)
if (training_step % FLAGS.eval_step_interval) == 0 or is_last_step:
set_size = audio_processor.set_size('validation')
total_accuracy = 0
total_conf_matrix = None
for i in xrange(0, set_size, FLAGS.batch_size):
validation_fingerprints, validation_ground_truth = (
audio_processor.get_data(FLAGS.batch_size, i, model_settings, 0.0,
0.0, 0, 'validation', sess))
# Run a validation step and capture training summaries for TensorBoard
# with the `merged` op.
validation_summary, validation_accuracy, conf_matrix = sess.run(
[merged_summaries, evaluation_step, confusion_matrix],
feed_dict={
fingerprint_input: validation_fingerprints,
ground_truth_input: validation_ground_truth,
dropout_prob: 1.0
})
validation_writer.add_summary(validation_summary, training_step)
batch_size = min(FLAGS.batch_size, set_size - i)
total_accuracy += (validation_accuracy * batch_size) / set_size
if total_conf_matrix is None:
total_conf_matrix = conf_matrix
else:
total_conf_matrix += conf_matrix
tf.logging.info('Confusion Matrix:\n %s' % (total_conf_matrix))
tf.logging.info('Step %d: Validation accuracy = %.1f%% (N=%d)' %
(training_step, total_accuracy * 100, set_size))
# Save the model checkpoint periodically.
if (training_step % FLAGS.save_step_interval == 0 or
training_step == training_steps_max):
checkpoint_path = os.path.join(FLAGS.train_dir,
FLAGS.model_architecture + '.ckpt')
tf.logging.info('Saving to "%s-%d"', checkpoint_path, training_step)
saver.save(sess, checkpoint_path, global_step=training_step)
set_size = audio_processor.set_size('testing')
tf.logging.info('set_size=%d', set_size)
total_accuracy = 0
total_conf_matrix = None
for i in xrange(0, set_size, FLAGS.batch_size):
test_fingerprints, test_ground_truth = audio_processor.get_data(
FLAGS.batch_size, i, model_settings, 0.0, 0.0, 0, 'testing', sess)
test_accuracy, conf_matrix = sess.run(
[evaluation_step, confusion_matrix],
feed_dict={
fingerprint_input: test_fingerprints,
ground_truth_input: test_ground_truth,
dropout_prob: 1.0
})
batch_size = min(FLAGS.batch_size, set_size - i)
total_accuracy += (test_accuracy * batch_size) / set_size
if total_conf_matrix is None:
total_conf_matrix = conf_matrix
else:
total_conf_matrix += conf_matrix
tf.logging.info('Confusion Matrix:\n %s' % (total_conf_matrix))
tf.logging.info('Final test accuracy = %.1f%% (N=%d)' % (total_accuracy * 100,
set_size))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--data_url',
type=str,
# pylint: disable=line-too-long
default='http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz',
# pylint: enable=line-too-long
help='Location of speech training data archive on the web.')
parser.add_argument(
'--data_dir',
type=str,
default='/tmp/speech_dataset/',
help="""\
Where to download the speech training data to.
""")
parser.add_argument(
'--background_volume',
type=float,
default=0.1,
help="""\
How loud the background noise should be, between 0 and 1.
""")
parser.add_argument(
'--background_frequency',
type=float,
default=0.8,
help="""\
How many of the training samples have background noise mixed in.
""")
parser.add_argument(
'--silence_percentage',
type=float,
default=10.0,
help="""\
How much of the training data should be silence.
""")
parser.add_argument(
'--unknown_percentage',
type=float,
default=10.0,
help="""\
How much of the training data should be unknown words.
""")
parser.add_argument(
'--time_shift_ms',
type=float,
default=100.0,
help="""\
Range to randomly shift the training audio by in time.
""")
parser.add_argument(
'--testing_percentage',
type=int,
default=10,
help='What percentage of wavs to use as a test set.')
parser.add_argument(
'--validation_percentage',
type=int,
default=10,
help='What percentage of wavs to use as a validation set.')
parser.add_argument(
'--sample_rate',
type=int,
default=16000,
help='Expected sample rate of the wavs',)
parser.add_argument(
'--clip_duration_ms',
type=int,
default=1000,
help='Expected duration in milliseconds of the wavs',)
parser.add_argument(
'--window_size_ms',
type=float,
default=30.0,
help='How long each spectrogram timeslice is.',)
parser.add_argument(
'--window_stride_ms',
type=float,
default=10.0,
help='How far to move in time between spectogram timeslices.',)
parser.add_argument(
'--feature_bin_count',
type=int,
default=40,
help='How many bins to use for the MFCC fingerprint',
)
parser.add_argument(
'--how_many_training_steps',
type=str,
default='15000,3000',
help='How many training loops to run',)
parser.add_argument(
'--eval_step_interval',
type=int,
default=400,
help='How often to evaluate the training results.')
parser.add_argument(
'--learning_rate',
type=str,
default='0.001,0.0001',
help='How large a learning rate to use when training.')
parser.add_argument(
'--batch_size',
type=int,
default=100,
help='How many items to train with at once',)
parser.add_argument(
'--summaries_dir',
type=str,
default='/tmp/retrain_logs',
help='Where to save summary logs for TensorBoard.')
parser.add_argument(
'--wanted_words',
type=str,
default='yes,no,up,down,left,right,on,off,stop,go',
help='Words to use (others will be added to an unknown label)',)
parser.add_argument(
'--train_dir',
type=str,
default='/tmp/speech_commands_train',
help='Directory to write event logs and checkpoint.')
parser.add_argument(
'--save_step_interval',
type=int,
default=100,
help='Save model checkpoint every save_steps.')
parser.add_argument(
'--start_checkpoint',
type=str,
default='',
help='If specified, restore this pretrained model before any training.')
parser.add_argument(
'--model_architecture',
type=str,
default='conv',
help='What model architecture to use')
parser.add_argument(
'--check_nans',
type=bool,
default=False,
help='Whether to check for invalid numbers during processing')
parser.add_argument(
'--quantize',
type=bool,
default=False,
help='Whether to train the model for eight-bit deployment')
parser.add_argument(
'--preprocess',
type=str,
default='mfcc',
help='Spectrogram processing mode. Can be "mfcc" or "average"')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
|
tensorflow-master
|
tensorflow/examples/speech_commands/train.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Converts a trained checkpoint into a frozen model for mobile inference.
Once you've trained a model using the `train.py` script, you can use this tool
to convert it into a binary GraphDef file that can be loaded into the Android,
iOS, or Raspberry Pi example code. Here's an example of how to run it:
bazel run tensorflow/examples/speech_commands/freeze -- \
--sample_rate=16000 --dct_coefficient_count=40 --window_size_ms=20 \
--window_stride_ms=10 --clip_duration_ms=1000 \
--model_architecture=conv \
--start_checkpoint=/tmp/speech_commands_train/conv.ckpt-1300 \
--output_file=/tmp/my_frozen_graph.pb
One thing to watch out for is that you need to pass in the same arguments for
`sample_rate` and other command line variables here as you did for the training
script.
The resulting graph has an input for WAV-encoded data named 'wav_data', one for
raw PCM data (as floats in the range -1.0 to 1.0) called 'decoded_sample_data',
and the output is called 'labels_softmax'.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os.path
import sys
import tensorflow as tf
from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio
import input_data
import models
from tensorflow.python.framework import graph_util
FLAGS = None
def create_inference_graph(wanted_words, sample_rate, clip_duration_ms,
clip_stride_ms, window_size_ms, window_stride_ms,
feature_bin_count, model_architecture, preprocess):
"""Creates an audio model with the nodes needed for inference.
Uses the supplied arguments to create a model, and inserts the input and
output nodes that are needed to use the graph for inference.
Args:
wanted_words: Comma-separated list of the words we're trying to recognize.
sample_rate: How many samples per second are in the input audio files.
clip_duration_ms: How many samples to analyze for the audio pattern.
clip_stride_ms: How often to run recognition. Useful for models with cache.
window_size_ms: Time slice duration to estimate frequencies from.
window_stride_ms: How far apart time slices should be.
feature_bin_count: Number of frequency bands to analyze.
model_architecture: Name of the kind of model to generate.
preprocess: How the spectrogram is processed to produce features, for
example 'mfcc' or 'average'.
Raises:
Exception: If the preprocessing mode isn't recognized.
"""
words_list = input_data.prepare_words_list(wanted_words.split(','))
model_settings = models.prepare_model_settings(
len(words_list), sample_rate, clip_duration_ms, window_size_ms,
window_stride_ms, feature_bin_count, preprocess)
runtime_settings = {'clip_stride_ms': clip_stride_ms}
wav_data_placeholder = tf.placeholder(tf.string, [], name='wav_data')
decoded_sample_data = contrib_audio.decode_wav(
wav_data_placeholder,
desired_channels=1,
desired_samples=model_settings['desired_samples'],
name='decoded_sample_data')
spectrogram = contrib_audio.audio_spectrogram(
decoded_sample_data.audio,
window_size=model_settings['window_size_samples'],
stride=model_settings['window_stride_samples'],
magnitude_squared=True)
if preprocess == 'average':
fingerprint_input = tf.nn.pool(
tf.expand_dims(spectrogram, -1),
window_shape=[1, model_settings['average_window_width']],
strides=[1, model_settings['average_window_width']],
pooling_type='AVG',
padding='SAME')
elif preprocess == 'mfcc':
fingerprint_input = contrib_audio.mfcc(
spectrogram,
sample_rate,
dct_coefficient_count=model_settings['fingerprint_width'])
else:
raise Exception('Unknown preprocess mode "%s" (should be "mfcc" or'
' "average")' % (preprocess))
fingerprint_size = model_settings['fingerprint_size']
reshaped_input = tf.reshape(fingerprint_input, [-1, fingerprint_size])
logits = models.create_model(
reshaped_input, model_settings, model_architecture, is_training=False,
runtime_settings=runtime_settings)
# Create an output to use for inference.
tf.nn.softmax(logits, name='labels_softmax')
def main(_):
# Create the model and load its weights.
sess = tf.InteractiveSession()
create_inference_graph(
FLAGS.wanted_words, FLAGS.sample_rate, FLAGS.clip_duration_ms,
FLAGS.clip_stride_ms, FLAGS.window_size_ms, FLAGS.window_stride_ms,
FLAGS.feature_bin_count, FLAGS.model_architecture, FLAGS.preprocess)
if FLAGS.quantize:
tf.contrib.quantize.create_eval_graph()
models.load_variables_from_checkpoint(sess, FLAGS.start_checkpoint)
# Turn all the variables into inline constants inside the graph and save it.
frozen_graph_def = graph_util.convert_variables_to_constants(
sess, sess.graph_def, ['labels_softmax'])
tf.train.write_graph(
frozen_graph_def,
os.path.dirname(FLAGS.output_file),
os.path.basename(FLAGS.output_file),
as_text=False)
tf.logging.info('Saved frozen graph to %s', FLAGS.output_file)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--sample_rate',
type=int,
default=16000,
help='Expected sample rate of the wavs',)
parser.add_argument(
'--clip_duration_ms',
type=int,
default=1000,
help='Expected duration in milliseconds of the wavs',)
parser.add_argument(
'--clip_stride_ms',
type=int,
default=30,
help='How often to run recognition. Useful for models with cache.',)
parser.add_argument(
'--window_size_ms',
type=float,
default=30.0,
help='How long each spectrogram timeslice is',)
parser.add_argument(
'--window_stride_ms',
type=float,
default=10.0,
help='How long the stride is between spectrogram timeslices',)
parser.add_argument(
'--feature_bin_count',
type=int,
default=40,
help='How many bins to use for the MFCC fingerprint',
)
parser.add_argument(
'--start_checkpoint',
type=str,
default='',
help='If specified, restore this pretrained model before any training.')
parser.add_argument(
'--model_architecture',
type=str,
default='conv',
help='What model architecture to use')
parser.add_argument(
'--wanted_words',
type=str,
default='yes,no,up,down,left,right,on,off,stop,go',
help='Words to use (others will be added to an unknown label)',)
parser.add_argument(
'--output_file', type=str, help='Where to save the frozen graph.')
parser.add_argument(
'--quantize',
type=bool,
default=False,
help='Whether to train the model for eight-bit deployment')
parser.add_argument(
'--preprocess',
type=str,
default='mfcc',
help='Spectrogram processing mode. Can be "mfcc" or "average"')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
|
tensorflow-master
|
tensorflow/examples/speech_commands/freeze.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Model definitions for simple speech recognition.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import hashlib
import math
import os.path
import random
import re
import sys
import tarfile
import numpy as np
from six.moves import urllib
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio
from tensorflow.python.ops import io_ops
from tensorflow.python.platform import gfile
from tensorflow.python.util import compat
MAX_NUM_WAVS_PER_CLASS = 2**27 - 1 # ~134M
SILENCE_LABEL = '_silence_'
SILENCE_INDEX = 0
UNKNOWN_WORD_LABEL = '_unknown_'
UNKNOWN_WORD_INDEX = 1
BACKGROUND_NOISE_DIR_NAME = '_background_noise_'
RANDOM_SEED = 59185
def prepare_words_list(wanted_words):
"""Prepends common tokens to the custom word list.
Args:
wanted_words: List of strings containing the custom words.
Returns:
List with the standard silence and unknown tokens added.
"""
return [SILENCE_LABEL, UNKNOWN_WORD_LABEL] + wanted_words
def which_set(filename, validation_percentage, testing_percentage):
"""Determines which data partition the file should belong to.
We want to keep files in the same training, validation, or testing sets even
if new ones are added over time. This makes it less likely that testing
samples will accidentally be reused in training when long runs are restarted
for example. To keep this stability, a hash of the filename is taken and used
to determine which set it should belong to. This determination only depends on
the name and the set proportions, so it won't change as other files are added.
It's also useful to associate particular files as related (for example words
spoken by the same person), so anything after '_nohash_' in a filename is
ignored for set determination. This ensures that 'bobby_nohash_0.wav' and
'bobby_nohash_1.wav' are always in the same set, for example.
Args:
filename: File path of the data sample.
validation_percentage: How much of the data set to use for validation.
testing_percentage: How much of the data set to use for testing.
Returns:
String, one of 'training', 'validation', or 'testing'.
"""
base_name = os.path.basename(filename)
# We want to ignore anything after '_nohash_' in the file name when
# deciding which set to put a wav in, so the data set creator has a way of
# grouping wavs that are close variations of each other.
hash_name = re.sub(r'_nohash_.*$', '', base_name)
# This looks a bit magical, but we need to decide whether this file should
# go into the training, testing, or validation sets, and we want to keep
# existing files in the same set even if more files are subsequently
# added.
# To do that, we need a stable way of deciding based on just the file name
# itself, so we do a hash of that and then use that to generate a
# probability value that we use to assign it.
hash_name_hashed = hashlib.sha1(compat.as_bytes(hash_name)).hexdigest()
percentage_hash = ((int(hash_name_hashed, 16) %
(MAX_NUM_WAVS_PER_CLASS + 1)) *
(100.0 / MAX_NUM_WAVS_PER_CLASS))
if percentage_hash < validation_percentage:
result = 'validation'
elif percentage_hash < (testing_percentage + validation_percentage):
result = 'testing'
else:
result = 'training'
return result
def load_wav_file(filename):
"""Loads an audio file and returns a float PCM-encoded array of samples.
Args:
filename: Path to the .wav file to load.
Returns:
Numpy array holding the sample data as floats between -1.0 and 1.0.
"""
with tf.Session(graph=tf.Graph()) as sess:
wav_filename_placeholder = tf.placeholder(tf.string, [])
wav_loader = io_ops.read_file(wav_filename_placeholder)
wav_decoder = contrib_audio.decode_wav(wav_loader, desired_channels=1)
return sess.run(
wav_decoder,
feed_dict={wav_filename_placeholder: filename}).audio.flatten()
def save_wav_file(filename, wav_data, sample_rate):
"""Saves audio sample data to a .wav audio file.
Args:
filename: Path to save the file to.
wav_data: 2D array of float PCM-encoded audio data.
sample_rate: Samples per second to encode in the file.
"""
with tf.Session(graph=tf.Graph()) as sess:
wav_filename_placeholder = tf.placeholder(tf.string, [])
sample_rate_placeholder = tf.placeholder(tf.int32, [])
wav_data_placeholder = tf.placeholder(tf.float32, [None, 1])
wav_encoder = contrib_audio.encode_wav(wav_data_placeholder,
sample_rate_placeholder)
wav_saver = io_ops.write_file(wav_filename_placeholder, wav_encoder)
sess.run(
wav_saver,
feed_dict={
wav_filename_placeholder: filename,
sample_rate_placeholder: sample_rate,
wav_data_placeholder: np.reshape(wav_data, (-1, 1))
})
def get_features_range(model_settings):
"""Returns the expected min/max for generated features.
Args:
model_settings: Information about the current model being trained.
Returns:
Min/max float pair holding the range of features.
Raises:
Exception: If preprocessing mode isn't recognized.
"""
# TODO(petewarden): These values have been derived from the observed ranges
# of spectrogram and MFCC inputs. If the preprocessing pipeline changes,
# they may need to be updated.
if model_settings['preprocess'] == 'average':
features_min = 0.0
features_max = 127.5
elif model_settings['preprocess'] == 'mfcc':
features_min = -247.0
features_max = 30.0
else:
raise Exception('Unknown preprocess mode "%s" (should be "mfcc" or'
' "average")' % (model_settings['preprocess']))
return features_min, features_max
class AudioProcessor(object):
"""Handles loading, partitioning, and preparing audio training data."""
def __init__(self, data_url, data_dir, silence_percentage, unknown_percentage,
wanted_words, validation_percentage, testing_percentage,
model_settings, summaries_dir):
if data_dir:
self.data_dir = data_dir
self.maybe_download_and_extract_dataset(data_url, data_dir)
self.prepare_data_index(silence_percentage, unknown_percentage,
wanted_words, validation_percentage,
testing_percentage)
self.prepare_background_data()
self.prepare_processing_graph(model_settings, summaries_dir)
def maybe_download_and_extract_dataset(self, data_url, dest_directory):
"""Download and extract data set tar file.
If the data set we're using doesn't already exist, this function
downloads it from the TensorFlow.org website and unpacks it into a
directory.
If the data_url is none, don't download anything and expect the data
directory to contain the correct files already.
Args:
data_url: Web location of the tar file containing the data set.
dest_directory: File path to extract data to.
"""
if not data_url:
return
if not os.path.exists(dest_directory):
os.makedirs(dest_directory)
filename = data_url.split('/')[-1]
filepath = os.path.join(dest_directory, filename)
if not os.path.exists(filepath):
def _progress(count, block_size, total_size):
sys.stdout.write(
'\r>> Downloading %s %.1f%%' %
(filename, float(count * block_size) / float(total_size) * 100.0))
sys.stdout.flush()
try:
filepath, _ = urllib.request.urlretrieve(data_url, filepath, _progress)
except:
tf.logging.error('Failed to download URL: %s to folder: %s', data_url,
filepath)
tf.logging.error('Please make sure you have enough free space and'
' an internet connection')
raise
print()
statinfo = os.stat(filepath)
tf.logging.info('Successfully downloaded %s (%d bytes)', filename,
statinfo.st_size)
tarfile.open(filepath, 'r:gz').extractall(dest_directory)
def prepare_data_index(self, silence_percentage, unknown_percentage,
wanted_words, validation_percentage,
testing_percentage):
"""Prepares a list of the samples organized by set and label.
The training loop needs a list of all the available data, organized by
which partition it should belong to, and with ground truth labels attached.
This function analyzes the folders below the `data_dir`, figures out the
right
labels for each file based on the name of the subdirectory it belongs to,
and uses a stable hash to assign it to a data set partition.
Args:
silence_percentage: How much of the resulting data should be background.
unknown_percentage: How much should be audio outside the wanted classes.
wanted_words: Labels of the classes we want to be able to recognize.
validation_percentage: How much of the data set to use for validation.
testing_percentage: How much of the data set to use for testing.
Returns:
Dictionary containing a list of file information for each set partition,
and a lookup map for each class to determine its numeric index.
Raises:
Exception: If expected files are not found.
"""
# Make sure the shuffling and picking of unknowns is deterministic.
random.seed(RANDOM_SEED)
wanted_words_index = {}
for index, wanted_word in enumerate(wanted_words):
wanted_words_index[wanted_word] = index + 2
self.data_index = {'validation': [], 'testing': [], 'training': []}
unknown_index = {'validation': [], 'testing': [], 'training': []}
all_words = {}
# Look through all the subfolders to find audio samples
search_path = os.path.join(self.data_dir, '*', '*.wav')
for wav_path in gfile.Glob(search_path):
_, word = os.path.split(os.path.dirname(wav_path))
word = word.lower()
# Treat the '_background_noise_' folder as a special case, since we expect
# it to contain long audio samples we mix in to improve training.
if word == BACKGROUND_NOISE_DIR_NAME:
continue
all_words[word] = True
set_index = which_set(wav_path, validation_percentage, testing_percentage)
# If it's a known class, store its detail, otherwise add it to the list
# we'll use to train the unknown label.
if word in wanted_words_index:
self.data_index[set_index].append({'label': word, 'file': wav_path})
else:
unknown_index[set_index].append({'label': word, 'file': wav_path})
if not all_words:
raise Exception('No .wavs found at ' + search_path)
for index, wanted_word in enumerate(wanted_words):
if wanted_word not in all_words:
raise Exception('Expected to find ' + wanted_word +
' in labels but only found ' +
', '.join(all_words.keys()))
# We need an arbitrary file to load as the input for the silence samples.
# It's multiplied by zero later, so the content doesn't matter.
silence_wav_path = self.data_index['training'][0]['file']
for set_index in ['validation', 'testing', 'training']:
set_size = len(self.data_index[set_index])
silence_size = int(math.ceil(set_size * silence_percentage / 100))
for _ in range(silence_size):
self.data_index[set_index].append({
'label': SILENCE_LABEL,
'file': silence_wav_path
})
# Pick some unknowns to add to each partition of the data set.
random.shuffle(unknown_index[set_index])
unknown_size = int(math.ceil(set_size * unknown_percentage / 100))
self.data_index[set_index].extend(unknown_index[set_index][:unknown_size])
# Make sure the ordering is random.
for set_index in ['validation', 'testing', 'training']:
random.shuffle(self.data_index[set_index])
# Prepare the rest of the result data structure.
self.words_list = prepare_words_list(wanted_words)
self.word_to_index = {}
for word in all_words:
if word in wanted_words_index:
self.word_to_index[word] = wanted_words_index[word]
else:
self.word_to_index[word] = UNKNOWN_WORD_INDEX
self.word_to_index[SILENCE_LABEL] = SILENCE_INDEX
def prepare_background_data(self):
"""Searches a folder for background noise audio, and loads it into memory.
It's expected that the background audio samples will be in a subdirectory
named '_background_noise_' inside the 'data_dir' folder, as .wavs that match
the sample rate of the training data, but can be much longer in duration.
If the '_background_noise_' folder doesn't exist at all, this isn't an
error, it's just taken to mean that no background noise augmentation should
be used. If the folder does exist, but it's empty, that's treated as an
error.
Returns:
List of raw PCM-encoded audio samples of background noise.
Raises:
Exception: If files aren't found in the folder.
"""
self.background_data = []
background_dir = os.path.join(self.data_dir, BACKGROUND_NOISE_DIR_NAME)
if not os.path.exists(background_dir):
return self.background_data
with tf.Session(graph=tf.Graph()) as sess:
wav_filename_placeholder = tf.placeholder(tf.string, [])
wav_loader = io_ops.read_file(wav_filename_placeholder)
wav_decoder = contrib_audio.decode_wav(wav_loader, desired_channels=1)
search_path = os.path.join(self.data_dir, BACKGROUND_NOISE_DIR_NAME,
'*.wav')
for wav_path in gfile.Glob(search_path):
wav_data = sess.run(
wav_decoder,
feed_dict={wav_filename_placeholder: wav_path}).audio.flatten()
self.background_data.append(wav_data)
if not self.background_data:
raise Exception('No background wav files were found in ' + search_path)
def prepare_processing_graph(self, model_settings, summaries_dir):
"""Builds a TensorFlow graph to apply the input distortions.
Creates a graph that loads a WAVE file, decodes it, scales the volume,
shifts it in time, adds in background noise, calculates a spectrogram, and
then builds an MFCC fingerprint from that.
This must be called with an active TensorFlow session running, and it
creates multiple placeholder inputs, and one output:
- wav_filename_placeholder_: Filename of the WAV to load.
- foreground_volume_placeholder_: How loud the main clip should be.
- time_shift_padding_placeholder_: Where to pad the clip.
- time_shift_offset_placeholder_: How much to move the clip in time.
- background_data_placeholder_: PCM sample data for background noise.
- background_volume_placeholder_: Loudness of mixed-in background.
- output_: Output 2D fingerprint of processed audio.
Args:
model_settings: Information about the current model being trained.
summaries_dir: Path to save training summary information to.
Raises:
ValueError: If the preprocessing mode isn't recognized.
"""
with tf.get_default_graph().name_scope('data'):
desired_samples = model_settings['desired_samples']
self.wav_filename_placeholder_ = tf.placeholder(
tf.string, [], name='wav_filename')
wav_loader = io_ops.read_file(self.wav_filename_placeholder_)
wav_decoder = contrib_audio.decode_wav(
wav_loader, desired_channels=1, desired_samples=desired_samples)
# Allow the audio sample's volume to be adjusted.
self.foreground_volume_placeholder_ = tf.placeholder(
tf.float32, [], name='foreground_volume')
scaled_foreground = tf.multiply(wav_decoder.audio,
self.foreground_volume_placeholder_)
# Shift the sample's start position, and pad any gaps with zeros.
self.time_shift_padding_placeholder_ = tf.placeholder(
tf.int32, [2, 2], name='time_shift_padding')
self.time_shift_offset_placeholder_ = tf.placeholder(
tf.int32, [2], name='time_shift_offset')
padded_foreground = tf.pad(
scaled_foreground,
self.time_shift_padding_placeholder_,
mode='CONSTANT')
sliced_foreground = tf.slice(padded_foreground,
self.time_shift_offset_placeholder_,
[desired_samples, -1])
# Mix in background noise.
self.background_data_placeholder_ = tf.placeholder(
tf.float32, [desired_samples, 1], name='background_data')
self.background_volume_placeholder_ = tf.placeholder(
tf.float32, [], name='background_volume')
background_mul = tf.multiply(self.background_data_placeholder_,
self.background_volume_placeholder_)
background_add = tf.add(background_mul, sliced_foreground)
background_clamp = tf.clip_by_value(background_add, -1.0, 1.0)
# Run the spectrogram and MFCC ops to get a 2D 'fingerprint' of the audio.
spectrogram = contrib_audio.audio_spectrogram(
background_clamp,
window_size=model_settings['window_size_samples'],
stride=model_settings['window_stride_samples'],
magnitude_squared=True)
tf.summary.image(
'spectrogram', tf.expand_dims(spectrogram, -1), max_outputs=1)
# The number of buckets in each FFT row in the spectrogram will depend on
# how many input samples there are in each window. This can be quite
# large, with a 160 sample window producing 127 buckets for example. We
# don't need this level of detail for classification, so we often want to
# shrink them down to produce a smaller result. That's what this section
# implements. One method is to use average pooling to merge adjacent
# buckets, but a more sophisticated approach is to apply the MFCC
# algorithm to shrink the representation.
if model_settings['preprocess'] == 'average':
self.output_ = tf.nn.pool(
tf.expand_dims(spectrogram, -1),
window_shape=[1, model_settings['average_window_width']],
strides=[1, model_settings['average_window_width']],
pooling_type='AVG',
padding='SAME')
tf.summary.image('shrunk_spectrogram', self.output_, max_outputs=1)
elif model_settings['preprocess'] == 'mfcc':
self.output_ = contrib_audio.mfcc(
spectrogram,
wav_decoder.sample_rate,
dct_coefficient_count=model_settings['fingerprint_width'])
tf.summary.image(
'mfcc', tf.expand_dims(self.output_, -1), max_outputs=1)
else:
raise ValueError('Unknown preprocess mode "%s" (should be "mfcc" or'
' "average")' % (model_settings['preprocess']))
# Merge all the summaries and write them out to /tmp/retrain_logs (by
# default)
self.merged_summaries_ = tf.summary.merge_all(scope='data')
if summaries_dir:
self.summary_writer_ = tf.summary.FileWriter(summaries_dir + '/data',
tf.get_default_graph())
def set_size(self, mode):
"""Calculates the number of samples in the dataset partition.
Args:
mode: Which partition, must be 'training', 'validation', or 'testing'.
Returns:
Number of samples in the partition.
"""
return len(self.data_index[mode])
def get_data(self, how_many, offset, model_settings, background_frequency,
background_volume_range, time_shift, mode, sess):
"""Gather samples from the data set, applying transformations as needed.
When the mode is 'training', a random selection of samples will be returned,
otherwise the first N clips in the partition will be used. This ensures that
validation always uses the same samples, reducing noise in the metrics.
Args:
how_many: Desired number of samples to return. -1 means the entire
contents of this partition.
offset: Where to start when fetching deterministically.
model_settings: Information about the current model being trained.
background_frequency: How many clips will have background noise, 0.0 to
1.0.
background_volume_range: How loud the background noise will be.
time_shift: How much to randomly shift the clips by in time.
mode: Which partition to use, must be 'training', 'validation', or
'testing'.
sess: TensorFlow session that was active when processor was created.
Returns:
List of sample data for the transformed samples, and list of label indexes
Raises:
ValueError: If background samples are too short.
"""
# Pick one of the partitions to choose samples from.
candidates = self.data_index[mode]
if how_many == -1:
sample_count = len(candidates)
else:
sample_count = max(0, min(how_many, len(candidates) - offset))
# Data and labels will be populated and returned.
data = np.zeros((sample_count, model_settings['fingerprint_size']))
labels = np.zeros(sample_count)
desired_samples = model_settings['desired_samples']
use_background = self.background_data and (mode == 'training')
pick_deterministically = (mode != 'training')
# Use the processing graph we created earlier to repeatedly to generate the
# final output sample data we'll use in training.
for i in xrange(offset, offset + sample_count):
# Pick which audio sample to use.
if how_many == -1 or pick_deterministically:
sample_index = i
else:
sample_index = np.random.randint(len(candidates))
sample = candidates[sample_index]
# If we're time shifting, set up the offset for this sample.
if time_shift > 0:
time_shift_amount = np.random.randint(-time_shift, time_shift)
else:
time_shift_amount = 0
if time_shift_amount > 0:
time_shift_padding = [[time_shift_amount, 0], [0, 0]]
time_shift_offset = [0, 0]
else:
time_shift_padding = [[0, -time_shift_amount], [0, 0]]
time_shift_offset = [-time_shift_amount, 0]
input_dict = {
self.wav_filename_placeholder_: sample['file'],
self.time_shift_padding_placeholder_: time_shift_padding,
self.time_shift_offset_placeholder_: time_shift_offset,
}
# Choose a section of background noise to mix in.
if use_background or sample['label'] == SILENCE_LABEL:
background_index = np.random.randint(len(self.background_data))
background_samples = self.background_data[background_index]
if len(background_samples) <= model_settings['desired_samples']:
raise ValueError(
'Background sample is too short! Need more than %d'
' samples but only %d were found' %
(model_settings['desired_samples'], len(background_samples)))
background_offset = np.random.randint(
0, len(background_samples) - model_settings['desired_samples'])
background_clipped = background_samples[background_offset:(
background_offset + desired_samples)]
background_reshaped = background_clipped.reshape([desired_samples, 1])
if sample['label'] == SILENCE_LABEL:
background_volume = np.random.uniform(0, 1)
elif np.random.uniform(0, 1) < background_frequency:
background_volume = np.random.uniform(0, background_volume_range)
else:
background_volume = 0
else:
background_reshaped = np.zeros([desired_samples, 1])
background_volume = 0
input_dict[self.background_data_placeholder_] = background_reshaped
input_dict[self.background_volume_placeholder_] = background_volume
# If we want silence, mute out the main sample but leave the background.
if sample['label'] == SILENCE_LABEL:
input_dict[self.foreground_volume_placeholder_] = 0
else:
input_dict[self.foreground_volume_placeholder_] = 1
# Run the graph to produce the output audio.
summary, data_tensor = sess.run(
[self.merged_summaries_, self.output_], feed_dict=input_dict)
self.summary_writer_.add_summary(summary)
data[i - offset, :] = data_tensor.flatten()
label_index = self.word_to_index[sample['label']]
labels[i - offset] = label_index
return data, labels
def get_features_for_wav(self, wav_filename, model_settings, sess):
"""Applies the feature transformation process to the input_wav.
Runs the feature generation process (generally producing a spectrogram from
the input samples) on the WAV file. This can be useful for testing and
verifying implementations being run on other platforms.
Args:
wav_filename: The path to the input audio file.
model_settings: Information about the current model being trained.
sess: TensorFlow session that was active when processor was created.
Returns:
Numpy data array containing the generated features.
"""
desired_samples = model_settings['desired_samples']
input_dict = {
self.wav_filename_placeholder_: wav_filename,
self.time_shift_padding_placeholder_: [[0, 0], [0, 0]],
self.time_shift_offset_placeholder_: [0, 0],
self.background_data_placeholder_: np.zeros([desired_samples, 1]),
self.background_volume_placeholder_: 0,
self.foreground_volume_placeholder_: 1,
}
# Run the graph to produce the output audio.
data_tensor = sess.run([self.output_], feed_dict=input_dict)
return data_tensor
def get_unprocessed_data(self, how_many, model_settings, mode):
"""Retrieve sample data for the given partition, with no transformations.
Args:
how_many: Desired number of samples to return. -1 means the entire
contents of this partition.
model_settings: Information about the current model being trained.
mode: Which partition to use, must be 'training', 'validation', or
'testing'.
Returns:
List of sample data for the samples, and list of labels in one-hot form.
"""
candidates = self.data_index[mode]
if how_many == -1:
sample_count = len(candidates)
else:
sample_count = how_many
desired_samples = model_settings['desired_samples']
words_list = self.words_list
data = np.zeros((sample_count, desired_samples))
labels = []
with tf.Session(graph=tf.Graph()) as sess:
wav_filename_placeholder = tf.placeholder(tf.string, [])
wav_loader = io_ops.read_file(wav_filename_placeholder)
wav_decoder = contrib_audio.decode_wav(
wav_loader, desired_channels=1, desired_samples=desired_samples)
foreground_volume_placeholder = tf.placeholder(tf.float32, [])
scaled_foreground = tf.multiply(wav_decoder.audio,
foreground_volume_placeholder)
for i in range(sample_count):
if how_many == -1:
sample_index = i
else:
sample_index = np.random.randint(len(candidates))
sample = candidates[sample_index]
input_dict = {wav_filename_placeholder: sample['file']}
if sample['label'] == SILENCE_LABEL:
input_dict[foreground_volume_placeholder] = 0
else:
input_dict[foreground_volume_placeholder] = 1
data[i, :] = sess.run(scaled_foreground, feed_dict=input_dict).flatten()
label_index = self.word_to_index[sample['label']]
labels.append(words_list[label_index])
return data, labels
|
tensorflow-master
|
tensorflow/examples/speech_commands/input_data.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for data input for speech commands."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.examples.speech_commands import freeze
from tensorflow.python.framework import test_util
from tensorflow.python.platform import test
class FreezeTest(test.TestCase):
@test_util.run_deprecated_v1
def testCreateInferenceGraphWithMfcc(self):
with self.cached_session() as sess:
freeze.create_inference_graph(
wanted_words='a,b,c,d',
sample_rate=16000,
clip_duration_ms=1000.0,
clip_stride_ms=30.0,
window_size_ms=30.0,
window_stride_ms=10.0,
feature_bin_count=40,
model_architecture='conv',
preprocess='mfcc')
self.assertIsNotNone(sess.graph.get_tensor_by_name('wav_data:0'))
self.assertIsNotNone(
sess.graph.get_tensor_by_name('decoded_sample_data:0'))
self.assertIsNotNone(sess.graph.get_tensor_by_name('labels_softmax:0'))
ops = [node.op for node in sess.graph_def.node]
self.assertEqual(1, ops.count('Mfcc'))
@test_util.run_deprecated_v1
def testCreateInferenceGraphWithoutMfcc(self):
with self.cached_session() as sess:
freeze.create_inference_graph(
wanted_words='a,b,c,d',
sample_rate=16000,
clip_duration_ms=1000.0,
clip_stride_ms=30.0,
window_size_ms=30.0,
window_stride_ms=10.0,
feature_bin_count=40,
model_architecture='conv',
preprocess='average')
self.assertIsNotNone(sess.graph.get_tensor_by_name('wav_data:0'))
self.assertIsNotNone(
sess.graph.get_tensor_by_name('decoded_sample_data:0'))
self.assertIsNotNone(sess.graph.get_tensor_by_name('labels_softmax:0'))
ops = [node.op for node in sess.graph_def.node]
self.assertEqual(0, ops.count('Mfcc'))
@test_util.run_deprecated_v1
def testFeatureBinCount(self):
with self.cached_session() as sess:
freeze.create_inference_graph(
wanted_words='a,b,c,d',
sample_rate=16000,
clip_duration_ms=1000.0,
clip_stride_ms=30.0,
window_size_ms=30.0,
window_stride_ms=10.0,
feature_bin_count=80,
model_architecture='conv',
preprocess='average')
self.assertIsNotNone(sess.graph.get_tensor_by_name('wav_data:0'))
self.assertIsNotNone(
sess.graph.get_tensor_by_name('decoded_sample_data:0'))
self.assertIsNotNone(sess.graph.get_tensor_by_name('labels_softmax:0'))
ops = [node.op for node in sess.graph_def.node]
self.assertEqual(0, ops.count('Mfcc'))
if __name__ == '__main__':
test.main()
|
tensorflow-master
|
tensorflow/examples/speech_commands/freeze_test.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Saves out a .wav file with synthesized conversational data and labels.
The best way to estimate the real-world performance of an audio recognition
model is by running it against a continuous stream of data, the way that it
would be used in an application. Training evaluations are only run against
discrete individual samples, so the results aren't as realistic.
To make it easy to run evaluations against audio streams, this script uses
samples from the testing partition of the data set, mixes them in at random
positions together with background noise, and saves out the result as one long
audio file.
Here's an example of generating a test file:
bazel run tensorflow/examples/speech_commands:generate_streaming_test_wav -- \
--data_dir=/tmp/my_wavs --background_dir=/tmp/my_backgrounds \
--background_volume=0.1 --test_duration_seconds=600 \
--output_audio_file=/tmp/streaming_test.wav \
--output_labels_file=/tmp/streaming_test_labels.txt
Once you've created a streaming audio file, you can then use the
test_streaming_accuracy tool to calculate accuracy metrics for a model.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import math
import sys
import numpy as np
import tensorflow as tf
import input_data
import models
FLAGS = None
def mix_in_audio_sample(track_data, track_offset, sample_data, sample_offset,
clip_duration, sample_volume, ramp_in, ramp_out):
"""Mixes the sample data into the main track at the specified offset.
Args:
track_data: Numpy array holding main audio data. Modified in-place.
track_offset: Where to mix the sample into the main track.
sample_data: Numpy array of audio data to mix into the main track.
sample_offset: Where to start in the audio sample.
clip_duration: How long the sample segment is.
sample_volume: Loudness to mix the sample in at.
ramp_in: Length in samples of volume increase stage.
ramp_out: Length in samples of volume decrease stage.
"""
ramp_out_index = clip_duration - ramp_out
track_end = min(track_offset + clip_duration, track_data.shape[0])
track_end = min(track_end,
track_offset + (sample_data.shape[0] - sample_offset))
sample_range = track_end - track_offset
for i in range(sample_range):
if i < ramp_in:
envelope_scale = i / ramp_in
elif i > ramp_out_index:
envelope_scale = (clip_duration - i) / ramp_out
else:
envelope_scale = 1
sample_input = sample_data[sample_offset + i]
track_data[track_offset
+ i] += sample_input * envelope_scale * sample_volume
def main(_):
words_list = input_data.prepare_words_list(FLAGS.wanted_words.split(','))
model_settings = models.prepare_model_settings(
len(words_list), FLAGS.sample_rate, FLAGS.clip_duration_ms,
FLAGS.window_size_ms, FLAGS.window_stride_ms, FLAGS.feature_bin_count,
'mfcc')
audio_processor = input_data.AudioProcessor(
'', FLAGS.data_dir, FLAGS.silence_percentage, 10,
FLAGS.wanted_words.split(','), FLAGS.validation_percentage,
FLAGS.testing_percentage, model_settings, FLAGS.data_dir)
output_audio_sample_count = FLAGS.sample_rate * FLAGS.test_duration_seconds
output_audio = np.zeros((output_audio_sample_count,), dtype=np.float32)
# Set up background audio.
background_crossover_ms = 500
background_segment_duration_ms = (
FLAGS.clip_duration_ms + background_crossover_ms)
background_segment_duration_samples = int(
(background_segment_duration_ms * FLAGS.sample_rate) / 1000)
background_segment_stride_samples = int(
(FLAGS.clip_duration_ms * FLAGS.sample_rate) / 1000)
background_ramp_samples = int(
((background_crossover_ms / 2) * FLAGS.sample_rate) / 1000)
# Mix the background audio into the main track.
how_many_backgrounds = int(
math.ceil(output_audio_sample_count / background_segment_stride_samples))
for i in range(how_many_backgrounds):
output_offset = int(i * background_segment_stride_samples)
background_index = np.random.randint(len(audio_processor.background_data))
background_samples = audio_processor.background_data[background_index]
background_offset = np.random.randint(
0, len(background_samples) - model_settings['desired_samples'])
background_volume = np.random.uniform(0, FLAGS.background_volume)
mix_in_audio_sample(output_audio, output_offset, background_samples,
background_offset, background_segment_duration_samples,
background_volume, background_ramp_samples,
background_ramp_samples)
# Mix the words into the main track, noting their labels and positions.
output_labels = []
word_stride_ms = FLAGS.clip_duration_ms + FLAGS.word_gap_ms
word_stride_samples = int((word_stride_ms * FLAGS.sample_rate) / 1000)
clip_duration_samples = int(
(FLAGS.clip_duration_ms * FLAGS.sample_rate) / 1000)
word_gap_samples = int((FLAGS.word_gap_ms * FLAGS.sample_rate) / 1000)
how_many_words = int(
math.floor(output_audio_sample_count / word_stride_samples))
all_test_data, all_test_labels = audio_processor.get_unprocessed_data(
-1, model_settings, 'testing')
for i in range(how_many_words):
output_offset = (
int(i * word_stride_samples) + np.random.randint(word_gap_samples))
output_offset_ms = (output_offset * 1000) / FLAGS.sample_rate
is_unknown = np.random.randint(100) < FLAGS.unknown_percentage
if is_unknown:
wanted_label = input_data.UNKNOWN_WORD_LABEL
else:
wanted_label = words_list[2 + np.random.randint(len(words_list) - 2)]
test_data_start = np.random.randint(len(all_test_data))
found_sample_data = None
index_lookup = np.arange(len(all_test_data), dtype=np.int32)
np.random.shuffle(index_lookup)
for test_data_offset in range(len(all_test_data)):
test_data_index = index_lookup[(
test_data_start + test_data_offset) % len(all_test_data)]
current_label = all_test_labels[test_data_index]
if current_label == wanted_label:
found_sample_data = all_test_data[test_data_index]
break
mix_in_audio_sample(output_audio, output_offset, found_sample_data, 0,
clip_duration_samples, 1.0, 500, 500)
output_labels.append({'label': wanted_label, 'time': output_offset_ms})
input_data.save_wav_file(FLAGS.output_audio_file, output_audio,
FLAGS.sample_rate)
tf.logging.info('Saved streaming test wav to %s', FLAGS.output_audio_file)
with open(FLAGS.output_labels_file, 'w') as f:
for output_label in output_labels:
f.write('%s, %f\n' % (output_label['label'], output_label['time']))
tf.logging.info('Saved streaming test labels to %s', FLAGS.output_labels_file)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--data_url',
type=str,
# pylint: disable=line-too-long
default='http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz',
# pylint: enable=line-too-long
help='Location of speech training data')
parser.add_argument(
'--data_dir',
type=str,
default='/tmp/speech_dataset',
help="""\
Where to download the speech training data to.
""")
parser.add_argument(
'--background_dir',
type=str,
default='',
help="""\
Path to a directory of .wav files to mix in as background noise during training.
""")
parser.add_argument(
'--background_volume',
type=float,
default=0.1,
help="""\
How loud the background noise should be, between 0 and 1.
""")
parser.add_argument(
'--background_frequency',
type=float,
default=0.8,
help="""\
How many of the training samples have background noise mixed in.
""")
parser.add_argument(
'--silence_percentage',
type=float,
default=10.0,
help="""\
How much of the training data should be silence.
""")
parser.add_argument(
'--testing_percentage',
type=int,
default=10,
help='What percentage of wavs to use as a test set.')
parser.add_argument(
'--validation_percentage',
type=int,
default=10,
help='What percentage of wavs to use as a validation set.')
parser.add_argument(
'--sample_rate',
type=int,
default=16000,
help='Expected sample rate of the wavs.',)
parser.add_argument(
'--clip_duration_ms',
type=int,
default=1000,
help='Expected duration in milliseconds of the wavs.',)
parser.add_argument(
'--window_size_ms',
type=float,
default=30.0,
help='How long each spectrogram timeslice is',)
parser.add_argument(
'--window_stride_ms',
type=float,
default=10.0,
help='How long the stride is between spectrogram timeslices',)
parser.add_argument(
'--feature_bin_count',
type=int,
default=40,
help='How many bins to use for the MFCC fingerprint',
)
parser.add_argument(
'--wanted_words',
type=str,
default='yes,no,up,down,left,right,on,off,stop,go',
help='Words to use (others will be added to an unknown label)',)
parser.add_argument(
'--output_audio_file',
type=str,
default='/tmp/speech_commands_train/streaming_test.wav',
help='File to save the generated test audio to.')
parser.add_argument(
'--output_labels_file',
type=str,
default='/tmp/speech_commands_train/streaming_test_labels.txt',
help='File to save the generated test labels to.')
parser.add_argument(
'--test_duration_seconds',
type=int,
default=600,
help='How long the generated test audio file should be.',)
parser.add_argument(
'--word_gap_ms',
type=int,
default=2000,
help='How long the average gap should be between words.',)
parser.add_argument(
'--unknown_percentage',
type=int,
default=30,
help='What percentage of words should be unknown.')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
|
tensorflow-master
|
tensorflow/examples/speech_commands/generate_streaming_test_wav.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Declaring how_tos a python package.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
|
tensorflow-master
|
tensorflow/examples/how_tos/__init__.py
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Converts MNIST data to TFRecords file format with Example protos."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import sys
import tensorflow as tf
from tensorflow.contrib.learn.python.learn.datasets import mnist
FLAGS = None
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def convert_to(data_set, name):
"""Converts a dataset to tfrecords."""
images = data_set.images
labels = data_set.labels
num_examples = data_set.num_examples
if images.shape[0] != num_examples:
raise ValueError('Images size %d does not match label size %d.' %
(images.shape[0], num_examples))
rows = images.shape[1]
cols = images.shape[2]
depth = images.shape[3]
filename = os.path.join(FLAGS.directory, name + '.tfrecords')
print('Writing', filename)
with tf.python_io.TFRecordWriter(filename) as writer:
for index in range(num_examples):
image_raw = images[index].tostring()
example = tf.train.Example(
features=tf.train.Features(
feature={
'height': _int64_feature(rows),
'width': _int64_feature(cols),
'depth': _int64_feature(depth),
'label': _int64_feature(int(labels[index])),
'image_raw': _bytes_feature(image_raw)
}))
writer.write(example.SerializeToString())
def main(unused_argv):
# Get the data.
data_sets = mnist.read_data_sets(FLAGS.directory,
dtype=tf.uint8,
reshape=False,
validation_size=FLAGS.validation_size)
# Convert to Examples and write the result to TFRecords.
convert_to(data_sets.train, 'train')
convert_to(data_sets.validation, 'validation')
convert_to(data_sets.test, 'test')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--directory',
type=str,
default='/tmp/data',
help='Directory to download data files and write the converted result'
)
parser.add_argument(
'--validation_size',
type=int,
default=5000,
help="""\
Number of examples to separate from the training data for the validation
set.\
"""
)
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
|
tensorflow-master
|
tensorflow/examples/how_tos/reading_data/convert_to_records.py
|
tensorflow-master
|
tensorflow/examples/how_tos/reading_data/__init__.py
|
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Train and Eval the MNIST network.
This version is like fully_connected_feed.py but uses data converted
to a TFRecords file containing tf.train.Example protocol buffers.
See:
https://www.tensorflow.org/guide/reading_data#reading_from_files
for context.
YOU MUST run convert_to_records before running this (but you only need to
run it once).
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os.path
import sys
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import mnist
# Basic model parameters as external flags.
FLAGS = None
# Constants used for dealing with the files, matches convert_to_records.
TRAIN_FILE = 'train.tfrecords'
VALIDATION_FILE = 'validation.tfrecords'
def decode(serialized_example):
"""Parses an image and label from the given `serialized_example`."""
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'label': tf.FixedLenFeature([], tf.int64),
})
# Convert from a scalar string tensor (whose single string has
# length mnist.IMAGE_PIXELS) to a uint8 tensor with shape
# [mnist.IMAGE_PIXELS].
image = tf.decode_raw(features['image_raw'], tf.uint8)
image.set_shape((mnist.IMAGE_PIXELS))
# Convert label from a scalar uint8 tensor to an int32 scalar.
label = tf.cast(features['label'], tf.int32)
return image, label
def augment(image, label):
"""Placeholder for data augmentation."""
# OPTIONAL: Could reshape into a 28x28 image and apply distortions
# here. Since we are not applying any distortions in this
# example, and the next step expects the image to be flattened
# into a vector, we don't bother.
return image, label
def normalize(image, label):
"""Convert `image` from [0, 255] -> [-0.5, 0.5] floats."""
image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
return image, label
def inputs(train, batch_size, num_epochs):
"""Reads input data num_epochs times.
Args:
train: Selects between the training (True) and validation (False) data.
batch_size: Number of examples per returned batch.
num_epochs: Number of times to read the input data, or 0/None to
train forever.
Returns:
A tuple (images, labels), where:
* images is a float tensor with shape [batch_size, mnist.IMAGE_PIXELS]
in the range [-0.5, 0.5].
* labels is an int32 tensor with shape [batch_size] with the true label,
a number in the range [0, mnist.NUM_CLASSES).
This function creates a one_shot_iterator, meaning that it will only iterate
over the dataset once. On the other hand there is no special initialization
required.
"""
if not num_epochs:
num_epochs = None
filename = os.path.join(FLAGS.train_dir, TRAIN_FILE
if train else VALIDATION_FILE)
with tf.name_scope('input'):
# TFRecordDataset opens a binary file and reads one record at a time.
# `filename` could also be a list of filenames, which will be read in order.
dataset = tf.data.TFRecordDataset(filename)
# The map transformation takes a function and applies it to every element
# of the dataset.
dataset = dataset.map(decode)
dataset = dataset.map(augment)
dataset = dataset.map(normalize)
# The shuffle transformation uses a finite-sized buffer to shuffle elements
# in memory. The parameter is the number of elements in the buffer. For
# completely uniform shuffling, set the parameter to be the same as the
# number of elements in the dataset.
dataset = dataset.shuffle(1000 + 3 * batch_size)
dataset = dataset.repeat(num_epochs)
dataset = dataset.batch(batch_size)
iterator = tf.compat.v1.data.make_one_shot_iterator(dataset)
return iterator.get_next()
def run_training():
"""Train MNIST for a number of steps."""
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
# Input images and labels.
image_batch, label_batch = inputs(
train=True, batch_size=FLAGS.batch_size, num_epochs=FLAGS.num_epochs)
# Build a Graph that computes predictions from the inference model.
logits = mnist.inference(image_batch, FLAGS.hidden1, FLAGS.hidden2)
# Add to the Graph the loss calculation.
loss = mnist.loss(logits, label_batch)
# Add to the Graph operations that train the model.
train_op = mnist.training(loss, FLAGS.learning_rate)
# The op for initializing the variables.
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
# Create a session for running operations in the Graph.
with tf.Session() as sess:
# Initialize the variables (the trained variables and the
# epoch counter).
sess.run(init_op)
try:
step = 0
while True: # Train until OutOfRangeError
start_time = time.time()
# Run one step of the model. The return values are
# the activations from the `train_op` (which is
# discarded) and the `loss` op. To inspect the values
# of your ops or variables, you may include them in
# the list passed to sess.run() and the value tensors
# will be returned in the tuple from the call.
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
# Print an overview fairly often.
if step % 100 == 0:
print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value,
duration))
step += 1
except tf.errors.OutOfRangeError:
print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs,
step))
def main(_):
run_training()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--learning_rate',
type=float,
default=0.01,
help='Initial learning rate.')
parser.add_argument(
'--num_epochs',
type=int,
default=2,
help='Number of epochs to run trainer.')
parser.add_argument(
'--hidden1',
type=int,
default=128,
help='Number of units in hidden layer 1.')
parser.add_argument(
'--hidden2',
type=int,
default=32,
help='Number of units in hidden layer 2.')
parser.add_argument('--batch_size', type=int, default=100, help='Batch size.')
parser.add_argument(
'--train_dir',
type=str,
default='/tmp/data',
help='Directory with the training data.')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
|
tensorflow-master
|
tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an 'AS IS' BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ======================================
"""Experimental support for defining XLA shardings."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as _np # Avoids becoming a part of public Tensorflow API.
from tensorflow.compiler.xla import xla_data_pb2
from tensorflow.core.framework import attr_value_pb2
class Sharding(object):
"""A class to support adding sharding attributes to Ops.
Use the factory constructors and then call apply_to_tensor:
Sharding.replicate().apply_to_tensor(tensor)
"""
def __init__(self, proto=None):
"""Do not use this constructor; use the factory functions below."""
self._proto = proto
@classmethod
def replicate(cls):
"""Returns a replicated sharding attribute.
This causes an op to be computed in its entirety independently on all
cores in the XLA device.
"""
return Sharding(
proto=xla_data_pb2.OpSharding(type=xla_data_pb2.OpSharding.REPLICATED))
@classmethod
def assign_device(cls, core):
"""Returns an AssignDevice sharding attribute.
This causes an op to be computed in its entirety only on one core in
the XLA device.
Args:
core: The core to assign this Op to.
"""
return Sharding(
proto=xla_data_pb2.OpSharding(
type=xla_data_pb2.OpSharding.MAXIMAL,
tile_assignment_dimensions=[1],
tile_assignment_devices=[core]))
@classmethod
def tile(cls, tile_assignment):
"""Returns a Tiled sharding attribute.
This causes an op to be partially computed on multiple cores in the
XLA device.
Args:
tile_assignment: An np.ndarray describing the topology of the tiling and
which device will compute which part of the topology.
Raises:
TypeError: tile_assignment was not of np.array type.
TODO(jmolloy): This concept is nefarious and is not
something we really want to expose to users (especially as the
contract for tile_assignment is very strict).
"""
if not isinstance(tile_assignment, _np.ndarray):
raise TypeError('Tile assignment must be of type np.ndarray')
dims = list(tile_assignment.shape)
flattened_devices = tile_assignment.reshape(-1, order='C')
return Sharding(
proto=xla_data_pb2.OpSharding(
type=xla_data_pb2.OpSharding.OTHER,
tile_assignment_dimensions=dims,
tile_assignment_devices=list(flattened_devices)))
@classmethod
def split(cls, tensor, split_dimension, num_devices):
"""Returns a Sharding that splits a tensor across a dimension.
This creates a Tiled attribute, similar to tile(), but easier to use for the
common case of tiling a tensor N ways in one dimension.
Args:
tensor: A tf.Tensor to split.
split_dimension: The dimension number to split.
num_devices: The number of cores to split `tensor` over.
Raises:
ValueError: The tensor to split was smaller in the split dimension than
the number of devices to split over.
"""
shape = tensor.shape.as_list()
if (shape[split_dimension] is not None and
shape[split_dimension] < num_devices):
raise ValueError('Split dimension was smaller than the required number '
'of splits: shape=%r, dimension=%r, num_devices=%r' %
(shape, split_dimension, num_devices))
tile_assignment_dims = [1] * len(shape)
tile_assignment_dims[split_dimension] = num_devices
return Sharding(
proto=xla_data_pb2.OpSharding(
type=xla_data_pb2.OpSharding.OTHER,
tile_assignment_dimensions=tile_assignment_dims,
tile_assignment_devices=range(num_devices)))
def apply_to_tensor(self, tensor, assign_tuple_sharding=False):
"""Applies this Sharding attribute to `tensor`.
Args:
tensor: A tf.Tensor to split.
assign_tuple_sharding: If the sharding type should be a tuple.
"""
if len(tensor.op.outputs) > 1 or assign_tuple_sharding:
proto = self._get_or_create_tuple_proto(tensor.op)
# We can't mutate an element of old_proto.tuple_shardings, so create
# a new proto.
tuple_shardings = list(proto.tuple_shardings)
tuple_shardings[tensor.value_index] = self._proto
proto = xla_data_pb2.OpSharding(
type=xla_data_pb2.OpSharding.TUPLE, tuple_shardings=tuple_shardings)
else:
proto = self._proto
attr_value = attr_value_pb2.AttrValue(s=proto.SerializeToString())
# TODO(jmolloy): This need to be seriously revisited before declaring this
# API available for public use.
# pylint: disable=protected-access
tensor.op._set_attr('_XlaSharding', attr_value)
@property
def proto(self):
"""Return the sharding protobuf of type xla_data_pb2.OpSharding."""
return self._proto
def _get_or_create_tuple_proto(self, op):
try:
attr = op.get_attr('_XlaSharding')
proto = xla_data_pb2.OpSharding()
proto.ParseFromString(attr)
return proto
except ValueError:
return self._create_tuple_proto(op)
def _create_tuple_proto(self, op):
shardings = [
xla_data_pb2.OpSharding(type=xla_data_pb2.OpSharding.REPLICATED)
for _ in op.outputs
]
return xla_data_pb2.OpSharding(
type=xla_data_pb2.OpSharding.TUPLE, tuple_shardings=shardings)
# Helpers for the above factory functions that allow easy application of
# shardings, for example:
# tensor = xla_sharding.replicate(tensor)
def replicate(tensor, assign_tuple_sharding=False):
Sharding.replicate().apply_to_tensor(
tensor,
assign_tuple_sharding=assign_tuple_sharding)
return tensor
def assign_device(tensor, device, assign_tuple_sharding=False):
Sharding.assign_device(device).apply_to_tensor(
tensor,
assign_tuple_sharding=assign_tuple_sharding)
return tensor
def tile(tensor, tile_assignment, assign_tuple_sharding=False):
Sharding.tile(tile_assignment).apply_to_tensor(
tensor,
assign_tuple_sharding=assign_tuple_sharding
)
return tensor
def split(tensor, split_dimension, num_devices, assign_tuple_sharding=False):
Sharding.split(tensor, split_dimension, num_devices).apply_to_tensor(
tensor,
assign_tuple_sharding=assign_tuple_sharding)
return tensor
|
tensorflow-master
|
tensorflow/compiler/xla/experimental/xla_sharding/xla_sharding.py
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an 'AS IS' BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ======================================
"""XLA LiteralProto utilities."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as _np # Avoids becoming a part of public Tensorflow API.
from tensorflow.compiler.xla import xla_data_pb2
from tensorflow.compiler.xla.python_api import types
from tensorflow.compiler.xla.python_api import xla_shape
def ConvertLiteralToNumpyArray(literal):
"""Converts a XLA literal to a Numpy array."""
element_type = literal.shape.element_type
if element_type == xla_data_pb2.TUPLE:
return tuple(
ConvertLiteralToNumpyArray(subliteral)
for subliteral in literal.tuple_literals)
type_record = types.MAP_XLA_TYPE_TO_RECORD[element_type]
if not literal.shape.dimensions:
return _np.array(
getattr(literal, type_record.literal_field_name)[0],
type_record.numpy_dtype)
else:
# Infer the proper Numpy order from the LiteralProto's layout. The repeated
# field representing the array's content in the Literal is linearized.
# Reading is done in two steps:
#
# 1. Read the array as 1D from the LiteralProto repeated field.
# 2. Reshape the array to its proper shape, using the right order depending
# on the LiteralProto's layout.
layout_order = literal.shape.layout.minor_to_major
numpy_shape = tuple(literal.shape.dimensions)
if layout_order == range(len(literal.shape.dimensions)):
numpy_reshaper = lambda arr: arr.reshape(numpy_shape, order='F')
elif layout_order == range(len(literal.shape.dimensions) - 1, -1, -1):
numpy_reshaper = lambda arr: arr.reshape(numpy_shape, order='C')
else:
raise NotImplementedError('Unsupported layout: {0}'.format(layout_order))
ndarray = _np.array(
getattr(literal, type_record.literal_field_name),
copy=False,
dtype=type_record.numpy_dtype)
return numpy_reshaper(ndarray)
def _ConvertNumpyArrayToLiteral(ndarray):
"""Converts a Numpy array to a XLA literal."""
type_record = types.MAP_DTYPE_TO_RECORD[str(ndarray.dtype)]
literal = xla_data_pb2.LiteralProto()
literal.shape.CopyFrom(xla_shape.CreateShapeFromNumpy(ndarray).message)
if ndarray.ndim == 0:
getattr(literal, type_record.literal_field_name).append(
ndarray.astype(type_record.literal_field_type).item())
else:
# Ndarrays with boolean dtypes need special type conversion with protobufs
if ndarray.dtype in {_np.bool_, _np.dtype('bool')}:
for element in _np.nditer(ndarray):
getattr(literal, type_record.literal_field_name).append(
type_record.literal_field_type(element))
else:
ndarray_flat = ndarray.ravel(order='A')
getattr(literal, type_record.literal_field_name).extend(ndarray_flat)
return literal
def ConvertNumpyArrayToLiteral(value):
"""Converts a Numpy array or a nested tuple thereof to an XLA literal."""
if isinstance(value, tuple):
literal = xla_data_pb2.LiteralProto()
literal.shape.CopyFrom(xla_shape.CreateShapeFromNumpy(value).message)
for component in value:
component_literal = literal.tuple_literals.add()
component_literal.CopyFrom(ConvertNumpyArrayToLiteral(component))
return literal
else:
return _ConvertNumpyArrayToLiteral(value)
|
tensorflow-master
|
tensorflow/compiler/xla/python_api/xla_literal.py
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an 'AS IS' BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ======================================
"""Utilities for XLA-specific Python types."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as _np # Avoids becoming a part of public Tensorflow API.
from tensorflow.compiler.xla import xla_data_pb2
from tensorflow.python.framework import dtypes
# Records corresponsence between a XLA primitive type and Python/Numpy types.
#
# primitive_type: value of type xla_data_pb2.PrimitiveType
# numpy_dtype: corresponsing Numpy "dtype" (like np.float32)
# literal_field_name: name of the field in the LiteralProto message elements
# of this type go into.
# literal_field_type: type of the field named 'literal_field_name'.
#
# TODO(eliben): figure out how to avoid knowing the extra Python type and the
# astype cast when writing into Literals.
TypeConversionRecord = collections.namedtuple('TypeConversionRecord', [
'primitive_type', 'numpy_dtype', 'literal_field_name', 'literal_field_type'
])
# Maps from XLA primitive types to TypeConversionRecord.
MAP_XLA_TYPE_TO_RECORD = {
xla_data_pb2.BF16:
TypeConversionRecord(
primitive_type=xla_data_pb2.BF16,
numpy_dtype=dtypes.bfloat16.as_numpy_dtype,
literal_field_name='bf16s',
literal_field_type=float),
xla_data_pb2.F16:
TypeConversionRecord(
primitive_type=xla_data_pb2.F16,
numpy_dtype=_np.float16,
literal_field_name='f16s',
literal_field_type=float),
xla_data_pb2.F32:
TypeConversionRecord(
primitive_type=xla_data_pb2.F32,
numpy_dtype=_np.float32,
literal_field_name='f32s',
literal_field_type=float),
xla_data_pb2.F64:
TypeConversionRecord(
primitive_type=xla_data_pb2.F64,
numpy_dtype=_np.float64,
literal_field_name='f64s',
literal_field_type=float),
xla_data_pb2.S8:
TypeConversionRecord(
primitive_type=xla_data_pb2.S8,
numpy_dtype=_np.int8,
literal_field_name='s8s',
literal_field_type=int),
xla_data_pb2.S16:
TypeConversionRecord(
primitive_type=xla_data_pb2.S16,
numpy_dtype=_np.int16,
literal_field_name='s16s',
literal_field_type=int),
xla_data_pb2.S32:
TypeConversionRecord(
primitive_type=xla_data_pb2.S32,
numpy_dtype=_np.int32,
literal_field_name='s32s',
literal_field_type=int),
xla_data_pb2.S64:
TypeConversionRecord(
primitive_type=xla_data_pb2.S64,
numpy_dtype=_np.int64,
literal_field_name='s64s',
literal_field_type=int),
xla_data_pb2.U8:
TypeConversionRecord(
primitive_type=xla_data_pb2.U8,
numpy_dtype=_np.uint8,
literal_field_name='s8s',
literal_field_type=int),
xla_data_pb2.U16:
TypeConversionRecord(
primitive_type=xla_data_pb2.U16,
numpy_dtype=_np.uint16,
literal_field_name='s16s',
literal_field_type=int),
xla_data_pb2.U32:
TypeConversionRecord(
primitive_type=xla_data_pb2.U32,
numpy_dtype=_np.uint32,
literal_field_name='s32s',
literal_field_type=int),
xla_data_pb2.U64:
TypeConversionRecord(
primitive_type=xla_data_pb2.U64,
numpy_dtype=_np.uint64,
literal_field_name='s64s',
literal_field_type=int),
xla_data_pb2.PRED:
TypeConversionRecord(
primitive_type=xla_data_pb2.PRED,
numpy_dtype=_np.bool,
literal_field_name='preds',
literal_field_type=bool)
}
# Maps from Numpy dtypes to TypeConversionRecord.
# Note the conversion on the key. Numpy has a known issue wherein dtype hashing
# doesn't work as expected (https://github.com/numpy/numpy/issues/7242). Thus,
# when keying by dtype in this dict, we use the string form of dtypes.
MAP_DTYPE_TO_RECORD = {
str(_np.dtype(record.numpy_dtype)): record
for record in MAP_XLA_TYPE_TO_RECORD.values()
}
|
tensorflow-master
|
tensorflow/compiler/xla/python_api/types.py
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an 'AS IS' BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ======================================
"""XLA Shape utilities."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as _np # Avoids becoming a part of public Tensorflow API.
from six.moves import xrange
from tensorflow.compiler.xla import xla_data_pb2
from tensorflow.compiler.xla.python_api import types
class Shape(object):
"""Wraps a xla_data_pb2.ShapeProto message with a convenient Python type.
Provides direct access to the underlying xla_data_pb2.ShapeProto message in
the
message attribute, along with accessor wrappers to the message's fields.
Avoid direct access to .message unless interacting directly with protobuf APIs
like CopyFrom. In other words, prefer hauling the shape around in a Shape, and
only access .message when strictly required by the protobuf API.
"""
def __init__(self, element_type, dimensions, layout=None):
"""Creates a new XLA Shape.
Args:
element_type: element type from xla_data_pb2.
dimensions: sequence of dimensions sizes (integers), or sequence
of Shapes in the case of a tuple, i.e. when element_type is
TUPLE.
layout: optional minor_to_major sequence for layout. If not given, the
default major-to-minor layout is used.
Raises:
ValueError: if element_type is TUPLE but dimensions are not Shape objects.
"""
self.message = xla_data_pb2.ShapeProto()
self.message.element_type = element_type
if element_type == xla_data_pb2.TUPLE:
if not all(isinstance(subshape, Shape) for subshape in dimensions):
raise ValueError(
'XLA tuple requires sequence of Shape objects as dimensions')
self._tuple_shapes = tuple(dimensions)
for component_shape in self._tuple_shapes:
component_message = self.message.tuple_shapes.add()
component_message.CopyFrom(component_shape.message)
else:
self.message.dimensions.extend(dimensions)
if layout is None:
layout = list(reversed(range(len(dimensions))))
self.message.layout.format = xla_data_pb2.DENSE
self.message.layout.minor_to_major.extend(layout)
def element_type(self):
return self.message.element_type
def is_tuple(self):
return self.element_type() == xla_data_pb2.TUPLE
def dimensions(self):
if self.is_tuple():
raise ValueError('Tuple shape has no dimensions. Try tuple_shapes()?')
return self.message.dimensions
def tuple_shapes(self):
"""If this is a tuple, returns its sequence of constituent Shape objects.
Returns:
Tuple sub-shapes.
Raises:
ValueError: if this is not a tuple.
"""
if not self.is_tuple():
raise ValueError('tuple_shapes() called on a non-tuple shape')
return self._tuple_shapes
def layout(self):
return self.message.layout
@staticmethod
def from_pyval(pyval):
return CreateShapeFromNumpy(pyval)
def _CreateShapeFromNumpy(ndarray): # pylint: disable=invalid-name
"""Create a Shape from a given Numpy array.
Args:
ndarray: Numpy array.
Returns:
A Shape object.
"""
element_type = types.MAP_DTYPE_TO_RECORD[str(ndarray.dtype)].primitive_type
dimensions = ndarray.shape
# Set the shape's layout based on the ordering of ndarray.
# Numpy arrays come in two orders: Fortran (column-major) and C (row-major).
if _np.isfortran(ndarray):
# Column-major layout. This corresponds to a "dimension order is
# minor-to-major" layout in XLA.
layout = range(ndarray.ndim)
else:
# Row-major layout. This corresponds to a "dimension order is
# major-to-minor" layout int XLA.
layout = list(reversed(xrange(ndarray.ndim)))
return Shape(element_type, dimensions, layout)
def CreateShapeFromNumpy(value): # pylint: disable=invalid-name
"""Create a Shape from a Numpy array or a nested tuple structure thereof.
Args:
value: Numpy array or (possibly nested) tuple structure that bottoms out in
Numpy arrays.
Returns:
A Shape object.
"""
if isinstance(value, tuple):
return Shape(
xla_data_pb2.TUPLE,
[CreateShapeFromNumpy(component) for component in value])
else:
return _CreateShapeFromNumpy(value)
def CreateShapeFromDtypeAndTuple(dtype, shape_tuple): # pylint: disable=invalid-name
"""Create a shape from a Numpy dtype and a sequence of nonnegative integers.
Args:
dtype: a numpy dtype, e.g. np.dtype('int32').
shape_tuple: a sequence of nonnegative integers.
Returns:
A Shape object.
"""
element_type = types.MAP_DTYPE_TO_RECORD[str(dtype)].primitive_type
return Shape(element_type, shape_tuple)
|
tensorflow-master
|
tensorflow/compiler/xla/python_api/xla_shape.py
|
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for the XRT client."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.compiler.xla.python import xla_client
from tensorflow.compiler.xla.python import xrt
from tensorflow.python.platform import test
def BuildAddAndScaleComputation(shape1, shape2):
"""Builds the computation (a + b) * 3."""
b = xla_client.ComputationBuilder("add-and-scale")
x = b.ParameterWithShape(shape1)
y = b.ParameterWithShape(shape2)
dtype = shape1.numpy_dtype().type
b.Mul(b.Add(x, y), b.Constant(dtype(3)))
return b.Build()
# TODO(phawkins): add more tests, beyond a simple "hello world" example.
class XrtBackendTest(test.TestCase):
def testBasics(self):
(worker,), _ = test.create_local_cluster(num_workers=1, num_ps=0)
self.assertTrue(worker.target.startswith("grpc://"))
tf_context = xrt.get_tf_context(worker.target[len("grpc://"):], "worker")
backend = xrt.XrtBackend(tf_context, "XLA_CPU")
a = np.arange(10)
b = np.arange(10)
c = BuildAddAndScaleComputation(
xla_client.shape_from_pyval(a), xla_client.shape_from_pyval(b))
executable = c.Compile(backend=backend)
output = xla_client.execute_with_python_values(
executable, (a, b), backend=backend)
self.assertAllEqual(output, (a + b) * 3)
def testTuples(self):
(worker,), _ = test.create_local_cluster(num_workers=1, num_ps=0)
self.assertTrue(worker.target.startswith("grpc://"))
tf_context = xrt.get_tf_context(worker.target[len("grpc://"):], "worker")
backend = xrt.XrtBackend(tf_context, "XLA_CPU")
a = np.random.randn(10)
b = np.random.randn(15, 3)
pieces = [
xla_client.Buffer.from_pyval(a, backend=backend),
xla_client.Buffer.from_pyval(b, backend=backend)
]
t = xla_client.Buffer.make_tuple(pieces, backend=backend)
a_out, b_out = t.destructure()
self.assertAllEqual(a, a_out.to_py())
self.assertAllEqual(b, b_out.to_py())
if __name__ == "__main__":
test.main()
|
tensorflow-master
|
tensorflow/compiler/xla/python/xrt_test.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""An XLA client in Python."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
import collections
import enum # pylint: disable=g-bad-import-order
import inspect
import itertools
import os
import numpy as np
import six
# Note this module does *not* depend on any Python protocol buffers. The XLA
# Python bindings are currently packaged both as part of jaxlib and as part
# of TensorFlow. If we use protocol buffers here, then importing both jaxlib
# and TensorFlow may fail with duplicate protocol buffer message definitions.
from tensorflow.compiler.xla.python import xla_extension as _xla
from tensorflow.compiler.xla.python.xla_extension import ops
# Most functions are snake_case for consistency with other modules, whereas
# method names of ComputationBuilder and Computation are CamelCase for
# consistency with XLA.
# pylint: disable=invalid-name
@six.add_metaclass(abc.ABCMeta)
class Backend(object):
"""Abstract base class for XLA backends."""
def __init__(self, platform):
"""Creates a new Backend.
Args:
platform: A string naming the platform; for example 'gpu'.
"""
self.platform = platform
@abc.abstractmethod
def device_count(self):
"""Returns the number of devices known to the backend."""
@abc.abstractmethod
def buffer_from_pyval(self, pyval, device=0):
"""Allocates a fresh buffer and populates it with `pyval`."""
def buffers_from_pyvals(self, pyvals_and_devices):
"""Allocates buffers and populates them with `pyvals`."""
return [
self.buffer_from_pyval(pyval, device)
for pyval, device in pyvals_and_devices
]
@abc.abstractmethod
def make_tuple(self, c_buffers, device_ordinal):
"""Makes a tuple from a sequence of backend buffer objects."""
@abc.abstractmethod
def compile(self, computation, compile_options):
"""Compiles a computation. Returns an executable."""
class LocalBackend(Backend):
"""XLA backend implemented using the in-process xla::LocalClient API."""
def __init__(self, platform, client):
"""Creates a new LocalBackend.
Args:
platform: A string; the user-visible platform name, e.g. 'gpu'.
client: An _xla.PyLocalClient object.
"""
super(LocalBackend, self).__init__(platform)
self.client = client
def device_count(self):
return self.client.DeviceCount()
def buffer_from_pyval(self, pyval, device=0):
return _xla.PyLocalBuffer.from_python(pyval, self.client, device)
def buffers_from_pyvals(self, pyvals_and_devices):
return _xla.PyLocalBuffer.from_python_values(pyvals_and_devices,
self.client)
def make_tuple(self, c_buffers, device_ordinal):
return _xla.PyLocalBuffer.make_tuple(c_buffers, self.client, device_ordinal)
def compile(self, c_computation, compile_options):
options = _xla.ExecutableBuildOptions()
options.num_replicas = compile_options.num_replicas
if compile_options.result_layout:
options.result_layout = compile_options.result_layout
options.debug_options.xla_cpu_fast_math_honor_infs = True
options.debug_options.xla_cpu_fast_math_honor_nans = True
return _xla.LocalExecutable.Compile(c_computation,
compile_options.argument_layouts,
options, self.client,
compile_options.device_assignment)
def _cpu_backend_factory():
client = _xla.LocalClient.Get(
platform='cpu', xla_platform_id='Host', asynchronous=True)
return LocalBackend(platform='cpu', client=client)
def _gpu_backend_factory():
"""Returns a GPU backend. BFC allocator is used by default."""
allocator = os.getenv('XLA_PYTHON_CLIENT_ALLOCATOR', 'default').lower()
memory_fraction = os.getenv('XLA_PYTHON_CLIENT_MEM_FRACTION')
preallocate = os.getenv('XLA_PYTHON_CLIENT_PREALLOCATE')
if allocator not in ('default', 'platform', 'bfc'):
raise ValueError(
'XLA_PYTHON_CLIENT_ALLOCATOR env var must be "default", "platform", or '
'"bfc", got "%s"' % allocator)
config = _xla.AllocatorConfig()
if allocator == 'default':
config.kind = _xla.AllocatorConfig.Kind.DEFAULT
if allocator == 'platform':
config.kind = _xla.AllocatorConfig.Kind.PLATFORM
if allocator == 'bfc':
config.kind = _xla.AllocatorConfig.Kind.BFC
if memory_fraction:
config.memory_fraction = float(memory_fraction)
config.preallocate = preallocate not in ('0', 'false', 'False')
client = _xla.LocalClient.Get(
platform='gpu', xla_platform_id='CUDA', asynchronous=True,
allocator_config=config)
return LocalBackend(platform='gpu', client=client)
# Backend factories, keyed by user-visible name, in increasing priority order.
_local_backend_factories = collections.OrderedDict([
('cpu', _cpu_backend_factory),
('gpu', _gpu_backend_factory),
])
def register_local_backend_factory(name, factory):
_local_backend_factories[name] = factory
_local_backends = None
def _get_local_backends():
"""Instantiates all known local backends."""
global _local_backends
if _local_backends is not None:
return _local_backends
_local_backends = collections.OrderedDict()
for name, factory in _local_backend_factories.items():
try:
backend = factory()
except RuntimeError:
# If the backend isn't built into the binary, or if it has no devices, we
# expect a RuntimeError.
continue
_local_backends[name] = backend
return _local_backends
def get_local_backend(name=None):
"""Returns a local backend.
Args:
name: the backend name. If `None`, a default local backend is returned,
typically `gpu` if one is present, or `cpu` if not. If a string, the named
backend is returned or an exception raised.
Returns:
A LocalBackend object.
"""
backends = _get_local_backends()
if name is not None:
try:
return backends[name]
except KeyError:
raise RuntimeError('Unknown backend {}'.format(name))
return list(backends.values())[-1]
class OpMetadata(object):
"""Python representation of a xla.OpMetadata protobuf."""
__slots__ = ('op_type', 'op_name', 'source_file', 'source_line')
def __init__(self, op_type='', op_name='', source_file='', source_line=0):
self.op_type = op_type
self.op_name = op_name
self.source_file = source_file
self.source_line = source_line
def CurrentSourceInfoMetadata(op_type=None, op_name=None, skip_frames=1):
"""Helper for use in source mapping that returns an OpMetadata object."""
full_filename, lineno = inspect.stack()[skip_frames][1:3]
filename = os.path.basename(full_filename)
return OpMetadata(
op_type=op_type,
op_name=op_name,
source_file=filename,
source_line=lineno)
PrimitiveType = _xla.PrimitiveType
XLA_ELEMENT_TYPE_TO_DTYPE = {
PrimitiveType.PRED: np.dtype('bool'),
PrimitiveType.S8: np.dtype('int8'),
PrimitiveType.S16: np.dtype('int16'),
PrimitiveType.S32: np.dtype('int32'),
PrimitiveType.S64: np.dtype('int64'),
PrimitiveType.U8: np.dtype('uint8'),
PrimitiveType.U16: np.dtype('uint16'),
PrimitiveType.U32: np.dtype('uint32'),
PrimitiveType.U64: np.dtype('uint64'),
PrimitiveType.F16: np.dtype('float16'),
PrimitiveType.F32: np.dtype('float32'),
PrimitiveType.F64: np.dtype('float64'),
PrimitiveType.C64: np.dtype('complex64'),
PrimitiveType.C128: np.dtype('complex128'),
PrimitiveType.TUPLE: np.dtype(np.object),
}
# Note the conversion on the key. Numpy has a known issue wherein dtype hashing
# doesn't work as expected (https://github.com/numpy/numpy/issues/7242). Thus,
# when keying by dtype in this dict, we use the string form of dtypes.
DTYPE_TO_XLA_ELEMENT_TYPE = {
str(dt): et for et, dt in XLA_ELEMENT_TYPE_TO_DTYPE.items()
}
def dtype_to_etype(dtype):
"""Convenience function for reading DTYPE_TO_XLA_ELEMENT_TYPE."""
return DTYPE_TO_XLA_ELEMENT_TYPE[str(np.dtype(dtype))]
Shape = _xla.Shape
Shape.__doc__ = """
A Shape is an object defined in C++ that duck types like the following class:
class Shape(object):
'''Represents an XLA shape.
A shape is either an array shape, having rank-many integer
dimensions and an element type (represented by a Numpy dtype), or it
is a tuple shape, having a shape for every tuple component:
type shape =
TupleShape of shape list
| ArrayShape of { dimensions: int list; element_type: dtype }
'''
@staticmethod
def tuple_shape(tuple_shapes) -> Shape:
"Construct a tuple shape."
@staticmethod
def array_shape(element_type, dimensions, minor_to_major=None) -> Shape:
@staticmethod
def from_pyval(pyval) -> Shape:
"Returns a Shape that describes a tuple-tree of Numpy arrays."
def __init__(self, str) -> Shape:
"Parses a shape string."
def __eq__(self, other: Shape) -> bool:
def __ne__(self, other: Shape) -> bool:
def __hash__(self):
def __repr__(self):
def is_tuple(self) -> bool:
def is_array(self) -> bool:
def tuple_shapes(self) -> [Shape]:
def numpy_dtype(self) -> np.dtype:
"Like element_type(), but returns dtype('O') for a tuple shape."
def xla_element_type(self) -> PrimitiveType:
def element_type(self) -> np.dtype:
def dimensions(self) -> (int, int, ...):
def rank(self) -> int:
def with_major_to_minor_layout_if_absent(self) -> Shape:
"Returns a copy with missing layouts set to major-to-minor."
def to_serialized_proto(self) -> bytes:
"Returns 'shape' as a serialized proto."
"""
ProgramShape = _xla.ProgramShape
ProgramShape.__doc__ = """
A ProgramShape is a C++ object that duck types like the following class.
class ProgramShape(object):
def __init__(self, parameter_shapes, result_shape):
def parameter_shapes(self) -> [Shape]:
def result_shape(self) -> Shape:
def __repr__(self):
"""
class Buffer(object):
"""Represents a handle to data owned by XLA.
The referent is ready for use in executing a local, compiled
Computation. On XLA platforms involving a device (e.g. GPU), this
means the referent is in device memory.
"""
@staticmethod
def from_pyval(pyval, device=0, backend=None):
"""Copies the `pyval` to a freshly allocated on-device buffer."""
backend = backend or get_local_backend()
return backend.buffer_from_pyval(pyval, device)
@staticmethod
def from_pyvals(pyvals_and_devices, backend=None):
"""Copies multiple Python values to freshly allocated on-device buffers.
Arguments:
pyvals_and_devices: a list of `(pyval, device)` pairs, where `pyval` is a
Python value to copy (e.g., a NumPy array), and `device` is an integer
device ordinal.
backend: a Backend object, or `None` to use the default local backend.
Returns:
A list of `Buffer` objects corresponding to `pyvals_and_devices`.
"""
backend = backend or get_local_backend()
return backend.buffers_from_pyvals(pyvals_and_devices)
@staticmethod
def make_tuple(buffers, backend=None, device=0):
backend = backend or get_local_backend()
return backend.make_tuple(buffers, device_ordinal=device)
# Buffer is not an instantiable type and exists only for its static methods.
# The underlying buffer objects are C++ object with the following
# API:
# def shape(self) -> Shape:
# def device(self) -> int:
# def delete(self):
# def destructure(self) -> [Buffer]
# def is_deleted(self) -> bool:
# def block_host_until_ready(self):
# """Blocks the calling thread until the buffer is ready on device."""
# def copy_to_host_async(self):
# """Requests a copy of the buffer to the host.
#
# Does not block waiting for the copy. Values fetched are available via
# `to_py()`; the purpose of `copy_to_host_async` is to prefetch values
# for subsequent `to_py()` calls, especially when requesting many values
# at once.
# """
# def to_py(self):
# """Returns the value of the buffer as a Python tuple tree of ndarrays."""
#
# TODO(phawkins): remove Buffer and its static methods completely, have
# clients call methods on Backend to create buffers.
# TODO(phawkins): Alias for backward compatibility. Remove after JAX drops
# compatibility with Jaxlib versions older than 0.1.13.
LocalBuffer = Buffer
def shape_from_pyval(pyval):
"""Returns a Shape that describes a tuple-tree of Numpy arrays."""
def convert(pyval):
if isinstance(pyval, tuple):
return Shape.tuple_shape(tuple(convert(elt) for elt in pyval))
else:
return Shape.array_shape(pyval.dtype, np.shape(pyval))
return convert(pyval)
def transfer_to_infeed(value, device_ordinal=0):
"""Transfers the given value into the XLA infeed queue.
XLA's infeed queue is a single queue that feeds the "XLA virtual machine" with
a totally ordered stream of values. This is dequeued from XLA computations via
the Infeed() operation.
Args:
value: the value that the caller would like to enqueue into the XLA infeed
queue
device_ordinal: the device to infeed the value to. Each device has a
distinct infeed queue.
"""
# TODO(phawkins): support non-default backends.
backend = get_local_backend()
backend.client.TransferToInfeed(value, device_ordinal)
def transfer_from_outfeed(shape, device_ordinal=0):
"""Transfers a literal of the given shape from `device_ordinal`'s outfeed.
Args:
shape: The shape of the value to transfer from outfeed.
device_ordinal: The device ordinal to transfer the outfeed value from. Each
device has a distinct outfeed queue..
Returns:
The literal value that is produced from the outfeed queue.
"""
# TODO(phawkins): support non-default backends.
backend = get_local_backend()
return backend.client.TransferFromOutfeed(
shape.with_major_to_minor_layout_if_absent(), device_ordinal)
DeviceAssignment = _xla.DeviceAssignment
DeviceAssignment.__doc__ = """
A DeviceAssignment is a C++ object with the following signature.
def create(assignment):
'''Builds a device assignment.
Args:
assignment: a 2D numpy array of device ordinal integers, indexed by
[replica][computation_in_replica].
Returns:
A device assignment.
'''
def replica_count():
'''Returns the number of replicas.'''
def computation_count():
'''Returns the number of computations per replica.'''
"""
class CompileOptions(object):
"""Python object for XLA compile options.
These options can be passed to the 'compile' step when using a local XLA
client.
"""
def __init__(self):
self.xla_dump_to = None
self.dump_hlo_pass_re = None
self.dump_hlo_module_re = None
self.dump_hlo_as_text = None
self.dump_hlo_as_proto = None
self.hlo_profile = None
self.num_replicas = 1
self.argument_layouts = None
self.result_layout = None
self.device_assignment = None
class Computation(object):
"""Python wrapper for an XLA Computation.
A Computation can be compiled to form an Executable, or used as a
subcomputation in ComputationBuilder methods.
"""
def __init__(self, c_computation, backend=None):
self._c_computation = c_computation
# The backend argument is deprecated. Pass a backend to Compile() instead.
self._backend = backend
@property
def computation(self):
return self._c_computation
def GetSerializedProto(self):
"""Gets the serialized HloModuleProto proto object in this computation.
Returns:
A string containing a serialized HloModuleProto proto containing the
computation and its dependencies.
"""
return self.computation.GetSerializedProto()
def GetHloText(self):
"""Get the textual HLO representation of this computation.
Returns:
A string containing the textual HLO.
"""
return self.computation.GetHloText()
def GetHloDotGraph(self):
"""Get a Graphviz Dot representation of this computation.
Returns:
A string containing the graphviz dot graph.
"""
return self.computation.GetHloDotGraph()
def Compile(self, argument_shapes=None, compile_options=None, backend=None):
"""Compiles a computation.
Computations are the result of a "ComputationBuild'ing" process.
Arguments:
argument_shapes: Deprecated. Use compile_options.argument_layouts instead.
compile_options: options to use for compilation, includes an optional laid
out result shape for the computation.
backend: a `Backend` for which an executable should be generated.
Returns:
A Executable instance.
"""
backend = backend or self._backend or get_local_backend()
compile_options = compile_options or CompileOptions()
if argument_shapes:
compile_options.argument_layouts = argument_shapes
return backend.compile(self.computation, compile_options)
def GetProgramShape(self):
return self._c_computation.GetProgramShape()
def GetReturnValueShape(self):
return self._c_computation.GetProgramShape().result_shape()
# An Executable is a C++ class that duck types with the following API:
# class Executable(object):
# def DeviceOrdinals(self) -> [int]:
# def Execute(self, arguments : [Buffer]) -> Buffer:
# """Execute on one replica with Buffer arguments and return value."""
#
# def ExecutePerReplica(self, arguments: [[Buffer]]) -> [Buffer]:
# """Execute on many replicas with Buffer arguments and return value.
#
# Args:
# arguments: A sequence of sequences of Buffers. The i'th inner sequence
# comprises the arguments for execution on the i'th replica.
#
# Returns:
# A list of the computation's outputs for each replica, as a Buffer. If
# a shallow sequence of arguments was passed in for `arguments`, then the
# sole, zero'th replica's output is returned instead, as a Buffer.
# """
#
# There are different implementations of Executable for the Local and XRT
# backends.
def execute_with_python_values(executable, arguments=(), backend=None):
"""Execute on one replica with Python values as arguments and output."""
backend = backend or get_local_backend()
def put(arg):
return Buffer.from_pyval(
arg, device=executable.DeviceOrdinals()[0], backend=backend)
arguments = [put(arg) for arg in arguments]
return executable.Execute(arguments).to_py()
def execute_with_python_values_replicated(executable, arguments, backend=None):
"""Execute on many replicas with Python values as arguments and output.
Arguments:
executable: the program to run.
arguments: a list of lists of Python values indexed by
`[replica][arg_num]` to pass as inputs.
backend: the backend we are targeting.
Returns:
A list of python values, one per replica.
"""
backend = backend or get_local_backend()
device_ordinals = executable.DeviceOrdinals()
# pylint: disable=g-complex-comprehension
flat_args = [(arg, device_ordinals[replica])
for replica, replica_args in enumerate(arguments)
for arg in replica_args]
flat_arg_buffers = Buffer.from_pyvals(flat_args, backend=backend)
arg_buffers = []
for replica_args in arguments:
arg_buffers.append(flat_arg_buffers[:len(replica_args)])
flat_arg_buffers = flat_arg_buffers[len(replica_args):]
return [out.to_py() for out in executable.ExecutePerReplica(arg_buffers)]
class PaddingType(enum.Enum):
VALID = 1
SAME = 2
def _convert_padding_type_to_pad_values(padding_type, lhs_dims, rhs_dims,
window_strides):
"""Maps PaddingType or string to pad values (list of pairs of ints)."""
if not isinstance(padding_type, (str, PaddingType)):
msg = 'padding_type must be str or PaddingType, got {}.'
raise TypeError(msg.format(type(padding_type)))
if isinstance(padding_type, str):
if padding_type.upper() == 'VALID':
padding_type = PaddingType.VALID
elif padding_type.upper() == 'SAME':
padding_type = PaddingType.SAME
else:
msg = 'Unknown padding type string: expected "VALID" or "SAME", got {}.'
raise ValueError(msg.format(padding_type))
if padding_type == PaddingType.VALID:
return [(0, 0)] * len(window_strides)
elif padding_type == PaddingType.SAME:
out_shape = np.ceil(np.true_divide(lhs_dims, window_strides)).astype(int)
pad_sizes = [
max((out_size - 1) * stride + filter_size - in_size, 0)
for out_size, stride, filter_size, in_size in zip(
out_shape, window_strides, rhs_dims, lhs_dims)
]
return [(pad_size // 2, pad_size - pad_size // 2) for pad_size in pad_sizes]
else:
msg = 'Unexpected PaddingType value: {}'
raise ValueError(msg.format(padding_type))
class ComputationBuilder(object):
"""XLA computation builder.
Enqueues XLA ops in sequence and in order to build a
Computation, which in turn can be compiled into a
LocalExecutable, which in turn can be locally executed.
"""
# The methods of this class map 1-to-1 onto the XLA C++
# computation builder API. Therefore, there's no need to laboriously list
# arguments and return values for every method, especially where it's obvious.
#
# pylint: disable=g-doc-return-or-yield
# pylint: disable=g-doc-args
def __init__(self, name):
self._builder = _xla.XlaBuilder(name)
self._parameter_numbering = itertools.count()
def Build(self, root=None, backend=None):
"""Builds a `Computation` from the contents of the builder.
Args:
root: if not None, the operator containing the return value of the
computation.
Returns:
A `Computation`.
"""
if root is not None:
return Computation(self._builder.Build(root), backend=backend)
else:
return Computation(self._builder.Build(), backend=backend)
def GetShape(self, operand):
return self._builder.GetShape(operand)
def SetOpMetadata(self, op_metadata):
"""Set metadata for operations that are about to be enqueued."""
self._builder.SetOpMetadata(op_metadata)
def ClearOpMetadata(self):
"""Clear metadata for operations that are about to be enqueued."""
self._builder.ClearOpMetadata()
def Infeed(self, shape):
"""Enqueues an infeed op onto the computation.
Infeed operations dequeue data of the given shape from the device's infeed
queue for subsequent use in the computation.
Returns:
An XlaOp.
"""
return ops.Infeed(self._builder,
shape.with_major_to_minor_layout_if_absent())
def Outfeed(self, operand):
"""Enqueues an outfeed op onto the computation.
Outfeed operations enqueue data, using the given operand, onto the XLA
outfeed queue for subsequent dequeue via the client API.
"""
return ops.Outfeed(operand, self._builder.GetShape(operand), '')
def Constant(self, value):
"""Enqueues a constant op onto the computation.
Args:
value: value for the constant, as a np.array with an explicit dtype set to
one of the supported types.
Returns:
An XlaOp.
"""
return ops.ConstantLiteral(self._builder, value)
def ConstantF32Scalar(self, value):
"""Convenience method to enqueue a scalar F32 constant op.
Args:
value: a floating-point number.
Returns:
An XlaOp.
"""
return self.Constant(np.array(value, dtype=np.float32))
def ConstantF64Scalar(self, value):
"""Convenience method to enqueue a scalar F32 constant op.
Args:
value: a floating-point number.
Returns:
An XlaOp.
"""
return self.Constant(np.array(value, dtype=np.float64))
def ConstantS32Scalar(self, value):
"""Convenience method to enqueue a scalar S32 constant op.
Args:
value: a floating-point number.
Returns:
An XlaOp.
"""
return self.Constant(np.array(value, dtype=np.int32))
def ConstantS64Scalar(self, value):
"""Convenience method to enqueue a scalar S64 constant op.
Args:
value: a floating-point number.
Returns:
An XlaOp.
"""
return self.Constant(np.array(value, dtype=np.int64))
def ConstantPredScalar(self, value):
"""Convenience method to enqueue a scalar PRED constant op.
Args:
value: a boolean value.
Returns:
An XlaOp.
"""
return self.Constant(np.array(value, dtype=np.bool))
def ParameterWithShape(self, shape, name=None, parameter_num=None):
"""Enqueues a Parameter op onto the computation, given a shape.
Args:
shape: the parameter's shape as a Shape object.
name: optional string name for the parameter.
parameter_num: parameter number in the computation function. If None, the
next linear parameter number is used. The default value capability can
be used for auto-numbering. If you're using auto-numbering for some
parameters, use it for *all* parameters to avoid clashes.
Returns:
An XlaOp.
"""
if name is None:
name = ''
if parameter_num is None:
parameter_num = next(self._parameter_numbering)
return ops.Parameter(self._builder, parameter_num,
shape.with_major_to_minor_layout_if_absent(),
name.encode('utf8'))
def ParameterFromNumpy(self, value, name=None, parameter_num=None):
"""Enqueues a Parameter op onto the computation.
Args:
value: a Numpy array, or a nested tuple thereof, from which the shape is
inferred.
name: as in ParameterWithShape.
parameter_num: as in ParameterWithShape.
Returns:
An XlaOp.
"""
return self.ParameterWithShape(
shape_from_pyval(value), name=name, parameter_num=parameter_num)
def Iota(self, dtype, size):
"""Enqueues an iota constant onto the computation.
Args:
dtype: expected numpy dtype of the output.
size: integer, the number of elements in the array.
Returns:
An XlaOp representing the added iota constant.
"""
element_type = DTYPE_TO_XLA_ELEMENT_TYPE[str(np.dtype(dtype))]
return ops.Iota(self._builder, element_type, size)
def BroadcastedIota(self, dtype, shape, dimension):
"""Enqueues a broadcasted iota constant onto the computation.
Args:
dtype: expected numpy dtype of the output.
shape: tuple of integers, the expected output shape (dimensions).
dimension: positive integer, dimension along which to increment values.
Returns:
An XlaOp representing the added broadcasted iota constant.
"""
element_type = DTYPE_TO_XLA_ELEMENT_TYPE[str(np.dtype(dtype))]
xla_shape = _xla.Shape.array_shape(element_type, shape, None)
return ops.Iota(self._builder, xla_shape, dimension)
def Concatenate(self, operands, dimension):
"""Enqueues a concatenate operation onto the computation.
Args:
operands: the operands to concatenate.
dimension: the dimension in which to perform the concatenation.
Returns:
An XlaOp representing the added concatenate op.
"""
return ops.ConcatInDim(self._builder, list(operands), dimension)
def ReplicaId(self):
"""Enqueues a ReplicaId operation onto the computation.
Returns:
A LocalOp representing the replica id.
"""
return _xla.ops.ReplicaId(self._builder)
def Pad(self, operand, padding_value, padding_config):
"""Enqueues a Pad operation onto the computation.
Args:
operand: XlaOp representing the array to pad.
padding_value: XlaOp representing the scalar pad value.
padding_config: either a PaddingConfig or a list of integer triples
(edge_padding_low, edge_padding_high, interior_padding) representing the
configuration of the padding operation.
Returns:
An XlaOp representing the added Pad op.
"""
if isinstance(padding_config, tuple) or isinstance(padding_config, list):
padding_config = GetPaddingConfigFromTriples(padding_config)
return ops.Pad(operand, padding_value, padding_config)
def Reshape(self, operand, dimensions, new_sizes):
"""Enqueues a reshape op onto the computation.
Args:
operand: XlaOp representing the array to be reshaped.
dimensions: sequence of integers encoding the order in which dimensions
are collapsed or None, in which case dimensions are flattened in order.
new_sizes: sequence of integers encoding the new dimension sizes (shape).
Returns:
An XlaOp representing the added Reshape op.
"""
if dimensions is None:
ndim = len(self.GetShape(operand).dimensions())
dimensions = tuple(range(ndim))
return ops.Reshape(operand, dimensions, new_sizes)
def AllReduce(self, operand, computation, replica_groups=None):
"""AllReduce op.
Args:
operand: XlaOp representing the input array
computation: a Computation object - binary reduction function.
replica_groups: optional, list of lists of ints encoding a partition of
the set {0, 1, ..., num_replicas} into equally-sized replica groups
within which the all-to-all is performed. If not supplied or None (the
default), all replicas belong to the same group.
Returns:
An XlaOp that represents the all-reduced result.
"""
replica_groups_protos = _get_replica_groups_protos(replica_groups)
return ops.AllReduce(operand, computation.computation,
replica_groups_protos, None)
def AllToAll(self,
operand,
split_dimension,
concat_dimension,
replica_groups=None):
"""AllToAll op.
Args:
operand: XlaOp representing the input array
split_dimension: the dimension along which the operand is split
concat_dimension: the dimension along which the split blocks are
concatenated
replica_groups: optional, list of lists of ints encoding a partition of
the set {0, 1, ..., num_replicas} into equally-sized replica groups
within which the all-to-all is performed. If not supplied or None (the
default), all replicas belong to the same group.
Returns:
An XlaOp that represents the all-to-all concatenation.
"""
replica_groups_protos = _get_replica_groups_protos(replica_groups)
if not replica_groups:
split_count = 1
else:
split_count = len(replica_groups[0])
if not all(split_count == len(g) for g in replica_groups):
raise ValueError('Replica groups must be equally sized')
return ops.AllToAll(operand, split_dimension, concat_dimension, split_count,
replica_groups_protos)
def CrossReplicaSum(self, operand, replica_groups=None):
"""CrossReplicaSum op.
Args:
operand: the operand to sum across replica instances.
replica_groups: optional, list of lists of ints encoding a partition of
the set {0, 1, ..., num_replicas} into equally-sized replica groups
within which the cross-replica sum is performed. If not supplied or None
(the default), all replicas belong to the same group.
Returns:
An XlaOp that represents on each replica the sum of its group's values.
"""
replica_groups_protos = _get_replica_groups_protos(replica_groups)
return ops.CrossReplicaSum(operand, replica_groups_protos)
def Trans(self, operand):
"""Specialized matrix transpose op."""
return ops.Transpose(operand, [1, 0])
def Transpose(self, operand, permutation):
"""Transpose op."""
return ops.Transpose(operand, permutation)
def SelectAndScatter(self, operand, select, window_dimensions, window_strides,
padding, source, init_value, scatter):
"""Select and scatter op, used by the gradient of ReduceWindow.
Args:
operand: XlaOp for array of dimension N and type T over which the windows
slide.
select: Computation of type (T, T) -> Pred to apply to the elements of
each window to indicate which element is selected.
window_dimensions: sequence of N integers for dimensions of the window.
window_strides: sequence of N integers for the strides of the window.
padding: PaddingType representing either 'SAME' or 'VALID ' padding.
source: XlaOp for array of type T with values to scatter.
init_value: XlaOp of scalar type T for initial out value.
scatter: Computation of type (T, T) -> T to apply to each scatter source
element with its destination element.
Returns:
An XlaOp representing the added SelectAndScatter op.
"""
pads = _convert_padding_type_to_pad_values(
padding,
self.GetShape(operand).dimensions(), window_dimensions, window_strides)
return ops.SelectAndScatterWithGeneralPadding(operand, select.computation,
window_dimensions,
window_strides, pads, source,
init_value,
scatter.computation)
def Slice(self, operand, start_indices, limit_indices, strides=None):
"""Enqueues a slice operation onto the computation.
Args:
operand: XlaOp for the N dimensional array to be sliced.
start_indices: iterable of N integers containing the starting indices of
the slice for each dimension.
limit_indices: iterable of N integers containing the ending indices
(exclusive) of the slice for each dimension.
strides: optional iterable of N integers containing the stride sizes for
each dimension.
Returns:
An XlaOp representing the added Slice op.
"""
if strides is None:
start_indices = list(start_indices)
strides = [1] * len(start_indices)
return ops.Slice(operand, start_indices, limit_indices, strides)
def DynamicSlice(self, operand, start_indices, slice_sizes):
"""Enqueues a slice op with dynamic start indices onto the computation.
Args:
operand: XlaOp for the N dimensional array to be sliced.
start_indices: XlaOp for the 1D array of N integers containing the
starting indices of the slice.
slice_sizes: iterable of N integers containing the slice sizes in each
dimension.
Returns:
An XlaOp representing the added DynamicSlice op.
"""
slice_sizes = list(slice_sizes)
if isinstance(start_indices, _xla.XlaOp):
start_indices = [
ops.Reshape(ops.Slice(start_indices, [i], [i + 1], [1]), [])
for i in range(len(slice_sizes))
]
return ops.DynamicSlice(operand, list(start_indices), slice_sizes)
def DynamicUpdateSlice(self, operand, update, start_indices):
"""Enqueues a dynamic update slice operation onto the computation.
Args:
operand: XlaOp for the N dimensional array to be updated.
update: N dimensional array comprising the slice update.
start_indices: Rank-1 array of N integers comprising the starting indices
of the slice along each dimension.
Returns:
An XlaOp representing the added DynamicUpdateSlice op.
"""
if isinstance(start_indices, _xla.XlaOp):
ndims = self._builder.GetShape(start_indices).dimensions()[0]
start_indices = [
ops.Reshape(ops.Slice(start_indices, [i], [i + 1], [1]), [])
for i in range(ndims)
]
return ops.DynamicUpdateSlice(operand, update, list(start_indices))
def Tuple(self, *elems):
"""Enqueues a tuple operation onto the computation.
Args:
elems: a sequence of tuple operands (each a XlaOp).
Returns:
An XlaOp representing the added Tuple op.
"""
return ops.Tuple(self._builder, list(elems))
def Call(self, computation_to_apply, operands):
"""Enqueues a call operation onto the computation.
Args:
computation_to_apply: a Computation object.
operands: an iterable of XlaOp. The number and types of operands must
match the arity of computation_to_apply.
Returns:
An XlaOp representing the added call op.
"""
return ops.Call(self._builder, computation_to_apply.computation,
list(operands))
def CustomCall(self,
call_target_name,
operands,
shape_with_layout,
operand_shapes_with_layout,
opaque=None):
"""Enqueues a custom call operation onto the computation.
Args:
call_target_name: the name of the function to call.
operands: an iterable of XlaOp. The number and types of operands must
match the arity of `operand_shapes_with_layout`.
shape_with_layout: the shape of the operator's output, with layout.
operand_shapes_with_layout: the shapes of `operands`, including the
expected layouts.
opaque: an opaque string passed to the backend.
Returns:
An XlaOp representing the added custom call op.
"""
opaque = opaque or b''
return ops.CustomCall(self._builder, call_target_name,
list(operands), shape_with_layout,
list(operand_shapes_with_layout), opaque)
def Map(self, operands, computation_to_apply, dimensions):
"""Enqueues a map operation onto the computation.
Args:
operands: an iterable of XlaOp.
computation_to_apply: a Computation object.
dimensions: dimensions over which to apply map the function.
Returns:
An XlaOp representing the added Map op.
"""
return ops.Map(self._builder, list(operands),
computation_to_apply.computation, dimensions, [])
def Reduce(self, operand, init_value, computation_to_apply, dimensions):
"""Enqueues a reduction operation onto the computation.
Args:
operand: reduction operand (XlaOp).
init_value: reduction initial value (XlaOp).
computation_to_apply: a Computation object - binary reduction function.
dimensions: sequence of dimensions (integers) to reduce on.
Returns:
An XlaOp representing the added Reduce op.
"""
return ops.Reduce(self._builder, [operand], [init_value],
computation_to_apply.computation, dimensions)
def ReduceWindow(self, operand, init_value, computation_to_apply,
window_dimensions, window_strides, padding):
"""Enqueues a windowed reduction operation onto the computation.
Args:
operand: reduction operand (XlaOp).
init_value: reduction initial value (XlaOp).
computation_to_apply: a binary reduction function (Computation).
window_dimensions: dimensions of window (sequence of integers).
window_strides: strides for window (sequence of integers).
padding: PaddingType representing either 'SAME' or 'VALID' padding.
Returns:
An XlaOp representing the added ReduceWindow op.
"""
pads = _convert_padding_type_to_pad_values(
padding,
self.GetShape(operand).dimensions(), window_dimensions, window_strides)
return ops.ReduceWindowWithGeneralPadding(operand, init_value,
computation_to_apply.computation,
window_dimensions, window_strides,
(), (), pads)
def ReduceWindowWithGeneralPadding(self, operand, init_value,
computation_to_apply, window_dimensions,
window_strides, base_dilations,
window_dilations, padding):
"""Enqueues a windowed reduction operation onto the computation.
Args:
operand: reduction operand (XlaOp).
init_value: reduction initial value (XlaOp).
computation_to_apply: a binary reduction function (Computation).
window_dimensions: dimensions of window (sequence of integers).
window_strides: strides for window (sequence of integers).
base_dilations: dilations for the base (sequence of integers).
window_dilations: dilations for window (sequence of integers).
padding: length-N array-like of pairs of integers of (low, high) padding.
Returns:
An XlaOp representing the added ReduceWindow op.
"""
return ops.ReduceWindowWithGeneralPadding(operand, init_value,
computation_to_apply.computation,
window_dimensions, window_strides,
base_dilations, window_dilations,
padding)
def RngNormal(self, mu, sigma, dims):
"""Enqueues an RngNormal operation onto the computation.
Args:
mu: An XlaOp to an F32 scalar specifying the mean.
sigma: An XlaOp to an F32 scalar specifying the standard deviation.
dims: A 1D array-like of nonnegative integers specifying the dimensions.
Returns: a XlaOp to the generated array of F32 values.
"""
shape = _xla.Shape.array_shape(self.GetShape(mu).xla_element_type(), dims)
return ops.RngNormal(mu, sigma, shape)
def RngUniform(self, a, b, dims):
"""Enqueues an RngUniform operation onto the computation.
Args:
a: a XlaOp to an F32, S32, or U32 scalar (consistent with the type of b)
specifying the low end of the interval [a, b) over which values are
generated.
b: a XlaOp to an F32, S32, or U32 scalar (consistent with the type of a)
specifying the high end of the interval [a, b) over which values are
generated.
dims: A 1D array-like of nonnegative integers specifying the dimensions.
Returns: a XlaOp to the generated array of values with the same numeric type
(F32, S32, or U32) as the arguments a and b.
"""
shape = _xla.Shape.array_shape(self.GetShape(a).xla_element_type(), dims)
return ops.RngUniform(a, b, shape)
def While(self, cond, body, init):
"""Enqueues a While operation onto the computation.
Args:
cond: a Computation for the loop condition, which has type T -> PRED
body: a Computation for the loop body, which has type T -> T
init: a XlaOp for the initial parameter, which has type T
Returns: a XlaOp representing the While operation.
"""
return ops.While(cond.computation, body.computation, init)
def Conditional(self, pred, true_operand, true_computation, false_operand,
false_computation):
"""Enqueues a Conditional operation onto the computation.
Args:
predicate: a XlaOp to test, which has scalar type PRED
true_operand: a XlaOp of type T_0
true_computation: a Computation to apply to true_operand, type T_0 -> S
false_operand: a ComputationDatahandle of type T_1
false_computation: a Computation to apply to false_operand, type T_1 -> S
Returns: a XlaOp representing the Conditional operation.
"""
return ops.Conditional(pred, true_operand, true_computation.computation,
false_operand, false_computation.computation)
def IsConstant(self, operand):
"""Checks whether the given operand is a compile-time constant.
Args:
operand: a ComputationDataHandle to test.
Returns: bool indicating whether `operand` is a compile-time constant,
meaning its value does not depend on any parametersor, or on stateful
operators such as `RngNormal` or `Infeed`.
"""
return self._builder.IsConstant(operand)
def BuildConstantSubGraph(self, operand):
"""Builds a constant sub graph.
Args:
operand: a XlaOp to test.
Returns: a Computation that is rooted on the given `operand` which is a
compile-time constant.
"""
return ops.BuildConstantSubGraph(operand)
def DotGeneral(self, lhs, rhs, dimension_numbers):
"""Enqueues a general dot operation onto the computation.
Args:
lhs: XlaOp for the left-hand-side array.
rhs: XlaOp for the right-hand-side array.
dimension_numbers: either a DotDimensionNumbers or a nested tuple
((lhs_contract, rhs_contract), (lhs_batch, rhs_batch)) of lists of
integers representing the dimensions to treat as contracting dimensions
and batch dimensions on each input operand.
Returns: a XlaOp representing the DotGeneral operation.
"""
if isinstance(dimension_numbers, tuple):
dimension_numbers = GetDotDimensionsFromLists(dimension_numbers)
return ops.DotGeneral(lhs, rhs, dimension_numbers)
def Conv(self, lhs, rhs, window_strides, padding,
feature_group_count=1, batch_group_count=1):
"""Enqueues a Conv operation onto the computation.
Args:
lhs: XlaOp for the rank N+2 array of inputs.
rhs: XlaOp for the rank N+2 array of kernel weights.
window_strides: length-N array-like of integer kernel strides.
padding: PaddingType representing either 'SAME' or 'VALID' padding.
feature_group_count: number of feature groups for grouped convolution.
batch_group_count: number of batch groups for grouped convolution.
Returns: a XlaOp representing the Conv operation.
"""
pads = _convert_padding_type_to_pad_values(
padding,
self.GetShape(lhs).dimensions()[2:],
self.GetShape(rhs).dimensions()[2:], window_strides)
return self.ConvGeneralDilated(
lhs,
rhs,
window_strides,
pads, [], [],
dimension_numbers=None,
feature_group_count=feature_group_count,
batch_group_count=batch_group_count)
def ConvWithGeneralPadding(self,
lhs,
rhs,
window_strides,
padding,
lhs_dilation,
rhs_dilation,
feature_group_count=1,
batch_group_count=1):
"""Enqueues a ConvWithGeneralPadding operation onto the computation.
Args:
lhs: XlaOp for the rank N+2 array of inputs.
rhs: XlaOp for the rank N+2 array of kernel weights.
window_strides: length-N array-like of kernel strides.
padding: length-N array-like of pairs of integers of (low, high) padding.
lhs_dilation: length-N array-like of dilation factors.
rhs_dilation: length-N array-like of dilation factors.
feature_group_count: number of feature groups for grouped convolution.
batch_group_count: number of batch groups for grouped convolution.
Returns:
A ComputationdataHandle representing the added ConvWithGeneralPadding op.
"""
return self.ConvGeneralDilated(
lhs,
rhs,
list(window_strides),
list(padding),
list(lhs_dilation),
list(rhs_dilation),
dimension_numbers=None,
feature_group_count=feature_group_count,
batch_group_count=batch_group_count)
def _GetConvDimensionNumbers(self, num_spatial_dims):
"""Create ConvolutionDimensionNumbers proto for convolutions."""
nd = num_spatial_dims
dimension_numbers = ConvolutionDimensionNumbers()
dimension_numbers.input_batch_dimension = 0
dimension_numbers.input_feature_dimension = 1
dimension_numbers.output_batch_dimension = 0
dimension_numbers.output_feature_dimension = 1
dimension_numbers.kernel_output_feature_dimension = 0
dimension_numbers.kernel_input_feature_dimension = 1
dimension_numbers.input_spatial_dimensions.extend(range(2, 2 + nd))
dimension_numbers.kernel_spatial_dimensions.extend(range(2, 2 + nd))
dimension_numbers.output_spatial_dimensions.extend(range(2, 2 + nd))
return dimension_numbers
def ConvGeneralDilated(self,
lhs,
rhs,
window_strides,
padding,
lhs_dilation,
rhs_dilation,
dimension_numbers=None,
feature_group_count=1,
batch_group_count=1):
"""Enqueues a ConvGeneralDilated operation onto the computation.
Args:
lhs: XlaOp for the rank N+2 array of inputs.
rhs: XlaOp for the rank N+2 array of kernel weights.
window_strides: length-N array-like of integer kernel strides.
padding: length-N array-like of pairs of integers of (low, high) padding.
lhs_dilation: length-N array-like of integer dilation factors.
rhs_dilation: length-N array-like of integer dilation factors.
dimension_numbers: optional, either a ConvolutionDimensionNumbers object
or a tuple (lhs_spec, rhs_spec, out_spec). Each element is a string of
length N+2 identifying by position: (1) batch dimensions in lhs, rhs,
and the output with the character 'N', (2) feature dimensions in lhs
and the output with the character 'C', (3) input and output feature
dimensions in rhs with the characters 'I' and 'O' respectively, and
(4) spatial dimension correspondences between lhs, rhs, and the output
using any distinct characters. For example, to indicate dimension
numbers consistent with the Conv operation with two spatial
dimensions, one could use ('NCHW', 'OIHW', 'NCHW'). As another
example, to indicate dimension numbers consistent with the TensorFlow
Conv2D operation, one could use ('NHWC', 'HWIO', 'NHWC'). When using
the latter form of convolution dimension specification, window strides
are associated with spatial dimension character labels according to
the order in which the labels appear in the rhs_spec string, so that
window_strides[0] is matched with the dimension corresponding to the
first character appearing in rhs_spec that is not 'I' or 'O'. By
default, use the same dimension numbering as Conv and
ConvWithGeneralPadding.
feature_group_count: number of feature groups for grouped convolution.
batch_group_count: number of batch groups for grouped convolution.
Returns: a XlaOp representing the ConvGenralDilated operation.
"""
if dimension_numbers is None:
dimension_numbers = self._GetConvDimensionNumbers(len(window_strides))
elif isinstance(dimension_numbers, tuple):
lhs_spec, rhs_spec, out_spec = dimension_numbers
dimension_numbers = ConvolutionDimensionNumbers()
dimension_numbers.input_batch_dimension = lhs_spec.index('N')
dimension_numbers.input_feature_dimension = lhs_spec.index('C')
dimension_numbers.output_batch_dimension = out_spec.index('N')
dimension_numbers.output_feature_dimension = out_spec.index('C')
dimension_numbers.kernel_output_feature_dimension = rhs_spec.index('O')
dimension_numbers.kernel_input_feature_dimension = rhs_spec.index('I')
dimension_numbers.kernel_spatial_dimensions.extend(
i for i, c in enumerate(rhs_spec) if c not in {'I', 'O'})
dimension_numbers.input_spatial_dimensions.extend(
sorted((i for i, c in enumerate(lhs_spec) if c not in {'N', 'C'}),
key=lambda i: rhs_spec.index(lhs_spec[i])))
dimension_numbers.output_spatial_dimensions.extend(
sorted((i for i, c in enumerate(out_spec) if c not in {'N', 'C'}),
key=lambda i: rhs_spec.index(out_spec[i])))
return ops.ConvGeneralDilated(lhs, rhs, window_strides, padding,
lhs_dilation, rhs_dilation, dimension_numbers,
feature_group_count, batch_group_count)
def Sort(self, operand, dimension=-1):
"""Enqueues a sort operation onto the computation."""
return ops.Sort(self._builder, [operand], dimension)
def SortKeyVal(self, keys, values, dimension=-1):
"""Enqueues a key-value sort operation onto the computation."""
return ops.Sort(self._builder, [keys, values], dimension)
def QR(self, a, full_matrices=True):
"""Enqueues a QR decomposition onto the computation."""
return self.Tuple(*ops.QR(a, full_matrices))
def TriangularSolve(self,
a,
b,
left_side=False,
lower=False,
transpose_a=False,
conjugate_a=False,
unit_diagonal=False):
"""Enqueues a triangular-solve operation onto the computation."""
if not transpose_a:
transpose = _xla.TriangularSolveOptions_Transpose.NO_TRANSPOSE
if conjugate_a:
a = self.Conj(a)
else:
transpose = (
_xla.TriangularSolveOptions_Transpose.ADJOINT
if conjugate_a else _xla.TriangularSolveOptions_Transpose.TRANSPOSE)
return ops.TriangularSolve(a, b, left_side, lower, unit_diagonal, transpose)
def Eigh(self, a, full_matrices=True):
"""Enqueues a symmetric/Hermitian eigendecomposition."""
return self.Tuple(*ops.Eigh(a, full_matrices))
def SVD(self, a):
"""Enqueues a singular value decomposition."""
return self.Tuple(*ops.SVD(a))
def Scatter(self, a, scatter_indices, updates, update_computation,
dimension_numbers):
"""Enqueues a Scatter operation onto the computation."""
return ops.Scatter(a, scatter_indices, updates,
update_computation.computation, dimension_numbers)
def Fft(self, operand, fft_type, fft_lengths):
"""Enqueues a FFT operation onto the computation."""
return ops.Fft(operand, fft_type, fft_lengths)
FftType = _xla.FftType
_UNARY_OPS = [
'Not',
'Clz',
'Abs',
'Exp',
'Expm1',
'Floor',
'Round',
'Ceil',
'Log',
'Log1p',
'Sign',
'Cos',
'Sin',
'Tanh',
'IsFinite',
'Sqrt',
'Rsqrt',
'Square',
'Reciprocal',
'Neg',
'Erf',
'Erfc',
'ErfInv',
'Lgamma',
'Digamma',
'Acos',
'Asin',
'Atan',
'Tan',
'Acosh',
'Asinh',
'Atanh',
'Cosh',
'Sinh',
'Real',
'Imag',
'Conj',
]
_BINARY_OPS = [
'Eq',
'Ne',
'Ge',
'Gt',
'Lt',
'Le',
'Add',
'Sub',
'Mul',
'Div',
'Rem',
'Max',
'Min',
'And',
'Or',
'Xor',
'Pow',
'ShiftLeft',
'ShiftRightArithmetic',
'ShiftRightLogical',
'Atan2',
'Complex',
]
_OTHER_OPS = [
'BitcastConvertType',
'Broadcast',
'BroadcastInDim',
'Cholesky',
'Clamp',
'Collapse',
'CollectivePermute',
'ConvertElementType',
'Dot',
'Gather',
'GetTupleElement',
'Rev',
'Select',
'SliceInDim',
]
def _forward_methods_to_local_builder():
"""Forward remaining ComputationBuilder methods to the C API.
Set up methods, corresponding to XLA operations,
whose calls are forwarded in a boilerplate manner to the underlying
_xla.ops API.
"""
def forward_op(target_method):
def forward(builder, *args, **kwargs):
del builder
return target_method(*args, **kwargs)
return forward
for method_name in itertools.chain(_UNARY_OPS, _BINARY_OPS, _OTHER_OPS):
forward = forward_op(getattr(ops, method_name))
forward.__name__ = method_name
setattr(ComputationBuilder, method_name, forward)
_forward_methods_to_local_builder()
def register_cpu_custom_call_target(name, fn):
"""Registers a CPU custom call target.
Args:
name: bytes containing the name of the function.
fn: a PyCapsule object containing the function pointer.
"""
_xla.RegisterCpuCustomCallTarget(name, fn)
class PaddingConfigDimension(object):
"""Python representation of a xla.PaddingConfigDimension protobuf."""
__slots__ = ('edge_padding_low', 'edge_padding_high', 'interior_padding')
def __init__(self):
self.edge_padding_low = 0
self.edge_padding_high = 0
self.interior_padding = 0
class PaddingConfig(object):
"""Python representation of a xla.PaddingConfig protobuf."""
__slots__ = ('dimensions',)
def __init__(self):
self.dimensions = []
def GetPaddingConfigFromTriples(triples):
"""Create PaddingConfig proto from list of triples of integers."""
padding_config = PaddingConfig()
for lo, hi, interior in triples:
dimension = PaddingConfigDimension()
dimension.edge_padding_low = lo
dimension.edge_padding_high = hi
dimension.interior_padding = interior
padding_config.dimensions.append(dimension)
return padding_config
class DotDimensionNumbers(object):
"""Python representation of a xla.DotDimensionNumbers protobuf."""
__slots__ = ('lhs_contracting_dimensions', 'rhs_contracting_dimensions',
'lhs_batch_dimensions', 'rhs_batch_dimensions')
def __init__(self):
self.lhs_contracting_dimensions = []
self.rhs_contracting_dimensions = []
self.lhs_batch_dimensions = []
self.rhs_batch_dimensions = []
def GetDotDimensionsFromLists(dimension_numbers):
(lhs_contract, rhs_contract), (lhs_batch, rhs_batch) = dimension_numbers
dot_dims_proto = DotDimensionNumbers()
dot_dims_proto.lhs_contracting_dimensions.extend(lhs_contract)
dot_dims_proto.rhs_contracting_dimensions.extend(rhs_contract)
dot_dims_proto.lhs_batch_dimensions.extend(lhs_batch)
dot_dims_proto.rhs_batch_dimensions.extend(rhs_batch)
return dot_dims_proto
class ConvolutionDimensionNumbers(object):
"""Python representation of a xla.ConvolutionDimensionNumbers protobuf."""
__slots__ = ('input_batch_dimension', 'input_feature_dimension',
'input_spatial_dimensions', 'kernel_input_feature_dimension',
'kernel_output_feature_dimension', 'kernel_spatial_dimensions',
'output_batch_dimension', 'output_feature_dimension',
'output_spatial_dimensions')
def __init__(self):
self.input_batch_dimension = 0
self.input_feature_dimension = 0
self.input_spatial_dimensions = []
self.kernel_input_feature_dimension = 0
self.kernel_output_feature_dimension = 0
self.kernel_spatial_dimensions = []
self.output_batch_dimension = 0
self.output_feature_dimension = 0
self.output_spatial_dimensions = []
class GatherDimensionNumbers(object):
"""Python representation of a xla.GatherDimensionNumbers protobuf."""
__slots__ = ('offset_dims', 'collapsed_slice_dims', 'start_index_map',
'index_vector_dim')
def __init__(self):
self.offset_dims = []
self.collapsed_slice_dims = []
self.start_index_map = []
self.index_vector_dim = 0
class ScatterDimensionNumbers(object):
"""Python representation of a xla.ScatterDimensionNumbers protobuf."""
__slots__ = ('update_window_dims', 'inserted_window_dims',
'scatter_dims_to_operand_dims', 'index_vector_dim')
def __init__(self):
self.update_window_dims = []
self.inserted_window_dims = []
self.scatter_dims_to_operand_dims = []
self.index_vector_dim = 0
class ReplicaGroup(object):
"""Python representation of a xla.ReplicaGroup protobuf."""
__slots__ = ('replica_ids',)
def __init__(self):
self.replica_ids = []
def _make_replica_group_proto(replica_group):
replica_group_proto = ReplicaGroup()
replica_group_proto.replica_ids.extend(replica_group)
return replica_group_proto
def _get_replica_groups_protos(replica_groups):
if replica_groups is None:
replica_groups_protos = [] # special value for XLA API
else:
replica_groups = list(replica_groups)
replica_groups_protos = [
_make_replica_group_proto(group) for group in replica_groups
]
return replica_groups_protos
|
tensorflow-master
|
tensorflow/compiler/xla/python/xla_client.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for the Python extension-based XLA client."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import itertools
import threading
import numpy as np
from tensorflow.compiler.xla.python import custom_call_for_test
from tensorflow.compiler.xla.python import xla_client
import unittest
class ComputationTest(unittest.TestCase):
"""Base class for running an XLA Computation through the local client."""
def _NewComputation(self, name=None):
if name is None:
name = self.id()
return xla_client.ComputationBuilder(name)
def _Execute(self, c, arguments):
compiled_c = c.Build().Compile()
return xla_client.execute_with_python_values(compiled_c, arguments)
def _ExecuteAndAssertWith(self, assert_func, c, arguments, expected):
assert expected is not None
result = self._Execute(c, arguments)
# Numpy's comparison methods are a bit too lenient by treating inputs as
# "array-like", meaning that scalar 4 will be happily compared equal to
# [[4]]. We'd like to be more strict so assert shapes as well.
self.assertEqual(np.asanyarray(result).shape, np.asanyarray(expected).shape)
assert_func(result, expected)
def _ExecuteAndCompareExact(self, c, arguments=(), expected=None):
self._ExecuteAndAssertWith(np.testing.assert_equal, c, arguments, expected)
def _ExecuteAndCompareClose(self,
c,
arguments=(),
expected=None,
rtol=1e-7,
atol=0):
self._ExecuteAndAssertWith(
functools.partial(np.testing.assert_allclose, rtol=rtol, atol=atol), c,
arguments, expected)
def NumpyArrayF32(*args, **kwargs):
"""Convenience wrapper to create Numpy arrays with a np.float32 dtype."""
return np.array(*args, dtype=np.float32, **kwargs)
def NumpyArrayF64(*args, **kwargs):
"""Convenience wrapper to create Numpy arrays with a np.float64 dtype."""
return np.array(*args, dtype=np.float64, **kwargs)
def NumpyArrayS32(*args, **kwargs):
"""Convenience wrapper to create Numpy arrays with a np.int32 dtype."""
return np.array(*args, dtype=np.int32, **kwargs)
def NumpyArrayS64(*args, **kwargs):
"""Convenience wrapper to create Numpy arrays with a np.int64 dtype."""
return np.array(*args, dtype=np.int64, **kwargs)
def NumpyArrayBool(*args, **kwargs):
"""Convenience wrapper to create Numpy arrays with a np.bool dtype."""
return np.array(*args, dtype=np.bool, **kwargs)
class ComputationPrinting(unittest.TestCase):
def ExampleComputation(self):
builder = xla_client.ComputationBuilder("acomputation")
p0 = builder.ParameterFromNumpy(np.float32(0))
p1 = builder.ParameterFromNumpy(np.zeros((4,), np.float32))
builder.Mul(p0, p1)
return builder.Build()
def testComputationToHloText(self):
computation = self.ExampleComputation()
hlo_text = computation.GetHloText()
self.assertTrue(hlo_text.startswith("HloModule acomputation"))
def testComputationToHloGraph(self):
computation = self.ExampleComputation()
hlo_dot_graph = computation.GetHloDotGraph()
self.assertTrue(hlo_dot_graph.startswith("digraph "))
class ComputationsWithConstantsTest(ComputationTest):
"""Tests focusing on Constant ops."""
def testConstantScalarSumS8(self):
c = self._NewComputation()
c.Add(c.Constant(np.int8(1)), c.Constant(np.int8(2)))
self._ExecuteAndCompareExact(c, expected=np.int8(3))
def testConstantScalarSumF32(self):
c = self._NewComputation()
c.Add(c.ConstantF32Scalar(1.11), c.ConstantF32Scalar(3.14))
self._ExecuteAndCompareClose(c, expected=4.25)
def testConstantScalarSumF64(self):
c = self._NewComputation()
c.Add(c.ConstantF64Scalar(1.11), c.ConstantF64Scalar(3.14))
self._ExecuteAndCompareClose(c, expected=4.25)
def testConstantScalarSumS32(self):
c = self._NewComputation()
c.Add(c.ConstantS32Scalar(1), c.ConstantS32Scalar(2))
self._ExecuteAndCompareClose(c, expected=3)
def testConstantScalarSumS64(self):
c = self._NewComputation()
c.Add(c.ConstantS64Scalar(1), c.ConstantS64Scalar(2))
self._ExecuteAndCompareClose(c, expected=3)
def testConstantVectorMulF16(self):
c = self._NewComputation()
c.Mul(
c.Constant(np.array([2.5, 3.3, -1.2, 0.7], np.float16)),
c.Constant(np.array([-1.2, 2, -2, -3], np.float16)))
self._ExecuteAndCompareClose(
c, expected=np.array([-3, 6.6, 2.4, -2.1], np.float16), rtol=2e-3)
def testConstantVectorMulF32(self):
c = self._NewComputation()
c.Mul(
c.Constant(NumpyArrayF32([2.5, 3.3, -1.2, 0.7])),
c.Constant(NumpyArrayF32([-1.2, 2, -2, -3])))
self._ExecuteAndCompareClose(c, expected=[-3, 6.6, 2.4, -2.1])
def testConstantVectorMulF64(self):
c = self._NewComputation()
c.Mul(
c.Constant(NumpyArrayF64([2.5, 3.3, -1.2, 0.7])),
c.Constant(NumpyArrayF64([-1.2, 2, -2, -3])))
self._ExecuteAndCompareClose(c, expected=[-3, 6.6, 2.4, -2.1])
def testConstantVectorScalarDivF32(self):
c = self._NewComputation()
c.Div(
c.Constant(NumpyArrayF32([1.5, 2.5, 3.0, -10.8])),
c.ConstantF32Scalar(2.0))
self._ExecuteAndCompareClose(c, expected=[0.75, 1.25, 1.5, -5.4])
def testConstantVectorScalarDivF64(self):
c = self._NewComputation()
c.Div(
c.Constant(NumpyArrayF64([1.5, 2.5, 3.0, -10.8])),
c.ConstantF64Scalar(2.0))
self._ExecuteAndCompareClose(c, expected=[0.75, 1.25, 1.5, -5.4])
def testConstantVectorScalarPowF32(self):
c = self._NewComputation()
c.Pow(c.Constant(NumpyArrayF32([1.5, 2.5, 3.0])), c.ConstantF32Scalar(2.))
self._ExecuteAndCompareClose(c, expected=[2.25, 6.25, 9.])
def testConstantVectorScalarPowF64(self):
c = self._NewComputation()
c.Pow(c.Constant(NumpyArrayF64([1.5, 2.5, 3.0])), c.ConstantF64Scalar(2.))
self._ExecuteAndCompareClose(c, expected=[2.25, 6.25, 9.])
def testIota(self):
c = self._NewComputation()
c.Iota(np.float32, 10)
self._ExecuteAndCompareExact(c, expected=np.arange(10, dtype=np.float32))
def testBroadcastedIota(self):
c = self._NewComputation()
c.BroadcastedIota(np.int64, (2, 3), 1)
expected = np.array([[0, 1, 2], [0, 1, 2]], dtype=np.int64)
self._ExecuteAndCompareExact(c, expected=expected)
def testBooleanAnd(self):
c = self._NewComputation()
c.And(
c.Constant(NumpyArrayBool([True, False, True, False])),
c.Constant(NumpyArrayBool([True, True, False, False])))
self._ExecuteAndCompareExact(c, expected=[True, False, False, False])
def testBooleanOr(self):
c = self._NewComputation()
c.Or(
c.Constant(NumpyArrayBool([True, False, True, False])),
c.Constant(NumpyArrayBool([True, True, False, False])))
self._ExecuteAndCompareExact(c, expected=[True, True, True, False])
def testBooleanXor(self):
c = self._NewComputation()
c.Xor(
c.Constant(NumpyArrayBool([True, False, True, False])),
c.Constant(NumpyArrayBool([True, True, False, False])))
self._ExecuteAndCompareExact(c, expected=[False, True, True, False])
def testSum2DF32(self):
c = self._NewComputation()
c.Add(
c.Constant(NumpyArrayF32([[1, 2, 3], [4, 5, 6]])),
c.Constant(NumpyArrayF32([[1, -1, 1], [-1, 1, -1]])))
self._ExecuteAndCompareClose(c, expected=[[2, 1, 4], [3, 6, 5]])
def testShiftLeft(self):
c = self._NewComputation()
c.ShiftLeft(c.Constant(NumpyArrayS32([3])), c.Constant(NumpyArrayS32([2])))
self._ExecuteAndCompareClose(c, expected=[12])
def testShiftRightArithmetic(self):
c = self._NewComputation()
c.ShiftRightArithmetic(
c.Constant(NumpyArrayS32([-2])), c.Constant(NumpyArrayS32([1])))
self._ExecuteAndCompareClose(c, expected=[-1])
def testShiftRightLogical(self):
c = self._NewComputation()
c.ShiftRightLogical(
c.Constant(NumpyArrayS32([-1])), c.Constant(NumpyArrayS32([1])))
self._ExecuteAndCompareClose(c, expected=[2**31 - 1])
def testSum2DF64(self):
c = self._NewComputation()
c.Add(
c.Constant(NumpyArrayF64([[1, 2, 3], [4, 5, 6]])),
c.Constant(NumpyArrayF64([[1, -1, 1], [-1, 1, -1]])))
self._ExecuteAndCompareClose(c, expected=[[2, 1, 4], [3, 6, 5]])
def testSum2DWith1DBroadcastDim0F32(self):
# sum of a 2D array with a 1D array where the latter is replicated across
# dimension 0 to match the former's shape.
c = self._NewComputation()
c.Add(
c.Constant(NumpyArrayF32([[1, 2, 3], [4, 5, 6], [7, 8, 9]])),
c.Constant(NumpyArrayF32([10, 20, 30])),
broadcast_dimensions=(0,))
self._ExecuteAndCompareClose(
c, expected=[[11, 12, 13], [24, 25, 26], [37, 38, 39]])
def testSum2DWith1DBroadcastDim0F64(self):
# sum of a 2D array with a 1D array where the latter is replicated across
# dimension 0 to match the former's shape.
c = self._NewComputation()
c.Add(
c.Constant(NumpyArrayF64([[1, 2, 3], [4, 5, 6], [7, 8, 9]])),
c.Constant(NumpyArrayF64([10, 20, 30])),
broadcast_dimensions=(0,))
self._ExecuteAndCompareClose(
c, expected=[[11, 12, 13], [24, 25, 26], [37, 38, 39]])
def testSum2DWith1DBroadcastDim1F32(self):
# sum of a 2D array with a 1D array where the latter is replicated across
# dimension 1 to match the former's shape.
c = self._NewComputation()
c.Add(
c.Constant(NumpyArrayF32([[1, 2, 3], [4, 5, 6], [7, 8, 9]])),
c.Constant(NumpyArrayF32([10, 20, 30])),
broadcast_dimensions=(1,))
self._ExecuteAndCompareClose(
c, expected=[[11, 22, 33], [14, 25, 36], [17, 28, 39]])
def testSum2DWith1DBroadcastDim1F64(self):
# sum of a 2D array with a 1D array where the latter is replicated across
# dimension 1 to match the former's shape.
c = self._NewComputation()
c.Add(
c.Constant(NumpyArrayF64([[1, 2, 3], [4, 5, 6], [7, 8, 9]])),
c.Constant(NumpyArrayF64([10, 20, 30])),
broadcast_dimensions=(1,))
self._ExecuteAndCompareClose(
c, expected=[[11, 22, 33], [14, 25, 36], [17, 28, 39]])
def testConstantAxpyF32(self):
c = self._NewComputation()
c.Add(
c.Mul(
c.ConstantF32Scalar(2),
c.Constant(NumpyArrayF32([2.2, 3.3, 4.4, 5.5]))),
c.Constant(NumpyArrayF32([100, -100, 200, -200])))
self._ExecuteAndCompareClose(c, expected=[104.4, -93.4, 208.8, -189])
def testConstantAxpyF64(self):
c = self._NewComputation()
c.Add(
c.Mul(
c.ConstantF64Scalar(2),
c.Constant(NumpyArrayF64([2.2, 3.3, 4.4, 5.5]))),
c.Constant(NumpyArrayF64([100, -100, 200, -200])))
self._ExecuteAndCompareClose(c, expected=[104.4, -93.4, 208.8, -189])
def testCustomCall(self):
c = self._NewComputation()
for name, fn in custom_call_for_test.cpu_custom_call_targets.items():
xla_client.register_cpu_custom_call_target(name, fn)
c.CustomCall(
b"test_subtract_f32",
operands=(c.ConstantF32Scalar(1.25), c.ConstantF32Scalar(0.5)),
shape_with_layout=xla_client.Shape.array_shape(
np.dtype(np.float32), (), ()),
operand_shapes_with_layout=(
xla_client.Shape.array_shape(np.dtype(np.float32), (), ()),
xla_client.Shape.array_shape(np.dtype(np.float32), (), ()),
))
self._ExecuteAndCompareClose(c, expected=0.75)
class ParametersTest(ComputationTest):
"""Tests focusing on Parameter ops and argument-passing."""
def setUp(self):
self.f32_scalar_2 = NumpyArrayF32(2.0)
self.f32_4vector = NumpyArrayF32([-2.3, 3.3, -4.3, 5.3])
self.f64_scalar_2 = NumpyArrayF64(2.0)
self.f64_4vector = NumpyArrayF64([-2.3, 3.3, -4.3, 5.3])
self.s32_scalar_3 = NumpyArrayS32(3)
self.s32_4vector = NumpyArrayS32([10, 15, -2, 7])
self.s64_scalar_3 = NumpyArrayS64(3)
self.s64_4vector = NumpyArrayS64([10, 15, -2, 7])
def testScalarTimesVectorAutonumberF32(self):
c = self._NewComputation()
p0 = c.ParameterFromNumpy(self.f32_scalar_2)
p1 = c.ParameterFromNumpy(self.f32_4vector)
c.Mul(p0, p1)
self._ExecuteAndCompareClose(
c,
arguments=[self.f32_scalar_2, self.f32_4vector],
expected=[-4.6, 6.6, -8.6, 10.6])
def testScalarTimesVectorAutonumberF64(self):
c = self._NewComputation()
p0 = c.ParameterFromNumpy(self.f64_scalar_2)
p1 = c.ParameterFromNumpy(self.f64_4vector)
c.Mul(p0, p1)
self._ExecuteAndCompareClose(
c,
arguments=[self.f64_scalar_2, self.f64_4vector],
expected=[-4.6, 6.6, -8.6, 10.6])
def testScalarTimesVectorS32(self):
c = self._NewComputation()
p0 = c.ParameterFromNumpy(self.s32_scalar_3)
p1 = c.ParameterFromNumpy(self.s32_4vector)
c.Mul(p0, p1)
self._ExecuteAndCompareExact(
c,
arguments=[self.s32_scalar_3, self.s32_4vector],
expected=[30, 45, -6, 21])
def testScalarTimesVectorS64(self):
c = self._NewComputation()
p0 = c.ParameterFromNumpy(self.s64_scalar_3)
p1 = c.ParameterFromNumpy(self.s64_4vector)
c.Mul(p0, p1)
self._ExecuteAndCompareExact(
c,
arguments=[self.s64_scalar_3, self.s64_4vector],
expected=[30, 45, -6, 21])
def testScalarMinusVectorExplicitNumberingF32(self):
# Use explicit numbering and pass parameter_num first. Sub is used since
# it's not commutative and can help catch parameter reversal within the
# computation.
c = self._NewComputation()
p1 = c.ParameterFromNumpy(self.f32_4vector, parameter_num=1)
p0 = c.ParameterFromNumpy(self.f32_scalar_2, parameter_num=0)
c.Sub(p1, p0)
self._ExecuteAndCompareClose(
c,
arguments=[self.f32_scalar_2, self.f32_4vector],
expected=[-4.3, 1.3, -6.3, 3.3])
def testScalarMinusVectorExplicitNumberingF64(self):
# Use explicit numbering and pass parameter_num first. Sub is used since
# it's not commutative and can help catch parameter reversal within the
# computation.
c = self._NewComputation()
p1 = c.ParameterFromNumpy(self.f64_4vector, parameter_num=1)
p0 = c.ParameterFromNumpy(self.f64_scalar_2, parameter_num=0)
c.Sub(p1, p0)
self._ExecuteAndCompareClose(
c,
arguments=[self.f64_scalar_2, self.f64_4vector],
expected=[-4.3, 1.3, -6.3, 3.3])
class BufferTest(ComputationTest):
"""Tests focusing on execution with Buffers."""
def _Execute(self, c, arguments):
compiled_c = c.Build().Compile()
arg_buffers = [xla_client.Buffer.from_pyval(arg) for arg in arguments]
result_buffer = compiled_c.Execute(arg_buffers)
return result_buffer.to_py()
def testConstantSum(self):
c = self._NewComputation()
c.Add(c.ConstantF32Scalar(1.11), c.ConstantF32Scalar(3.14))
self._ExecuteAndCompareClose(c, expected=4.25)
def testOneParameterSum(self):
c = self._NewComputation()
c.Add(c.ParameterFromNumpy(NumpyArrayF32(0.)), c.ConstantF32Scalar(3.14))
self._ExecuteAndCompareClose(
c, arguments=[NumpyArrayF32(1.11)], expected=4.25)
def testTwoParameterSum(self):
c = self._NewComputation()
c.Add(
c.ParameterFromNumpy(NumpyArrayF32(0.)),
c.ParameterFromNumpy(NumpyArrayF32(0.)))
self._ExecuteAndCompareClose(
c, arguments=[NumpyArrayF32(1.11),
NumpyArrayF32(3.14)], expected=4.25)
def testCannotCallWithDeletedBuffers(self):
c = self._NewComputation()
c.Add(c.ParameterFromNumpy(NumpyArrayF32(0.)), c.ConstantF32Scalar(3.14))
arg = NumpyArrayF32(1.11)
compiled_c = c.Build().Compile()
arg_buffer = xla_client.Buffer.from_pyval(arg)
arg_buffer.delete()
with self.assertRaises(RuntimeError):
compiled_c.Execute([arg_buffer])
def testDestructureTupleEmpty(self):
t = ()
local_buffer = xla_client.Buffer.from_pyval(t)
pieces = local_buffer.destructure()
self.assertFalse(local_buffer.is_deleted())
self.assertEqual(len(pieces), 0)
def testDestructureTupleOneArrayElement(self):
t = (np.array([1, 2, 3, 4], dtype=np.int32),)
local_buffer = xla_client.Buffer.from_pyval(t)
pieces = local_buffer.destructure()
self.assertFalse(local_buffer.is_deleted())
self.assertEqual(len(pieces), 1)
array = pieces[0]
got = array.to_py()
want = NumpyArrayS32([1, 2, 3, 4])
np.testing.assert_equal(want, got)
def testDestructureTupleTwoArrayElementDifferentType(self):
t = (
np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32),
np.array([2, 3, 4, 5], dtype=np.int32),
)
local_buffer = xla_client.Buffer.from_pyval(t)
# Run the test twice to verify that the original tuple buffer remains valid
# even after destructuring.
for _ in range(2):
pieces = local_buffer.destructure()
self.assertFalse(local_buffer.is_deleted())
self.assertEqual(len(pieces), 2)
array0, array1 = pieces
got = array0.to_py()
want = NumpyArrayF32([1.0, 2.0, 3.0, 4.0])
np.testing.assert_equal(want, got)
got = array1.to_py()
want = NumpyArrayS32([2, 3, 4, 5])
np.testing.assert_equal(want, got)
def testDestructureTupleNested(self):
t = ((NumpyArrayF32([1.0, 2.0]), NumpyArrayS32([3, 4])), NumpyArrayS32([5]))
local_buffer = xla_client.Buffer.from_pyval(t)
pieces = local_buffer.destructure()
self.assertFalse(local_buffer.is_deleted())
self.assertEqual(len(pieces), 2)
tuple0, array1 = pieces
got = array1.to_py()
want = NumpyArrayS32([5])
np.testing.assert_equal(want, got)
got = tuple0.to_py()
self.assertEqual(type(got), tuple)
self.assertEqual(len(got), 2)
np.testing.assert_equal(NumpyArrayF32([1.0, 2.0]), got[0])
np.testing.assert_equal(NumpyArrayS32([3, 4]), got[1])
def testMakeTuple(self):
t = (
np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32),
np.array([2, 3, 4, 5], dtype=np.int32),
)
b0 = xla_client.Buffer.from_pyval(t[0])
b1 = xla_client.Buffer.from_pyval(t[1])
btup = xla_client.Buffer.make_tuple([b0, b1], device=0)
pieces = btup.destructure()
self.assertEqual(len(pieces), 2)
array0, array1 = pieces
np.testing.assert_equal(
np.array([1, 2, 3, 4], dtype=np.float32), array0.to_py())
np.testing.assert_equal(
np.array([2, 3, 4, 5], dtype=np.int32), array1.to_py())
def testShape(self):
pyval = np.array([[1., 2.]], np.float32)
local_buffer = xla_client.Buffer.from_pyval(pyval)
xla_shape = local_buffer.shape()
self.assertEqual(xla_shape.dimensions(), (1, 2))
self.assertEqual(np.dtype(xla_shape.element_type()), np.dtype(np.float32))
def testBlockHostUntilReadyWorks(self):
arg = np.array([[1., 2.]], np.float32)
arg_buffer = xla_client.Buffer.from_pyval(arg)
arg_buffer.block_host_until_ready()
# This test merely checks that nothing goes awry when we call
# block_host_until_ready(); it's difficult to test anything else.
def testCopyToHost(self):
arg0 = np.array([[1., 2.]], np.float32)
arg1 = np.array([[3., 4.]], np.float32)
arg0_buffer = xla_client.Buffer.from_pyval(arg0)
arg1_buffer = xla_client.Buffer.from_pyval(arg1)
# Prefetch two buffers using copy_to_host_async, and then retrieve their
# values using to_py.
arg0_buffer.copy_to_host_async()
arg0_buffer.copy_to_host_async() # Duplicate calls don't do anything.
arg1_buffer.copy_to_host_async()
np.testing.assert_equal(arg0, arg0_buffer.to_py())
np.testing.assert_equal(arg1, arg1_buffer.to_py())
# copy_to_host_async does nothing after to_py is called.
arg0_buffer.copy_to_host_async()
np.testing.assert_equal(arg0, arg0_buffer.to_py())
class SingleOpTest(ComputationTest):
"""Tests for single ops.
The goal here is smoke testing - to exercise the most basic functionality of
single XLA ops. As minimal as possible number of additional ops are added
around the op being tested.
"""
def testConcatenateF32(self):
c = self._NewComputation()
args = (
c.Constant(NumpyArrayF32([1.0, 2.0, 3.0])),
c.Constant(NumpyArrayF32([4.0, 5.0, 6.0])),
)
c.Concatenate(args, dimension=0)
self._ExecuteAndCompareClose(c, expected=[1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
def testConcatenateF64(self):
c = self._NewComputation()
args = (
c.Constant(NumpyArrayF64([1.0, 2.0, 3.0])),
c.Constant(NumpyArrayF64([4.0, 5.0, 6.0])),
)
c.Concatenate(args, dimension=0)
self._ExecuteAndCompareClose(c, expected=[1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
def testConvertElementType(self):
xla_types = {
np.bool: xla_client.PrimitiveType.PRED,
np.int32: xla_client.PrimitiveType.S32,
np.int64: xla_client.PrimitiveType.S64,
np.float32: xla_client.PrimitiveType.F32,
np.float64: xla_client.PrimitiveType.F64,
}
def _ConvertAndTest(template, src_dtype, dst_dtype):
c = self._NewComputation()
x = c.Constant(np.array(template, dtype=src_dtype))
c.ConvertElementType(x, xla_types[dst_dtype])
result = xla_client.execute_with_python_values(c.Build().Compile())
expected = np.array(template, dtype=dst_dtype)
self.assertEqual(result.shape, expected.shape)
self.assertEqual(result.dtype, expected.dtype)
np.testing.assert_equal(result, expected)
x = [0, 1, 0, 0, 1]
for src_dtype, dst_dtype in itertools.product(xla_types, xla_types):
_ConvertAndTest(x, src_dtype, dst_dtype)
def testBitcastConvertType(self):
xla_x32_types = {
np.int32: xla_client.PrimitiveType.S32,
np.float32: xla_client.PrimitiveType.F32,
}
xla_x64_types = {
np.int64: xla_client.PrimitiveType.S64,
np.float64: xla_client.PrimitiveType.F64,
}
def _ConvertAndTest(template, src_dtype, dst_dtype, dst_etype):
c = self._NewComputation()
x = c.Constant(np.array(template, dtype=src_dtype))
c.BitcastConvertType(x, dst_etype)
result = xla_client.execute_with_python_values(c.Build().Compile())
expected = np.array(template, src_dtype).view(dst_dtype)
self.assertEqual(result.shape, expected.shape)
self.assertEqual(result.dtype, expected.dtype)
np.testing.assert_equal(result, expected)
x = [0, 1, 0, 0, 1]
for xla_types in [xla_x32_types, xla_x64_types]:
for src_dtype, dst_dtype in itertools.product(xla_types, xla_types):
_ConvertAndTest(x, src_dtype, dst_dtype, xla_types[dst_dtype])
# TODO(b/123523486) implement AllToAll on CPU
def DISABLED_testAllToAllOneReplica(self):
samples = [
NumpyArrayF32([97.0]),
NumpyArrayF32([64.0, 117.0]),
NumpyArrayF32([[2.0, 3.0], [4.0, 5.0]]),
]
for lhs in samples[:1]:
c = self._NewComputation()
c.AllToAll(c.Constant(lhs), 0, 0)
self._ExecuteAndCompareExact(c, expected=lhs)
def testCrossReplicaSumOneReplica(self):
samples = [
NumpyArrayF32(42.0),
NumpyArrayF32([97.0]),
NumpyArrayF32([64.0, 117.0]),
NumpyArrayF32([[2.0, 3.0], [4.0, 5.0]]),
]
for lhs in samples:
c = self._NewComputation()
c.CrossReplicaSum(c.Constant(lhs))
self._ExecuteAndCompareExact(c, expected=lhs)
def testReplicaId(self):
c = self._NewComputation()
_ = c.ReplicaId()
self._ExecuteAndCompareExact(c, expected=0)
def testCrossReplicaSumOneReplicaWithSingletonGroup(self):
samples = [
NumpyArrayF32(42.0),
NumpyArrayF32([97.0]),
NumpyArrayF32([64.0, 117.0]),
NumpyArrayF32([[2.0, 3.0], [4.0, 5.0]]),
]
for lhs in samples:
c = self._NewComputation()
c.CrossReplicaSum(c.Constant(lhs), [[0]])
self._ExecuteAndCompareExact(c, expected=lhs)
def testDotMatrixVectorF32(self):
c = self._NewComputation()
lhs = NumpyArrayF32([[2.0, 3.0], [4.0, 5.0]])
rhs = NumpyArrayF32([[10.0], [20.0]])
c.Dot(c.Constant(lhs), c.Constant(rhs))
self._ExecuteAndCompareClose(c, expected=np.dot(lhs, rhs))
def testDotMatrixVectorF64(self):
c = self._NewComputation()
lhs = NumpyArrayF64([[2.0, 3.0], [4.0, 5.0]])
rhs = NumpyArrayF64([[10.0], [20.0]])
c.Dot(c.Constant(lhs), c.Constant(rhs))
self._ExecuteAndCompareClose(c, expected=np.dot(lhs, rhs))
def testDotMatrixMatrixF32(self):
c = self._NewComputation()
lhs = NumpyArrayF32([[2.0, 3.0], [4.0, 5.0]])
rhs = NumpyArrayF32([[10.0, 20.0], [100.0, 200.0]])
c.Dot(c.Constant(lhs), c.Constant(rhs))
self._ExecuteAndCompareClose(c, expected=np.dot(lhs, rhs))
def testDotMatrixMatrixF64(self):
c = self._NewComputation()
lhs = NumpyArrayF64([[2.0, 3.0], [4.0, 5.0]])
rhs = NumpyArrayF64([[10.0, 20.0], [100.0, 200.0]])
c.Dot(c.Constant(lhs), c.Constant(rhs))
self._ExecuteAndCompareClose(c, expected=np.dot(lhs, rhs))
def testDotGeneral(self):
c = self._NewComputation()
rng = np.random.RandomState(0)
lhs = NumpyArrayF32(rng.randn(10, 3, 4))
rhs = NumpyArrayF32(rng.randn(10, 4, 5))
dimension_numbers = (([2], [1]), ([0], [0]))
c.DotGeneral(c.Constant(lhs), c.Constant(rhs), dimension_numbers)
self._ExecuteAndCompareClose(c, expected=np.matmul(lhs, rhs))
def testDotGeneralWithDotDimensionNumbersProto(self):
c = self._NewComputation()
rng = np.random.RandomState(0)
lhs = NumpyArrayF32(rng.randn(10, 3, 4))
rhs = NumpyArrayF32(rng.randn(10, 4, 5))
dimension_numbers = xla_client.DotDimensionNumbers()
dimension_numbers.lhs_contracting_dimensions.append(2)
dimension_numbers.rhs_contracting_dimensions.append(1)
dimension_numbers.lhs_batch_dimensions.append(0)
dimension_numbers.rhs_batch_dimensions.append(0)
c.DotGeneral(c.Constant(lhs), c.Constant(rhs), dimension_numbers)
self._ExecuteAndCompareClose(c, expected=np.matmul(lhs, rhs))
def testConvF32Same(self):
c = self._NewComputation()
a = lambda *dims: np.arange(np.prod(dims)).reshape(dims).astype("float32")
lhs = a(1, 2, 3, 4)
rhs = a(1, 2, 1, 2) * 10
c.Conv(
c.Constant(lhs), c.Constant(rhs), [1, 1], xla_client.PaddingType.SAME)
result = np.array([[[
[640., 700., 760., 300.],
[880., 940., 1000., 380.],
[1120., 1180., 1240., 460.],
]]])
self._ExecuteAndCompareClose(c, expected=result)
def testConvF32Valid(self):
c = self._NewComputation()
a = lambda *dims: np.arange(np.prod(dims)).reshape(dims).astype("float32")
lhs = a(1, 2, 3, 4)
rhs = a(1, 2, 1, 2) * 10
c.Conv(
c.Constant(lhs), c.Constant(rhs), [2, 1], xla_client.PaddingType.VALID)
result = np.array([[[
[640., 700., 760.],
[1120., 1180., 1240.],
]]])
self._ExecuteAndCompareClose(c, expected=result)
def testConvWithGeneralPaddingF32(self):
c = self._NewComputation()
a = lambda *dims: np.arange(np.prod(dims)).reshape(dims).astype("float32")
lhs = a(1, 1, 2, 3)
rhs = a(1, 1, 1, 2) * 10
strides = [1, 1]
pads = [(1, 0), (0, 1)]
lhs_dilation = (2, 1)
rhs_dilation = (1, 1)
c.ConvWithGeneralPadding(
c.Constant(lhs), c.Constant(rhs), strides, pads, lhs_dilation,
rhs_dilation)
result = np.array([[[
[0., 0., 0.],
[10., 20., 0.],
[0., 0., 0.],
[40., 50., 0.],
]]])
self._ExecuteAndCompareClose(c, expected=result)
def testConvGeneralDilatedF32(self):
c = self._NewComputation()
a = lambda *dims: np.arange(np.prod(dims)).reshape(dims).astype("float32")
lhs = a(1, 1, 2, 3)
rhs = a(1, 1, 1, 2) * 10
strides = [1, 1]
pads = [(1, 0), (0, 1)]
lhs_dilation = (2, 1)
rhs_dilation = (1, 1)
dimension_numbers = ("NCHW", "OIHW", "NCHW")
c.ConvGeneralDilated(
c.Constant(lhs), c.Constant(rhs), strides, pads, lhs_dilation,
rhs_dilation, dimension_numbers)
result = np.array([[[
[0., 0., 0.],
[10., 20., 0.],
[0., 0., 0.],
[40., 50., 0.],
]]])
self._ExecuteAndCompareClose(c, expected=result)
def testConvGeneralDilatedPermutedF32(self):
c = self._NewComputation()
a = lambda *dims: np.arange(np.prod(dims)).reshape(dims).astype("float32")
lhs = a(1, 1, 2, 3)
rhs = a(1, 1, 1, 2) * 10
strides = [1, 1]
pads = [(1, 0), (0, 1)]
lhs_dilation = (2, 1)
rhs_dilation = (1, 1)
dimension_numbers = ("NHWC", "OIHW", "CWNH")
c.ConvGeneralDilated(
c.Constant(np.transpose(lhs, (0, 2, 3, 1))), c.Constant(rhs), strides,
pads, lhs_dilation, rhs_dilation, dimension_numbers)
result = np.array([[[[0., 0., 0.], [10., 20., 0.], [0., 0., 0.],
[40., 50., 0.]]]])
self._ExecuteAndCompareClose(c, expected=np.transpose(result, (1, 3, 0, 2)))
def testConvGeneralDilatedGroupedConvolutionF32(self):
c = self._NewComputation()
a = lambda *dims: np.arange(np.prod(dims)).reshape(dims).astype("float32")
lhs = a(1, 2, 2, 3)
rhs = a(2, 1, 1, 2) * 10
strides = [1, 1]
pads = [(1, 0), (0, 1)]
lhs_dilation = (2, 1)
rhs_dilation = (1, 1)
dimension_numbers = ("NCHW", "OIHW", "NCHW")
feature_group_count = 2
c.ConvGeneralDilated(
c.Constant(lhs), c.Constant(rhs), strides, pads, lhs_dilation,
rhs_dilation, dimension_numbers, feature_group_count)
result = np.array([[[
[0., 0., 0.],
[10., 20., 0.],
[0., 0., 0.],
[40., 50., 0.],
], [
[0., 0., 0.],
[330., 380., 160.],
[0., 0., 0.],
[480., 530., 220.],
]]])
self._ExecuteAndCompareClose(c, expected=result)
def testBooleanNot(self):
c = self._NewComputation()
arr = NumpyArrayBool([True, False, True])
c.Not(c.Constant(arr))
self._ExecuteAndCompareClose(c, expected=~arr)
def testCountLeadingZeros(self):
c = self._NewComputation()
arr = NumpyArrayS32([0x7FFF, 0x12345678])
c.Clz(c.Constant(arr))
self._ExecuteAndCompareClose(c, expected=[17, 3])
def testExp(self):
c = self._NewComputation()
arr = NumpyArrayF32([3.3, 12.1])
c.Exp(c.Constant(arr))
self._ExecuteAndCompareClose(c, expected=np.exp(arr))
def testExpm1(self):
c = self._NewComputation()
arr = NumpyArrayF32([3.3, 12.1])
c.Expm1(c.Constant(arr))
self._ExecuteAndCompareClose(c, expected=np.expm1(arr))
def testRound(self):
c = self._NewComputation()
arr = NumpyArrayF32([3.3, 12.1])
c.Round(c.Constant(arr))
self._ExecuteAndCompareClose(c, expected=np.round(arr))
def testLog(self):
c = self._NewComputation()
arr = NumpyArrayF32([3.3, 12.1])
c.Log(c.Constant(arr))
self._ExecuteAndCompareClose(c, expected=np.log(arr))
def testLog1p(self):
c = self._NewComputation()
arr = NumpyArrayF32([3.3, 12.1])
c.Log1p(c.Constant(arr))
self._ExecuteAndCompareClose(c, expected=np.log1p(arr))
def testNeg(self):
c = self._NewComputation()
arr = NumpyArrayF32([3.3, 12.1])
c.Neg(c.Constant(arr))
self._ExecuteAndCompareClose(c, expected=-arr)
def testFloor(self):
c = self._NewComputation()
arr = NumpyArrayF32([3.3, 12.1])
c.Floor(c.Constant(arr))
self._ExecuteAndCompareClose(c, expected=np.floor(arr))
def testCeil(self):
c = self._NewComputation()
arr = NumpyArrayF32([3.3, 12.1])
c.Ceil(c.Constant(arr))
self._ExecuteAndCompareClose(c, expected=np.ceil(arr))
def testAbs(self):
c = self._NewComputation()
arr = NumpyArrayF32([3.3, -12.1, 2.4, -1.])
c.Abs(c.Constant(arr))
self._ExecuteAndCompareClose(c, expected=np.abs(arr))
def testTanh(self):
c = self._NewComputation()
arr = NumpyArrayF32([3.3, 12.1])
c.Tanh(c.Constant(arr))
self._ExecuteAndCompareClose(c, expected=np.tanh(arr))
def testTrans(self):
def _TransposeAndTest(array):
c = self._NewComputation()
c.Trans(c.Constant(array))
self._ExecuteAndCompareClose(c, expected=array.T)
# Test square and non-square matrices in both default (C) and F orders.
for array_fun in [NumpyArrayF32, NumpyArrayF64]:
_TransposeAndTest(array_fun([[1, 2, 3], [4, 5, 6]]))
_TransposeAndTest(array_fun([[1, 2, 3], [4, 5, 6]], order="F"))
_TransposeAndTest(array_fun([[1, 2], [4, 5]]))
_TransposeAndTest(array_fun([[1, 2], [4, 5]], order="F"))
def testTranspose(self):
def _TransposeAndTest(array, permutation):
c = self._NewComputation()
c.Transpose(c.Constant(array), permutation)
expected = np.transpose(array, permutation)
self._ExecuteAndCompareClose(c, expected=expected)
_TransposeAndTest(NumpyArrayF32([[1, 2, 3], [4, 5, 6]]), [0, 1])
_TransposeAndTest(NumpyArrayF32([[1, 2, 3], [4, 5, 6]]), [1, 0])
_TransposeAndTest(NumpyArrayF32([[1, 2], [4, 5]]), [0, 1])
_TransposeAndTest(NumpyArrayF32([[1, 2], [4, 5]]), [1, 0])
arr = np.random.RandomState(0).randn(2, 3, 4).astype(np.float32)
for permutation in itertools.permutations(range(arr.ndim)):
_TransposeAndTest(arr, permutation)
_TransposeAndTest(np.asfortranarray(arr), permutation)
def testEq(self):
c = self._NewComputation()
c.Eq(
c.Constant(NumpyArrayS32([1, 2, 3, 4])),
c.Constant(NumpyArrayS32([4, 2, 3, 1])))
self._ExecuteAndCompareExact(c, expected=[False, True, True, False])
def testNe(self):
c = self._NewComputation()
c.Ne(
c.Constant(NumpyArrayS32([1, 2, 3, 4])),
c.Constant(NumpyArrayS32([4, 2, 3, 1])))
self._ExecuteAndCompareExact(c, expected=[True, False, False, True])
c.Ne(
c.Constant(NumpyArrayF32([-2.0, 0.0,
float("nan"),
float("nan")])),
c.Constant(NumpyArrayF32([2.0, -0.0, 1.0, float("nan")])))
self._ExecuteAndAssertWith(
np.testing.assert_allclose, c, (), expected=[True, False, True, True])
def testGt(self):
c = self._NewComputation()
c.Gt(
c.Constant(NumpyArrayS32([1, 2, 3, 4, 9])),
c.Constant(NumpyArrayS32([1, 0, 2, 7, 12])))
self._ExecuteAndCompareExact(c, expected=[False, True, True, False, False])
def testGe(self):
c = self._NewComputation()
c.Ge(
c.Constant(NumpyArrayS32([1, 2, 3, 4, 9])),
c.Constant(NumpyArrayS32([1, 0, 2, 7, 12])))
self._ExecuteAndCompareExact(c, expected=[True, True, True, False, False])
def testLt(self):
c = self._NewComputation()
c.Lt(
c.Constant(NumpyArrayS32([1, 2, 3, 4, 9])),
c.Constant(NumpyArrayS32([1, 0, 2, 7, 12])))
self._ExecuteAndCompareExact(c, expected=[False, False, False, True, True])
def testLe(self):
c = self._NewComputation()
c.Le(
c.Constant(NumpyArrayS32([1, 2, 3, 4, 9])),
c.Constant(NumpyArrayS32([1, 0, 2, 7, 12])))
self._ExecuteAndCompareExact(c, expected=[True, False, False, True, True])
def testMax(self):
c = self._NewComputation()
c.Max(
c.Constant(NumpyArrayF32([1.0, 2.0, 3.0, 4.0, 9.0])),
c.Constant(NumpyArrayF32([1.0, 0.0, 2.0, 7.0, 12.0])))
self._ExecuteAndCompareExact(c, expected=[1.0, 2.0, 3.0, 7.0, 12.0])
def testMaxExplicitBroadcastDim0(self):
c = self._NewComputation()
c.Max(
c.Constant(NumpyArrayF32([[1, 2, 3], [4, 5, 6], [7, 8, 9]])),
c.Constant(NumpyArrayF32([3, 4, 5])),
broadcast_dimensions=(0,))
self._ExecuteAndCompareExact(c, expected=[[3, 3, 3], [4, 5, 6], [7, 8, 9]])
def testMaxExplicitBroadcastDim1(self):
c = self._NewComputation()
c.Max(
c.Constant(NumpyArrayF32([[1, 2, 3], [4, 5, 6], [7, 8, 9]])),
c.Constant(NumpyArrayF32([3, 4, 5])),
broadcast_dimensions=(1,))
self._ExecuteAndCompareExact(c, expected=[[3, 4, 5], [4, 5, 6], [7, 8, 9]])
def testMin(self):
c = self._NewComputation()
c.Min(
c.Constant(NumpyArrayF32([1.0, 2.0, 3.0, 4.0, 9.0])),
c.Constant(NumpyArrayF32([1.0, 0.0, 2.0, 7.0, 12.0])))
self._ExecuteAndCompareExact(c, expected=[1.0, 0.0, 2.0, 4.0, 9.0])
def testPad(self):
c = self._NewComputation()
c.Pad(
c.Constant(NumpyArrayF32([[1.0, 2.0], [3.0, 4.0]])),
c.Constant(NumpyArrayF32(0.0)), [(1, 2, 1), (0, 1, 0)])
self._ExecuteAndCompareClose(
c,
expected=[[0.0, 0.0, 0.0], [1.0, 2.0, 0.0], [0.0, 0.0, 0.0],
[3.0, 4.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
def testPadWithPaddingConfig(self):
c = self._NewComputation()
padding_config = xla_client.PaddingConfig()
for lo, hi, interior in [(1, 2, 1), (0, 1, 0)]:
dimension = xla_client.PaddingConfigDimension()
dimension.edge_padding_low = lo
dimension.edge_padding_high = hi
dimension.interior_padding = interior
padding_config.dimensions.append(dimension)
c.Pad(
c.Constant(NumpyArrayF32([[1.0, 2.0], [3.0, 4.0]])),
c.Constant(NumpyArrayF32(0.0)), padding_config)
self._ExecuteAndCompareClose(
c,
expected=[[0.0, 0.0, 0.0], [1.0, 2.0, 0.0], [0.0, 0.0, 0.0],
[3.0, 4.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
def testReshape(self):
c = self._NewComputation()
c.Reshape(
c.Constant(NumpyArrayS32([[1, 2], [3, 4], [5, 6]])),
dimensions=[0, 1],
new_sizes=[2, 3])
self._ExecuteAndCompareExact(c, expected=[[1, 2, 3], [4, 5, 6]])
def testCollapse(self):
c = self._NewComputation()
c.Collapse(
c.Constant(NumpyArrayS32([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])),
dimensions=[1, 2])
self._ExecuteAndCompareExact(c, expected=[[1, 2, 3, 4], [5, 6, 7, 8]])
def testRev(self):
c = self._NewComputation()
c.Rev(
c.Constant(NumpyArrayS32([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])),
dimensions=[0, 2])
self._ExecuteAndCompareExact(
c, expected=[[[6, 5], [8, 7]], [[2, 1], [4, 3]]])
def testClampF32(self):
c = self._NewComputation()
c.Clamp(
c.Constant(NumpyArrayF32(-1)),
c.Constant(NumpyArrayF32([-2, -1, 0, 1, 2, 3])),
c.Constant(NumpyArrayF32(2)))
self._ExecuteAndCompareExact(c, expected=[-1, -1, 0, 1, 2, 2])
def testClampS32(self):
c = self._NewComputation()
c.Clamp(
c.Constant(NumpyArrayS32(-1)),
c.Constant(NumpyArrayS32([-2, -1, 0, 1, 2, 3])),
c.Constant(NumpyArrayS32(2)))
self._ExecuteAndCompareExact(c, expected=[-1, -1, 0, 1, 2, 2])
def testSelect(self):
c = self._NewComputation()
c.Select(
c.Constant(NumpyArrayBool([True, False, False, True, False])),
c.Constant(NumpyArrayS32([1, 2, 3, 4, 5])),
c.Constant(NumpyArrayS32([-1, -2, -3, -4, -5])))
self._ExecuteAndCompareExact(c, expected=[1, -2, -3, 4, -5])
def testSlice(self):
c = self._NewComputation()
c.Slice(
c.Constant(NumpyArrayS32([[1, 2, 3], [4, 5, 6], [7, 8, 9]])), [1, 0],
[3, 2])
self._ExecuteAndCompareExact(c, expected=[[4, 5], [7, 8]])
def testSliceInDim(self):
c = self._NewComputation()
c.SliceInDim(
c.Constant(NumpyArrayS32([[1, 2, 3], [4, 5, 6], [7, 8, 9]])),
start_index=1,
limit_index=2,
stride=1,
dimno=1)
self._ExecuteAndCompareExact(c, expected=[[2], [5], [8]])
c.SliceInDim(
c.Constant(NumpyArrayS32([[1, 2, 3], [4, 5, 6], [7, 8, 9]])),
start_index=0,
limit_index=3,
stride=2,
dimno=0)
self._ExecuteAndCompareExact(c, expected=[[1, 2, 3], [7, 8, 9]])
def testDynamicSlice(self):
c = self._NewComputation()
c.DynamicSlice(
c.Constant(NumpyArrayS32([[1, 2, 3], [4, 5, 6], [7, 8, 9]])),
c.Constant(NumpyArrayS32([1, 0])), [2, 2])
self._ExecuteAndCompareExact(c, expected=[[4, 5], [7, 8]])
def testDynamicUpdateSlice(self):
c = self._NewComputation()
c.DynamicUpdateSlice(
c.Constant(NumpyArrayS32([[1, 2, 3], [4, 5, 6], [7, 8, 9]])),
c.Constant(NumpyArrayS32([[1, 2], [3, 4]])),
c.Constant(NumpyArrayS32([1, 1])))
self._ExecuteAndCompareExact(c, expected=[[1, 2, 3], [4, 1, 2], [7, 3, 4]])
def testTuple(self):
c = self._NewComputation()
c.Tuple(
c.ConstantS32Scalar(42), c.Constant(NumpyArrayF32([1.0, 2.0])),
c.Constant(NumpyArrayBool([True, False, False, True])))
result = xla_client.execute_with_python_values(c.Build().Compile())
self.assertIsInstance(result, tuple)
np.testing.assert_equal(result[0], 42)
np.testing.assert_allclose(result[1], [1.0, 2.0])
np.testing.assert_equal(result[2], [True, False, False, True])
def testGetTupleElement(self):
c = self._NewComputation()
c.GetTupleElement(
c.Tuple(
c.ConstantS32Scalar(42), c.Constant(NumpyArrayF32([1.0, 2.0])),
c.Constant(NumpyArrayBool([True, False, False, True]))), 1)
self._ExecuteAndCompareClose(c, expected=[1.0, 2.0])
def testBroadcast(self):
c = self._NewComputation()
c.Broadcast(c.Constant(NumpyArrayS32([10, 20, 30, 40])), sizes=(3,))
self._ExecuteAndCompareExact(
c, expected=[[10, 20, 30, 40], [10, 20, 30, 40], [10, 20, 30, 40]])
def testBroadcastInDim(self):
c = self._NewComputation()
c.BroadcastInDim(c.Constant(NumpyArrayS32([1, 2])), [2, 2], [0])
self._ExecuteAndCompareExact(c, expected=[[1, 1], [2, 2]])
c.BroadcastInDim(c.Constant(NumpyArrayS32([1, 2])), [2, 2], [1])
self._ExecuteAndCompareExact(c, expected=[[1, 2], [1, 2]])
def testRngNormal(self):
shape = (2, 3)
c = self._NewComputation()
c.RngNormal(
c.Constant(NumpyArrayF32(0.)),
c.Constant(NumpyArrayF32(1.)),
dims=shape)
result = xla_client.execute_with_python_values(c.Build().Compile())
# since the result is random, we just check shape and uniqueness
self.assertEqual(result.shape, shape)
self.assertEqual(len(np.unique(result)), np.prod(shape))
def testRngUniformF32(self):
lo, hi = 2., 4.
shape = (2, 3)
c = self._NewComputation()
c.RngUniform(
c.Constant(NumpyArrayF32(lo)),
c.Constant(NumpyArrayF32(hi)),
dims=shape)
result = xla_client.execute_with_python_values(c.Build().Compile())
# since the result is random, we just check shape, uniqueness, and range
self.assertEqual(result.shape, shape)
self.assertEqual(len(np.unique(result)), np.prod(shape))
self.assertTrue(np.all(lo <= result))
self.assertTrue(np.all(result < hi))
def testRngUniformS32(self):
lo, hi = 2, 4
shape = (2, 3)
c = self._NewComputation()
c.RngUniform(
c.Constant(NumpyArrayS32(lo)),
c.Constant(NumpyArrayS32(hi)),
dims=shape)
result = xla_client.execute_with_python_values(c.Build().Compile())
# since the result is random, we just check shape, integrality, and range
self.assertEqual(result.shape, shape)
self.assertEqual(result.dtype, np.int32)
self.assertTrue(np.all(lo <= result))
self.assertTrue(np.all(result < hi))
def testCholesky(self):
l = np.array([[4, 0, 0, 0], [6, 5, 0, 0], [2, 14, 16, 0], [3, 6, 1, 4]],
dtype=np.float32)
c = self._NewComputation()
c.Cholesky(c.Constant(np.dot(l, l.T)))
self._ExecuteAndCompareClose(c, expected=l, rtol=1e-4)
def testSort(self):
keys = np.array([[2, 4, 1, 3], [3, 1, 4, 2]], dtype=np.float32)
c = self._NewComputation()
c.Sort(c.Constant(keys))
self._ExecuteAndCompareClose(
c, expected=np.array([[1, 2, 3, 4], [1, 2, 3, 4]], dtype=np.float32))
def testSortKeyVal(self):
keys = np.array([[2, 4, 1, 3], [3, 1, 4, 2]], dtype=np.float32)
values = np.array([[0, 1, 2, 3], [4, 5, 6, 7]], dtype=np.int32)
c = self._NewComputation()
c.SortKeyVal(c.Constant(keys), c.Constant(values), dimension=0)
result = xla_client.execute_with_python_values(c.Build().Compile())
self.assertIsInstance(result, tuple)
np.testing.assert_allclose(result[0], [[2, 1, 1, 2], [3, 4, 4, 3]])
np.testing.assert_equal(result[1], [[0, 5, 2, 7], [4, 1, 6, 3]])
def testQR(self):
a = np.array(
[[4, 6, 8, 10], [6, 45, 54, 63], [8, 54, 146, 166], [10, 63, 166, 310]],
dtype=np.float32)
c = self._NewComputation()
c.QR(c.Constant(a), full_matrices=True)
q, r = self._Execute(c, ())
np.testing.assert_allclose(np.dot(q, r), a, rtol=1e-4)
def testEigh(self):
a = np.array(
[[4, 6, 8, 10], [6, 45, 54, 63], [8, 54, 146, 166], [10, 63, 166, 310]],
dtype=np.float32)
a = (a + a.T) / 2
c = self._NewComputation()
c.Eigh(c.Constant(a), full_matrices=True)
# TODO(b/129396575): Turn this test back on when it passes without fastmath.
# v, w = self._Execute(c, ())
# self.assertLess(np.linalg.norm(np.dot(a, v) - w * v), 1e-3)
def testSVD(self):
a = np.array(
[[4, 6, 8, 10], [6, 45, 54, 63], [8, 54, 146, 166], [10, 63, 166, 310]],
dtype=np.float32)
c = self._NewComputation()
c.SVD(c.Constant(a))
u, d, v = self._Execute(c, ())
self.assertLess(np.linalg.norm(a - np.matmul(u * d, v.T)), 1e-3)
def testTriangularSolve(self):
a_vals = np.array(
[[2, 0, 0, 0], [3, 6, 0, 0], [4, 7, 9, 0], [5, 8, 10, 11]],
dtype=np.float32)
b_vals = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]],
dtype=np.float32)
c = self._NewComputation()
c.TriangularSolve(
c.Constant(a_vals),
c.Constant(b_vals),
left_side=False,
lower=True,
transpose_a=True)
self._ExecuteAndCompareClose(
c,
expected=np.array([
[0.5, 0.08333334, 0.04629629, 0.03367003],
[2.5, -0.25, -0.1388889, -0.1010101],
[4.5, -0.58333331, -0.32407406, -0.23569024],
],
dtype=np.float32),
rtol=1e-4)
def testIsConstant(self):
c = self._NewComputation()
a = c.ConstantS32Scalar(3)
b = c.ConstantS32Scalar(1)
x = c.ParameterFromNumpy(NumpyArrayS32(0))
const_expr = c.Sub(b, a)
non_const_expr = c.Mul(const_expr, x)
self.assertTrue(c.IsConstant(const_expr))
self.assertFalse(c.IsConstant(non_const_expr))
# self.assertTrue(c.IsConstant(c.Sub(c.Add(x, a), x))) # TODO(b/77245564)
def testGather(self):
a = np.arange(9).astype(np.int32).reshape((3, 3))
indices = np.array([[[0, 2], [2, 1]], [[1, 2], [2, 0]]], dtype=np.int32)
dnums = xla_client.GatherDimensionNumbers()
dnums.offset_dims.append(1)
dnums.offset_dims.append(2)
dnums.start_index_map.append(0)
dnums.start_index_map.append(1)
dnums.index_vector_dim = 2
c = self._NewComputation()
c.Gather(c.Constant(a), c.Constant(indices), dnums, slice_sizes=[1, 1])
g = self._Execute(c, ())
expected = np.array([[[[2, 7]]], [[[5, 6]]]], dtype=np.int32)
np.testing.assert_allclose(g, expected, rtol=1e-4)
def testFft(self):
shape = [2, 3, 4, 5]
rng = np.random.RandomState(0)
a = rng.randn(*shape) + 1.0j * rng.randn(*shape)
a = a.astype(np.complex64)
# FFT
c = self._NewComputation()
c.Fft(c.Constant(a), xla_client.FftType.FFT, shape[-3:])
self._ExecuteAndCompareClose(c, expected=np.fft.fftn(a, axes=(1, 2, 3)),
rtol=1e-4)
# IFFT
c = self._NewComputation()
c.Fft(c.Constant(a), xla_client.FftType.IFFT, shape[-3:])
self._ExecuteAndCompareClose(c, expected=np.fft.ifftn(a, axes=(1, 2, 3)),
rtol=1e-4)
# RFFT
b = rng.randn(*shape).astype(np.float32)
c = self._NewComputation()
c.Fft(c.Constant(b), xla_client.FftType.RFFT, shape[-3:])
self._ExecuteAndCompareClose(c, expected=np.fft.rfftn(b, axes=(1, 2, 3)),
rtol=1e-4)
# IRFFT
c = self._NewComputation()
c.Fft(c.Constant(a), xla_client.FftType.IRFFT, [3, 4, 8])
self._ExecuteAndCompareClose(c, expected=np.fft.irfftn(a, axes=(1, 2, 3)),
rtol=1e-4)
class EmbeddedComputationsTest(ComputationTest):
"""Tests for XLA graphs with embedded computations (such as maps)."""
def _CreateConstantS32Computation(self):
"""Computation (f32) -> s32 that returns a constant 1 for any input."""
c = self._NewComputation("constant_s32_one")
# TODO(eliben): consider adding a nicer way to create new parameters without
# having to create dummy Numpy arrays or populating Shape messages. Perhaps
# we need our own (Python-client-own) way to represent Shapes conveniently.
c.ParameterFromNumpy(NumpyArrayF32(0))
c.ConstantS32Scalar(1)
return c.Build()
def _CreateConstantS64Computation(self):
"""Computation (f64) -> s64 that returns a constant 1 for any input."""
c = self._NewComputation("constant_s64_one")
# TODO(eliben): consider adding a nicer way to create new parameters without
# having to create dummy Numpy arrays or populating Shape messages. Perhaps
# we need our own (Python-client-own) way to represent Shapes conveniently.
c.ParameterFromNumpy(NumpyArrayF64(0))
c.ConstantS64Scalar(1)
return c.Build()
def _CreateConstantF32Computation(self):
"""Computation (f32) -> f32 that returns a constant 1.0 for any input."""
c = self._NewComputation("constant_f32_one")
c.ParameterFromNumpy(NumpyArrayF32(0))
c.ConstantF32Scalar(1.0)
return c.Build()
def _CreateConstantF64Computation(self):
"""Computation (f64) -> f64 that returns a constant 1.0 for any input."""
c = self._NewComputation("constant_f64_one")
c.ParameterFromNumpy(NumpyArrayF64(0))
c.ConstantF64Scalar(1.0)
return c.Build()
def _CreateMulF32By2Computation(self):
"""Computation (f32) -> f32 that multiplies its parameter by 2."""
c = self._NewComputation("mul_f32_by2")
c.Mul(c.ParameterFromNumpy(NumpyArrayF32(0)), c.ConstantF32Scalar(2.0))
return c.Build()
def _CreateMulF32ByParamComputation(self):
"""Computation (f32) -> f32 that multiplies one parameter by the other."""
c = self._NewComputation("mul_f32_by_param")
c.Mul(
c.ParameterFromNumpy(NumpyArrayF32(0)),
c.ParameterFromNumpy(NumpyArrayF32(0)))
return c.Build()
def _CreateMulF64By2Computation(self):
"""Computation (f64) -> f64 that multiplies its parameter by 2."""
c = self._NewComputation("mul_f64_by2")
c.Mul(c.ParameterFromNumpy(NumpyArrayF64(0)), c.ConstantF64Scalar(2.0))
return c.Build()
def _CreateBinaryAddS32Computation(self):
"""Computation (s32, s32) -> s32 that adds its two parameters."""
c = self._NewComputation("add_param0_by_param1")
c.Add(
c.ParameterFromNumpy(NumpyArrayS32(0)),
c.ParameterFromNumpy(NumpyArrayS32(0)))
return c.Build()
def _CreateBinaryAddF32Computation(self):
"""Computation (f32, f32) -> f32 that adds its two parameters."""
c = self._NewComputation("add_param0_by_param1")
c.Add(
c.ParameterFromNumpy(NumpyArrayF32(0)),
c.ParameterFromNumpy(NumpyArrayF32(0)))
return c.Build()
def _CreateBinaryAddF64Computation(self):
"""Computation (f64, f64) -> f64 that adds its two parameters."""
c = self._NewComputation("add_param0_by_param1")
c.Add(
c.ParameterFromNumpy(NumpyArrayF64(0)),
c.ParameterFromNumpy(NumpyArrayF64(0)))
return c.Build()
def _CreateBinaryDivF32Computation(self):
"""Computation (f32, f32) -> f32 that divides its two parameters."""
c = self._NewComputation("div_param0_by_param1")
c.Div(
c.ParameterFromNumpy(NumpyArrayF32(0)),
c.ParameterFromNumpy(NumpyArrayF32(0)))
return c.Build()
def _CreateBinaryDivF64Computation(self):
"""Computation (f64, f64) -> f64 that divides its two parameters."""
c = self._NewComputation("div_param0_by_param1")
c.Div(
c.ParameterFromNumpy(NumpyArrayF64(0)),
c.ParameterFromNumpy(NumpyArrayF64(0)))
return c.Build()
def _CreateTestF32Lt10Computation(self):
"""Computation (f32) -> bool that tests if its parameter is less than 10."""
c = self._NewComputation("test_f32_lt_10")
c.Lt(c.ParameterFromNumpy(NumpyArrayF32(0)), c.ConstantF32Scalar(10.))
return c.Build()
def _CreateTestF64Lt10Computation(self):
"""Computation (f64) -> bool that tests if its parameter is less than 10."""
c = self._NewComputation("test_f64_lt_10")
c.Lt(c.ParameterFromNumpy(NumpyArrayF64(0)), c.ConstantF64Scalar(10.))
return c.Build()
def _CreateBinaryGeF32Computation(self):
"""Computation (f32, f32) -> bool that tests first_param >= second_param."""
c = self._NewComputation("param0_lt_param1")
c.Ge(
c.ParameterFromNumpy(NumpyArrayF32(0)),
c.ParameterFromNumpy(NumpyArrayF32(0)))
return c.Build()
def _CreateBinaryGeF64Computation(self):
"""Computation (f64, f64) -> bool that tests first_param >= second_param."""
c = self._NewComputation("param0_lt_param1")
c.Ge(
c.ParameterFromNumpy(NumpyArrayF64(0)),
c.ParameterFromNumpy(NumpyArrayF64(0)))
return c.Build()
def _MakeSample3DArrayF32(self):
return NumpyArrayF32([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]],
[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])
def _MakeSample3DArrayF64(self):
return NumpyArrayF64([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]],
[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])
def testCallF32(self):
c = self._NewComputation()
c.Call(
self._CreateMulF32By2Computation(),
operands=(c.ConstantF32Scalar(5.0),))
self._ExecuteAndCompareClose(c, expected=10.0)
def testCallF64(self):
c = self._NewComputation()
c.Call(
self._CreateMulF64By2Computation(),
operands=(c.ConstantF64Scalar(5.0),))
self._ExecuteAndCompareClose(c, expected=10.0)
def testMapEachElementToS32Constant(self):
c = self._NewComputation()
c.Map([c.Constant(NumpyArrayF32([1.0, 2.0, 3.0, 4.0]))],
self._CreateConstantS32Computation(), [0])
self._ExecuteAndCompareExact(c, expected=[1, 1, 1, 1])
def testMapEachElementToS64Constant(self):
c = self._NewComputation()
c.Map([c.Constant(NumpyArrayF64([1.0, 2.0, 3.0, 4.0]))],
self._CreateConstantS64Computation(), [0])
self._ExecuteAndCompareExact(c, expected=[1, 1, 1, 1])
def testMapMulBy2F32(self):
c = self._NewComputation()
c.Map([c.Constant(NumpyArrayF32([1.0, 2.0, 3.0, 4.0]))],
self._CreateMulF32By2Computation(), [0])
self._ExecuteAndCompareClose(c, expected=[2.0, 4.0, 6.0, 8.0])
def testMapMulBy2F64(self):
c = self._NewComputation()
c.Map([c.Constant(NumpyArrayF64([1.0, 2.0, 3.0, 4.0]))],
self._CreateMulF64By2Computation(), [0])
self._ExecuteAndCompareClose(c, expected=[2.0, 4.0, 6.0, 8.0])
def testSimpleMapChainF32(self):
# Chains a map of constant-f32 with a map of mul-by-2
c = self._NewComputation()
const_f32 = c.Map([c.Constant(NumpyArrayF32([1.0, 2.0, 3.0, 4.0]))],
self._CreateConstantF32Computation(), [0])
c.Map([const_f32], self._CreateMulF32By2Computation(), [0])
self._ExecuteAndCompareClose(c, expected=[2.0, 2.0, 2.0, 2.0])
def testSimpleMapChainF64(self):
# Chains a map of constant-f64 with a map of mul-by-2
c = self._NewComputation()
const_f64 = c.Map([c.Constant(NumpyArrayF64([1.0, 2.0, 3.0, 4.0]))],
self._CreateConstantF64Computation(), [0])
c.Map([const_f64], self._CreateMulF64By2Computation(), [0])
self._ExecuteAndCompareClose(c, expected=[2.0, 2.0, 2.0, 2.0])
def testDivVectorsWithMapF32(self):
c = self._NewComputation()
c.Map((c.Constant(NumpyArrayF32([1.0, 2.0, 3.0, 4.0])),
c.Constant(NumpyArrayF32([5.0, 5.0, 4.0, 4.0]))),
self._CreateBinaryDivF32Computation(), [0])
self._ExecuteAndCompareClose(c, expected=[0.2, 0.4, 0.75, 1.0])
def testDivVectorsWithMapF64(self):
c = self._NewComputation()
c.Map((c.Constant(NumpyArrayF64([1.0, 2.0, 3.0, 4.0])),
c.Constant(NumpyArrayF64([5.0, 5.0, 4.0, 4.0]))),
self._CreateBinaryDivF64Computation(), [0])
self._ExecuteAndCompareClose(c, expected=[0.2, 0.4, 0.75, 1.0])
def testSelectAndScatterF32(self):
c = self._NewComputation()
c.SelectAndScatter(
c.Constant(NumpyArrayF32([[1., 2., 6.], [4., 5., 3.]])),
select=self._CreateBinaryGeF32Computation(),
window_dimensions=(2, 1),
window_strides=(1, 2),
padding=xla_client.PaddingType.VALID,
source=c.Constant(NumpyArrayF32([[0.1, 0.2]])),
init_value=c.Constant(NumpyArrayF32(1)),
scatter=self._CreateBinaryAddF32Computation())
self._ExecuteAndCompareClose(c, expected=[[1., 1., 1.2], [1.1, 1., 1.]])
def testSelectAndScatterF64(self):
c = self._NewComputation()
c.SelectAndScatter(
c.Constant(NumpyArrayF64([[1., 2., 6.], [4., 5., 3.]])),
select=self._CreateBinaryGeF64Computation(),
window_dimensions=(2, 1),
window_strides=(1, 2),
padding=xla_client.PaddingType.VALID,
source=c.Constant(NumpyArrayF64([[0.1, 0.2]])),
init_value=c.Constant(NumpyArrayF64(1)),
scatter=self._CreateBinaryAddF64Computation())
self._ExecuteAndCompareClose(c, expected=[[1., 1., 1.2], [1.1, 1., 1.]])
def testReduce1DtoScalarF32(self):
c = self._NewComputation()
c.Reduce(
operand=c.Constant(NumpyArrayF32([1.0, 2.0, 3.0, 4.0])),
init_value=c.ConstantF32Scalar(0),
computation_to_apply=self._CreateBinaryAddF32Computation(),
dimensions=[0])
self._ExecuteAndCompareClose(c, expected=10)
def testReduce1DtoScalarF64(self):
c = self._NewComputation()
c.Reduce(
operand=c.Constant(NumpyArrayF64([1.0, 2.0, 3.0, 4.0])),
init_value=c.ConstantF64Scalar(0),
computation_to_apply=self._CreateBinaryAddF64Computation(),
dimensions=[0])
self._ExecuteAndCompareClose(c, expected=10)
def testReduce2DTo1DDim0F32(self):
input_array = NumpyArrayF32([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
c = self._NewComputation()
c.Reduce(
operand=c.Constant(input_array),
init_value=c.ConstantF32Scalar(0),
computation_to_apply=self._CreateBinaryAddF32Computation(),
dimensions=[0])
self._ExecuteAndCompareClose(c, expected=[5, 7, 9])
def testReduce2DTo1DDim0F64(self):
input_array = NumpyArrayF64([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
c = self._NewComputation()
c.Reduce(
operand=c.Constant(input_array),
init_value=c.ConstantF64Scalar(0),
computation_to_apply=self._CreateBinaryAddF64Computation(),
dimensions=[0])
self._ExecuteAndCompareClose(c, expected=[5, 7, 9])
def testReduce2DTo1DDim1F32(self):
input_array = NumpyArrayF32([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
c = self._NewComputation()
c.Reduce(
operand=c.Constant(input_array),
init_value=c.ConstantF32Scalar(0),
computation_to_apply=self._CreateBinaryAddF32Computation(),
dimensions=[1])
self._ExecuteAndCompareClose(c, expected=[6, 15])
def testReduce2DTo1DDim1F64(self):
input_array = NumpyArrayF64([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
c = self._NewComputation()
c.Reduce(
operand=c.Constant(input_array),
init_value=c.ConstantF64Scalar(0),
computation_to_apply=self._CreateBinaryAddF64Computation(),
dimensions=[1])
self._ExecuteAndCompareClose(c, expected=[6, 15])
def testReduce3DAllPossibleWaysF32(self):
input_array = self._MakeSample3DArrayF32()
def _ReduceAndTest(*dims):
c = self._NewComputation()
c.Reduce(
operand=c.Constant(input_array),
init_value=c.ConstantF32Scalar(0),
computation_to_apply=self._CreateBinaryAddF32Computation(),
dimensions=dims)
self._ExecuteAndCompareClose(
c, expected=np.sum(input_array, axis=tuple(dims)))
_ReduceAndTest(0)
_ReduceAndTest(0, 1)
_ReduceAndTest(0, 2)
_ReduceAndTest(1, 2)
_ReduceAndTest(0, 1, 2)
def testReduce3DAllPossibleWaysF64(self):
input_array = self._MakeSample3DArrayF64()
def _ReduceAndTest(*dims):
c = self._NewComputation()
c.Reduce(
operand=c.Constant(input_array),
init_value=c.ConstantF64Scalar(0),
computation_to_apply=self._CreateBinaryAddF64Computation(),
dimensions=dims)
self._ExecuteAndCompareClose(
c, expected=np.sum(input_array, axis=tuple(dims)))
_ReduceAndTest(0)
_ReduceAndTest(0)
_ReduceAndTest(0, 1)
_ReduceAndTest(0, 2)
_ReduceAndTest(1, 2)
_ReduceAndTest(0, 1, 2)
def testReduceWindowValidUnitStridesF32(self):
input_array = NumpyArrayF32([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
c = self._NewComputation()
c.ReduceWindow(
operand=c.Constant(input_array),
init_value=c.ConstantF32Scalar(0),
computation_to_apply=self._CreateBinaryAddF32Computation(),
window_dimensions=(2, 1),
window_strides=(1, 1),
padding=xla_client.PaddingType.VALID)
self._ExecuteAndCompareClose(c, expected=[[5., 7., 9.]])
def testReduceWindowSameUnitStridesF32(self):
input_array = NumpyArrayF32([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
c = self._NewComputation()
c.ReduceWindow(
operand=c.Constant(input_array),
init_value=c.ConstantF32Scalar(0),
computation_to_apply=self._CreateBinaryAddF32Computation(),
window_dimensions=(2, 1),
window_strides=(1, 1),
padding=xla_client.PaddingType.SAME)
self._ExecuteAndCompareClose(c, expected=[[5., 7., 9.], [4., 5., 6.]])
def testReduceWindowValidGeneralStridesF32(self):
input_array = NumpyArrayF32([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
c = self._NewComputation()
c.ReduceWindow(
operand=c.Constant(input_array),
init_value=c.ConstantF32Scalar(0),
computation_to_apply=self._CreateBinaryAddF32Computation(),
window_dimensions=(2, 1),
window_strides=(1, 2),
padding=xla_client.PaddingType.VALID)
self._ExecuteAndCompareClose(c, expected=[[5., 9.]])
def testReduceWindowValidUnitStridesF64(self):
input_array = NumpyArrayF64([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
c = self._NewComputation()
c.ReduceWindow(
operand=c.Constant(input_array),
init_value=c.ConstantF64Scalar(0),
computation_to_apply=self._CreateBinaryAddF64Computation(),
window_dimensions=(2, 1),
window_strides=(1, 1),
padding=xla_client.PaddingType.VALID)
self._ExecuteAndCompareClose(c, expected=[[5., 7., 9.]])
def testReduceWindowSameUnitStridesF64(self):
input_array = NumpyArrayF64([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
c = self._NewComputation()
c.ReduceWindow(
operand=c.Constant(input_array),
init_value=c.ConstantF64Scalar(0),
computation_to_apply=self._CreateBinaryAddF64Computation(),
window_dimensions=(2, 1),
window_strides=(1, 1),
padding=xla_client.PaddingType.SAME)
self._ExecuteAndCompareClose(c, expected=[[5., 7., 9.], [4., 5., 6.]])
def testReduceWindowValidGeneralStridesF64(self):
input_array = NumpyArrayF64([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
c = self._NewComputation()
c.ReduceWindow(
operand=c.Constant(input_array),
init_value=c.ConstantF64Scalar(0),
computation_to_apply=self._CreateBinaryAddF64Computation(),
window_dimensions=(2, 1),
window_strides=(1, 2),
padding=xla_client.PaddingType.VALID)
self._ExecuteAndCompareClose(c, expected=[[5., 9.]])
def testWhileF32(self):
cond = self._CreateTestF32Lt10Computation()
body = self._CreateMulF32By2Computation()
c = self._NewComputation()
init = c.ConstantF32Scalar(1.)
c.While(cond, body, init)
self._ExecuteAndCompareClose(c, expected=16.)
def testWhileF64(self):
cond = self._CreateTestF64Lt10Computation()
body = self._CreateMulF64By2Computation()
c = self._NewComputation()
init = c.ConstantF64Scalar(1.)
c.While(cond, body, init)
self._ExecuteAndCompareClose(c, expected=16.)
def testConditionalTrue(self):
c = self._NewComputation()
pred = c.ConstantPredScalar(True)
true_operand = c.ConstantF32Scalar(3.)
true_computation = self._CreateMulF32By2Computation()
false_operand = c.ConstantF32Scalar(2.)
false_computation = self._CreateConstantF32Computation()
c.Conditional(pred, true_operand, true_computation, false_operand,
false_computation)
self._ExecuteAndCompareClose(c, expected=6.)
def testConditionalFalse(self):
c = self._NewComputation()
pred = c.ConstantPredScalar(False)
true_operand = c.ConstantF32Scalar(3.)
true_computation = self._CreateMulF32By2Computation()
false_operand = c.ConstantF32Scalar(2.)
false_computation = self._CreateConstantF32Computation()
c.Conditional(pred, true_operand, true_computation, false_operand,
false_computation)
self._ExecuteAndCompareClose(c, expected=1.)
def testInfeedS32Values(self):
to_infeed = NumpyArrayS32([1, 2, 3, 4])
c = self._NewComputation()
c.Infeed(xla_client.shape_from_pyval(to_infeed[0]))
compiled_c = c.Build().Compile()
for item in to_infeed:
xla_client.transfer_to_infeed(item)
for item in to_infeed:
result = xla_client.execute_with_python_values(compiled_c)
self.assertEqual(result, item)
def testInfeedThenOutfeedS32(self):
to_round_trip = NumpyArrayS32([1, 2, 3, 4])
c = self._NewComputation()
x = c.Infeed(xla_client.shape_from_pyval(to_round_trip[0]))
c.Outfeed(x)
compiled_c = c.Build().Compile()
for want in to_round_trip:
execution = threading.Thread(target=lambda: compiled_c.Execute([]))
execution.start()
xla_client.transfer_to_infeed(want)
got = xla_client.transfer_from_outfeed(
xla_client.shape_from_pyval(to_round_trip[0]))
execution.join()
self.assertEqual(want, got)
def testScatter(self):
a = np.arange(9).astype(np.int32).reshape((3, 3))
scatter_indices = np.array([0, 2], dtype=np.int32)
updates = np.array([[10, 20, 30], [70, 80, 90]], dtype=np.int32)
dnums = xla_client.ScatterDimensionNumbers()
dnums.update_window_dims.append(1)
dnums.inserted_window_dims.append(0)
dnums.scatter_dims_to_operand_dims.append(0)
dnums.index_vector_dim = 1
c = self._NewComputation()
c.Scatter(
c.Constant(a), c.Constant(scatter_indices), c.Constant(updates),
self._CreateBinaryAddS32Computation(), dnums)
expected = np.array([[10, 21, 32], [3, 4, 5], [76, 87, 98]], dtype=np.int32)
self._ExecuteAndCompareClose(c, expected=expected)
class ErrorTest(ComputationTest):
def setUp(self):
self.f32_scalar_2 = NumpyArrayF32(2.0)
self.s32_scalar_2 = NumpyArrayS32(2)
def testCompileWithWrongElementTypeInLayout(self):
c = self._NewComputation()
c.SetOpMetadata(xla_client.CurrentSourceInfoMetadata())
c.ParameterFromNumpy(self.s32_scalar_2)
c.ClearOpMetadata()
options = xla_client.CompileOptions()
options.argument_layouts = [
xla_client.Shape.array_shape(np.dtype(np.float32), [])
]
def TestFun():
return c.Build().Compile(compile_options=options)
self.assertRaisesRegexp(
RuntimeError, r".*Invalid argument shape.*"
r"expected s32\[\], got f32\[\].*", TestFun)
def testInvokeWithWrongElementType(self):
c = self._NewComputation()
c.SetOpMetadata(xla_client.CurrentSourceInfoMetadata())
c.ParameterFromNumpy(self.s32_scalar_2)
c.ClearOpMetadata()
def TestFun():
return xla_client.execute_with_python_values(c.Build().Compile(),
[self.f32_scalar_2])
self.assertRaisesRegexp(
RuntimeError, r"Invalid argument: Argument does not match.*"
r"want s32\[\], got f32\[\].*", TestFun)
class ComputationRootTest(ComputationTest):
"""Tests related to setting the root of the computation."""
def testComputationRootDifferentFromLastOp(self):
c = self._NewComputation()
x = c.ParameterFromNumpy(NumpyArrayF32(2.0))
result = c.Add(x, c.ConstantF32Scalar(3.14))
extra = c.Add(result, c.ConstantF32Scalar(1.618)) # pylint: disable=unused-variable
arg = NumpyArrayF32(1.0)
compiled_c = c.Build(result).Compile()
ans = xla_client.execute_with_python_values(compiled_c, [arg])
np.testing.assert_allclose(ans, 4.14)
if __name__ == "__main__":
unittest.main()
|
tensorflow-master
|
tensorflow/compiler/xla/python/xla_client_test.py
|
tensorflow-master
|
tensorflow/compiler/xla/python/__init__.py
|
|
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""XLA backend that runs XRT operators via TensorFlow remote eager.
This module implements the Python XLA client's `Backend` abstraction using XRT,
which embeds XLA's compiler/runtime operations as TensorFlow
operations. The module uses TensorFlow's remote eager RPC API to invoke XRT
operations.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# pylint: disable=g-direct-tensorflow-import
from tensorflow.compiler.xla.python import xla_client
from tensorflow.compiler.xla.python import xla_extension as _xla
# pylint: enable=g-direct-tensorflow-import
def get_tf_context(target, worker):
"""Returns a TensorFlow RPC client object.
Args:
target: string; a host:port pair (e.g., '10.0.101.1:8470') naming an XRT
server.
worker: string; the task name of the remote TensorFlow worker.
"""
client = _xla.xrt.GetTfClient(target, worker)
options = _xla.xrt.XrtTfContextOptions()
options.max_queue_size = 10000
return _xla.xrt.XrtTfContext.Create(options, client, worker, 0)
class XrtBackend(xla_client.Backend):
"""XLA backend using XRT.
Args:
tf_context: an XrtTfContext object.
tf_device_type: the type of TensorFlow device to use for XRT (e.g. `"TPU"`).
"""
def __init__(self, tf_context, tf_device_type, platform="tpu"):
super(XrtBackend, self).__init__(platform)
self.tf_device_type = tf_device_type
self.context = _xla.xrt.XrtContext.Create(tf_context, tf_device_type)
def device_count(self):
return self.context.DeviceCount()
def buffer_from_pyval(self, pyval, device=0):
return _xla.xrt.XrtBuffer.from_literal(self.context, device, pyval)
def make_tuple(self, buffers, device_ordinal):
return _xla.xrt.XrtBuffer.make_tuple(self.context, buffers, device_ordinal)
def compile(self, computation, compile_options):
# pylint: disable=protected-access
program_shape = computation.GetProgramShape()
# pylint: enable=protected-access
proto = computation.GetSerializedProto()
# TODO(phawkins): use the layouts in compile_options.
arg_shapes = [
shape.with_major_to_minor_layout_if_absent()
for shape in program_shape.parameter_shapes()
]
result_shape = (
program_shape.result_shape().with_major_to_minor_layout_if_absent())
device_assignment = _xla.xrt.AssignDevices(compile_options.num_replicas, 1)
return _xla.xrt.XrtExecutable.Compile(self.context, proto, arg_shapes,
result_shape, device_assignment)
|
tensorflow-master
|
tensorflow/compiler/xla/python/xrt.py
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Proof that transforming (A*C)+(B*C) <=> (A+B)*C is "safe" if C=2^k.
Specifically, for all floating-point values A, B, and C, if
- C is equal to +/- 2^k for some (possibly negative) integer k, and
- A, B, C, A*C, B*C, and A+B are not subnormal, zero, or inf,
then there exists a rounding mode rm in [RTZ, RNE] such that
(A*C) + (B*C) == (A+B) * C (computed with rounding mode rm).
Informally, this means that the equivalence holds for powers of 2 C, modulo
flushing to zero or inf, and modulo rounding of intermediate results.
Requires z3 python bindings; try `pip install z3-solver`.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import z3
# We do float16 because it lets the solver run much faster. These results
# should generalize to fp32 and fp64, and you can verify this by changing the
# value of FLOAT_TY (and then waiting a while).
FLOAT_TY = z3.Float16
a = z3.FP("a", FLOAT_TY())
b = z3.FP("b", FLOAT_TY())
c = z3.FP("c", FLOAT_TY())
s = z3.Solver()
# C must be a power of 2, i.e. significand bits must all be 0.
s.add(z3.Extract(FLOAT_TY().sbits() - 1, 0, z3.fpToIEEEBV(c)) == 0)
for rm in [z3.RTZ(), z3.RNE()]:
z3.set_default_rounding_mode(rm)
before = a * c + b * c
after = (a + b) * c
# Check that before == after, allowing that 0 == -0.
s.add(
z3.Not(
z3.Or(
before == after, #
z3.And(z3.fpIsZero(before), z3.fpIsZero(after)))))
for x in [
(a * c),
(b * c),
(a + b),
]:
s.add(z3.Not(z3.fpIsSubnormal(x)))
s.add(z3.Not(z3.fpIsZero(x)))
s.add(z3.Not(z3.fpIsInf(x)))
if s.check() == z3.sat:
m = s.model()
print("Counterexample found!")
print(m)
print("a*c: ", z3.simplify(m[a] * m[c]))
print("b*c: ", z3.simplify(m[b] * m[c]))
print("a+b: ", z3.simplify(m[a] + m[b]))
print("a*c + b*c: ", z3.simplify(m[a] * m[c] + m[b] * m[c]))
print("(a+b) * c: ", z3.simplify((m[a] + m[b]) * m[c]))
else:
print("Proved!")
|
tensorflow-master
|
tensorflow/compiler/xla/service/algebraic_simplifier_proof_distributive_property.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Test cases for ternary operators."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_math_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import googletest
class TernaryOpsTest(xla_test.XLATestCase):
def _testTernary(self, op, a, b, c, expected):
with self.session() as session:
with self.test_scope():
pa = array_ops.placeholder(dtypes.as_dtype(a.dtype), a.shape, name="a")
pb = array_ops.placeholder(dtypes.as_dtype(b.dtype), b.shape, name="b")
pc = array_ops.placeholder(dtypes.as_dtype(c.dtype), c.shape, name="c")
output = op(pa, pb, pc)
result = session.run(output, {pa: a, pb: b, pc: c})
self.assertAllClose(result, expected, rtol=1e-3)
def testLinspace(self):
self._testTernary(
math_ops.linspace,
np.float32(1),
np.float32(2),
np.int32(1),
expected=np.array([1], dtype=np.float32))
self._testTernary(
math_ops.linspace,
np.float32(1),
np.float32(4),
np.int32(3),
expected=np.array([1, 2.5, 4], dtype=np.float32))
def testRange(self):
self._testTernary(
math_ops.range,
np.int32(1),
np.int32(2),
np.int32(1),
expected=np.array([1], dtype=np.int32))
self._testTernary(
math_ops.range,
np.int32(1),
np.int32(7),
np.int32(2),
expected=np.array([1, 3, 5], dtype=np.int32))
def testSelect(self):
for dtype in self.numeric_types:
self._testTernary(
array_ops.where,
np.array(False),
np.array(2, dtype=dtype),
np.array(7, dtype=dtype),
expected=np.array(7, dtype=dtype))
self._testTernary(
array_ops.where,
np.array(True),
np.array([1, 2, 3, 4], dtype=dtype),
np.array([5, 6, 7, 8], dtype=dtype),
expected=np.array([1, 2, 3, 4], dtype=dtype))
self._testTernary(
array_ops.where,
np.array(False),
np.array([[1, 2], [3, 4], [5, 6]], dtype=dtype),
np.array([[7, 8], [9, 10], [11, 12]], dtype=dtype),
expected=np.array([[7, 8], [9, 10], [11, 12]], dtype=dtype))
self._testTernary(
array_ops.where,
np.array([0, 1, 1, 0], dtype=np.bool),
np.array([1, 2, 3, 4], dtype=dtype),
np.array([5, 6, 7, 8], dtype=dtype),
expected=np.array([5, 2, 3, 8], dtype=dtype))
self._testTernary(
array_ops.where,
np.array([0, 1, 0], dtype=np.bool),
np.array([[1, 2], [3, 4], [5, 6]], dtype=dtype),
np.array([[7, 8], [9, 10], [11, 12]], dtype=dtype),
expected=np.array([[7, 8], [3, 4], [11, 12]], dtype=dtype))
def testSelectV2(self):
for dtype in self.numeric_types:
self._testTernary(
array_ops.where_v2,
np.array(False),
np.array(2, dtype=dtype),
np.array(7, dtype=dtype),
expected=np.array(7, dtype=dtype))
self._testTernary(
array_ops.where_v2,
np.array(True),
np.array([1, 2, 3, 4], dtype=dtype),
np.array([5, 6, 7, 8], dtype=dtype),
expected=np.array([1, 2, 3, 4], dtype=dtype))
self._testTernary(
array_ops.where_v2,
np.array(False),
np.array([[1, 2], [3, 4], [5, 6]], dtype=dtype),
np.array([[7, 8], [9, 10], [11, 12]], dtype=dtype),
expected=np.array([[7, 8], [9, 10], [11, 12]], dtype=dtype))
self._testTernary(
array_ops.where_v2,
np.array([0, 1, 1, 0], dtype=np.bool),
np.array([1, 2, 3, 4], dtype=dtype),
np.array([5, 6, 7, 8], dtype=dtype),
expected=np.array([5, 2, 3, 8], dtype=dtype))
# Broadcast the condition
self._testTernary(
array_ops.where_v2,
np.array([0, 1], dtype=np.bool),
np.array([[1, 2], [3, 4], [5, 6]], dtype=dtype),
np.array([[7, 8], [9, 10], [11, 12]], dtype=dtype),
expected=np.array([[7, 2], [9, 4], [11, 6]], dtype=dtype))
# Broadcast the then branch to the else
self._testTernary(
array_ops.where_v2,
np.array([[0, 1], [1, 0], [1, 1]], dtype=np.bool),
np.array([[1, 2]], dtype=dtype),
np.array([[7, 8], [9, 10], [11, 12]], dtype=dtype),
expected=np.array([[7, 2], [1, 10], [1, 2]], dtype=dtype))
# Broadcast the else branch to the then
self._testTernary(
array_ops.where_v2,
np.array([[1, 0], [0, 1], [0, 0]], dtype=np.bool),
np.array([[7, 8], [9, 10], [11, 12]], dtype=dtype),
np.array([[1, 2]], dtype=dtype),
expected=np.array([[7, 2], [1, 10], [1, 2]], dtype=dtype))
# Broadcast the then/else branches to the condition
self._testTernary(
array_ops.where_v2,
np.array([[1, 0], [0, 1], [1, 1]], dtype=np.bool),
np.array(7, dtype=dtype),
np.array(8, dtype=dtype),
expected=np.array([[7, 8], [8, 7], [7, 7]], dtype=dtype))
self._testTernary(
array_ops.where_v2,
np.array([[1, 0], [0, 1], [0, 0]], dtype=np.bool),
np.array(7, dtype=dtype),
np.array([8, 9], dtype=dtype),
expected=np.array([[7, 9], [8, 7], [8, 9]], dtype=dtype))
def testSlice(self):
for dtype in self.numeric_types:
self._testTernary(
array_ops.slice,
np.array([[], [], []], dtype=dtype),
np.array([1, 0], dtype=np.int32),
np.array([2, 0], dtype=np.int32),
expected=np.array([[], []], dtype=dtype))
self._testTernary(
array_ops.slice,
np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=dtype),
np.array([0, 1], dtype=np.int32),
np.array([2, 1], dtype=np.int32),
expected=np.array([[2], [5]], dtype=dtype))
def testClipByValue(self):
for dtype in self.numeric_types - self.complex_types:
test_cases = [
(np.array([2, 4, 5], dtype=dtype), dtype(7)), #
(dtype(1), np.array([2, 4, 5], dtype=dtype)), #
(np.array([-2, 7, 7], dtype=dtype), np.array([-2, 9, 8], dtype=dtype))
]
x = np.array([-2, 10, 6], dtype=dtype)
for lower, upper in test_cases:
self._testTernary(
gen_math_ops._clip_by_value,
x,
lower,
upper,
expected=np.minimum(np.maximum(x, lower), upper))
if __name__ == "__main__":
googletest.main()
|
tensorflow-master
|
tensorflow/compiler/tests/ternary_ops_test.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for slicing."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import parameterized
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import googletest
class ReshapeTest(xla_test.XLATestCase, parameterized.TestCase):
@parameterized.named_parameters(('32_bit_index', dtypes.int32),
('64_bit_index', dtypes.int64))
def testBasic(self, index_dtype):
for dtype in self.numeric_types:
with self.session():
i = array_ops.placeholder(dtype, shape=[2, 3])
with self.test_scope():
shape = constant_op.constant([3, 2], dtype=index_dtype)
o = array_ops.reshape(i, shape)
params = {
i: [[1, 2, 3], [4, 5, 6]],
}
result = o.eval(feed_dict=params)
self.assertAllEqual([[1, 2], [3, 4], [5, 6]], result)
if __name__ == '__main__':
googletest.main()
|
tensorflow-master
|
tensorflow/compiler/tests/reshape_op_test.py
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for xla handling of placeholder_with_default."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.compiler.tests import xla_test
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import googletest
class PlaceholderTest(xla_test.XLATestCase):
def test_placeholder_with_default_default(self):
with self.session() as sess, self.test_scope():
v = resource_variable_ops.ResourceVariable(4.0)
ph = array_ops.placeholder_with_default(v, shape=[])
out = ph * 2
sess.run(variables.variables_initializer([v]))
self.assertEqual(8.0, self.evaluate(out))
def test_placeholder_with_default_fed(self):
with self.session() as sess, self.test_scope():
v = resource_variable_ops.ResourceVariable(4.0)
ph = array_ops.placeholder_with_default(v, shape=[])
out = ph * 2
sess.run(variables.variables_initializer([v]))
self.assertEqual(2.0, sess.run(out, {ph: 1.0}))
if __name__ == '__main__':
googletest.main()
|
tensorflow-master
|
tensorflow/compiler/tests/placeholder_test.py
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for tensorflow.ops.tf.scatter_nd."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import numpy as np
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import errors
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
def _AsType(v, vtype):
return v.astype(vtype) if isinstance(v, np.ndarray) else vtype(v)
def _FlatInnerDims(tensor, ndims=2):
shape = list(tensor.shape)
return tensor.reshape(
[functools.reduce(lambda x, y: x * y, shape[:-ndims + 1], 1)] +
shape[-ndims + 1:])
def _FlatOuterDims(tensor, ndims=2):
shape = list(tensor.shape)
return tensor.reshape(
shape[:ndims - 1] +
[functools.reduce(lambda x, y: x * y, shape[ndims - 1:], 1)])
def _NumpyScatterNd(ref, indices, updates, op):
ixdim = indices.shape[-1]
num_updates = indices.size // ixdim
total_nd = len(ref.shape)
slice_size = 1
for i in range(ixdim, total_nd):
slice_size *= ref.shape[i]
flat_indices = _FlatInnerDims(indices)
flat_updates = updates.reshape((num_updates, slice_size))
output_flat = _FlatOuterDims(ref, ixdim + 1)
for ix_updates, ix_output in enumerate(flat_indices):
ix_output = tuple(ix_output)
output_flat[ix_output] = op(output_flat[ix_output],
flat_updates[ix_updates])
return output_flat.reshape(ref.shape)
def _NumpyUpdate(indices, updates, shape):
ref = np.zeros(shape, dtype=updates.dtype)
return _NumpyScatterNd(ref, indices, updates, lambda p, u: u)
class ScatterNdTest(xla_test.XLATestCase):
def _VariableRankTest(self,
np_scatter,
tf_scatter,
vtype,
itype,
repeat_indices=False):
np.random.seed(8)
ref_shapes = [(3, 6), (3, 6), (3, 6, 9), (3, 6, 9), (3, 6, 9), (3, 6, 9)]
indices_shapes = [(2,), (2, 2), (2,), (2, 2), (2, 3), (2, 3, 3)]
for ref_shape, indices_shape in zip(ref_shapes, indices_shapes):
num_updates = indices_shape[0]
ixdim = indices_shape[-1]
indexable_area_shape = ()
for i in range(ixdim):
indexable_area_shape += (ref_shape[i],)
all_indices = [
list(coord)
for coord, _ in np.ndenumerate(np.empty(indexable_area_shape, vtype))
]
np.random.shuffle(all_indices)
indices = np.array(all_indices[:num_updates])
if num_updates > 1 and repeat_indices:
indices = indices[:num_updates // 2]
for _ in range(num_updates - num_updates // 2):
indices = np.append(
indices, [indices[np.random.randint(num_updates // 2)]], axis=0)
np.random.shuffle(indices)
indices = _AsType(indices[:num_updates], itype)
updates_shape = (num_updates,)
for i in range(ixdim, len(ref_shape)):
updates_shape += (ref_shape[i],)
updates = _AsType(np.random.randn(*(updates_shape)), vtype)
# Scatter via numpy
np_out = np_scatter(indices, updates, ref_shape)
# Scatter via tensorflow
tf_out = tf_scatter(indices, updates, ref_shape)
self.assertAllClose(np_out, tf_out)
def _VariableRankTests(self, np_scatter, tf_scatter):
for vtype in self.numeric_types:
for itype in set([np.int32, np.int64]).intersection(set(self.int_types)):
self._VariableRankTest(np_scatter, tf_scatter, vtype, itype)
def _runScatterNd(self, indices, updates, shape):
with self.session():
updates_placeholder = array_ops.placeholder(updates.dtype)
indices_placeholder = array_ops.placeholder(indices.dtype)
with self.test_scope():
output = array_ops.scatter_nd(indices_placeholder, updates_placeholder,
shape)
feed_dict = {updates_placeholder: updates, indices_placeholder: indices}
return output.eval(feed_dict=feed_dict)
def testSimple(self):
indices = np.array([[4], [3], [1], [7]], dtype=np.int32)
updates = np.array([9, 10, 11, 12], dtype=np.float32)
expected = np.array([0, 11, 0, 10, 9, 0, 0, 12], dtype=np.int32)
self.assertAllEqual(expected, self._runScatterNd(indices, updates, [8]))
def testRepeatedIndices(self):
indices = np.array([[0], [1], [0], [1]], dtype=np.int32)
updates = np.array([9, 10, 11, 12], dtype=np.float32)
expected = np.array([20, 22], dtype=np.int32)
self.assertAllEqual(expected, self._runScatterNd(indices, updates, [2]))
def testSimple2(self):
indices = np.array([[1, 0], [1, 1]], dtype=np.int32)
updates = np.array([11., 12.], dtype=np.float32)
expected = np.array([[0., 0.], [11., 12.], [0., 0.]], dtype=np.float32)
self.assertAllEqual(expected, self._runScatterNd(indices, updates, [3, 2]))
def testSimple3(self):
indices = np.array([[1]], dtype=np.int32)
updates = np.array([[11., 12.]], dtype=np.float32)
expected = np.array([[0., 0.], [11., 12.], [0., 0.]])
self.assertAllEqual(expected, self._runScatterNd(indices, updates, [3, 2]))
def testVariableRankUpdate(self):
self._VariableRankTests(_NumpyUpdate, self._runScatterNd)
def testExtraIndicesDimensions(self):
indices = np.zeros([1, 1, 2], np.int32)
updates = np.zeros([1, 1], np.int32)
expected = np.zeros([2, 2], dtype=np.int32)
self.assertAllEqual(expected, self._runScatterNd(indices, updates, [2, 2]))
def testRank3InvalidShape1(self):
indices = np.zeros([3, 2, 2], np.int32)
updates = np.zeros([2, 2, 2], np.int32)
with self.assertRaisesWithPredicateMatch(errors.InvalidArgumentError,
"Must have updates.shape"):
self._runScatterNd(indices, updates, [2, 2, 2])
def testRank3InvalidShape2(self):
indices = np.zeros([2, 2, 1], np.int32)
updates = np.zeros([2, 2], np.int32)
with self.assertRaisesWithPredicateMatch(errors.InvalidArgumentError,
"Must have updates.shape"):
self._runScatterNd(indices, updates, [2, 2, 2])
def testScatterOutOfRange(self):
updates = np.array([-3, -4, -5]).astype(np.float32)
# Indices all in range, no problem.
indices = np.array([[2], [0], [5]], dtype=np.int32)
self._runScatterNd(indices, updates, [6])
# Indices out of range should not fail. It produces implementation-defined
# output.
indices = np.array([[-1], [0], [5]], dtype=np.int32)
self._runScatterNd(indices, updates, [6])
indices = np.array([[2], [0], [6]], dtype=np.int32)
self._runScatterNd(indices, updates, [6])
class ScatterNdTensorTest(xla_test.XLATestCase):
def _runScatter(self, op):
indices_np = np.array([[4], [3], [1], [7]], dtype=np.int32)
updates_np = np.array([9, 10, 11, 12], dtype=np.float32)
with self.session() as sess, self.test_scope():
indices = array_ops.placeholder(indices_np.dtype, shape=indices_np.shape)
updates = array_ops.placeholder(updates_np.dtype, shape=updates_np.shape)
t = array_ops.ones([8], dtype=np.float32)
out = op(t, indices, updates)
return sess.run(out, feed_dict={indices: indices_np, updates: updates_np})
def testAdd(self):
self.assertAllEqual(
self._runScatter(array_ops.tensor_scatter_add),
np.array([1, 12, 1, 11, 10, 1, 1, 13], dtype=np.float32))
def testSub(self):
self.assertAllEqual(
self._runScatter(array_ops.tensor_scatter_sub),
np.array([1, -10, 1, -9, -8, 1, 1, -11], dtype=np.float32))
def testUpdate(self):
self.assertAllEqual(
self._runScatter(array_ops.tensor_scatter_update),
np.array([1, 11, 1, 10, 9, 1, 1, 12], dtype=np.float32))
if __name__ == "__main__":
test.main()
|
tensorflow-master
|
tensorflow/compiler/tests/scatter_nd_op_test.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Test cases for segment reduction ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import numpy as np
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import googletest
class SegmentReductionOpsTest(xla_test.XLATestCase):
"""Test cases for segment reduction ops."""
def _segmentReduction(self, op, data, indices, num_segments):
with self.session() as sess, self.test_scope():
d = array_ops.placeholder(data.dtype, shape=data.shape)
if isinstance(indices, int):
i = array_ops.placeholder(np.int32, shape=[])
else:
i = array_ops.placeholder(indices.dtype, shape=indices.shape)
return sess.run(op(d, i, num_segments), {d: data, i: indices})
def _unsortedSegmentSum(self, data, indices, num_segments):
return self._segmentReduction(math_ops.unsorted_segment_sum, data, indices,
num_segments)
def _unsortedSegmentProd(self, data, indices, num_segments):
return self._segmentReduction(math_ops.unsorted_segment_prod, data, indices,
num_segments)
def _unsortedSegmentMin(self, data, indices, num_segments):
return self._segmentReduction(math_ops.unsorted_segment_min, data, indices,
num_segments)
def _unsortedSegmentMax(self, data, indices, num_segments):
return self._segmentReduction(math_ops.unsorted_segment_max, data, indices,
num_segments)
def testUnsortedSegmentSum0DIndices1DData(self):
for dtype in self.numeric_types:
self.assertAllClose(
np.array(
[[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 1, 2, 3, 4, 5],
[0, 0, 0, 0, 0, 0]],
dtype=dtype),
self._unsortedSegmentSum(
np.array([0, 1, 2, 3, 4, 5], dtype=dtype), 2, 4))
def testUnsortedSegmentSum1DIndices1DData(self):
for dtype in self.numeric_types:
self.assertAllClose(
np.array([1, 3, 2, 9], dtype=dtype),
self._unsortedSegmentSum(
np.array([0, 1, 2, 3, 4, 5], dtype=dtype),
np.array([3, 0, 2, 1, 3, 3], dtype=np.int32), 4))
def testUnsortedSegmentSum1DIndices1DDataNegativeIndices(self):
for dtype in self.numeric_types:
self.assertAllClose(
np.array([6, 3, 0, 6], dtype=dtype),
self._unsortedSegmentSum(
np.array([0, 1, 2, 3, 4, 5, 6], dtype=dtype),
np.array([3, -1, 0, 1, 0, -1, 3], dtype=np.int32), 4))
def testUnsortedSegmentSum1DIndices2DDataDisjoint(self):
for dtype in self.numeric_types:
data = np.array(
[[0, 1, 2, 3], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43],
[50, 51, 52, 53]],
dtype=dtype)
indices = np.array([8, 1, 0, 3, 7], dtype=np.int32)
num_segments = 10
y = self._unsortedSegmentSum(data, indices, num_segments)
self.assertAllClose(
np.array(
[[30, 31, 32, 33], [20, 21, 22, 23], [0, 0, 0, 0],
[40, 41, 42, 43], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0],
[50, 51, 52, 53], [0, 1, 2, 3], [0, 0, 0, 0]],
dtype=dtype), y)
def testUnsortedSegmentSum1DIndices2DDataNonDisjoint(self):
for dtype in self.numeric_types:
data = np.array(
[[0, 1, 2, 3], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43],
[50, 51, 52, 53]],
dtype=dtype)
indices = np.array([0, 1, 2, 0, 1], dtype=np.int32)
num_segments = 4
y = self._unsortedSegmentSum(data, indices, num_segments)
self.assertAllClose(
np.array(
[[40, 42, 44, 46], [70, 72, 74, 76], [30, 31, 32, 33],
[0, 0, 0, 0]],
dtype=dtype), y)
def testUnsortedSegmentSum2DIndices3DData(self):
for dtype in self.numeric_types:
data = np.array(
[[[0, 1, 2], [10, 11, 12]], [[100, 101, 102], [110, 111, 112]], [[
200, 201, 202
], [210, 211, 212]], [[300, 301, 302], [310, 311, 312]]],
dtype=dtype)
indices = np.array([[3, 5], [3, 1], [5, 0], [6, 2]], dtype=np.int32)
num_segments = 8
y = self._unsortedSegmentSum(data, indices, num_segments)
self.assertAllClose(
np.array(
[[210, 211, 212], [110, 111, 112], [310, 311, 312], [
100, 102, 104
], [0, 0, 0.], [210, 212, 214], [300, 301, 302], [0, 0, 0]],
dtype=dtype), y)
def testUnsortedSegmentSum1DIndices3DData(self):
for dtype in self.numeric_types:
data = np.array(
[[[0, 1, 2], [10, 11, 12]], [[100, 101, 102], [110, 111, 112]], [[
200, 201, 202
], [210, 211, 212]], [[300, 301, 302], [310, 311, 312]]],
dtype=dtype)
indices = np.array([3, 0, 2, 5], dtype=np.int32)
num_segments = 6
y = self._unsortedSegmentSum(data, indices, num_segments)
self.assertAllClose(
np.array(
[[[100, 101, 102.], [110, 111, 112]], [[0, 0, 0], [0, 0, 0]],
[[200, 201, 202], [210, 211, 212]], [[0, 1, 2.], [10, 11, 12]],
[[0, 0, 0], [0, 0, 0]], [[300, 301, 302], [310, 311, 312]]],
dtype=dtype), y)
def testUnsortedSegmentSumShapeError(self):
for dtype in self.numeric_types:
data = np.ones((4, 8, 7), dtype=dtype)
indices = np.ones((3, 2), dtype=np.int32)
num_segments = 4
self.assertRaises(
ValueError,
functools.partial(self._segmentReduction,
math_ops.unsorted_segment_sum, data, indices,
num_segments))
def testUnsortedSegmentOps1DIndices1DDataNegativeIndices(self):
"""Tests for min, max, and prod ops.
These share most of their implementation with sum, so we only test basic
functionality.
"""
for dtype in self.numeric_types:
self.assertAllClose(
np.array([8, 3, 1, 0], dtype=dtype),
self._unsortedSegmentProd(
np.array([0, 1, 2, 3, 4, 5, 6], dtype=dtype),
np.array([3, -1, 0, 1, 0, -1, 3], dtype=np.int32), 4))
for dtype in self.int_types | self.float_types:
minval = dtypes.as_dtype(dtype).min
maxval = dtypes.as_dtype(dtype).max
self.assertAllClose(
np.array([2, 3, maxval, 0], dtype=dtype),
self._unsortedSegmentMin(
np.array([0, 1, 2, 3, 4, 5, 6], dtype=dtype),
np.array([3, -1, 0, 1, 0, -1, 3], dtype=np.int32), 4))
self.assertAllClose(
np.array([4, 3, minval, 6], dtype=dtype),
self._unsortedSegmentMax(
np.array([0, 1, 2, 3, 4, 5, 6], dtype=dtype),
np.array([3, -1, 0, 1, 0, -1, 3], dtype=np.int32), 4))
if __name__ == "__main__":
googletest.main()
|
tensorflow-master
|
tensorflow/compiler/tests/segment_reduction_ops_test.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for Local Response Normalization ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import numpy as np
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_nn_ops
from tensorflow.python.ops import nn
from tensorflow.python.platform import googletest
CPU_DEVICE = "/job:localhost/replica:0/task:0/cpu:0"
# Local response normalization tests. The forward tests are copied from
# tensorflow/python/kernel_tests/lrn_op_test.py
class LRNTest(xla_test.XLATestCase):
def _LRN(self, input_image, lrn_depth_radius=5, bias=1.0, alpha=1.0,
beta=0.5):
"""Compute expected result."""
output = copy.deepcopy(input_image)
batch_size = input_image.shape[0]
rows = input_image.shape[1]
cols = input_image.shape[2]
depth = input_image.shape[3]
for b in range(batch_size):
for r in range(rows):
for c in range(cols):
for d in range(depth):
begin = max(0, d - lrn_depth_radius)
end = min(depth, d + lrn_depth_radius + 1)
patch = input_image[b, r, c, begin:end]
output[b, r, c, d] /= (
np.power(bias + alpha * np.sum(patch * patch), beta))
return output
def _RunAndVerify(self, dtype):
with self.session():
# random shape
shape = np.random.randint(1, 16, size=4)
# Make depth at least 2 to make it meaningful
shape[3] += 1
p = array_ops.placeholder(dtype, shape=shape)
# random depth_radius, bias, alpha, beta
lrn_depth_radius = np.random.randint(1, shape[3])
bias = 1.0 + np.random.rand()
alpha = 2.0 * np.random.rand()
beta = 2.0 * np.random.rand()
with self.test_scope():
lrn_t = nn.local_response_normalization(
p,
name="lrn",
depth_radius=lrn_depth_radius,
bias=bias,
alpha=alpha,
beta=beta)
params = {p: np.random.rand(*shape).astype("f")}
result = lrn_t.eval(feed_dict=params)
expected = self._LRN(
params[p],
lrn_depth_radius=lrn_depth_radius,
bias=bias,
alpha=alpha,
beta=beta)
err = np.amax(np.abs(result - expected))
print("LRN error for bias ", bias, "alpha ", alpha, " beta ", beta, " is ",
err)
if dtype == dtypes.float32:
self.assertTrue(err < 1e-4)
else:
self.assertTrue(err < 1e-2)
self.assertShapeEqual(expected, lrn_t)
def testCompute(self):
for _ in range(2):
self._RunAndVerify(dtypes.float32)
def testLrnGrad(self):
# Test for LRNGrad that compares against the CPU implementation.
shape = [1, 2, 3, 4]
total_size = np.prod(shape)
in_image_vals = np.arange(1, total_size + 1, dtype=np.float32)
out_image_vals = np.arange(1, total_size + 1, dtype=np.float32)
out_grads_vals = np.arange(1, total_size + 1, dtype=np.float32)
depth_radius = np.random.randint(1, shape[3])
bias = 1.0 + np.random.rand()
alpha = 1.0 * np.random.rand()
beta = 1.0 * np.random.rand()
with self.session():
in_image = constant_op.constant(in_image_vals, shape=shape)
out_image = constant_op.constant(out_image_vals, shape=shape)
out_grads = constant_op.constant(out_grads_vals, shape=shape)
with ops.device(CPU_DEVICE):
expected = gen_nn_ops.lrn_grad(out_grads, in_image, out_image,
depth_radius, bias, alpha, beta)
with self.test_scope():
actual = gen_nn_ops.lrn_grad(out_grads, in_image, out_image,
depth_radius, bias, alpha, beta)
expected_val = self.evaluate(expected)
actual_val = self.evaluate(actual)
self.assertAllClose(actual_val, expected_val, rtol=1e-3)
if __name__ == "__main__":
googletest.main()
|
tensorflow-master
|
tensorflow/compiler/tests/lrn_ops_test.py
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Test cases for XLA devices."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_control_flow_ops
from tensorflow.python.platform import test
class XlaDeviceTest(xla_test.XLATestCase):
def testCopies(self):
"""Tests that copies onto and off XLA devices work."""
shapes = [[0], [1], [1, 0], [1024, 0], [1024, 1], [3, 777], [777, 3],
[16384, 1], [1, 16384], [1, 20000, 1, 1]]
for dtype in self.numeric_types:
for shape in shapes:
with self.session() as sess:
with ops.device("CPU"):
x = array_ops.placeholder(dtype, shape)
with self.test_scope():
y = x + x
with ops.device("CPU"):
z = array_ops.identity(y)
inputs = np.random.randint(-100, 100, shape).astype(dtype)
result = sess.run(z, {x: inputs})
self.assertAllCloseAccordingToType(result, inputs + inputs)
def testCopiesOfUnsupportedTypesFailGracefully(self):
"""Tests that copies of unsupported types don't crash."""
test_types = set([
np.uint8, np.uint16, np.uint32, np.uint64, np.int8, np.int16, np.int32,
np.int64, np.float16, np.float32, np.float16,
dtypes.bfloat16.as_numpy_dtype
])
shape = (10, 10)
for unsupported_dtype in test_types - self.all_types:
with self.session() as sess:
with ops.device("CPU"):
x = array_ops.placeholder(unsupported_dtype, shape)
with self.test_scope():
y, = array_ops.identity_n([x])
with ops.device("CPU"):
z = array_ops.identity(y)
inputs = np.random.randint(-100, 100, shape)
inputs = inputs.astype(unsupported_dtype)
# Execution should either succeed or raise an InvalidArgumentError,
# but not crash. Even "unsupported types" may succeed here since some
# backends (e.g., the CPU backend) are happy to handle buffers of
# unsupported types, even if they cannot compute with them.
try:
sess.run(z, {x: inputs})
except errors.InvalidArgumentError:
pass
def testControlTrigger(self):
with self.session() as sess:
with self.test_scope():
x = gen_control_flow_ops.control_trigger()
self.evaluate(x)
if __name__ == "__main__":
test.main()
|
tensorflow-master
|
tensorflow/compiler/tests/xla_device_test.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for FFT via the XLA JIT."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
import numpy as np
import scipy.signal as sps
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops.signal import signal
from tensorflow.python.platform import googletest
BATCH_DIMS = (3, 5)
RTOL = 0.02 # Eigen/cuFFT differ widely from np, especially for FFT3D
ATOL = 1e-3
def pick_10(x):
x = list(x)
np.random.seed(123)
np.random.shuffle(x)
return x[:10]
def to_32bit(x):
if x.dtype == np.complex128:
return x.astype(np.complex64)
if x.dtype == np.float64:
return x.astype(np.float32)
return x
POWS_OF_2 = 2**np.arange(3, 12)
INNER_DIMS_1D = list((x,) for x in POWS_OF_2)
POWS_OF_2 = 2**np.arange(3, 8) # To avoid OOM on GPU.
INNER_DIMS_2D = pick_10(itertools.product(POWS_OF_2, POWS_OF_2))
INNER_DIMS_3D = pick_10(itertools.product(POWS_OF_2, POWS_OF_2, POWS_OF_2))
class FFTTest(xla_test.XLATestCase):
def _VerifyFftMethod(self, inner_dims, complex_to_input, input_to_expected,
tf_method):
for indims in inner_dims:
print("nfft =", indims)
shape = BATCH_DIMS + indims
data = np.arange(np.prod(shape) * 2) / np.prod(indims)
np.random.seed(123)
np.random.shuffle(data)
data = np.reshape(data.astype(np.float32).view(np.complex64), shape)
data = to_32bit(complex_to_input(data))
expected = to_32bit(input_to_expected(data))
with self.session() as sess:
with self.test_scope():
ph = array_ops.placeholder(
dtypes.as_dtype(data.dtype), shape=data.shape)
out = tf_method(ph)
value = sess.run(out, {ph: data})
self.assertAllClose(expected, value, rtol=RTOL, atol=ATOL)
def testContribSignalSTFT(self):
ws = 512
hs = 128
dims = (ws * 20,)
shape = BATCH_DIMS + dims
data = np.arange(np.prod(shape)) / np.prod(dims)
np.random.seed(123)
np.random.shuffle(data)
data = np.reshape(data.astype(np.float32), shape)
window = sps.get_window("hann", ws)
expected = sps.stft(
data, nperseg=ws, noverlap=ws - hs, boundary=None, window=window)[2]
expected = np.swapaxes(expected, -1, -2)
expected *= window.sum() # scipy divides by window sum
with self.session() as sess:
with self.test_scope():
ph = array_ops.placeholder(
dtypes.as_dtype(data.dtype), shape=data.shape)
out = signal.stft(ph, ws, hs)
grad = gradients_impl.gradients(out, ph,
grad_ys=array_ops.ones_like(out))
# For gradients, we simply verify that they compile & execute.
value, _ = sess.run([out, grad], {ph: data})
self.assertAllClose(expected, value, rtol=RTOL, atol=ATOL)
def testFFT(self):
self._VerifyFftMethod(INNER_DIMS_1D, lambda x: x, np.fft.fft,
signal.fft)
def testFFT2D(self):
self._VerifyFftMethod(INNER_DIMS_2D, lambda x: x, np.fft.fft2,
signal.fft2d)
def testFFT3D(self):
self._VerifyFftMethod(INNER_DIMS_3D, lambda x: x,
lambda x: np.fft.fftn(x, axes=(-3, -2, -1)),
signal.fft3d)
def testIFFT(self):
self._VerifyFftMethod(INNER_DIMS_1D, lambda x: x, np.fft.ifft,
signal.ifft)
def testIFFT2D(self):
self._VerifyFftMethod(INNER_DIMS_2D, lambda x: x, np.fft.ifft2,
signal.ifft2d)
def testIFFT3D(self):
self._VerifyFftMethod(INNER_DIMS_3D, lambda x: x,
lambda x: np.fft.ifftn(x, axes=(-3, -2, -1)),
signal.ifft3d)
def testRFFT(self):
self._VerifyFftMethod(
INNER_DIMS_1D, np.real, lambda x: np.fft.rfft(x, n=x.shape[-1]),
lambda x: signal.rfft(x, fft_length=[x.shape[-1].value]))
def testRFFT2D(self):
def _tf_fn(x):
return signal.rfft2d(
x, fft_length=[x.shape[-2].value, x.shape[-1].value])
self._VerifyFftMethod(
INNER_DIMS_2D, np.real,
lambda x: np.fft.rfft2(x, s=[x.shape[-2], x.shape[-1]]), _tf_fn)
def testRFFT3D(self):
def _to_expected(x):
return np.fft.rfftn(
x, axes=(-3, -2, -1), s=[x.shape[-3], x.shape[-2], x.shape[-1]])
def _tf_fn(x):
return signal.rfft3d(
x,
fft_length=[x.shape[-3].value, x.shape[-2].value, x.shape[-1].value])
self._VerifyFftMethod(INNER_DIMS_3D, np.real, _to_expected, _tf_fn)
def testRFFT3DMismatchedSize(self):
def _to_expected(x):
return np.fft.rfftn(
x,
axes=(-3, -2, -1),
s=[x.shape[-3] // 2, x.shape[-2], x.shape[-1] * 2])
def _tf_fn(x):
return signal.rfft3d(
x,
fft_length=[
x.shape[-3].value // 2, x.shape[-2].value, x.shape[-1].value * 2
])
self._VerifyFftMethod(INNER_DIMS_3D, np.real, _to_expected, _tf_fn)
def testIRFFT(self):
def _tf_fn(x):
return signal.irfft(x, fft_length=[2 * (x.shape[-1].value - 1)])
self._VerifyFftMethod(
INNER_DIMS_1D, lambda x: np.fft.rfft(np.real(x), n=x.shape[-1]),
lambda x: np.fft.irfft(x, n=2 * (x.shape[-1] - 1)), _tf_fn)
def testIRFFT2D(self):
def _tf_fn(x):
return signal.irfft2d(
x, fft_length=[x.shape[-2].value, 2 * (x.shape[-1].value - 1)])
self._VerifyFftMethod(
INNER_DIMS_2D,
lambda x: np.fft.rfft2(np.real(x), s=[x.shape[-2], x.shape[-1]]),
lambda x: np.fft.irfft2(x, s=[x.shape[-2], 2 * (x.shape[-1] - 1)]),
_tf_fn)
def testIRFFT3D(self):
def _to_input(x):
return np.fft.rfftn(
np.real(x),
axes=(-3, -2, -1),
s=[x.shape[-3], x.shape[-2], x.shape[-1]])
def _to_expected(x):
return np.fft.irfftn(
x,
axes=(-3, -2, -1),
s=[x.shape[-3], x.shape[-2], 2 * (x.shape[-1] - 1)])
def _tf_fn(x):
return signal.irfft3d(
x,
fft_length=[
x.shape[-3].value, x.shape[-2].value, 2 * (x.shape[-1].value - 1)
])
self._VerifyFftMethod(INNER_DIMS_3D, _to_input, _to_expected, _tf_fn)
def testIRFFT3DMismatchedSize(self):
def _to_input(x):
return np.fft.rfftn(
np.real(x),
axes=(-3, -2, -1),
s=[x.shape[-3] // 2, x.shape[-2], x.shape[-1] * 2])
def _to_expected(x):
return np.fft.irfftn(
x,
axes=(-3, -2, -1),
s=[x.shape[-3] // 2, x.shape[-2], x.shape[-1] * 2])
def _tf_fn(x):
return signal.irfft3d(
x,
fft_length=[
x.shape[-3].value // 2, x.shape[-2].value, x.shape[-1].value * 2
])
self._VerifyFftMethod(INNER_DIMS_3D, _to_input, _to_expected, _tf_fn)
if __name__ == "__main__":
googletest.main()
|
tensorflow-master
|
tensorflow/compiler/tests/fft_test.py
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for tensorflow.ops.tf.gather_nd."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import errors
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
class GatherNdTest(xla_test.XLATestCase):
def _runGather(self, params, indices):
with self.session():
paramsp = array_ops.placeholder(params.dtype)
indicesp = array_ops.placeholder(indices.dtype)
with self.test_scope():
gather_nd_t = array_ops.gather_nd(paramsp, indicesp)
feed_dict = {paramsp: params, indicesp: indices}
return gather_nd_t.eval(feed_dict=feed_dict)
def testSimpleDtype(self):
for dtype in self.numeric_types:
self.assertAllEqual(
np.array([7, 7, 8], dtype=dtype),
self._runGather(
np.array([8, 1, 2, 3, 7, 5], dtype=dtype),
np.array([[4], [4], [0]], np.int32)))
def testEmptyIndicesAndParamsOKButJustEmptyParamsFails(self):
with self.session():
params = np.ones((3, 3), dtype=np.float32)
indices_empty = np.empty((0, 2), dtype=np.int32)
gather_nd_ok_val = self._runGather(params, indices_empty)
self.assertAllClose(np.empty((0,), dtype=np.float32), gather_nd_ok_val)
indices_empty = np.empty((0, 1), dtype=np.int32)
gather_nd_ok_val = self._runGather(params, indices_empty)
self.assertAllClose(np.empty((0, 3), dtype=np.float32), gather_nd_ok_val)
params_empty = np.empty((0, 3), dtype=np.float32)
indices_empty = np.empty((0, 2), dtype=np.int32)
gather_nd_ok_val = self._runGather(params_empty, indices_empty)
self.assertAllClose(np.empty((0,), dtype=np.float32), gather_nd_ok_val)
params_empty = np.empty((0, 3), dtype=np.float32)
indices_nonempty = np.zeros((1, 2), dtype=np.int32)
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError, r"Gather dimension 0 is of size zero"):
self._runGather(params_empty, indices_nonempty)
def testIndexScalar(self):
params = np.array(
[[-8, -1, -2, -3, -7, -5], [8, 1, 2, 3, 7, 5]], dtype=np.float32).T
indices = np.array([4, 1], dtype=np.int32)
gather_nd_val = self._runGather(params, indices)
self.assertAllEqual(np.array(7), gather_nd_val)
def testParamsRankLargerThanIndexIndexScalarSlices(self):
params = np.array(
[[-8, -1, -2, -3, -7, -5], [8, 1, 2, 3, 7, 5]], dtype=np.float32).T
indices = np.array(
[
4,
], dtype=np.int32)
gather_nd_val = self._runGather(params, indices)
self.assertAllEqual(np.array([-7, 7]), gather_nd_val)
def testParamsRankLargerThanIndexSlices(self):
params = np.array(
[[-8, -1, -2, -3, -7, -5], [8, 1, 2, 3, 7, 5]], dtype=np.float32).T
indices = np.array([[4], [4], [0]], np.int32)
gather_nd_val = self._runGather(params, indices)
self.assertAllEqual(np.array([[-7, 7], [-7, 7], [-8, 8]]), gather_nd_val)
def testHigherRankParamsLargerThanIndexSlices(self):
params = np.array(
[[[-8, -1, -2, -3, -7, -5], [8, 1, 2, 3, 7, 5]],
[[-80, -10, -20, -30, -70, -50], [80, 10, 20, 30, 70, 50]]],
dtype=np.float32).T
indices = np.array([[4], [4], [0]], np.int32)
gather_nd_val = self._runGather(params, indices)
self.assertAllEqual(params[[4, 4, 0]], gather_nd_val)
def testEmptyIndicesLastRankMeansCopyEntireTensor(self):
params = np.array(
[[[-8, -1, -2, -3, -7, -5], [8, 1, 2, 3, 7, 5]],
[[-80, -10, -20, -30, -70, -50], [80, 10, 20, 30, 70, 50]]],
dtype=np.float32).T
indices = np.array([[], []], dtype=np.int32) # Size (2, 0)
gather_nd_val = self._runGather(params, indices)
self.assertAllEqual(
np.vstack((params[np.newaxis, :], params[np.newaxis, :])),
gather_nd_val)
def testHigherRankParamsAndIndicesLargerThanIndexSlices(self):
params = np.array(
[[[-8, -1, -2, -3, -7, -5], [8, 1, 2, 3, 7, 5]],
[[-80, -10, -20, -30, -70, -50], [80, 10, 20, 30, 70, 50]]],
dtype=np.float32).T
indices = np.array([[[3], [2], [1]], [[4], [4], [0]]], np.int32)
gather_nd_val = self._runGather(params, indices)
self.assertAllEqual(params[[3, 2, 1, 4, 4, 0]].reshape(2, 3, 2, 2),
gather_nd_val)
def testHigherRankParams(self):
shape = (10, 20, 5, 1, 17)
params = np.random.rand(*shape).astype(np.float32)
indices = np.vstack(
[np.random.randint(0, s, size=2000, dtype=np.int32) for s in shape]).T
gather_nd_val = self._runGather(params, indices)
expected = params[tuple(indices.T)]
self.assertAllEqual(expected, gather_nd_val)
def testHigherRankParamsAndIndices(self):
shape = (10, 20, 5, 1, 17)
params = np.random.rand(*shape).astype(np.float32)
indices = np.vstack(
[np.random.randint(0, s, size=2000, dtype=np.int32) for s in shape]).T
indices_reshaped = indices.reshape([10, 10, 20, 5])
gather_nd_val = self._runGather(params, indices_reshaped)
expected = params[tuple(indices.T)]
self.assertAllEqual(expected.reshape([10, 10, 20]), gather_nd_val)
if __name__ == "__main__":
test.main()
|
tensorflow-master
|
tensorflow/compiler/tests/gather_nd_op_test.py
|
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for bucketize_op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors_impl
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
class BucketizationOpTest(xla_test.XLATestCase):
def testInt(self):
with self.session() as sess:
p = array_ops.placeholder(dtypes.int32)
with self.test_scope():
op = math_ops._bucketize(p, boundaries=[0, 3, 8, 11])
expected_out = [0, 1, 1, 2, 2, 3, 3, 4, 4]
self.assertAllEqual(expected_out,
sess.run(op, {p: [-5, 0, 2, 3, 5, 8, 10, 11, 12]}))
def testFloat(self):
with self.session() as sess:
p = array_ops.placeholder(dtypes.float32)
with self.test_scope():
op = math_ops._bucketize(p, boundaries=[0., 3., 8., 11.])
expected_out = [0, 1, 1, 2, 2, 3, 3, 4, 4]
self.assertAllEqual(
expected_out,
sess.run(op, {p: [-5., 0., 2., 3., 5., 8., 10., 11., 12.]}))
def test2DInput(self):
with self.session() as sess:
p = array_ops.placeholder(dtypes.float32)
with self.test_scope():
op = math_ops._bucketize(p, boundaries=[0, 3, 8, 11])
expected_out = [[0, 1, 1, 2, 2], [3, 3, 4, 4, 1]]
self.assertAllEqual(
expected_out, sess.run(op,
{p: [[-5, 0, 2, 3, 5], [8, 10, 11, 12, 0]]}))
def testInvalidBoundariesOrder(self):
with self.session() as sess:
p = array_ops.placeholder(dtypes.int32)
with self.test_scope():
op = math_ops._bucketize(p, boundaries=[0, 8, 3, 11])
with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
"Expected sorted boundaries"):
sess.run(op, {p: [-5, 0]})
def testBoundariesNotList(self):
with self.session():
with self.assertRaisesRegexp(TypeError, "Expected list.*"):
p = array_ops.placeholder(dtypes.int32)
with self.test_scope():
math_ops._bucketize(p, boundaries=0)
if __name__ == "__main__":
test.main()
|
tensorflow-master
|
tensorflow/compiler/tests/bucketize_op_test.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for reading and writing variables."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_state_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.platform import googletest
from tensorflow.python.training.gradient_descent import GradientDescentOptimizer
class VariableOpsTest(xla_test.XLATestCase):
"""Test cases for resource variable operators."""
def testWriteEmptyShape(self):
# Verifies that we can pass an uninitialized variable with an empty shape,
# assign it a value, and successfully return it.
for dtype in self.numeric_types:
with self.session() as sess, self.test_scope():
zeros = np.zeros([3, 0], dtype=dtype)
v = resource_variable_ops.ResourceVariable(zeros)
p = array_ops.placeholder(dtype)
x = v.assign(p)
with ops.control_dependencies([x]):
y = v.read_value()
self.assertAllClose(zeros, sess.run(y, {p: zeros}))
def testOneWriteOneOutput(self):
# Regression test for a bug where computations with one non-constant
# output and one variable update were mishandled.
for dtype in self.numeric_types:
init = np.array([[1, 2j], [3, 4]]).astype(dtype)
with self.session() as sess, self.test_scope():
v = resource_variable_ops.ResourceVariable(init)
sess.run(variables.variables_initializer([v]))
p = array_ops.placeholder(dtype)
x = v.assign_add(p)
with ops.control_dependencies([x]):
y = v.read_value()
self.assertAllClose(
np.array([[2, 1 + 2j], [4, 5]]).astype(dtype), sess.run(y, {p: 1}))
def testSparseRead0DIndices(self):
for dtype in self.numeric_types:
init = np.array([[0, 1, 2, 3], [4, 5, 6, 7], [8j, 9, 10,
11]]).astype(dtype)
with self.session() as sess, self.test_scope():
v = resource_variable_ops.ResourceVariable(init)
sess.run(variables.variables_initializer([v]))
x = v.sparse_read(2)
self.assertAllClose(
np.array([8j, 9, 10, 11]).astype(dtype), self.evaluate(x))
def testSparseRead1DIndices(self):
for dtype in self.numeric_types:
init = np.array([[0, 1, 2, 3], [4, 5, 6j, 7], [8, 9, 10,
11]]).astype(dtype)
with self.session() as sess, self.test_scope():
v = resource_variable_ops.ResourceVariable(init)
sess.run(variables.variables_initializer([v]))
x = v.sparse_read([2, 1])
self.assertAllClose(
np.array([[8, 9, 10, 11], [4, 5, 6j, 7]]).astype(dtype),
self.evaluate(x))
def testSparseRead2DIndices(self):
for dtype in self.numeric_types:
init = np.array([[0, 1, 2j, 3], [4, 5, 6, 7], [8, 9, 10,
11]]).astype(dtype)
with self.session() as sess, self.test_scope():
v = resource_variable_ops.ResourceVariable(init)
sess.run(variables.variables_initializer([v]))
x = v.sparse_read([[2, 1], [0, 2]])
self.assertAllClose(
np.array([[[8, 9, 10, 11], [4, 5, 6, 7]],
[[0, 1, 2j, 3], [8, 9, 10, 11]]]).astype(dtype),
self.evaluate(x))
def testSparseRead2DIndices3DTensor(self):
for dtype in self.numeric_types:
init = np.array([[[0, 1, 2], [3, 4, 5]], [[10, 11, 12], [13, 14, 15]],
[[20, 21, 22], [23, 24j, 25]],
[[30, 31, 32], [33, 34, 35]]]).astype(dtype)
with self.session() as sess, self.test_scope():
v = resource_variable_ops.ResourceVariable(init)
sess.run(variables.variables_initializer([v]))
x = v.sparse_read([[2, 1], [3, 0]])
self.assertAllClose(
np.array(
[[[[20, 21, 22], [23, 24j, 25]], [[10, 11, 12], [13, 14, 15]]],
[[[30, 31, 32], [33, 34, 35]], [[0, 1, 2], [3, 4, 5]]]
],).astype(dtype), self.evaluate(x))
def testShape(self):
for dtype in self.numeric_types:
init = np.ones([2, 3]).astype(dtype)
with self.session() as session, self.test_scope():
v = resource_variable_ops.ResourceVariable(init)
session.run(variables.variables_initializer([v]))
h = v.handle
s32, s64 = session.run([
resource_variable_ops.variable_shape(h),
resource_variable_ops.variable_shape(h, out_type=dtypes.int64)
])
self.assertEqual(s32.dtype, np.int32)
self.assertEqual(s64.dtype, np.int64)
self.assertAllEqual(s32, [2, 3])
self.assertAllEqual(s64, [2, 3])
def testReadWrite(self):
"""Tests initialization, reading, and writing a resource variable."""
for dtype in self.numeric_types:
with self.session() as session:
with self.test_scope():
with variable_scope.variable_scope("ascope", use_resource=True):
x = variable_scope.get_variable(
"x",
shape=[],
dtype=dtype,
initializer=init_ops.constant_initializer(2))
a = x.read_value()
with ops.control_dependencies([a]):
b = state_ops.assign(x, dtype(47))
with ops.control_dependencies([b]):
c = x.read_value()
with ops.control_dependencies([c]):
d = state_ops.assign_add(x, np.array(6 + 2j).astype(dtype))
with ops.control_dependencies([d]):
e = state_ops.assign_sub(x, dtype(3))
with ops.control_dependencies([e]):
f = x.read_value()
session.run(variables.global_variables_initializer())
v1, v2, v3 = session.run([a, c, f])
self.assertAllClose(dtype(2), v1)
self.assertAllClose(dtype(47), v2)
self.assertAllClose(np.array(50 + 2j).astype(dtype), v3)
def testTraining(self):
"""Tests a gradient descent step for a simple model."""
with self.session() as session:
with self.test_scope():
with variable_scope.variable_scope("ascope", use_resource=True):
w = variable_scope.get_variable(
"w",
shape=[4, 2],
dtype=dtypes.float32,
initializer=init_ops.constant_initializer(
np.array([[1, 2], [3, 4], [5, 6], [7, 8]], dtype=np.float32)))
b = variable_scope.get_variable(
"b",
shape=[2],
dtype=dtypes.float32,
initializer=init_ops.constant_initializer(
np.array([2, 3], dtype=np.float32)))
x = array_ops.placeholder(dtypes.float32, shape=[1, 4])
y = math_ops.matmul(x, w) + b
loss = math_ops.reduce_sum(y)
optimizer = GradientDescentOptimizer(0.1)
train = optimizer.minimize(loss)
session.run(variables.global_variables_initializer())
session.run(train, {x: np.array([[7, 3, 5, 9]], dtype=np.float32)})
vw, vb = session.run([w, b])
self.assertAllClose(
np.array(
[[0.3, 1.3], [2.7, 3.7], [4.5, 5.5], [6.1, 7.1]],
dtype=np.float32),
vw,
rtol=1e-4)
self.assertAllClose(np.array([1.9, 2.9], dtype=np.float32), vb, rtol=1e-4)
def testWriteOfAliasedTensor(self):
for dtype in self.numeric_types:
init = np.array([[1, 2j], [3, 4]]).astype(dtype)
update = np.array([[7, 1j], [2, 11]]).astype(dtype)
with self.session() as sess, self.test_scope():
v = resource_variable_ops.ResourceVariable(init)
sess.run(variables.variables_initializer([v]))
p = array_ops.placeholder(dtype)
q = array_ops.identity(p)
x = v.read_value()
# Writes the value of 'p' to 'v', but keeps a reference to the original
# value of 'v' so the variable update cannot reuse its buffer.
with ops.control_dependencies([x]):
y = v.assign(q)
result = sess.run([x, y, q], {p: update})
self.assertAllClose(init, result[0])
self.assertAllClose(update, result[1])
self.assertAllClose(update, result[2])
def testScatterAdd(self):
with self.session() as sess, self.test_scope():
handle = resource_variable_ops.var_handle_op(
dtype=dtypes.int32, shape=[2, 1])
sess.run(
resource_variable_ops.assign_variable_op(
handle, constant_op.constant([[1], [7]], dtype=dtypes.int32)))
sess.run(
resource_variable_ops.resource_scatter_add(
handle, [0], constant_op.constant([[2]], dtype=dtypes.int32)))
read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32)
self.assertAllEqual(self.evaluate(read), [[3], [7]])
def testScatterSub(self):
with self.session() as sess, self.test_scope():
handle = resource_variable_ops.var_handle_op(
dtype=dtypes.int32, shape=[2, 1])
sess.run(
resource_variable_ops.assign_variable_op(
handle, constant_op.constant([[4], [1]], dtype=dtypes.int32)))
sess.run(
resource_variable_ops.resource_scatter_sub(
handle, [1], constant_op.constant([[2]], dtype=dtypes.int32)))
read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32)
self.assertAllEqual(self.evaluate(read), [[4], [-1]])
def testScatterMul(self):
with self.session() as sess, self.test_scope():
handle = resource_variable_ops.var_handle_op(
dtype=dtypes.int32, shape=[1, 1])
sess.run(
resource_variable_ops.assign_variable_op(
handle, constant_op.constant([[1]], dtype=dtypes.int32)))
sess.run(
resource_variable_ops.resource_scatter_mul(
handle, [0], constant_op.constant([[5]], dtype=dtypes.int32)))
read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32)
self.assertEqual(self.evaluate(read), [[5]])
def testScatterDiv(self):
with self.session() as sess, self.test_scope():
handle = resource_variable_ops.var_handle_op(
dtype=dtypes.int32, shape=[1, 1])
sess.run(
resource_variable_ops.assign_variable_op(
handle, constant_op.constant([[6]], dtype=dtypes.int32)))
sess.run(
resource_variable_ops.resource_scatter_div(
handle, [0], constant_op.constant([[3]], dtype=dtypes.int32)))
read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32)
self.assertAllEqual(self.evaluate(read), [[2]])
def testScatterMin(self):
with self.session() as sess, self.test_scope():
handle = resource_variable_ops.var_handle_op(
dtype=dtypes.int32, shape=[1, 1])
sess.run(
resource_variable_ops.assign_variable_op(
handle, constant_op.constant([[6]], dtype=dtypes.int32)))
sess.run(
resource_variable_ops.resource_scatter_min(
handle, [0], constant_op.constant([[3]], dtype=dtypes.int32)))
read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32)
self.assertEqual(self.evaluate(read), [[3]])
def testScatterMax(self):
with self.session() as sess, self.test_scope():
handle = resource_variable_ops.var_handle_op(
dtype=dtypes.int32, shape=[1, 1])
sess.run(
resource_variable_ops.assign_variable_op(
handle, constant_op.constant([[6]], dtype=dtypes.int32)))
sess.run(
resource_variable_ops.resource_scatter_max(
handle, [0], constant_op.constant([[3]], dtype=dtypes.int32)))
read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32)
self.assertEqual(self.evaluate(read), [[6]])
def testScatterUpdate(self):
with self.session() as sess, self.test_scope():
handle = resource_variable_ops.var_handle_op(
dtype=dtypes.int32, shape=[1, 1])
sess.run(
resource_variable_ops.assign_variable_op(
handle, constant_op.constant([[6]], dtype=dtypes.int32)))
sess.run(
resource_variable_ops.resource_scatter_update(
handle, [0], constant_op.constant([[3]], dtype=dtypes.int32)))
read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32)
self.assertEqual(self.evaluate(read), [[3]])
def testScatterAddScalar(self):
with self.session() as sess, self.test_scope():
handle = resource_variable_ops.var_handle_op(
dtype=dtypes.int32, shape=[1, 1])
sess.run(
resource_variable_ops.assign_variable_op(
handle, constant_op.constant([[1]], dtype=dtypes.int32)))
sess.run(
resource_variable_ops.resource_scatter_add(
handle, [0], constant_op.constant(2, dtype=dtypes.int32)))
read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32)
self.assertEqual(self.evaluate(read), [[3]])
def testScatterSubScalar(self):
with self.session() as sess, self.test_scope():
handle = resource_variable_ops.var_handle_op(
dtype=dtypes.int32, shape=[1, 1])
sess.run(
resource_variable_ops.assign_variable_op(
handle, constant_op.constant([[1]], dtype=dtypes.int32)))
sess.run(
resource_variable_ops.resource_scatter_sub(
handle, [0], constant_op.constant(2, dtype=dtypes.int32)))
read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32)
self.assertEqual(self.evaluate(read), [[-1]])
def testScatterMulScalar(self):
with self.session() as sess, self.test_scope():
handle = resource_variable_ops.var_handle_op(
dtype=dtypes.int32, shape=[1, 1])
sess.run(
resource_variable_ops.assign_variable_op(
handle, constant_op.constant([[1]], dtype=dtypes.int32)))
sess.run(
resource_variable_ops.resource_scatter_mul(
handle, [0], constant_op.constant(5, dtype=dtypes.int32)))
read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32)
self.assertEqual(self.evaluate(read), [[5]])
def testScatterDivScalar(self):
with self.session() as sess, self.test_scope():
handle = resource_variable_ops.var_handle_op(
dtype=dtypes.int32, shape=[1, 1])
sess.run(
resource_variable_ops.assign_variable_op(
handle, constant_op.constant([[6]], dtype=dtypes.int32)))
sess.run(
resource_variable_ops.resource_scatter_div(
handle, [0], constant_op.constant(3, dtype=dtypes.int32)))
read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32)
self.assertEqual(self.evaluate(read), [[2]])
def testScatterMinScalar(self):
with self.session() as sess, self.test_scope():
handle = resource_variable_ops.var_handle_op(
dtype=dtypes.int32, shape=[1, 1])
sess.run(
resource_variable_ops.assign_variable_op(
handle, constant_op.constant([[6]], dtype=dtypes.int32)))
sess.run(
resource_variable_ops.resource_scatter_min(
handle, [0], constant_op.constant(3, dtype=dtypes.int32)))
read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32)
self.assertEqual(self.evaluate(read), [[3]])
def testScatterMaxScalar(self):
with self.session() as sess, self.test_scope():
handle = resource_variable_ops.var_handle_op(
dtype=dtypes.int32, shape=[1, 1])
sess.run(
resource_variable_ops.assign_variable_op(
handle, constant_op.constant([[6]], dtype=dtypes.int32)))
sess.run(
resource_variable_ops.resource_scatter_max(
handle, [0], constant_op.constant(3, dtype=dtypes.int32)))
read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32)
self.assertEqual(self.evaluate(read), [[6]])
def testScatterNdAddOps(self):
with self.session() as sess, self.test_scope():
handle = resource_variable_ops.var_handle_op(
dtype=dtypes.float32, shape=[8])
sess.run(
resource_variable_ops.assign_variable_op(
handle, constant_op.constant([1] * 8, dtype=dtypes.float32)))
indices = constant_op.constant([[4], [3], [1], [7]], dtype=dtypes.int32)
updates = constant_op.constant([9, 10, 11, 12], dtype=dtypes.float32)
expected = np.array([1, 12, 1, 11, 10, 1, 1, 13])
sess.run(gen_state_ops.resource_scatter_nd_add(handle, indices, updates))
read = resource_variable_ops.read_variable_op(
handle, dtype=dtypes.float32)
self.assertAllClose(expected, self.evaluate(read))
def testScatterNdUpdateAddOps(self):
with self.session() as sess, self.test_scope():
handle = resource_variable_ops.var_handle_op(
dtype=dtypes.float32, shape=[8])
sess.run(
resource_variable_ops.assign_variable_op(
handle, constant_op.constant([1] * 8, dtype=dtypes.float32)))
indices = constant_op.constant([[4], [3], [1], [7]], dtype=dtypes.int32)
updates = constant_op.constant([9, 10, 11, 12], dtype=dtypes.float32)
expected = np.array([1, 11, 1, 10, 9, 1, 1, 12])
sess.run(
gen_state_ops.resource_scatter_nd_update(handle, indices, updates))
read = resource_variable_ops.read_variable_op(
handle, dtype=dtypes.float32)
self.assertAllClose(expected, self.evaluate(read))
class StridedSliceAssignChecker(object):
"""Compares the results of a slice assignment using Tensorflow and numpy."""
def __init__(self, test, x, dtype):
self.dtype = dtype
self.test = test
self.x_np = np.array(x).astype(dtype)
# Randomly start on mode 0 or 1.
self.which_mode = np.random.randint(2, size=1)[0]
def __setitem__(self, index, value):
self.which_mode = 1 - self.which_mode
value = np.array(value).astype(self.dtype)
with self.test.session() as sess, self.test.test_scope():
x = constant_op.constant(self.x_np, dtype=self.dtype)
var = resource_variable_ops.ResourceVariable(x)
sess.run(variables.variables_initializer([var]))
if self.which_mode == 0:
val = sess.run(var[index].assign(value))
else:
assert self.which_mode == 1
val = sess.run(state_ops.assign(var[index], value))
valnp = np.copy(self.x_np)
valnp[index] = np.array(value)
self.test.assertAllEqual(val, valnp)
class SliceAssignTest(xla_test.XLATestCase):
def testSliceAssign(self):
for dtype in self.numeric_types:
checker = StridedSliceAssignChecker(
self, [[1, 2, 3], [4, 5, 6]], dtype=dtype)
# No-op assignment
checker[:] = [[10, 20, 30], [40, 50, 60]]
# Checks trivial (1,1) shape tensor
checker[1:2, 1:2] = [[66]]
# shrink shape changes
checker[1:2, 1] = [66]
checker[1, 1:2] = [66]
if dtype != dtypes.bfloat16.as_numpy_dtype:
# TODO(b/68813416): valnp call above results in an ndarray and not a
# number for bfloat16s.
checker[1, 1] = 66
# newaxis shape changes
checker[:, None, :] = [[[10, 20, 30]], [[40, 50, 50]]]
# shrink and newaxis
checker[None, None, 0, 0:1] = [[[99]]]
# Non unit strides
checker[::1, 1::-1] = [[3, 33], [4, 44]]
# degenerate interval
checker[8:10, 0] = []
checker[8:10, 8:10] = [[]]
# Assign vector to scalar (rank-0) using newaxis
checker2 = StridedSliceAssignChecker(self, 222, dtype=dtype)
if dtype != dtypes.bfloat16.as_numpy_dtype:
# TODO(b/68813416): valnp call above results in an ndarray and not a
# number for bfloat16s.
checker2[()] = 6 # no indices
checker2[...] = 6 # ellipsis
checker2[None] = [6] # new axis
def testUninitialized(self):
with self.assertRaisesRegexp(errors.FailedPreconditionError,
"uninitialized variable"):
with self.session() as sess, self.test_scope():
v = resource_variable_ops.ResourceVariable([1, 2])
sess.run(v[:].assign([1, 2]))
if __name__ == "__main__":
googletest.main()
|
tensorflow-master
|
tensorflow/compiler/tests/variable_ops_test.py
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Utilities for helping test ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
def ConvertBetweenDataFormats(x, data_format_src, data_format_dst):
"""Converts 4D tensor between data formats."""
valid_data_formats = ["NHWC", "NCHW", "HWNC", "HWCN"]
if data_format_src not in valid_data_formats:
raise ValueError("data_format_src must be of %s, got %s." %
(valid_data_formats, data_format_src))
if data_format_dst not in valid_data_formats:
raise ValueError("data_format_dst must be of %s, got %s." %
(valid_data_formats, data_format_dst))
if len(x.shape) != 4:
raise ValueError("x must be 4D, got shape %s." % x.shape)
if data_format_src == data_format_dst:
return x
dim_map = {d: i for i, d in enumerate(data_format_src)}
transpose_dims = [dim_map[d] for d in data_format_dst]
return np.transpose(x, transpose_dims)
def PermuteDimsBetweenDataFormats(dims, data_format_src, data_format_dst):
"""Get new shape for converting between data formats."""
valid_data_formats = ["NHWC", "NCHW", "HWNC", "HWCN"]
if data_format_src not in valid_data_formats:
raise ValueError("data_format_src must be of %s, got %s." %
(valid_data_formats, data_format_src))
if data_format_dst not in valid_data_formats:
raise ValueError("data_format_dst must be of %s, got %s." %
(valid_data_formats, data_format_dst))
if len(dims) != 4:
raise ValueError("dims must be of length 4, got %s." % dims)
if data_format_src == data_format_dst:
return dims
dim_map = {d: i for i, d in enumerate(data_format_src)}
permuted_dims = [dims[dim_map[d]] for d in data_format_dst]
return permuted_dims
_JIT_WARMUP_ITERATIONS = 10
def RunWithWarmup(sess, op_to_run, feed_dict, options=None, run_metadata=None):
"""Runs a graph a few times to ensure that its clusters are compiled."""
for _ in xrange(0, _JIT_WARMUP_ITERATIONS):
sess.run(op_to_run, feed_dict, options=options)
return sess.run(
op_to_run, feed_dict, options=options, run_metadata=run_metadata)
|
tensorflow-master
|
tensorflow/compiler/tests/test_utils.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for reduction operators."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import itertools
from absl.testing import parameterized
import numpy as np
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors_impl
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import googletest
@parameterized.named_parameters(('32_bit_index', dtypes.int32),
('64_bit_index', dtypes.int64))
class ReduceOpsTest(xla_test.XLATestCase, parameterized.TestCase):
def _testReduction(self,
tf_reduce_fn,
np_reduce_fn,
dtype,
test_inputs,
index_dtype,
rtol=1e-4,
atol=1e-4):
"""Tests that the output of 'tf_reduce_fn' matches numpy's output."""
for test_input in test_inputs:
with self.session() as sess:
with self.test_scope():
a = array_ops.placeholder(dtype)
index = array_ops.placeholder(index_dtype)
out = tf_reduce_fn(a, index)
result = sess.run(out, {a: test_input, index: [0]})
self.assertAllClose(
result, np_reduce_fn(test_input, axis=0), rtol=rtol, atol=atol)
result = sess.run(out, {a: test_input, index: [1]})
self.assertAllClose(
result, np_reduce_fn(test_input, axis=1), rtol=rtol, atol=atol)
result = sess.run(out, {a: test_input, index: [-1]})
self.assertAllClose(
result, np_reduce_fn(test_input, axis=1), rtol=rtol, atol=atol)
with self.assertRaisesWithPredicateMatch(
errors_impl.InvalidArgumentError, 'Invalid reduction dim'):
sess.run(out, {a: test_input, index: [-33]})
with self.assertRaisesWithPredicateMatch(
errors_impl.InvalidArgumentError, 'Invalid reduction dim'):
sess.run(out, {a: test_input, index: [2]})
REAL_DATA = [
np.zeros(shape=(2, 0)),
np.zeros(shape=(0, 30)),
np.arange(1, 7).reshape(2, 3),
np.arange(-10, -4).reshape(2, 3),
np.arange(-4, 2).reshape(2, 3),
]
COMPLEX_DATA = [
np.zeros(shape=(2, 0)).astype(np.complex64),
np.zeros(shape=(0, 30)).astype(np.complex64),
np.arange(1, 13, dtype=np.float32).view(np.complex64).reshape(2, 3),
np.arange(-14, -2, dtype=np.float32).view(np.complex64).reshape(2, 3),
np.arange(-4, 8, dtype=np.float32).view(np.complex64).reshape(2, 3),
]
NONEMPTY_REAL_DATA = [x for x in REAL_DATA if np.size(x) > 0]
NONEMPTY_COMPLEX_DATA = [x for x in COMPLEX_DATA if np.size(x) > 0]
BOOL_DATA = [
np.array([], dtype=np.bool).reshape(2, 0),
np.array([], dtype=np.bool).reshape(0, 3),
np.array([[False, True, False], [True, True, False]]),
]
ONES = [np.ones([34000, 2])]
def testReduceSumF32(self, index_dtype):
self._testReduction(math_ops.reduce_sum, np.sum, np.float32, self.REAL_DATA,
index_dtype)
def testReduceSumC64(self, index_dtype):
self._testReduction(math_ops.reduce_sum, np.sum, np.complex64,
self.COMPLEX_DATA, index_dtype)
def testReduceProdF32(self, index_dtype):
self._testReduction(math_ops.reduce_prod, np.prod, np.float32,
self.REAL_DATA, index_dtype)
def testReduceProdC64(self, index_dtype):
self._testReduction(math_ops.reduce_prod, np.prod, np.complex64,
self.COMPLEX_DATA, index_dtype)
def testReduceMin(self, index_dtype):
def reference_min(dtype, inp, axis):
"""Wrapper around np.amin that returns +infinity for an empty input."""
if inp.shape[axis] == 0:
if np.issubdtype(dtype, np.floating):
return np.full(inp.shape[0:axis] + inp.shape[axis + 1:], float('inf'))
return np.full(inp.shape[0:axis] + inp.shape[axis + 1:],
np.iinfo(dtype).max)
return np.amin(inp, axis)
for dtype in set(self.all_types).intersection(
[np.float32, np.int32, np.int64]):
self._testReduction(math_ops.reduce_min,
functools.partial(reference_min, dtype), dtype,
self.REAL_DATA, index_dtype)
def testReduceMax(self, index_dtype):
def reference_max(dtype, inp, axis):
"""Wrapper around np.amax that returns -infinity for an empty input."""
if inp.shape[axis] == 0:
if np.issubdtype(dtype, np.floating):
return np.full(inp.shape[0:axis] + inp.shape[axis + 1:],
float('-inf'))
return np.full(inp.shape[0:axis] + inp.shape[axis + 1:],
np.iinfo(dtype).min)
return np.amax(inp, axis)
for dtype in set(self.all_types).intersection(
[np.float32, np.int32, np.int64]):
self._testReduction(math_ops.reduce_max,
functools.partial(reference_max, dtype), dtype,
self.REAL_DATA, index_dtype)
def testReduceMeanF32(self, index_dtype):
# TODO(phawkins): mean on XLA currently returns 0 instead of NaN when
# reducing across zero inputs.
self._testReduction(math_ops.reduce_mean, np.mean, np.float32,
self.NONEMPTY_REAL_DATA, index_dtype)
def testReduceMeanF16(self, index_dtype):
if np.float16 in self.all_types:
self._testReduction(math_ops.reduce_mean, np.mean, np.float16, self.ONES,
index_dtype)
def testReduceMeanC64(self, index_dtype):
self._testReduction(math_ops.reduce_mean, np.mean, np.complex64,
self.NONEMPTY_COMPLEX_DATA, index_dtype)
def testReduceAll(self, index_dtype):
self._testReduction(math_ops.reduce_all, np.all, np.bool, self.BOOL_DATA,
index_dtype)
def testReduceAny(self, index_dtype):
self._testReduction(math_ops.reduce_any, np.any, np.bool, self.BOOL_DATA,
index_dtype)
class ReduceOpPrecisionTest(xla_test.XLATestCase):
def _testReduceSum(self,
expected_result,
dtype,
test_inputs,
rtol=1e-3,
atol=1e-4):
"""Tests reduce sum on a list of input arrays.
For each array in test_inputs, check that performing reduce sum on the array
produces a value that is close to the expected result.
Args:
expected_result: the expected result.
dtype: the data type of the reduce sum operation.
test_inputs: a list of input arrays for the reduce sum operation.
rtol: the relative error.
atol: the absolute error.
"""
for test_input in test_inputs:
with self.session() as sess:
with self.test_scope():
a = array_ops.placeholder(dtype)
index = array_ops.placeholder(dtypes.int32)
out = math_ops.reduce_sum(a, index)
result = sess.run(out, {
a: np.array(test_input, dtype=dtype),
index: [0]
})
# Compare the results using float32 type.
self.assertAllClose(
np.float32(result),
np.float32(expected_result),
rtol=rtol,
atol=atol)
def testReduceSumF16(self):
"""Tests the reduce sum of float16 doesn't lose too much precision."""
if np.float16 not in self.all_types:
return
f16_max = np.finfo(np.float16).max
self._testReduceSum(
f16_max, np.float16,
itertools.permutations([f16_max, f16_max, f16_max * (-1.0)], 3))
def testReduceSumBF16(self):
"""Tests the reduce sum of bfloat16 doesn't lose too much precision."""
if dtypes.bfloat16.as_numpy_dtype not in self.all_types:
return
bf16_max = np.float32(dtypes.bfloat16.max)
f32_max = dtypes.float32.max
value = min(bf16_max, f32_max - bf16_max) / 2
self._testReduceSum(
dtypes.bfloat16.as_numpy_dtype(value), dtypes.bfloat16.as_numpy_dtype,
itertools.permutations([bf16_max, value, bf16_max * (-1.0)], 3))
if __name__ == '__main__':
googletest.main()
|
tensorflow-master
|
tensorflow/compiler/tests/reduce_ops_test.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Test cases for XLA devices."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.client import session as session_lib
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
class XlaDeviceGpuTest(test.TestCase):
def testCopiesToAndFromGpuWork(self):
"""Tests that copies between GPU and XLA devices work."""
if not test.is_gpu_available():
return
with session_lib.Session() as sess:
x = array_ops.placeholder(dtypes.float32, [2])
with ops.device("GPU"):
y = x * 2
with ops.device("device:XLA_CPU:0"):
z = y * y
with ops.device("GPU"):
w = y + z
result = sess.run(w, {x: [1.5, 0.5]})
self.assertAllClose(result, [12., 2.], rtol=1e-3)
if __name__ == "__main__":
test.main()
|
tensorflow-master
|
tensorflow/compiler/tests/xla_device_gpu_test.py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for random-number generation ops in the XLA JIT compiler."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import numpy as np
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops.distributions import special_math
from tensorflow.python.platform import googletest
class RandomOpsTest(xla_test.XLATestCase):
"""Test cases for random-number generating operators."""
def _random_types(self):
return set(self.numeric_types) - set(
self.complex_types) - {np.uint64, np.int64, np.uint8, np.int8}
def _testRngIsNotConstant(self, rng, dtype):
# Tests that 'rng' does not always return the same value.
with self.session() as sess:
with self.test_scope():
x = rng(dtype)
# The random-number generator, if working correctly, should produce the
# same output multiple times with low probability.
y = self.evaluate(x)
z = self.evaluate(x)
w = self.evaluate(x)
# We use exact equality here. If the random-number generator is producing
# deterministic output, all three outputs will be bitwise identical.
self.assertTrue((not np.array_equal(y, z)) or
(not np.array_equal(z, w)) or (not np.array_equal(y, w)))
def testRandomUniformIsNotConstant(self):
def rng(dtype):
dtype = dtypes.as_dtype(dtype)
return random_ops.random_uniform(shape=[2], dtype=dtype, maxval=dtype.max)
for dtype in self._random_types():
self._testRngIsNotConstant(rng, dtype)
def testRandomNormalIsNotConstant(self):
def rng(dtype):
return random_ops.random_normal(shape=[2], dtype=dtype)
for dtype in self._random_types() & self.float_types:
self._testRngIsNotConstant(rng, dtype)
def testRandomNormalMean(self):
for dtype in self._random_types() & self.float_types:
with self.session():
with self.test_scope():
normal = random_ops.random_normal([1024],
dtype=dtype,
mean=1.4,
stddev=1.2)
mean = math_ops.reduce_mean(normal)
x = self.evaluate(mean)
self.assertAllClose(x, 1.4, rtol=1e-1, atol=1e-1)
def testRandomNormalVariance(self):
for dtype in self._random_types() & self.float_types:
with self.session():
with self.test_scope():
normal = random_ops.random_normal([1024],
dtype=dtype,
mean=2.3,
stddev=2.0)
variance = math_ops.reduce_variance(normal)
x = self.evaluate(variance)
self.assertAllClose(x, 4.0, rtol=1e-1, atol=1e-1)
def testRandomUniformIsInRange(self):
for dtype in self._random_types():
# TODO (b/112272078): enable bfloat16 for CPU and GPU when the bug is
# fixed.
if (self.device in ["XLA_GPU", "XLA_CPU"
]) and (dtype in [dtypes.bfloat16, dtypes.half]):
continue
with self.session() as sess:
with self.test_scope():
x = random_ops.random_uniform(
shape=[1000], dtype=dtype, minval=-2, maxval=33)
y = self.evaluate(x)
self.assertTrue((y >= -2).sum() == 1000)
self.assertTrue((y < 33).sum() == 1000)
def testTruncatedNormalIsNotConstant(self):
def rng(dtype):
return random_ops.truncated_normal(shape=[2], dtype=dtype)
self._testRngIsNotConstant(rng, dtypes.float32)
def testTruncatedNormalIsInRange(self):
count = 10000000
# TODO(b/34339814): make this test work with 16 bit float types.
for dtype in self._random_types() & {dtypes.float32, dtypes.float64}:
with self.session() as sess:
with self.test_scope():
x = random_ops.truncated_normal(shape=[count], dtype=dtype)
y = self.evaluate(x)
def normal_cdf(x):
return .5 * math.erfc(-x / math.sqrt(2))
def normal_pdf(x):
return math.exp(-(x**2) / 2.) / math.sqrt(2 * math.pi)
def probit(x, sess=sess):
return self.evaluate(special_math.ndtri(x))
a = -2.
b = 2.
mu = 0.
sigma = 1.
alpha = (a - mu) / sigma
beta = (b - mu) / sigma
z = normal_cdf(beta) - normal_cdf(alpha)
self.assertEqual((y >= a).sum(), count)
self.assertEqual((y <= b).sum(), count)
# For more information on these calculations, see:
# Burkardt, John. "The Truncated Normal Distribution".
# Department of Scientific Computing website. Florida State University.
expected_mean = mu + (normal_pdf(alpha) - normal_pdf(beta)) / z * sigma
actual_mean = np.mean(y)
self.assertAllClose(actual_mean, expected_mean, atol=2e-3)
expected_median = mu + probit(
(normal_cdf(alpha) + normal_cdf(beta)) / 2.) * sigma
actual_median = np.median(y)
self.assertAllClose(actual_median, expected_median, atol=1e-2)
expected_variance = sigma**2 * (1 + (
(alpha * normal_pdf(alpha) - beta * normal_pdf(beta)) / z) - (
(normal_pdf(alpha) - normal_pdf(beta)) / z)**2)
actual_variance = np.var(y)
self.assertAllClose(actual_variance, expected_variance, rtol=2*1e-3)
def testShuffle1d(self):
with self.session() as sess:
with self.test_scope():
x = math_ops.range(1 << 16)
shuffle = random_ops.random_shuffle(x)
result = self.evaluate(shuffle)
expected = range(1 << 16)
# Compare sets to avoid randomness behavior changes but make sure still
# have all the values.
self.assertAllEqual(set(result), set(expected))
def testShuffle2d(self):
with self.session() as sess:
with self.test_scope():
x = array_ops.diag(math_ops.range(20))
shuffle = random_ops.random_shuffle(x)
result = self.evaluate(shuffle)
expected = np.diag(range(20)).flatten()
# Compare sets to avoid randomness behavior changes but make sure still
# have all the values.
self.assertAllEqual(len(result.flatten()), len(expected))
self.assertAllEqual(set(result.flatten()), set(expected))
if __name__ == '__main__':
googletest.main()
|
tensorflow-master
|
tensorflow/compiler/tests/random_ops_test.py
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for tensorflow.ops.reverse_sequence_op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
class ReverseSequenceTest(xla_test.XLATestCase):
def _testReverseSequence(self,
x,
batch_axis,
seq_axis,
seq_lengths,
truth,
expected_err_re=None):
with self.session():
p = array_ops.placeholder(dtypes.as_dtype(x.dtype))
lengths = array_ops.placeholder(dtypes.as_dtype(seq_lengths.dtype))
with self.test_scope():
ans = array_ops.reverse_sequence(
p, batch_axis=batch_axis, seq_axis=seq_axis, seq_lengths=lengths)
if expected_err_re is None:
tf_ans = ans.eval(feed_dict={p: x, lengths: seq_lengths})
self.assertAllClose(tf_ans, truth, atol=1e-10)
else:
with self.assertRaisesOpError(expected_err_re):
ans.eval(feed_dict={p: x, lengths: seq_lengths})
def testSimple(self):
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32)
expected = np.array([[1, 2, 3], [6, 5, 4], [8, 7, 9]], dtype=np.int32)
self._testReverseSequence(
x,
batch_axis=0,
seq_axis=1,
seq_lengths=np.array([1, 3, 2], np.int32),
truth=expected)
def _testBasic(self, dtype, len_dtype):
x = np.asarray(
[[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12], [13, 14, 15, 16]],
[[17, 18, 19, 20], [21, 22, 23, 24]]],
dtype=dtype)
x = x.reshape(3, 2, 4, 1, 1)
x = x.transpose([2, 1, 0, 3, 4]) # permute axes 0 <=> 2
# reverse dim 2 up to (0:3, none, 0:4) along dim=0
seq_lengths = np.asarray([3, 0, 4], dtype=len_dtype)
truth_orig = np.asarray(
[
[[3, 2, 1, 4], [7, 6, 5, 8]], # reverse 0:3
[[9, 10, 11, 12], [13, 14, 15, 16]], # reverse none
[[20, 19, 18, 17], [24, 23, 22, 21]]
], # reverse 0:4 (all)
dtype=dtype)
truth_orig = truth_orig.reshape(3, 2, 4, 1, 1)
truth = truth_orig.transpose([2, 1, 0, 3, 4]) # permute axes 0 <=> 2
seq_axis = 0 # permute seq_axis and batch_axis (originally 2 and 0, resp.)
batch_axis = 2
self._testReverseSequence(x, batch_axis, seq_axis, seq_lengths, truth)
def testSeqLength(self):
for dtype in self.all_types:
for seq_dtype in self.all_types & {np.int32, np.int64}:
self._testBasic(dtype, seq_dtype)
if __name__ == "__main__":
test.main()
|
tensorflow-master
|
tensorflow/compiler/tests/reverse_sequence_op_test.py
|
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