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-rw-r--r--tensorflow/contrib/layers/python/layers/layers_test.py150
1 files changed, 145 insertions, 5 deletions
diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py
index d4ee85b550..15809ea180 100644
--- a/tensorflow/contrib/layers/python/layers/layers_test.py
+++ b/tensorflow/contrib/layers/python/layers/layers_test.py
@@ -27,7 +27,6 @@ from tensorflow.contrib.framework.python.ops import arg_scope
from tensorflow.contrib.framework.python.ops import variables
from tensorflow.contrib.layers.python.layers import layers as _layers
from tensorflow.contrib.layers.python.layers import regularizers
-from tensorflow.contrib.losses.python.losses import loss_ops
from tensorflow.python.client import session
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
@@ -49,6 +48,7 @@ from tensorflow.python.ops import state_ops
from tensorflow.python.ops import template
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables as variables_lib
+from tensorflow.python.ops.losses import losses
from tensorflow.python.platform import test
@@ -121,6 +121,76 @@ class AvgPool2DTest(test.TestCase):
self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
+class AvgPool3DTest(test.TestCase):
+
+ def testInvalidDataFormat(self):
+ depth, height, width = 3, 6, 9
+ images = np.random.uniform(size=(5, depth, height, width, 3))
+ with self.assertRaisesRegexp(ValueError,
+ 'data_format has to be either NCDHW or NDHWC.'):
+ _layers.avg_pool3d(images, [3, 3, 3], data_format='CDHWN')
+
+ def testCreateAvgPool(self):
+ depth, height, width = 3, 6, 9
+ images = np.random.uniform(size=(5, depth, height, width, 3))
+ output = _layers.avg_pool3d(images, [3, 3, 3])
+ self.assertEqual(output.op.name, 'AvgPool3D/AvgPool3D')
+ self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 4, 3])
+
+ def testCreateAvgPoolNCDHW(self):
+ depth, height, width = 3, 6, 9
+ images = np.random.uniform(size=(5, 2, depth, height, width))
+ output = _layers.avg_pool3d(images, [3, 3, 3], data_format='NCDHW')
+ self.assertEquals(output.op.name, 'AvgPool3D/transpose_1')
+ self.assertListEqual(output.get_shape().as_list(), [5, 2, 1, 2, 4])
+
+ def testCollectOutputs(self):
+ depth, height, width = 3, 6, 9
+ images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+ output = _layers.avg_pool3d(images, [3, 3, 3], outputs_collections='outputs')
+ output_collected = ops.get_collection('outputs')[0]
+ self.assertEqual(output_collected.aliases, ['AvgPool3D'])
+ self.assertEqual(output_collected, output)
+
+ def testCreateSquareAvgPool(self):
+ depth, height, width = 3, 6, 9
+ images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+ output = _layers.avg_pool3d(images, 3)
+ self.assertEqual(output.op.name, 'AvgPool3D/AvgPool3D')
+ self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 4, 3])
+
+ def testCreateAvgPoolWithScope(self):
+ depth, height, width = 3, 6, 9
+ images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+ output = _layers.avg_pool3d(images, [3, 3, 3], scope='pool1')
+ self.assertEqual(output.op.name, 'pool1/AvgPool3D')
+
+ def testCreateAvgPoolWithSamePadding(self):
+ depth, height, width = 3, 6, 9
+ images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+ output = _layers.avg_pool3d(images, [3, 3, 3], padding='SAME')
+ self.assertListEqual(output.get_shape().as_list(), [5, 2, 3, 5, 3])
+
+ def testCreateAvgPoolWithSamePaddingNCDHW(self):
+ depth, height, width = 3, 6, 9
+ images = random_ops.random_uniform((5, 3, depth, height, width), seed=1)
+ output = _layers.avg_pool3d(
+ images, [3, 3, 3], padding='SAME', data_format='NCDHW')
+ self.assertListEqual(output.get_shape().as_list(), [5, 3, 2, 3, 5])
+
+ def testCreateAvgPoolStrideWithSamePadding(self):
+ depth, height, width = 3, 6, 9
+ images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+ output = _layers.avg_pool3d(images, [3, 3, 3], stride=1, padding='SAME')
+ self.assertListEqual(output.get_shape().as_list(), [5, depth, height, width, 3])
+
+ def testGlobalAvgPool(self):
+ depth, height, width = 3, 6, 9
+ images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+ output = _layers.avg_pool3d(images, images.get_shape()[1:4], stride=1)
+ self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 1, 3])
+
+
class PoolTest(test.TestCase):
def testCreatePool(self):
@@ -1559,23 +1629,23 @@ class FCTest(test.TestCase):
inputs, 32, scope='fc1', weights_regularizer=regularizer)
self.assertEqual(
len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 1)
- self.assertEqual(len(loss_ops.get_regularization_losses()), 1)
+ self.assertEqual(len(losses.get_regularization_losses()), 1)
_layers.fully_connected(
inputs, 32, scope='fc1', weights_regularizer=regularizer, reuse=True)
self.assertEqual(
len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 1)
- self.assertEqual(len(loss_ops.get_regularization_losses()), 1)
+ self.assertEqual(len(losses.get_regularization_losses()), 1)
with variable_scope.variable_scope('outer', reuse=False):
_layers.fully_connected(inputs, 32, weights_regularizer=regularizer)
self.assertEqual(
len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 2)
- self.assertEqual(len(loss_ops.get_regularization_losses()), 2)
+ self.assertEqual(len(losses.get_regularization_losses()), 2)
with variable_scope.variable_scope('outer', reuse=True):
_layers.fully_connected(inputs, 32, weights_regularizer=regularizer)
self.assertEqual(
len(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)), 2)
- self.assertEqual(len(loss_ops.get_regularization_losses()), 2)
+ self.assertEqual(len(losses.get_regularization_losses()), 2)
def testCreateFCWithoutActivation(self):
height, width = 3, 3
@@ -2771,6 +2841,76 @@ class MaxPool2DTest(test.TestCase):
self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])
+class MaxPool3DTest(test.TestCase):
+
+ def testInvalidDataFormat(self):
+ depth, height, width = 3, 6, 9
+ images = np.random.uniform(size=(5, depth, height, width, 3))
+ with self.assertRaisesRegexp(ValueError,
+ 'data_format has to be either NCDHW or NDHWC.'):
+ _layers.max_pool3d(images, [3, 3, 3], data_format='CDHWN')
+
+ def testCreateMaxPool(self):
+ depth, height, width = 3, 6, 9
+ images = np.random.uniform(size=(5, depth, height, width, 3)).astype(np.float32)
+ output = _layers.max_pool3d(images, [3, 3, 3])
+ self.assertEqual(output.op.name, 'MaxPool3D/MaxPool3D')
+ self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 4, 3])
+
+ def testCreateMaxPoolNCDHW(self):
+ depth, height, width = 3, 6, 9
+ images = np.random.uniform(size=(5, 3, depth, height, width)).astype(np.float32)
+ output = _layers.max_pool3d(images, [3, 3, 3], data_format='NCDHW')
+ self.assertEquals(output.op.name, 'MaxPool3D/transpose_1')
+ self.assertListEqual(output.get_shape().as_list(), [5, 3, 1, 2, 4])
+
+ def testCollectOutputs(self):
+ depth, height, width = 3, 6, 9
+ images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+ output = _layers.max_pool3d(images, [3, 3, 3], outputs_collections='outputs')
+ output_collected = ops.get_collection('outputs')[0]
+ self.assertEqual(output_collected.aliases, ['MaxPool3D'])
+ self.assertEqual(output_collected, output)
+
+ def testCreateSquareMaxPool(self):
+ depth, height, width = 3, 6, 9
+ images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+ output = _layers.max_pool3d(images, 3)
+ self.assertEqual(output.op.name, 'MaxPool3D/MaxPool3D')
+ self.assertListEqual(output.get_shape().as_list(), [5, 1, 2, 4, 3])
+
+ def testCreateMaxPoolWithScope(self):
+ depth, height, width = 3, 6, 9
+ images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+ output = _layers.max_pool3d(images, [3, 3, 3], scope='pool1')
+ self.assertEqual(output.op.name, 'pool1/MaxPool3D')
+
+ def testCreateMaxPoolWithSamePadding(self):
+ depth, height, width = 3, 6, 9
+ images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+ output = _layers.max_pool3d(images, [3, 3, 3], padding='SAME')
+ self.assertListEqual(output.get_shape().as_list(), [5, 2, 3, 5, 3])
+
+ def testCreateMaxPoolWithSamePaddingNCDHW(self):
+ depth, height, width = 3, 6, 9
+ images = random_ops.random_uniform((5, 3, depth, height, width), seed=1)
+ output = _layers.max_pool3d(
+ images, [3, 3, 3], padding='SAME', data_format='NCDHW')
+ self.assertListEqual(output.get_shape().as_list(), [5, 3, 2, 3, 5])
+
+ def testCreateMaxPoolStrideWithSamePadding(self):
+ depth, height, width = 3, 6, 9
+ images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+ output = _layers.max_pool3d(images, [3, 3, 3], stride=1, padding='SAME')
+ self.assertListEqual(output.get_shape().as_list(), [5, depth, height, width, 3])
+
+ def testGlobalMaxPool(self):
+ depth, height, width = 3, 6, 9
+ images = random_ops.random_uniform((5, depth, height, width, 3), seed=1)
+ output = _layers.max_pool3d(images, images.get_shape()[1:4], stride=1)
+ self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 1, 3])
+
+
class OneHotEncodingTest(test.TestCase):
def testOneHotEncodingCreate(self):
generated by cgit v1.2.3 (git 2.39.1) at 2024-06-15 06:29:06 +0000