added gqa as eval dataset

open_flamingo/eval/eval_datasets.py

@@ -14,6 +14,7 @@
     "okvqa",
     "vizwiz",
     "textvqa",
+    "gqa",
     "hateful_memes",
     "imagenet",
 ]
@@ -104,7 +105,7 @@ def get_img_path(self, question):
             )
         elif self.dataset_name == "vizwiz":
             return os.path.join(self.image_dir_path, question["image_id"])
-        elif self.dataset_name == "textvqa":
+        elif self.dataset_name == "textvqa" or self.dataset_name == "gqa":
             return os.path.join(self.image_dir_path, f"{question['image_id']}.jpg")
         else:
             raise Exception(f"Unknown VQA dataset {self.dataset_name}")

open_flamingo/eval/eval_models/blip.py

@@ -108,6 +108,9 @@ def get_vizwiz_prompt(self, question, answer=None) -> str:
 
     def get_textvqa_prompt(self, question, answer=None) -> str:
         return f"Question:{question} Short answer:{answer if answer is not None else ''}"
+
+    def get_gqa_prompt(self, question, answer=None) -> str:
+        return f"Question:{question} Short answer:{answer if answer is not None else ''}"
 
     def get_coco_prompt(self, caption=None) -> str:
         return f"A photo of {caption if caption is not None else ''}"

open_flamingo/eval/eval_models/open_flamingo.py

@@ -287,6 +287,9 @@ def get_vizwiz_prompt(self, question, answer=None) -> str:
 
     def get_textvqa_prompt(self, question, answer=None) -> str:
         return f"<image>Question:{question} Short answer:{answer if answer is not None else ''}{'<|endofchunk|>' if answer is not None else ''}"
+
+    def get_gqa_prompt(self, question, answer=None) -> str:
+        return f"<image>Question:{question} Short answer:{answer if answer is not None else ''}{'<|endofchunk|>' if answer is not None else ''}"
 
     def get_coco_prompt(self, caption=None) -> str:
         return f"<image>Output:{caption if caption is not None else ''}{'<|endofchunk|>' if caption is not None else ''}"

open_flamingo/eval/evaluate.py

@@ -139,6 +139,14 @@
     default=False,
     help="Whether to evaluate on TextVQA.",
 )
+
+parser.add_argument(
+    "--eval_gqa",
+    action="store_true",
+    default=False,
+    help="Whether to evaluate on GQA.",
+)
+
 parser.add_argument(
     "--eval_imagenet",
     action="store_true",
@@ -346,6 +354,44 @@
     default=None,
 )
 
+# GQA Dataset
+parser.add_argument(
+    "--gqa_train_image_dir_path",
+    type=str,
+    help="Path to the gqa train images directory.",
+    default=None,
+)
+parser.add_argument(
+    "--gqa_train_questions_json_path",
+    type=str,
+    help="Path to the gqa questions json file.",
+    default=None,
+)
+parser.add_argument(
+    "--gqa_train_annotations_json_path",
+    type=str,
+    help="Path to the gqa annotations json file",
+    default=None,
+)
+parser.add_argument(
+    "--gqa_test_image_dir_path",
+    type=str,
+    help="Path to the gqa test images directory.",
+    default=None,
+)
+parser.add_argument(
+    "--gqa_test_questions_json_path",
+    type=str,
+    help="Path to the gqa questions json file",
+    default=None,
+)
+parser.add_argument(
+    "--gqa_test_annotations_json_path",
+    type=str,
+    help="Path to the gqa annotations json file",
+    default=None,
+)
+
 ## Imagenet dataset
 parser.add_argument("--imagenet_root", type=str, default="/tmp")
 
@@ -650,6 +696,44 @@ def main():
                         "stddev": np.nanstd(scores),
                     }
                 )
+
+    if args.eval_gqa:
+        print("Evaluating on GQA...")
+
+        #load cached demonstration features on GQA
+        if args.cached_demonstration_features is not None:
+            cached_features = torch.load(
+                f"{args.cached_demonstration_features}/imagenet.pkl", map_location="cpu"
+            )
+        else:
+            cached_features = None
+
+        for shot in args.shots:
+            scores = []
+            for seed, trial in zip(args.trial_seeds, range(args.num_trials)):
+                gqa_score = evaluate_vqa(
+                    args=args,
+                    eval_model=eval_model,
+                    num_shots=shot,
+                    seed=seed,
+                    dataset_name="gqa",
+                    max_new_tokens=10,
+                    cached_features=cached_features,
+                )
+                if args.rank == 0:
+                    print(f"Shots {shot} Trial {trial} GQA score: {gqa_score}")
+                    scores.append(gqa_score)
+
+            if args.rank == 0:
+                print(f"Shots {shot} Mean GQA score: {np.nanmean(scores)}")
+                results["gqa"].append(
+                    {
+                        "shots": shot,
+                        "trials": scores,
+                        "mean": np.nanmean(scores),
+                        "stddev": np.nanstd(scores),
+                    }
+                )
 
     if args.eval_imagenet:
         print("Evaluating on ImageNet...")
@@ -968,6 +1052,13 @@ def evaluate_vqa(
         test_image_dir_path = args.textvqa_image_dir_path
         test_questions_json_path = args.textvqa_test_questions_json_path
         test_annotations_json_path = args.textvqa_test_annotations_json_path
+    elif dataset_name == "gqa":
+        train_image_dir_path = args.gqa_train_image_dir_path
+        train_questions_json_path = args.gqa_train_questions_json_path
+        train_annotations_json_path = args.gqa_train_annotations_json_path
+        test_image_dir_path = args.gqa_test_image_dir_path
+        test_questions_json_path = args.gqa_test_questions_json_path
+        test_annotations_json_path = args.gqa_test_annotations_json_path
     else:
         raise ValueError(f"Unsupported dataset: {dataset_name}")