check config.data/config.model in trainers

docs/tutorial/model.md

@@ -10,13 +10,14 @@ class MyModel(ModelDesc):
 		return [InputDesc(...), InputDesc(...)]
 
 	def _build_graph(self, inputs):
+    tensorA, tensorB = inputs
 		# build the graph
 
 	def _get_optimizer(self):
 	  return tf.train.GradientDescentOptimizer(0.1)
 ```
 
-Basically, `_get_inputs` should define the metainfo of all the possible placeholders your graph may need.
+`_get_inputs` should define the metainfo of all the inputs your graph may need.
 `_build_graph` should add tensors/operations to the graph, where
 the argument `inputs` is the list of input tensors matching `_get_inputs`.
 

tensorpack/train/config.py

@@ -95,8 +95,9 @@ class TrainConfig(object):
             monitors = [TFEventWriter(), JSONWriter(), ScalarPrinter()]
         self.monitors = monitors
 
+        if model is not None:
+            assert_type(model, ModelDesc)
         self.model = model
-        assert_type(self.model, ModelDesc)
 
         if session_init is None:
             session_init = JustCurrentSession()

tensorpack/train/distributed.py

@@ -52,7 +52,7 @@ class DistributedReplicatedTrainer(MultiGPUTrainerBase):
             config (TrainConfig): the train config.
             server (tf.train.Server): the server object with ps and workers
         """
-
+        assert config.data is not None and config.model is not None
         self.server = server
         server_def = server.server_def
         self.cluster = tf.train.ClusterSpec(server_def.cluster)
@@ -83,7 +83,8 @@ class DistributedReplicatedTrainer(MultiGPUTrainerBase):
     @staticmethod
     def _average_grads(tower_grads, devices):
         """
-        Average grad with round-robin device selection.
+        Average grads from towers.
+        The device where the average happens is chosen with round-robin.
 
         Args:
             tower_grads: Ngpu x Nvar x 2
@@ -111,6 +112,9 @@ class DistributedReplicatedTrainer(MultiGPUTrainerBase):
     def _apply_shadow_vars(avg_grads):
         """
         Replace variables in avg_grads by shadow variables.
+
+        Args:
+            avg_grads: list of (grad, var) tuples
         """
         ps_var_grads = []
         for grad, var in avg_grads:

tensorpack/train/feedfree.py

@@ -58,6 +58,7 @@ def QueueInputTrainer(config, input_queue=None):
         config (TrainConfig): a `TrainConfig` instance. config.dataflow must exist.
         input_queue (tf.QueueBase): an input queue. Defaults to the :class:`QueueInput` default.
     """
+    assert (config.data is not None or config.dataflow is not None) and config.model is not None
     if config.data is not None:
         assert isinstance(config.data, QueueInput), config.data
     else:

tensorpack/train/multigpu.py

@@ -30,6 +30,7 @@ def _check_tf_version():
 
 
 def apply_prefetch_policy(config, gpu_prefetch=True):
+    assert (config.data is not None or config.dataflow is not None) and config.model is not None
     if config.data is None and config.dataflow is not None:
         # always use Queue prefetch
         config.data = QueueInput(config.dataflow)

tensorpack/train/simple.py

@@ -27,6 +27,7 @@ class SimpleTrainer(Trainer):
             "Got nr_tower={}, but doesn't support multigpu!" \
             " Use Sync/AsyncMultiGPUTrainer instead.".format(len(config.tower))
 
+        assert (config.data is not None or config.dataflow is not None) and config.model is not None
         if config.dataflow is None:
             self._input_source = config.data
         else: