CLIP (original) (raw)

This model was released on 2021-02-26 and added to Hugging Face Transformers on 2021-05-12.

CLIP is a is a multimodal vision and language model motivated by overcoming the fixed number of object categories when training a computer vision model. CLIP learns about images directly from raw text by jointly training on 400M (image, text) pairs. Pretraining on this scale enables zero-shot transfer to downstream tasks. CLIP uses an image encoder and text encoder to get visual features and text features. Both features are projected to a latent space with the same number of dimensions and their dot product gives a similarity score.

You can find all the original CLIP checkpoints under the OpenAI organization.

Click on the CLIP models in the right sidebar for more examples of how to apply CLIP to different image and language tasks.

The example below demonstrates how to calculate similarity scores between multiple text descriptions and an image with Pipeline or the AutoModel class.

import torch from transformers import pipeline

clip = pipeline( task="zero-shot-image-classification", model="openai/clip-vit-base-patch32", dtype=torch.bfloat16, device=0 ) labels = ["a photo of a cat", "a photo of a dog", "a photo of a car"] clip("http://images.cocodataset.org/val2017/000000039769.jpg", candidate_labels=labels)

Notes

Use CLIPImageProcessor to resize (or rescale) and normalizes images for the model.

CLIPConfig

class transformers.CLIPConfig

( 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 CLIPTextConfig.
vision_config (dict, optional) — Dictionary of configuration options used to initialize CLIPVisionConfig.
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 CLIP implementation.
kwargs (optional) — Dictionary of keyword arguments.

CLIPConfig is the configuration class to store the configuration of a CLIPModel. It is used to instantiate a 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 CLIPopenai/clip-vit-base-patch32 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 CLIPConfig, CLIPModel

configuration = CLIPConfig()

model = CLIPModel(configuration)

configuration = model.config

from transformers import CLIPTextConfig, CLIPVisionConfig

config_text = CLIPTextConfig() config_vision = CLIPVisionConfig()

config = CLIPConfig(text_config=config_text, vision_config=config_vision)

CLIPTextConfig

class transformers.CLIPTextConfig

( vocab_size = 49408 hidden_size = 512 intermediate_size = 2048 projection_dim = 512 num_hidden_layers = 12 num_attention_heads = 8 max_position_embeddings = 77 hidden_act = 'quick_gelu' layer_norm_eps = 1e-05 attention_dropout = 0.0 initializer_range = 0.02 initializer_factor = 1.0 pad_token_id = 1 bos_token_id = 49406 eos_token_id = 49407 **kwargs )

Parameters

vocab_size (int, optional, defaults to 49408) — Vocabulary size of the CLIP text model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling CLIPModel.
hidden_size (int, optional, defaults to 512) — Dimensionality of the encoder layers and the pooler layer.
intermediate_size (int, optional, defaults to 2048) — 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 8) — Number of attention heads for each attention layer in the Transformer encoder.
max_position_embeddings (int, optional, defaults to 77) — 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).
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).
pad_token_id (int, optional, defaults to 1) — Padding token id.
bos_token_id (int, optional, defaults to 49406) — Beginning of stream token id.
eos_token_id (int, optional, defaults to 49407) — End of stream token id.

This is the configuration class to store the configuration of a CLIPTextModel. It is used to instantiate a CLIP text encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the text encoder of the CLIPopenai/clip-vit-base-patch32 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 CLIPTextConfig, CLIPTextModel

configuration = CLIPTextConfig()

model = CLIPTextModel(configuration)

configuration = model.config

CLIPVisionConfig

class transformers.CLIPVisionConfig

( 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 CLIPVisionModel. It is used to instantiate a CLIP vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the vision encoder of the CLIPopenai/clip-vit-base-patch32 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 CLIPVisionConfig, CLIPVisionModel

configuration = CLIPVisionConfig()

model = CLIPVisionModel(configuration)

configuration = model.config

CLIPTokenizer

class transformers.CLIPTokenizer

( vocab: typing.Union[str, dict[str, int], NoneType] = None merges: typing.Union[str, list[str], NoneType] = None unk_token: str = '<|endoftext|>' bos_token: str = '<|startoftext|>' eos_token: str = '<|endoftext|>' pad_token: str = '<|endoftext|>' **kwargs )

Parameters

vocab (str, dict or list, optional) — Vocabulary dict to use for the tokenizer.
merges (str or list, optional) — Merges list to use for the BPE tokenizer.
unk_token (str, optional, defaults to "<|endoftext|>") — The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.
bos_token (str, optional, defaults to "<|startoftext|>") — The beginning of sequence token.
eos_token (str, optional, defaults to "<|endoftext|>") — The end of sequence token.
pad_token (str, optional, defaults to "<|endoftext|>") — The token used for padding, for example when batching sequences of different lengths.

Construct a CLIP tokenizer (backed by HuggingFace’s tokenizers library). Based on byte-level Byte-Pair-Encoding.

This tokenizer inherits from TokenizersBackend which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

get_special_tokens_mask

( token_ids_0: list[int] token_ids_1: Optional[list[int]] = None already_has_special_tokens: bool = False ) → A list of integers in the range [0, 1]

Parameters

token_ids_0 — List of IDs for the (possibly already formatted) sequence.
token_ids_1 — Unused when already_has_special_tokens=True. Must be None in that case.
already_has_special_tokens — Whether the sequence is already formatted with special tokens.

Returns

A list of integers in the range [0, 1]

1 for a special token, 0 for a sequence token.

Retrieve sequence ids from a token list that has no special tokens added.

For fast tokenizers, data collators call this with already_has_special_tokens=True to build a mask over an already-formatted sequence. In that case, we compute the mask by checking membership in all_special_ids.

save_vocabulary

( save_directory: str filename_prefix: typing.Optional[str] = None )

CLIPTokenizerFast

class transformers.CLIPTokenizer

( vocab: typing.Union[str, dict[str, int], NoneType] = None merges: typing.Union[str, list[str], NoneType] = None unk_token: str = '<|endoftext|>' bos_token: str = '<|startoftext|>' eos_token: str = '<|endoftext|>' pad_token: str = '<|endoftext|>' **kwargs )

Parameters

vocab (str, dict or list, optional) — Vocabulary dict to use for the tokenizer.
merges (str or list, optional) — Merges list to use for the BPE tokenizer.
unk_token (str, optional, defaults to "<|endoftext|>") — The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.
bos_token (str, optional, defaults to "<|startoftext|>") — The beginning of sequence token.
eos_token (str, optional, defaults to "<|endoftext|>") — The end of sequence token.
pad_token (str, optional, defaults to "<|endoftext|>") — The token used for padding, for example when batching sequences of different lengths.

Construct a CLIP tokenizer (backed by HuggingFace’s tokenizers library). Based on byte-level Byte-Pair-Encoding.

This tokenizer inherits from TokenizersBackend which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

CLIPImageProcessor

class transformers.CLIPImageProcessor

( do_resize: bool = True size: typing.Optional[dict[str, int]] = None resample: Resampling = <Resampling.BICUBIC: 3> do_center_crop: bool = True crop_size: typing.Optional[dict[str, int]] = None do_rescale: bool = True rescale_factor: typing.Union[int, float] = 0.00392156862745098 do_normalize: bool = True image_mean: typing.Union[float, list[float], NoneType] = None image_std: typing.Union[float, list[float], NoneType] = 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 bydo_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 preprocessmethod.
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 thepreprocess 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 preprocessmethod.
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 preprocessmethod.
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 [0.48145466, 0.4578275, 0.40821073]) — 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 [0.26862954, 0.26130258, 0.27577711]) — 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 CLIP image processor.

preprocess

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']] do_resize: typing.Optional[bool] = None size: typing.Optional[dict[str, int]] = None resample: typing.Optional[PIL.Image.Resampling] = None do_center_crop: typing.Optional[bool] = None crop_size: typing.Optional[int] = None do_rescale: typing.Optional[bool] = None rescale_factor: typing.Optional[float] = None do_normalize: typing.Optional[bool] = None image_mean: typing.Union[float, list[float], NoneType] = None image_std: typing.Union[float, list[float], NoneType] = None do_convert_rgb: typing.Optional[bool] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None data_format: typing.Optional[transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'> input_data_format: typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None **kwargs )

Parameters

images (ImageInput) — Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, 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 toTrue.
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.

Preprocess an image or batch of images.

CLIPImageProcessorFast

class transformers.CLIPImageProcessorFast

( **kwargs: typing_extensions.Unpack[transformers.processing_utils.ImagesKwargs] )

Constructs a fast Clip image processor.

preprocess

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']] *args **kwargs: typing_extensions.Unpack[transformers.processing_utils.ImagesKwargs] ) → <class 'transformers.image_processing_base.BatchFeature'>

Parameters

images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]) — 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 toTrue.
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

<class 'transformers.image_processing_base.BatchFeature'>

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.

CLIPProcessor

class transformers.CLIPProcessor

( image_processor = None tokenizer = None **kwargs )

Parameters

image_processor (CLIPImageProcessor, optional) — The image processor is a required input.
tokenizer (AutoTokenizer, optional) — The tokenizer is a required input.

Constructs a CLIP processor which wraps a CLIP image processor and a CLIP tokenizer into a single processor.

CLIPProcessor offers all the functionalities of CLIPImageProcessor and CLIPTokenizerFast. See thecall() and decode() for more information.

CLIPModel

class transformers.CLIPModel

( config: CLIPConfig )

Parameters

config (CLIPConfig) — 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 thefrom_pretrained() method to load the model weights.

The bare Clip Model outputting raw hidden-states without any specific head on top.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

( input_ids: typing.Optional[torch.LongTensor] = None pixel_values: typing.Optional[torch.FloatTensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None return_loss: typing.Optional[bool] = None interpolate_pos_encoding: bool = False **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → transformers.models.clip.modeling_clip.CLIPOutput or tuple(torch.FloatTensor)

Parameters

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. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
What are 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 usingCLIPImageProcessor. See CLIPImageProcessor.call() for details (CLIPProcessor usesCLIPImageProcessor 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?
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?
return_loss (bool, optional) — Whether or not to return the contrastive loss.
interpolate_pos_encoding (bool, defaults to False) — Whether to interpolate the pre-trained position encodings.

Returns

transformers.models.clip.modeling_clip.CLIPOutput or tuple(torch.FloatTensor)

A transformers.models.clip.modeling_clip.CLIPOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (CLIPConfig) 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 CLIPTextModel.
image_embeds (torch.FloatTensor of shape (batch_size, output_dim) — The image embeddings obtained by applying the projection layer to the pooled output of CLIPVisionModel.
text_model_output (<class '~modeling_outputs.BaseModelOutputWithPooling'>.text_model_output, defaults to None) — The output of the CLIPTextModel.
vision_model_output (<class '~modeling_outputs.BaseModelOutputWithPooling'>.vision_model_output, defaults to None) — The output of the CLIPVisionModel.

The CLIPModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

import torch from transformers import AutoProcessor, CLIPModel from transformers.image_utils import load_image

model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")

url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = load_image(url)

inputs = processor( ... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True ... )

with torch.inference_mode(): ... outputs = model(**inputs) logits_per_image = outputs.logits_per_image
probs = logits_per_image.softmax(dim=1)

get_text_features

( input_ids: Tensor attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None ) → text_features (torch.FloatTensor of shape (batch_size, output_dim)

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. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.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?
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?

Returns

text_features (torch.FloatTensor of shape (batch_size, output_dim)

The text embeddings obtained by applying the projection layer to the pooled output of CLIPTextModel.

Examples:

import torch from transformers import AutoTokenizer, CLIPModel

model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")

inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")

with torch.inference_mode(): ... text_features = model.get_text_features(**inputs)

get_image_features

( pixel_values: FloatTensor interpolate_pos_encoding: bool = False ) → image_features (torch.FloatTensor of shape (batch_size, output_dim)

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 usingCLIPImageProcessor. See CLIPImageProcessor.call() for details (CLIPProcessor usesCLIPImageProcessor for processing images).
interpolate_pos_encoding (bool, 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 pooled output of CLIPVisionModel.

Examples:

import torch from transformers import AutoProcessor, CLIPModel from transformers.image_utils import load_image

model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")

url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = load_image(url)

inputs = processor(images=image, return_tensors="pt")

with torch.inference_mode(): ... image_features = model.get_image_features(**inputs)

CLIPTextModel

class transformers.CLIPTextModel

( config: CLIPTextConfig )

Parameters

config (CLIPTextConfig) — 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 thefrom_pretrained() method to load the model weights.

The text model from CLIP without any head or projection on top.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → transformers.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)

Parameters

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. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.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?
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?

A transformers.modeling_outputs.BaseModelOutputWithPooling or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (CLIPConfig) 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 CLIPTextModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance 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, CLIPTextModel

model = CLIPTextModel.from_pretrained("openai/clip-vit-base-patch32") tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")

inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")

outputs = model(**inputs) last_hidden_state = outputs.last_hidden_state pooled_output = outputs.pooler_output

CLIPTextModelWithProjection

class transformers.CLIPTextModelWithProjection

( config: CLIPTextConfig )

Parameters

config (CLIPTextConfig) — 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 thefrom_pretrained() method to load the model weights.

The Clip Model with a projection layer on top (a linear layer on top of the pooled output).

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → transformers.models.clip.modeling_clip.CLIPTextModelOutput or tuple(torch.FloatTensor)

Parameters

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. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.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?
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?

Returns

transformers.models.clip.modeling_clip.CLIPTextModelOutput or tuple(torch.FloatTensor)

A transformers.models.clip.modeling_clip.CLIPTextModelOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (CLIPConfig) and inputs.

text_embeds (torch.FloatTensor of shape (batch_size, output_dim) optional returned when model is initialized with with_projection=True) — The text embeddings obtained by applying the projection layer to the pooler_output.
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional, defaults to None) — Sequence of hidden-states at the output of the last layer of the model.
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 CLIPTextModelWithProjection forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

import torch from transformers import AutoTokenizer, CLIPTextModelWithProjection

model = CLIPTextModelWithProjection.from_pretrained("openai/clip-vit-base-patch32") tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")

inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")

with torch.inference_mode(): ... outputs = model(**inputs) text_embeds = outputs.text_embeds

CLIPVisionModelWithProjection

class transformers.CLIPVisionModelWithProjection

( config: CLIPVisionConfig )

Parameters

config (CLIPVisionConfig) — 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 thefrom_pretrained() method to load the model weights.

The Clip Model with a projection layer on top (a linear layer on top of the pooled output).

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

( pixel_values: typing.Optional[torch.FloatTensor] = None interpolate_pos_encoding: bool = False **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → transformers.models.clip.modeling_clip.CLIPVisionModelOutput or tuple(torch.FloatTensor)

Parameters

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 usingCLIPImageProcessor. See CLIPImageProcessor.call() for details (CLIPProcessor usesCLIPImageProcessor for processing images).
interpolate_pos_encoding (bool, defaults to False) — Whether to interpolate the pre-trained position encodings.

Returns

transformers.models.clip.modeling_clip.CLIPVisionModelOutput or tuple(torch.FloatTensor)

A transformers.models.clip.modeling_clip.CLIPVisionModelOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (CLIPConfig) and inputs.

image_embeds (torch.FloatTensor of shape (batch_size, output_dim) optional returned when model is initialized with with_projection=True) — The image embeddings obtained by applying the projection layer to the pooler_output.
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional, defaults to None) — Sequence of hidden-states at the output of the last layer of the model.
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 CLIPVisionModelWithProjection forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

import torch from transformers import AutoProcessor, CLIPVisionModelWithProjection from transformers.image_utils import load_image

model = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-base-patch32") processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")

url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = load_image(url)

inputs = processor(images=image, return_tensors="pt")

with torch.inference_mode(): ... outputs = model(**inputs) image_embeds = outputs.image_embeds

CLIPVisionModel

class transformers.CLIPVisionModel

( config: CLIPVisionConfig )

Parameters

config (CLIPVisionConfig) — 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 thefrom_pretrained() method to load the model weights.

The vision model from CLIP without any head or projection on top.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

( pixel_values: typing.Optional[torch.FloatTensor] = None interpolate_pos_encoding: bool = False **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → transformers.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)

Parameters

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 usingCLIPImageProcessor. See CLIPImageProcessor.call() for details (CLIPProcessor usesCLIPImageProcessor for processing images).
interpolate_pos_encoding (bool, defaults to False) — Whether to interpolate the pre-trained position encodings.

A transformers.modeling_outputs.BaseModelOutputWithPooling or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (CLIPConfig) 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 CLIPVisionModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance 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 PIL import Image import requests from transformers import AutoProcessor, CLIPVisionModel

model = CLIPVisionModel.from_pretrained("openai/clip-vit-base-patch32") processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")

url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw)

inputs = processor(images=image, return_tensors="pt")

outputs = model(**inputs) last_hidden_state = outputs.last_hidden_state pooled_output = outputs.pooler_output

CLIPForImageClassification

class transformers.CLIPForImageClassification

( config: CLIPConfig )

Parameters

config (CLIPConfig) — 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 thefrom_pretrained() method to load the model weights.

CLIP vision encoder with an image classification head on top (a linear layer on top of the pooled final hidden states of the patch tokens) e.g. for ImageNet.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

( pixel_values: typing.Optional[torch.Tensor] = None labels: typing.Optional[torch.Tensor] = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → transformers.modeling_outputs.ImageClassifierOutput or tuple(torch.FloatTensor)

Parameters

pixel_values (torch.Tensor of shape (batch_size, num_channels, image_size, image_size), optional) — The tensors corresponding to the input images. Pixel values can be obtained usingCLIPImageProcessor. See CLIPImageProcessor.call() for details (CLIPProcessor usesCLIPImageProcessor for processing images).
labels (torch.LongTensor of shape (batch_size,), optional) — Labels for computing the image classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1 a classification loss is computed (Cross-Entropy).

A transformers.modeling_outputs.ImageClassifierOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (CLIPConfig) and inputs.

loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.
logits (torch.FloatTensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
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 stage) of shape (batch_size, sequence_length, hidden_size). Hidden-states (also called feature maps) of the model at the output of each stage.
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, patch_size, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The CLIPForImageClassification forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance 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 AutoImageProcessor, CLIPForImageClassification import torch from datasets import load_dataset

dataset = load_dataset("huggingface/cats-image") image = dataset["test"]["image"][0]

image_processor = AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32") model = CLIPForImageClassification.from_pretrained("openai/clip-vit-base-patch32")

inputs = image_processor(image, return_tensors="pt")

with torch.no_grad(): ... logits = model(**inputs).logits

predicted_label = logits.argmax(-1).item() print(model.config.id2label[predicted_label]) ...

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