338 lines
15 KiB
Python
338 lines
15 KiB
Python
# coding=utf-8
|
|
# Copyright 2023 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.
|
|
""" OWLv2 model configuration"""
|
|
|
|
import os
|
|
from typing import TYPE_CHECKING, Dict, Union
|
|
|
|
|
|
if TYPE_CHECKING:
|
|
pass
|
|
|
|
from ...configuration_utils import PretrainedConfig
|
|
from ...utils import logging
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
|
|
from ..deprecated._archive_maps import OWLV2_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
|
|
|
|
|
|
# Copied from transformers.models.owlvit.configuration_owlvit.OwlViTTextConfig with OwlViT->Owlv2, owlvit-base-patch32->owlv2-base-patch16, owlvit->owlv2, OWL-ViT->OWLv2
|
|
class Owlv2TextConfig(PretrainedConfig):
|
|
r"""
|
|
This is the configuration class to store the configuration of an [`Owlv2TextModel`]. It is used to instantiate an
|
|
Owlv2 text encoder according to the specified arguments, defining the model architecture. Instantiating a
|
|
configuration with the defaults will yield a similar configuration to that of the Owlv2
|
|
[google/owlv2-base-patch16](https://huggingface.co/google/owlv2-base-patch16) architecture.
|
|
|
|
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 49408):
|
|
Vocabulary size of the OWLv2 text model. Defines the number of different tokens that can be represented
|
|
by the `inputs_ids` passed when calling [`Owlv2TextModel`].
|
|
hidden_size (`int`, *optional*, defaults to 512):
|
|
Dimensionality of the encoder layers and the pooler layer.
|
|
intermediate_size (`int`, *optional*, defaults to 2048):
|
|
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
|
|
num_hidden_layers (`int`, *optional*, defaults to 12):
|
|
Number of hidden layers in the Transformer encoder.
|
|
num_attention_heads (`int`, *optional*, defaults to 8):
|
|
Number of attention heads for each attention layer in the Transformer encoder.
|
|
max_position_embeddings (`int`, *optional*, defaults to 16):
|
|
The maximum sequence length that this model might ever be used with. Typically set this to something large
|
|
just in case (e.g., 512 or 1024 or 2048).
|
|
hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
|
|
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
|
|
`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
|
|
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
|
|
The epsilon used by the layer normalization layers.
|
|
attention_dropout (`float`, *optional*, defaults to 0.0):
|
|
The dropout ratio for the attention probabilities.
|
|
initializer_range (`float`, *optional*, defaults to 0.02):
|
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
|
initializer_factor (`float`, *optional*, defaults to 1.0):
|
|
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
|
|
testing).
|
|
pad_token_id (`int`, *optional*, defaults to 0):
|
|
The id of the padding token in the input sequences.
|
|
bos_token_id (`int`, *optional*, defaults to 49406):
|
|
The id of the beginning-of-sequence token in the input sequences.
|
|
eos_token_id (`int`, *optional*, defaults to 49407):
|
|
The id of the end-of-sequence token in the input sequences.
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import Owlv2TextConfig, Owlv2TextModel
|
|
|
|
>>> # Initializing a Owlv2TextModel with google/owlv2-base-patch16 style configuration
|
|
>>> configuration = Owlv2TextConfig()
|
|
|
|
>>> # Initializing a Owlv2TextConfig from the google/owlv2-base-patch16 style configuration
|
|
>>> model = Owlv2TextModel(configuration)
|
|
|
|
>>> # Accessing the model configuration
|
|
>>> configuration = model.config
|
|
```"""
|
|
|
|
model_type = "owlv2_text_model"
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_size=49408,
|
|
hidden_size=512,
|
|
intermediate_size=2048,
|
|
num_hidden_layers=12,
|
|
num_attention_heads=8,
|
|
max_position_embeddings=16,
|
|
hidden_act="quick_gelu",
|
|
layer_norm_eps=1e-5,
|
|
attention_dropout=0.0,
|
|
initializer_range=0.02,
|
|
initializer_factor=1.0,
|
|
pad_token_id=0,
|
|
bos_token_id=49406,
|
|
eos_token_id=49407,
|
|
**kwargs,
|
|
):
|
|
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
|
|
|
|
self.vocab_size = vocab_size
|
|
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.max_position_embeddings = max_position_embeddings
|
|
self.hidden_act = hidden_act
|
|
self.layer_norm_eps = layer_norm_eps
|
|
self.attention_dropout = attention_dropout
|
|
self.initializer_range = initializer_range
|
|
self.initializer_factor = initializer_factor
|
|
|
|
@classmethod
|
|
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
|
|
cls._set_token_in_kwargs(kwargs)
|
|
|
|
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
|
|
|
|
# get the text config dict if we are loading from Owlv2Config
|
|
if config_dict.get("model_type") == "owlv2":
|
|
config_dict = config_dict["text_config"]
|
|
|
|
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
|
|
logger.warning(
|
|
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
|
|
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
|
|
)
|
|
|
|
return cls.from_dict(config_dict, **kwargs)
|
|
|
|
|
|
# Copied from transformers.models.owlvit.configuration_owlvit.OwlViTVisionConfig with OwlViT->Owlv2, owlvit-base-patch32->owlv2-base-patch16, owlvit->owlv2, OWL-ViT->OWLv2, 32->16
|
|
class Owlv2VisionConfig(PretrainedConfig):
|
|
r"""
|
|
This is the configuration class to store the configuration of an [`Owlv2VisionModel`]. It is used to instantiate
|
|
an OWLv2 image encoder according to the specified arguments, defining the model architecture. Instantiating a
|
|
configuration with the defaults will yield a similar configuration to that of the OWLv2
|
|
[google/owlv2-base-patch16](https://huggingface.co/google/owlv2-base-patch16) architecture.
|
|
|
|
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
|
documentation from [`PretrainedConfig`] for more information.
|
|
|
|
Args:
|
|
hidden_size (`int`, *optional*, defaults to 768):
|
|
Dimensionality of the encoder layers and the pooler layer.
|
|
intermediate_size (`int`, *optional*, defaults to 3072):
|
|
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
|
|
num_hidden_layers (`int`, *optional*, defaults to 12):
|
|
Number of hidden layers in the Transformer encoder.
|
|
num_attention_heads (`int`, *optional*, defaults to 12):
|
|
Number of attention heads for each attention layer in the Transformer encoder.
|
|
num_channels (`int`, *optional*, defaults to 3):
|
|
Number of channels in the input images.
|
|
image_size (`int`, *optional*, defaults to 768):
|
|
The size (resolution) of each image.
|
|
patch_size (`int`, *optional*, defaults to 16):
|
|
The size (resolution) of each patch.
|
|
hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
|
|
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
|
|
`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
|
|
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
|
|
The epsilon used by the layer normalization layers.
|
|
attention_dropout (`float`, *optional*, defaults to 0.0):
|
|
The dropout ratio for the attention probabilities.
|
|
initializer_range (`float`, *optional*, defaults to 0.02):
|
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
|
initializer_factor (`float`, *optional*, defaults to 1.0):
|
|
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
|
|
testing).
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import Owlv2VisionConfig, Owlv2VisionModel
|
|
|
|
>>> # Initializing a Owlv2VisionModel with google/owlv2-base-patch16 style configuration
|
|
>>> configuration = Owlv2VisionConfig()
|
|
|
|
>>> # Initializing a Owlv2VisionModel model from the google/owlv2-base-patch16 style configuration
|
|
>>> model = Owlv2VisionModel(configuration)
|
|
|
|
>>> # Accessing the model configuration
|
|
>>> configuration = model.config
|
|
```"""
|
|
|
|
model_type = "owlv2_vision_model"
|
|
|
|
def __init__(
|
|
self,
|
|
hidden_size=768,
|
|
intermediate_size=3072,
|
|
num_hidden_layers=12,
|
|
num_attention_heads=12,
|
|
num_channels=3,
|
|
image_size=768,
|
|
patch_size=16,
|
|
hidden_act="quick_gelu",
|
|
layer_norm_eps=1e-5,
|
|
attention_dropout=0.0,
|
|
initializer_range=0.02,
|
|
initializer_factor=1.0,
|
|
**kwargs,
|
|
):
|
|
super().__init__(**kwargs)
|
|
|
|
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.num_channels = num_channels
|
|
self.image_size = image_size
|
|
self.patch_size = patch_size
|
|
self.hidden_act = hidden_act
|
|
self.layer_norm_eps = layer_norm_eps
|
|
self.attention_dropout = attention_dropout
|
|
self.initializer_range = initializer_range
|
|
self.initializer_factor = initializer_factor
|
|
|
|
@classmethod
|
|
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
|
|
cls._set_token_in_kwargs(kwargs)
|
|
|
|
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
|
|
|
|
# get the vision config dict if we are loading from Owlv2Config
|
|
if config_dict.get("model_type") == "owlv2":
|
|
config_dict = config_dict["vision_config"]
|
|
|
|
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
|
|
logger.warning(
|
|
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
|
|
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
|
|
)
|
|
|
|
return cls.from_dict(config_dict, **kwargs)
|
|
|
|
|
|
# Copied from transformers.models.owlvit.configuration_owlvit.OwlViTConfig with OwlViT->Owlv2, owlvit-base-patch32->owlv2-base-patch16, owlvit->owlv2, OWL-ViT->OWLv2
|
|
class Owlv2Config(PretrainedConfig):
|
|
r"""
|
|
[`Owlv2Config`] is the configuration class to store the configuration of an [`Owlv2Model`]. It is used to
|
|
instantiate an OWLv2 model according to the specified arguments, defining the text model and vision model
|
|
configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the OWLv2
|
|
[google/owlv2-base-patch16](https://huggingface.co/google/owlv2-base-patch16) architecture.
|
|
|
|
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
|
documentation from [`PretrainedConfig`] for more information.
|
|
|
|
Args:
|
|
text_config (`dict`, *optional*):
|
|
Dictionary of configuration options used to initialize [`Owlv2TextConfig`].
|
|
vision_config (`dict`, *optional*):
|
|
Dictionary of configuration options used to initialize [`Owlv2VisionConfig`].
|
|
projection_dim (`int`, *optional*, defaults to 512):
|
|
Dimensionality of text and vision projection layers.
|
|
logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
|
|
The inital value of the *logit_scale* parameter. Default is used as per the original OWLv2
|
|
implementation.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not the model should return a dictionary. If `False`, returns a tuple.
|
|
kwargs (*optional*):
|
|
Dictionary of keyword arguments.
|
|
"""
|
|
|
|
model_type = "owlv2"
|
|
|
|
def __init__(
|
|
self,
|
|
text_config=None,
|
|
vision_config=None,
|
|
projection_dim=512,
|
|
logit_scale_init_value=2.6592,
|
|
return_dict=True,
|
|
**kwargs,
|
|
):
|
|
super().__init__(**kwargs)
|
|
|
|
if text_config is None:
|
|
text_config = {}
|
|
logger.info("text_config is None. Initializing the Owlv2TextConfig with default values.")
|
|
|
|
if vision_config is None:
|
|
vision_config = {}
|
|
logger.info("vision_config is None. initializing the Owlv2VisionConfig with default values.")
|
|
|
|
self.text_config = Owlv2TextConfig(**text_config)
|
|
self.vision_config = Owlv2VisionConfig(**vision_config)
|
|
|
|
self.projection_dim = projection_dim
|
|
self.logit_scale_init_value = logit_scale_init_value
|
|
self.return_dict = return_dict
|
|
self.initializer_factor = 1.0
|
|
|
|
@classmethod
|
|
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
|
|
cls._set_token_in_kwargs(kwargs)
|
|
|
|
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
|
|
|
|
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
|
|
logger.warning(
|
|
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
|
|
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
|
|
)
|
|
|
|
return cls.from_dict(config_dict, **kwargs)
|
|
|
|
@classmethod
|
|
def from_text_vision_configs(cls, text_config: Dict, vision_config: Dict, **kwargs):
|
|
r"""
|
|
Instantiate a [`Owlv2Config`] (or a derived class) from owlv2 text model configuration and owlv2 vision
|
|
model configuration.
|
|
|
|
Returns:
|
|
[`Owlv2Config`]: An instance of a configuration object
|
|
"""
|
|
config_dict = {}
|
|
config_dict["text_config"] = text_config
|
|
config_dict["vision_config"] = vision_config
|
|
|
|
return cls.from_dict(config_dict, **kwargs)
|