pllava-7b-demo / models /pllava /processing_pllava.py
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# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.
"""
Processor class for Llava.
"""
import itertools
from typing import List, Optional, Union
import PIL.Image
import numpy as np
from transformers import AutoTokenizer
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import (
ImageInput,
make_list_of_images,
valid_images,
infer_channel_dimension_format,
to_numpy_array,
get_image_size,
ChannelDimension,
)
from transformers.image_processing_utils import get_size_dict
from transformers.image_utils import PILImageResampling
from transformers.processing_utils import ProcessorMixin
from transformers.image_transforms import resize, pad, PaddingMode, to_channel_dimension_format, get_resize_output_image_size
from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from transformers.utils import TensorType
class PllavaProcessor(ProcessorMixin):
r"""
Constructs a Llava processor which wraps a Llava image processor and a Llava tokenizer into a single processor.
[`LlavaProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`LlamaTokenizerFast`]. See the
[`~LlavaProcessor.__call__`] and [`~LlavaProcessor.decode`] for more information.
Args:
image_processor ([`CLIPImageProcessor`], *optional*):
The image processor is a required input.
tokenizer ([`LlamaTokenizerFast`], *optional*):
The tokenizer is a required input.
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "CLIPImageProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(self, image_processor=None, tokenizer=None,
shortest_edge=336,
longest_edge=762,
center_pad=False):
self.shortest_edge = shortest_edge
self.longest_edge = longest_edge
self.center_pad = center_pad
super().__init__(image_processor, tokenizer)
def resize_crop_longshort(self, videos: list[list[np.ndarray]], input_data_format):
video_spatial_sizes = [get_image_size(images[0], input_data_format) for images in videos]
long_short_rates = [max(size) / min(size) for size in video_spatial_sizes]
min_long_short_rate = min(long_short_rates)
min_long_short_video_idx = long_short_rates.index(min_long_short_rate)
clip_resolution = self.image_processor.size['shortest_edge']
out_video_spatial_size = video_spatial_sizes[min_long_short_video_idx]
out_videos_short_edge = max(min(size) for size in video_spatial_sizes)
resize_longest_edge = max(max(size) for size in video_spatial_sizes)
resize_longest_edge = min(640, resize_longest_edge)
out_videos_short_edge = min(out_videos_short_edge, int(resize_longest_edge / min_long_short_rate))
out_videos_short_edge = max(out_videos_short_edge, clip_resolution)
if out_video_spatial_size[0] > out_video_spatial_size[1]: # h > w:
out_video_spatial_size = (int(out_videos_short_edge * min_long_short_rate), out_videos_short_edge )
else:
out_video_spatial_size = ( out_videos_short_edge, int(out_videos_short_edge * min_long_short_rate) )
videos = [
[self.resize(frame, input_data_format=input_data_format, shortest_edge=out_videos_short_edge, longest_edge=9999) for frame in frames]
for frames in videos
]
out_videos = []
for frames in videos:
out_frames = []
video_spatial_size = get_image_size(frames[0], input_data_format)
assert min(video_spatial_size) == out_videos_short_edge
overhead = (max(video_spatial_size) - max(out_video_spatial_size)) // 2
slice_start, slice_end = overhead // 2, overhead // 2 + max(out_video_spatial_size)
hslice, wslice = (slice(slice_start, slice_end), slice(None, None)) if video_spatial_size[0] > video_spatial_size[1] \
else (slice(None, None), slice(slice_start, slice_end)) # h > w
for frame in frames:
if input_data_format == ChannelDimension.FIRST:
out_frames.append(frame[..., hslice, wslice])
elif input_data_format == ChannelDimension.LAST:
out_frames.append(frame[..., hslice, wslice, :])
out_videos.append(out_frames)
return out_videos
@staticmethod
def _compute_num_blocks_and_overlaps(input_shape, resolution):
input_shape = np.array(input_shape)
resolution = np.array(resolution)
assert input_shape.max() >= resolution
num_blocks = np.ceil(input_shape / resolution).astype(np.int32).tolist()
overlaps = [0 if size % resolution==0
else int(np.floor((resolution - size % resolution) / (num_block - 1))) for num_block, size in zip(num_blocks, input_shape)]
return num_blocks, overlaps
def resize(
self,
image: np.ndarray,
resample: PILImageResampling = PILImageResampling.BICUBIC, # type: ignore
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
shortest_edge: int = None,
longest_edge: int = None,
**kwargs,
) -> np.ndarray:
"""
Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge
resized to keep the input aspect ratio.
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Size of the output image.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
Resampling filter to use when resiizing the image.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
shortest_edge = getattr(self, 'shortest_edge', None) if shortest_edge is None else shortest_edge
longest_edge = getattr(self, 'longest_edge', None) if longest_edge is None else longest_edge
default_to_square = False
output_size = get_resize_output_image_size(
image,
size=shortest_edge,
default_to_square=default_to_square,
max_size=longest_edge,
input_data_format=input_data_format,
)
clip_resolution = self.image_processor.size['shortest_edge']
if min(output_size) < clip_resolution:
output_size = get_resize_output_image_size(
image,
size=shortest_edge,
default_to_square=default_to_square,
input_data_format=input_data_format,
)
return resize(
image,
size=output_size,
resample=resample,
data_format=data_format,
input_data_format=input_data_format,
**kwargs,
)
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
images: ImageInput = None,
center_pad = None,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length=None,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
) -> 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 LlamaTokenizerFast's [`~LlamaTokenizerFast.__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 doctsring
of the above two methods for more information.
Args:
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).
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. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
number of channels, H and W are image height and width.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
max_length (`int`, *optional*):
Maximum length of the returned list and optionally padding length (see above).
truncation (`bool`, *optional*):
Activates truncation to cut input sequences longer than `max_length` to `max_length`.
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`.
"""
data=dict()
if images is not None:
if isinstance(images, list) and isinstance(images[0], PIL.Image.Image):
videos = [images] # one video
else:
videos = images
pixel_values_list = []
videos = [[to_numpy_array(image) for image in make_list_of_images(images)] for images in videos]
# images = [self.resize(image, ) if min(get_image_size(image, input_data_format)) < clip_resolution else image for image in images]
input_data_format = infer_channel_dimension_format(videos[0][0])
videos = self.resize_crop_longshort(videos, input_data_format)
for images in videos:
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."
)
center_pad = center_pad if center_pad is not None else self.center_pad
if center_pad:
images = [self.pad_to_square(image, 0, input_data_format, input_data_format) for image in images]
pixel_values = self.image_processor(images, return_tensors='np')["pixel_values"]
pixel_values_list.append(pixel_values)
pixel_values = np.concatenate(pixel_values_list)
data.update(pixel_values=pixel_values)
else:
data.update(pixel_values = None)
if text is not None:
text_inputs = self.tokenizer(
text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
)
data.update(**text_inputs)
return BatchFeature(data, tensor_type=return_tensors)
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
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))