address deprecation of tf.contrib

docs/tutorial/inference.md

@@ -48,7 +48,7 @@ A simple example of how it works:
 ```python
 pred_config = PredictConfig(
     model=YourModel(),
-    session_init=get_model_loader(model_path),
+    session_init=SmartInit(model_path),
     input_names=['input1', 'input2'],  # tensor names in the graph, or name of the declared inputs
     output_names=['output1', 'output2'])  # tensor names in the graph
 predictor = OfflinePredictor(pred_config)
@@ -161,7 +161,7 @@ with TowerContext('', is_training=False):
 ### Step 2: load the checkpoint
 
 You can just use `tf.train.Saver` for all the work.
-Alternatively, use tensorpack's `get_model_loader(path).init(tf.get_default_session())`
+Alternatively, use tensorpack's `SmartInit(path).init(tf.get_default_session())`
 
 Now, you've already built a graph for inference, and the checkpoint is also loaded.
 You may now:

scripts/checkpoint-prof.py

@@ -7,7 +7,7 @@ import numpy as np
 import tensorflow as tf
 
 from tensorpack import get_default_sess_config, get_op_tensor_name
-from tensorpack.tfutils.sessinit import get_model_loader
+from tensorpack.tfutils.sessinit import SmartInit
 from tensorpack.utils import logger
 
 if __name__ == '__main__':
@@ -25,7 +25,7 @@ if __name__ == '__main__':
     tf.train.import_meta_graph(args.meta, clear_devices=True)
     G = tf.get_default_graph()
     with tf.Session(config=get_default_sess_config()) as sess:
-        init = get_model_loader(args.model)
+        init = SmartInit(args.model)
         init.init(sess)
 
         feed = {}
@@ -52,16 +52,20 @@ if __name__ == '__main__':
         sess.run(fetches, feed_dict=feed, options=opt, run_metadata=meta)
 
         if args.print_flops:
-            tf.contrib.tfprof.model_analyzer.print_model_analysis(
-                G, run_meta=meta,
-                tfprof_options=tf.contrib.tfprof.model_analyzer.FLOAT_OPS_OPTIONS)
+            tf.profiler.profile(
+                G,
+                run_meta=meta,
+                cmd='op',
+                options=tf.profiler.ProfileOptionBuilder.float_operation())
 
         if args.print_params:
-            tf.contrib.tfprof.model_analyzer.print_model_analysis(
-                G, run_meta=meta,
-                tfprof_options=tf.contrib.tfprof.model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
+            tf.profiler.profile(
+                G,
+                run_meta=meta,
+                options=tf.profiler.ProfileOptionBuilder.trainable_variables_parameter())
 
         if args.print_timing:
-            tf.contrib.tfprof.model_analyzer.print_model_analysis(
-                G, run_meta=meta,
-                tfprof_options=tf.contrib.tfprof.model_analyzer.PRINT_ALL_TIMING_MEMORY)
+            tf.profiler.profile(
+                G,
+                run_meta=meta,
+                options=tf.profiler.ProfileOptionBuilder.time_and_memory())

tensorpack/graph_builder/utils.py

@@ -143,7 +143,7 @@ def allreduce_grads(all_grads, average):
     """
 
     if get_tf_version_tuple() <= (1, 12):
-        from tensorflow.contrib import nccl
+        from tensorflow.contrib import nccl  # deprecated
     else:
         from tensorflow.python.ops import nccl_ops as nccl
     nr_tower = len(all_grads)

tensorpack/models/batch_norm.py

@@ -296,12 +296,12 @@ def BatchNorm(inputs, axis=None, training=None, momentum=0.9, epsilon=1e-5,
 
                 if TF_version <= (1, 12):
                     try:
-                        from tensorflow.contrib.nccl.python.ops.nccl_ops import _validate_and_load_nccl_so
+                        from tensorflow.contrib.nccl.python.ops.nccl_ops import _validate_and_load_nccl_so  # deprecated
                     except Exception:
                         pass
                     else:
                         _validate_and_load_nccl_so()
-                    from tensorflow.contrib.nccl.ops import gen_nccl_ops
+                    from tensorflow.contrib.nccl.ops import gen_nccl_ops  # deprecated
                 else:
                     from tensorflow.python.ops import gen_nccl_ops
                 shared_name = re.sub('tower[0-9]+/', '', tf.get_variable_scope().name)

tensorpack/models/conv2d.py

@@ -50,7 +50,7 @@ def Conv2D(
     """
     if kernel_initializer is None:
         if get_tf_version_tuple() <= (1, 12):
-            kernel_initializer = tf.contrib.layers.variance_scaling_initializer(2.0)
+            kernel_initializer = tf.contrib.layers.variance_scaling_initializer(2.0)  # deprecated
         else:
             kernel_initializer = tf.keras.initializers.VarianceScaling(2.0, distribution='untruncated_normal')
     dilation_rate = shape2d(dilation_rate)
@@ -175,7 +175,7 @@ def Conv2DTranspose(
     """
     if kernel_initializer is None:
         if get_tf_version_tuple() <= (1, 12):
-            kernel_initializer = tf.contrib.layers.variance_scaling_initializer(2.0)
+            kernel_initializer = tf.contrib.layers.variance_scaling_initializer(2.0)  # deprecated
         else:
             kernel_initializer = tf.keras.initializers.VarianceScaling(2.0, distribution='untruncated_normal')
 

tensorpack/models/fc.py

@@ -48,7 +48,7 @@ def FullyConnected(
     """
     if kernel_initializer is None:
         if get_tf_version_tuple() <= (1, 12):
-            kernel_initializer = tf.contrib.layers.variance_scaling_initializer(2.0)
+            kernel_initializer = tf.contrib.layers.variance_scaling_initializer(2.0)  # deprecated
         else:
             kernel_initializer = tf.keras.initializers.VarianceScaling(2.0, distribution='untruncated_normal')
 

tensorpack/models/regularize.py

@@ -22,8 +22,8 @@ def _log_once(msg):
 
 
 if get_tf_version_tuple() <= (1, 12):
-    l2_regularizer = tf.contrib.layers.l2_regularizer
-    l1_regularizer = tf.contrib.layers.l1_regularizer
+    l2_regularizer = tf.contrib.layers.l2_regularizer  # deprecated
+    l1_regularizer = tf.contrib.layers.l1_regularizer  # deprecated
 else:
     # oh these little dirty details
     l2_regularizer = lambda x: tf.keras.regularizers.l2(x * 0.5)  # noqa

tensorpack/tfutils/dependency.py

@@ -32,7 +32,10 @@ def dependency_of_targets(targets, op):
         op = op.op
     assert isinstance(op, tf.Operation), op
 
-    from tensorflow.contrib.graph_editor import get_backward_walk_ops
+    try:
+        from tensorflow.contrib.graph_editor import get_backward_walk_ops  # deprecated
+    except ImportError:
+        from tensorflow.python.ops.op_selector import get_backward_walk_ops
     # alternative implementation can use graph_util.extract_sub_graph
     dependent_ops = get_backward_walk_ops(targets, control_inputs=True)
     return op in dependent_ops

tensorpack/tfutils/sesscreate.py

@@ -2,7 +2,7 @@
 # File: sesscreate.py
 
 
-from ..compat import tfv1 as tf, is_tfv2
+from ..compat import tfv1 as tf
 from ..utils import logger
 from .common import get_default_sess_config
 
@@ -70,8 +70,10 @@ class NewSessionCreator(tf.train.SessionCreator):
             return False
 
         def run(op):
-            if not is_tfv2():
-                from tensorflow.contrib.graph_editor import get_backward_walk_ops
+            try:
+                from tensorflow.contrib.graph_editor import get_backward_walk_ops  # deprecated
+            except ImportError:
+                from tensorflow.python.ops.op_selector import get_backward_walk_ops
 
             deps = get_backward_walk_ops(op, control_inputs=True)
             for dep_op in deps:
@@ -80,6 +82,7 @@ class NewSessionCreator(tf.train.SessionCreator):
                         "Initializer '{}' depends on a blocking op '{}'. "
                         "This initializer is likely to hang!".format(
                             op.name, dep_op.name))
+
             sess.run(op)
 
         run(tf.global_variables_initializer())

tensorpack/tfutils/varmanip.py

@@ -140,7 +140,7 @@ class SessionUpdate(object):
 def dump_session_params(path):
     """
     Dump value of all TRAINABLE + MODEL variables to a dict, and save as
-    npz format (loadable by :func:`sessinit.get_model_loader`).
+    npz format (loadable by :func:`sessinit.SmartInit`).
 
     Args:
         path(str): the file name to save the parameters. Must ends with npz.

tensorpack/train/tower.py

@@ -279,7 +279,10 @@ class SingleCostTrainer(TowerTrainer):
             if not self.XLA_COMPILE:
                 return compute_grad_from_inputs(*inputs)
             else:
-                from tensorflow.contrib.compiler import xla
+                try:
+                    from tensorflow.contrib.compiler import xla  # deprecated
+                except ImportError:
+                    from tensorflow.python.compiler.xla import xla
 
                 def xla_func():
                     grads = compute_grad_from_inputs(*inputs)