improve error messages in TrainConfig/PredictConfig type checking (#1029)

examples/basics/export-model.py

@@ -19,18 +19,18 @@ The steps are:
 
 1. train the model by
 
-    python export.py
+    python export-model.py
 
 2. export the model by
 
-    python export.py --export serving --load train_log/export/checkpoint
-    python export.py --export compact --load train_log/export/checkpoint
+    python export-model.py --export serving --load train_log/export/checkpoint
+    python export-model.py --export compact --load train_log/export/checkpoint
 
 3. run inference by
 
-    python export.py --apply default --load train_log/export/checkpoint
-    python export.py --apply inference_graph --load train_log/export/checkpoint
-    python export.py --apply compact --load /tmp/compact_graph.pb
+    python export-model.py --apply default --load train_log/export/checkpoint
+    python export-model.py --apply inference_graph --load train_log/export/checkpoint
+    python export-model.py --apply compact --load /tmp/compact_graph.pb
 """
 
 

scripts/ls-checkpoint.py

@@ -20,7 +20,7 @@ if __name__ == '__main__':
         params = dict(np.load(fpath))
         dic = {k: v.shape for k, v in six.iteritems(params)}
     else:
-        path = get_checkpoint_path(sys.argv[1])
+        path = get_checkpoint_path(fpath)
         reader = tf.train.NewCheckpointReader(path)
         dic = reader.get_variable_to_shape_map()
     pprint.pprint(dic)

tensorpack/predict/config.py

@@ -53,10 +53,13 @@ class PredictConfig(object):
             create_graph (bool): create a new graph, or use the default graph
                 when predictor is first initialized.
         """
-        def assert_type(v, tp):
-            assert isinstance(v, tp), v.__class__
+        def assert_type(v, tp, name):
+            assert isinstance(v, tp), \
+                "{} has to be type '{}', but an object of type '{}' found.".format(
+                    name, tp.__name__, v.__class__.__name__)
+
         if model is not None:
-            assert_type(model, ModelDescBase)
+            assert_type(model, ModelDescBase, 'model')
             assert inputs_desc is None and tower_func is None
             self.inputs_desc = model.get_inputs_desc()
             self.tower_func = TowerFuncWrapper(model.build_graph, self.inputs_desc)
@@ -70,7 +73,7 @@ class PredictConfig(object):
         if session_init is None:
             session_init = JustCurrentSession()
         self.session_init = session_init
-        assert_type(self.session_init, SessionInit)
+        assert_type(self.session_init, SessionInit, 'session_init')
 
         if session_creator is None:
             self.session_creator = tf.train.ChiefSessionCreator(config=get_default_sess_config())
@@ -82,13 +85,13 @@ class PredictConfig(object):
         if self.input_names is None:
             self.input_names = [k.name for k in self.inputs_desc]
         self.output_names = output_names
-        assert_type(self.output_names, list)
-        assert_type(self.input_names, list)
+        assert_type(self.output_names, list, 'output_names')
+        assert_type(self.input_names, list, 'input_names')
         if len(self.input_names) == 0:
             logger.warn('PredictConfig receives empty "input_names".')
         # assert len(self.input_names), self.input_names
         for v in self.input_names:
-            assert_type(v, six.string_types)
+            assert_type(v, six.string_types, 'Each item in input_names')
         assert len(self.output_names), self.output_names
 
         self.return_input = bool(return_input)

tensorpack/tfutils/sessinit.py

@@ -248,6 +248,7 @@ def get_model_loader(filename):
         SessInit: either a :class:`DictRestore` (if name ends with 'npy/npz') or
         :class:`SaverRestore` (otherwise).
     """
+    assert isinstance(filename, six.string_types), filename
     if filename.endswith('.npy'):
         assert tf.gfile.Exists(filename), filename
         return DictRestore(np.load(filename, encoding='latin1').item())

tensorpack/train/config.py

@@ -98,33 +98,35 @@ class TrainConfig(object):
         """
 
         # TODO type checker decorator
-        def assert_type(v, tp):
-            assert isinstance(v, tp), v.__class__
+        def assert_type(v, tp, name):
+            assert isinstance(v, tp), \
+                "{} has to be type '{}', but an object of type '{}' found.".format(
+                    name, tp.__name__, v.__class__.__name__)
 
         # process data & model
         assert data is None or dataflow is None, "dataflow and data cannot be both presented in TrainConfig!"
         if dataflow is not None:
-            assert_type(dataflow, DataFlow)
+            assert_type(dataflow, DataFlow, 'dataflow')
         if data is not None:
-            assert_type(data, InputSource)
+            assert_type(data, InputSource, 'data')
         self.dataflow = dataflow
         self.data = data
 
         if model is not None:
-            assert_type(model, ModelDescBase)
+            assert_type(model, ModelDescBase, 'model')
         self.model = model
 
         if callbacks is not None:
-            assert_type(callbacks, list)
+            assert_type(callbacks, list, 'callbacks')
         self.callbacks = callbacks
         if extra_callbacks is not None:
-            assert_type(extra_callbacks, list)
+            assert_type(extra_callbacks, list, 'extra_callbacks')
         self.extra_callbacks = extra_callbacks
         if monitors is not None:
-            assert_type(monitors, list)
+            assert_type(monitors, list, 'monitors')
         self.monitors = monitors
         if session_init is not None:
-            assert_type(session_init, SessionInit)
+            assert_type(session_init, SessionInit, 'session_init')
         self.session_init = session_init
 
         if session_creator is None: