remove some deprecation in trainers

tensorpack/train/feedfree.py

@@ -5,7 +5,6 @@
 
 import tensorflow as tf
 
-from ..utils.develop import log_deprecated
 from ..tfutils.tower import TowerContext, get_current_tower_context
 from .input_data import QueueInput, FeedfreeInput
 
@@ -21,7 +20,7 @@ class FeedfreeTrainerBase(Trainer):
     """
     def build_train_tower(self):
         """
-        Get input tensors from `self.input_method` and build the graph.
+        Get input tensors from `self.input_method` and build the forward graph.
         """
         def f():
             self._input_tensors = self._input_method.get_input_tensors()
@@ -64,7 +63,7 @@ class SingleCostFeedfreeTrainer(FeedfreeTrainerBase):
     def _get_cost_and_grad(self):
         """ get the cost and gradient"""
         self.build_train_tower()
-        cost = self.model.get_cost()
+        cost = self.model.get_cost()    # assume single cost
         opt = self.config.optimizer
         # GATE_NONE faster?
         grads = opt.compute_gradients(
@@ -90,7 +89,8 @@ class SimpleFeedfreeTrainer(SingleCostFeedfreeTrainer):
         assert isinstance(self._input_method, FeedfreeInput), self._input_method
         super(SimpleFeedfreeTrainer, self).__init__(config)
         assert len(self.config.tower) == 1, \
-            "SimpleFeedfreeTrainer doesn't support multigpu!"
+            "Got nr_tower={}, but doesn't support multigpu!" \
+            " Use Sync/AsyncMultiGPUTrainer instead.".format(len(self.config.tower))
 
     def _setup(self):
         super(SimpleFeedfreeTrainer, self)._setup()
@@ -101,7 +101,7 @@ class SimpleFeedfreeTrainer(SingleCostFeedfreeTrainer):
         # self.train_op = tf.group(*self._input_tensors)
 
 
-def QueueInputTrainer(config, input_queue=None, predict_tower=None):
+def QueueInputTrainer(config, input_queue=None):
     """
     A wrapper trainer which automatically wraps ``config.dataflow`` by a
     :class:`QueueInput`.
@@ -117,14 +117,8 @@ def QueueInputTrainer(config, input_queue=None, predict_tower=None):
     else:
         assert isinstance(config.data, QueueInput), config.data
 
+    # debug
     # from tensorpack.train.input_data import StagingInputWrapper, DummyConstantInput
     # config.data = StagingInputWrapper(config.data, ['/gpu:0'])
     # config.data = DummyConstantInput([[128,224,224,3], [128]])
-
-    if predict_tower is not None:
-        log_deprecated("Argument `predict_tower` in trainer", "Use TrainConfig(predict_tower=...) instead!")
-        config.predict_tower = predict_tower
-    assert len(config.tower) == 1, \
-        "Got nr_tower={}, but QueueInputTrainer doesn't support multigpu!" \
-        " Use Sync/AsyncMultiGPUTrainer instead.".format(len(config.tower))
     return SimpleFeedfreeTrainer(config)

tensorpack/train/input_data.py

@@ -293,7 +293,7 @@ class TensorInput(FeedfreeInput):
         """
         Args:
             get_tensor_fn: a function which returns a list of input tensors
-                when called.
+                when called. It will be called under a TowerContext.
             size(int): size of this input. Use None to leave it undefined.
         """
         self.get_tensor_fn = get_tensor_fn

tensorpack/train/multigpu.py

@@ -63,20 +63,15 @@ class SyncMultiGPUTrainer(MultiGPUTrainer,
     from each tower and averages them.
     """
 
-    def __init__(self, config, input_queue=None,
-                 average_cost=False):
+    def __init__(self, config):
         """
         Args:
-            config, input_queue: same as in :class:`QueueInputTrainer`.
-            average_cost (bool): average the cost (instead of gradients) from
-                each tower and did backprop only once. This option should make no
-                difference mathematically, but may affect speed.
+            config: same as in :class:`QueueInputTrainer`.
         """
         if config.dataflow is not None:
             # use queueinput by default. May need to avoid this in the future (when more input type is available)
-            self._input_method = QueueInput(config.dataflow, input_queue)
+            self._input_method = QueueInput(config.dataflow)
         else:
-            assert input_queue is None, input_queue
             self._input_method = config.data
 
         assert len(config.tower) >= 1, "MultiGPUTrainer must be used with at least one tower."
@@ -89,7 +84,6 @@ class SyncMultiGPUTrainer(MultiGPUTrainer,
                 self._input_method = StagingInputWrapper(self._input_method, devices)
 
         super(SyncMultiGPUTrainer, self).__init__(config)
-        self.average_cost = average_cost
 
     @staticmethod
     def _average_grads(tower_grads):
@@ -117,7 +111,7 @@ class SyncMultiGPUTrainer(MultiGPUTrainer,
 
     def _setup(self):
         super(SyncMultiGPUTrainer, self)._setup()
-        if not self.average_cost:
+
         grad_list = MultiGPUTrainer.multi_tower_grads(
             self.config.tower, lambda: self._get_cost_and_grad()[1])
 
@@ -128,21 +122,7 @@ class SyncMultiGPUTrainer(MultiGPUTrainer,
 
         grads = SyncMultiGPUTrainer._average_grads(grad_list)
         # grads = grad_list[0]
-        else:
-            def get_cost():
-                self.build_train_tower()
-                return self.model.get_cost()
-
-            cost_list = MultiGPUTrainer.multi_tower_costs(
-                self.config.tower, get_cost)
-            cost = tf.multiply(tf.add_n(cost_list), 1.0 / len(cost_list),
-                               name='averaged_cost')
-
-            opt = self.config.optimizer
-            grads = opt.compute_gradients(
-                cost,
-                gate_gradients=tf.train.Optimizer.GATE_NONE,
-                colocate_gradients_with_ops=True)
+
         self.train_op = self.config.optimizer.apply_gradients(grads, name='min_op')
 
 
@@ -154,19 +134,17 @@ class AsyncMultiGPUTrainer(MultiGPUTrainer,
     """
 
     def __init__(self, config,
-                 input_queue=None,
                  scale_gradient=True):
         """
         Args:
-            config, input_queue: same as in :class:`QueueInputTrainer`.
+            config: same as in :class:`QueueInputTrainer`.
             scale_gradient (bool): if True, will scale each gradient by
                 ``1.0/nr_tower``, to make Async and Sync Trainer have the same
                 effective learning rate.
         """
         if config.dataflow is not None:
-            self._input_method = QueueInput(config.dataflow, input_queue)
+            self._input_method = QueueInput(config.dataflow)
         else:
-            assert input_queue is None, input_queue
             self._input_method = config.data
         super(AsyncMultiGPUTrainer, self).__init__(config)