# Chinese-CLIP ## Overview The Chinese-CLIP model was proposed in [Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese](https://huggingface.co/papers/2211.01335) 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](https://github.com/OFA-Sys/Chinese-CLIP). 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](https://huggingface.co/OFA-Sys). ## Usage example The code snippet below shows how to compute image & text features and similarities: ```python >>> 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](https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16) - [OFA-Sys/chinese-clip-vit-large-patch14](https://huggingface.co/OFA-Sys/chinese-clip-vit-large-patch14) - [OFA-Sys/chinese-clip-vit-large-patch14-336px](https://huggingface.co/OFA-Sys/chinese-clip-vit-large-patch14-336px) - [OFA-Sys/chinese-clip-vit-huge-patch14](https://huggingface.co/OFA-Sys/chinese-clip-vit-huge-patch14) ## ChineseCLIPConfig[[transformers.ChineseCLIPConfig]] #### transformers.ChineseCLIPConfig[[transformers.ChineseCLIPConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.0.0rc2/src/transformers/models/chinese_clip/configuration_chinese_clip.py#L219) [ChineseCLIPConfig](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPConfig) is the configuration class to store the configuration of a [ChineseCLIPModel](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.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](https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16) architecture. Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.0.0rc2/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/v5.0.0rc2/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. Example: ```python >>> 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(text_config=config_text, vision_config=config_vision) ``` **Parameters:** text_config (`dict`, *optional*) : Dictionary of configuration options used to initialize [ChineseCLIPTextConfig](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPTextConfig). vision_config (`dict`, *optional*) : Dictionary of configuration options used to initialize [ChineseCLIPVisionConfig](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.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. ## ChineseCLIPTextConfig[[transformers.ChineseCLIPTextConfig]] #### transformers.ChineseCLIPTextConfig[[transformers.ChineseCLIPTextConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.0.0rc2/src/transformers/models/chinese_clip/configuration_chinese_clip.py#L24) This is the configuration class to store the configuration of a [ChineseCLIPModel](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.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](/docs/transformers/v5.0.0rc2/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/v5.0.0rc2/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. Example: ```python >>> 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 ``` **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](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.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](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.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. 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`. ## ChineseCLIPVisionConfig[[transformers.ChineseCLIPVisionConfig]] #### transformers.ChineseCLIPVisionConfig[[transformers.ChineseCLIPVisionConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.0.0rc2/src/transformers/models/chinese_clip/configuration_chinese_clip.py#L128) This is the configuration class to store the configuration of a [ChineseCLIPModel](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.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](/docs/transformers/v5.0.0rc2/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/v5.0.0rc2/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. Example: ```python >>> 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 ``` **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). ## ChineseCLIPImageProcessor[[transformers.ChineseCLIPImageProcessor]] #### transformers.ChineseCLIPImageProcessor[[transformers.ChineseCLIPImageProcessor]] [Source](https://github.com/huggingface/transformers/blob/v5.0.0rc2/src/transformers/models/chinese_clip/image_processing_chinese_clip.py#L55) Constructs a Chinese-CLIP image processor. preprocesstransformers.ChineseCLIPImageProcessor.preprocesshttps://github.com/huggingface/transformers/blob/v5.0.0rc2/src/transformers/models/chinese_clip/image_processing_chinese_clip.py#L168[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "do_resize", "val": ": typing.Optional[bool] = None"}, {"name": "size", "val": ": typing.Optional[dict[str, int]] = None"}, {"name": "resample", "val": ": typing.Optional[PIL.Image.Resampling] = None"}, {"name": "do_center_crop", "val": ": typing.Optional[bool] = None"}, {"name": "crop_size", "val": ": typing.Optional[int] = None"}, {"name": "do_rescale", "val": ": typing.Optional[bool] = None"}, {"name": "rescale_factor", "val": ": typing.Optional[float] = None"}, {"name": "do_normalize", "val": ": typing.Optional[bool] = None"}, {"name": "image_mean", "val": ": typing.Union[float, list[float], NoneType] = None"}, {"name": "image_std", "val": ": typing.Union[float, list[float], NoneType] = None"}, {"name": "do_convert_rgb", "val": ": typing.Optional[bool] = None"}, {"name": "return_tensors", "val": ": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"}, {"name": "data_format", "val": ": typing.Optional[transformers.image_utils.ChannelDimension] = "}, {"name": "input_data_format", "val": ": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"}]- **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.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.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.0 Preprocess an image or batch of images. **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. ## ChineseCLIPImageProcessorFast[[transformers.ChineseCLIPImageProcessorFast]] #### transformers.ChineseCLIPImageProcessorFast[[transformers.ChineseCLIPImageProcessorFast]] [Source](https://github.com/huggingface/transformers/blob/v5.0.0rc2/src/transformers/models/chinese_clip/image_processing_chinese_clip_fast.py#L23) Constructs a fast Chinese Clip image processor. preprocesstransformers.ChineseCLIPImageProcessorFast.preprocesshttps://github.com/huggingface/transformers/blob/v5.0.0rc2/src/transformers/image_processing_utils_fast.py#L839[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "*args", "val": ""}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.processing_utils.ImagesKwargs]"}]- **images** (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list, list, list]`) -- 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_convert_rgb** (`bool`, *optional*) -- Whether to convert the image to RGB. - **do_resize** (`bool`, *optional*) -- Whether to resize the image. - **size** (`Annotated[Union[int, list[int], tuple[int, ...], dict[str, int], NoneType], None]`) -- Describes the maximum input dimensions to the model. - **crop_size** (`Annotated[Union[int, list[int], tuple[int, ...], dict[str, int], NoneType], None]`) -- Size of the output image after applying `center_crop`. - **resample** (`Annotated[Union[PILImageResampling, int, NoneType], None]`) -- 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_rescale** (`bool`, *optional*) -- Whether to rescale the image. - **rescale_factor** (`float`, *optional*) -- Rescale factor to rescale the image by if `do_rescale` is set to `True`. - **do_normalize** (`bool`, *optional*) -- Whether to normalize the image. - **image_mean** (`Union[float, list[float], tuple[float, ...], NoneType]`) -- Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`. - **image_std** (`Union[float, list[float], tuple[float, ...], NoneType]`) -- Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to `True`. - **do_pad** (`bool`, *optional*) -- Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model. - **pad_size** (`Annotated[Union[int, list[int], tuple[int, ...], dict[str, int], NoneType], None]`) -- The size in `{"height": int, "width" int}` to pad the images to. Must be larger than any image size provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest height and width in the batch. Applied only when `do_pad=True.` - **do_center_crop** (`bool`, *optional*) -- Whether to center crop the image. - **data_format** (`Union[~image_utils.ChannelDimension, str, NoneType]`) -- Only `ChannelDimension.FIRST` is supported. Added for compatibility with slow processors. - **input_data_format** (`Union[~image_utils.ChannelDimension, str, NoneType]`) -- 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. - **device** (`Annotated[Union[str, torch.device, NoneType], None]`) -- The device to process the images on. If unset, the device is inferred from the input images. - **return_tensors** (`Annotated[Union[str, ~utils.generic.TensorType, NoneType], None]`) -- Returns stacked tensors if set to `pt, otherwise returns a list of tensors. - **disable_grouping** (`bool`, *optional*) -- Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157 - **image_seq_length** (`int`, *optional*) -- The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models.0``- **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.). - **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization. **Parameters:** images (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list, list, list]`) : 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_convert_rgb (`bool`, *optional*) : Whether to convert the image to RGB. do_resize (`bool`, *optional*) : Whether to resize the image. size (`Annotated[Union[int, list[int], tuple[int, ...], dict[str, int], NoneType], None]`) : Describes the maximum input dimensions to the model. crop_size (`Annotated[Union[int, list[int], tuple[int, ...], dict[str, int], NoneType], None]`) : Size of the output image after applying `center_crop`. resample (`Annotated[Union[PILImageResampling, int, NoneType], None]`) : 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_rescale (`bool`, *optional*) : Whether to rescale the image. rescale_factor (`float`, *optional*) : Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_normalize (`bool`, *optional*) : Whether to normalize the image. image_mean (`Union[float, list[float], tuple[float, ...], NoneType]`) : Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`. image_std (`Union[float, list[float], tuple[float, ...], NoneType]`) : Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to `True`. do_pad (`bool`, *optional*) : Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model. pad_size (`Annotated[Union[int, list[int], tuple[int, ...], dict[str, int], NoneType], None]`) : The size in `{"height": int, "width" int}` to pad the images to. Must be larger than any image size provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest height and width in the batch. Applied only when `do_pad=True.` do_center_crop (`bool`, *optional*) : Whether to center crop the image. data_format (`Union[~image_utils.ChannelDimension, str, NoneType]`) : Only `ChannelDimension.FIRST` is supported. Added for compatibility with slow processors. input_data_format (`Union[~image_utils.ChannelDimension, str, NoneType]`) : 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. device (`Annotated[Union[str, torch.device, NoneType], None]`) : The device to process the images on. If unset, the device is inferred from the input images. return_tensors (`Annotated[Union[str, ~utils.generic.TensorType, NoneType], None]`) : Returns stacked tensors if set to `pt, otherwise returns a list of tensors. disable_grouping (`bool`, *optional*) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157 image_seq_length (`int`, *optional*) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. **Returns:** ```` - **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.). - **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization. ## ChineseCLIPProcessor[[transformers.ChineseCLIPProcessor]] #### transformers.ChineseCLIPProcessor[[transformers.ChineseCLIPProcessor]] [Source](https://github.com/huggingface/transformers/blob/v5.0.0rc2/src/transformers/models/chinese_clip/processing_chinese_clip.py#L22) Constructs a Chinese-CLIP processor which wraps a Chinese-CLIP image processor and a Chinese-CLIP tokenizer into a single processor. [ChineseCLIPProcessor](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPProcessor) offers all the functionalities of [ChineseCLIPImageProcessor](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPImageProcessor) and [BertTokenizerFast](/docs/transformers/v5.0.0rc2/en/model_doc/electra#transformers.BertTokenizer). See the [__call__()](/docs/transformers/v5.0.0rc2/en/model_doc/tvp#transformers.TvpProcessor.__call__) and [decode()](/docs/transformers/v5.0.0rc2/en/main_classes/processors#transformers.ProcessorMixin.decode) for more information. **Parameters:** image_processor ([ChineseCLIPImageProcessor](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPImageProcessor), *optional*) : The image processor is a required input. tokenizer ([BertTokenizerFast](/docs/transformers/v5.0.0rc2/en/model_doc/electra#transformers.BertTokenizer), *optional*) : The tokenizer is a required input. ## ChineseCLIPModel[[transformers.ChineseCLIPModel]] #### transformers.ChineseCLIPModel[[transformers.ChineseCLIPModel]] [Source](https://github.com/huggingface/transformers/blob/v5.0.0rc2/src/transformers/models/chinese_clip/modeling_chinese_clip.py#L967) The bare Chinese Clip Model outputting raw hidden-states without any specific head on top. This model inherits from [PreTrainedModel](/docs/transformers/v5.0.0rc2/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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. forwardtransformers.ChineseCLIPModel.forwardhttps://github.com/huggingface/transformers/blob/v5.0.0rc2/src/transformers/models/chinese_clip/modeling_chinese_clip.py#L1085[{"name": "input_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "pixel_values", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "token_type_ids", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "return_loss", "val": ": typing.Optional[bool] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "interpolate_pos_encoding", "val": ": bool = False"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "**kwargs", "val": ""}]- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of input sequence tokens in the vocabulary. Padding will be ignored by default. Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.0.0rc2/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.0.0rc2/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.0.0rc2/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#input-ids) - **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) -- The tensors corresponding to the input images. Pixel values can be obtained using [ChineseCLIPImageProcessor](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPImageProcessor). See [ChineseCLIPImageProcessor.__call__()](/docs/transformers/v5.0.0rc2/en/model_doc/fuyu#transformers.FuyuImageProcessor.__call__) for details ([ChineseCLIPProcessor](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPProcessor) uses [ChineseCLIPImageProcessor](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPImageProcessor) for processing images). - **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?](../glossary#attention-mask) - **token_type_ids** (`torch.Tensor` 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?](../glossary#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.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) - **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. - **interpolate_pos_encoding** (`bool`, *optional*, defaults to `False`) -- Whether to interpolate the pre-trained position encodings. - **return_dict** (`bool`, *optional*) -- Whether or not to return a [ModelOutput](/docs/transformers/v5.0.0rc2/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0`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 ([ChineseCLIPConfig](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.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](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.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](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPVisionModel). - **text_model_output** (`.text_model_output`, defaults to `None`) -- The output of the [ChineseCLIPTextModel](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPTextModel). - **vision_model_output** (`.vision_model_output`, defaults to `None`) -- The output of the [ChineseCLIPVisionModel](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPVisionModel). The [ChineseCLIPModel](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.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: ```python >>> import torch >>> from transformers import AutoProcessor, ChineseCLIPModel >>> from transformers.image_utils import load_image >>> 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 = load_image(url) >>> inputs = processor(text=["杰尼龟", "妙蛙种子", "小火龙", "皮卡丘"], images=image, return_tensors="pt", padding=True) >>> with torch.inference_mode(): ... 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 ``` **Parameters:** config ([ChineseCLIPConfig](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.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()](/docs/transformers/v5.0.0rc2/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. **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 ([ChineseCLIPConfig](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.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](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.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](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPVisionModel). - **text_model_output** (`.text_model_output`, defaults to `None`) -- The output of the [ChineseCLIPTextModel](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPTextModel). - **vision_model_output** (`.vision_model_output`, defaults to `None`) -- The output of the [ChineseCLIPVisionModel](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPVisionModel). #### get_text_features[[transformers.ChineseCLIPModel.get_text_features]] [Source](https://github.com/huggingface/transformers/blob/v5.0.0rc2/src/transformers/models/chinese_clip/modeling_chinese_clip.py#L1004) Examples: ```python >>> import torch >>> 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") >>> with torch.inference_mode(): ... text_features = model.get_text_features(**inputs) >>> text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True) ``` **Parameters:** input_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`) : Indices of input sequence tokens in the vocabulary. Padding will be ignored by default. Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.0.0rc2/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.0.0rc2/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.0.0rc2/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#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?](../glossary#attention-mask) token_type_ids (`torch.Tensor` 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?](../glossary#token-type-ids) position_ids (`torch.Tensor` 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.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) **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. #### get_image_features[[transformers.ChineseCLIPModel.get_image_features]] [Source](https://github.com/huggingface/transformers/blob/v5.0.0rc2/src/transformers/models/chinese_clip/modeling_chinese_clip.py#L1044) Examples: ```python >>> import torch >>> from transformers import AutoProcessor, ChineseCLIPModel >>> from transformers.image_utils import load_image >>> 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 = load_image(url) >>> inputs = processor(images=image, return_tensors="pt") >>> with torch.inference_mode(): ... image_features = model.get_image_features(**inputs) >>> image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True) ``` **Parameters:** pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) : The tensors corresponding to the input images. Pixel values can be obtained using [ChineseCLIPImageProcessor](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPImageProcessor). See [ChineseCLIPImageProcessor.__call__()](/docs/transformers/v5.0.0rc2/en/model_doc/fuyu#transformers.FuyuImageProcessor.__call__) for details ([ChineseCLIPProcessor](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPProcessor) uses [ChineseCLIPImageProcessor](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPImageProcessor) for processing images). interpolate_pos_encoding (`bool`, *optional*, defaults to `False`) : Whether to interpolate the pre-trained position encodings. **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. ## ChineseCLIPTextModel[[transformers.ChineseCLIPTextModel]] #### transformers.ChineseCLIPTextModel[[transformers.ChineseCLIPTextModel]] [Source](https://github.com/huggingface/transformers/blob/v5.0.0rc2/src/transformers/models/chinese_clip/modeling_chinese_clip.py#L790) The text model from CHINESE_CLIP without any head or projection on top. This model inherits from [PreTrainedModel](/docs/transformers/v5.0.0rc2/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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. forwardtransformers.ChineseCLIPTextModel.forwardhttps://github.com/huggingface/transformers/blob/v5.0.0rc2/src/transformers/models/chinese_clip/modeling_chinese_clip.py#L829[{"name": "input_ids", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "token_type_ids", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "inputs_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "encoder_hidden_states", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "encoder_attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "past_key_values", "val": ": typing.Optional[transformers.cache_utils.Cache] = None"}, {"name": "use_cache", "val": ": typing.Optional[bool] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "**kwargs", "val": ""}]- **input_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of input sequence tokens in the vocabulary. Padding will be ignored by default. Indices can be obtained using [AutoTokenizer](/docs/transformers/v5.0.0rc2/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.0.0rc2/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.0.0rc2/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#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?](../glossary#attention-mask) - **token_type_ids** (`torch.Tensor` 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?](../glossary#token-type-ids) - **position_ids** (`torch.Tensor` 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.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) - **inputs_embeds** (`torch.Tensor` 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. - **encoder_hidden_states** (`torch.Tensor` 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.Tensor` 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** (`~cache_utils.Cache`, *optional*) -- Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Only [Cache](/docs/transformers/v5.0.0rc2/en/internal/generation_utils#transformers.Cache) instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). If no `past_key_values` are passed, [DynamicCache](/docs/transformers/v5.0.0rc2/en/internal/generation_utils#transformers.DynamicCache) will be initialized by default. The model will output the same cache format that is fed as input. If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `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`). - **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](/docs/transformers/v5.0.0rc2/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0[transformers.modeling_outputs.BaseModelOutputWithPooling](/docs/transformers/v5.0.0rc2/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [transformers.modeling_outputs.BaseModelOutputWithPooling](/docs/transformers/v5.0.0rc2/en/main_classes/output#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 ([ChineseCLIPConfig](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.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. The [ChineseCLIPTextModel](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.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. **Parameters:** config ([ChineseCLIPTextModel](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPTextModel)) : 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()](/docs/transformers/v5.0.0rc2/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. add_pooling_layer (`bool`, *optional*, defaults to `True`) : Whether to add a pooling layer **Returns:** `[transformers.modeling_outputs.BaseModelOutputWithPooling](/docs/transformers/v5.0.0rc2/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`` A [transformers.modeling_outputs.BaseModelOutputWithPooling](/docs/transformers/v5.0.0rc2/en/main_classes/output#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 ([ChineseCLIPConfig](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.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. ## ChineseCLIPVisionModel[[transformers.ChineseCLIPVisionModel]] #### transformers.ChineseCLIPVisionModel[[transformers.ChineseCLIPVisionModel]] [Source](https://github.com/huggingface/transformers/blob/v5.0.0rc2/src/transformers/models/chinese_clip/modeling_chinese_clip.py#L910) The vision model from CHINESE_CLIP without any head or projection on top. This model inherits from [PreTrainedModel](/docs/transformers/v5.0.0rc2/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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. forwardtransformers.ChineseCLIPVisionModel.forwardhttps://github.com/huggingface/transformers/blob/v5.0.0rc2/src/transformers/models/chinese_clip/modeling_chinese_clip.py#L925[{"name": "pixel_values", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "interpolate_pos_encoding", "val": ": bool = False"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "**kwargs", "val": ""}]- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) -- The tensors corresponding to the input images. Pixel values can be obtained using [ChineseCLIPImageProcessor](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPImageProcessor). See [ChineseCLIPImageProcessor.__call__()](/docs/transformers/v5.0.0rc2/en/model_doc/fuyu#transformers.FuyuImageProcessor.__call__) for details ([ChineseCLIPProcessor](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPProcessor) uses [ChineseCLIPImageProcessor](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPImageProcessor) for processing images). - **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 to `False`) -- Whether to interpolate the pre-trained position encodings. - **return_dict** (`bool`, *optional*) -- Whether or not to return a [ModelOutput](/docs/transformers/v5.0.0rc2/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0[transformers.modeling_outputs.BaseModelOutputWithPooling](/docs/transformers/v5.0.0rc2/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [transformers.modeling_outputs.BaseModelOutputWithPooling](/docs/transformers/v5.0.0rc2/en/main_classes/output#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 ([ChineseCLIPConfig](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.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. The [ChineseCLIPVisionModel](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.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: ```python >>> 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 ``` **Parameters:** config ([ChineseCLIPVisionConfig](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.ChineseCLIPVisionConfig)) : 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()](/docs/transformers/v5.0.0rc2/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. **Returns:** `[transformers.modeling_outputs.BaseModelOutputWithPooling](/docs/transformers/v5.0.0rc2/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`` A [transformers.modeling_outputs.BaseModelOutputWithPooling](/docs/transformers/v5.0.0rc2/en/main_classes/output#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 ([ChineseCLIPConfig](/docs/transformers/v5.0.0rc2/en/model_doc/chinese_clip#transformers.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.