Optimize the inspection of the dataset

gqa-lxmert.py

@@ -91,52 +91,49 @@ class GqaLxmert(datasets.GeneratorBasedBuilder):
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
         dl_dir = dl_manager.download_and_extract(_URLS)
-        self.ans2label = json.load(open(dl_dir["ans2label"]))
-        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))
-
+        trainval_features_path = os.path.join(dl_dir["trainval_feat"], TRAINVAL_FEAT_PATH)
+        test_features_path = os.path.join(dl_dir["test_feat"], TEST_FEAT_PATH)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": dl_dir["train"], "testset": False},
+                gen_kwargs={
+                    "filepath": dl_dir["train"],
+                    "ans2label_path": dl_dir["ans2label"],
+                    "features_path": trainval_features_path,
+                    "testset": False,
+                },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": dl_dir["valid"], "testset": False},
+                gen_kwargs={
+                    "filepath": dl_dir["valid"],
+                    "ans2label_path": dl_dir["ans2label"],
+                    "features_path": trainval_features_path,
+                    "testset": False,
+                },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                gen_kwargs={"filepath": dl_dir["testdev"], "testset": True},
+                gen_kwargs={
+                    "filepath": dl_dir["testdev"],
+                    "ans2label_path": dl_dir["ans2label"],
+                    "features_path": test_features_path,
+                    "testset": True,
+                },
             ),
         ]
 
-    def _load_features(self, filepath):
-        """Returns a dictionary mapping an image id to the corresponding image's objects features."""
-        id2features = {}
-        with open(filepath) as f:
-            reader = csv.DictReader(f, FIELDNAMES, delimiter="\t")
-            for i, item in enumerate(reader):
-                features = {}
-                img_h = int(item["img_h"])
-                img_w = int(item["img_w"])
-                num_boxes = int(item["num_boxes"])
-                features["features"] = np.frombuffer(base64.b64decode(item["features"]), dtype=np.float32).reshape(
-                    (num_boxes, -1)
-                )
-                boxes = np.frombuffer(base64.b64decode(item["boxes"]), dtype=np.float32).reshape((num_boxes, 4))
-                features["normalized_boxes"] = self._normalize_boxes(boxes, img_h, img_w)
-                id2features[item["img_id"]] = features
-        return id2features
-
-    def _normalize_boxes(self, boxes, img_h, img_w):
-        """ Normalizes the input boxes given the original image size."""
-        normalized_boxes = boxes.copy()
-        normalized_boxes[:, (0, 2)] /= img_w
-        normalized_boxes[:, (1, 3)] /= img_h
-        return normalized_boxes
-
-    def _generate_examples(self, filepath, testset=False):
+    def _generate_examples(self, filepath, ans2label_path, features_path, testset=False):
         """ Yields examples as (key, example) tuples."""
+        if not hasattr(self, "ans2label"):
+            with open(ans2label_path, encoding="utf-8") as f:
+                self.ans2label = json.load(f)
+        if testset:
+            self.test_id2features = self._load_features(features_path)
+        else:
+            if not hasattr(self, "trainval_id2features"):
+                self.trainval_id2features = self._load_features(features_path)
+
         with open(filepath, encoding="utf-8") as f:
             gqa = json.load(f)
             for id_, d in enumerate(gqa):
@@ -161,4 +158,29 @@ class GqaLxmert(datasets.GeneratorBasedBuilder):
                     "features": img_features["features"],
                     "normalized_boxes": img_features["normalized_boxes"],
                     "label": label,
-                }
+                }
+
+    def _load_features(self, filepath):
+        """Returns a dictionary mapping an image id to the corresponding image's objects features."""
+        id2features = {}
+        with open(filepath) as f:
+            reader = csv.DictReader(f, FIELDNAMES, delimiter="\t")
+            for i, item in enumerate(reader):
+                features = {}
+                img_h = int(item["img_h"])
+                img_w = int(item["img_w"])
+                num_boxes = int(item["num_boxes"])
+                features["features"] = np.frombuffer(base64.b64decode(item["features"]), dtype=np.float32).reshape(
+                    (num_boxes, -1)
+                )
+                boxes = np.frombuffer(base64.b64decode(item["boxes"]), dtype=np.float32).reshape((num_boxes, 4))
+                features["normalized_boxes"] = self._normalize_boxes(boxes, img_h, img_w)
+                id2features[item["img_id"]] = features
+        return id2features
+
+    def _normalize_boxes(self, boxes, img_h, img_w):
+        """ Normalizes the input boxes given the original image size."""
+        normalized_boxes = boxes.copy()
+        normalized_boxes[:, (0, 2)] /= img_w
+        normalized_boxes[:, (1, 3)] /= img_h
+        return normalized_boxes