Update dataset.py

dataset.py

@@ -1,17 +1,18 @@
 import os
+import json
 from datasets import DatasetInfo, GeneratorBasedBuilder, Features, Image, ClassLabel, Split
 from huggingface_hub import hf_hub_download
 
 class UIR25MReference(GeneratorBasedBuilder):
-    """UIR-2.5M Reference Dataset Loader"""
+    """UIR-2.5M Reference Dataset Loader with metadata mapping"""
 
     VERSION = "1.0.0"
 
-    # Directory where pretrained models will be downloaded
+    # Directory for pretrained models
     PRETRAINED_DIR = os.path.expanduser("./pretrained_models")
     os.makedirs(PRETRAINED_DIR, exist_ok=True)
 
-    # List of pretrained model files with Hugging Face URLs
+    # Pretrained models info
     PRETRAINED_MODELS = [
         "nafnet_maskdcpt_12d.pth",
         "nafnet_maskdcpt_5d.pth",
@@ -20,7 +21,6 @@ class UIR25MReference(GeneratorBasedBuilder):
         "restormer_maskdcpt_5d.pth",
         "swinir_maskdcpt_5d.pth"
     ]
-
     PRETRAINED_REPO = "Jiakui/MaskDCPT"
 
     def download_pretrained_models(self):
@@ -63,8 +63,8 @@ class UIR25MReference(GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         """
-        Leverage automatic split detection:
-        Expects directories named `train`, `test`, `validation` in external path
+        Automatic split detection:
+        Expects directories named train/test/validation
         """
         data_dir = dl_manager.download_and_extract("https://github.com/MILab-PKU/MaskDCPT.git")
         splits = []
@@ -80,18 +80,30 @@ class UIR25MReference(GeneratorBasedBuilder):
         return splits
 
     def _generate_examples(self, images_dir):
-        """Yield examples for Hugging Face dataset."""
+        """Yield examples using metadata JSON mapping"""
         low_dir = os.path.join(images_dir, "low_quality")
         high_dir = os.path.join(images_dir, "high_quality")
+        metadata_file = os.path.join(images_dir, "metadata.json")
+
+        if not os.path.exists(metadata_file):
+            raise FileNotFoundError(f"Metadata file not found: {metadata_file}")
+
+        with open(metadata_file, "r") as f:
+            metadata = json.load(f)
 
-        # Expect a mapping from low to high quality images and degradation type metadata
-        for idx, fname in enumerate(os.listdir(low_dir)):
-            if fname.lower().endswith((".png", ".jpg", ".jpeg")):
-                low_path = os.path.join(low_dir, fname)
-                high_path = os.path.join(high_dir, fname)
-                degradation_type = 0  # Replace with actual metadata mapping if available
+        for idx, sample in enumerate(metadata):
+            low_fname = sample["low_quality"]
+            high_fname = sample["high_quality"]
+            degradation_type = sample["degradation_type"]
+
+            low_path = os.path.join(low_dir, low_fname)
+            high_path = os.path.join(high_dir, high_fname)
+
+            if os.path.exists(low_path) and os.path.exists(high_path):
                 yield idx, {
                     "low_quality": low_path,
                     "high_quality": high_path,
                     "degradation_type": degradation_type
-                }
+                }
+            else:
+                print(f"Skipping missing files: {low_fname} or {high_fname}")