Update train and utils submodules

src/phenocam_snow/train.py

@@ -47,7 +47,6 @@ def main():
     parser.add_argument("--classes", nargs="+", help="The image classes to use.")
     args = parser.parse_args()
 
-    label_method = "via subdir"  # can't do "in notebook" from a script
     if args.new and args.existing:
         print("Cannot specify both --new and --existing")
     elif args.new:
@@ -56,7 +55,6 @@ def main():
             args.learning_rate,
             args.weight_decay,
             args.site_name,
-            label_method,
             args.n_train,
             args.n_test,
             args.classes,
@@ -82,7 +80,6 @@ def train_model_with_new_data(
     learning_rate: float,
     weight_decay: float,
     site_name: str,
-    label_method: str,
     n_train: int,
     n_test: int,
     classes: list[str],
@@ -97,8 +94,6 @@ def train_model_with_new_data(
     :type weight_decay: float
     :param site_name: The name of the PhenoCam site you want.
     :type site_name: str
-    :param label_method: How you wish to label images ("in notebook" or "via subdir").
-    :type label_method: str
     :param n_train: The number of training images to use.
     :type n_train: int
     :param n_test: The number of testing images to use.
@@ -109,10 +104,6 @@ def train_model_with_new_data(
     :return: The best model obtained during training.
     :rtype: PhenoCamResNet
     """
-    valid_label_methods = ["in notebook", "via subdir"]
-    if label_method not in valid_label_methods:
-        raise ValueError(f"{label_method} is not a valid label method")
-
     train_dir = f"{site_name}_train"
     test_dir = f"{site_name}_test"
     train_labels = f"{train_dir}/labels.csv"
@@ -121,20 +112,29 @@ def train_model_with_new_data(
     data_module = PhenoCamDataModule(
         site_name, train_dir, train_labels, test_dir, test_labels
     )
-    base_download_args = {"site_name": site_name}
-    base_label_args = {
-        "site_name": site_name,
-        "categories": classes,
-        "method": label_method,
-    }
     data_module.prepare_data(
-        train_download_args=base_download_args
-        + {"save_to": train_dir, "n_photos": n_train},
-        train_label_args=base_label_args
-        + {"img_dir": train_dir, "save_to": train_labels},
-        test_download_args=base_download_args
-        + {"save_to": test_dir, "n_photos": n_test},
-        test_label_args=base_label_args + {"img_dir": test_dir, "save_to": test_labels},
+        train_download_args={
+            "site_name": site_name,
+            "save_to": train_dir,
+            "n_photos": n_train,
+        },
+        train_label_args={
+            "site_name": site_name,
+            "categories": classes,
+            "img_dir": train_dir,
+            "save_to": train_labels,
+        },
+        test_download_args={
+            "site_name": site_name,
+            "save_to": test_dir,
+            "n_photos": n_test,
+        },
+        test_label_args={
+            "site_name": site_name,
+            "categories": classes,
+            "img_dir": test_dir,
+            "save_to": test_labels,
+        },
     )
 
     return _fit_and_eval_model(

src/phenocam_snow/utils.py

@@ -293,7 +293,8 @@ def label_images(
     """
     if type(img_dir) is not Path:
         img_dir = Path(img_dir)
-    assert img_dir.is_dir()
+    if not img_dir.is_dir():
+        raise ValueError(f"img_dir {img_dir} is not a directory")
 
     dircats = []
     for cat in categories:
@@ -307,24 +308,24 @@ def label_images(
             "Move images into the appropriate sub-directory then press any key to continue."
         )
 
-    filenames = []
+    data = []
     for dircat in dircats:
-        filenames.extend([os.path.basename(fpath) for fpath in dircat.glob("*.jpg")])
-    df = pd.DataFrame(
-        zip(filenames, categories), columns=["filename", "label"]
-    ).explode("filename")
-    with open(save_to, "w+") as f:
+        for fpath in dircat.glob("*.jpg"):
+            data.append((os.path.basename(fpath), os.path.basename(dircat)))
+    df = pd.DataFrame(data, columns=["filename", "label"])
+    with open(img_dir / save_to, "w+") as f:
         f.write(f"# Site: {site_name}\n")
         f.write("# Categories:\n")
         for i, cat in enumerate(categories):
             f.write(f"# {i}. {cat}\n")
-    df.to_csv(save_to, mode="a", index=False)
+    df.to_csv(img_dir / save_to, mode="a", index=False)
 
     for item in img_dir.glob("*"):
         if item.is_dir():
             for subitem in sorted(item.glob("*")):
                 new_path = Path(subitem.resolve().parent.parent).joinpath(subitem.name)
                 subitem.rename(new_path)
+            item.rmdir()
 
 
 def read_labels(labels_file: str | Path) -> pd.DataFrame: