Merge pull request #251 from EvolvingLMMs-Lab/fix/pbar

[Fix] Bring back process result pbar

lmms_eval/evaluator.py

@@ -477,6 +477,9 @@ def evaluate(
         # iterate over different filters used
         for filter_key in task.instances[0].filtered_resps.keys():
             doc_iterator = task.doc_iterator(rank=RANK, limit=limit, world_size=WORLD_SIZE)
+            doc_iterator_for_counting = itertools.islice(range(len(task.test_docs())), RANK, limit, WORLD_SIZE) if task.has_test_docs() else itertools.islice(range(len(task.validation_docs())), RANK, limit, WORLD_SIZE)
+            total_docs = sum(1 for _ in doc_iterator_for_counting)
+            pbar = tqdm(total=total_docs, desc=f"Postprocessing", disable=(RANK != 0))
             for doc_id, doc in doc_iterator:
                 requests = instances_by_doc_id[doc_id]
                 metrics = task.process_results(doc, [req.filtered_resps[filter_key] for req in requests])
@@ -514,6 +517,9 @@ def evaluate(
                     task_output.logged_samples.append(example)
                 for metric, value in metrics.items():
                     task_output.sample_metrics[(metric, filter_key)].append(value)
+                pbar.update(1)
+
+            pbar.close()
 
     if WORLD_SIZE > 1:
         # if multigpu, then gather data across all ranks to rank 0