image_processing_locateanything.py
| 1 | # Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. |
| 2 | # |
| 3 | # NVIDIA CORPORATION and its licensors retain all intellectual property |
| 4 | # and proprietary rights in and to this software, related documentation |
| 5 | # and any modifications thereto. Any use, reproduction, disclosure or |
| 6 | # distribution of this software and related documentation without an express |
| 7 | # license agreement from NVIDIA CORPORATION is strictly prohibited. |
| 8 | |
| 9 | """Image processor class for KimiVL.""" |
| 10 | |
| 11 | import math |
| 12 | import numpy as np |
| 13 | from PIL import Image |
| 14 | from typing import Optional, Union |
| 15 | |
| 16 | import torch |
| 17 | from torchvision.transforms import functional as TF |
| 18 | from transformers.image_utils import ImageInput, make_list_of_images, valid_images |
| 19 | from transformers.image_processing_utils import BaseImageProcessor, BatchFeature |
| 20 | from transformers.utils import TensorType |
| 21 | from transformers import AutoImageProcessor |
| 22 | |
| 23 | MEAN = (0.5, 0.5, 0.5) |
| 24 | STD = (0.5, 0.5, 0.5) |
| 25 | |
| 26 | |
| 27 | class LocateAnythingImageProcessor(BaseImageProcessor): |
| 28 | model_type = "locateanything" |
| 29 | |
| 30 | def __init__( |
| 31 | self, |
| 32 | patch_size: int = 14, |
| 33 | image_mean: tuple[float, float, float] = MEAN, |
| 34 | image_std: tuple[float, float, float] = STD, |
| 35 | in_token_limit: int = 4096, |
| 36 | merge_kernel_size: list[int, int] = [2, 2], |
| 37 | **kwargs, |
| 38 | ): |
| 39 | super().__init__(**kwargs) |
| 40 | self.in_token_limit = in_token_limit |
| 41 | self.patch_size = patch_size |
| 42 | self.image_mean = image_mean |
| 43 | self.image_std = image_std |
| 44 | self.merge_kernel_size = merge_kernel_size |
| 45 | |
| 46 | def rescale( |
| 47 | self, image: Image.Image, merge_kernel_size: list[int, int] = [2, 2] |
| 48 | ) -> Image.Image: |
| 49 | w, h = image.size |
| 50 | patch_size = self.patch_size |
| 51 | |
| 52 | if (w // patch_size) * (h // patch_size) > self.in_token_limit: |
| 53 | scale = math.sqrt(self.in_token_limit / ((w // patch_size) * (h // patch_size))) |
| 54 | new_w, new_h = int(w * scale), int(h * scale) |
| 55 | image = image.resize((new_w, new_h), Image.Resampling.BICUBIC) |
| 56 | |
| 57 | new_w, new_h = image.size |
| 58 | pad_size_h = merge_kernel_size[0] * patch_size |
| 59 | pad_size_w = merge_kernel_size[1] * patch_size |
| 60 | |
| 61 | target_w = math.ceil(new_w / pad_size_w) * pad_size_w |
| 62 | target_h = math.ceil(new_h / pad_size_h) * pad_size_h |
| 63 | |
| 64 | if target_w != new_w or target_h != new_h: |
| 65 | image = image.resize((target_w, target_h), Image.Resampling.BICUBIC) |
| 66 | |
| 67 | w, h = image.size |
| 68 | if w // patch_size >= 512 or h // patch_size >= 512: |
| 69 | raise ValueError("Exceed pos emb") |
| 70 | |
| 71 | return image |
| 72 | |
| 73 | def to_tensor(self, image: Image.Image) -> torch.Tensor: |
| 74 | return TF.to_tensor(image.convert("RGB")) |
| 75 | |
| 76 | def normalize(self, image: torch.Tensor) -> torch.Tensor: |
| 77 | return TF.normalize(image, self.image_mean, self.image_std) |
| 78 | |
| 79 | def patchify(self, image: torch.Tensor) -> tuple[torch.Tensor, list[int, int]]: |
| 80 | patch_size = self.patch_size |
| 81 | C, H, W = image.shape |
| 82 | patches = image.reshape(C, H // patch_size, patch_size, W // patch_size, patch_size) |
| 83 | patches = patches.permute(1, 3, 0, 2, 4) |
| 84 | patches = patches.contiguous().view(-1, C, patch_size, patch_size) |
| 85 | grid_hw = (H // patch_size, W // patch_size) |
| 86 | return patches, grid_hw |
| 87 | |
| 88 | def _preprocess(self, image: ImageInput) -> tuple[torch.Tensor, list[int, int]]: |
| 89 | """ |
| 90 | Preprocess image and patchify it. |
| 91 | Args: |
| 92 | image (`ImageInput`): |
| 93 | Image to preprocess. Expects pixel values ranging from 0 to 255. If pixel values range from 0 to 1, set `do_rescale=False`. |
| 94 | Returns: |
| 95 | patches: torch.Tensor |
| 96 | grid_hw: list[int, int] |
| 97 | """ |
| 98 | image = self.rescale(image, self.merge_kernel_size) |
| 99 | image = self.to_tensor(image) |
| 100 | image = self.normalize(image) |
| 101 | patches, grid_hw = self.patchify(image) |
| 102 | return patches, grid_hw |
| 103 | |
| 104 | def preprocess( |
| 105 | self, |
| 106 | images: ImageInput, |
| 107 | return_tensors: Optional[Union[str, TensorType]] = None, |
| 108 | ) -> BatchFeature: |
| 109 | images = make_list_of_images(images) |
| 110 | |
| 111 | if not valid_images(images): |
| 112 | raise ValueError( |
| 113 | "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " |
| 114 | "torch.Tensor, tf.Tensor or jax.ndarray." |
| 115 | ) |
| 116 | |
| 117 | pixel_values, image_grid_hws = [], [] |
| 118 | for image in images: |
| 119 | patches, image_grid_hw = self._preprocess(image) |
| 120 | pixel_values.append(patches) |
| 121 | image_grid_hws.append(image_grid_hw) |
| 122 | pixel_values = torch.concat(pixel_values, dim=0) |
| 123 | image_grid_hws = np.array(image_grid_hws) |
| 124 | data = {"pixel_values": pixel_values, "image_grid_hws": image_grid_hws} |
| 125 | |
| 126 | return BatchFeature(data=data, tensor_type=return_tensors) |
| 127 | |
| 128 | AutoImageProcessor.register("LocateAnythingImageProcessor", LocateAnythingImageProcessor) |