Add normalization to the positions of the detected objects

vqa-lxmert.py

@@ -77,7 +77,7 @@ class VqaV2Lxmert(datasets.GeneratorBasedBuilder):
                 "question_id": datasets.Value("int32"),
                 "image_id": datasets.Value("string"),
                 "features": datasets.Array2D(_SHAPE_FEATURES, dtype="float32"),
-                "boxes": datasets.Array2D(_SHAPE_BOXES, dtype="float32"),
+                "normalized_boxes": datasets.Array2D(_SHAPE_BOXES, dtype="float32"),
                 "answer_type": datasets.Value("string"),
                 "label": datasets.Sequence(
                     {
@@ -117,22 +117,24 @@ class VqaV2Lxmert(datasets.GeneratorBasedBuilder):
             reader = csv.DictReader(f, FIELDNAMES, delimiter="\t")
             for i, item in enumerate(reader):
                 features = {}
-                for key in ["img_h", "img_w", "num_boxes"]:
-                    features[key] = int(item[key])
-                num_boxes = features["num_boxes"]
-                decode_config = [
-                    ("objects_id", (num_boxes,), np.int64),
-                    ("objects_conf", (num_boxes,), np.float32),
-                    ("attrs_id", (num_boxes,), np.int64),
-                    ("attrs_conf", (num_boxes,), np.float32),
-                    ("boxes", (num_boxes, 4), np.float32),
-                    ("features", (num_boxes, -1), np.float32),
-                ]
-                for key, shape, dtype in decode_config:
-                    features[key] = np.frombuffer(base64.b64decode(item[key]), dtype=dtype).reshape(shape)
+                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):
+        """ Normalize 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, imgfeat):
         """ Yields examples as (key, example) tuples."""
         id2features = self._load_features(imgfeat)
@@ -148,7 +150,7 @@ class VqaV2Lxmert(datasets.GeneratorBasedBuilder):
                     "question_id": d["question_id"],
                     "image_id": d["img_id"],
                     "features": img_features["features"],
-                    "boxes": img_features["boxes"],
+                    "normalized_boxes": img_features["normalized_boxes"],
                     "answer_type": d["answer_type"],
                     "label": {
                         "ids": ids,