Update medianomaly.py

medianomaly.py

@@ -53,17 +53,54 @@ class Medianomaly(datasets.GeneratorBasedBuilder):
     ]
 
     def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features({
-                "image": datasets.Image(),
-                "label": datasets.ClassLabel(names=["normal", "abnormal"]),
-            }),
-            supervised_keys=("image", "label"),
-            homepage=_HOMEPAGE,
-            license="apache-2.0",
-            citation=_CITATION,
-        )
+        config_name = self.config.name.lower()
+        if config_name in ["rsna", "vincxr", "braintumor", "lag", "camelyon16"]:
+            return datasets.DatasetInfo(
+                description=_DESCRIPTION,
+                features=datasets.Features({
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(names=["normal", "abnormal"]),
+                }),
+                supervised_keys=("image", "label"),
+                homepage=_HOMEPAGE,
+                license="apache-2.0",
+                citation=_CITATION,
+            )
+        elif config_name == "brats2021":
+            return datasets.DatasetInfo(
+                description=_DESCRIPTION,
+                features=datasets.Features({
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(names=["normal", "abnormal"]),
+                    "annotation": datasets.Image(),
+                }),
+                supervised_keys=("image", "label"),
+                homepage=_HOMEPAGE,
+                license="apache-2.0",
+                citation=_CITATION,
+            )
+        elif config_name == "isic2018_task3":
+            return datasets.DatasetInfo(
+                description=_DESCRIPTION,
+                features=datasets.Features({
+                    "image": datasets.Image(),
+                    "labels": datasets.Sequence(datasets.Value("int32")),
+                    "MEL": datasets.ClassLabel(names=["melanoma", "non-melanoma"]),
+                    "NV": datasets.ClassLabel(names=["nevus", "non-nevus"]),
+                    "BCC": datasets.ClassLabel(names=["basal cell carcinoma", "non-basal cell carcinoma"]),
+                    "AKIEC": datasets.ClassLabel(names=["actinic keratosis", "non-actinic keratosis"]),
+                    "BKL": datasets.ClassLabel(names=["benign keratosis", "non-benign keratosis"]),
+                    "VASC": datasets.ClassLabel(names=["vascular lesion", "non-vascular lesion"]),
+                    "DF": datasets.ClassLabel(names=["dermatofibroma", "non-dermatofibroma"]),
+                }),
+                supervised_keys=("image", "labels"),
+                homepage=_HOMEPAGE,
+                license="apache-2.0",
+                citation=_CITATION,
+            )
+        else:
+            raise NotImplementedError(f"{config_name} is not implemented in Medianomaly.")
+
     
     def _split_generators(self, dl_manager):
         config_name = self.config.name.lower()
@@ -83,7 +120,26 @@ class Medianomaly(datasets.GeneratorBasedBuilder):
                     "samples": metadata["test"], "base_dir": data_dir, "config": config_name
                 }),
             ]
-    
+        elif config_name == "brats2021":
+            data_dir = os.path.join(archive_path, config_names[config_name])
+            return [
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={
+                    "samples": "train", "base_dir": data_dir, "config": config_name
+                }),
+                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={
+                    "samples": "test", "base_dir": data_dir, "config": config_name
+                }),
+            ]
+        elif config_name == "camelyon16":
+            data_dir = os.path.join(archive_path, config_names[config_name])
+            return [
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={
+                    "samples": "train", "base_dir": data_dir, "config": config_name
+                }),
+                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={
+                    "samples": "test", "base_dir": data_dir, "config": config_name
+                }),
+            ]
     
     def _generate_examples(self, samples, base_dir, config):
         if config in ["rsna", "vincxr", "braintumor", "lag"]:
@@ -95,4 +151,79 @@ class Medianomaly(datasets.GeneratorBasedBuilder):
                     yield idx, {
                         "image": image_path,
                         "label": label,
-                    }
+                    }
+
+        elif config == "brats2021":
+            if samples == "train":
+                base_dir = os.path.join(base_dir, "train")
+                for idx, item in enumerate(os.listdir(base_dir)):
+                    image_path = os.path.join(base_dir, item)
+                    yield idx, {
+                        "image": image_path,
+                        "label": 0,  # All training images are normal
+                    }
+            elif samples == "test":
+                image_dir_normal = os.path.join(base_dir, "test", "normal")
+                image_dir_tumor = os.path.join(base_dir, "test", "tumor")
+                annot_dir = os.path.join(base_dir, "test", "annotation")
+
+                idx = 0
+                for fname in os.listdir(image_dir_normal):
+                    if fname.endswith(".png"):
+                        image_path = os.path.join(image_dir_normal, fname)
+                        yield idx, {
+                            "image": image_path,
+                            "label": 0,
+                            "annotation": None,
+                        }
+                        idx += 1
+
+                for fname in os.listdir(image_dir_tumor):
+                    if fname.endswith(".png"):
+                        image_path = os.path.join(image_dir_tumor, fname)
+                        annot_name = fname.replace("flair", "seg")
+                        annot_path = os.path.join(annot_dir, annot_name)
+
+                        yield idx, {
+                            "image": image_path,
+                            "label": 1,
+                            "annotation": annot_path,
+                        }
+                        idx += 1
+
+        elif config == "camelyon16":
+            if samples == "train":
+                base_dir = os.path.join(base_dir, "train")
+                base_dir = os.path.join(base_dir, "good")
+                for idx, item in enumerate(os.listdir(base_dir)):
+                    image_path = os.path.join(base_dir, item)
+                    yield idx, {
+                        "image": image_path,
+                        "label": 0,  # All training images are normal
+                    }
+            elif samples == "test":
+                base_dir = os.path.join(base_dir, "test")
+                good_dir = os.path.join(base_dir, "good")
+                ungood_dir = os.path.join(base_dir, "ungood")
+
+                idx = 0
+                for item in os.listdir(good_dir):
+                    if item.endswith(".png"):
+                        image_path = os.path.join(base_dir, item)
+                        yield idx, {
+                            "image": image_path,
+                            "label": 0,
+                        }
+                        idx += 1
+                
+                for item in os.listdir(ungood_dir):
+                    if item.endswith(".png"):
+                        image_path = os.path.join(base_dir, item)
+                        yield idx, {
+                            "image": image_path,
+                            "label": 1,
+                        }
+                        idx += 1
+                        
+
+