fix bugs

modelscope/trainers/multi_modal/custom_diffusion/custom_diffusion_trainer.py

@@ -68,11 +68,6 @@ class CustomCheckpointProcessor(CheckpointProcessor):
 
 
 class CustomDiffusionDataset(Dataset):
-    """
-    A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
-    It pre-processes the images and the tokenizes prompts.
-    """
-
     def __init__(
         self,
         concepts_list,
@@ -85,6 +80,20 @@ class CustomDiffusionDataset(Dataset):
         hflip=False,
         aug=True,
     ):
+        """A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
+        It pre-processes the images and the tokenizes prompts.
+
+        Args:
+            concepts_list: contain multiple concepts, instance_prompt, class_prompt, etc.
+            tokenizer: pretrained tokenizer.
+            size: the size of images.
+            mask_size: the mask size of images.
+            center_crop: execute center crop or not.
+            with_prior_preservation: flag to add prior preservation loss.
+            hflip: whether to flip horizontally.
+            aug: perform data augmentation.
+
+        """
         self.size = size
         self.mask_size = mask_size
         self.center_crop = center_crop
@@ -219,84 +228,6 @@ class CustomDiffusionDataset(Dataset):
         return example
 
 
-class ClassDataset(Dataset):
-
-    def __init__(
-        self,
-        tokenizer,
-        class_data_root=None,
-        class_prompt=None,
-        class_num_images=None,
-        size=512,
-        center_crop=False,
-    ):
-        """A dataset to prepare  class images with the prompts for fine-tuning the model.
-            It pre-processes the images and the tokenizes prompts.
-
-        Args:
-            tokenizer: The tokenizer to use for tokenization.
-            class_data_root: The saved class data path.
-            class_prompt: The prompt to use for class images.
-            class_num_images: The number of class images to use.
-            size: The size to resize the images.
-            center_crop: Whether to do center crop or random crop.
-
-        """
-        self.size = size
-        self.center_crop = center_crop
-        self.tokenizer = tokenizer
-
-        if class_data_root is not None:
-            self.class_data_root = Path(class_data_root)
-            self.class_data_root.mkdir(parents=True, exist_ok=True)
-            self.class_images_path = list(self.class_data_root.iterdir())
-            if class_num_images is not None:
-                self.num_class_images = min(
-                    len(self.class_images_path), class_num_images)
-            else:
-                self.num_class_images = len(self.class_images_path)
-            self.class_prompt = class_prompt
-        else:
-            raise ValueError(
-                f"Class {self.class_data_root} class data root doesn't exists."
-            )
-
-        self.image_transforms = transforms.Compose([
-            transforms.Resize(
-                size, interpolation=transforms.InterpolationMode.BILINEAR),
-            transforms.CenterCrop(size)
-            if center_crop else transforms.RandomCrop(size),
-            transforms.ToTensor(),
-            transforms.Normalize([0.5], [0.5]),
-        ])
-
-    def __len__(self):
-        return self.num_class_images
-
-    def __getitem__(self, index):
-        example = {}
-
-        if self.class_data_root:
-            class_image = Image.open(
-                self.class_images_path[index % self.num_class_images])
-            class_image = exif_transpose(class_image)
-
-            if not class_image.mode == 'RGB':
-                class_image = class_image.convert('RGB')
-            example['pixel_values'] = self.image_transforms(class_image)
-
-            class_text_inputs = self.tokenizer(
-                self.class_prompt,
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                padding='max_length',
-                return_tensors='pt')
-            input_ids = torch.squeeze(class_text_inputs.input_ids)
-            example['input_ids'] = input_ids
-
-        return example
-
-
 class PromptDataset(Dataset):
 
     def __init__(self, prompt, num_samples):