Improve the use of size() of InputSource

tensorpack/callbacks/inference_runner.py

@@ -116,7 +116,7 @@ class InferenceRunnerBase(Callback):
 
         # iterate over the data, and run the hooked session
         self._input_source.reset_state()
-        for _ in tqdm.trange(self._input_source.size(), **get_tqdm_kwargs()):
+        for _ in tqdm.trange(self._size, **get_tqdm_kwargs()):
             feed = self._input_source.next_feed()
             self._hooked_sess.run(fetches=[], feed_dict=feed)
         summary_inferencer(self.trainer, self.infs)
@@ -252,7 +252,7 @@ class DataParallelInferenceRunner(InferenceRunnerBase):
             inf.before_inference()
 
         self._input_source.reset_state()
-        total = self._input_source.size()
+        total = self._size
         nr_tower = len(self._gpus)
         with tqdm.tqdm(total=total, **get_tqdm_kwargs()) as pbar:
             while total >= nr_tower:

tensorpack/train/config.py

@@ -65,7 +65,7 @@ class TrainConfig(object):
         # process data
         if 'dataset' in kwargs:
             dataflow = kwargs.pop('dataset')
-            log_deprecated("TrainConfig.dataset", "Use TrainConfig.dataflow instead.")
+            log_deprecated("TrainConfig.dataset", "Use TrainConfig.dataflow instead.", "2017-09-11")
         if dataflow is not None:
             assert data is None, "dataflow and data cannot be both presented in TrainConfig!"
             self.dataflow = dataflow
@@ -113,10 +113,6 @@ class TrainConfig(object):
             assert session_config is None, "Cannot set both session_creator and session_config!"
         self.session_config = session_config
 
-        if steps_per_epoch is None:
-            steps_per_epoch = kwargs.pop('step_per_epoch', None)
-            if steps_per_epoch is not None:
-                log_deprecated("step_per_epoch", "Use steps_per_epoch instead!", "2017-03-27")
         if steps_per_epoch is None:
             try:
                 if dataflow is not None:

tensorpack/train/input_source.py

@@ -72,6 +72,13 @@ class InputSource(object):
         """
         pass
 
+    def size(self):
+        """
+        Returns:
+            int: epoch size of the InputSource
+        """
+        return NotImplementedError()
+
 
 class FeedInput(InputSource):
     """ Input by iterating over a DataFlow and feed datapoints. """
@@ -271,7 +278,7 @@ class QueueInput(FeedfreeInput):
                 return get_tensors_inputs(self.input_placehdrs, ret, self._names)
 
 
-class BatchQueueInput(FeedfreeInput):
+class BatchQueueInput(QueueInput):
     """ Enqueue datapoints from a DataFlow to a TF queue.
         And the model receives batches formed by concatenating
         dequeued tensors.
@@ -285,9 +292,7 @@ class BatchQueueInput(FeedfreeInput):
                 should match the corresponding InputDesc of the model.
                 Defaults to a FIFO queue of size 3000.
         """
-        assert isinstance(ds, DataFlow), ds
-        self.queue = queue
-        self.ds = ds
+        super(BatchQueueInput, self).__init__(ds, queue)
         self.batch_size = int(batch_size)
 
     def size(self):
@@ -324,11 +329,6 @@ class BatchQueueInput(FeedfreeInput):
 
         self.thread = EnqueueThread(self.queue, self.ds, placehdrs_nobatch)
 
-    def get_callbacks(self):
-        cb = StartProcOrThread(self.thread)
-        cb.chief_only = False
-        return [cb]
-
     def get_input_tensors(self):
         with tf.device('/cpu:0'):
             ret = self.queue.dequeue_many(self.batch_size, name='input_deque')