update docs; assert non-empty dataset

Summary: Pull Request resolved: fairinternal/detectron2#316

Differential Revision: D18197894

Pulled By: ppwwyyxx

fbshipit-source-id: 9ef0daa6ebb23581bf845ca25126e73b2f28d5c8

MODEL_ZOO.md

@@ -19,7 +19,8 @@ Unless otherwise noted, the following settings are used for all runs:
   [Detectron1-Comparisons](configs/Detectron1-Comparisons/) for accuracy comparison,
   and [benchmarks](https://detectron2.readthedocs.io/notes/benchmarks.html)
   for speed comparison.
-* Inference speed is measured by `tools/train_net.py --eval-only`, with batch size 1 in detectron2 directly.
+* Inference speed is measured by `tools/train_net.py --eval-only`, or [inference_on_dataset()](https://detectron2.readthedocs.io/modules/evaluation.html#detectron2.evaluation.inference_on_dataset),
+  with batch size 1 in detectron2 directly.
   The actual deployment should in general be faster than the given inference
   speed due to more optimizations.
 * Training speed is averaged across the entire training.

detectron2/data/build.py

@@ -254,6 +254,8 @@ def get_detection_dataset_dicts(
     """
     assert len(dataset_names)
     dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in dataset_names]
+    for dataset_name, dicts in zip(dataset_names, dataset_dicts):
+        assert len(dicts), "Dataset '{}' is empty!".format(dataset_name)
 
     if proposal_files is not None:
         assert len(dataset_names) == len(proposal_files)

detectron2/evaluation/evaluator.py

@@ -92,7 +92,9 @@ def inference_on_dataset(model, data_loader, evaluator):
             wrap the given model and override its behavior of `.eval()` and `.train()`.
         data_loader: an iterable object with a length.
             The elements it generates will be the inputs to the model.
-        evaluator (DatasetEvaluator): the evaluator to run
+        evaluator (DatasetEvaluator): the evaluator to run. Use
+            :class:`DatasetEvaluators([])` if you only want to benchmark, but
+            don't want to do any evaluation.
 
     Returns:
         The return value of `evaluator.evaluate()`