remove some deprecations

tensorpack/callbacks/base.py

@@ -5,7 +5,6 @@
 import tensorflow as tf
 from abc import ABCMeta
 import six
-from ..utils.develop import log_deprecated
 from ..tfutils.common import get_op_or_tensor_by_name
 
 __all__ = ['Callback', 'ProxyCallback', 'CallbackFactory']
@@ -284,12 +283,9 @@ class CallbackFactory(Callback):
     Create a callback with some lambdas.
     """
     def __init__(self, setup_graph=None, before_train=None, trigger=None,
-                 after_train=None, trigger_epoch=None):
+                 after_train=None):
         """
         Each lambda takes ``self`` as the only argument.
-
-        Note:
-            trigger_epoch was deprecated.
         """
 
         self._cb_setup_graph = setup_graph
@@ -297,10 +293,6 @@ class CallbackFactory(Callback):
         self._cb_trigger = trigger
         self._cb_after_train = after_train
 
-        if trigger_epoch:
-            self._cb_trigger = trigger_epoch
-            log_deprecated("CallbackFactory(trigger_epoch=)", "Use trigger instead.", "2017-11-15")
-
     def _setup_graph(self):
         if self._cb_setup_graph:
             self._cb_setup_graph(self)

tensorpack/callbacks/inference_runner.py

@@ -127,10 +127,7 @@ class InferenceRunner(InferenceRunnerBase):
         return InferencerToHook(inf, fetches)
 
     def _setup_graph(self):
-        if self.trainer._API_VERSION == 1 and self.trainer._config.predict_tower is not None:
-            device = self.trainer._config.predict_tower[0]
-        else:
-            device = self._device
+        device = self._device
         assert self.trainer.tower_func is not None, "You must set tower_func of the trainer to use InferenceRunner!"
         input_callbacks = self._input_source.setup(self.trainer.inputs_desc)
 

tensorpack/dataflow/raw.py

@@ -110,7 +110,7 @@ class DataFromGenerator(DataFlow):
         else:
             self._gen = gen
         if size is not None:
-            log_deprecated("DataFromGenerator(size=)", "It doesn't make much sense.")
+            log_deprecated("DataFromGenerator(size=)", "It doesn't make much sense.", "2018-03-31")
 
     def get_data(self):
         # yield from

tensorpack/input_source/input_source.py

@@ -516,7 +516,7 @@ class StagingInput(FeedfreeInput):
         assert isinstance(input, FeedfreeInput), input
         self._input = input
         if towers is not None:
-            log_deprecated("StagingInput(towers=) has no effect! Devices are handled automatically.")
+            log_deprecated("StagingInput(towers=)", "Devices are handled automatically.", "2018-03-31")
 
         self._nr_stage = nr_stage
         self._areas = []

tensorpack/models/conv2d.py

@@ -6,7 +6,6 @@
 import tensorflow as tf
 from .common import layer_register, VariableHolder, rename_get_variable
 from ..utils.argtools import shape2d, shape4d
-from ..utils.develop import log_deprecated
 
 __all__ = ['Conv2D', 'Deconv2D']
 
@@ -113,16 +112,7 @@ def Deconv2D(x, out_channel, kernel_shape,
     in_channel = in_shape[channel_axis]
     assert in_channel is not None, "[Deconv2D] Input cannot have unknown channel!"
 
-    out_shape = out_channel
-    if isinstance(out_shape, int):
-        out_channel = out_shape
-    else:
-        log_deprecated("Deconv2D(out_shape=[...])",
-                       "Use an integer 'out_channel' instead!", "2017-11-18")
-        for k in out_shape:
-            if not isinstance(k, int):
-                raise ValueError("[Deconv2D] out_shape {} is invalid!".format(k))
-        out_channel = out_shape[channel_axis - 1]   # out_shape doesn't have batch
+    assert isinstance(out_channel, int), out_channel
 
     if W_init is None:
         W_init = tf.contrib.layers.xavier_initializer_conv2d()
@@ -141,11 +131,6 @@ def Deconv2D(x, out_channel, kernel_shape,
             trainable=True)
         ret = layer.apply(x, scope=tf.get_variable_scope())
 
-    # Check that we only supports out_shape = in_shape * stride
-    out_shape3 = ret.get_shape().as_list()[1:]
-    if not isinstance(out_shape, int):
-        assert list(out_shape) == out_shape3, "{} != {}".format(out_shape, out_shape3)
-
     ret.variables = VariableHolder(W=layer.kernel)
     if use_bias:
         ret.variables.b = layer.bias

tensorpack/tfutils/summary.py

@@ -220,7 +220,7 @@ def add_moving_summary(*args, **kwargs):
     if not isinstance(args[0], list):
         v = args
     else:
-        log_deprecated("Call add_moving_summary with positional args instead of a list!")
+        log_deprecated("Call add_moving_summary with positional args instead of a list!", eos="2018-02-28")
         v = args[0]
     for x in v:
         assert isinstance(x, tf.Tensor), x

tensorpack/train/base.py

@@ -21,7 +21,7 @@ from ..callbacks.steps import MaintainStepCounter
 
 from .config import TrainConfig, DEFAULT_MONITORS, DEFAULT_CALLBACKS
 
-__all__ = ['StopTraining', 'TrainConfig', 'Trainer']
+__all__ = ['StopTraining', 'Trainer']
 
 
 class StopTraining(BaseException):

tensorpack/train/config.py

@@ -12,7 +12,6 @@ from ..utils import logger
 from ..tfutils import (JustCurrentSession, SessionInit)
 from ..tfutils.sesscreate import NewSessionCreator
 from ..input_source import InputSource
-from ..utils.develop import log_deprecated
 
 __all__ = ['TrainConfig', 'DEFAULT_CALLBACKS', 'DEFAULT_MONITORS']
 
@@ -151,15 +150,6 @@ class TrainConfig(object):
             assert self.nr_tower == 1, "Cannot set both nr_tower and tower in TrainConfig!"
             self.tower = tower
 
-        predict_tower = kwargs.pop('predict_tower', None)
-        if predict_tower is not None:
-            log_deprecated("TrainConfig(predict_tower=)",
-                           "InferenceRunner now accepts a 'device' argument.", "2017-12-31")
-        self.predict_tower = predict_tower
-        if isinstance(self.predict_tower, int):
-            self.predict_tower = [self.predict_tower]
-        # --------------------------------------------------------------
-
         assert len(kwargs) == 0, 'Unknown arguments: {}'.format(str(kwargs.keys()))
 
     @property