fix: checkpoint

Signed-off-by: ivelin <ivelin.eth@gmail.com>

ui_refexp.py

@@ -67,40 +67,40 @@ _METADATA_URLS = {
 
 
 def tfrecord2dict(raw_tfr_dataset: None):
-  """Filter and convert refexp tfrecord file to dict object."""
-  count = 0
-  donut_refexp_dict = []
-  for raw_record in raw_tfr_dataset:
-    count += 1
-    example = tf.train.Example()
-    example.ParseFromString(raw_record.numpy())
-    # print(f"total UI objects in this sample: {len(example.features.feature['image/object/bbox/xmin'].float_list.value)}")
-    # print(f"feature keys: {example.features.feature.keys}")
-    donut_refexp = {}
-    image_id = example.features.feature['image/id'].bytes_list.value[0].decode()
-    image_path=zipurl_template.format(image_id = image_id)
-    donut_refexp["image_path"] = image_path
-    donut_refexp["question"] = example.features.feature["image/ref_exp/text"].bytes_list.value[0].decode()
-    object_idx = example.features.feature["image/ref_exp/label"].int64_list.value[0]
-    object_idx = int(object_idx)
-    # print(f"object_idx: {object_idx}")
-    object_bb = {}
-    # print(f"example.features.feature['image/object/bbox/xmin']: {example.features.feature['image/object/bbox/xmin'].float_list.value[object_idx]}")
-    object_bb["xmin"] = example.features.feature['image/object/bbox/xmin'].float_list.value[object_idx]
-    object_bb["ymin"] = example.features.feature['image/object/bbox/ymin'].float_list.value[object_idx]
-    object_bb["xmax"] = example.features.feature['image/object/bbox/xmax'].float_list.value[object_idx]
-    object_bb["ymax"] = example.features.feature['image/object/bbox/ymax'].float_list.value[object_idx]
-    donut_refexp["answer"] = object_bb
-    donut_refexp_dict.append(donut_refexp)
-    if count != 3:
-      continue
-    print(f"Donut refexp: {donut_refexp}")
-    # for key, feature in example.features.feature.items():
-    #   if key in ['image/id', "image/ref_exp/text", "image/ref_exp/label", 'image/object/bbox/xmin', 'image/object/bbox/ymin', 'image/object/bbox/xmax', 'image/object/bbox/ymax']:
-    #     print(key, feature)
-
-  print(f"Total samples in the raw dataset: {count}")
-  return donut_refexp_dict
+    """Filter and convert refexp tfrecord file to dict object."""
+    count = 0
+    donut_refexp_dict = []
+    for raw_record in raw_tfr_dataset:
+        count += 1
+        example = tf.train.Example()
+        example.ParseFromString(raw_record.numpy())
+        # print(f"total UI objects in this sample: {len(example.features.feature['image/object/bbox/xmin'].float_list.value)}")
+        # print(f"feature keys: {example.features.feature.keys}")
+        donut_refexp = {}
+        image_id = example.features.feature['image/id'].bytes_list.value[0].decode()
+        image_path = zipurl_template.format(image_id=image_id)
+        donut_refexp["image_path"] = image_path
+        donut_refexp["question"] = example.features.feature["image/ref_exp/text"].bytes_list.value[0].decode()
+        object_idx = example.features.feature["image/ref_exp/label"].int64_list.value[0]
+        object_idx = int(object_idx)
+        # print(f"object_idx: {object_idx}")
+        object_bb = {}
+        # print(f"example.features.feature['image/object/bbox/xmin']: {example.features.feature['image/object/bbox/xmin'].float_list.value[object_idx]}")
+        object_bb["xmin"] = example.features.feature['image/object/bbox/xmin'].float_list.value[object_idx]
+        object_bb["ymin"] = example.features.feature['image/object/bbox/ymin'].float_list.value[object_idx]
+        object_bb["xmax"] = example.features.feature['image/object/bbox/xmax'].float_list.value[object_idx]
+        object_bb["ymax"] = example.features.feature['image/object/bbox/ymax'].float_list.value[object_idx]
+        donut_refexp["answer"] = object_bb
+        donut_refexp_dict.append(donut_refexp)
+        if count != 3:
+            continue
+        print(f"Donut refexp: {donut_refexp}")
+        # for key, feature in example.features.feature.items():
+        #   if key in ['image/id', "image/ref_exp/text", "image/ref_exp/label", 'image/object/bbox/xmin', 'image/object/bbox/ymin', 'image/object/bbox/xmax', 'image/object/bbox/ymax']:
+        #     print(key, feature)
+
+    print(f"Total samples in the raw dataset: {count}")
+    return donut_refexp_dict
 
 
 class UIRefExp(datasets.GeneratorBasedBuilder):
@@ -120,34 +120,35 @@ class UIRefExp(datasets.GeneratorBasedBuilder):
     # data = datasets.load_dataset('my_dataset', 'first_domain')
     # data = datasets.load_dataset('my_dataset', 'second_domain')
     BUILDER_CONFIGS = [
-         datasets.BuilderConfig(
-             name="ui_refexp",
-             version=VERSION,
-             description="Contains 66k+ unique UI screens. For each UI, we present a screenshot (JPG file) and the text shown on the screen that was extracted using an OCR model.",
-         )
-    #    ,
-    #     # datasets.BuilderConfig(
-    #     #     name="screenshots_captions_filtered",
-    #     #     version=VERSION,
-    #     #     description="Contains 25k unique UI screens. For each UI, we present a screenshot (JPG file) and the text shown on the screen that was extracted using an OCR model. Filtering was done as discussed in this paper: https://aclanthology.org/2020.acl-main.729.pdf",
-    #     # ),
-    # ]
-
-    DEFAULT_CONFIG_NAME = "screenshots_captions_filtered"
+        datasets.BuilderConfig(
+            name="ui_refexp",
+            version=VERSION,
+            description="Contains 66k+ unique UI screens. For each UI, we present a screenshot (JPG file) and the text shown on the screen that was extracted using an OCR model.",
+        )
+        #    ,
+        #     # datasets.BuilderConfig(
+        #     #     name="screenshots_captions_filtered",
+        #     #     version=VERSION,
+        #     #     description="Contains 25k unique UI screens. For each UI, we present a screenshot (JPG file) and the text shown on the screen that was extracted using an OCR model. Filtering was done as discussed in this paper: https://aclanthology.org/2020.acl-main.729.pdf",
+        #     # ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "ui_refexp"
 
     def _info(self):
         features = datasets.Features(
             {
                 "screenshot": datasets.Image(),
-                "prompt": datasets.Value("string"), # click the search button next to menu drawer at the top of the screen
-                "target_bounding_box": dict, # [xmin, ymin, xmax, ymax], normalized screen reference values between 0 and 1
+                # click the search button next to menu drawer at the top of the screen
+                "prompt": datasets.Value("string"),
+                # [xmin, ymin, xmax, ymax], normalized screen reference values between 0 and 1
+                "target_bounding_box": dict,
             }
         )
 
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=features,
-            supervised_keys=("screenshot","prompt", "target_bounding_box"),
             homepage=_HOMEPAGE,
             license=_LICENSE,
             citation=_CITATION,
@@ -161,51 +162,48 @@ class UIRefExp(datasets.GeneratorBasedBuilder):
         # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        image_urls = _DATA_URLs[self.config.name]
-        image_archive = dl_manager.download(image_urls)
         # download and extract TFRecord labeling metadata
         local_tfrs = {}
-        for split, tfrecord_url in _METADATA_URLS:
+        for split, tfrecord_url in _METADATA_URLS[self.config.name].items():
             local_tfr_file = dl_manager.download(tfrecord_url)
             local_tfrs[split] = local_tfr_file
-        
+        # download image files
+        image_urls = _DATA_URLs[self.config.name]
+        archive_path = dl_manager.download(image_urls)
+
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "root_dir": data_dir,
-                    "metadata_file": local_tfrs["train"], 
+                    "metadata_file": local_tfrs["train"],
                     "images": dl_manager.iter_archive(archive_path),
                     "split": "train",
-                    
+
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "root_dir": data_dir,
-                    "metadata_file": local_tfrs["validation"], 
+                    "metadata_file": local_tfrs["validation"],
                     "images": dl_manager.iter_archive(archive_path),
                     "split": "validation",
-                },                
+                },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "root_dir": data_dir,
-                    "metadata_file": local_tfrs["test"], 
+                    "metadata_file": local_tfrs["test"],
                     "images": dl_manager.iter_archive(archive_path),
                     "split": "test",
-                },                
+                },
             )
         ]
 
     def _generate_examples(
         self,
-        root_dir,
         metadata_file,
         images,
         split,  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
@@ -214,12 +212,12 @@ class UIRefExp(datasets.GeneratorBasedBuilder):
         # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is here for legacy reason (tfds) and is not important in itself.
         # filter tfrecord and convert to json
-        
+
         with open(metadata_path, encoding="utf-8") as f:
             files_to_keep = set(f.read().split("\n"))
         for file_path, file_obj in images:
             if file_path.startswith(_IMAGES_DIR):
-                if file_path[len(_IMAGES_DIR) : -len(".jpg")] in files_to_keep:
+                if file_path[len(_IMAGES_DIR): -len(".jpg")] in files_to_keep:
                     label = file_path.split("/")[2]
                     yield file_path, {
                         "image": {"path": file_path, "bytes": file_obj.read()},