Support is_one and is_ten for noface-inet

face-obfuscated-imagenet.py

@@ -135,6 +135,8 @@ class FaceObfuscatedImagenet(datasets.GeneratorBasedBuilder):
                 {
                     "image": datasets.Image(),
                     "label": datasets.ClassLabel(names=formatted_class_names),
+                    "is_one": datasets.Value("bool"),
+                    "is_ten": datasets.Value("bool"),
                 }
             ),
             supervised_keys=("image", "label"),  # type: ignore
@@ -149,8 +151,10 @@ class FaceObfuscatedImagenet(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         if dl_manager.is_streaming:
-            raise NotImplementedError("Streaming is not yet supported for this dataset.")
-            
+            raise NotImplementedError(
+                "Streaming is not yet supported for this dataset."
+            )
+
         # cache location for the dataset downloads
         inet_train_path = (
             "/gpfs/data/shared/imagenet/ILSVRC2012/ILSVRC2012_img_train.tar"
@@ -347,6 +351,8 @@ class FaceObfuscatedImagenet(datasets.GeneratorBasedBuilder):
                                 yield example_idx, {
                                     "image": img,
                                     "label": label,
+                                    "is_one": False,
+                                    "is_ten": False,
                                 }
                                 example_idx += 1
 
@@ -385,6 +391,8 @@ class FaceObfuscatedImagenet(datasets.GeneratorBasedBuilder):
                     yield example_idx, {
                         "image": img,
                         "label": label,
+                        "is_one": False,
+                        "is_ten": False,
                     }
 
     def _generate_nonblur_subset(
@@ -421,6 +429,8 @@ class FaceObfuscatedImagenet(datasets.GeneratorBasedBuilder):
                                 yield example_idx, {
                                     "image": img,
                                     "label": new_label,
+                                    "is_one": False,
+                                    "is_ten": False,
                                 }
                                 example_idx += 1
 
@@ -469,70 +479,81 @@ class FaceObfuscatedImagenet(datasets.GeneratorBasedBuilder):
                     yield example_idx, {
                         "image": img,
                         "label": new_label,
+                        "is_one": False,
+                        "is_ten": False,
                     }
                     example_idx += 1
 
     def _generate_noface(self, archive_path, split, train_txt_url, val_txt_url):
         def is_valid_file(path):
             return os.path.isfile(path) and os.path.getsize(path) > 0
-    
+
         num_failed = 0
-    
+
         if split == "train":
             with urllib.request.urlopen(train_txt_url) as f:
                 train_ids = [line.decode("utf-8").strip() for line in f if line.strip()]
-    
+
             if not hasattr(self, "_mapping"):
                 with urllib.request.urlopen(self._CLASS_INDEX_URL) as response:
                     self._mapping = json.load(response)
-    
+
             wnid_to_label = {
                 wnid: idx for idx, wnid in enumerate(self._get_class_names())
             }
-    
+
             valid_entries = []
             for file_id in train_ids:
-                wnid = file_id.split("_")[0]
-                img_name = file_id
+                # inputs are like n01440764_2708-0-0
+                # Split to get all parts: ["n01440764_2708", "0", "0"]
+                parts = file_id.split("-")
+                base_file_id = parts[0]  # "n01440764_2708"
+                is_one = parts[1] == "1" if len(parts) > 1 else False
+                is_ten = parts[2] == "1" if len(parts) > 2 else False
+
+                wnid = base_file_id.split("_")[0]
+                img_name = base_file_id
                 found = False
                 for ext in [".JPEG", ".jpg"]:
                     candidate_path = os.path.join(
                         archive_path, "train_blurred", wnid, img_name + ext
                     )
                     if is_valid_file(candidate_path):
-                        valid_entries.append((candidate_path, wnid))
+                        valid_entries.append((candidate_path, wnid, is_one, is_ten))
                         found = True
                         break
                 if not found:
                     num_failed += 1
-    
+
             print(f"[train] Skipped {num_failed} invalid or zero-byte files.")
-    
+
             example_idx = 0
-            for img_path, wnid in valid_entries:
+            for img_path, wnid, is_one, is_ten in valid_entries:
                 label = wnid_to_label[wnid]
                 img = Image.open(img_path)
                 if img.mode != "RGB":
                     img = img.convert("RGB")
-    
+
                 yield example_idx, {
                     "image": img,
                     "label": label,
+                    "is_one": is_one,
+                    "is_ten": is_ten,
                 }
                 example_idx += 1
-    
+
         if split == "validation":
             with urllib.request.urlopen(val_txt_url) as f:
                 val_ids = [line.decode("utf-8").strip() for line in f if line.strip()]
-    
+
             if not hasattr(self, "_mapping"):
                 with urllib.request.urlopen(self._CLASS_INDEX_URL) as response:
                     self._mapping = json.load(response)
-    
+
             wnid_to_label = {
                 wnid: idx for idx, wnid in enumerate(self._get_class_names())
             }
-    
+
             valid_entries = []
             for file_id in val_ids:
                 wnid = file_id.split("_")[0]
@@ -548,19 +569,21 @@ class FaceObfuscatedImagenet(datasets.GeneratorBasedBuilder):
                         break
                 if not found:
                     num_failed += 1
-    
+
             print(f"[validation] Skipped {num_failed} invalid or zero-byte files.")
-    
+
             example_idx = 0
             for img_path, wnid in valid_entries:
                 label = wnid_to_label[wnid]
                 img = Image.open(img_path)
                 if img.mode != "RGB":
                     img = img.convert("RGB")
-    
+
                 yield example_idx, {
                     "image": img,
                     "label": label,
+                    "is_one": False,
+                    "is_ten": False,
                 }
                 example_idx += 1