File size: 6,686 Bytes
c866901 | 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 | """Image processor class for Kimi-K3.
"""
import json
from typing import Any, Dict, Optional, Union
import numpy as np
import torch
from PIL import Image
from transformers.image_processing_utils import (BaseImageProcessor,
BatchFeature)
from transformers.utils import TensorType
from .media_utils import (MediaInput, TransparentBgConfig, _to_tensor,
ensure_media_type, image_to_np, navit_patchify,
navit_resize_image, normalize)
class KimiK3VisionProcessor(BaseImageProcessor):
model_type = "kimi_k3"
def __init__(
self,
media_proc_cfg: dict,
**kwargs,
):
super().__init__(**kwargs)
self.media_proc_cfg = media_proc_cfg
@property
def _transparent_bg_config(self) -> Optional[TransparentBgConfig]:
cfg = self.media_proc_cfg.get("transparent_bg_config")
if cfg is None:
return None
if isinstance(cfg, TransparentBgConfig):
return cfg
return TransparentBgConfig(**cfg)
@property
def _transparent_bg_fill_stage(self) -> str:
return self.media_proc_cfg.get("transparent_bg_fill_stage",
"before_resize")
def media_tokens_calculator(self, media: MediaInput):
media = ensure_media_type(
media,
transparent_bg_config=self._transparent_bg_config,
transparent_bg_fill_stage=self._transparent_bg_fill_stage,
)
ret = self.get_resize_config(media)
return ret['num_tokens']
@classmethod
def make_image_prompt(cls, width: int, height: int) -> str:
"""Build the K3 image placeholder with resolution info."""
return (f"<|media_begin|>image {width}x{height}"
f"<|media_content|><|media_pad|><|media_end|>")
def get_resize_config(self, media_input: MediaInput) -> dict:
if media_input['type'] == 'image':
w, h = media_input['image'].size
ret = navit_resize_image(
w, h, self.media_proc_cfg['patch_size'],
self.media_proc_cfg['merge_kernel_size'],
self.media_proc_cfg['in_patch_limit'],
self.media_proc_cfg['patch_limit_on_one_side'],
self.media_proc_cfg['fixed_output_tokens'])
return ret
else:
raise ValueError("Unsupported type: {}".format(
media_input['type']))
def resize_image(self, image: Image.Image, new_width: int, new_height: int,
pad_width: int, pad_height: int) -> np.ndarray:
image_np = image_to_np(
image,
(new_width, new_height),
"resize",
transparent_bg_config=self._transparent_bg_config,
transparent_bg_fill_stage=self._transparent_bg_fill_stage,
)
image_np = np.pad(
image_np,
((0, pad_height), (0, pad_width), (0, 0)),
mode="constant",
constant_values=0,
)
return image_np
def preprocess(
self,
medias: list[MediaInput],
return_tensors: Optional[Union[str, TensorType]] = None,
) -> BatchFeature:
"""
Preprocess a atom vision input (images) into model-ready tensors.
Args:
medias: List of MediaInput.
return_tensors: Desired output format ('pt', 'np', 'tf', or None).
Returns:
BatchFeature containing 'pixel_values' and 'grid_thws' tensors.
"""
if not isinstance(medias, list):
medias = [medias]
if medias:
pixel_values = []
for item in medias:
item = ensure_media_type(
item,
transparent_bg_config=self._transparent_bg_config,
transparent_bg_fill_stage=self._transparent_bg_fill_stage,
)
resize_config = self.get_resize_config(item)
new_width, new_height, pad_width, pad_height = resize_config[
'new_width'], resize_config['new_height'], resize_config[
'pad_width'], resize_config['pad_height']
if item['type'] == 'image':
image = item['image']
image_np = self.resize_image(image, new_width, new_height,
pad_width, pad_height)
pixel_values.append(np.expand_dims(image_np, axis=0))
else:
raise ValueError("Unsupported type: {}".format(
item['type']))
normalized_pixel_values = []
image_std_inv = 1.0 / np.array(self.media_proc_cfg['image_std'])
image_mean = np.array(self.media_proc_cfg['image_mean'])
for pixels in pixel_values:
pixels = normalize(pixels, image_mean, image_std_inv)
pixels_and_thw = navit_patchify(
pixels,
self.media_proc_cfg['patch_size'],
)
normalized_pixel_values.append(pixels_and_thw)
pixel_values = torch.cat([
_to_tensor(pixel_value['pixel_values'])
for pixel_value in normalized_pixel_values
])
grid_thws = torch.cat([
_to_tensor(pixel_value['grid_thw'],
dtype=torch.int64).unsqueeze(0)
for pixel_value in normalized_pixel_values
])
data = {
'pixel_values': pixel_values,
'grid_thws': grid_thws,
}
else:
data = {}
return BatchFeature(data=data, tensor_type=return_tensors)
def __repr__(self):
return f"KimiK3VisionProcessor(media_proc_cfg={self.media_proc_cfg})"
def to_dict(self) -> Dict[str, Any]:
output = super().to_dict()
output["media_proc_cfg"] = self.media_proc_cfg
if "media_processor" in output:
del output["media_processor"]
return output
@classmethod
def from_dict(cls, config_dict: Dict[str, Any], **kwargs):
config = config_dict.copy()
media_proc_cfg = config.pop("media_proc_cfg", {})
return cls(media_proc_cfg=media_proc_cfg, **config, **kwargs)
def to_json_string(self):
dictionary = self.to_dict()
for key, value in dictionary.items():
if hasattr(value, 'tolist'):
dictionary[key] = value.tolist()
return json.dumps(dictionary, indent=2, sort_keys=True) + "\n"
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