Supply dataset CSV file instead of dir with hardcoded CSV filename

1 files changed, 14 insertions, 10 deletions

diff --git a/data/dreambooth/csv.py b/data/dreambooth/csv.py
index 04df4c6..e70c068 100644
--- a/data/dreambooth/csv.py
+++ b/data/dreambooth/csv.py
@@ -11,7 +11,7 @@ from torchvision import transforms
 class CSVDataModule(pl.LightningDataModule):
     def __init__(self,
                  batch_size,
-                 data_root,
+                 data_file,
                  tokenizer,
                  instance_prompt,
                  class_data_root=None,
@@ -24,7 +24,12 @@ class CSVDataModule(pl.LightningDataModule):
                  collate_fn=None):
         super().__init__()
 
-        self.data_root = data_root
+        self.data_file = Path(data_file)
+
+        if not self.data_file.is_file():
+            raise ValueError("data_file must be a file")
+
+        self.data_root = self.data_file.parent
         self.tokenizer = tokenizer
         self.instance_prompt = instance_prompt
         self.class_data_root = class_data_root
@@ -38,7 +43,7 @@ class CSVDataModule(pl.LightningDataModule):
         self.batch_size = batch_size
 
     def prepare_data(self):
-        metadata = pd.read_csv(f'{self.data_root}/list.csv')
+        metadata = pd.read_csv(self.data_file)
         image_paths = [os.path.join(self.data_root, f_path) for f_path in metadata['image'].values]
         captions = [caption for caption in metadata['caption'].values]
         skips = [skip for skip in metadata['skip'].values]
@@ -50,14 +55,13 @@ class CSVDataModule(pl.LightningDataModule):
         self.data_train, self.data_val = random_split(self.data_full, [train_set_size, valid_set_size])
 
         train_dataset = CSVDataset(self.data_train, self.tokenizer, instance_prompt=self.instance_prompt,
-                                   class_data_root=self.class_data_root,
-                                   class_prompt=self.class_prompt, size=self.size, repeats=self.repeats,
-                                   interpolation=self.interpolation, identifier=self.identifier,
-                                   center_crop=self.center_crop)
+                                   class_data_root=self.class_data_root, class_prompt=self.class_prompt,
+                                   size=self.size, interpolation=self.interpolation, identifier=self.identifier,
+                                   center_crop=self.center_crop, repeats=self.repeats)
         val_dataset = CSVDataset(self.data_val, self.tokenizer, instance_prompt=self.instance_prompt,
-                                 class_data_root=self.class_data_root,
-                                 class_prompt=self.class_prompt, size=self.size, interpolation=self.interpolation,
-                                 identifier=self.identifier, center_crop=self.center_crop)
+                                 class_data_root=self.class_data_root, class_prompt=self.class_prompt,
+                                 size=self.size, interpolation=self.interpolation, identifier=self.identifier,
+                                 center_crop=self.center_crop)
         self.train_dataloader_ = DataLoader(train_dataset, batch_size=self.batch_size,
                                             shuffle=True, collate_fn=self.collate_fn)
         self.val_dataloader_ = DataLoader(val_dataset, batch_size=self.batch_size, collate_fn=self.collate_fn)