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# Copyright 2021 TF-Transformers Authors.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# ====================================================================
""" ViT model configuration """
from tf_transformers.core import TransformerConfig
[docs]class CLIPImageConfig(TransformerConfig):
r"""
This is the configuration class to store the configuration of a :class:`~tf_transformers.models.ViTModel`.
It is used to instantiate an ViT 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 ViT `google/vit-base-patch16-224
<https://huggingface.co/google/vit-base-patch16-224>`__ architecture.
Configuration objects inherit from :class:`~tf_transformers.models.TransformerConfig` and can be used to control the model
outputs. Read the documentation from :class:`~tf_transformers.models.TransformerConfig` for more information.
Args:
vocab_size (:obj:`int`, `optional`, defaults to 30522):
Vocabulary size of the ALBERT model. Defines the number of different tokens that can be represented by the
:obj:`inputs_ids` passed when calling :class:`~tf_transformers.model.BertModel` or
:class:`~tf_transformers.models.BertEncoder`.
embedding_size (:obj:`int`, `optional`, defaults to 128):
Dimensionality of vocabulary embeddings.
embedding_projection_size (:obj:`int`):
Dimensionality of the encoder layers and the pooler layer. Useful for Bert.
num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (:obj:`int`, `optional`, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
attention_head_size (:obj:`int`):
Size of attention heads in each layer. Normally (embedding_size//num_attention_heads).
intermediate_size (:obj:`int`, `optional`, defaults to 3072):
The dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string,
:obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and many are supported.
hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
(e.g., 512 or 1024 or 2048).
type_vocab_size (:obj:`int`, `optional`, defaults to 2):
The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.BertModel` or
:class:`~transformers.TFBertModel`.
initializer_range (:obj:`float`, `optional`, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-12):
The epsilon used by the layer normalization layers.
classifier_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
The dropout ratio for attached classifiers.
position_embedding_type (:obj:`str`, `optional`, defaults to :obj:`"absolute"`):
Type of position embedding. Choose one of :obj:`"absolute"`, :obj:`"relative_key"`,
:obj:`"relative_key_query"`. For positional embeddings use :obj:`"absolute"`. For more information on
:obj:`"relative_key"`, please refer to `Self-Attention with Relative Position Representations (Shaw et al.)
<https://arxiv.org/abs/1803.02155>`__. For more information on :obj:`"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>`__.
num_hidden_groups (:obj:`int`, `optional`, defaults to 1):
Number of groups for the hidden layers, parameters in the same group are shared.
image_size (:obj:`int`, `optional`, defaults to :obj:`224`):
The size (resolution) of each image.
patch_size (:obj:`int`, `optional`, defaults to :obj:`16`):
The size (resolution) of each patch.
num_channels (:obj:`int`, `optional`, defaults to :obj:`3`):
The number of input channels.
num_labels (:obj:`int`, `optional`, defaults to :obj:`1000`):
Total number of labels by which model has been pre-trained
Examples::
>>> from tf_transformers.models import CLIPImageConfig, CLIPImageEncoder
>>> # Initializing an 'google/vit-base-patch16-224' style configuration
>>> configuration = CLIPImageConfig()
>>> # Initializing an ViT different style configuration
>>> configuration_new = CLIPImageConfig(
... embedding_size=768,
... num_attention_heads=12,
... intermediate_size=3072,
... )
>>> # Initializing a model from the original configuration
>>> model = CLIPImageEncoder.from_config(configuration)
>>> # Accessing the model configuration
>>> configuration = model._config_dict # This has more details than original configuration
>>> # To get a config
>>> model_name = 'openai/clip-vit-base-patch32'
>>> config = CLIPImage.get_config(model_name)
"""
def __init__(
self,
vocab_size=None,
embedding_size=768,
num_hidden_layers=12,
num_attention_heads=12,
attention_head_size=64,
intermediate_size=3072,
hidden_act="quick_gelu",
intermediate_act="quick_gelu",
hidden_dropout_prob=0,
attention_probs_dropout_prob=0,
max_position_embeddings=None,
type_vocab_size=-1,
initializer_range=0.02,
layer_norm_epsilon=1e-5,
position_embedding_type="absolute",
image_size=224,
patch_size=16,
num_channels=3,
num_labels=None,
projection_dim=512,
):
super().__init__(
vocab_size=vocab_size,
embedding_size=embedding_size,
num_hidden_layers=num_hidden_layers,
num_attention_heads=num_attention_heads,
attention_head_size=attention_head_size,
hidden_act=hidden_act,
intermediate_act=intermediate_act,
intermediate_size=intermediate_size,
hidden_dropout_prob=hidden_dropout_prob,
attention_probs_dropout_prob=attention_probs_dropout_prob,
max_position_embeddings=max_position_embeddings,
type_vocab_size=type_vocab_size,
initializer_range=initializer_range,
layer_norm_epsilon=layer_norm_epsilon,
position_embedding_type=position_embedding_type,
image_size=image_size,
patch_size=patch_size,
num_channels=num_channels,
num_labels=num_labels,
projection_dim=projection_dim,
)
[docs]class CLIPTextConfig(TransformerConfig):
r"""
This is the configuration class to store the configuration of a :class:`~tf_transformers.models.ViTModel`.
It is used to instantiate an ViT 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 ViT `google/vit-base-patch16-224
<https://huggingface.co/google/vit-base-patch16-224>`__ architecture.
Configuration objects inherit from :class:`~tf_transformers.models.TransformerConfig` and can be used to control the model
outputs. Read the documentation from :class:`~tf_transformers.models.TransformerConfig` for more information.
Args:
vocab_size (:obj:`int`, `optional`, defaults to 30522):
Vocabulary size of the ALBERT model. Defines the number of different tokens that can be represented by the
:obj:`inputs_ids` passed when calling :class:`~tf_transformers.model.BertModel` or
:class:`~tf_transformers.models.BertEncoder`.
embedding_size (:obj:`int`, `optional`, defaults to 128):
Dimensionality of vocabulary embeddings.
embedding_projection_size (:obj:`int`):
Dimensionality of the encoder layers and the pooler layer. Useful for Bert.
num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (:obj:`int`, `optional`, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
attention_head_size (:obj:`int`):
Size of attention heads in each layer. Normally (embedding_size//num_attention_heads).
intermediate_size (:obj:`int`, `optional`, defaults to 3072):
The dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string,
:obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and many are supported.
hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
(e.g., 512 or 1024 or 2048).
type_vocab_size (:obj:`int`, `optional`, defaults to 2):
The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.BertModel` or
:class:`~transformers.TFBertModel`.
initializer_range (:obj:`float`, `optional`, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_epsilon (:obj:`float`, `optional`, defaults to 1e-12):
The epsilon used by the layer normalization layers.
classifier_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
The dropout ratio for attached classifiers.
position_embedding_type (:obj:`str`, `optional`, defaults to :obj:`"absolute"`):
Type of position embedding. Choose one of :obj:`"absolute"`, :obj:`"relative_key"`,
:obj:`"relative_key_query"`. For positional embeddings use :obj:`"absolute"`. For more information on
:obj:`"relative_key"`, please refer to `Self-Attention with Relative Position Representations (Shaw et al.)
<https://arxiv.org/abs/1803.02155>`__. For more information on :obj:`"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>`__.
num_hidden_groups (:obj:`int`, `optional`, defaults to 1):
Number of groups for the hidden layers, parameters in the same group are shared.
image_size (:obj:`int`, `optional`, defaults to :obj:`224`):
The size (resolution) of each image.
patch_size (:obj:`int`, `optional`, defaults to :obj:`16`):
The size (resolution) of each patch.
num_channels (:obj:`int`, `optional`, defaults to :obj:`3`):
The number of input channels.
num_labels (:obj:`int`, `optional`, defaults to :obj:`1000`):
Total number of labels by which model has been pre-trained
Examples::
>>> from tf_transformers.models import CLIPImageConfig, CLIPImageEncoder
>>> # Initializing an 'openai/clip-vit-base-patch32' style configuration
>>> configuration = CLIPImageConfig()
>>> vision_config = configuration['vision_config']
>>> text_config = configuration['text_config]
>>> # Initializing a model from the original configuration
>>> vision_encoder = CLIPImageEncoder.from_config(vision_config)
>>> text_encoder = CLIPTextEncoder.from_config(text_config)
>>> model = CLIPEncoder(vision_encoder, text_encoder, is_training=False, use_dropout=False)
>>> configuration = model._config_dict # This has more details than original configuration
>>> # To get a model config
>>> model_name = 'openai/clip-vit-base-patch32'
>>> config = CLIPImage.get_config(model_name)
"""
def __init__(
self,
vocab_size=49408,
embedding_size=512,
num_hidden_layers=12,
num_attention_heads=8,
attention_head_size=64,
intermediate_size=2048,
hidden_act="quick_gelu",
intermediate_act="quick_gelu",
hidden_dropout_prob=0,
attention_probs_dropout_prob=0,
max_position_embeddings=77,
type_vocab_size=-1,
initializer_range=0.02,
layer_norm_epsilon=1e-5,
position_embedding_type="absolute",
image_size=None,
patch_size=None,
num_channels=None,
num_labels=None,
projection_dim=512,
):
super().__init__(
vocab_size=vocab_size,
embedding_size=embedding_size,
num_hidden_layers=num_hidden_layers,
num_attention_heads=num_attention_heads,
attention_head_size=attention_head_size,
hidden_act=hidden_act,
intermediate_act=intermediate_act,
intermediate_size=intermediate_size,
hidden_dropout_prob=hidden_dropout_prob,
attention_probs_dropout_prob=attention_probs_dropout_prob,
max_position_embeddings=max_position_embeddings,
type_vocab_size=type_vocab_size,
initializer_range=initializer_range,
layer_norm_epsilon=layer_norm_epsilon,
position_embedding_type=position_embedding_type,
image_size=image_size,
patch_size=patch_size,
num_channels=num_channels,
num_labels=num_labels,
projection_dim=projection_dim,
)