Chinese-CLIP
Overview
Chinese-CLIP An Yang, Junshu Pan, Junyang Lin, Rui Men, Yichang Zhang, Jingren Zhou, Chang Zhou Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese で提案されました。周、張周。 Chinese-CLIP は、中国語の画像とテキストのペアの大規模なデータセットに対する CLIP (Radford et al., 2021) の実装です。クロスモーダル検索を実行できるほか、ゼロショット画像分類、オープンドメインオブジェクト検出などのビジョンタスクのビジョンバックボーンとしても機能します。オリジナルの中国語-CLIPコードはこのリンクで。
論文の要約は次のとおりです。
CLIP の大成功 (Radford et al., 2021) により、視覚言語の事前訓練のための対照学習の研究と応用が促進されました。この研究では、ほとんどのデータが公開されているデータセットから取得された中国語の画像とテキストのペアの大規模なデータセットを構築し、新しいデータセットで中国語の CLIP モデルを事前トレーニングします。当社では、7,700 万から 9 億 5,800 万のパラメータにわたる、複数のサイズの 5 つの中国 CLIP モデルを開発しています。さらに、モデルのパフォーマンスを向上させるために、最初に画像エンコーダーをフリーズさせてモデルをトレーニングし、次にすべてのパラメーターを最適化してトレーニングする 2 段階の事前トレーニング方法を提案します。私たちの包括的な実験では、中国の CLIP がゼロショット学習と微調整のセットアップで MUGE、Flickr30K-CN、および COCO-CN 上で最先端のパフォーマンスを達成でき、ゼロで競争力のあるパフォーマンスを達成できることを実証しています。 - ELEVATER ベンチマークでの評価に基づくショット画像の分類 (Li et al., 2022)。コード、事前トレーニング済みモデル、デモがリリースされました。
Chinese-CLIP モデルは、OFA-Sys によって提供されました。
Usage example
以下のコード スニペットは、画像とテキストの特徴と類似性を計算する方法を示しています。
>>> from PIL import Image
>>> import requests
>>> from transformers import ChineseCLIPProcessor, ChineseCLIPModel
>>> model = ChineseCLIPModel.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16")
>>> processor = ChineseCLIPProcessor.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16")
>>> url = "https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/pokemon.jpeg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> # Squirtle, Bulbasaur, Charmander, Pikachu in English
>>> texts = ["杰尼龟", "妙蛙种子", "小火龙", "皮卡丘"]
>>> # compute image feature
>>> inputs = processor(images=image, return_tensors="pt")
>>> image_features = model.get_image_features(**inputs)
>>> image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True) # normalize
>>> # compute text features
>>> inputs = processor(text=texts, padding=True, return_tensors="pt")
>>> text_features = model.get_text_features(**inputs)
>>> text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True) # normalize
>>> # compute image-text similarity scores
>>> inputs = processor(text=texts, images=image, return_tensors="pt", padding=True)
>>> outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
>>> probs = logits_per_image.softmax(dim=1) # probs: [[1.2686e-03, 5.4499e-02, 6.7968e-04, 9.4355e-01]]
現在、次のスケールの事前トレーニング済み Chinese-CLIP モデルが 🤗 Hub で利用可能です。
- OFA-Sys/chinese-clip-vit-base-patch16
- OFA-Sys/chinese-clip-vit-large-patch14
- OFA-Sys/chinese-clip-vit-large-patch14-336px
- OFA-Sys/chinese-clip-vit-huge-patch14
ChineseCLIPConfig
class transformers.ChineseCLIPConfig
< source >( text_config = None vision_config = None projection_dim = 512 logit_scale_init_value = 2.6592 **kwargs )
Parameters
- text_config (
dict
, optional) — Dictionary of configuration options used to initialize ChineseCLIPTextConfig. - vision_config (
dict
, optional) — Dictionary of configuration options used to initialize ChineseCLIPVisionConfig. - projection_dim (
int
, optional, defaults to 512) — 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 ChineseCLIP implementation. - kwargs (optional) — Dictionary of keyword arguments.
ChineseCLIPConfig is the configuration class to store the configuration of a ChineseCLIPModel. It is used to instantiate Chinese-CLIP 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 Chinese-CLIP OFA-Sys/chinese-clip-vit-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.
Example:
>>> from transformers import ChineseCLIPConfig, ChineseCLIPModel
>>> # Initializing a ChineseCLIPConfig with OFA-Sys/chinese-clip-vit-base-patch16 style configuration
>>> configuration = ChineseCLIPConfig()
>>> # Initializing a ChineseCLIPModel (with random weights) from the OFA-Sys/chinese-clip-vit-base-patch16 style configuration
>>> model = ChineseCLIPModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> # We can also initialize a ChineseCLIPConfig from a ChineseCLIPTextConfig and a ChineseCLIPVisionConfig
>>> # Initializing a ChineseCLIPTextConfig and ChineseCLIPVisionConfig configuration
>>> config_text = ChineseCLIPTextConfig()
>>> config_vision = ChineseCLIPVisionConfig()
>>> config = ChineseCLIPConfig.from_text_vision_configs(config_text, config_vision)
from_text_vision_configs
< source >( text_config: ChineseCLIPTextConfig vision_config: ChineseCLIPVisionConfig **kwargs )
Instantiate a ChineseCLIPConfig (or a derived class) from Chinese-CLIP text model configuration and Chinese-CLIP vision model configuration. Returns: ChineseCLIPConfig: An instance of a configuration object
ChineseCLIPTextConfig
class transformers.ChineseCLIPTextConfig
< 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 initializer_factor = 1.0 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 CHINESE_CLIP model. Defines the number of different tokens that can be represented by theinputs_ids
passed when calling ChineseCLIPModel. - 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 (
str
orCallable
, 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_ids
passed when calling ChineseCLIPModel. - 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). - 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 ChineseCLIPModel. It is used to instantiate a Chinese CLIP 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 Chinese CLIP [OFA-Sys/chinese-clip-vit-base-patch16](https: //huggingface.co/OFA-Sys/chinese-clip-vit-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.
Example:
>>> from transformers import ChineseCLIPTextConfig, ChineseCLIPTextModel
>>> # Initializing a ChineseCLIPTextConfig with OFA-Sys/chinese-clip-vit-base-patch16 style configuration
>>> configuration = ChineseCLIPTextConfig()
>>> # Initializing a ChineseCLIPTextModel (with random weights) from the OFA-Sys/chinese-clip-vit-base-patch16 style configuration
>>> model = ChineseCLIPTextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
ChineseCLIPVisionConfig
class transformers.ChineseCLIPVisionConfig
< source >( 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-05 attention_dropout = 0.0 initializer_range = 0.02 initializer_factor = 1.0 **kwargs )
Parameters
- 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. - projection_dim (
int
, optional, defaults to 512) — Dimentionality of text and vision projection layers. - 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) — The number of input channels. - 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
orfunction
, 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).
This is the configuration class to store the configuration of a ChineseCLIPModel. It is used to instantiate an ChineseCLIP 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 ChineseCLIP [OFA-Sys/chinese-clip-vit-base-patch16](https: //huggingface.co/OFA-Sys/chinese-clip-vit-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.
Example:
>>> from transformers import ChineseCLIPVisionConfig, ChineseCLIPVisionModel
>>> # Initializing a ChineseCLIPVisionConfig with OFA-Sys/chinese-clip-vit-base-patch16 style configuration
>>> configuration = ChineseCLIPVisionConfig()
>>> # Initializing a ChineseCLIPVisionModel (with random weights) from the OFA-Sys/chinese-clip-vit-base-patch16 style configuration
>>> model = ChineseCLIPVisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
ChineseCLIPImageProcessor
class transformers.ChineseCLIPImageProcessor
< source >( do_resize: bool = True size: typing.Dict[str, int] = None resample: Resampling = <Resampling.BICUBIC: 3> do_center_crop: bool = True crop_size: typing.Dict[str, int] = None do_rescale: bool = True rescale_factor: typing.Union[int, float] = 0.00392156862745098 do_normalize: bool = True image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None do_convert_rgb: bool = True **kwargs )
Parameters
- do_resize (
bool
, optional, defaults toTrue
) — Whether to resize the image’s (height, width) dimensions to the specifiedsize
. Can be overridden bydo_resize
in thepreprocess
method. - size (
Dict[str, int]
optional, defaults to{"shortest_edge" -- 224}
): Size of the image after resizing. The shortest edge of the image is resized to size[“shortest_edge”], with the longest edge resized to keep the input aspect ratio. Can be overridden bysize
in thepreprocess
method. - resample (
PILImageResampling
, optional, defaults toResampling.BICUBIC
) — Resampling filter to use if resizing the image. Can be overridden byresample
in thepreprocess
method. - do_center_crop (
bool
, optional, defaults toTrue
) — Whether to center crop the image to the specifiedcrop_size
. Can be overridden bydo_center_crop
in thepreprocess
method. - crop_size (
Dict[str, int]
optional, defaults to 224) — Size of the output image after applyingcenter_crop
. Can be overridden bycrop_size
in thepreprocess
method. - do_rescale (
bool
, optional, defaults toTrue
) — Whether to rescale the image by the specified scalerescale_factor
. Can be overridden bydo_rescale
in thepreprocess
method. - rescale_factor (
int
orfloat
, optional, defaults to1/255
) — Scale factor to use if rescaling the image. Can be overridden byrescale_factor
in thepreprocess
method. - do_normalize (
bool
, optional, defaults toTrue
) — Whether to normalize the image. Can be overridden bydo_normalize
in thepreprocess
method. - image_mean (
float
orList[float]
, optional, defaults toIMAGENET_STANDARD_MEAN
) — Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by theimage_mean
parameter in thepreprocess
method. - image_std (
float
orList[float]
, optional, defaults toIMAGENET_STANDARD_STD
) — Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by theimage_std
parameter in thepreprocess
method. Can be overridden by theimage_std
parameter in thepreprocess
method. - do_convert_rgb (
bool
, optional, defaults toTrue
) — Whether to convert the image to RGB.
Constructs a Chinese-CLIP image processor.
preprocess
< source >( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]] do_resize: bool = None size: typing.Dict[str, int] = None resample: Resampling = None do_center_crop: bool = None crop_size: int = None do_rescale: bool = None rescale_factor: float = None do_normalize: bool = None image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None do_convert_rgb: bool = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None data_format: typing.Optional[transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'> input_data_format: typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None **kwargs )
Parameters
- 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, setdo_rescale=False
. - do_resize (
bool
, optional, defaults toself.do_resize
) — Whether to resize the image. - size (
Dict[str, int]
, optional, defaults toself.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 toself.resample
) — Resampling filter to use if resizing the image. This can be one of the enumPILImageResampling
. Only has an effect ifdo_resize
is set toTrue
. - do_center_crop (
bool
, optional, defaults toself.do_center_crop
) — Whether to center crop the image. - crop_size (
Dict[str, int]
, optional, defaults toself.crop_size
) — Size of the center crop. Only has an effect ifdo_center_crop
is set toTrue
. - do_rescale (
bool
, optional, defaults toself.do_rescale
) — Whether to rescale the image. - rescale_factor (
float
, optional, defaults toself.rescale_factor
) — Rescale factor to rescale the image by ifdo_rescale
is set toTrue
. - do_normalize (
bool
, optional, defaults toself.do_normalize
) — Whether to normalize the image. - image_mean (
float
orList[float]
, optional, defaults toself.image_mean
) — Image mean to use for normalization. Only has an effect ifdo_normalize
is set toTrue
. - image_std (
float
orList[float]
, optional, defaults toself.image_std
) — Image standard deviation to use for normalization. Only has an effect ifdo_normalize
is set toTrue
. - do_convert_rgb (
bool
, optional, defaults toself.do_convert_rgb
) — Whether to convert the image to RGB. - return_tensors (
str
orTensorType
, 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 typetf.Tensor
.TensorType.PYTORCH
or'pt'
: Return a batch of typetorch.Tensor
.TensorType.NUMPY
or'np'
: Return a batch of typenp.ndarray
.TensorType.JAX
or'jax'
: Return a batch of typejax.numpy.ndarray
.
- Unset: Return a list of
- data_format (
ChannelDimension
orstr
, optional, defaults toChannelDimension.FIRST
) — The channel dimension format for the output image. Can be one of:"channels_first"
orChannelDimension.FIRST
: image in (num_channels, height, width) format."channels_last"
orChannelDimension.LAST
: image in (height, width, num_channels) format.- Unset: Use the channel dimension format of the input image.
- input_data_format (
ChannelDimension
orstr
, 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"
orChannelDimension.FIRST
: image in (num_channels, height, width) format."channels_last"
orChannelDimension.LAST
: image in (height, width, num_channels) format."none"
orChannelDimension.NONE
: image in (height, width) format.
Preprocess an image or batch of images.
ChineseCLIPFeatureExtractor
ChineseCLIPProcessor
class transformers.ChineseCLIPProcessor
< source >( image_processor = None tokenizer = None **kwargs )
Parameters
- image_processor (ChineseCLIPImageProcessor, optional) — The image processor is a required input.
- tokenizer (BertTokenizerFast, optional) — The tokenizer is a required input.
Constructs a Chinese-CLIP processor which wraps a Chinese-CLIP image processor and a Chinese-CLIP tokenizer into a single processor.
ChineseCLIPProcessor offers all the functionalities of ChineseCLIPImageProcessor and BertTokenizerFast.
See the __call__()
and decode() for more information.
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.
ChineseCLIPModel
class transformers.ChineseCLIPModel
< source >( config: ChineseCLIPConfig )
Parameters
- config (ChineseCLIPConfig) — 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 is 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.LongTensor] = 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 ) → transformers.models.chinese_clip.modeling_chinese_clip.ChineseCLIPOutput
or tuple(torch.FloatTensor)
Parameters
- input_ids (
torch.LongTensor
of 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.Tensor
of 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.
- token_type_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
, 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.
- position_ids (
torch.LongTensor
of 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]
. - pixel_values (
torch.FloatTensor
of 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 ChineseCLIPImageProcessor.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. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail. - return_dict (
bool
, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.models.chinese_clip.modeling_chinese_clip.ChineseCLIPOutput
or tuple(torch.FloatTensor)
A transformers.models.chinese_clip.modeling_chinese_clip.ChineseCLIPOutput
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.chinese_clip.configuration_chinese_clip.ChineseCLIPConfig'>
) and inputs.
- loss (
torch.FloatTensor
of shape(1,)
, optional, returned whenreturn_loss
isTrue
) — Contrastive loss for image-text similarity. - logits_per_image:(
torch.FloatTensor
of shape(image_batch_size, text_batch_size)
) — The scaled dot product scores betweenimage_embeds
andtext_embeds
. This represents the image-text similarity scores. - logits_per_text:(
torch.FloatTensor
of shape(text_batch_size, image_batch_size)
) — The scaled dot product scores betweentext_embeds
andimage_embeds
. This represents the text-image similarity scores. - text_embeds(
torch.FloatTensor
of shape(batch_size, output_dim
) — The text embeddings obtained by applying the projection layer to the pooled output of ChineseCLIPTextModel. - image_embeds(
torch.FloatTensor
of shape(batch_size, output_dim
) — The image embeddings obtained by applying the projection layer to the pooled output of ChineseCLIPVisionModel. - text_model_output(
BaseModelOutputWithPoolingAndCrossAttentions
): The output of the ChineseCLIPTextModel. - vision_model_output(
BaseModelOutputWithPoolingAndCrossAttentions
): The output of the ChineseCLIPVisionModel.
The ChineseCLIPModel 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, ChineseCLIPModel
>>> model = ChineseCLIPModel.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16")
>>> processor = AutoProcessor.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16")
>>> url = "https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/pokemon.jpeg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(text=["杰尼龟", "妙蛙种子", "小火龙", "皮卡丘"], images=image, return_tensors="pt", padding=True)
>>> outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
>>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
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 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.LongTensor
of shape(batch_size, sequence_length)
) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
- attention_mask (
torch.FloatTensor
of 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.
- token_type_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
, 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.
- position_ids (
torch.LongTensor
of 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]
. - head_mask (
torch.FloatTensor
of 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.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
, optional) — Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_ids
indices 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. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — Whether or not to return the hidden states of all layers. Seehidden_states
under 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 final [CLS] hidden state of Text-Transformer.
The ChineseCLIPModel 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, ChineseCLIPModel
>>> model = ChineseCLIPModel.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16")
>>> tokenizer = AutoTokenizer.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16")
>>> inputs = tokenizer(["杰尼龟", "妙蛙种子", "小火龙", "皮卡丘"], padding=True, return_tensors="pt")
>>> text_features = model.get_text_features(**inputs)
>>> text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)
get_image_features
< source >( pixel_values: typing.Optional[torch.FloatTensor] = None output_attentions: typing.Optional[bool] = 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.FloatTensor
of 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 ChineseCLIPImageProcessor.call() for details. - output_attentions (
bool
, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — Whether or not to return the hidden states of all layers. Seehidden_states
under 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 final [CLS] hidden state of Vision-Transformer.
The ChineseCLIPModel 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, ChineseCLIPModel
>>> model = ChineseCLIPModel.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16")
>>> processor = AutoProcessor.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16")
>>> url = "https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/pokemon.jpeg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="pt")
>>> image_features = model.get_image_features(**inputs)
>>> image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True)
ChineseCLIPTextModel
class transformers.ChineseCLIPTextModel
< source >( config add_pooling_layer = True )
Parameters
- config (ChineseCLIPConfig) — 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 CHINESE_CLIP without any head or projection on top. This model is 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.
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in Attention is all you need by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
To behave as an decoder the model needs to be initialized with the is_decoder
argument of the configuration set
to True
. To be used in a Seq2Seq model, the model needs to initialized with both is_decoder
argument and
add_cross_attention
set to True
; an encoder_hidden_states
is then expected as an input to the forward pass.
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 encoder_hidden_states: typing.Optional[torch.Tensor] = None encoder_attention_mask: typing.Optional[torch.Tensor] = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None use_cache: typing.Optional[bool] = 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.LongTensor
of 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.Tensor
of 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.
- token_type_ids (
torch.LongTensor
of shape(batch_size, sequence_length)
, 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.
- position_ids (
torch.LongTensor
of 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]
. - pixel_values (
torch.FloatTensor
of 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 ChineseCLIPImageProcessor.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. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail. - return_dict (
bool
, optional) — Whether or not to return a ModelOutput instead of a plain tuple. - encoder_hidden_states (
torch.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
, optional) — Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. - encoder_attention_mask (
torch.FloatTensor
of shape(batch_size, sequence_length)
, optional) — Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in[0, 1]
:- 1 for tokens that are not masked,
- 0 for tokens that are masked.
- past_key_values (
tuple(tuple(torch.FloatTensor))
of lengthconfig.n_layers
with each tuple having 4 tensors of shape(batch_size, num_heads, sequence_length - 1, embed_size_per_head)
) — Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.If
past_key_values
are used, the user can optionally input only the lastdecoder_input_ids
(those that don’t have their past key value states given to this model) of shape(batch_size, 1)
instead of alldecoder_input_ids
of shape(batch_size, sequence_length)
. - use_cache (
bool
, optional) — If set toTrue
,past_key_values
key value states are returned and can be used to speed up decoding (seepast_key_values
).
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 (ChineseCLIPConfig) and inputs.
-
last_hidden_state (
torch.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
) — Sequence of hidden-states at the output of the last layer of the model. -
pooler_output (
torch.FloatTensor
of 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=True
is 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=True
is 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=True
andconfig.add_cross_attention=True
is 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=True
is 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=True
2 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=True
in the cross-attention blocks) that can be used (seepast_key_values
input) to speed up sequential decoding.
The ChineseCLIPTextModel 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.
Example:
>>> from transformers import AutoTokenizer, ChineseCLIPTextModel
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16")
>>> model = ChineseCLIPTextModel.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
ChineseCLIPVisionModel
class transformers.ChineseCLIPVisionModel
< source >( config: ChineseCLIPVisionConfig )
Parameters
- config (ChineseCLIPConfig) — 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 CHINESE_CLIP without any head or projection on top. This model is 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_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → transformers.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)
Parameters
- pixel_values (
torch.FloatTensor
of 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 ChineseCLIPImageProcessor.call() for details. - output_attentions (
bool
, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail. - output_hidden_states (
bool
, optional) — Whether or not to return the hidden states of all layers. Seehidden_states
under 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.BaseModelOutputWithPooling or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutputWithPooling 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.chinese_clip.configuration_chinese_clip.ChineseCLIPVisionConfig'>
) and inputs.
-
last_hidden_state (
torch.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
) — Sequence of hidden-states at the output of the last layer of the model. -
pooler_output (
torch.FloatTensor
of 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=True
is 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=True
is 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.
The ChineseCLIPVisionModel 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 CLIPProcessor, ChineseCLIPVisionModel
>>> model = ChineseCLIPVisionModel.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16")
>>> processor = CLIPProcessor.from_pretrained("OFA-Sys/chinese-clip-vit-base-patch16")
>>> url = "https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/pokemon.jpeg"
>>> 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