Spaces:

yizhangliu
/

Grounded-Segment-Anything

Running on T4

Grounded-Segment-Anything / transformers_4_35_0 /models /altclip /configuration_altclip.py

liuyizhang

add transformers_4_35_0

1ce5e18 8 months ago

No virus

19.1 kB

	# coding=utf-8
	# Copyright 2022 WenXiang ZhongzhiCheng LedellWu LiuGuang BoWenZhang 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.
	""" AltCLIP model configuration"""
	import os
	from typing import Union

	from ...configuration_utils import PretrainedConfig
	from ...utils import logging


	logger = logging.get_logger(__name__)

	ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP = {
	"BAAI/AltCLIP": "https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json",
	# See all AltCLIP models at https://huggingface.co/models?filter=altclip
	}


	class AltCLIPTextConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`AltCLIPTextModel`]. It is used to instantiate a
	AltCLIP text model according to the specified arguments, defining the model architecture. Instantiating a
	configuration with the defaults will yield a similar configuration to that of the AltCLIP
	[BAAI/AltCLIP](https://huggingface.co/BAAI/AltCLIP) 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 250002):
	Vocabulary size of the AltCLIP model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`AltCLIPTextModel`].
	hidden_size (`int`, optional, defaults to 1024):
	Dimensionality of the encoder layers and the pooler layer.
	num_hidden_layers (`int`, optional, defaults to 24):
	Number of hidden layers in the Transformer encoder.
	num_attention_heads (`int`, optional, defaults to 16):
	Number of attention heads for each attention layer in the Transformer encoder.
	intermediate_size (`int`, optional, defaults to 4096):
	Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
	hidden_act (`str` or `Callable`, optional, defaults to `"gelu"`):
	The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
	`"relu"`, `"silu"` and `"gelu_new"` are supported.
	hidden_dropout_prob (`float`, optional, defaults to 0.1):
	The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
	attention_probs_dropout_prob (`float`, optional, defaults to 0.1):
	The dropout ratio for the attention probabilities.
	max_position_embeddings (`int`, optional, defaults to 514):
	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).
	type_vocab_size (`int`, optional, defaults to 2):
	The vocabulary size of the `token_type_ids` passed when calling [`AltCLIPTextModel`]
	initializer_range (`float`, optional, defaults to 0.02):
	The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
	layer_norm_eps (`float`, optional, defaults to 1e-5):
	The epsilon used by the layer normalization layers.
	position_embedding_type (`str`, optional, defaults to `"absolute"`):
	Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
	positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
	[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
	For more information on `"relative_key_query"`, please refer to Method 4 in [Improve Transformer Models
	with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
	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`.
	project_dim (`int`, optional, defaults to 768):
	The dimentions of the teacher model before the mapping layer.

	Examples:

	```python
	>>> from transformers import AltCLIPTextModel, AltCLIPTextConfig

	>>> # Initializing a AltCLIPTextConfig with BAAI/AltCLIP style configuration
	>>> configuration = AltCLIPTextConfig()

	>>> # Initializing a AltCLIPTextModel (with random weights) from the BAAI/AltCLIP style configuration
	>>> model = AltCLIPTextModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```"""
	model_type = "altclip_text_model"

	def __init__(
	self,
	vocab_size=250002,
	hidden_size=1024,
	num_hidden_layers=24,
	num_attention_heads=16,
	intermediate_size=4096,
	hidden_act="gelu",
	hidden_dropout_prob=0.1,
	attention_probs_dropout_prob=0.1,
	max_position_embeddings=514,
	type_vocab_size=1,
	initializer_range=0.02,
	initializer_factor=0.02,
	layer_norm_eps=1e-05,
	pad_token_id=1,
	bos_token_id=0,
	eos_token_id=2,
	position_embedding_type="absolute",
	use_cache=True,
	project_dim=768,
	**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.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads
	self.hidden_act = hidden_act
	self.intermediate_size = intermediate_size
	self.hidden_dropout_prob = hidden_dropout_prob
	self.attention_probs_dropout_prob = attention_probs_dropout_prob
	self.max_position_embeddings = max_position_embeddings
	self.type_vocab_size = type_vocab_size
	self.initializer_range = initializer_range
	self.initializer_factor = initializer_factor
	self.layer_norm_eps = layer_norm_eps
	self.position_embedding_type = position_embedding_type
	self.use_cache = use_cache
	self.project_dim = project_dim


	class AltCLIPVisionConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`AltCLIPModel`]. It is used to instantiate an
	AltCLIP model according to the specified arguments, defining the model architecture. Instantiating a configuration
	with the defaults will yield a similar configuration to that of the AltCLIP
	[BAAI/AltCLIP](https://huggingface.co/BAAI/AltCLIP) 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.
	image_size (`int`, optional, defaults to 224):
	The size (resolution) of each image.
	patch_size (`int`, optional, defaults to 32):
	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-5):
	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):
	A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
	testing).

	Example:

	```python
	>>> from transformers import AltCLIPVisionConfig, AltCLIPVisionModel

	>>> # Initializing a AltCLIPVisionConfig with BAAI/AltCLIP style configuration
	>>> configuration = AltCLIPVisionConfig()

	>>> # Initializing a AltCLIPVisionModel (with random weights) from the BAAI/AltCLIP style configuration
	>>> model = AltCLIPVisionModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```"""

	model_type = "altclip_vision_model"

	def __init__(
	self,
	hidden_size=768,
	intermediate_size=3072,
	projection_dim=512,
	num_hidden_layers=12,
	num_attention_heads=12,
	num_channels=3,
	image_size=224,
	patch_size=32,
	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.projection_dim = projection_dim
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads
	self.num_channels = num_channels
	self.patch_size = patch_size
	self.image_size = image_size
	self.initializer_range = initializer_range
	self.initializer_factor = initializer_factor
	self.attention_dropout = attention_dropout
	self.layer_norm_eps = layer_norm_eps
	self.hidden_act = hidden_act

	@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 AltCLIPConfig
	if config_dict.get("model_type") == "altclip":
	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)


	class AltCLIPConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`AltCLIPModel`]. It is used to instantiate an
	AltCLIP model according to the specified arguments, defining the model architecture. Instantiating a configuration
	with the defaults will yield a similar configuration to that of the AltCLIP
	[BAAI/AltCLIP](https://huggingface.co/BAAI/AltCLIP) 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 [`AltCLIPTextConfig`].
	vision_config (`dict`, optional):
	Dictionary of configuration options used to initialize [`AltCLIPVisionConfig`].
	projection_dim (`int`, optional, defaults to 768):
	Dimentionality of text and vision projection layers.
	logit_scale_init_value (`float`, optional, defaults to 2.6592):
	The inital value of the logit_scale paramter. Default is used as per the original CLIP implementation.
	kwargs (optional):
	Dictionary of keyword arguments.

	Example:

	```python
	>>> from transformers import AltCLIPConfig, AltCLIPModel

	>>> # Initializing a AltCLIPConfig with BAAI/AltCLIP style configuration
	>>> configuration = AltCLIPConfig()

	>>> # Initializing a AltCLIPModel (with random weights) from the BAAI/AltCLIP style configuration
	>>> model = AltCLIPModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config

	>>> # We can also initialize a AltCLIPConfig from a AltCLIPTextConfig and a AltCLIPVisionConfig

	>>> # Initializing a AltCLIPText and AltCLIPVision configuration
	>>> config_text = AltCLIPTextConfig()
	>>> config_vision = AltCLIPVisionConfig()

	>>> config = AltCLIPConfig.from_text_vision_configs(config_text, config_vision)
	```"""

	model_type = "altclip"

	def __init__(
	self, text_config=None, vision_config=None, projection_dim=768, logit_scale_init_value=2.6592, **kwargs
	):
	# If `_config_dict` exist, we use them for the backward compatibility.
	# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
	# of confusion!).
	text_config_dict = kwargs.pop("text_config_dict", None)
	vision_config_dict = kwargs.pop("vision_config_dict", None)

	super().__init__(**kwargs)

	# Instead of simply assigning `[text\|vision]_config_dict` to `[text\|vision]_config`, we use the values in
	# `[text\|vision]_config_dict` to update the values in `[text\|vision]_config`. The values should be same in most
	# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
	if text_config_dict is not None:
	if text_config is None:
	text_config = {}

	# This is the complete result when using `text_config_dict`.
	_text_config_dict = AltCLIPTextConfig(**text_config_dict).to_dict()

	# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
	for key, value in _text_config_dict.items():
	if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
	# If specified in `text_config_dict`
	if key in text_config_dict:
	message = (
	f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
	f'The value `text_config_dict["{key}"]` will be used instead.'
	)
	# If inferred from default argument values (just to be super careful)
	else:
	message = (
	f"`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The "
	f'value `text_config["{key}"]` will be overriden.'
	)
	logger.warning(message)

	# Update all values in `text_config` with the ones in `_text_config_dict`.
	text_config.update(_text_config_dict)

	if vision_config_dict is not None:
	if vision_config is None:
	vision_config = {}

	# This is the complete result when using `vision_config_dict`.
	_vision_config_dict = AltCLIPVisionConfig(**vision_config_dict).to_dict()
	# convert keys to string instead of integer
	if "id2label" in _vision_config_dict:
	_vision_config_dict["id2label"] = {
	str(key): value for key, value in _vision_config_dict["id2label"].items()
	}

	# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
	for key, value in _vision_config_dict.items():
	if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
	# If specified in `vision_config_dict`
	if key in vision_config_dict:
	message = (
	f"`{key}` is found in both `vision_config_dict` and `vision_config` but with different "
	f'values. The value `vision_config_dict["{key}"]` will be used instead.'
	)
	# If inferred from default argument values (just to be super careful)
	else:
	message = (
	f"`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. "
	f'The value `vision_config["{key}"]` will be overriden.'
	)
	logger.warning(message)

	# Update all values in `vision_config` with the ones in `_vision_config_dict`.
	vision_config.update(_vision_config_dict)

	if text_config is None:
	text_config = {}
	logger.info("`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.")

	if vision_config is None:
	vision_config = {}
	logger.info("`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.")

	self.text_config = AltCLIPTextConfig(**text_config)
	self.vision_config = AltCLIPVisionConfig(**vision_config)

	self.projection_dim = projection_dim
	self.logit_scale_init_value = logit_scale_init_value
	self.initializer_factor = 1.0

	@classmethod
	def from_text_vision_configs(cls, text_config: AltCLIPTextConfig, vision_config: AltCLIPVisionConfig, **kwargs):
	r"""
	Instantiate a [`AltCLIPConfig`] (or a derived class) from altclip text model configuration and altclip vision
	model configuration.

	Returns:
	[`AltCLIPConfig`]: An instance of a configuration object
	"""

	return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)