Update rsna-2023-abdominal-trauma-detection.py

rsna-2023-abdominal-trauma-detection.py

@@ -156,86 +156,46 @@ class RSNA2023AbdominalTraumaDetectionSegmentation(datasets.GeneratorBasedBuilde
         # segmentation: 206 segmentations and the relevant imgs, train_series_meta.csv, train_dicom_tags.parquet
         # classification: 4711 all imgs, train.csv, train_series_meta.csv, train_dicom_tags.parquet
         # classification-with-mask: 206 segmentations and the relevant imgs, train.csv, train_series_meta.csv, train_dicom_tags.parquet
-        self.series_meta_df = pd.read_csv(
+        series_meta_df = pd.read_csv(
             dl_manager.download_and_extract(
                 urllib.parse.urljoin(_URL, "train_series_meta.csv")
             )
         )
+        series_meta_df["img_download_url"] = series_meta_df.apply(
+            lambda x: urllib.parse.urljoin(
+                _URL,
+                f"train_images/{int(x['patient_id'])}/{int(x['series_id'])}.nii.gz",
+            ),
+            axis=1,
+        )
+        series_meta_df["seg_download_url"] = series_meta_df.apply(
+            lambda x: urllib.parse.urljoin(
+                _URL, f"segmentations/{int(x['series_id'])}.nii.gz"
+            ),
+            axis=1,
+        )
         if (
             self.config.name == "classification-with-mask"
             or self.config.name == "segmentation"
         ):
-            self.series_meta_df = self.series_meta_df.loc[
-                self.series_meta_df["has_segmentation"] == 1
-            ]
+            series_meta_df = series_meta_df.loc[series_meta_df["has_segmentation"] == 1]
 
-            train_series_meta_df, test_series_meta_df = train_test_split(
-                self.series_meta_df, test_size=0.1, random_state=42, shuffle=True
+            series_meta_df["img_cache_path"] = dl_manager.download(
+                series_meta_df["img_download_url"].tolist()
             )
-
-            train_img_files = dl_manager.download(
-                train_series_meta_df.apply(
-                    lambda x: urllib.parse.urljoin(
-                        _URL,
-                        f"train_images/{int(x['patient_id'])}/{int(x['series_id'])}.nii.gz",
-                    ),
-                    axis=1,
-                ).tolist()
-            )
-            test_img_files = dl_manager.download(
-                test_series_meta_df.apply(
-                    lambda x: urllib.parse.urljoin(
-                        _URL,
-                        f"train_images/{int(x['patient_id'])}/{int(x['series_id'])}.nii.gz",
-                    ),
-                    axis=1,
-                ).tolist()
-            )
-            train_seg_files = dl_manager.download(
-                train_series_meta_df.apply(
-                    lambda x: urllib.parse.urljoin(
-                        _URL, f"segmentations/{int(x['series_id'])}.nii.gz"
-                    ),
-                    axis=1,
-                ).tolist()
-            )
-            test_seg_files = dl_manager.download(
-                test_series_meta_df.apply(
-                    lambda x: urllib.parse.urljoin(
-                        _URL, f"segmentations/{int(x['series_id'])}.nii.gz"
-                    ),
-                    axis=1,
-                ).tolist()
-            )
-        else:
-            train_series_meta_df, test_series_meta_df = train_test_split(
-                self.series_meta_df, test_size=0.1, random_state=42, shuffle=True
+            series_meta_df["seg_cache_path"] = dl_manager.download(
+                series_meta_df["seg_download_url"].tolist()
             )
 
-            train_img_files = dl_manager.download(
-                train_series_meta_df.apply(
-                    lambda x: urllib.parse.urljoin(
-                        _URL,
-                        f"train_images/{int(x['patient_id'])}/{int(x['series_id'])}.nii.gz",
-                    ),
-                    axis=1,
-                ).tolist()
-            )
-            test_img_files = dl_manager.download(
-                test_series_meta_df.apply(
-                    lambda x: urllib.parse.urljoin(
-                        _URL,
-                        f"train_images/{int(x['patient_id'])}/{int(x['series_id'])}.nii.gz",
-                    ),
-                    axis=1,
-                ).tolist()
+        else:
+            series_meta_df["img_cache_path"] = dl_manager.download(
+                series_meta_df["img_download_url"].tolist()
             )
-            train_seg_files = None
-            test_seg_files = None
+            series_meta_df["seg_cache_path"] = None
 
-        dicom_tags_df = datasets.load_dataset("parquet", data_files=urllib.parse.urljoin(_URL, "train_dicom_tags.parquet"))[
-            "train"
-        ].to_pandas()[
+        dicom_tags_df = datasets.load_dataset(
+            "parquet", data_files=urllib.parse.urljoin(_URL, "train_dicom_tags.parquet")
+        )["train"].to_pandas()[
             [
                 "SeriesInstanceUID",
                 "PixelRepresentation",
@@ -256,55 +216,72 @@ class RSNA2023AbdominalTraumaDetectionSegmentation(datasets.GeneratorBasedBuilde
                 "BitsStored": "bits_stored",
             }
         )
-        self.series_meta_df = pd.merge(
-            left=self.series_meta_df, right=dicom_tags_df, how="inner", on="series_id"
+        series_meta_df = pd.merge(
+            left=series_meta_df, right=dicom_tags_df, how="inner", on="series_id"
+        )
+
+        self.labels_df = (
+            pd.read_csv(
+                dl_manager.download_and_extract(urllib.parse.urljoin(_URL, "train.csv"))
+            )
+            if self.config.name != "segmentation"
+            else None
+        )
+
+        train_series_meta_df, test_series_meta_df = train_test_split(
+            series_meta_df, test_size=0.1, random_state=42, shuffle=True
         )
-        
-        self.labels_df = pd.read_csv(
-            dl_manager.download_and_extract(urllib.parse.urljoin(_URL, "train.csv"))
-        ) if self.config.name != "segmentation" else None
 
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "series_ids": train_series_meta_df["series_id"].tolist(),
-                    "img_files": train_img_files,
-                    "seg_files": train_seg_files,
+                    "filepaths": train_series_meta_df[
+                        [
+                            "series_id",
+                            "patient_id",
+                            "img_cache_path",
+                            "seg_cache_path",
+                            "incomplete_organ",
+                            "aortic_hu",
+                            "pixel_representation",
+                            "bits_allocated",
+                            "bits_stored",
+                        ]
+                    ].to_dict("records"),
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "series_ids": test_series_meta_df["series_id"].tolist(),
-                    "img_files": test_img_files,
-                    "seg_files": test_seg_files,
+                    "filepaths": test_series_meta_df[
+                        [
+                            "series_id",
+                            "patient_id",
+                            "img_cache_path",
+                            "seg_cache_path",
+                            "incomplete_organ",
+                            "aortic_hu",
+                            "pixel_representation",
+                            "bits_allocated",
+                            "bits_stored",
+                        ]
+                    ].to_dict("records"),
                 },
             ),
         ]
 
     def _generate_examples(
         self,
-        series_ids,
-        img_files,
-        seg_files,
+        filepaths,
     ):
         if self.config.name == "segmentation":
-            for key, (series_id, img_path, seg_path) in enumerate(
-                zip(series_ids, img_files, seg_files)
-            ):
-                series_meta = (
-                    self.series_meta_df.loc[
-                        self.series_meta_df["series_id"] == series_id
-                    ]
-                    .iloc[0]
-                    .to_dict()
-                )
+            for key, series_meta in enumerate(filepaths):
                 yield key, {
-                    "img_path": img_path,
-                    "seg_path": seg_path,
+                    "img_path": series_meta["img_cache_path"],
+                    "seg_path": series_meta["seg_cache_path"],
                     "metadata": {
-                        "series_id": series_id,
+                        "series_id": series_meta["series_id"],
                         "patient_id": series_meta["patient_id"],
                         "incomplete_organ": series_meta["incomplete_organ"],
                         "aortic_hu": series_meta["aortic_hu"],
@@ -315,26 +292,18 @@ class RSNA2023AbdominalTraumaDetectionSegmentation(datasets.GeneratorBasedBuilde
                 }
 
         elif self.config.name == "classification-with-mask":
-            for key, (series_id, img_path, seg_path) in enumerate(
-                zip(series_ids, img_files, seg_files)
-            ):
-                series_meta = (
-                    self.series_meta_df.loc[
-                        self.series_meta_df["series_id"] == series_id
-                    ]
-                    .iloc[0]
-                    .to_dict()
-                )
-                patient_id = series_meta["patient_id"]
+            for key, series_meta in enumerate(filepaths):
                 label_data = (
-                    self.labels_df.loc[self.labels_df["patient_id"] == patient_id]
+                    self.labels_df.loc[
+                        self.labels_df["patient_id"] == series_meta["patient_id"]
+                    ]
                     .iloc[0]
                     .to_dict()
                 )
 
                 yield key, {
-                    "img_path": img_path,
-                    "seg_path": seg_path,
+                    "img_path": series_meta["img_cache_path"],
+                    "seg_path": series_meta["seg_cache_path"],
                     "bowel": np.argmax(
                         [label_data["bowel_healthy"], label_data["bowel_injury"]]
                     ),
@@ -367,7 +336,7 @@ class RSNA2023AbdominalTraumaDetectionSegmentation(datasets.GeneratorBasedBuilde
                     ),
                     "any_injury": label_data["any_injury"],
                     "metadata": {
-                        "series_id": series_id,
+                        "series_id": series_meta["series_id"],
                         "patient_id": series_meta["patient_id"],
                         "incomplete_organ": series_meta["incomplete_organ"],
                         "aortic_hu": series_meta["aortic_hu"],
@@ -377,23 +346,17 @@ class RSNA2023AbdominalTraumaDetectionSegmentation(datasets.GeneratorBasedBuilde
                     },
                 }
         else:
-            for key, (series_id, img_path) in enumerate(zip(series_ids, img_files)):
-                series_meta = (
-                    self.series_meta_df.loc[
-                        self.series_meta_df["series_id"] == series_id
-                    ]
-                    .iloc[0]
-                    .to_dict()
-                )
-                patient_id = series_meta["patient_id"]
+            for key, series_meta in enumerate(filepaths):
                 label_data = (
-                    self.labels_df.loc[self.labels_df["patient_id"] == patient_id]
+                    self.labels_df.loc[
+                        self.labels_df["patient_id"] == series_meta["patient_id"]
+                    ]
                     .iloc[0]
                     .to_dict()
                 )
 
                 yield key, {
-                    "img_path": img_path,
+                    "img_path": series_meta["img_cache_path"],
                     "bowel": np.argmax(
                         [label_data["bowel_healthy"], label_data["bowel_injury"]]
                     ),
@@ -426,7 +389,7 @@ class RSNA2023AbdominalTraumaDetectionSegmentation(datasets.GeneratorBasedBuilde
                     ),
                     "any_injury": label_data["any_injury"],
                     "metadata": {
-                        "series_id": series_id,
+                        "series_id": series_meta["series_id"],
                         "patient_id": series_meta["patient_id"],
                         "incomplete_organ": series_meta["incomplete_organ"],
                         "aortic_hu": series_meta["aortic_hu"],