Fix evaluation set image features url

gqa-lxmert.py

@@ -44,21 +44,19 @@ seeking to address key shortcomings of previous visual question answering (VQA)
 
 _URLS = {
     "train": "https://nlp.cs.unc.edu/data/lxmert_data/gqa/train.json",
-    "train_feat": "https://nlp.cs.unc.edu/data/lxmert_data/vg_gqa_imgfeat/vg_gqa_obj36.zip",
     "dev": "https://nlp.cs.unc.edu/data/lxmert_data/gqa/valid.json",
-    "dev_feat": "https://nlp.cs.unc.edu/data/lxmert_data/vg_gqa_imgfeat/gqa_testdev_obj36.zip",
+    "feat": "https://nlp.cs.unc.edu/data/lxmert_data/vg_gqa_imgfeat/vg_gqa_obj36.zip",
     "ans2label": "https://raw.githubusercontent.com/airsplay/lxmert/master/data/gqa/trainval_ans2label.json",
 }
 
-_TRAIN_IMG_PATH = "vg_gqa_imgfeat/vg_gqa_obj36.tsv"
-_DEV_IMG_PATH = "vg_gqa_imgfeat/gqa_testdev_obj36.tsv"
+_FEAT_PATH = "vg_gqa_imgfeat/vg_gqa_obj36.tsv"
 
 FIELDNAMES = [
     "img_id", "img_h", "img_w", "objects_id", "objects_conf", "attrs_id", "attrs_conf", "num_boxes", "boxes", "features"
 ]
 
-SHAPE_FEATURES = (36, 2048)
-SHAPE_BOXES = (36, 4)
+_SHAPE_FEATURES = (36, 2048)
+_SHAPE_BOXES = (36, 4)
 
 
 class GqaLxmert(datasets.GeneratorBasedBuilder):
@@ -75,8 +73,8 @@ class GqaLxmert(datasets.GeneratorBasedBuilder):
                 "question": datasets.Value("string"),
                 "question_id": datasets.Value("int32"),
                 "image_id": datasets.Value("string"),
-                "features": datasets.Array2D(SHAPE_FEATURES, dtype="float32"),
-                "boxes": datasets.Array2D(SHAPE_BOXES, dtype="float32"),
+                "features": datasets.Array2D(_SHAPE_FEATURES, dtype="float32"),
+                "boxes": datasets.Array2D(_SHAPE_BOXES, dtype="float32"),
                 "label": datasets.Value("int32"),
             }
         )
@@ -91,15 +89,16 @@ class GqaLxmert(datasets.GeneratorBasedBuilder):
         """Returns SplitGenerators."""
         dl_dir = dl_manager.download_and_extract(_URLS)
         self.ans2label = json.load(open(dl_dir["ans2label"]))
+        self.id2features = self._load_features(os.path.join(dl_dir["feat"], _FEAT_PATH))
 
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": dl_dir["train"], "imgfeat": os.path.join(dl_dir["train_feat"], _TRAIN_IMG_PATH)},
+                gen_kwargs={"filepath": dl_dir["train"]},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": dl_dir["dev"], "imgfeat": os.path.join(dl_dir["dev_feat"], _DEV_IMG_PATH)},
+                gen_kwargs={"filepath": dl_dir["dev"]},
             ),
         ]
 
@@ -126,13 +125,12 @@ class GqaLxmert(datasets.GeneratorBasedBuilder):
                 id2features[item["img_id"]] = features
         return id2features
 
-    def _generate_examples(self, filepath, imgfeat):
+    def _generate_examples(self, filepath):
         """ Yields examples as (key, example) tuples."""
-        id2features = self._load_features(imgfeat)
         with open(filepath, encoding="utf-8") as f:
             gqa = json.load(f)
             for id_, d in enumerate(gqa):
-                img_features = id2features[d["img_id"]]
+                img_features = self.id2features[d["img_id"]]
                 label = self.ans2label[next(iter(d["label"]))]
                 yield id_, {
                     "question": d["sent"],