Fix multiproc metrics in no_trainer examples (#16865)

examples/pytorch/image-classification/run_image_classification_no_trainer.py

@@ -457,12 +457,21 @@ def main():
                 break
 
         model.eval()
+        samples_seen = 0
         for step, batch in enumerate(eval_dataloader):
             outputs = model(**batch)
             predictions = outputs.logits.argmax(dim=-1)
+            predictions, references = accelerator.gather((predictions, batch["labels"]))
+            # If we are in a multiprocess environment, the last batch has duplicates
+            if accelerator.num_processes > 1:
+                if step == len(eval_dataloader):
+                    predictions = predictions[: len(eval_dataloader.dataset) - samples_seen]
+                    references = references[: len(eval_dataloader.dataset) - samples_seen]
+                else:
+                    samples_seen += references.shape[0]
             metric.add_batch(
-                predictions=accelerator.gather(predictions),
-                references=accelerator.gather(batch["labels"]),
+                predictions=predictions,
+                references=references,
             )
 
         eval_metric = metric.compute()