ALIGN
概要
ALIGNモデルは、「Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision」という論文でChao Jia、Yinfei Yang、Ye Xia、Yi-Ting Chen、Zarana Parekh、Hieu Pham、Quoc V. Le、Yunhsuan Sung、Zhen Li、Tom Duerigによって提案されました。ALIGNはマルチモーダルな視覚言語モデルです。これは画像とテキストの類似度や、ゼロショット画像分類に使用できます。ALIGNはEfficientNetを視覚エンコーダーとして、BERTをテキストエンコーダーとして搭載したデュアルエンコーダー構造を特徴とし、対照学習によって視覚とテキストの表現を整合させることを学びます。それまでの研究とは異なり、ALIGNは巨大でノイジーなデータセットを活用し、コーパスのスケールを利用して単純な方法ながら最先端の表現を達成できることを示しています。
論文の要旨は以下の通りです：
事前学習された表現は、多くの自然言語処理（NLP）および知覚タスクにとって重要になっています。NLPにおける表現学習は、人間のアノテーションのない生のテキストでの学習へと移行していますが、視覚および視覚言語の表現は依然として精巧な学習データセットに大きく依存しており、これは高価であったり専門知識を必要としたりします。視覚アプリケーションの場合、ImageNetやOpenImagesのような明示的なクラスラベルを持つデータセットを使用して学習されることがほとんどです。視覚言語の場合、Conceptual Captions、MSCOCO、CLIPなどの人気のあるデータセットはすべて、それぞれ無視できないデータ収集（およびクリーニング）プロセスを含みます。このコストのかかるキュレーションプロセスはデータセットのサイズを制限し、訓練されたモデルのスケーリングを妨げます。本論文では、Conceptual Captionsデータセットの高価なフィルタリングや後処理ステップなしで得られた、10億を超える画像alt-textペアのノイズの多いデータセットを活用します。シンプルなデュアルエンコーダーアーキテクチャは、対照損失を使用して画像とテキストペアの視覚的および言語的表現を整合させることを学習します。我々は、コーパスの規模がそのノイズを補い、このような単純な学習スキームでも最先端の表現につながることを示します。我々の視覚表現は、ImageNetやVTABなどの分類タスクへの転移において強力な性能を発揮します。整合した視覚的および言語的表現は、ゼロショット画像分類を可能にし、また、より洗練されたクロスアテンションモデルと比較しても、Flickr30KおよびMSCOCO画像テキスト検索ベンチマークにおいて新たな最先端の結果を達成します。また、これらの表現は、複雑なテキストおよびテキスト+画像のクエリを用いたクロスモーダル検索を可能にします。
このモデルはAlara Dirikにより提供されました。 オリジナルのコードは公開されておらず、この実装は元論文に基づいたKakao Brainの実装をベースにしています。
使用例
ALIGNはEfficientNetを使用して視覚的特徴を、BERTを使用してテキスト特徴を取得します。テキストと視覚の両方の特徴は、同一の次元を持つ潜在空間に射影されます。射影された画像とテキスト特徴間のドット積が類似度スコアとして使用されます。
AlignProcessorは、テキストのエンコードと画像の前処理を両方行うために、EfficientNetImageProcessorとBertTokenizerを単一のインスタンスにラップします。以下の例は、AlignProcessorとAlignModelを使用して画像-テキスト類似度スコアを取得する方法を示しています。
import requests
import torch
from PIL import Image
from transformers import AlignProcessor, AlignModel
processor = AlignProcessor.from_pretrained("kakaobrain/align-base")
model = AlignModel.from_pretrained("kakaobrain/align-base")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
candidate_labels = ["an image of a cat", "an image of a dog"]
inputs = processor(text=candidate_labels, images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
# this is the image-text similarity score
logits_per_image = outputs.logits_per_image
# we can take the softmax to get the label probabilities
probs = logits_per_image.softmax(dim=1)
print(probs)参考資料
ALIGNの使用を開始するのに役立つ公式のHugging Faceとコミュニティ（🌎で示されている）の参考資料の一覧です。
- ALIGNとCOYO-700Mデータセットに関するブログ投稿。
- ゼロショット画像分類デモ。
kakaobrain/align-baseモデルのモデルカード。
ここに参考資料を提出したい場合は、気兼ねなくPull Requestを開いてください。私たちはそれをレビューいたします！参考資料は、既存のものを複製するのではなく、何か新しいことを示すことが理想的です。
AlignConfig
class transformers.AlignConfig
< source >( text_config = None vision_config = None projection_dim = 640 temperature_init_value = 1.0 initializer_range = 0.02 **kwargs )
Parameters
- text_config (
dict, optional) — Dictionary of configuration options used to initialize AlignTextConfig. - vision_config (
dict, optional) — Dictionary of configuration options used to initialize AlignVisionConfig. - projection_dim (
int, optional, defaults to 640) — Dimensionality of text and vision projection layers. - temperature_init_value (
float, optional, defaults to 1.0) — The initial value of the temperature parameter. Default is used as per the original ALIGN implementation. - initializer_range (
float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices. - kwargs (optional) — Dictionary of keyword arguments.
AlignConfig is the configuration class to store the configuration of a AlignModel. It is used to instantiate a ALIGN 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 ALIGN kakaobrain/align-base architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.
Example:
>>> from transformers import AlignConfig, AlignModel
>>> # Initializing a AlignConfig with kakaobrain/align-base style configuration
>>> configuration = AlignConfig()
>>> # Initializing a AlignModel (with random weights) from the kakaobrain/align-base style configuration
>>> model = AlignModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> # We can also initialize a AlignConfig from a AlignTextConfig and a AlignVisionConfig
>>> from transformers import AlignTextConfig, AlignVisionConfig
>>> # Initializing ALIGN Text and Vision configurations
>>> config_text = AlignTextConfig()
>>> config_vision = AlignVisionConfig()
>>> config = AlignConfig.from_text_vision_configs(config_text, config_vision)from_text_vision_configs
< source >( text_config: AlignTextConfig vision_config: AlignVisionConfig **kwargs ) → AlignConfig
Instantiate a AlignConfig (or a derived class) from align text model configuration and align vision model configuration.
AlignTextConfig
class transformers.AlignTextConfig
< source >( vocab_size = 30522 hidden_size = 768 num_hidden_layers = 12 num_attention_heads = 12 intermediate_size = 3072 hidden_act = 'gelu' hidden_dropout_prob = 0.1 attention_probs_dropout_prob = 0.1 max_position_embeddings = 512 type_vocab_size = 2 initializer_range = 0.02 layer_norm_eps = 1e-12 pad_token_id = 0 position_embedding_type = 'absolute' use_cache = True **kwargs )
Parameters
- vocab_size (
int, optional, defaults to 30522) — Vocabulary size of the Align Text model. Defines the number of different tokens that can be represented by theinputs_idspassed when calling AlignTextModel. - hidden_size (
int, optional, defaults to 768) — Dimensionality of the encoder layers and the pooler layer. - 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. - intermediate_size (
int, optional, defaults to 3072) — Dimensionality of the “intermediate” (often named feed-forward) layer in the Transformer encoder. - hidden_act (
strorCallable, 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 512) — 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 thetoken_type_idspassed when calling AlignTextModel. - 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-12) — The epsilon used by the layer normalization layers. - pad_token_id (
int, optional, defaults to 0) — Padding token id. - 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.). For more information on"relative_key_query", please refer to Method 4 in Improve Transformer Models with Better Relative Position Embeddings (Huang et al.). - use_cache (
bool, optional, defaults toTrue) — Whether or not the model should return the last key/values attentions (not used by all models). Only relevant ifconfig.is_decoder=True.
This is the configuration class to store the configuration of a AlignTextModel. It is used to instantiate a ALIGN 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 text encoder of the ALIGN kakaobrain/align-base architecture. The default values here are copied from BERT.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.
Example:
>>> from transformers import AlignTextConfig, AlignTextModel
>>> # Initializing a AlignTextConfig with kakaobrain/align-base style configuration
>>> configuration = AlignTextConfig()
>>> # Initializing a AlignTextModel (with random weights) from the kakaobrain/align-base style configuration
>>> model = AlignTextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.configAlignVisionConfig
class transformers.AlignVisionConfig
< source >( num_channels: int = 3 image_size: int = 600 width_coefficient: float = 2.0 depth_coefficient: float = 3.1 depth_divisor: int = 8 kernel_sizes: typing.List[int] = [3, 3, 5, 3, 5, 5, 3] in_channels: typing.List[int] = [32, 16, 24, 40, 80, 112, 192] out_channels: typing.List[int] = [16, 24, 40, 80, 112, 192, 320] depthwise_padding: typing.List[int] = [] strides: typing.List[int] = [1, 2, 2, 2, 1, 2, 1] num_block_repeats: typing.List[int] = [1, 2, 2, 3, 3, 4, 1] expand_ratios: typing.List[int] = [1, 6, 6, 6, 6, 6, 6] squeeze_expansion_ratio: float = 0.25 hidden_act: str = 'swish' hidden_dim: int = 2560 pooling_type: str = 'mean' initializer_range: float = 0.02 batch_norm_eps: float = 0.001 batch_norm_momentum: float = 0.99 drop_connect_rate: float = 0.2 **kwargs )
Parameters
- num_channels (
int, optional, defaults to 3) — The number of input channels. - image_size (
int, optional, defaults to 600) — The input image size. - width_coefficient (
float, optional, defaults to 2.0) — Scaling coefficient for network width at each stage. - depth_coefficient (
float, optional, defaults to 3.1) — Scaling coefficient for network depth at each stage. - depth_divisor
int, optional, defaults to 8) — A unit of network width. - kernel_sizes (
List[int], optional, defaults to[3, 3, 5, 3, 5, 5, 3]) — List of kernel sizes to be used in each block. - in_channels (
List[int], optional, defaults to[32, 16, 24, 40, 80, 112, 192]) — List of input channel sizes to be used in each block for convolutional layers. - out_channels (
List[int], optional, defaults to[16, 24, 40, 80, 112, 192, 320]) — List of output channel sizes to be used in each block for convolutional layers. - depthwise_padding (
List[int], optional, defaults to[]) — List of block indices with square padding. - strides (
List[int], optional, defaults to[1, 2, 2, 2, 1, 2, 1]) — List of stride sizes to be used in each block for convolutional layers. - num_block_repeats (
List[int], optional, defaults to[1, 2, 2, 3, 3, 4, 1]) — List of the number of times each block is to repeated. - expand_ratios (
List[int], optional, defaults to[1, 6, 6, 6, 6, 6, 6]) — List of scaling coefficient of each block. - squeeze_expansion_ratio (
float, optional, defaults to 0.25) — Squeeze expansion ratio. - hidden_act (
strorfunction, optional, defaults to"silu") — The non-linear activation function (function or string) in each block. If string,"gelu","relu","selu",“gelu_new”,“silu”and“mish”` are supported. - hidden_dim (
int, optional, defaults to 1280) — The hidden dimension of the layer before the classification head. - pooling_type (
strorfunction, optional, defaults to"mean") — Type of final pooling to be applied before the dense classification head. Available options are ["mean","max"] - initializer_range (
float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices. - batch_norm_eps (
float, optional, defaults to 1e-3) — The epsilon used by the batch normalization layers. - batch_norm_momentum (
float, optional, defaults to 0.99) — The momentum used by the batch normalization layers. - drop_connect_rate (
float, optional, defaults to 0.2) — The drop rate for skip connections.
This is the configuration class to store the configuration of a AlignVisionModel. It is used to instantiate a ALIGN vision 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 vision encoder of the ALIGN kakaobrain/align-base architecture. The default values are copied from EfficientNet (efficientnet-b7)
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.
Example:
>>> from transformers import AlignVisionConfig, AlignVisionModel
>>> # Initializing a AlignVisionConfig with kakaobrain/align-base style configuration
>>> configuration = AlignVisionConfig()
>>> # Initializing a AlignVisionModel (with random weights) from the kakaobrain/align-base style configuration
>>> model = AlignVisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.configAlignProcessor
class transformers.AlignProcessor
< source >( image_processor tokenizer )
Constructs an ALIGN processor which wraps EfficientNetImageProcessor and
BertTokenizer/BertTokenizerFast into a single processor that interits both the image processor and
tokenizer functionalities. See the __call__() and decode() for more
information.
The preferred way of passing kwargs is as a dictionary per modality, see usage example below.
from transformers import AlignProcessor
from PIL import Image
model_id = "kakaobrain/align-base"
processor = AlignProcessor.from_pretrained(model_id)
processor(
images=your_pil_image,
text=["What is that?"],
images_kwargs = {"crop_size": {"height": 224, "width": 224}},
text_kwargs = {"padding": "do_not_pad"},
common_kwargs = {"return_tensors": "pt"},
)This method forwards all its arguments to BertTokenizerFast’s batch_decode(). Please refer to the docstring of this method for more information.
This method forwards all its arguments to BertTokenizerFast’s decode(). Please refer to the docstring of this method for more information.
AlignModel
class transformers.AlignModel
< source >( config: AlignConfig )
Parameters
- config (AlignConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >( input_ids: typing.Optional[torch.LongTensor] = None pixel_values: typing.Optional[torch.FloatTensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None return_loss: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None )
Parameters
- input_ids (
torch.LongTensorof shape(batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
What are input IDs? attention_mask (
torch.Tensorof shape(batch_size, sequence_length), optional): Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:- 1 for tokens that are not masked,
- 0 for tokens that are masked.
What are attention masks? position_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1].What are position IDs? token_type_ids (
torch.LongTensorof shape({0}), optional): Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
What are token type IDs? head_mask (
torch.FloatTensorof shape(num_heads,)or(num_layers, num_heads), optional): Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
inputs_embeds (
torch.FloatTensorof shape({0}, hidden_size), optional): Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. pixel_values (torch.FloatTensorof shape(batch_size, num_channels, height, width)): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using AutoImageProcessor. See EfficientNetImageProcessor.call() for details. return_loss (bool, optional): Whether or not to return the contrastive loss. output_attentions (bool, optional): Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. output_hidden_states (bool, optional): Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. return_dict (bool, optional): Whether or not to return a ModelOutput instead of a plain tuple.
The AlignModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
get_text_features
< source >( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → text_features (torch.FloatTensor of shape (batch_size, output_dim)
Parameters
- input_ids (
torch.LongTensorof shape(batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
- attention_mask (
torch.Tensorof shape(batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:- 1 for tokens that are not masked,
- 0 for tokens that are masked.
- position_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]. - token_type_ids (
torch.LongTensorof shape({0}), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
- head_mask (
torch.FloatTensorof shape(num_heads,)or(num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
- inputs_embeds (
torch.FloatTensorof shape({0}, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. - output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. - return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
Returns
text_features (torch.FloatTensor of shape (batch_size, output_dim)
The text embeddings obtained by applying the projection layer to the pooled output of AlignTextModel.
The AlignModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
>>> from transformers import AutoTokenizer, AlignModel
>>> model = AlignModel.from_pretrained("kakaobrain/align-base")
>>> tokenizer = AutoTokenizer.from_pretrained("kakaobrain/align-base")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
>>> text_features = model.get_text_features(**inputs)get_image_features
< source >( pixel_values: typing.Optional[torch.FloatTensor] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → image_features (torch.FloatTensor of shape (batch_size, output_dim)
Parameters
- pixel_values (
torch.FloatTensorof shape(batch_size, num_channels, height, width)) — Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using AutoImageProcessor. See EfficientNetImageProcessor.call() for details. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. - return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
Returns
image_features (torch.FloatTensor of shape (batch_size, output_dim)
The image embeddings obtained by applying the projection layer to the pooled output of AlignVisionModel.
The AlignModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, AlignModel
>>> model = AlignModel.from_pretrained("kakaobrain/align-base")
>>> processor = AutoProcessor.from_pretrained("kakaobrain/align-base")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="pt")
>>> image_features = model.get_image_features(**inputs)AlignTextModel
class transformers.AlignTextModel
< source >( config: AlignTextConfig add_pooling_layer: bool = True )
Parameters
- config (AlignConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
The text model from ALIGN without any head or projection on top. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or tuple(torch.FloatTensor)
Parameters
- input_ids (
torch.LongTensorof shape(batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
- attention_mask (
torch.Tensorof shape(batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:- 1 for tokens that are not masked,
- 0 for tokens that are masked.
- position_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]. - token_type_ids (
torch.LongTensorof shape({0}), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
- head_mask (
torch.FloatTensorof shape(num_heads,)or(num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
- inputs_embeds (
torch.FloatTensorof shape({0}, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. - output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. - return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (<class 'transformers.models.align.configuration_align.AlignTextConfig'>) and inputs.
-
last_hidden_state (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model. -
pooler_output (
torch.FloatTensorof shape(batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. -
hidden_states (
tuple(torch.FloatTensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor(one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
-
attentions (
tuple(torch.FloatTensor), optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.FloatTensor(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length).Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-
cross_attentions (
tuple(torch.FloatTensor), optional, returned whenoutput_attentions=Trueandconfig.add_cross_attention=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.FloatTensor(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length).Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
-
past_key_values (
tuple(tuple(torch.FloatTensor)), optional, returned whenuse_cache=Trueis passed or whenconfig.use_cache=True) — Tuple oftuple(torch.FloatTensor)of lengthconfig.n_layers, with each tuple having 2 tensors of shape(batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally ifconfig.is_encoder_decoder=True2 additional tensors of shape(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
config.is_encoder_decoder=Truein the cross-attention blocks) that can be used (seepast_key_valuesinput) to speed up sequential decoding.
The AlignTextModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
>>> from transformers import AutoTokenizer, AlignTextModel
>>> model = AlignTextModel.from_pretrained("kakaobrain/align-base")
>>> tokenizer = AutoTokenizer.from_pretrained("kakaobrain/align-base")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled (EOS token) statesAlignVisionModel
class transformers.AlignVisionModel
< source >( config: AlignVisionConfig )
Parameters
- config (AlignConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
The vision model from ALIGN without any head or projection on top. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >( pixel_values: typing.Optional[torch.FloatTensor] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention or tuple(torch.FloatTensor)
Parameters
- pixel_values (
torch.FloatTensorof shape(batch_size, num_channels, height, width)) — Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using AutoImageProcessor. See EfficientNetImageProcessor.call() for details. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. - return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (<class 'transformers.models.align.configuration_align.AlignVisionConfig'>) and inputs.
-
last_hidden_state (
torch.FloatTensorof shape(batch_size, num_channels, height, width)) — Sequence of hidden-states at the output of the last layer of the model. -
pooler_output (
torch.FloatTensorof shape(batch_size, hidden_size)) — Last layer hidden-state after a pooling operation on the spatial dimensions. -
hidden_states (
tuple(torch.FloatTensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor(one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape(batch_size, num_channels, height, width).Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
The AlignVisionModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, AlignVisionModel
>>> model = AlignVisionModel.from_pretrained("kakaobrain/align-base")
>>> processor = AutoProcessor.from_pretrained("kakaobrain/align-base")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled CLS states