class DeepseekOCRProcessor(ProcessorMixin):
tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
attributes = ["tokenizer"]
def __init__(
self,
tokenizer: LlamaTokenizerFast,
patch_size: int = 16,
downsample_ratio: int = 4,
image_mean: tuple[float, float, float] = (0.5, 0.5, 0.5),
image_std: tuple[float, float, float] = (0.5, 0.5, 0.5),
normalize: bool = True,
image_token: str = "<image>",
pad_token: str = "<｜▁pad▁｜>",
add_special_token: bool = False,
sft_format: str = "deepseek",
mask_prompt: bool = True,
ignore_id: int = -100,
image_size: int = IMAGE_SIZE,
base_size: int = BASE_SIZE,
strategy: Literal["v1", "v2"] = "v1",
**kwargs,
):
self.image_size = image_size
self.base_size = base_size
# image token calculation strategy for
# Deepseek-OCR and Deepseek-OCR-2
self.strategy = strategy
assert strategy in ["v1", "v2"], "Only 'v1' and 'v2' strategies are supported."
self.patch_size = 16
self.image_mean = image_mean
self.image_std = image_std
self.normalize = normalize
self.downsample_ratio = 4
self.image_transform = ImageTransform(
mean=image_mean, std=image_std, normalize=normalize
)
self.tokenizer = tokenizer
self.tokenizer.padding_side = "left" # must set this，padding side with make a difference in batch inference # noqa: E501
# add the pad_token as special token to use 'tokenizer.pad_token'
# and 'tokenizer.pad_token_id'
if self.tokenizer.pad_token is None:
self.tokenizer.add_special_tokens({"pad_token": pad_token})
# add image token
self.image_token_id = self.tokenizer.vocab.get(image_token)
self.image_token = image_token
self.pad_token = pad_token
self.add_special_token = add_special_token
self.sft_format = sft_format
self.mask_prompt = mask_prompt
self.ignore_id = ignore_id
super().__init__(
tokenizer,
**kwargs,
)
@property
def bos_id(self):
return self.tokenizer.bos_token_id
@property
def eos_id(self):
return self.tokenizer.eos_token_id
@property
def pad_id(self):
return self.tokenizer.pad_token_id
def encode(self, text: str, bos: bool = True, eos: bool = False):
t = self.tokenizer.encode(text, add_special_tokens=False)
if bos:
t = [self.bos_id] + t
if eos:
t = t + [self.eos_id]
return t
def decode(self, t: list[int], **kwargs) -> str:
return self.tokenizer.decode(t, **kwargs)
def process_one(
self,
prompt: str,
images: list[Image.Image],
crop_mode: bool = CROP_MODE,
):
"""
Args:
prompt (str): the formatted prompt;
images (List[ImageType]): the list of images;
crop_mode (bool): if True, then crop the image;
Returns:
outputs (BaseProcessorOutput): the output of the processor,
- input_ids (torch.LongTensor): [N + image tokens]
- target_ids (torch.LongTensor): [N + image tokens]
- pixel_values (torch.FloatTensor): [n_patches, 3, H, W]
- image_id (int): the id of the image token
- num_image_tokens (List[int]): the number of image tokens
"""
assert prompt is not None and images is not None, (
"prompt and images must be used at the same time."
)
sft_format = prompt
(
input_ids,
pixel_values,
images_crop,
images_seq_mask,
images_spatial_crop,
num_image_tokens,
_,
) = self.tokenize_with_images(
conversation=sft_format,
images=images,
bos=True,
eos=True,
cropping=crop_mode,
)
prepare = BatchFeature(
data=dict(
input_ids=input_ids,
pixel_values=pixel_values,
images_crop=images_crop,
images_seq_mask=images_seq_mask,
images_spatial_crop=images_spatial_crop,
num_image_tokens=num_image_tokens,
),
tensor_type="pt",
)
return prepare
def __call__(
self,
*,
prompt: str,
images: list[Image.Image],
crop_mode: bool = CROP_MODE,
**kwargs,
):
prepare = self.process_one(
prompt=prompt,
images=images,
crop_mode=crop_mode,
)
return prepare
def tokenize_with_images(
self,
conversation: str,
images: list[Image.Image],
bos: bool = True,
eos: bool = True,
cropping: bool = True,
):
"""Tokenize text with <image> tags."""
assert conversation.count(self.image_token) == len(images)
text_splits = conversation.split(self.image_token)
images_list, images_crop_list, images_seq_mask, images_spatial_crop = (
[],
[],
[],
[],
)
image_shapes = []
num_image_tokens = []
tokenized_str = []
for text_sep, image in zip(text_splits, images):
tokenized_sep = self.encode(text_sep, bos=False, eos=False)
tokenized_str += tokenized_sep
images_seq_mask += [False] * len(tokenized_sep)
image_shapes.append(image.size)
images_crop_raw = []
if image.size[0] <= self.image_size and image.size[1] <= self.image_size:
crop_ratio = [1, 1]
elif cropping:
images_crop_raw, crop_ratio = dynamic_preprocess(
image, image_size=self.image_size
)
else:
crop_ratio = [1, 1]
if not cropping:
image = image.resize((self.image_size, self.image_size))
global_view = ImageOps.pad(
image,
(self.base_size, self.base_size),
color=tuple(int(x * 255) for x in self.image_transform.mean),
)
images_list.append(self.image_transform(global_view))
num_width_tiles, num_height_tiles = crop_ratio
images_spatial_crop.append([num_width_tiles, num_height_tiles])
if num_width_tiles > 1 or num_height_tiles > 1:
for cropped_image in images_crop_raw:
images_crop_list.append(self.image_transform(cropped_image))
num_queries = math.ceil(
(self.image_size // self.patch_size) / self.downsample_ratio
)
num_queries_base = math.ceil(
(self.base_size // self.patch_size) / self.downsample_ratio
)
num_tokens_base = (
(num_queries_base * (num_queries_base + 1))
if self.strategy == "v1"
else num_queries_base * num_queries_base
)
tokenized_image = [self.image_token_id] * num_tokens_base
tokenized_image += [self.image_token_id]
if num_width_tiles > 1 or num_height_tiles > 1:
num_tokens_per_row = (
num_queries * num_width_tiles + 1
if self.strategy == "v1"
else num_queries * num_width_tiles
)
local_row = [self.image_token_id] * num_tokens_per_row
tokenized_image += local_row * (num_queries * num_height_tiles)
tokenized_str += tokenized_image
images_seq_mask += [True] * len(tokenized_image)
num_image_tokens.append(len(tokenized_image))
"""process the last text split"""
tokenized_sep = self.encode(text_splits[-1], bos=False, eos=False)
tokenized_str += tokenized_sep
images_seq_mask += [False] * len(tokenized_sep)
"""add the bos and eos tokens"""
if bos:
tokenized_str = [self.bos_id] + tokenized_str
images_seq_mask = [False] + images_seq_mask
if eos:
tokenized_str = tokenized_str + [self.eos_id]
images_seq_mask = images_seq_mask + [False]
assert len(tokenized_str) == len(images_seq_mask), (
f"tokenize_with_images func: tokenized_str's length {len(tokenized_str)} "
f"is not equal to images_seq_mask's length {len(images_seq_mask)}."
)
masked_tokenized_str = []
for token_index in tokenized_str:
if token_index != self.image_token_id:
masked_tokenized_str.append(token_index)
else:
masked_tokenized_str.append(self.ignore_id)
assert (
len(tokenized_str) == len(images_seq_mask) == len(masked_tokenized_str)
), (
f"tokenized_str's length {len(tokenized_str)}, "
f"input_ids' length {len(masked_tokenized_str)}, "
f"images_seq_mask's length {len(images_seq_mask)}, are not equal."
)
input_ids = torch.LongTensor(tokenized_str)
target_ids = torch.LongTensor(masked_tokenized_str)
images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool)
# set input_ids < 0 | input_ids == self.image_token_id as ignore_id
target_ids[(input_ids < 0) | (input_ids == self.image_token_id)] = (
self.ignore_id
)
input_ids[input_ids < 0] = self.pad_id
# Remove the ending eos token
assert input_ids[-1] == self.eos_id
input_ids = input_ids[:-1]
target_ids = target_ids[:-1]
images_seq_mask = images_seq_mask[:-1]
if len(images_list) == 0:
pixel_values = torch.zeros((0, 3, self.base_size, self.base_size))
images_spatial_crop = torch.zeros((0, 2), dtype=torch.long)
images_crop = torch.zeros((0, 3, self.image_size, self.image_size))
else:
pixel_values = torch.stack(images_list, dim=0)
images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long)
if images_crop_list:
images_crop = torch.stack(images_crop_list, dim=0)
else:
images_crop = torch.zeros((0, 3, self.image_size, self.image_size))
input_ids = input_ids.unsqueeze(0)
return (
input_ids,
pixel_values,
images_crop,
images_seq_mask,
images_spatial_crop,
num_image_tokens,
image_shapes,
)