From 1ed4e69f7bfc660cca00132dbb9f26bf2b578422 Mon Sep 17 00:00:00 2001
From: Lukasz Kaiser <lukaszkaiser@google.com>
Date: Wed, 18 Jan 2017 17:14:33 -0800
Subject: [PATCH] Default reuse should be None, never False. Correct it in
layers and add tests. Change: 144903238
---
tensorflow/python/layers/convolutional.py | 10 +-
.../python/layers/convolutional_test.py | 127 ++++++++++++++----
tensorflow/python/layers/core.py | 2 +-
tensorflow/python/layers/core_test.py | 10 ++
tensorflow/python/layers/normalization.py | 2 +-
.../python/layers/normalization_test.py | 15 +++
6 files changed, 135 insertions(+), 31 deletions(-)
diff --git a/tensorflow/python/layers/convolutional.py b/tensorflow/python/layers/convolutional.py
index a476b0f72a3..3b96d4362fd 100644
--- a/tensorflow/python/layers/convolutional.py
+++ b/tensorflow/python/layers/convolutional.py
@@ -268,7 +268,7 @@ def conv1d(inputs,
activity_regularizer=None,
trainable=True,
name=None,
- reuse=False):
+ reuse=None):
"""Functional interface for 1D convolution layer (e.g. temporal convolution).
This layer creates a convolution kernel that is convolved
@@ -435,7 +435,7 @@ def conv2d(inputs,
activity_regularizer=None,
trainable=True,
name=None,
- reuse=False):
+ reuse=None):
"""Functional interface for the 2D convolution layer.
This layer creates a convolution kernel that is convolved
@@ -608,7 +608,7 @@ def conv3d(inputs,
activity_regularizer=None,
trainable=True,
name=None,
- reuse=False):
+ reuse=None):
"""Functional interface for the 3D convolution layer.
This layer creates a convolution kernel that is convolved
@@ -867,7 +867,7 @@ def separable_conv2d(inputs,
activity_regularizer=None,
trainable=True,
name=None,
- reuse=False):
+ reuse=None):
"""Functional interface for the depthwise separable 2D convolution layer.
This layer performs a depthwise convolution that acts separately on
@@ -1128,7 +1128,7 @@ def conv2d_transpose(inputs,
activity_regularizer=None,
trainable=True,
name=None,
- reuse=False):
+ reuse=None):
"""Transposed convolution layer (sometimes called Deconvolution).
The need for transposed convolutions generally arises
diff --git a/tensorflow/python/layers/convolutional_test.py b/tensorflow/python/layers/convolutional_test.py
index c47e92c5824..1a5fe5c9b7d 100644
--- a/tensorflow/python/layers/convolutional_test.py
+++ b/tensorflow/python/layers/convolutional_test.py
@@ -18,11 +18,16 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
+import numpy as np
+
from tensorflow.python.framework import ops
from tensorflow.python.layers import convolutional as conv_layers
+from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import variable_scope
+from tensorflow.python.ops import variables
from tensorflow.python.platform import test
@@ -191,21 +196,45 @@ class ConvTest(test.TestCase):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 3), seed=1)
conv_layers.conv2d(images, 32, [3, 3], name='conv1')
- self.assertEqual(
- len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 2)
+ self.assertEqual(len(variables.trainable_variables()), 2)
conv_layers.conv2d(images, 32, [3, 3], name='conv1', reuse=True)
- self.assertEqual(
- len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 2)
+ self.assertEqual(len(variables.trainable_variables()), 2)
+
+ def testFunctionalConv2DReuseFromScope(self):
+ with variable_scope.variable_scope('scope'):
+ height, width = 7, 9
+ images = random_ops.random_uniform((5, height, width, 3), seed=1)
+ conv_layers.conv2d(images, 32, [3, 3], name='conv1')
+ self.assertEqual(len(variables.trainable_variables()), 2)
+ with variable_scope.variable_scope('scope', reuse=True):
+ conv_layers.conv2d(images, 32, [3, 3], name='conv1')
+ self.assertEqual(len(variables.trainable_variables()), 2)
+
+ def testFunctionalConv2DInitializerFromScope(self):
+ with self.test_session() as sess:
+ with variable_scope.variable_scope(
+ 'scope', initializer=init_ops.ones_initializer()):
+ height, width = 7, 9
+ images = random_ops.random_uniform((5, height, width, 3), seed=1)
+ conv_layers.conv2d(images, 32, [3, 3], name='conv1')
+ weights = variables.trainable_variables()
+ # Check the names of weights in order.
+ self.assertTrue('kernel' in weights[0].name)
+ self.assertTrue('bias' in weights[1].name)
+ sess.run(variables.global_variables_initializer())
+ weights = sess.run(weights)
+ # Check that the kernel weights got initialized to ones (from scope)
+ self.assertAllClose(weights[0], np.ones((3, 3, 3, 32)))
+ # Check that the bias still got initialized to zeros.
+ self.assertAllClose(weights[1], np.zeros((32)))
def testFunctionalConv2DNoReuse(self):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 3), seed=1)
conv_layers.conv2d(images, 32, [3, 3])
- self.assertEqual(
- len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 2)
+ self.assertEqual(len(variables.trainable_variables()), 2)
conv_layers.conv2d(images, 32, [3, 3])
- self.assertEqual(
- len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 4)
+ self.assertEqual(len(variables.trainable_variables()), 4)
class SeparableConv2DTest(test.TestCase):
@@ -323,22 +352,48 @@ class SeparableConv2DTest(test.TestCase):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 3), seed=1)
conv_layers.separable_conv2d(images, 32, [3, 3], name='sepconv1')
- self.assertEqual(
- len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 3)
+ self.assertEqual(len(variables.trainable_variables()), 3)
conv_layers.separable_conv2d(
images, 32, [3, 3], name='sepconv1', reuse=True)
- self.assertEqual(
- len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 3)
+ self.assertEqual(len(variables.trainable_variables()), 3)
+
+ def testFunctionalConv2DReuseFromScope(self):
+ with variable_scope.variable_scope('scope'):
+ height, width = 7, 9
+ images = random_ops.random_uniform((5, height, width, 3), seed=1)
+ conv_layers.separable_conv2d(images, 32, [3, 3], name='sepconv1')
+ self.assertEqual(len(variables.trainable_variables()), 3)
+ with variable_scope.variable_scope('scope', reuse=True):
+ conv_layers.separable_conv2d(images, 32, [3, 3], name='sepconv1')
+ self.assertEqual(len(variables.trainable_variables()), 3)
+
+ def testFunctionalConv2DInitializerFromScope(self):
+ with self.test_session() as sess:
+ with variable_scope.variable_scope(
+ 'scope', initializer=init_ops.ones_initializer()):
+ height, width = 7, 9
+ images = random_ops.random_uniform((5, height, width, 3), seed=1)
+ conv_layers.separable_conv2d(images, 32, [3, 3], name='sepconv1')
+ weights = variables.trainable_variables()
+ # Check the names of weights in order.
+ self.assertTrue('depthwise_kernel' in weights[0].name)
+ self.assertTrue('pointwise_kernel' in weights[1].name)
+ self.assertTrue('bias' in weights[2].name)
+ sess.run(variables.global_variables_initializer())
+ weights = sess.run(weights)
+ # Check that the kernel weights got initialized to ones (from scope)
+ self.assertAllClose(weights[0], np.ones((3, 3, 3, 1)))
+ self.assertAllClose(weights[1], np.ones((1, 1, 3, 32)))
+ # Check that the bias still got initialized to zeros.
+ self.assertAllClose(weights[2], np.zeros((32)))
def testFunctionalConv2DNoReuse(self):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 3), seed=1)
conv_layers.separable_conv2d(images, 32, [3, 3])
- self.assertEqual(
- len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 3)
+ self.assertEqual(len(variables.trainable_variables()), 3)
conv_layers.separable_conv2d(images, 32, [3, 3])
- self.assertEqual(
- len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 6)
+ self.assertEqual(len(variables.trainable_variables()), 6)
def testSeparableConv2DDepthwiseRegularizer(self):
height, width = 7, 9
@@ -511,21 +566,45 @@ class Conv2DTransposeTest(test.TestCase):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 3), seed=1)
conv_layers.conv2d_transpose(images, 32, [3, 3], name='deconv1')
- self.assertEqual(
- len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 2)
+ self.assertEqual(len(variables.trainable_variables()), 2)
conv_layers.conv2d_transpose(images, 32, [3, 3], name='deconv1', reuse=True)
- self.assertEqual(
- len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 2)
+ self.assertEqual(len(variables.trainable_variables()), 2)
+
+ def testFunctionalConv2DTransposeReuseFromScope(self):
+ with variable_scope.variable_scope('scope'):
+ height, width = 7, 9
+ images = random_ops.random_uniform((5, height, width, 3), seed=1)
+ conv_layers.conv2d_transpose(images, 32, [3, 3], name='deconv1')
+ self.assertEqual(len(variables.trainable_variables()), 2)
+ with variable_scope.variable_scope('scope', reuse=True):
+ conv_layers.conv2d_transpose(images, 32, [3, 3], name='deconv1')
+ self.assertEqual(len(variables.trainable_variables()), 2)
+
+ def testFunctionalConv2DTransposeInitializerFromScope(self):
+ with self.test_session() as sess:
+ with variable_scope.variable_scope(
+ 'scope', initializer=init_ops.ones_initializer()):
+ height, width = 7, 9
+ images = random_ops.random_uniform((5, height, width, 3), seed=1)
+ conv_layers.conv2d_transpose(images, 32, [3, 3], name='deconv1')
+ weights = variables.trainable_variables()
+ # Check the names of weights in order.
+ self.assertTrue('kernel' in weights[0].name)
+ self.assertTrue('bias' in weights[1].name)
+ sess.run(variables.global_variables_initializer())
+ weights = sess.run(weights)
+ # Check that the kernel weights got initialized to ones (from scope)
+ self.assertAllClose(weights[0], np.ones((3, 3, 32, 3)))
+ # Check that the bias still got initialized to zeros.
+ self.assertAllClose(weights[1], np.zeros((32)))
def testFunctionalConv2DTransposeNoReuse(self):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 3), seed=1)
conv_layers.conv2d_transpose(images, 32, [3, 3])
- self.assertEqual(
- len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 2)
+ self.assertEqual(len(variables.trainable_variables()), 2)
conv_layers.conv2d_transpose(images, 32, [3, 3])
- self.assertEqual(
- len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 4)
+ self.assertEqual(len(variables.trainable_variables()), 4)
if __name__ == '__main__':
diff --git a/tensorflow/python/layers/core.py b/tensorflow/python/layers/core.py
index b8b6cd97da7..92894e14472 100644
--- a/tensorflow/python/layers/core.py
+++ b/tensorflow/python/layers/core.py
@@ -167,7 +167,7 @@ def dense(
activity_regularizer=None,
trainable=True,
name=None,
- reuse=False):
+ reuse=None):
"""Functional interface for the densely-connected layer.
This layer implements the operation:
diff --git a/tensorflow/python/layers/core_test.py b/tensorflow/python/layers/core_test.py
index 50e3ebc72f5..cfcee7b788f 100644
--- a/tensorflow/python/layers/core_test.py
+++ b/tensorflow/python/layers/core_test.py
@@ -207,6 +207,16 @@ class DenseTest(test.TestCase):
vars2 = variables.trainable_variables()
self.assertEqual(vars1, vars2)
+ def testFunctionalDenseTwiceReuseFromScope(self):
+ with variable_scope.variable_scope('scope'):
+ inputs = random_ops.random_uniform((5, 3), seed=1)
+ core_layers.dense(inputs, 2, name='my_dense')
+ vars1 = variables.trainable_variables()
+ with variable_scope.variable_scope('scope', reuse=True):
+ core_layers.dense(inputs, 2, name='my_dense')
+ vars2 = variables.trainable_variables()
+ self.assertEqual(vars1, vars2)
+
def testFunctionalDenseInitializerFromScope(self):
with self.test_session() as sess:
with variable_scope.variable_scope(
diff --git a/tensorflow/python/layers/normalization.py b/tensorflow/python/layers/normalization.py
index fcbc69f2c52..4a59d779483 100644
--- a/tensorflow/python/layers/normalization.py
+++ b/tensorflow/python/layers/normalization.py
@@ -257,7 +257,7 @@ def batch_normalization(inputs,
training=False,
trainable=True,
name=None,
- reuse=False):
+ reuse=None):
"""Functional interface for the batch normalization layer.
Reference: http://arxiv.org/abs/1502.03167
diff --git a/tensorflow/python/layers/normalization_test.py b/tensorflow/python/layers/normalization_test.py
index 93efc09ca06..91b7cb6f483 100644
--- a/tensorflow/python/layers/normalization_test.py
+++ b/tensorflow/python/layers/normalization_test.py
@@ -26,6 +26,7 @@ from tensorflow.python.layers import normalization as normalization_layers
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 import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
@@ -454,6 +455,20 @@ class BNTest(test.TestCase):
self.assertAlmostEqual(np.mean(normed_np_output), 0., places=2)
self.assertAlmostEqual(np.std(normed_np_output), 1., places=1)
+ def testFunctionalReuseFromScope(self):
+ inputs = variables.Variable(
+ np.random.random((5, 4, 3, 6)), dtype=dtypes.float32)
+ epsilon = 1e-3
+ training = array_ops.placeholder(dtype='bool')
+ with variable_scope.variable_scope('scope'):
+ _ = normalization_layers.batch_norm(
+ inputs, axis=-1, momentum=0.9, epsilon=epsilon, training=training)
+ self.assertEqual(len(variables.global_variables()), 5)
+ with variable_scope.variable_scope('scope', reuse=True):
+ _ = normalization_layers.batch_norm(
+ inputs, axis=-1, momentum=0.9, epsilon=epsilon, training=training)
+ self.assertEqual(len(variables.global_variables()), 5)
+
def testNoCenter(self):
bn = normalization_layers.BatchNormalization(axis=1, center=False)
inputs = random_ops.random_uniform((5, 4, 3), seed=1)