Update

1 files changed, 4 insertions, 7 deletions

diff --git a/data/csv.py b/data/csv.py
index b058a3e..5de3ac7 100644
--- a/data/csv.py
+++ b/data/csv.py
@@ -100,28 +100,25 @@ def generate_buckets(
     return buckets, bucket_items, bucket_assignments
 
 
-def collate_fn(weight_dtype: torch.dtype, tokenizer: CLIPTokenizer, examples):
-    with_prior = all("class_prompt_ids" in example for example in examples)
-
+def collate_fn(dtype: torch.dtype, tokenizer: CLIPTokenizer, with_prior_preservation: bool, examples):
     prompt_ids = [example["prompt_ids"] for example in examples]
     nprompt_ids = [example["nprompt_ids"] for example in examples]
 
     input_ids = [example["instance_prompt_ids"] for example in examples]
     pixel_values = [example["instance_images"] for example in examples]
 
-    if with_prior:
+    if with_prior_preservation:
         input_ids += [example["class_prompt_ids"] for example in examples]
         pixel_values += [example["class_images"] for example in examples]
 
     pixel_values = torch.stack(pixel_values)
-    pixel_values = pixel_values.to(dtype=weight_dtype, memory_format=torch.contiguous_format)
+    pixel_values = pixel_values.to(dtype=dtype, memory_format=torch.contiguous_format)
 
     prompts = unify_input_ids(tokenizer, prompt_ids)
     nprompts = unify_input_ids(tokenizer, nprompt_ids)
     inputs = unify_input_ids(tokenizer, input_ids)
 
     batch = {
-        "with_prior": torch.tensor([with_prior] * len(examples)),
         "prompt_ids": prompts.input_ids,
         "nprompt_ids": nprompts.input_ids,
         "input_ids": inputs.input_ids,
@@ -285,7 +282,7 @@ class VlpnDataModule():
             size=self.size, interpolation=self.interpolation,
         )
 
-        collate_fn_ = partial(collate_fn, self.dtype, self.tokenizer)
+        collate_fn_ = partial(collate_fn, self.dtype, self.tokenizer, self.num_class_images != 0)
 
         self.train_dataloader = DataLoader(
             train_dataset,