268 lines
12 KiB
Python
268 lines
12 KiB
Python
# coding=utf-8
|
|
# Copyright 2024 HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from typing import Optional, Union
|
|
|
|
import numpy as np
|
|
|
|
from ...image_processing_utils import BatchFeature
|
|
from ...image_utils import ImageInput
|
|
from ...processing_utils import ImagesKwargs, MultiModalData, ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack
|
|
from ...tokenization_utils_base import PreTokenizedInput, TextInput
|
|
from ...utils import is_vision_available
|
|
|
|
|
|
if is_vision_available():
|
|
from .image_processing_emu3 import smart_resize
|
|
|
|
|
|
class Emu3TextKwargs(TextKwargs, total=False):
|
|
return_for_image_generation: bool
|
|
|
|
|
|
class Emu3ImagesKwargs(ImagesKwargs, total=False):
|
|
ratio: str
|
|
image_area: int
|
|
|
|
|
|
class Emu3ProcessorKwargs(ProcessingKwargs, total=False):
|
|
text_kwargs: Emu3TextKwargs
|
|
images_kwargs: Emu3ImagesKwargs
|
|
_defaults = {
|
|
"text_kwargs": {
|
|
"return_for_image_generation": False,
|
|
"return_mm_token_type_ids": False,
|
|
},
|
|
"images_kwargs": {
|
|
"ratio": "1:1",
|
|
"image_area": 518400,
|
|
},
|
|
}
|
|
|
|
|
|
class Emu3Processor(ProcessorMixin):
|
|
r"""
|
|
Constructs a Emu3 processor which wraps a Emu3 image processor and a GPT2 tokenizer into a single
|
|
processor.
|
|
|
|
[`Emu3Processor`] offers all the functionalities of [`Emu3ImageProcessor`] and [`GPT2TokenizerFast`].
|
|
See the [`~Emu3Processor.__call__`] and [`~Emu3Processor.decode`] for more information.
|
|
|
|
Args:
|
|
image_processor ([`Emu3ImageProcessor`]):
|
|
The image processor is a required input.
|
|
tokenizer ([`Emu3TokenizerFast`]):
|
|
The tokenizer is a required input.
|
|
chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
|
|
in a chat into a tokenizable string.
|
|
"""
|
|
|
|
attributes = ["image_processor", "tokenizer"]
|
|
tokenizer_class = ("GPT2Tokenizer", "GPT2TokenizerFast")
|
|
image_processor_class = "Emu3ImageProcessor"
|
|
|
|
def __init__(
|
|
self,
|
|
image_processor,
|
|
tokenizer,
|
|
chat_template=None,
|
|
**kwargs,
|
|
):
|
|
self.image_token = tokenizer.image_token # image_token as placeholder to be replaced by vq-vae tokens
|
|
self.image_token_id = tokenizer.image_token_id
|
|
self.image_start_token = tokenizer.boi_token # "<|image start|>" fixed tokens for start and end of image
|
|
self.image_end_token = tokenizer.eoi_token # "<|image end|>"
|
|
self.fake_token_around_image = tokenizer.image_wrapper_token # "<|image token|>" every image starts with it
|
|
self.eof_token = tokenizer.eof_token # "<|extra_201|>"
|
|
self.bos_token = tokenizer.bos_token
|
|
self.downsample_ratio = 8
|
|
super().__init__(image_processor, tokenizer, chat_template=chat_template)
|
|
|
|
def __call__(
|
|
self,
|
|
images: Optional[ImageInput] = None,
|
|
text: Optional[Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]]] = None,
|
|
audio=None,
|
|
videos=None,
|
|
**kwargs: Unpack[Emu3ProcessorKwargs],
|
|
) -> BatchFeature:
|
|
"""
|
|
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
|
|
and `kwargs` arguments to Emu3TokenizerFast's [`~Emu3TokenizerFast.__call__`] if `text` is not `None` to encode
|
|
the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
|
|
CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring
|
|
of the above two methods for more information.
|
|
|
|
Args:
|
|
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
|
|
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
|
|
tensor. Both channels-first and channels-last formats are supported.
|
|
text (`str`, `list[str]`, `list[list[str]]`):
|
|
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
|
|
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
|
|
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
|
|
return_tensors (`str` or [`~utils.TensorType`], *optional*):
|
|
If set, will return tensors of a particular framework. Acceptable values are:
|
|
|
|
- `'tf'`: Return TensorFlow `tf.constant` objects.
|
|
- `'pt'`: Return PyTorch `torch.Tensor` objects.
|
|
- `'np'`: Return NumPy `np.ndarray` objects.
|
|
- `'jax'`: Return JAX `jnp.ndarray` objects.
|
|
|
|
Returns:
|
|
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
|
|
|
|
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
|
|
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
|
|
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
|
|
`None`).
|
|
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
|
|
"""
|
|
# check if images and text inputs are reversed for BC
|
|
|
|
if isinstance(text, str):
|
|
text = [text]
|
|
elif not isinstance(text, list) and not isinstance(text[0], str):
|
|
raise TypeError("Invalid input text. Please provide a string, or a list of strings")
|
|
|
|
output_kwargs = self._merge_kwargs(
|
|
Emu3ProcessorKwargs,
|
|
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
|
|
**kwargs,
|
|
)
|
|
return_for_image_generation = output_kwargs["text_kwargs"].pop("return_for_image_generation", False)
|
|
ratio = output_kwargs["images_kwargs"].pop("ratio", None)
|
|
image_area = output_kwargs["images_kwargs"].pop("image_area", None)
|
|
|
|
if return_for_image_generation and images is not None:
|
|
raise ValueError("You should not provide `images` when `return_for_image_generation=True`")
|
|
|
|
if not return_for_image_generation and text is None and images is None:
|
|
raise ValueError("You must provide either text or images when `return_for_image_generation=False`")
|
|
|
|
image_features = {}
|
|
image_start_tokens = f"{self.image_start_token}"
|
|
image_end_tokens = f"{self.eof_token}{self.image_end_token}"
|
|
|
|
# generate text from image + text input, so we add placeholders for image tokens
|
|
if not return_for_image_generation and images is not None:
|
|
image_features = self.image_processor(images, **output_kwargs["images_kwargs"])
|
|
image_sizes = iter(image_features.image_sizes)
|
|
|
|
prompt_strings = []
|
|
for sample in text:
|
|
while self.image_token in sample:
|
|
image_size = next(image_sizes)
|
|
height, width = image_size
|
|
height = height // self.downsample_ratio
|
|
width = width // self.downsample_ratio
|
|
image_seq_length = height * (width + 1) # +1 for extra row when converting to BPE in modeling code
|
|
|
|
image_placeholder = f"{image_start_tokens}{height}*{width}{self.fake_token_around_image}{'<placeholder>' * image_seq_length}{image_end_tokens}"
|
|
sample = sample.replace(self.image_token, image_placeholder, 1)
|
|
sample = f"{self.bos_token}{sample}" # add BOS because GPT tokenizer doesn't add it
|
|
prompt_strings.append(sample)
|
|
text = [sample.replace("<placeholder>", self.image_token) for sample in prompt_strings]
|
|
|
|
# generate image from text input, so we add begin-of-image tokens from where image generation starts
|
|
elif return_for_image_generation:
|
|
height, width = self.calculate_generate_size(ratio, image_area, self.downsample_ratio)
|
|
image_prompt = f"{image_start_tokens}{height}*{width}{self.fake_token_around_image}"
|
|
text = [f"{self.bos_token}{sample}{image_prompt}" for sample in text]
|
|
image_features["image_sizes"] = [[height, width]] * len(text)
|
|
|
|
# else just generate from text-only input, and we do no special treatment for text
|
|
return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
|
|
return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
|
|
text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None)
|
|
self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
|
|
|
|
if return_mm_token_type_ids:
|
|
array_ids = np.array(text_inputs["input_ids"])
|
|
mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
|
|
mm_token_type_ids[array_ids == self.image_token_id] = 1
|
|
text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
|
|
|
|
return BatchFeature(data={**text_inputs, **image_features}, tensor_type=return_tensors)
|
|
|
|
def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
|
|
"""
|
|
Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
|
|
|
|
Args:
|
|
image_sizes (`list[list[int]]`, *optional*):
|
|
The input sizes formatted as (height, width) per each image.
|
|
|
|
Returns:
|
|
`MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
|
|
input modalities, along with other useful data.
|
|
"""
|
|
|
|
vision_data = {}
|
|
if image_sizes is not None:
|
|
num_image_tokens = []
|
|
for height, width in image_sizes:
|
|
height, width = smart_resize(
|
|
height,
|
|
width,
|
|
self.image_processor.spatial_factor,
|
|
self.image_processor.min_pixels,
|
|
self.image_processor.max_pixels,
|
|
)
|
|
height = height // self.downsample_ratio
|
|
width = width // self.downsample_ratio
|
|
image_seq_length = height * (width + 1) # +1 for extra row when converting to BPE in modeling code
|
|
num_image_tokens.append(image_seq_length)
|
|
|
|
num_image_patches = [1] * len(image_sizes)
|
|
vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
|
|
|
|
return MultiModalData(**vision_data)
|
|
|
|
def calculate_generate_size(self, ratio, image_area, spatial_factor):
|
|
width, height = map(int, ratio.split(":"))
|
|
current_area = width * height
|
|
target_ratio = (image_area / current_area) ** 0.5
|
|
|
|
token_height = int(round(height * target_ratio / spatial_factor))
|
|
token_width = int(round(width * target_ratio / spatial_factor))
|
|
return token_height, token_width
|
|
|
|
def postprocess(self, images: ImageInput, **kwargs):
|
|
return self.image_processor.postprocess(images, **kwargs)
|
|
|
|
def batch_decode(self, *args, **kwargs):
|
|
"""
|
|
This method forwards all its arguments to Emu3TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
|
|
refer to the docstring of this method for more information.
|
|
"""
|
|
return self.tokenizer.batch_decode(*args, **kwargs)
|
|
|
|
def decode(self, *args, **kwargs):
|
|
"""
|
|
This method forwards all its arguments to Emu3TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
|
|
the docstring of this method for more information.
|
|
"""
|
|
return self.tokenizer.decode(*args, **kwargs)
|
|
|
|
@property
|
|
def model_input_names(self):
|
|
tokenizer_input_names = self.tokenizer.model_input_names
|
|
image_processor_input_names = self.image_processor.model_input_names
|
|
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
|
|
|
|
|
|
__all__ = ["Emu3Processor"]
|