[rllib] Better error message for unsupported non-atari image observat…

…ion sizes (#3444)

python/ray/rllib/models/visionnet.py

@@ -16,7 +16,7 @@ def _build_layers_v2(self, input_dict, num_outputs, options):
         inputs = input_dict["obs"]
         filters = options.get("conv_filters")
         if not filters:
-            filters = get_filter_config(options)
+            filters = get_filter_config(inputs)
 
         activation = get_activation_fn(options.get("conv_activation"))
 
@@ -47,7 +47,7 @@ def _build_layers_v2(self, input_dict, num_outputs, options):
             return flatten(fc2), flatten(fc1)
 
 
-def get_filter_config(options):
+def get_filter_config(inputs):
     filters_84x84 = [
         [16, [8, 8], 4],
         [32, [4, 4], 2],
@@ -58,12 +58,15 @@ def get_filter_config(options):
         [32, [4, 4], 2],
         [256, [11, 11], 1],
     ]
-    dim = options.get("dim")
-    if dim == 84:
+    shape = inputs.shape.as_list()[1:]
+    if len(shape) == 3 and shape[:2] == [84, 84]:
         return filters_84x84
-    elif dim == 42:
+    elif len(shape) == 3 and shape[:2] == [42, 42]:
         return filters_42x42
     else:
         raise ValueError(
-            "No default configuration for image size={}".format(dim) +
-            ", you must specify `conv_filters` manually as a model option.")
+            "No default configuration for obs input {}".format(inputs) +
+            ", you must specify `conv_filters` manually as a model option. "
+            "Default configurations are only available for inputs of size "
+            "[?, 42, 42, K] and [?, 84, 84, K]. You may alternatively want "
+            "to use a custom model or preprocessor.")

python/ray/rllib/test/test_catalog.py

@@ -72,13 +72,13 @@ def testDefaultModels(self):
 
         with tf.variable_scope("test1"):
             p1 = ModelCatalog.get_model({
-                "obs": np.zeros((10, 3), dtype=np.float32)
+                "obs": tf.zeros((10, 3), dtype=tf.float32)
             }, Box(0, 1, shape=(3, ), dtype=np.float32), 5, {})
             self.assertEqual(type(p1), FullyConnectedNetwork)
 
         with tf.variable_scope("test2"):
             p2 = ModelCatalog.get_model({
-                "obs": np.zeros((10, 84, 84, 3), dtype=np.float32)
+                "obs": tf.zeros((10, 84, 84, 3), dtype=tf.float32)
             }, Box(0, 1, shape=(84, 84, 3), dtype=np.float32), 5, {})
             self.assertEqual(type(p2), VisionNetwork)