fix: checkpoint

ui_refexp.py

@@ -18,6 +18,7 @@
 import csv
 import glob
 import os
+import tensorflow as tf
 
 import datasets
 
@@ -65,7 +66,43 @@ _METADATA_URLS = {
 }
 
 
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+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
+
+
 class UIRefExp(datasets.GeneratorBasedBuilder):
     """Dataset with (image, question, answer) fields derive from UIBert RefExp."""
 
@@ -124,15 +161,21 @@ 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
-        my_urls = _DATA_URLs[self.config.name]
-        image_archive = dl_manager.download(my_urls)
+        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:
+            local_tfr_file = dl_manager.download(tfrecord_url)
+            local_tfrs[split] = local_tfr_file
+        
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
                     "root_dir": data_dir,
-                    "tfrecords_file": , 
+                    "metadata_file": local_tfrs["train"], 
                     "images": dl_manager.iter_archive(archive_path),
                     "split": "train",
                     
@@ -143,6 +186,7 @@ class UIRefExp(datasets.GeneratorBasedBuilder):
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
                     "root_dir": data_dir,
+                    "metadata_file": local_tfrs["validation"], 
                     "images": dl_manager.iter_archive(archive_path),
                     "split": "validation",
                 },                
@@ -152,6 +196,7 @@ class UIRefExp(datasets.GeneratorBasedBuilder):
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
                     "root_dir": data_dir,
+                    "metadata_file": local_tfrs["test"], 
                     "images": dl_manager.iter_archive(archive_path),
                     "split": "test",
                 },                
@@ -161,21 +206,22 @@ class UIRefExp(datasets.GeneratorBasedBuilder):
     def _generate_examples(
         self,
         root_dir,
+        metadata_file,
+        images,
         split,  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     ):
         """Yields examples as (key, example) tuples."""
         # 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.
-
-        screen_glob = sorted(glob.glob(os.path.join(root_dir, "**/*.jpg")))
-        hierarchy_glob = sorted(glob.glob(os.path.join(root_dir, "**/*.json")))
-        caption_glob = sorted(glob.glob(os.path.join(root_dir, "**/*.txt")))
-        for idx, (screen_filepath, hierarchy_filepath, caption_filepath) in enumerate(
-            zip(screen_glob, hierarchy_glob, caption_glob)
-        ):
-            with open(hierarchy_filepath, "r", encoding="utf-8") as f:
-                hierarchy = f.read()
-            with open(caption_filepath, "r", encoding="utf-8") as f:
-                caption = f.read()
-
-            yield idx, {"screenshot_path": screen_filepath, "hierarchy": hierarchy, "caption": caption}
+        # 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:
+                    label = file_path.split("/")[2]
+                    yield file_path, {
+                        "image": {"path": file_path, "bytes": file_obj.read()},
+                        "label": label,
+                    }