Assign unused images in validation dataset to train dataset

1 files changed, 8 insertions, 3 deletions

diff --git a/data/textual_inversion/csv.py b/data/textual_inversion/csv.py
index f306c7a..e082511 100644
--- a/data/textual_inversion/csv.py
+++ b/data/textual_inversion/csv.py
@@ -18,7 +18,8 @@ class CSVDataModule(pl.LightningDataModule):
                  repeats=100,
                  interpolation="bicubic",
                  placeholder_token="*",
-                 center_crop=False):
+                 center_crop=False,
+                 valid_set_size=None):
         super().__init__()
 
         self.data_file = Path(data_file)
@@ -33,6 +34,7 @@ class CSVDataModule(pl.LightningDataModule):
         self.placeholder_token = placeholder_token
         self.center_crop = center_crop
         self.interpolation = interpolation
+        self.valid_set_size = valid_set_size
 
         self.batch_size = batch_size
 
@@ -44,8 +46,11 @@ class CSVDataModule(pl.LightningDataModule):
         self.data_full = [(img, cap) for img, cap, skip in zip(image_paths, captions, skips) if skip != "x"]
 
     def setup(self, stage=None):
-        train_set_size = int(len(self.data_full) * 0.8)
-        valid_set_size = len(self.data_full) - train_set_size
+        valid_set_size = int(len(self.data_full) * 0.2)
+        if self.valid_set_size:
+            valid_set_size = math.min(valid_set_size, self.valid_set_size)
+        train_set_size = len(self.data_full) - valid_set_size
+
         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, size=self.size, repeats=self.repeats, interpolation=self.interpolation,