error for wrong param dtype

docs/tutorial/inference.md

@@ -152,8 +152,9 @@ with TowerContext('', is_training=False):
 
   It's also very common to change the graph for inference.
   For example, you may need a different data layout for CPU inference,
-  or you may need placeholders in the inference graph (which may not even exist in
-  the training graph). However metagraph is not designed to be easily modified at all.
+  you may need placeholders in the inference graph (which may not even exist in
+  the training graph), you may add/remove static shapes for inference, etc.
+  However metagraph is not designed to be easily modified at all.
 
   Due to the above reasons, to do inference, it's best to recreate a clean graph (and save it if needed) by yourself.
 ```

tensorpack/callbacks/param.py

@@ -77,6 +77,10 @@ class GraphVarParam(HyperParam):
 
     def set_value(self, v):
         """ Assign the variable a new value. """
+        if not self.var.dtype.is_floating and isinstance(v, float):
+            raise ValueError(
+                "HyperParam {} has type '{}'. Cannot update it using float values.".format(
+                    self.name, self.var.dtype))
         self.var.load(v)
 
     def get_value(self):

tensorpack/dataflow/imgaug/convert.py

@@ -44,7 +44,7 @@ class Grayscale(ColorSpace):
 
 
 class ToUint8(PhotometricAugmentor):
-    """ Convert image to uint8. Useful to reduce communication overhead. """
+    """ Clip and convert image to uint8. Useful to reduce communication overhead. """
     def _augment(self, img, _):
         return np.clip(img, 0, 255).astype(np.uint8)
 

tensorpack/input_source/input_source.py

@@ -134,7 +134,7 @@ class FeedfreeInput(InputSource):
 class EnqueueThread(ShareSessionThread):
     def __init__(self, queue, ds, placehdrs):
         super(EnqueueThread, self).__init__()
-        self.name = 'EnqueueThread ' + queue.name
+        self.name = 'EnqueueThread: enqueue dataflow to TF queue "{}"'.format(queue.name)
         self.daemon = True
         self.dataflow = ds
         self.queue = queue