File size: 11,136 Bytes
4ea39ff |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 |
"""Image processor class for KimiVL."""
import math
import numpy as np
from PIL import Image
from typing import Optional, Union
import torch
import torchvision.transforms as T
from torchvision.transforms.functional import InterpolationMode
from transformers.image_utils import ImageInput, make_list_of_images, valid_images
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
from transformers.utils import TensorType
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
best_ratio_diff = float('inf')
best_ratio = (1, 1)
area = width * height
for ratio in target_ratios:
target_aspect_ratio = ratio[0] / ratio[1]
ratio_diff = abs(aspect_ratio - target_aspect_ratio)
if ratio_diff < best_ratio_diff:
best_ratio_diff = ratio_diff
best_ratio = ratio
elif ratio_diff == best_ratio_diff:
if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
best_ratio = ratio
return best_ratio
def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False):
orig_width, orig_height = image.size
aspect_ratio = orig_width / orig_height
# calculate the existing image aspect ratio
target_ratios = set(
(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
i * j <= max_num and i * j >= min_num)
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
# find the closest aspect ratio to the target
target_aspect_ratio = find_closest_aspect_ratio(
aspect_ratio, target_ratios, orig_width, orig_height, image_size)
# calculate the target width and height
target_width = image_size * target_aspect_ratio[0]
target_height = image_size * target_aspect_ratio[1]
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
# resize the image
resized_img = image.resize((target_width, target_height))
processed_images = []
for i in range(blocks):
box = (
(i % (target_width // image_size)) * image_size,
(i // (target_width // image_size)) * image_size,
((i % (target_width // image_size)) + 1) * image_size,
((i // (target_width // image_size)) + 1) * image_size
)
# split the image
split_img = resized_img.crop(box)
processed_images.append(split_img)
assert len(processed_images) == blocks
if use_thumbnail and len(processed_images) != 1:
thumbnail_img = image.resize((image_size, image_size))
processed_images.append(thumbnail_img)
return processed_images
def dynamic_preprocess_msac1(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False):
orig_width, orig_height = image.size
aspect_ratio = orig_width / orig_height
# calculate the existing image aspect ratio
target_ratios = set(
(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
i * j <= max_num and i * j >= min_num)
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
# find the closest aspect ratio to the target
target_aspect_ratio = find_closest_aspect_ratio(
aspect_ratio, target_ratios, orig_width, orig_height, image_size)
# calculate the target width and height
target_width = image_size * target_aspect_ratio[0]
target_height = image_size * target_aspect_ratio[1]
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
# resize the image
resized_img = image.resize((target_width, target_height))
processed_images = []
for i in range(blocks):
box = (
(i % (target_width // image_size)) * image_size,
(i // (target_width // image_size)) * image_size,
((i % (target_width // image_size)) + 1) * image_size,
((i // (target_width // image_size)) + 1) * image_size
)
# split the image
split_img = resized_img.crop(box)
processed_images.append(split_img)
assert len(processed_images) == blocks
if use_thumbnail and len(processed_images) != 1:
thumbnail_img = image.resize((image_size, image_size))
processed_images.append(thumbnail_img)
return processed_images, target_aspect_ratio
def dynamic_preprocess_msac2(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False, prior_aspect_ratio=None):
orig_width, orig_height = image.size
aspect_ratio = orig_width / orig_height
# calculate the existing image aspect ratio
target_ratios = set(
(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
i * j <= max_num and i * j >= min_num)
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
new_target_ratios = []
if prior_aspect_ratio is not None:
for i in target_ratios:
if prior_aspect_ratio[0]%i[0] != 0 or prior_aspect_ratio[1]%i[1] != 0:
new_target_ratios.append(i)
else:
continue
# find the closest aspect ratio to the target
target_aspect_ratio = find_closest_aspect_ratio(
aspect_ratio, new_target_ratios, orig_width, orig_height, image_size)
# calculate the target width and height
target_width = image_size * target_aspect_ratio[0]
target_height = image_size * target_aspect_ratio[1]
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
# resize the image
resized_img = image.resize((target_width, target_height))
processed_images = []
for i in range(blocks):
box = (
(i % (target_width // image_size)) * image_size,
(i // (target_width // image_size)) * image_size,
((i % (target_width // image_size)) + 1) * image_size,
((i // (target_width // image_size)) + 1) * image_size
)
# split the image
split_img = resized_img.crop(box)
processed_images.append(split_img)
assert len(processed_images) == blocks
if use_thumbnail and len(processed_images) != 1:
thumbnail_img = image.resize((image_size, image_size))
processed_images.append(thumbnail_img)
return processed_images
class SAILVLImageProcessor(BaseImageProcessor):
model_type = "sailvl"
def __init__(
self,
patch_size: int = 14,
image_mean: tuple[float, float, float] = IMAGENET_MEAN,
image_std: tuple[float, float, float] = IMAGENET_STD,
max_dynamic_patch: int = 10,
image_size: int = 448,
use_msac: bool = False,
**kwargs,
):
super().__init__(**kwargs)
self.patch_size = patch_size
self.image_mean = image_mean
self.image_std = image_std
self.max_dynamic_patch = max_dynamic_patch
self.image_size = image_size
self.use_msac = use_msac
def build_transform(self, input_size):
MEAN, STD = self.image_mean, self.image_std
transform = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=MEAN, std=STD)
])
return transform
def load_image(self, image, input_size=448, max_num=6, upscale=False):
# image = Image.open(image_file).convert('RGB')
if upscale:
image = image.resize((image.width * 2, image.height * 2), Image.BILINEAR)
transform = self.build_transform(input_size=input_size)
images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
pixel_values = [transform(image) for image in images]
pixel_values = torch.stack(pixel_values)
return pixel_values
def load_image_msac(self, image, input_size=448, max_num=6, upscale=False):
# image = Image.open(image_file).convert('RGB')
if upscale:
image = image.resize((image.width * 2, image.height * 2), Image.BILINEAR)
transform = self.build_transform(input_size=input_size)
images,target_aspect_ratio = dynamic_preprocess_msac1(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
images = images[:-1] + dynamic_preprocess_msac2(image,max_num=max_num,image_size=input_size,use_thumbnail=False,prior_aspect_ratio=target_aspect_ratio) + images[-1:]
pixel_values = [transform(image) for image in images]
pixel_values = torch.stack(pixel_values)
return pixel_values
def preprocess(
self,
images: ImageInput,
return_tensors: Optional[Union[str, TensorType]] = None,
) -> BatchFeature:
images = make_list_of_images(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
# print('图片个数：',len(images))
image_num = len(images)
if image_num > 1:
# image_path = [x['value'] for x in message if x['type'] == 'image']
num_patches_list = []
pixel_values_list = []
for image_idx, image_pil in enumerate(images):
upscale_flag = False
curr_pixel_values = self.load_image(
image_pil, max_num=self.max_dynamic_patch, upscale=upscale_flag, input_size=self.image_size).cuda().to(torch.bfloat16)
num_patches_list.append(curr_pixel_values.size(0))
pixel_values_list.append(curr_pixel_values)
pixel_values = torch.cat(pixel_values_list, dim=0)
elif image_num == 1:
# image_path = [x['value'] for x in message if x['type'] == 'image'][0]
image_pil = images[0]
upscale_flag = False
if self.use_msac:
pixel_values = self.load_image_msac(
image_pil, max_num=self.max_dynamic_patch, upscale=upscale_flag, input_size=self.image_size).cuda().to(torch.bfloat16)
else:
pixel_values = self.load_image(
image_pil, max_num=self.max_dynamic_patch, upscale=upscale_flag, input_size=self.image_size).cuda().to(torch.bfloat16)
num_patches_list = [pixel_values.size(0)]
else:
pixel_values = None
num_patches_list = None
# pixel_values, image_grid_hws = [], []
# for image in images:
# patches, image_grid_hw = self._preprocess(image)
# pixel_values.append(patches)
# image_grid_hws.append(image_grid_hw)
# pixel_values = torch.concat(pixel_values, dim=0)
# image_grid_hws = np.array(image_grid_hws)
data = {"pixel_values": pixel_values, "num_patches_list": num_patches_list}
return BatchFeature(data=data, tensor_type=return_tensors) |