Add test set

vqa-lxmert.py

@@ -47,11 +47,14 @@ _URLS = {
     "train_feat": "https://nlp.cs.unc.edu/data/lxmert_data/mscoco_imgfeat/train2014_obj36.zip",
     "valid": "https://nlp.cs.unc.edu/data/lxmert_data/vqa/valid.json",
     "valid_feat": "https://nlp.cs.unc.edu/data/lxmert_data/mscoco_imgfeat/val2014_obj36.zip",
+    "test": "https://nlp.cs.unc.edu/data/lxmert_data/vqa/test.json",
+    "test_feat": "https://nlp.cs.unc.edu/data/lxmert_data/mscoco_imgfeat/test2015_obj36.zip",
     "ans2label": "https://raw.githubusercontent.com/airsplay/lxmert/master/data/vqa/trainval_ans2label.json",
 }
 
-_TRAIN_IMG_PATH = "train2014_obj36.tsv"
-_VALID_IMG_PATH = "mscoco_imgfeat/val2014_obj36.tsv"
+_TRAIN_FEAT_PATH = "train2014_obj36.tsv"
+_VALID_FEAT_PATH = "mscoco_imgfeat/val2014_obj36.tsv"
+_TEST_FEAT_PATH = "mscoco_imgfeat/test2015_obj36.tsv"
 
 FIELDNAMES = [
     "img_id", "img_h", "img_w", "objects_id", "objects_conf", "attrs_id", "attrs_conf", "num_boxes", "boxes", "features"
@@ -102,11 +105,15 @@ class VqaV2Lxmert(datasets.GeneratorBasedBuilder):
         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"], "imgfeat": os.path.join(dl_dir["train_feat"], _TRAIN_FEAT_PATH)},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": dl_dir["valid"], "imgfeat": os.path.join(dl_dir["valid_feat"], _VALID_IMG_PATH)},
+                gen_kwargs={"filepath": dl_dir["valid"], "imgfeat": os.path.join(dl_dir["valid_feat"], _VALID_FEAT_PATH)},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": dl_dir["test"], "imgfeat": os.path.join(dl_dir["test_feat"], _TEST_FEAT_PATH), "labeled": False},
             ),
         ]
 
@@ -135,26 +142,42 @@ class VqaV2Lxmert(datasets.GeneratorBasedBuilder):
         normalized_boxes[:, (1, 3)] /= img_h
         return normalized_boxes
 
-    def _generate_examples(self, filepath, imgfeat):
+    def _generate_examples(self, filepath, imgfeat, labeled=True):
         """ Yields examples as (key, example) tuples."""
         id2features = self._load_features(imgfeat)
         with open(filepath, encoding="utf-8") as f:
             vqa = json.load(f)
-            for id_, d in enumerate(vqa):
-                img_features = id2features[d["img_id"]]
-                ids = [self.ans2label[x] for x in d["label"].keys()]
-                weights = list(d["label"].values())
-                yield id_, {
-                    "question": d["sent"],
-                    "question_type": d["question_type"],
-                    "question_id": d["question_id"],
-                    "image_id": d["img_id"],
-                    "features": img_features["features"],
-                    "normalized_boxes": img_features["normalized_boxes"],
-                    "answer_type": d["answer_type"],
-                    "label": {
-                        "ids": ids,
-                        "weights": weights,
-                    },
-                }
-
+            if labeled:
+                for id_, d in enumerate(vqa):
+                    img_features = id2features[d["img_id"]]
+                    ids = [self.ans2label[x] for x in d["label"].keys()]
+                    weights = list(d["label"].values())
+                    yield id_, {
+                        "question": d["sent"],
+                        "question_type": d["question_type"],
+                        "question_id": d["question_id"],
+                        "image_id": d["img_id"],
+                        "features": img_features["features"],
+                        "normalized_boxes": img_features["normalized_boxes"],
+                        "answer_type": d["answer_type"],
+                        "label": {
+                            "ids": ids,
+                            "weights": weights,
+                        },
+                    }
+            else:
+                for id_, d in enumerate(vqa):
+                    img_features = id2features[d["img_id"]]
+                    yield id_, {
+                        "question": d["sent"],
+                        "question_type": "",
+                        "question_id": d["question_id"],
+                        "image_id": d["img_id"],
+                        "features": img_features["features"],
+                        "normalized_boxes": img_features["normalized_boxes"],
+                        "answer_type": "",
+                        "label": {
+                            "ids": [],
+                            "weights": [],
+                        },
+                    }