Add test set

gqa-lxmert.py

@@ -44,12 +44,15 @@ seeking to address key shortcomings of previous visual question answering (VQA)
 
 _URLS = {
     "train": "https://nlp.cs.unc.edu/data/lxmert_data/gqa/train.json",
-    "dev": "https://nlp.cs.unc.edu/data/lxmert_data/gqa/valid.json",
-    "feat": "https://nlp.cs.unc.edu/data/lxmert_data/vg_gqa_imgfeat/vg_gqa_obj36.zip",
+    "val": "https://nlp.cs.unc.edu/data/lxmert_data/gqa/valid.json",
+    "trainval_feat": "https://nlp.cs.unc.edu/data/lxmert_data/vg_gqa_imgfeat/vg_gqa_obj36.zip",
+    "test": "https://nlp.cs.unc.edu/data/lxmert_data/gqa/testdev.json",
+    "test_feat": "https://nlp.cs.unc.edu/data/lxmert_data/vg_gqa_imgfeat/gqa_testdev_obj36.zip",
     "ans2label": "https://raw.githubusercontent.com/airsplay/lxmert/master/data/gqa/trainval_ans2label.json",
 }
 
-_FEAT_PATH = "vg_gqa_imgfeat/vg_gqa_obj36.tsv"
+TRAINVAL_FEAT_PATH = "vg_gqa_imgfeat/vg_gqa_obj36.tsv"
+TEST_FEAT_PATH = "vg_gqa_imgfeat/gqa_testdev_obj36.tsv"
 
 FIELDNAMES = [
     "img_id", "img_h", "img_w", "objects_id", "objects_conf", "attrs_id", "attrs_conf", "num_boxes", "boxes", "features"
@@ -89,16 +92,21 @@ 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))
+        self.trainval_id2features = self._load_features(os.path.join(dl_dir["trainval_feat"], TRAINVAL_FEAT_PATH))
+        self.test_id2features = self._load_features(os.path.join(dl_dir["test_feat"], TEST_FEAT_PATH))
 
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": dl_dir["train"]},
+                gen_kwargs={"filepath": dl_dir["train"], "testset": False},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": dl_dir["dev"]},
+                gen_kwargs={"filepath": dl_dir["val"], "testset": False},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": dl_dir["test"], "testset": True},
             ),
         ]
 
@@ -127,13 +135,25 @@ class GqaLxmert(datasets.GeneratorBasedBuilder):
         normalized_boxes[:, (1, 3)] /= img_h
         return normalized_boxes
 
-    def _generate_examples(self, filepath):
+    def _generate_examples(self, filepath, testset=False):
         """ Yields examples as (key, example) tuples."""
         with open(filepath, encoding="utf-8") as f:
             gqa = json.load(f)
             for id_, d in enumerate(gqa):
-                img_features = self.id2features[d["img_id"]]
-                label = self.ans2label[next(iter(d["label"]))]
+                # test_id2features only contains features of a subset of samples in testdev.json 
+                if testset:
+                    try:
+                        img_features = self.test_id2features[d["img_id"]]
+                    except KeyError:
+                        continue
+                    label_key = next(iter(d["label"]))
+                    if label_key not in self.ans2label:
+                        print ("Ignored one sample because of unseen label.")
+                        continue
+                    label = self.ans2label[label_key]
+                else:
+                    img_features = self.trainval_id2features[d["img_id"]]
+                    label = self.ans2label[next(iter(d["label"]))]
                 yield id_, {
                     "question": d["sent"],
                     "question_id": d["question_id"],