Update vasr.py

vasr.py

@@ -32,7 +32,11 @@ _HOMEPAGE = "https://vasr-dataset.github.io/"
 _LICENSE = "https://creativecommons.org/licenses/by/4.0/"
 
 _URL = "https://huggingface.co/datasets/nlphuji/vasr/blob/main"
-split2file = {'train': 'train_gold.csv', 'validation': 'dev_gold.csv', 'test': 'test_gold_unlabeled.csv'} 
+_URLS = {
+    "train": os.path.join(_URL, "train_gold_unlabeled.csv"),
+    "dev": os.path.join(_URL, "dev_gold.csv"),
+    "test": os.path.join(_URL, "test_gold.csv"),
+}
 
 class Vasr(datasets.GeneratorBasedBuilder):
     VERSION = datasets.Version("1.1.0")
@@ -42,20 +46,13 @@ class Vasr(datasets.GeneratorBasedBuilder):
     # BUILDER_CONFIG_CLASS = MyBuilderConfig
 
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="test", version=VERSION, description="vasr gold test dataset (unlabeled)"),
-        datasets.BuilderConfig(name="dev", version=VERSION, description="vasr gold dev dataset (labeled)"),
-        datasets.BuilderConfig(name="train", version=VERSION, description="vasr gold train dataset (labeled)"),
+        datasets.BuilderConfig(name="v1", version=VERSION, description="vasr gold test dataset"),
     ]
-    KEYS_LABELED = ["A", "A'", "B", "B'", 'candidates', 'label', 'A_verb', "A'_verb", 'B_verb', "B'_verb", 'diff_item_A', 'diff_item_A_str_first', "diff_item_A'", "diff_item_A'_str_first", "A_str", "A'_str", "B_str", "B'_str"]
-    KEYS_UNLABELED = ["A", "A'", "B", 'candidates_images', 'A_str', "A'_str", "B"]
-
-    print("*** in Vasr class ***")
+    DATASET_KEYS = ["A", "A'", "B", "B'", 'candidates', 'label', "A_str", "A'_str", "B_str", "B'_str"]
 
     def _info(self):
-        print("*** in Vasr info ***")
-        print(vars(self))
-        print(f"Curr config name: {self.config.name}")
-        feats_dict = {
+        features = datasets.Features(
+            {
                 "A": datasets.Image(),
                 "A'": datasets.Image(),
                 "B": datasets.Image(),
@@ -63,23 +60,12 @@ class Vasr(datasets.GeneratorBasedBuilder):
                 "candidates_images": [datasets.Image()],
                 "label": datasets.Value("int64"),
                 "candidates": [datasets.Value("string")],
-                "A_verb": datasets.Value("string"),
-                "A'_verb": datasets.Value("string"),
-                "B_verb": datasets.Value("string"),
-                "B'_verb": datasets.Value("string"),
-                "diff_item_A": datasets.Value("string"),
-                "diff_item_A_str_first": datasets.Value("string"),
-                "diff_item_A'": datasets.Value("string"),
-                "diff_item_A'_str_first": datasets.Value("string"),
                 "A_str": datasets.Value("string"),
                 "A'_str": datasets.Value("string"),
                 "B_str": datasets.Value("string"),
                 "B'_str": datasets.Value("string"),
             }
-        if self.config.name == 'test': 
-            print("Filtering feats dict")
-            feats_dict = {k:v for k,v in feats_dict.items() if k in self.KEYS_UNLABELED}
-        features = datasets.Features(feats_dict)
+        )
         return datasets.DatasetInfo(
             # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
@@ -105,13 +91,15 @@ class Vasr(datasets.GeneratorBasedBuilder):
         data_dir = dl_manager.download_and_extract({
             "images_dir": hf_hub_url(repo_id="nlphuji/vasr", repo_type='dataset', filename="vasr_images.zip")
         })
-        print(f"in split gen, config name: {self.config.name}")
-        print(f"in split gen, config name: {split2file[self.config.name]}")
-        examples = hf_hub_url(repo_id="nlphuji/vasr", repo_type='dataset', filename=split2file[self.config.name])
+        test_examples = hf_hub_url(repo_id="nlphuji/vasr", repo_type='dataset', filename="test_gold.csv")
+        dev_examples = hf_hub_url(repo_id="nlphuji/vasr", repo_type='dataset', filename="dev_gold.csv")
+        train_examples = hf_hub_url(repo_id="nlphuji/vasr", repo_type='dataset', filename="train_gold.csv")
 
-        gen = datasets.SplitGenerator(name=self.config.name, gen_kwargs={**data_dir, **{'examples_csv': examples}})
+        train_gen = datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={**data_dir, **{'examples_csv': train_examples}})
+        dev_gen = datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={**data_dir, **{'examples_csv': dev_examples}})
+        test_gen = datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={**data_dir, **{'examples_csv': test_examples}})
 
-        return [gen]
+        return [train_gen, dev_gen, test_gen]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, examples_csv, images_dir):
@@ -119,15 +107,6 @@ class Vasr(datasets.GeneratorBasedBuilder):
 
         df = pd.read_csv(examples_csv)
 
-        has_label = 'D_img' in df.columns
-
-        d_keys = self.KEYS_LABELED if has_label else self.KEYS_UNLABELED
-        dataset = self.config.name
-        print(f'Curr config: {dataset}')
-        print(f'curr keys')
-        print(self.info.features)
-        print(type(self.info.features))
-        
         for r_idx, r in df.iterrows():
             r_dict = r.to_dict()
             r_dict['candidates'] = json.loads(r_dict['candidates'])
@@ -137,22 +116,12 @@ class Vasr(datasets.GeneratorBasedBuilder):
             r_dict["A_str"] = r_dict['A_img']
             r_dict["A'_str"] = r_dict['B_img']
             r_dict["B_str"] = r_dict['C_img']
-            if has_label:
-                r_dict["B'_str"] = r_dict['D_img']
-                imgs_list = ['A_img', 'B_img', 'C_img', 'D_img']
-            else:
-                imgs_list = ['A_img', 'B_img', 'C_img']
-            for img in imgs_list:
+            r_dict["B'_str"] = r_dict['D_img']
+            for img in ['A_img', 'B_img', 'C_img', 'D_img']:
                 r_dict[img] = os.path.join(images_dir, "vasr_images", r_dict[img])
             r_dict["A"] = r_dict['A_img']
             r_dict["A'"] = r_dict['B_img']
             r_dict["B"] = r_dict['C_img']
-            if has_label:
-                r_dict["B'"] = r_dict['D_img']
-                r_dict["A'_verb"] = r_dict['B_verb']
-                r_dict["B_verb"] = r_dict['C_verb']
-                r_dict["B'_verb"] = r_dict['D_verb']
-                r_dict["diff_item_A'"] = r_dict['diff_item_B']
-                r_dict["diff_item_A'_str_first"] = r_dict['diff_item_B_str_first']
-            relevant_r_dict = {k:v for k,v in r_dict.items() if k in d_keys or k == 'candidates_images'}
+            r_dict["B'"] = r_dict['D_img']
+            relevant_r_dict = {k:v for k,v in r_dict.items() if k in self.DATASET_KEYS or k == 'candidates_images'}
             yield r_idx, relevant_r_dict