1007 lines
47 KiB
Python
1007 lines
47 KiB
Python
# coding=utf-8
|
|
# Copyright 2024 The Qwen team, Alibaba Group and the 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.
|
|
"""Qwen2VL model configuration"""
|
|
from typing import Dict, Iterable, List, Optional, Union
|
|
|
|
import numpy as np
|
|
from transformers import (
|
|
AutoImageProcessor,
|
|
AutoProcessor,
|
|
BaseImageProcessor,
|
|
BatchFeature,
|
|
PretrainedConfig,
|
|
ProcessorMixin,
|
|
TensorType,
|
|
)
|
|
from transformers.image_transforms import (
|
|
convert_to_rgb,
|
|
normalize,
|
|
rescale,
|
|
resize,
|
|
to_channel_dimension_format,
|
|
)
|
|
from transformers.image_utils import (
|
|
ChannelDimension,
|
|
ImageInput,
|
|
PILImageResampling,
|
|
VideoInput,
|
|
get_image_size,
|
|
infer_channel_dimension_format,
|
|
is_pil_image,
|
|
is_valid_image,
|
|
make_list_of_images,
|
|
to_numpy_array,
|
|
valid_images,
|
|
validate_preprocess_arguments,
|
|
)
|
|
from transformers.modeling_rope_utils import rope_config_validation
|
|
from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize
|
|
from transformers.processing_utils import ProcessingKwargs, Unpack, VideosKwargs
|
|
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
|
|
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
|
|
|
|
|
|
def is_valid_list_of_images(images: List):
|
|
return images and all(is_valid_image(image) for image in images)
|
|
|
|
|
|
class Qwen2_5_VLVisionConfig(PretrainedConfig):
|
|
model_type = "qwen2_5_vl"
|
|
base_config_key = "vision_config"
|
|
|
|
def __init__(
|
|
self,
|
|
depth=32,
|
|
hidden_size=3584,
|
|
hidden_act="silu",
|
|
intermediate_size=3420,
|
|
num_heads=16,
|
|
in_channels=3,
|
|
patch_size=14,
|
|
spatial_merge_size=2,
|
|
temporal_patch_size=2,
|
|
tokens_per_second=4,
|
|
window_size=112,
|
|
out_hidden_size=3584,
|
|
fullatt_block_indexes=[7, 15, 23, 31],
|
|
**kwargs,
|
|
):
|
|
super().__init__(**kwargs)
|
|
|
|
self.depth = depth
|
|
self.hidden_size = hidden_size
|
|
self.hidden_act = hidden_act
|
|
self.intermediate_size = intermediate_size
|
|
self.num_heads = num_heads
|
|
self.in_channels = in_channels
|
|
self.patch_size = patch_size
|
|
self.spatial_merge_size = spatial_merge_size
|
|
self.temporal_patch_size = temporal_patch_size
|
|
self.tokens_per_second = tokens_per_second
|
|
self.window_size = window_size
|
|
self.fullatt_block_indexes = fullatt_block_indexes
|
|
self.out_hidden_size = out_hidden_size
|
|
|
|
|
|
class Qwen2_5_VLConfig(PretrainedConfig):
|
|
r"""
|
|
This is the configuration class to store the configuration of a [`Qwen2_5_VLModel`]. It is used to instantiate a
|
|
Qwen2-VL model according to the specified arguments, defining the model architecture. Instantiating a configuration
|
|
with the defaults will yield a similar configuration to that of
|
|
Qwen2-VL-7B-Instruct [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct).
|
|
|
|
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
|
documentation from [`PretrainedConfig`] for more information.
|
|
|
|
|
|
Args:
|
|
vocab_size (`int`, *optional*, defaults to 152064):
|
|
Vocabulary size of the Qwen2_5_VL model. Defines the number of different tokens that can be represented by the
|
|
`inputs_ids` passed when calling [`Qwen2_5_VLModel`]
|
|
hidden_size (`int`, *optional*, defaults to 8192):
|
|
Dimension of the hidden representations.
|
|
intermediate_size (`int`, *optional*, defaults to 29568):
|
|
Dimension of the MLP representations.
|
|
num_hidden_layers (`int`, *optional*, defaults to 80):
|
|
Number of hidden layers in the Transformer encoder.
|
|
num_attention_heads (`int`, *optional*, defaults to 64):
|
|
Number of attention heads for each attention layer in the Transformer encoder.
|
|
num_key_value_heads (`int`, *optional*, defaults to 8):
|
|
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
|
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
|
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
|
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
|
by meanpooling all the original heads within that group. For more details checkout [this
|
|
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
|
|
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
|
The non-linear activation function (function or string) in the decoder.
|
|
max_position_embeddings (`int`, *optional*, defaults to 32768):
|
|
The maximum sequence length that this model might ever be used with.
|
|
initializer_range (`float`, *optional*, defaults to 0.02):
|
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
|
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
|
|
The epsilon used by the rms normalization layers.
|
|
use_cache (`bool`, *optional*, defaults to `True`):
|
|
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
|
relevant if `config.is_decoder=True`.
|
|
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
|
Whether the model's input and output word embeddings should be tied.
|
|
rope_theta (`float`, *optional*, defaults to 1000000.0):
|
|
The base period of the RoPE embeddings.
|
|
use_sliding_window (`bool`, *optional*, defaults to `False`):
|
|
Whether to use sliding window attention.
|
|
sliding_window (`int`, *optional*, defaults to 4096):
|
|
Sliding window attention (SWA) window size. If not specified, will default to `4096`.
|
|
max_window_layers (`int`, *optional*, defaults to 80):
|
|
The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
|
|
attention_dropout (`float`, *optional*, defaults to 0.0):
|
|
The dropout ratio for the attention probabilities.
|
|
vision_config (`Dict`, *optional*):
|
|
The config for the visual encoder initialization.
|
|
rope_scaling (`Dict`, *optional*):
|
|
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
|
|
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
|
|
accordingly.
|
|
Expected contents:
|
|
`rope_type` (`str`):
|
|
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
|
|
'llama3'], with 'default' being the original RoPE implementation.
|
|
`factor` (`float`, *optional*):
|
|
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
|
|
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
|
|
original maximum pre-trained length.
|
|
`original_max_position_embeddings` (`int`, *optional*):
|
|
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
|
|
pretraining.
|
|
`attention_factor` (`float`, *optional*):
|
|
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
|
|
computation. If unspecified, it defaults to value recommended by the implementation, using the
|
|
`factor` field to infer the suggested value.
|
|
`beta_fast` (`float`, *optional*):
|
|
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
|
|
ramp function. If unspecified, it defaults to 32.
|
|
`beta_slow` (`float`, *optional*):
|
|
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
|
|
ramp function. If unspecified, it defaults to 1.
|
|
`short_factor` (`List[float]`, *optional*):
|
|
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
|
|
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
|
|
size divided by the number of attention heads divided by 2
|
|
`long_factor` (`List[float]`, *optional*):
|
|
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
|
|
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
|
|
size divided by the number of attention heads divided by 2
|
|
`low_freq_factor` (`float`, *optional*):
|
|
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
|
|
`high_freq_factor` (`float`, *optional*):
|
|
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
|
|
|
|
```python
|
|
>>> from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLConfig
|
|
|
|
>>> # Initializing a Qwen2_5_VL style configuration
|
|
>>> configuration = Qwen2_5_VLConfig()
|
|
|
|
>>> # Initializing a model from the Qwen2-VL-7B style configuration
|
|
>>> model = Qwen2_5_VLForConditionalGeneration(configuration)
|
|
|
|
>>> # Accessing the model configuration
|
|
>>> configuration = model.config
|
|
```"""
|
|
|
|
model_type = "qwen2_5_vl"
|
|
sub_configs = {"vision_config": Qwen2_5_VLVisionConfig}
|
|
keys_to_ignore_at_inference = ["past_key_values"]
|
|
# Default tensor parallel plan for base model `Qwen2_5_VL`
|
|
base_model_tp_plan = {
|
|
"layers.*.self_attn.q_proj": "colwise",
|
|
"layers.*.self_attn.k_proj": "colwise",
|
|
"layers.*.self_attn.v_proj": "colwise",
|
|
"layers.*.self_attn.o_proj": "rowwise",
|
|
"layers.*.mlp.gate_proj": "colwise",
|
|
"layers.*.mlp.up_proj": "colwise",
|
|
"layers.*.mlp.down_proj": "rowwise",
|
|
}
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_size=152064,
|
|
hidden_size=8192,
|
|
intermediate_size=29568,
|
|
num_hidden_layers=80,
|
|
num_attention_heads=64,
|
|
num_key_value_heads=8,
|
|
hidden_act="silu",
|
|
max_position_embeddings=32768,
|
|
initializer_range=0.02,
|
|
rms_norm_eps=1e-05,
|
|
use_cache=True,
|
|
tie_word_embeddings=False,
|
|
rope_theta=1000000.0,
|
|
use_sliding_window=False,
|
|
sliding_window=4096,
|
|
max_window_layers=80,
|
|
attention_dropout=0.0,
|
|
vision_config=None,
|
|
rope_scaling=None,
|
|
**kwargs,
|
|
):
|
|
if isinstance(vision_config, dict):
|
|
self.vision_config = self.sub_configs["vision_config"](**vision_config)
|
|
elif vision_config is None:
|
|
self.vision_config = self.sub_configs["vision_config"]()
|
|
|
|
self.vocab_size = vocab_size
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.hidden_size = hidden_size
|
|
self.intermediate_size = intermediate_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
self.use_sliding_window = use_sliding_window
|
|
self.sliding_window = sliding_window
|
|
self.max_window_layers = max_window_layers
|
|
|
|
# for backward compatibility
|
|
if num_key_value_heads is None:
|
|
num_key_value_heads = num_attention_heads
|
|
|
|
self.num_key_value_heads = num_key_value_heads
|
|
self.hidden_act = hidden_act
|
|
self.initializer_range = initializer_range
|
|
self.rms_norm_eps = rms_norm_eps
|
|
self.use_cache = use_cache
|
|
self.rope_theta = rope_theta
|
|
self.attention_dropout = attention_dropout
|
|
self.rope_scaling = rope_scaling
|
|
|
|
# Validate the correctness of rotary position embeddings parameters
|
|
# BC: if there is a 'type' field, move it to 'rope_type'.
|
|
# and change type from 'mrope' to 'default' because `mrope` does defeault RoPE calculations
|
|
# one can set it to "linear"/"dynamic" etc. to have scaled RoPE
|
|
# TODO: @raushan update config in the hub
|
|
if self.rope_scaling is not None and "type" in self.rope_scaling:
|
|
if self.rope_scaling["type"] == "mrope":
|
|
self.rope_scaling["type"] = "default"
|
|
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
|
|
rope_config_validation(self, ignore_keys={"mrope_section"})
|
|
|
|
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
|
|
|
|
|
|
# FIXME: workaround of obsolete transformers version
|
|
|
|
|
|
class Qwen2_5_VLVideosProcessorKwargs(VideosKwargs, total=False):
|
|
fps: Union[List[float], float]
|
|
|
|
|
|
class Qwen2_5_VLProcessorKwargs(ProcessingKwargs, total=False):
|
|
videos_kwargs: Qwen2_5_VLVideosProcessorKwargs
|
|
_defaults = {
|
|
"text_kwargs": {
|
|
"padding": False,
|
|
},
|
|
"videos_kwargs": {"fps": 2.0},
|
|
}
|
|
|
|
|
|
class Qwen2_5_VLProcessor(ProcessorMixin):
|
|
r"""
|
|
Constructs a Qwen2.5-VL processor which wraps a Qwen2.5-VL image processor and a Qwen2 tokenizer into a single processor.
|
|
[`Qwen2_5_VLProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`]. See the
|
|
[`~Qwen2_5_VLProcessor.__call__`] and [`~Qwen2_5_VLProcessor.decode`] for more information.
|
|
Args:
|
|
image_processor ([`Qwen2VLImageProcessor`], *optional*):
|
|
The image processor is a required input.
|
|
tokenizer ([`Qwen2TokenizerFast`], *optional*):
|
|
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"]
|
|
valid_kwargs = ["chat_template"]
|
|
|
|
image_processor_class = "AutoImageProcessor"
|
|
tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
|
|
|
|
def __init__(
|
|
self, image_processor=None, tokenizer=None, chat_template=None, **kwargs
|
|
):
|
|
self.image_token = (
|
|
"<|image_pad|>"
|
|
if not hasattr(tokenizer, "image_token")
|
|
else tokenizer.image_token
|
|
)
|
|
self.video_token = (
|
|
"<|video_pad|>"
|
|
if not hasattr(tokenizer, "video_token")
|
|
else tokenizer.video_token
|
|
)
|
|
super().__init__(image_processor, tokenizer, chat_template=chat_template)
|
|
|
|
def __call__(
|
|
self,
|
|
images: ImageInput = None,
|
|
text: Union[
|
|
TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]
|
|
] = None,
|
|
videos: VideoInput = None,
|
|
**kwargs: Unpack[Qwen2_5_VLProcessorKwargs],
|
|
) -> 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 Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
|
|
the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwrags` arguments to
|
|
Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__call__`] if `vision_infos` is not `None`.
|
|
|
|
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).
|
|
videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
|
|
The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
|
|
tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
|
|
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`.
|
|
- **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
|
|
- **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
|
|
- **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
|
|
- **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`.
|
|
"""
|
|
output_kwargs = self._merge_kwargs(
|
|
Qwen2_5_VLProcessorKwargs,
|
|
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
|
|
**kwargs,
|
|
)
|
|
if images is not None:
|
|
image_inputs = self.image_processor(
|
|
images=images, videos=None, **output_kwargs["images_kwargs"]
|
|
)
|
|
image_grid_thw = image_inputs["image_grid_thw"]
|
|
else:
|
|
image_inputs = {}
|
|
image_grid_thw = None
|
|
|
|
if videos is not None:
|
|
videos_inputs = self.image_processor(
|
|
images=None, videos=videos, **output_kwargs["images_kwargs"]
|
|
)
|
|
video_grid_thw = videos_inputs["video_grid_thw"]
|
|
|
|
fps = output_kwargs["videos_kwargs"].pop("fps", 2.0)
|
|
if isinstance(fps, (int, float)):
|
|
second_per_grid_ts = [
|
|
self.image_processor.temporal_patch_size / fps
|
|
] * len(video_grid_thw)
|
|
elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw):
|
|
second_per_grid_ts = [
|
|
self.image_processor.temporal_patch_size / tmp for tmp in fps
|
|
]
|
|
else:
|
|
raise ValueError(
|
|
f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number."
|
|
)
|
|
videos_inputs.update({"second_per_grid_ts": second_per_grid_ts})
|
|
|
|
else:
|
|
videos_inputs = {}
|
|
video_grid_thw = None
|
|
|
|
if not isinstance(text, list):
|
|
text = [text]
|
|
|
|
if image_grid_thw is not None:
|
|
merge_length = self.image_processor.merge_size**2
|
|
index = 0
|
|
for i in range(len(text)):
|
|
while self.image_token in text[i]:
|
|
text[i] = text[i].replace(
|
|
self.image_token,
|
|
"<|placeholder|>"
|
|
* (image_grid_thw[index].prod() // merge_length),
|
|
1,
|
|
)
|
|
index += 1
|
|
text[i] = text[i].replace("<|placeholder|>", self.image_token)
|
|
|
|
if video_grid_thw is not None:
|
|
merge_length = self.image_processor.merge_size**2
|
|
index = 0
|
|
for i in range(len(text)):
|
|
while self.video_token in text[i]:
|
|
text[i] = text[i].replace(
|
|
self.video_token,
|
|
"<|placeholder|>"
|
|
* (video_grid_thw[index].prod() // merge_length),
|
|
1,
|
|
)
|
|
index += 1
|
|
text[i] = text[i].replace("<|placeholder|>", self.video_token)
|
|
|
|
text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
|
|
|
|
return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs})
|
|
|
|
def batch_decode(self, *args, **kwargs):
|
|
"""
|
|
This method forwards all its arguments to Qwen2TokenizerFast'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 Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
|
|
the docstring of this method for more information.
|
|
"""
|
|
return self.tokenizer.decode(*args, **kwargs)
|
|
|
|
def post_process_image_text_to_text(self, generated_outputs):
|
|
"""
|
|
Post-process the output of the model to decode the text.
|
|
|
|
Args:
|
|
generated_outputs (`torch.Tensor` or `np.ndarray`):
|
|
The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
|
|
or `(sequence_length,)`.
|
|
|
|
Returns:
|
|
`List[str]`: The decoded text.
|
|
"""
|
|
return self.tokenizer.batch_decode(
|
|
generated_outputs,
|
|
skip_special_tokens=True,
|
|
clean_up_tokenization_spaces=False,
|
|
)
|
|
|
|
@property
|
|
def model_input_names(self):
|
|
tokenizer_input_names = self.tokenizer.model_input_names
|
|
image_processor_input_names = self.image_processor.model_input_names
|
|
names_from_processor = list(
|
|
dict.fromkeys(tokenizer_input_names + image_processor_input_names)
|
|
)
|
|
return names_from_processor + ["second_per_grid_ts"]
|
|
|
|
|
|
class Qwen2_5_VLImageProcessor(BaseImageProcessor):
|
|
r"""
|
|
Constructs a Qwen2.5-VL image processor that dynamically resizes images based on the original images.
|
|
|
|
Args:
|
|
do_resize (`bool`, *optional*, defaults to `True`):
|
|
Whether to resize the image's (height, width) dimensions.
|
|
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
|
|
Resampling filter to use when resizing the image.
|
|
do_rescale (`bool`, *optional*, defaults to `True`):
|
|
Whether to rescale the image by the specified scale `rescale_factor`.
|
|
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
|
|
Scale factor to use if rescaling the image.
|
|
do_normalize (`bool`, *optional*, defaults to `True`):
|
|
Whether to normalize the image.
|
|
image_mean (`float` or `List[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
|
|
Mean to use if normalizing the image. This is a float or list of floats for each channel in the image.
|
|
image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
|
|
Standard deviation to use if normalizing the image. This is a float or list of floats for each channel in the image.
|
|
do_convert_rgb (`bool`, *optional*, defaults to `True`):
|
|
Whether to convert the image to RGB.
|
|
min_pixels (`int`, *optional*, defaults to `56 * 56`):
|
|
The min pixels of the image to resize the image.
|
|
max_pixels (`int`, *optional*, defaults to `28 * 28 * 1280`):
|
|
The max pixels of the image to resize the image.
|
|
patch_size (`int`, *optional*, defaults to 14):
|
|
The spacial patch size of the vision encoder.
|
|
temporal_patch_size (`int`, *optional*, defaults to 2):
|
|
The temporal patch size of the vision encoder.
|
|
merge_size (`int`, *optional*, defaults to 2):
|
|
The merge size of the vision encoder to llm encoder.
|
|
"""
|
|
|
|
model_input_names = [
|
|
"pixel_values",
|
|
"image_grid_thw",
|
|
"pixel_values_videos",
|
|
"video_grid_thw",
|
|
"second_per_grid_ts",
|
|
]
|
|
|
|
def __init__(
|
|
self,
|
|
do_resize: bool = True,
|
|
resample: PILImageResampling = PILImageResampling.BICUBIC,
|
|
do_rescale: bool = True,
|
|
rescale_factor: Union[int, float] = 1 / 255,
|
|
do_normalize: bool = True,
|
|
image_mean: Optional[Union[float, List[float]]] = None,
|
|
image_std: Optional[Union[float, List[float]]] = None,
|
|
do_convert_rgb: bool = True,
|
|
min_pixels: int = 56 * 56,
|
|
max_pixels: int = 28 * 28 * 1280,
|
|
patch_size: int = 14,
|
|
temporal_patch_size: int = 2,
|
|
merge_size: int = 2,
|
|
**kwargs,
|
|
) -> None:
|
|
super().__init__(**kwargs)
|
|
self.do_resize = do_resize
|
|
self.resample = resample
|
|
self.do_rescale = do_rescale
|
|
self.rescale_factor = rescale_factor
|
|
self.do_normalize = do_normalize
|
|
self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
|
|
self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
|
|
self.min_pixels = min_pixels
|
|
self.max_pixels = max_pixels
|
|
self.patch_size = patch_size
|
|
self.temporal_patch_size = temporal_patch_size
|
|
self.merge_size = merge_size
|
|
self.size = {"min_pixels": min_pixels, "max_pixels": max_pixels}
|
|
self.do_convert_rgb = do_convert_rgb
|
|
|
|
def rescale(
|
|
self,
|
|
image: np.ndarray,
|
|
scale: float,
|
|
data_format: Optional[Union[str, ChannelDimension]] = None,
|
|
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
|
**kwargs,
|
|
) -> np.ndarray:
|
|
"""
|
|
Rescale an image by a scale factor. image = image * scale.
|
|
|
|
Args:
|
|
image (`np.ndarray`):
|
|
Image to rescale.
|
|
scale (`float`):
|
|
The scaling factor to rescale pixel values by.
|
|
data_format (`str` or `ChannelDimension`, *optional*):
|
|
The channel dimension format for the output image. If unset, the channel dimension format of the input
|
|
image is used. Can be one of:
|
|
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
|
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
|
input_data_format (`ChannelDimension` or `str`, *optional*):
|
|
The channel dimension format for the input image. If unset, the channel dimension format is inferred
|
|
from the input image. Can be one of:
|
|
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
|
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
|
|
|
Returns:
|
|
`np.ndarray`: The rescaled image.
|
|
"""
|
|
return rescale(
|
|
image,
|
|
scale=scale,
|
|
data_format=data_format,
|
|
input_data_format=input_data_format,
|
|
**kwargs,
|
|
)
|
|
|
|
def normalize(
|
|
self,
|
|
image: np.ndarray,
|
|
mean: Union[float, Iterable[float]],
|
|
std: Union[float, Iterable[float]],
|
|
data_format: Optional[Union[str, ChannelDimension]] = None,
|
|
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
|
**kwargs,
|
|
) -> np.ndarray:
|
|
"""
|
|
Normalize an image. image = (image - image_mean) / image_std.
|
|
|
|
Args:
|
|
image (`np.ndarray`):
|
|
Image to normalize.
|
|
mean (`float` or `Iterable[float]`):
|
|
Image mean to use for normalization.
|
|
std (`float` or `Iterable[float]`):
|
|
Image standard deviation to use for normalization.
|
|
data_format (`str` or `ChannelDimension`, *optional*):
|
|
The channel dimension format for the output image. If unset, the channel dimension format of the input
|
|
image is used. Can be one of:
|
|
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
|
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
|
input_data_format (`ChannelDimension` or `str`, *optional*):
|
|
The channel dimension format for the input image. If unset, the channel dimension format is inferred
|
|
from the input image. Can be one of:
|
|
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
|
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
|
|
|
Returns:
|
|
`np.ndarray`: The normalized image.
|
|
"""
|
|
return normalize(
|
|
image,
|
|
mean=mean,
|
|
std=std,
|
|
data_format=data_format,
|
|
input_data_format=input_data_format,
|
|
**kwargs,
|
|
)
|
|
|
|
def _preprocess(
|
|
self,
|
|
images: Union[ImageInput, VideoInput],
|
|
do_resize: bool = None,
|
|
resample: PILImageResampling = None,
|
|
do_rescale: bool = None,
|
|
rescale_factor: float = None,
|
|
do_normalize: bool = None,
|
|
image_mean: Optional[Union[float, List[float]]] = None,
|
|
image_std: Optional[Union[float, List[float]]] = None,
|
|
do_convert_rgb: bool = None,
|
|
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
|
|
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
|
):
|
|
"""
|
|
Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`.
|
|
|
|
Args:
|
|
images (`ImageInput`):
|
|
Image or batch of images to preprocess. Expects pixel values ranging from 0 to 255. If pixel values range from 0 to 1, set `do_rescale=False`.
|
|
vision_info (`List[Dict]`, *optional*):
|
|
Optional list of dictionaries containing additional information about vision inputs.
|
|
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
|
|
Whether to resize the image.
|
|
resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
|
|
Resampling filter to use if resizing the image. This can be one of the `PILImageResampling` enums.
|
|
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
|
|
Whether to rescale the image.
|
|
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
|
|
Scale factor to use if rescaling the image.
|
|
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
|
|
Whether to normalize the image.
|
|
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
|
|
Mean to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
|
|
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
|
|
Standard deviation to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
|
|
do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
|
|
Whether to convert the image to RGB.
|
|
data_format (`ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`):
|
|
The channel dimension format for the output image. Can be one of:
|
|
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
|
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
|
- Unset: Use the channel dimension format of the input image.
|
|
input_data_format (`ChannelDimension` or `str`, *optional*):
|
|
The channel dimension format for the input image. Can be one of:
|
|
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
|
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
|
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
|
|
"""
|
|
images = make_list_of_images(images)
|
|
|
|
if do_convert_rgb:
|
|
images = [convert_to_rgb(image) for image in images]
|
|
|
|
# All transformations expect numpy arrays.
|
|
images = [to_numpy_array(image) for image in images]
|
|
|
|
if input_data_format is None:
|
|
# We assume that all images have the same channel dimension format.
|
|
input_data_format = infer_channel_dimension_format(images[0])
|
|
|
|
height, width = get_image_size(images[0], channel_dim=input_data_format)
|
|
resized_height, resized_width = height, width
|
|
processed_images = []
|
|
for image in images:
|
|
if do_resize:
|
|
resized_height, resized_width = smart_resize(
|
|
height,
|
|
width,
|
|
factor=self.patch_size * self.merge_size,
|
|
min_pixels=self.min_pixels,
|
|
max_pixels=self.max_pixels,
|
|
)
|
|
image = resize(
|
|
image,
|
|
size=(resized_height, resized_width),
|
|
resample=resample,
|
|
input_data_format=input_data_format,
|
|
)
|
|
|
|
if do_rescale:
|
|
image = self.rescale(
|
|
image, scale=rescale_factor, input_data_format=input_data_format
|
|
)
|
|
|
|
if do_normalize:
|
|
image = self.normalize(
|
|
image=image,
|
|
mean=image_mean,
|
|
std=image_std,
|
|
input_data_format=input_data_format,
|
|
)
|
|
|
|
image = to_channel_dimension_format(
|
|
image, data_format, input_channel_dim=input_data_format
|
|
)
|
|
processed_images.append(image)
|
|
|
|
patches = np.array(processed_images)
|
|
if data_format == ChannelDimension.LAST:
|
|
patches = patches.transpose(0, 3, 1, 2)
|
|
if patches.shape[0] % self.temporal_patch_size != 0:
|
|
repeats = np.repeat(
|
|
patches[-1][np.newaxis], self.temporal_patch_size - 1, axis=0
|
|
)
|
|
patches = np.concatenate([patches, repeats], axis=0)
|
|
channel = patches.shape[1]
|
|
grid_t = patches.shape[0] // self.temporal_patch_size
|
|
grid_h, grid_w = (
|
|
resized_height // self.patch_size,
|
|
resized_width // self.patch_size,
|
|
)
|
|
patches = patches.reshape(
|
|
grid_t,
|
|
self.temporal_patch_size,
|
|
channel,
|
|
grid_h // self.merge_size,
|
|
self.merge_size,
|
|
self.patch_size,
|
|
grid_w // self.merge_size,
|
|
self.merge_size,
|
|
self.patch_size,
|
|
)
|
|
patches = patches.transpose(0, 3, 6, 4, 7, 2, 1, 5, 8)
|
|
flatten_patches = patches.reshape(
|
|
grid_t * grid_h * grid_w,
|
|
channel * self.temporal_patch_size * self.patch_size * self.patch_size,
|
|
)
|
|
|
|
return flatten_patches, (grid_t, grid_h, grid_w)
|
|
|
|
def preprocess(
|
|
self,
|
|
images: ImageInput,
|
|
videos: VideoInput = None,
|
|
do_resize: bool = None,
|
|
size: Dict[str, int] = None,
|
|
resample: PILImageResampling = None,
|
|
do_rescale: bool = None,
|
|
rescale_factor: float = None,
|
|
do_normalize: bool = None,
|
|
image_mean: Optional[Union[float, List[float]]] = None,
|
|
image_std: Optional[Union[float, List[float]]] = None,
|
|
do_convert_rgb: bool = None,
|
|
return_tensors: Optional[Union[str, TensorType]] = None,
|
|
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
|
|
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
|
):
|
|
"""
|
|
Args:
|
|
images (`ImageInput`):
|
|
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
|
|
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
|
|
videos (`VideoInput`):
|
|
Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If
|
|
passing in videos with pixel values between 0 and 1, set `do_rescale=False`.
|
|
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
|
|
Whether to resize the image.
|
|
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
|
|
Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
|
|
the longest edge resized to keep the input aspect ratio.
|
|
resample (`int`, *optional*, defaults to `self.resample`):
|
|
Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
|
|
has an effect if `do_resize` is set to `True`.
|
|
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
|
|
Whether to rescale the image.
|
|
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
|
|
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
|
|
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
|
|
Whether to normalize the image.
|
|
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
|
|
Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
|
|
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
|
|
Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
|
|
`True`.
|
|
do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
|
|
Whether to convert the image to RGB.
|
|
return_tensors (`str` or `TensorType`, *optional*):
|
|
The type of tensors to return. Can be one of:
|
|
- Unset: Return a list of `np.ndarray`.
|
|
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
|
|
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
|
|
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
|
|
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
|
|
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
|
|
The channel dimension format for the output image. Can be one of:
|
|
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
|
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
|
- Unset: Use the channel dimension format of the input image.
|
|
input_data_format (`ChannelDimension` or `str`, *optional*):
|
|
The channel dimension format for the input image. If unset, the channel dimension format is inferred
|
|
from the input image. Can be one of:
|
|
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
|
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
|
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
|
|
|
|
"""
|
|
do_resize = do_resize if do_resize is not None else self.do_resize
|
|
size = size if size is not None else self.size
|
|
resample = resample if resample is not None else self.resample
|
|
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
|
|
rescale_factor = (
|
|
rescale_factor if rescale_factor is not None else self.rescale_factor
|
|
)
|
|
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
|
|
image_mean = image_mean if image_mean is not None else self.image_mean
|
|
image_std = image_std if image_std is not None else self.image_std
|
|
do_convert_rgb = (
|
|
do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
|
|
)
|
|
|
|
def make_flat_list_of_images(
|
|
images: Union[List[ImageInput], ImageInput],
|
|
) -> ImageInput:
|
|
"""
|
|
Ensure that the output is a flat list of images. If the input is a single image, it is converted to a list of length 1.
|
|
If the input is a nested list of images, it is converted to a flat list of images.
|
|
Args:
|
|
images (`Union[List[ImageInput], ImageInput]`):
|
|
The input image.
|
|
Returns:
|
|
list: A list of images or a 4d array of images.
|
|
"""
|
|
# If the input is a nested list of images, we flatten it
|
|
if (
|
|
isinstance(images, (list, tuple))
|
|
and all(isinstance(images_i, (list, tuple)) for images_i in images)
|
|
and all(is_valid_list_of_images(images_i) for images_i in images)
|
|
):
|
|
return [img for img_list in images for img in img_list]
|
|
|
|
if isinstance(images, (list, tuple)) and is_valid_list_of_images(images):
|
|
if is_pil_image(images[0]) or images[0].ndim == 3:
|
|
return images
|
|
if images[0].ndim == 4:
|
|
return [img for img_list in images for img in img_list]
|
|
|
|
if is_valid_image(images):
|
|
if is_pil_image(images) or images.ndim == 3:
|
|
return [images]
|
|
if images.ndim == 4:
|
|
return list(images)
|
|
|
|
raise ValueError(f"Could not make a flat list of images from {images}")
|
|
|
|
def make_batched_videos(videos) -> VideoInput:
|
|
"""
|
|
Ensure that the input is a list of videos.
|
|
Args:
|
|
videos (`VideoInput`):
|
|
Video or videos to turn into a list of videos.
|
|
Returns:
|
|
list: A list of videos.
|
|
"""
|
|
if (
|
|
isinstance(videos, (list, tuple))
|
|
and isinstance(videos[0], (list, tuple))
|
|
and is_valid_image(videos[0][0])
|
|
):
|
|
# case 1: nested batch of videos so we flatten it
|
|
if not is_pil_image(videos[0][0]) and videos[0][0].ndim == 4:
|
|
videos = [
|
|
[video for batch_list in batched_videos for video in batch_list]
|
|
for batched_videos in videos
|
|
]
|
|
# case 2: list of videos represented as list of video frames
|
|
return videos
|
|
|
|
elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]):
|
|
if is_pil_image(videos[0]) or videos[0].ndim == 3:
|
|
return [videos]
|
|
elif videos[0].ndim == 4:
|
|
return [list(video) for video in videos]
|
|
|
|
elif is_valid_image(videos):
|
|
if is_pil_image(videos) or videos.ndim == 3:
|
|
return [[videos]]
|
|
elif videos.ndim == 4:
|
|
return [list(videos)]
|
|
|
|
raise ValueError(f"Could not make batched video from {videos}")
|
|
|
|
if images is not None:
|
|
images = make_flat_list_of_images(images)
|
|
if videos is not None:
|
|
videos = make_batched_videos(videos)
|
|
|
|
if images is not None and 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."
|
|
)
|
|
|
|
validate_preprocess_arguments(
|
|
rescale_factor=rescale_factor,
|
|
do_normalize=do_normalize,
|
|
image_mean=image_mean,
|
|
image_std=image_std,
|
|
do_resize=do_resize,
|
|
size=size,
|
|
resample=resample,
|
|
)
|
|
|
|
if images is not None:
|
|
pixel_values, vision_grid_thws = [], []
|
|
for image in images:
|
|
patches, image_grid_thw = self._preprocess(
|
|
image,
|
|
do_resize=do_resize,
|
|
resample=resample,
|
|
do_rescale=do_rescale,
|
|
rescale_factor=rescale_factor,
|
|
do_normalize=do_normalize,
|
|
image_mean=image_mean,
|
|
image_std=image_std,
|
|
data_format=data_format,
|
|
do_convert_rgb=do_convert_rgb,
|
|
input_data_format=input_data_format,
|
|
)
|
|
pixel_values.extend(patches)
|
|
vision_grid_thws.append(image_grid_thw)
|
|
pixel_values = np.array(pixel_values)
|
|
vision_grid_thws = np.array(vision_grid_thws)
|
|
data = {"pixel_values": pixel_values, "image_grid_thw": vision_grid_thws}
|
|
|
|
if videos is not None:
|
|
pixel_values, vision_grid_thws = [], []
|
|
for images in videos:
|
|
patches, video_grid_thw = self._preprocess(
|
|
images,
|
|
do_resize=do_resize,
|
|
resample=resample,
|
|
do_rescale=do_rescale,
|
|
rescale_factor=rescale_factor,
|
|
do_normalize=do_normalize,
|
|
image_mean=image_mean,
|
|
image_std=image_std,
|
|
data_format=data_format,
|
|
do_convert_rgb=do_convert_rgb,
|
|
input_data_format=input_data_format,
|
|
)
|
|
pixel_values.extend(patches)
|
|
vision_grid_thws.append(video_grid_thw)
|
|
pixel_values = np.array(pixel_values)
|
|
vision_grid_thws = np.array(vision_grid_thws)
|
|
data = {
|
|
"pixel_values_videos": pixel_values,
|
|
"video_grid_thw": vision_grid_thws,
|
|
}
|
|
|
|
return BatchFeature(data=data, tensor_type=return_tensors)
|
|
|
|
|
|
AutoImageProcessor.register(Qwen2_5_VLConfig, None, Qwen2_5_VLImageProcessor, None)
|
|
AutoProcessor.register(Qwen2_5_VLConfig, Qwen2_5_VLProcessor)
|