intial json for preprocessor

processor_dfine.py

@@ -1,62 +0,0 @@
-from transformers import ProcessorMixin
-from PIL import Image
-import torchvision.transforms as T
-import torch
-import numpy as np
-
-class DFineProcessor(ProcessorMixin):
-    def __init__(self, size=640):
-        self.size = size
-
-    def resize_with_aspect_ratio(self, image):
-        original_width, original_height = image.size
-        ratio = min(self.size / original_width, self.size / original_height)
-        new_width = int(original_width * ratio)
-        new_height = int(original_height * ratio)
-        image = image.resize((new_width, new_height), Image.BILINEAR)
-
-        new_image = Image.new("RGB", (self.size, self.size))
-        pad_w = (self.size - new_width) // 2
-        pad_h = (self.size - new_height) // 2
-        new_image.paste(image, (pad_w, pad_h))
-
-        return new_image, ratio, pad_w, pad_h
-
-    def __call__(self, images):
-        if not isinstance(images, (list, tuple)):
-            images = [images]
-
-        tensors, orig_sizes, ratios, pad_ws, pad_hs = [], [], [], [], []
-
-        for image in images:
-            if isinstance(image, np.ndarray):
-                image = Image.fromarray(image)
-            elif not isinstance(image, Image.Image):
-                raise ValueError("Input must be PIL.Image, numpy.ndarray, or list of them.")
-
-            resized, ratio, pad_w, pad_h = self.resize_with_aspect_ratio(image)
-            tensor = T.ToTensor()(resized)
-
-            tensors.append(tensor)
-            orig_sizes.append([resized.size[1], resized.size[0]])
-            ratios.append(ratio)
-            pad_ws.append(pad_w)
-            pad_hs.append(pad_h)
-
-        batch_tensor = torch.stack(tensors)
-        return {
-            "images": batch_tensor,
-            "orig_target_sizes": torch.tensor(orig_sizes),
-            "ratio": torch.tensor(ratios),
-            "pad_w": torch.tensor(pad_ws),
-            "pad_h": torch.tensor(pad_hs),
-        }
-
-    def save_pretrained(self, save_directory):
-        # Optional: save size or metadata here if needed
-        pass
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
-        # Optionally load metadata like `size` from processor_config.json
-        return cls()