#
Chinese-CLIP

##
Overview

The Chinese-CLIP model was proposed in Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese by An Yang, Junshu Pan, Junyang Lin, Rui Men, Yichang Zhang, Jingren Zhou, Chang Zhou. Chinese-CLIP is an implementation of CLIP (Radford et al., 2021) on a large-scale dataset of Chinese image-text pairs. It is capable of performing cross-modal retrieval and also playing as a vision backbone for vision tasks like zero-shot image classification, open-domain object detection, etc. The original Chinese-CLIP code is released at this link.

The abstract from the paper is the following:

The tremendous success of CLIP (Radford et al., 2021) has promoted the research and application of contrastive learning for vision-language pretraining. In this work, we construct a large-scale dataset of image-text pairs in Chinese, where most data are retrieved from publicly available datasets, and we pretrain Chinese CLIP models on the new dataset. We develop 5 Chinese CLIP models of multiple sizes, spanning from 77 to 958 million parameters. Furthermore, we propose a two-stage pretraining method, where the model is first trained with the image encoder frozen and then trained with all parameters being optimized, to achieve enhanced model performance. Our comprehensive experiments demonstrate that Chinese CLIP can achieve the state-of-the-art performance on MUGE, Flickr30K-CN, and COCO-CN in the setups of zero-shot learning and finetuning, and it is able to achieve competitive performance in zero-shot image classification based on the evaluation on the ELEVATER benchmark (Li et al., 2022). Our codes, pretrained models, and demos have been released.

The Chinese-CLIP model was contributed by OFA-Sys.

##
Usage example

The code snippet below shows how to compute image & text features and similarities:

    >>> 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]]

Currently, following scales of pretrained Chinese-CLIP models are available on 🤗 Hub:

- 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) — Dimensionality of text and vision projection layers.
- logit_scale_init_value (`float`, optional, defaults to 2.6592) — The initial value of the logit_scale parameter. 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 the `inputs_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` or `Callable`, optional, defaults to `"gelu"`) — The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported.
- hidden_dropout_prob (`float`, optional, defaults to 0.1) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
- attention_probs_dropout_prob (`float`, optional, defaults to 0.1) — The dropout ratio for the attention probabilities.
- max_position_embeddings (`int`, optional, defaults to 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 the `token_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 to `True`) — Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`.

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) — Dimensionality 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` or `function`, optional, defaults to `"quick_gelu"`) — The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
- layer_norm_eps (`float`, optional, defaults to 1e-05) — The epsilon used by the layer normalization layers.
- attention_dropout (`float`, optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
- initializer_range (`float`, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- initializer_factor (`float`, optional, defaults to 1.0) — A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing).

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 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: Dict = None resample: Resampling = <Resampling.BICUBIC: 3> do_center_crop: bool = True crop_size: Dict = None do_rescale: bool = True rescale_factor: Union = 0.00392156862745098 do_normalize: bool = True image_mean: Union = None image_std: Union = None do_convert_rgb: bool = True **kwargs )

Parameters

- do_resize (`bool`, optional, defaults to `True`) — Whether to resize the image’s (height, width) dimensions to the specified `size`. Can be overridden by `do_resize` in the `preprocess` 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 by `size` in the `preprocess` method.
- resample (`PILImageResampling`, optional, defaults to `Resampling.BICUBIC`) — Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
- do_center_crop (`bool`, optional, defaults to `True`) — Whether to center crop the image to the specified `crop_size`. Can be overridden by `do_center_crop` in the `preprocess` method.
- crop_size (`Dict[str, int]` optional, defaults to 224) — Size of the output image after applying `center_crop`. Can be overridden by `crop_size` in the `preprocess` method.
- do_rescale (`bool`, optional, defaults to `True`) — Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in the `preprocess` method.
- rescale_factor (`int` or `float`, optional, defaults to `1/255`) — Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess` method.
- do_normalize (`bool`, optional, defaults to `True`) — Whether to normalize the image. Can be overridden by `do_normalize` in the `preprocess` method.
- image_mean (`float` or `List[float]`, optional, defaults to `IMAGENET_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 the `image_mean` parameter in the `preprocess` method.
- image_std (`float` or `List[float]`, optional, defaults to `IMAGENET_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 the `image_std` parameter in the `preprocess` method. Can be overridden by the `image_std` parameter in the `preprocess` method.
- do_convert_rgb (`bool`, optional, defaults to `True`) — Whether to convert the image to RGB.

Constructs a Chinese-CLIP image processor.

####
preprocess

< source >

( images: Union do_resize: bool = None size: Dict = 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: Union = None image_std: Union = None do_convert_rgb: bool = None return_tensors: Union = None data_format: Optional = <ChannelDimension.FIRST: 'channels_first'> input_data_format: Union = None )

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, set `do_rescale=False`.
- do_resize (`bool`, optional, defaults to `self.do_resize`) — Whether to resize the image.
- size (`Dict[str, int]`, optional, defaults to `self.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 to `self.resample`) — Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only has an effect if `do_resize` is set to `True`.
- do_center_crop (`bool`, optional, defaults to `self.do_center_crop`) — Whether to center crop the image.
- crop_size (`Dict[str, int]`, optional, defaults to `self.crop_size`) — Size of the center crop. Only has an effect if `do_center_crop` is set to `True`.
- do_rescale (`bool`, optional, defaults to `self.do_rescale`) — Whether to rescale the image.
- rescale_factor (`float`, optional, defaults to `self.rescale_factor`) — Rescale factor to rescale the image by if `do_rescale` is set to `True`.
- do_normalize (`bool`, optional, defaults to `self.do_normalize`) — Whether to normalize the image.
- image_mean (`float` or `List[float]`, optional, defaults to `self.image_mean`) — Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
- image_std (`float` or `List[float]`, optional, defaults to `self.image_std`) — Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to `True`.
- do_convert_rgb (`bool`, optional, defaults to `self.do_convert_rgb`) — Whether to convert the image to RGB.
- return_tensors (`str` or `TensorType`, 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 type `tf.Tensor`.
  - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
  - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
  - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
- data_format (`ChannelDimension` or `str`, optional, defaults to `ChannelDimension.FIRST`) — The channel dimension format for the output image. Can be one of:
  - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
  - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
  - Unset: Use the channel dimension format of the input image.
- input_data_format (`ChannelDimension` or `str`, 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"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
  - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
  - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.

Preprocess an image or batch of images.

##
ChineseCLIPFeatureExtractor

###
class transformers.ChineseCLIPFeatureExtractor

< source >

( *args **kwargs )

##
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.

####
batch_decode

< source >

( *args **kwargs )

This method forwards all its arguments to BertTokenizerFast’s batch_decode(). Please refer to the docstring of this method for more information.

####
decode

< source >

( *args **kwargs )

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: Optional = None pixel_values: Optional = None attention_mask: Optional = None token_type_ids: Optional = None position_ids: Optional = None return_loss: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None interpolate_pos_encoding: bool = False return_dict: Optional = 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.

  What are input IDs?

- 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.

  What are attention masks?

- 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.

  What are token type IDs?

- 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]`.

  What are position IDs?

- 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. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_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 when `return_loss` is `True`) — Contrastive loss for image-text similarity.
- logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`) — The scaled dot product scores between `image_embeds` and `text_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 between `text_embeds` and `image_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: Optional = None attention_mask: Optional = None token_type_ids: Optional = None position_ids: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = 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.

  What are input IDs?

- 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.

  What are attention masks?

- 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.

  What are token type IDs?

- 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]`.

  What are position IDs?

- 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 passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_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. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- interpolate_pos_encoding (`bool`, optional, defaults `False`) — Whether to interpolate the pre-trained position encodings.
- 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: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None interpolate_pos_encoding: bool = False return_dict: Optional = 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. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- interpolate_pos_encoding (`bool`, optional, defaults `False`) — Whether to interpolate the pre-trained position encodings.
- 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: Optional = None attention_mask: Optional = None token_type_ids: Optional = None position_ids: Optional = None head_mask: Optional = None inputs_embeds: Optional = None encoder_hidden_states: Optional = None encoder_attention_mask: Optional = None past_key_values: Optional = None use_cache: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = 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.

  What are input IDs?

- 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.

  What are attention masks?

- 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.

  What are token type IDs?

- 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]`.

  What are position IDs?

- 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. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_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 length `config.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 last `decoder_input_ids` (those that don’t have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.

- use_cache (`bool`, optional) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_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 when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.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 when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.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 when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.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 when `use_cache=True` is passed or when `config.use_cache=True`) — Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if `config.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 (see `past_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: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None interpolate_pos_encoding: bool = False return_dict: Optional = 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. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- interpolate_pos_encoding (`bool`, optional, defaults `False`) — Whether to interpolate the pre-trained position encodings.
- 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 when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.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 when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.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

< > Update on GitHub