TowerFuncWrapper -> TowerFunc

docs/conf.py

@@ -384,6 +384,7 @@ _DEPRECATED_NAMES = set([
     'get_nr_gpu',
     'TrainingMonitor',
     'PeakMemoryTracker',
+    'TowerFuncWrapper',
 
     'PrefetchData',
     'MultiProcessPrefetchData',
@@ -391,7 +392,7 @@ _DEPRECATED_NAMES = set([
     'MultiThreadPrefetchData',
 
     # deprecated or renamed symbolic code
-    'Deconv2D', 'psnr',
+    'Deconv2D',
 
     # shouldn't appear in doc:
     'l2_regularizer', 'l1_regularizer',

examples/GAN/GAN.py

@@ -8,7 +8,7 @@ import tensorflow as tf
 from tensorpack import BatchNorm, DataFlow, ModelDescBase, StagingInput, TowerTrainer, argscope
 from tensorpack.graph_builder import DataParallelBuilder, LeastLoadedDeviceSetter
 from tensorpack.tfutils.summary import add_moving_summary
-from tensorpack.tfutils.tower import TowerContext, TowerFuncWrapper
+from tensorpack.tfutils.tower import TowerContext, TowerFunc
 from tensorpack.utils import logger
 from tensorpack.utils.argtools import memoized_method
 
@@ -105,7 +105,7 @@ class GANTrainer(TowerTrainer):
         not needed. Just calling model.build_graph directly is OK.
         """
         # Build the graph
-        self.tower_func = TowerFuncWrapper(model.build_graph, model.inputs())
+        self.tower_func = TowerFunc(model.build_graph, model.inputs())
         with TowerContext('', is_training=True):
             self.tower_func(*input.get_input_tensors())
         opt = model.get_optimizer()
@@ -127,7 +127,7 @@ class GANTrainer(TowerTrainer):
             model.build_graph(*inputs)
             return [model.d_loss, model.g_loss]
 
-        self.tower_func = TowerFuncWrapper(get_cost, model.get_input_signature())
+        self.tower_func = TowerFunc(get_cost, model.get_input_signature())
         devices = [LeastLoadedDeviceSetter(d, raw_devices) for d in raw_devices]
         cost_list = DataParallelBuilder.build_on_towers(
             list(range(num_gpu)),
@@ -167,7 +167,7 @@ class SeparateGANTrainer(TowerTrainer):
         self.register_callback(cbs)
 
         # Build the graph
-        self.tower_func = TowerFuncWrapper(model.build_graph, model.inputs())
+        self.tower_func = TowerFunc(model.build_graph, model.inputs())
         with TowerContext('', is_training=True), \
                 argscope(BatchNorm, ema_update='internal'):
             # should not hook the EMA updates to both train_op, it will hurt training speed.

examples/keras/mnist-keras.py

@@ -120,7 +120,7 @@ if __name__ == '__main__':
 
     if get_num_gpu() <= 1:
         # single GPU:
-        launch_train_with_config(cfg, QueueInputTrainer())
+        launch_train_with_config(cfg, SimpleTrainer())
     else:
         # multi GPU:
         launch_train_with_config(cfg, SyncMultiGPUTrainerParameterServer(2))

tensorpack/callbacks/inference_runner.py

@@ -114,7 +114,7 @@ class InferenceRunner(InferenceRunnerBase):
             infs (list): a list of :class:`Inferencer` instances.
             tower_name (str): the name scope of the tower to build. Need to set a
                 different one if multiple InferenceRunner are used.
-            tower_func (tfutils.TowerFuncWrapper or None): the tower function to be used to build the graph.
+            tower_func (tfutils.TowerFunc or None): the tower function to be used to build the graph.
                 By defaults to call `trainer.tower_func` under a `training=False` TowerContext,
                 but you can change it to a different tower function
                 if you need to inference with several different graphs.
@@ -196,7 +196,7 @@ class DataParallelInferenceRunner(InferenceRunnerBase):
             gpus (int or list[int]): #gpus, or list of GPU id
             tower_name (str): the name scope of the tower to build. Need to set a
                 different one if multiple InferenceRunner are used.
-            tower_func (tfutils.TowerFuncWrapper or None): the tower function to be used to build the graph.
+            tower_func (tfutils.TowerFunc or None): the tower function to be used to build the graph.
                 The tower function will be called under a `training=False` TowerContext.
                 The default is `trainer.tower_func`,
                 but you can change it to a different tower function

tensorpack/graph_builder/model_desc.py

@@ -6,7 +6,6 @@ from collections import namedtuple
 import tensorflow as tf
 
 from ..utils.argtools import memoized_method
-from ..utils.develop import deprecated
 from ..tfutils.common import get_op_tensor_name
 from ..compat import backport_tensor_spec, tfv1
 
@@ -174,7 +173,3 @@ class ModelDesc(ModelDescBase):
         A subclass is expected to implement this method.
         """
         raise NotImplementedError()
-
-    @deprecated("Just use `build_graph` instead!")
-    def _build_graph_get_cost(self, *inputs):
-        return self.build_graph(*inputs)

tensorpack/predict/config.py

@@ -9,7 +9,7 @@ from ..graph_builder import ModelDescBase
 from ..tfutils import get_default_sess_config
 from ..tfutils.sessinit import JustCurrentSession, SessionInit
 from ..tfutils.sesscreate import NewSessionCreator
-from ..tfutils.tower import TowerFuncWrapper
+from ..tfutils.tower import TowerFunc
 from ..utils import logger
 
 __all__ = ['PredictConfig']
@@ -36,7 +36,7 @@ class PredictConfig(object):
         This can be provided in the following ways:
 
         1. `model`: a :class:`ModelDesc` instance. It will contain a tower function by itself.
-        2. `tower_func`: a :class:`tfutils.TowerFuncWrapper` instance.
+        2. `tower_func`: a :class:`tfutils.TowerFunc` instance.
             Provide a tower function instance directly.
         3. `tower_func`: a symbolic function and `input_signature`: the signature of the function.
             Provide both a function and its signature.
@@ -52,8 +52,8 @@ class PredictConfig(object):
         Args:
             model (ModelDescBase): to be used to construct a tower function.
             tower_func: a callable which takes input tensors (by positional args) and construct a tower.
-                or a :class:`tfutils.TowerFuncWrapper` instance.
-            input_signature ([tf.TensorSpec]): if tower_func is a plain function (instead of a TowerFuncWrapper),
+                or a :class:`tfutils.TowerFunc` instance.
+            input_signature ([tf.TensorSpec]): if tower_func is a plain function (instead of a TowerFunc),
                 this describes the list of inputs it takes.
 
             input_names (list): a list of input tensor names. Defaults to match input_signature.
@@ -85,13 +85,13 @@ class PredictConfig(object):
             assert_type(model, ModelDescBase, 'model')
             assert input_signature is None and tower_func is None
             self.input_signature = model.get_input_signature()
-            self.tower_func = TowerFuncWrapper(model.build_graph, self.input_signature)
+            self.tower_func = TowerFunc(model.build_graph, self.input_signature)
         else:
-            if isinstance(tower_func, TowerFuncWrapper):
+            if isinstance(tower_func, TowerFunc):
                 input_signature = tower_func.input_signature
             assert input_signature is not None and tower_func is not None
             self.input_signature = input_signature
-            self.tower_func = TowerFuncWrapper(tower_func, input_signature)
+            self.tower_func = TowerFunc(tower_func, input_signature)
 
         if session_init is None:
             session_init = JustCurrentSession()

tensorpack/tfutils/symbolic_functions.py

@@ -5,7 +5,6 @@
 import tensorflow as tf
 
 from ..compat import tfv1
-from ..utils.develop import deprecated
 
 __all__ = ['print_stat', 'rms']
 
@@ -37,7 +36,6 @@ def rms(x, name=None):
 
 
 # don't hurt to leave it here
-@deprecated("Please implement it by yourself.", "2018-04-28")
 def psnr(prediction, ground_truth, maxp=None, name='psnr'):
     """`Peak Signal to Noise Ratio <https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio>`_.
 

tensorpack/tfutils/tower.py

@@ -15,7 +15,8 @@ from ..utils.naming import MOVING_SUMMARY_OPS_KEY
 from .collection import CollectionGuard
 from .common import get_op_or_tensor_by_name, get_op_tensor_name
 
-__all__ = ['get_current_tower_context', 'BaseTowerContext', 'TowerContext', 'TowerFuncWrapper',
+__all__ = ['get_current_tower_context', 'BaseTowerContext', 'TowerContext',
+           'TowerFuncWrapper', 'TowerFunc',
            'TowerTensorHandle', 'TowerTensorHandles']
 
 _CurrentTowerContext = None
@@ -245,9 +246,9 @@ def TowerContext(tower_name, is_training, vs_name=''):
         return PredictTowerContext(tower_name, vs_name=vs_name)
 
 
-class TowerFuncWrapper(object):
+class TowerFunc(object):
     """
-    A wrapper around a tower function (see
+    A tower function (see
     [tutorial on tower function](http://tensorpack.readthedocs.io/tutorial/trainer.html#tower-trainer)).
     It keeps track of the name scope, variable scope and input/output tensors
     each time the function is called.
@@ -279,10 +280,10 @@ class TowerFuncWrapper(object):
 
     def __new__(cls, tower_fn, _):
         # to avoid double-wrapping a function
-        if isinstance(tower_fn, TowerFuncWrapper):
+        if isinstance(tower_fn, TowerFunc):
             return tower_fn
         else:
-            return super(TowerFuncWrapper, cls).__new__(cls)
+            return super(TowerFunc, cls).__new__(cls)
 
     def __call__(self, *args):
         ctx = get_current_tower_context()
@@ -311,6 +312,9 @@ class TowerFuncWrapper(object):
         return self._input_signature
 
 
+TowerFuncWrapper = TowerFunc
+
+
 class TowerTensorHandles(object):
     """
     Wrap a list of :class:`TowerTensorHandle`,

tensorpack/train/config.py

@@ -176,9 +176,14 @@ class AutoResumeTrainConfig(TrainConfig):
 
     Note that the functionality requires the logging directory to obtain
     necessary information from a previous run.
-    In some cases (e.g. when using Horovod), the directory is not
-    available, or the directories are different for different workers,
-    then this class may not function properly.
+    If you have unconventional setup of logging directory, this class will not
+    work for you, for example:
+
+        1. If you save the checkpoint to a different directory rather than the
+           logging directory.
+
+        2. If in distributed training the directory is not
+           available to every worker, or the directories are different for different workers.
     """
     def __init__(self, always_resume=True, **kwargs):
         """
@@ -189,7 +194,7 @@ class AutoResumeTrainConfig(TrainConfig):
             kwargs: same as in :class:`TrainConfig`.
 
         Note:
-            The main goal of this class is to let a training job to resume
+            The main goal of this class is to let a training job resume
             without changing any line of code or command line arguments.
             So it's useful to let resume take priority over user-provided arguments sometimes.
 

tensorpack/train/interface.py

@@ -85,7 +85,7 @@ def launch_train_with_config(config, trainer):
 
     # This is the only place where the `ModelDesc` abstraction is useful.
     # We should gradually stay away from this unuseful abstraction.
-    # TowerFuncWrapper is a better abstraction (similar to tf.defun in the future)
+    # TowerFunc is a better abstraction (similar to tf.function in the future)
     trainer.setup_graph(
         model.get_input_signature(), input,
         model.build_graph, model.get_optimizer)

tensorpack/train/tower.py

@@ -9,7 +9,7 @@ from ..compat import tfv1, is_tfv2
 from ..input_source import PlaceholderInput
 from ..predict.base import OnlinePredictor
 from ..tfutils.gradproc import FilterNoneGrad
-from ..tfutils.tower import PredictTowerContext, TowerFuncWrapper, get_current_tower_context
+from ..tfutils.tower import PredictTowerContext, TowerFunc, get_current_tower_context
 from ..utils import logger
 from ..utils.argtools import call_only_once, memoized
 from ..utils.develop import HIDE_DOC
@@ -38,13 +38,13 @@ class TowerTrainer(Trainer):
 
     @call_only_once
     def _set_tower_func(self, tower_func):
-        assert isinstance(tower_func, TowerFuncWrapper), tower_func
+        assert isinstance(tower_func, TowerFunc), tower_func
         self._tower_func = tower_func
 
     @property
     def tower_func(self):
         """
-        A :class:`TowerFuncWrapper` instance.
+        A :class:`TowerFunc` instance.
         See [tutorial on tower function](http://tensorpack.readthedocs.io/tutorial/trainer.html#tower-trainer)
         for more information.
         """
@@ -215,7 +215,7 @@ class SingleCostTrainer(TowerTrainer):
             It must follows the `rules of tower function.
             <http://tensorpack.readthedocs.io/tutorial/trainer.html#tower-trainer>`_.
         """
-        get_cost_fn = TowerFuncWrapper(get_cost_fn, input_signature)
+        get_cost_fn = TowerFunc(get_cost_fn, input_signature)
         get_opt_fn = memoized(get_opt_fn)
         self.tower_func = get_cost_fn
 

tensorpack/train/trainers.py

@@ -18,7 +18,7 @@ from ..tfutils.sesscreate import NewSessionCreator
 from ..tfutils.tower import TrainTowerContext
 from ..utils import logger
 from ..utils.argtools import map_arg
-from ..utils.develop import HIDE_DOC
+from ..utils.develop import HIDE_DOC, deprecated
 from .tower import SingleCostTrainer
 
 __all__ = ['NoOpTrainer', 'SimpleTrainer',
@@ -66,6 +66,7 @@ class NoOpTrainer(SimpleTrainer):
 
 # Only exists for type check & back-compatibility
 class QueueInputTrainer(SimpleTrainer):
+    @deprecated("SimpleTrainer is sufficient!", "2019-12-31")
     def _setup_graph(self, input, get_cost_fn, get_opt_fn):
         assert isinstance(input, QueueInput), input
         return super(QueueInputTrainer, self)._setup_graph(input, get_cost_fn, get_opt_fn)