get_tf_version_number -> get_tf_version_tuple

examples/DeepQNetwork/DQNModel.py

@@ -4,13 +4,11 @@
 
 import abc
 import tensorflow as tf
-import tensorpack
 from tensorpack import ModelDesc
 from tensorpack.utils import logger
 from tensorpack.tfutils import (
     varreplace, summary, get_current_tower_context, optimizer, gradproc)
 from tensorpack.tfutils.scope_utils import auto_reuse_variable_scope
-assert tensorpack.tfutils.common.get_tf_version_number() >= 1.2
 
 
 class Model(ModelDesc):

examples/FasterRCNN/train.py

@@ -22,7 +22,7 @@ assert six.PY3, "FasterRCNN requires Python 3!"
 from tensorpack import *
 from tensorpack.tfutils.summary import add_moving_summary
 from tensorpack.tfutils import optimizer
-from tensorpack.tfutils.common import get_tf_version_number
+from tensorpack.tfutils.common import get_tf_version_tuple
 import tensorpack.utils.viz as tpviz
 
 from coco import COCODetection
@@ -514,7 +514,7 @@ if __name__ == '__main__':
     parser.add_argument('--config', help="A list of KEY=VALUE to overwrite those defined in config.py",
                         nargs='+')
 
-    if get_tf_version_number() < 1.6:
+    if get_tf_version_tuple() < (1, 6):
         # https://github.com/tensorflow/tensorflow/issues/14657
         logger.warn("TF<1.6 has a bug which may lead to crash in FasterRCNN training if you're unlucky.")
 

examples/GAN/Improved-WGAN.py

@@ -4,7 +4,7 @@
 # Author: Yuxin Wu
 
 from tensorpack import *
-from tensorpack.tfutils import get_tf_version_number
+from tensorpack.tfutils import get_tf_version_tuple
 from tensorpack.tfutils.summary import add_moving_summary
 from tensorpack.tfutils.scope_utils import auto_reuse_variable_scope
 import tensorflow as tf
@@ -83,7 +83,7 @@ class Model(DCGAN.Model):
 
 
 if __name__ == '__main__':
-    assert get_tf_version_number() >= 1.4
+    assert get_tf_version_tuple() >= (1, 4)
     args = DCGAN.get_args(default_batch=64, default_z_dim=128)
     M = Model(shape=args.final_size, batch=args.batch, z_dim=args.z_dim)
     if args.sample:

tensorpack/callbacks/saver.py

@@ -8,7 +8,6 @@ import os
 
 from .base import Callback
 from ..utils import logger
-from ..tfutils.common import get_tf_version_number
 
 __all__ = ['ModelSaver', 'MinSaver', 'MaxSaver']
 
@@ -51,13 +50,6 @@ class ModelSaver(Callback):
             vars.extend(tf.get_collection(key))
         vars = list(set(vars))
         self.path = os.path.join(self.checkpoint_dir, 'model')
-        if get_tf_version_number() <= 1.1:
-            self.saver = tf.train.Saver(
-                var_list=vars,
-                max_to_keep=self._max_to_keep,
-                keep_checkpoint_every_n_hours=self._keep_every_n_hours,
-                write_version=tf.train.SaverDef.V2)
-        else:
         self.saver = tf.train.Saver(
             var_list=vars,
             max_to_keep=self._max_to_keep,

tensorpack/graph_builder/utils.py

@@ -8,7 +8,7 @@ import tensorflow as tf
 
 from ..tfutils.varreplace import custom_getter_scope
 from ..tfutils.scope_utils import under_name_scope, cached_name_scope
-from ..tfutils.common import get_tf_version_number
+from ..tfutils.common import get_tf_version_tuple
 from ..utils.argtools import call_only_once
 from ..utils import logger
 
@@ -67,7 +67,7 @@ class LeastLoadedDeviceSetter(object):
         self.ps_sizes = [0] * len(self.ps_devices)
 
     def __call__(self, op):
-        if get_tf_version_number() >= 1.8:
+        if get_tf_version_tuple() >= (1, 8):
             from tensorflow.python.training.device_util import canonicalize
         else:
             def canonicalize(name):    # tensorflow/tensorflow#11484

tensorpack/models/_old_batch_norm.py

@@ -7,7 +7,7 @@ from tensorflow.python.training import moving_averages
 from ..utils import logger
 from ..utils.argtools import get_data_format
 from ..tfutils.tower import get_current_tower_context
-from ..tfutils.common import get_tf_version_number
+from ..tfutils.common import get_tf_version_tuple
 
 from .common import layer_register, VariableHolder
 from .tflayer import convert_to_tflayer_args
@@ -128,7 +128,7 @@ def BatchNorm(inputs, training=None, momentum=0.9, epsilon=1e-5,
             xn = tf.squeeze(xn, [1, 2])
     else:
         if ctx.is_training:
-            assert get_tf_version_number() >= 1.4, \
+            assert get_tf_version_tuple() >= (1, 4), \
                 "Fine tuning a BatchNorm model with fixed statistics is only " \
                 "supported after https://github.com/tensorflow/tensorflow/pull/12580 "
             if ctx.is_main_training_tower:  # only warn in first tower

tensorpack/models/batch_norm.py

@@ -11,7 +11,7 @@ import six
 from ..utils import logger
 from ..utils.argtools import get_data_format
 from ..tfutils.tower import get_current_tower_context
-from ..tfutils.common import get_tf_version_number
+from ..tfutils.common import get_tf_version_tuple
 from ..tfutils.collection import backup_collection, restore_collection
 from .common import layer_register, VariableHolder
 from .tflayer import convert_to_tflayer_args, rename_get_variable
@@ -155,9 +155,9 @@ def BatchNorm(inputs, axis=None, training=None, momentum=0.9, epsilon=1e-5,
     if training is None:
         training = ctx.is_training
     training = bool(training)
-    TF_version = get_tf_version_number()
+    TF_version = get_tf_version_tuple()
     if not training and ctx.is_training:
-        assert TF_version >= 1.4, \
+        assert TF_version >= (1, 4), \
             "Fine tuning a BatchNorm model with fixed statistics is only " \
             "supported after https://github.com/tensorflow/tensorflow/pull/12580 "
         if ctx.is_main_training_tower:  # only warn in first tower
@@ -178,7 +178,7 @@ def BatchNorm(inputs, axis=None, training=None, momentum=0.9, epsilon=1e-5,
                 gamma_initializer=gamma_initializer,
                 fused=(ndims == 4 and axis in [1, 3]),
                 _reuse=tf.get_variable_scope().reuse)
-            if TF_version >= 1.5:
+            if TF_version >= (1, 5):
                 tf_args['virtual_batch_size'] = virtual_batch_size
             else:
                 assert virtual_batch_size is None, "Feature not supported in this version of TF!"
@@ -220,7 +220,7 @@ def BatchNorm(inputs, axis=None, training=None, momentum=0.9, epsilon=1e-5,
         batch_mean_square = tf.reduce_mean(tf.square(inputs), axis=red_axis)
 
         if sync_statistics == 'nccl':
-            if six.PY3 and TF_version <= 1.9 and ctx.is_main_training_tower:
+            if six.PY3 and TF_version <= (1, 9) and ctx.is_main_training_tower:
                 logger.warn("A TensorFlow bug will cause cross-GPU BatchNorm to fail. "
                             "Apply this patch: https://github.com/tensorflow/tensorflow/pull/20360")
 

tensorpack/models/conv2d.py

@@ -4,7 +4,7 @@
 
 import tensorflow as tf
 from .common import layer_register, VariableHolder
-from ..tfutils.common import get_tf_version_number
+from ..tfutils.common import get_tf_version_tuple
 from ..utils.argtools import shape2d, shape4d, get_data_format
 from .tflayer import rename_get_variable, convert_to_tflayer_args
 
@@ -86,14 +86,14 @@ def Conv2D(
 
         out_channel = filters
         assert out_channel % split == 0
-        assert dilation_rate == (1, 1) or get_tf_version_number() >= 1.5, 'TF>=1.5 required for group dilated conv'
+        assert dilation_rate == (1, 1) or get_tf_version_tuple() >= (1, 5), 'TF>=1.5 required for group dilated conv'
 
         kernel_shape = shape2d(kernel_size)
         filter_shape = kernel_shape + [in_channel / split, out_channel]
         stride = shape4d(strides, data_format=data_format)
 
         kwargs = dict(data_format=data_format)
-        if get_tf_version_number() >= 1.5:
+        if get_tf_version_tuple() >= (1, 5):
             kwargs['dilations'] = shape4d(dilation_rate, data_format=data_format)
 
         W = tf.get_variable(

tensorpack/models/tflayer.py

@@ -6,7 +6,7 @@ import six
 import functools
 
 from ..utils.argtools import get_data_format
-from ..tfutils.common import get_tf_version_number
+from ..tfutils.common import get_tf_version_tuple
 from ..tfutils.varreplace import custom_getter_scope
 
 
@@ -112,7 +112,7 @@ def rename_tflayer_get_variable():
 
 
 def monkeypatch_tf_layers():
-    if get_tf_version_number() < 1.4:
+    if get_tf_version_tuple() < (1, 4):
         if not hasattr(tf.layers, 'Dense'):
             from tensorflow.python.layers.core import Dense
             tf.layers.Dense = Dense

tensorpack/predict/base.py

@@ -6,11 +6,10 @@ from abc import abstractmethod, ABCMeta
 import tensorflow as tf
 import six
 
-from ..tfutils.common import get_tensors_by_names, get_tf_version_number
+from ..tfutils.common import get_tensors_by_names
 from ..tfutils.tower import PredictTowerContext
 from ..input_source import PlaceholderInput
 from ..utils.develop import log_deprecated
-from ..utils.argtools import log_once
 from ..utils.utils import execute_only_once
 
 __all__ = ['PredictorBase', 'AsyncPredictorBase',
@@ -110,9 +109,7 @@ class OnlinePredictor(PredictorBase):
         self.input_tensors = input_tensors
         self.output_tensors = output_tensors
         self.sess = sess
-        self._use_callable = get_tf_version_number() >= 1.2
 
-        if self._use_callable:
         if sess is not None:
             self._callable = sess.make_callable(
                 fetches=output_tensors,
@@ -120,9 +117,6 @@ class OnlinePredictor(PredictorBase):
                 accept_options=self.ACCEPT_OPTIONS)
         else:
             self._callable = None
-        else:
-            log_once(
-                "TF>=1.2 is recommended for better performance of predictor!", 'warn')
 
     def _do_call_old(self, dp):
         feed = dict(zip(self.input_tensors, dp))

tensorpack/tfutils/common.py

@@ -5,6 +5,7 @@
 import tensorflow as tf
 from six.moves import map
 from ..utils.argtools import graph_memoized
+from ..utils.develop import deprecated
 
 __all__ = ['get_default_sess_config',
            'get_global_step_value',
@@ -12,7 +13,6 @@ __all__ = ['get_default_sess_config',
            # 'get_op_tensor_name',
            # 'get_tensors_by_names',
            # 'get_op_or_tensor_by_name',
-           # 'get_tf_version_number',
            ]
 
 
@@ -132,8 +132,13 @@ def get_op_or_tensor_by_name(name):
         return list(map(f, name))
 
 
+@deprecated("You should use get_tf_version_tuple instead due to the existence of TF 1.10")
 def get_tf_version_number():
+    return float('.'.join(tf.VERSION.split('.')[:2]))
+
+
+def get_tf_version_tuple():
     """
-    Return a float (for comparison), indicating tensorflow version.
+    Return TensorFlow version as a 2-element tuple (for comparison).
     """
-    return float('.'.join(tf.VERSION.split('.')[:2]))
+    return tuple(map(int, tf.VERSION.split('.')[:2]))

tensorpack/tfutils/scope_utils.py

@@ -7,7 +7,7 @@ import functools
 from contextlib import contextmanager
 
 from ..utils.argtools import graph_memoized
-from .common import get_tf_version_number
+from .common import get_tf_version_tuple
 
 __all__ = ['auto_reuse_variable_scope', 'cached_name_scope', 'under_name_scope']
 
@@ -39,7 +39,7 @@ def auto_reuse_variable_scope(func):
         h = hash((tf.get_default_graph(), scope.name))
         # print("Entering " + scope.name + " reuse: " + str(h in used_scope))
         if h in used_scope:
-            if get_tf_version_number() >= 1.5:
+            if get_tf_version_tuple() >= (1, 5):
                 with tf.variable_scope(scope, reuse=True, auxiliary_name_scope=False):
                     return func(*args, **kwargs)
             else:

tensorpack/tfutils/varreplace.py

@@ -5,7 +5,7 @@
 import tensorflow as tf
 from contextlib import contextmanager
 
-from .common import get_tf_version_number
+from .common import get_tf_version_tuple
 
 __all__ = ['freeze_variables', 'remap_variables']
 
@@ -13,7 +13,7 @@ __all__ = ['freeze_variables', 'remap_variables']
 @contextmanager
 def custom_getter_scope(custom_getter):
     scope = tf.get_variable_scope()
-    if get_tf_version_number() >= 1.5:
+    if get_tf_version_tuple() >= (1, 5):
         with tf.variable_scope(
                 scope, custom_getter=custom_getter,
                 auxiliary_name_scope=False):

tests/test_infogan.py

 from case_script import TestPythonScript
 
-from tensorpack.tfutils.common import get_tf_version_number
+from tensorpack.tfutils.common import get_tf_version_tuple
 
 
 class InfoGANTest(TestPythonScript):
@@ -10,6 +10,6 @@ class InfoGANTest(TestPythonScript):
         return '../examples/GAN/InfoGAN-mnist.py'
 
     def test(self):
-        if get_tf_version_number() < 1.4:
+        if get_tf_version_tuple() < (1, 4):
             return True     # requires leaky_relu
         self.assertSurvive(self.script, args=None)