Fix run_llava.py

llava/eval/run_llava.py

@@ -95,6 +95,7 @@ def eval_model(args):
 
     image_files = image_parser(args)
     images = load_images(image_files)
+    image_sizes = [x.size for x in images]
     images_tensor = process_images(
         images,
         image_processor,
@@ -107,36 +108,20 @@ def eval_model(args):
         .cuda()
     )
 
-    stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
-    keywords = [stop_str]
-    stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
-
     with torch.inference_mode():
         output_ids = model.generate(
             input_ids,
             images=images_tensor,
+            image_sizes=image_sizes,
             do_sample=True if args.temperature > 0 else False,
             temperature=args.temperature,
             top_p=args.top_p,
             num_beams=args.num_beams,
             max_new_tokens=args.max_new_tokens,
             use_cache=True,
-            stopping_criteria=[stopping_criteria],
         )
 
-    input_token_len = input_ids.shape[1]
-    n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
-    if n_diff_input_output > 0:
-        print(
-            f"[Warning] {n_diff_input_output} output_ids are not the same as the input_ids"
-        )
-    outputs = tokenizer.batch_decode(
-        output_ids[:, input_token_len:], skip_special_tokens=True
-    )[0]
-    outputs = outputs.strip()
-    if outputs.endswith(stop_str):
-        outputs = outputs[: -len(stop_str)]
-    outputs = outputs.strip()
+    outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
     print(outputs)