Modified the tests as a workaround the TF #29439 issue and modified t…

…he crf.py and misc_io.py to work with TF 1.13.1 and 1.14.0rc0.

niftynet/contrib/segmentation_selective_sampler/test_sampler_selective.py

@@ -11,6 +11,7 @@
 from niftynet.io.image_reader import ImageReader
 from niftynet.io.image_sets_partitioner import ImageSetsPartitioner
 from niftynet.utilities.util_common import ParserNamespace
+from tests.niftynet_testcase import NiftyNetTestCase
 
 
 ### utility function for testing purposes
@@ -192,7 +193,7 @@ def get_dynamic_window_reader():
     return reader
 
 
-class SelectiveSamplerTest(tf.test.TestCase):
+class SelectiveSamplerTest(NiftyNetTestCase):
     def test_3d_init(self):
         constraint_built = Constraint(compulsory_labels=[1],
                                       min_ratio=0.000001,
@@ -203,7 +204,7 @@ def test_3d_init(self):
                                    constraint=constraint_built,
                                    windows_per_image=2,
                                    queue_length=10)
-        with self.test_session() as sess:
+        with self.cached_session() as sess:
             sampler.run_threads(sess, num_threads=1)
             out = sess.run(sampler.pop_batch_op())
             self.assertTrue(check_constraint(out['label'], constraint_built))
@@ -218,7 +219,7 @@ def test_3d_init(self):
     #                              batch_size=2,
     #                              windows_per_image=10,
     #                              queue_length=10)
-    #     with self.test_session() as sess:
+    #     with self.cached_session() as sess:
     #         sampler.run_threads(sess, num_threads=2)
     #         out = sess.run(sampler.pop_batch_op())
     #         self.assertAllClose(out['image'].shape, (2, 10, 9, 1))
@@ -234,7 +235,7 @@ def test_3d_init(self):
     #                                    min_num_labels=2),
     #                                windows_per_image=2,
     #                                queue_length=2)
-    #     with self.test_session() as sess:
+    #     with self.cached_session() as sess:
     #         sampler.run_threads(sess, num_threads=2)
     #         out = sess.run(sampler.pop_batch_op())
     #         test = np.zeros_like(out['label'])
@@ -262,7 +263,7 @@ def test_3d_init(self):
     #    sampler.close_all()
 
 
-# class RandomCoordinatesTest(tf.test.TestCase):
+# class RandomCoordinatesTest(NiftyNetTestCase):
 #     def test_coordinates(self):
 #         coords = rand_choice_coordinates(
 #             subject_id=1,

niftynet/engine/handler_early_stopping.py

@@ -77,7 +77,6 @@ def check_should_stop(performance_history, mode='mean', min_delta=0.03,
         dividing.
     :param performance_history: a list of size patience with the performance
     history
-    :param patience: see above
     :param min_delta: threshold for smoothness
     :param kernel_size: hyperparameter for median smoothing
     :param k_splits: number of splits if using 'validation_up'

niftynet/io/misc_io.py

@@ -1,6 +1,5 @@
 # -*- coding: utf-8 -*-
 """Utilities functions for file and path management"""
-
 from __future__ import absolute_import, print_function, unicode_literals
 
 import errno
@@ -766,9 +765,18 @@ def set_logger(file_name=None):
     :return:
     """
     # pylint: disable=no-name-in-module
-    from tensorflow.python.platform.tf_logging import _get_logger
+    # This is done so if the user had TF 1.12.1 or a new version the code
+    # does not brake. First part of the try is renaming the TF 1.12.1 to
+    # fit the TF 1.13.1>= naming scheme, while the second is just a normal
+    # import for TF 1.13.1>=
+    try:
+        # pylint: disable=no-name-in-module
+        from tensorflow.python.platform.tf_logging import \
+            _get_logger as get_logger
+    except ImportError:
+        from tensorflow.python.platform.tf_logging import get_logger
 
-    logger = _get_logger()
+    logger = get_logger()
     tf.logging.set_verbosity(tf.logging.INFO)
     logger.handlers = []
 
@@ -793,9 +801,18 @@ def close_logger():
     :return:
     """
     # pylint: disable=no-name-in-module
-    from tensorflow.python.platform.tf_logging import _get_logger
+    # This is done so if the user had TF 1.12.1 or a new version the code
+    # does not brake. First part of the try is renaming the TF 1.12.1 to
+    # fit the TF 1.13.1>= naming scheme, while the second is just a normal
+    # import for TF 1.13.1>=
+    try:
+        # pylint: disable=no-name-in-module
+        from tensorflow.python.platform.tf_logging import \
+            _get_logger as get_logger
+    except ImportError:
+        from tensorflow.python.platform.tf_logging import get_logger
 
-    logger = _get_logger()
+    logger = get_logger()
     for handler in reversed(logger.handlers):
         try:
             handler.flush()

niftynet/layer/crf.py

@@ -281,18 +281,44 @@ def _simple_hash(key):
         hash_vector = tf.constant(hash_vector, dtype=tf.int64)
         return tf.reduce_sum(tf.to_int64(key) * hash_vector, 1)
 
-    hash_table = tf.contrib.lookup.MutableDenseHashTable(
-        tf.int64, tf.int64,
-        default_value=tf.constant([-1] * n_ch, dtype=tf.int64),
-        empty_key=-2,
-        initial_num_buckets=8,
-        checkpoint=False)
-    index_table = tf.contrib.lookup.MutableDenseHashTable(
-        tf.int64, tf.int64,
-        default_value=0,
-        empty_key=-1,
-        initial_num_buckets=8,
-        checkpoint=False)
+    # This is done so if the user had TF 1.12.1 or a new version the code
+    # does not brake. First part of the try is for TF 1.12.1 where the
+    # deleted_key keyword was missing, while the second is just a normal
+    # usage for TF 1.13.1>=
+    try:
+        hash_table = tf.contrib.lookup.MutableDenseHashTable(
+            tf.int64, tf.int64,
+            default_value=tf.constant([-1] * 100, dtype=tf.int64),
+            empty_key=-2,
+            initial_num_buckets=8,
+            checkpoint=False
+        )
+    except TypeError:
+        hash_table = tf.contrib.lookup.MutableDenseHashTable(
+            tf.int64, tf.int64,
+            default_value=tf.constant([-1] * n_ch, dtype=tf.int64),
+            empty_key=-3,
+            deleted_key=-2,
+            initial_num_buckets=8,
+            checkpoint=False
+        )
+    try:
+        index_table = tf.contrib.lookup.MutableDenseHashTable(
+            tf.int64, tf.int64,
+            default_value=0,
+            empty_key=-1,
+            initial_num_buckets=8,
+            checkpoint=False
+        )
+    except TypeError:
+        index_table = tf.contrib.lookup.MutableDenseHashTable(
+            tf.int64, tf.int64,
+            default_value=0,
+            empty_key=-2,
+            deleted_key=-1,
+            initial_num_buckets=8,
+            checkpoint=False
+        )
 
     # canonical simplex (p.4 in [Adams et al 2010])
     canonical = \

requirements-cpu.txt

@@ -3,7 +3,7 @@ nibabel>=2.1.0
 numpy>=1.13.3, <= 1.14.5
 scipy>=0.18
 configparser
-tensorflow==1.12.2
+tensorflow>=1.12.2
 pandas
 pillow
 blinker

requirements-gpu.txt

@@ -3,7 +3,7 @@ nibabel>=2.1.0
 numpy>=1.13.3, <= 1.14.5
 scipy>=0.18
 configparser
-tensorflow-gpu==1.12.2
+tensorflow-gpu>=1.12.2
 pandas
 pillow
 blinker

tests/activation_test.py

@@ -5,9 +5,9 @@
 from tensorflow.contrib.layers.python.layers import regularizers
 
 from niftynet.layer.activation import ActiLayer
+from tests.niftynet_testcase import NiftyNetTestCase
 
-
-class ActivationTest(tf.test.TestCase):
+class ActivationTest(NiftyNetTestCase):
     def get_3d_input(self):
         input_shape = (2, 16, 16, 16, 8)
         x = tf.ones(input_shape)
@@ -26,7 +26,7 @@ def run_test(self, is_3d, type_str, expected_shape):
         activation_layer = ActiLayer(func=type_str)
         out_acti = activation_layer(x)
         print(activation_layer)
-        with self.test_session() as sess:
+        with self.cached_session() as sess:
             sess.run(tf.global_variables_initializer())
             out = sess.run(out_acti)
             self.assertAllClose(out.shape, expected_shape)
@@ -66,7 +66,7 @@ def test_3d_prelu_reg_shape(self):
                                 name='regularized')
         out_prelu = prelu_layer(x)
         print(prelu_layer)
-        with self.test_session() as sess:
+        with self.cached_session() as sess:
             sess.run(tf.global_variables_initializer())
             out = sess.run(out_prelu)
             self.assertAllClose((2, 16, 16, 16, 8), out.shape)
@@ -76,7 +76,7 @@ def test_3d_dropout_shape(self):
         dropout_layer = ActiLayer(func='dropout')
         out_dropout = dropout_layer(x, keep_prob=0.8)
         print(dropout_layer)
-        with self.test_session() as sess:
+        with self.cached_session() as sess:
             sess.run(tf.global_variables_initializer())
             out = sess.run(out_dropout)
             self.assertAllClose((2, 16, 16, 16, 8), out.shape)
@@ -119,7 +119,7 @@ def test_2d_prelu_reg_shape(self):
                                 name='regularized')
         out_prelu = prelu_layer(x)
         print(prelu_layer)
-        with self.test_session() as sess:
+        with self.cached_session() as sess:
             sess.run(tf.global_variables_initializer())
             out = sess.run(out_prelu)
             self.assertAllClose((2, 16, 16, 8), out.shape)
@@ -129,7 +129,7 @@ def test_2d_dropout_shape(self):
         dropout_layer = ActiLayer(func='dropout')
         out_dropout = dropout_layer(x, keep_prob=0.8)
         print(dropout_layer)
-        with self.test_session() as sess:
+        with self.cached_session() as sess:
             sess.run(tf.global_variables_initializer())
             out = sess.run(out_dropout)
             self.assertAllClose((2, 16, 16, 8), out.shape)

tests/additive_upsample_test.py

@@ -4,6 +4,7 @@
 import tensorflow as tf
 
 from niftynet.layer.additive_upsample import AdditiveUpsampleLayer
+from tests.niftynet_testcase import NiftyNetTestCase
 
 def get_3d_input():
     input_shape = (2, 16, 16, 16, 4)
@@ -15,7 +16,7 @@ def get_2d_input():
     x = tf.ones(input_shape)
     return x
 
-class AdditiveUpsampleTest(tf.test.TestCase):
+class AdditiveUpsampleTest(NiftyNetTestCase):
     def run_test(self, new_size, n_splits, expected_shape, is_3d=True):
         if is_3d:
             x = get_3d_input()
@@ -26,7 +27,7 @@ def run_test(self, new_size, n_splits, expected_shape, is_3d=True):
             new_size=new_size, n_splits=n_splits)
         resized = resize_layer(x)
         print(resize_layer)
-        with self.test_session() as sess:
+        with self.cached_session() as sess:
             out = sess.run(resized)
             self.assertAllClose(out.shape, expected_shape)
 

tests/affine_augmentation_test.py

@@ -5,9 +5,9 @@
 import tensorflow as tf
 
 from niftynet.layer.affine_augmentation import AffineAugmentationLayer
+from tests.niftynet_testcase import NiftyNetTestCase
 
-
-class RandRotationTest(tf.test.TestCase):
+class RandRotationTest(NiftyNetTestCase):
     def get_3d_image(self):
         image_shape = (1, 200, 200, 1)
         elements = tf.range(np.prod(image_shape), dtype=tf.float32)
@@ -28,7 +28,7 @@ def test_2d_shape(self):
         inverse_augment_layer = augment_layer.inverse()
         inverse = inverse_augment_layer(deformed)
 
-        with self.test_session() as sess:
+        with self.cached_session() as sess:
             test_out = sess.run([input_tensor, deformed, inverse])
             original, deformed_image, resumed_image = test_out
             to_compare = resumed_image > 0
@@ -49,7 +49,7 @@ def test_3d_shape(self):
         inverse = inverse_augment_layer(deformed)
 
         # with tf.Session() as sess:
-        with self.test_session() as sess:
+        with self.cached_session() as sess:
             test_out = sess.run([input_tensor, deformed, inverse])
             original, deformed_image, resumed_image = test_out
             to_compare = resumed_image > 0