Randomize dataset across cycles

1 files changed, 5 insertions, 9 deletions

diff --git a/data/csv.py b/data/csv.py
index 818fcd9..3af9925 100644
--- a/data/csv.py
+++ b/data/csv.py
@@ -192,7 +192,7 @@ class VlpnDataModule():
         valid_set_size: Optional[int] = None,
         train_set_pad: Optional[int] = None,
         valid_set_pad: Optional[int] = None,
-        seed: Optional[int] = None,
+        generator: Optional[torch.Generator] = None,
         filter: Optional[Callable[[VlpnDataItem], bool]] = None,
         dtype: torch.dtype = torch.float32,
     ):
@@ -224,10 +224,10 @@ class VlpnDataModule():
         self.valid_set_size = valid_set_size
         self.train_set_pad = train_set_pad if train_set_pad is not None else batch_size
         self.valid_set_pad = valid_set_pad if valid_set_pad is not None else batch_size
-        self.seed = seed
         self.filter = filter
         self.batch_size = batch_size
         self.dtype = dtype
+        self.generator = generator
 
     def prepare_items(self, template, expansions, data) -> list[VlpnDataItem]:
         tpl_image = template["image"] if "image" in template else "{}"
@@ -304,16 +304,12 @@ class VlpnDataModule():
         train_set_size = max(num_images - valid_set_size, 1)
         valid_set_size = num_images - train_set_size
 
-        generator = torch.Generator(device="cpu")
-        if self.seed is not None:
-            generator = generator.manual_seed(self.seed)
-
         collate_fn_ = partial(collate_fn, self.dtype, self.tokenizer, self.with_guidance, self.num_class_images != 0)
 
         if valid_set_size == 0:
             data_train, data_val = items, items
         else:
-            data_train, data_val = random_split(items, [train_set_size, valid_set_size], generator=generator)
+            data_train, data_val = random_split(items, [train_set_size, valid_set_size], generator=self.generator)
 
         data_train = self.pad_items(data_train, self.num_class_images)
 
@@ -324,7 +320,7 @@ class VlpnDataModule():
             data_train, self.tokenizer,
             num_buckets=self.num_buckets, progressive_buckets=self.progressive_buckets,
             bucket_step_size=self.bucket_step_size, bucket_max_pixels=self.bucket_max_pixels,
-            batch_size=self.batch_size, fill_batch=True, generator=generator,
+            batch_size=self.batch_size, fill_batch=True, generator=self.generator,
             size=self.size, interpolation=self.interpolation, color_jitter=self.color_jitter,
             num_class_images=self.num_class_images, dropout=self.dropout, shuffle=self.shuffle,
         )
@@ -344,7 +340,7 @@ class VlpnDataModule():
                 data_val, self.tokenizer,
                 num_buckets=self.num_buckets, progressive_buckets=True,
                 bucket_step_size=self.bucket_step_size, bucket_max_pixels=self.bucket_max_pixels,
-                batch_size=self.batch_size, generator=generator,
+                batch_size=self.batch_size, generator=self.generator,
                 size=self.size, interpolation=self.interpolation, color_jitter=self.color_jitter,
             )