Code deduplication

1 files changed, 42 insertions, 5 deletions

diff --git a/data/csv.py b/data/csv.py
index 9ad7dd6..f5fc8e6 100644
--- a/data/csv.py
+++ b/data/csv.py
@@ -1,7 +1,7 @@
 import math
 import torch
 import json
-import copy
+from functools import partial
 from pathlib import Path
 from typing import NamedTuple, Optional, Union, Callable
 
@@ -99,6 +99,41 @@ def generate_buckets(
     return buckets, bucket_items, bucket_assignments
 
 
+def collate_fn(
+    num_class_images: int,
+    weight_dtype: torch.dtype,
+    prompt_processor: PromptProcessor,
+    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]
+
+    # concat class and instance examples for prior preservation
+    if num_class_images != 0 and "class_prompt_ids" in examples[0]:
+        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)
+
+    prompts = prompt_processor.unify_input_ids(prompt_ids)
+    nprompts = prompt_processor.unify_input_ids(nprompt_ids)
+    inputs = prompt_processor.unify_input_ids(input_ids)
+
+    batch = {
+        "prompt_ids": prompts.input_ids,
+        "nprompt_ids": nprompts.input_ids,
+        "input_ids": inputs.input_ids,
+        "pixel_values": pixel_values,
+        "attention_mask": inputs.attention_mask,
+    }
+
+    return batch
+
+
 class VlpnDataItem(NamedTuple):
     instance_image_path: Path
     class_image_path: Path
@@ -129,7 +164,7 @@ class VlpnDataModule():
         valid_set_repeat: int = 1,
         seed: Optional[int] = None,
         filter: Optional[Callable[[VlpnDataItem], bool]] = None,
-        collate_fn=None,
+        dtype: torch.dtype = torch.float32,
         num_workers: int = 0
     ):
         super().__init__()
@@ -158,9 +193,9 @@ class VlpnDataModule():
         self.valid_set_repeat = valid_set_repeat
         self.seed = seed
         self.filter = filter
-        self.collate_fn = collate_fn
         self.num_workers = num_workers
         self.batch_size = batch_size
+        self.dtype = dtype
 
     def prepare_items(self, template, expansions, data) -> list[VlpnDataItem]:
         image = template["image"] if "image" in template else "{}"
@@ -254,14 +289,16 @@ class VlpnDataModule():
             size=self.size, interpolation=self.interpolation,
         )
 
+        collate_fn_ = partial(collate_fn, self.num_class_images, self.dtype, self.prompt_processor)
+
         self.train_dataloader = DataLoader(
             train_dataset,
-            batch_size=None, pin_memory=True, collate_fn=self.collate_fn, num_workers=self.num_workers
+            batch_size=None, pin_memory=True, collate_fn=collate_fn_, num_workers=self.num_workers
         )
 
         self.val_dataloader = DataLoader(
             val_dataset,
-            batch_size=None, pin_memory=True, collate_fn=self.collate_fn, num_workers=self.num_workers
+            batch_size=None, pin_memory=True, collate_fn=collate_fn_, num_workers=self.num_workers
         )