Hugging Face's logo

Buckets:
hf-doc-build
/
doc-dev

Files
xet
hf-doc-build/doc-dev / transformers /pr_33962 /en /model_doc /clip.md
rtrm's picture
rtrm
preview code
|
download
raw
88.7 kB

CLIP

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)

import requests
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModel

model = AutoModel.from_pretrained("openai/clip-vit-base-patch32", dtype=torch.bfloat16, attn_implementation="sdpa")
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)
labels = ["a photo of a cat", "a photo of a dog", "a photo of a car"]

inputs = processor(text=labels, images=image, return_tensors="pt", padding=True)

outputs = model(**inputs)
logits_per_image = outputs.logits_per_image
probs = logits_per_image.softmax(dim=1)
most_likely_idx = probs.argmax(dim=1).item()
most_likely_label = labels[most_likely_idx]
print(f"Most likely label: {most_likely_label} with probability: {probs[0][most_likely_idx].item():.3f}")

Notes

CLIPConfig[[transformers.CLIPConfig]]

class transformers.CLIPConfigtransformers.CLIPConfighttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/configuration_clip.py#L226[{"name": "text_config", "val": " = None"}, {"name": "vision_config", "val": " = None"}, {"name": "projection_dim", "val": " = 512"}, {"name": "logit_scale_init_value", "val": " = 2.6592"}, {"name": "**kwargs", "val": ""}]- 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.0

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 CLIP openai/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

>>> # Initializing a CLIPConfig with openai/clip-vit-base-patch32 style configuration
>>> configuration = CLIPConfig()

>>> # Initializing a CLIPModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
>>> model = CLIPModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

>>> # We can also initialize a CLIPConfig from a CLIPTextConfig and a CLIPVisionConfig
>>> from transformers import CLIPTextConfig, CLIPVisionConfig

>>> # Initializing a CLIPText and CLIPVision configuration
>>> config_text = CLIPTextConfig()
>>> config_vision = CLIPVisionConfig()

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

CLIPTextConfig[[transformers.CLIPTextConfig]]

class transformers.CLIPTextConfigtransformers.CLIPTextConfighttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/configuration_clip.py#L33[{"name": "vocab_size", "val": " = 49408"}, {"name": "hidden_size", "val": " = 512"}, {"name": "intermediate_size", "val": " = 2048"}, {"name": "projection_dim", "val": " = 512"}, {"name": "num_hidden_layers", "val": " = 12"}, {"name": "num_attention_heads", "val": " = 8"}, {"name": "max_position_embeddings", "val": " = 77"}, {"name": "hidden_act", "val": " = 'quick_gelu'"}, {"name": "layer_norm_eps", "val": " = 1e-05"}, {"name": "attention_dropout", "val": " = 0.0"}, {"name": "initializer_range", "val": " = 0.02"}, {"name": "initializer_factor", "val": " = 1.0"}, {"name": "pad_token_id", "val": " = 1"}, {"name": "bos_token_id", "val": " = 49406"}, {"name": "eos_token_id", "val": " = 49407"}, {"name": "**kwargs", "val": ""}]- 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.0

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 CLIP openai/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

>>> # Initializing a CLIPTextConfig with openai/clip-vit-base-patch32 style configuration
>>> configuration = CLIPTextConfig()

>>> # Initializing a CLIPTextModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
>>> model = CLIPTextModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

CLIPVisionConfig[[transformers.CLIPVisionConfig]]

class transformers.CLIPVisionConfigtransformers.CLIPVisionConfighttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/configuration_clip.py#L134[{"name": "hidden_size", "val": " = 768"}, {"name": "intermediate_size", "val": " = 3072"}, {"name": "projection_dim", "val": " = 512"}, {"name": "num_hidden_layers", "val": " = 12"}, {"name": "num_attention_heads", "val": " = 12"}, {"name": "num_channels", "val": " = 3"}, {"name": "image_size", "val": " = 224"}, {"name": "patch_size", "val": " = 32"}, {"name": "hidden_act", "val": " = 'quick_gelu'"}, {"name": "layer_norm_eps", "val": " = 1e-05"}, {"name": "attention_dropout", "val": " = 0.0"}, {"name": "initializer_range", "val": " = 0.02"}, {"name": "initializer_factor", "val": " = 1.0"}, {"name": "**kwargs", "val": ""}]- 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).0

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 CLIP openai/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

>>> # Initializing a CLIPVisionConfig with openai/clip-vit-base-patch32 style configuration
>>> configuration = CLIPVisionConfig()

>>> # Initializing a CLIPVisionModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
>>> model = CLIPVisionModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

CLIPTokenizer[[transformers.CLIPTokenizer]]

class transformers.CLIPTokenizertransformers.CLIPTokenizerhttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/tokenization_clip.py#L254[{"name": "vocab_file", "val": ""}, {"name": "merges_file", "val": ""}, {"name": "errors", "val": " = 'replace'"}, {"name": "unk_token", "val": " = '<|endoftext|>'"}, {"name": "bos_token", "val": " = '<|startoftext|>'"}, {"name": "eos_token", "val": " = '<|endoftext|>'"}, {"name": "pad_token", "val": " = '<|endoftext|>'"}, {"name": "**kwargs", "val": ""}]- vocab_file (str) -- Path to the vocabulary file.

  • merges_file (str) -- Path to the merges file.
  • errors (str, optional, defaults to "replace") -- Paradigm to follow when decoding bytes to UTF-8. See bytes.decode for more information.
  • 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.0

Construct a CLIP tokenizer. Based on byte-level Byte-Pair-Encoding.

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

build_inputs_with_special_tokenstransformers.CLIPTokenizer.build_inputs_with_special_tokenshttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/tokenization_clip.py#L339[{"name": "token_ids_0", "val": ": list"}, {"name": "token_ids_1", "val": ": typing.Optional[list[int]] = None"}]- token_ids_0 (list[int]) -- List of IDs to which the special tokens will be added.

  • token_ids_1 (list[int], optional) -- Optional second list of IDs for sequence pairs.0list[int]List of input IDs with the appropriate special tokens.

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A CLIP sequence has the following format:

  • single sequence: <|startoftext|> X <|endoftext|>

Pairs of sequences are not the expected use case, but they will be handled without a separator.

get_special_tokens_masktransformers.CLIPTokenizer.get_special_tokens_maskhttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/tokenization_clip.py#L366[{"name": "token_ids_0", "val": ": list"}, {"name": "token_ids_1", "val": ": typing.Optional[list[int]] = None"}, {"name": "already_has_special_tokens", "val": ": bool = False"}]- token_ids_0 (list[int]) -- List of IDs.

  • token_ids_1 (list[int], optional) -- Optional second list of IDs for sequence pairs.
  • already_has_special_tokens (bool, optional, defaults to False) -- Whether or not the token list is already formatted with special tokens for the model.0list[int]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. This method is called when adding special tokens using the tokenizer prepare_for_model method.

create_token_type_ids_from_sequencestransformers.CLIPTokenizer.create_token_type_ids_from_sequenceshttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/tokenization_clip.py#L394[{"name": "token_ids_0", "val": ": list"}, {"name": "token_ids_1", "val": ": typing.Optional[list[int]] = None"}]- token_ids_0 (list[int]) -- List of IDs.

  • token_ids_1 (list[int], optional) -- Optional second list of IDs for sequence pairs.0list[int]List of zeros.

Create a mask from the two sequences passed. CLIP does not make use of token type ids, therefore a list of zeros is returned.

save_vocabularytransformers.CLIPTokenizer.save_vocabularyhttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/tokenization_clip.py#L489[{"name": "save_directory", "val": ": str"}, {"name": "filename_prefix", "val": ": typing.Optional[str] = None"}]

CLIPTokenizerFast[[transformers.CLIPTokenizerFast]]

class transformers.CLIPTokenizerFasttransformers.CLIPTokenizerFasthttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/tokenization_clip_fast.py#L31[{"name": "vocab_file", "val": " = None"}, {"name": "merges_file", "val": " = None"}, {"name": "tokenizer_file", "val": " = None"}, {"name": "unk_token", "val": " = '<|endoftext|>'"}, {"name": "bos_token", "val": " = '<|startoftext|>'"}, {"name": "eos_token", "val": " = '<|endoftext|>'"}, {"name": "pad_token", "val": " = '<|endoftext|>'"}, {"name": "**kwargs", "val": ""}]- vocab_file (str, optional) -- Path to the vocabulary file.

  • merges_file (str, optional) -- Path to the merges file.
  • tokenizer_file (str, optional) -- The path to a tokenizer file to use instead of the vocab file.
  • 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.0

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

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

build_inputs_with_special_tokenstransformers.CLIPTokenizerFast.build_inputs_with_special_tokenshttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/tokenization_clip_fast.py#L109[{"name": "token_ids_0", "val": ": list"}, {"name": "token_ids_1", "val": ": typing.Optional[list[int]] = None"}]- token_ids_0 (list[int]) -- List of IDs to which the special tokens will be added.

  • token_ids_1 (list[int], optional) -- Optional second list of IDs for sequence pairs.0list[int]List of input IDs with the appropriate special tokens.

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A CLIP sequence has the following format:

  • single sequence: <|startoftext|> X <|endoftext|>

Pairs of sequences are not the expected use case, but they will be handled without a separator.

create_token_type_ids_from_sequencestransformers.CLIPTokenizerFast.create_token_type_ids_from_sequenceshttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/tokenization_clip_fast.py#L136[{"name": "token_ids_0", "val": ": list"}, {"name": "token_ids_1", "val": ": typing.Optional[list[int]] = None"}]- token_ids_0 (list[int]) -- List of IDs.

  • token_ids_1 (list[int], optional) -- Optional second list of IDs for sequence pairs.0list[int]List of zeros.

Create a mask from the two sequences passed. CLIP does not make use of token type ids, therefore a list of zeros is returned.

CLIPImageProcessor[[transformers.CLIPImageProcessor]]

class transformers.CLIPImageProcessortransformers.CLIPImageProcessorhttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/image_processing_clip.py#L54[{"name": "do_resize", "val": ": bool = True"}, {"name": "size", "val": ": typing.Optional[dict[str, int]] = None"}, {"name": "resample", "val": ": Resampling = <Resampling.BICUBIC: 3>"}, {"name": "do_center_crop", "val": ": bool = True"}, {"name": "crop_size", "val": ": typing.Optional[dict[str, int]] = None"}, {"name": "do_rescale", "val": ": bool = True"}, {"name": "rescale_factor", "val": ": typing.Union[int, float] = 0.00392156862745098"}, {"name": "do_normalize", "val": ": bool = True"}, {"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": ": bool = True"}, {"name": "**kwargs", "val": ""}]- 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 [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.0

Constructs a CLIP image processor.

preprocesstransformers.CLIPImageProcessor.preprocesshttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/image_processing_clip.py#L202[{"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] = <ChannelDimension.FIRST: 'channels_first'>"}, {"name": "input_data_format", "val": ": typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None"}, {"name": "**kwargs", "val": ""}]- 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.

CLIPImageProcessorFast[[transformers.CLIPImageProcessorFast]]

class transformers.CLIPImageProcessorFasttransformers.CLIPImageProcessorFasthttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/image_processing_clip_fast.py#L23[{"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.processing_utils.ImagesKwargs]"}]

Constructs a fast Clip image processor.

preprocesstransformers.CLIPImageProcessorFast.preprocesshttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/image_processing_utils_fast.py#L710[{"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['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 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[str, ~image_utils.ChannelDimension, NoneType]) -- Only ChannelDimension.FIRST is supported. Added for compatibility with slow processors.
  • input_data_format (Union[str, ~image_utils.ChannelDimension, 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[str, None], optional) -- 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/381570<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[[transformers.CLIPProcessor]]

class transformers.CLIPProcessortransformers.CLIPProcessorhttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/processing_clip.py#L22[{"name": "image_processor", "val": " = None"}, {"name": "tokenizer", "val": " = None"}, {"name": "**kwargs", "val": ""}]- image_processor (CLIPImageProcessor, optional) -- The image processor is a required input.

  • tokenizer (AutoTokenizer, optional) -- The tokenizer is a required input.0

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 the call() and decode() for more information.

CLIPModel[[transformers.CLIPModel]]

class transformers.CLIPModeltransformers.CLIPModelhttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/modeling_clip.py#L822[{"name": "config", "val": ": CLIPConfig"}]- 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 the from_pretrained() method to load the model weights.0

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.

forwardtransformers.CLIPModel.forwardhttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/modeling_clip.py#L938[{"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": "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"}]- 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() and PreTrainedTokenizer.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 using CLIPImageProcessor. See CLIPImageProcessor.call() for details (CLIPProcessor uses CLIPImageProcessor 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.

  • 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, defaults to False) -- Whether to interpolate the pre-trained position encodings.0transformers.models.clip.modeling_clip.CLIPOutput or tuple(torch.FloatTensor)A transformers.models.clip.modeling_clip.CLIPOutput 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 (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 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:

>>> 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  # this is the image-text similarity score
>>> probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities

get_text_featurestransformers.CLIPModel.get_text_featureshttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/modeling_clip.py#L862[{"name": "input_ids", "val": ": Tensor"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[torch.Tensor] = None"}]- 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() and PreTrainedTokenizer.call() for details.

What are input IDs?

  • attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) -- Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,
    • 0 for tokens that are masked.

    What are attention masks?

  • 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?0text_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_featurestransformers.CLIPModel.get_image_featureshttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/modeling_clip.py#L899[{"name": "pixel_values", "val": ": FloatTensor"}, {"name": "interpolate_pos_encoding", "val": ": bool = False"}]- 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 CLIPImageProcessor. See CLIPImageProcessor.call() for details (CLIPProcessor uses CLIPImageProcessor for processing images).

  • interpolate_pos_encoding (bool, defaults to False) -- Whether to interpolate the pre-trained position encodings.0image_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[[transformers.CLIPTextModel]]

class transformers.CLIPTextModeltransformers.CLIPTextModelhttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/modeling_clip.py#L663[{"name": "config", "val": ": CLIPTextConfig"}]- 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 the from_pretrained() method to load the model weights.0

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.

forwardtransformers.CLIPTextModel.forwardhttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/modeling_clip.py#L681[{"name": "input_ids", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}]- 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() and PreTrainedTokenizer.call() for details.

What are input IDs?

  • attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) -- Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,
    • 0 for tokens that are masked.

    What are attention masks?

  • 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?

  • 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.0transformers.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)A transformers.modeling_outputs.BaseModelOutputWithPooling or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (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 Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

>>> from transformers import AutoTokenizer, 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  # pooled (EOS token) states

CLIPTextModelWithProjection[[transformers.CLIPTextModelWithProjection]]

class transformers.CLIPTextModelWithProjectiontransformers.CLIPTextModelWithProjectionhttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/modeling_clip.py#L1030[{"name": "config", "val": ": CLIPTextConfig"}]- 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 the from_pretrained() method to load the model weights.0

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.

forwardtransformers.CLIPTextModelWithProjection.forwardhttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/modeling_clip.py#L1053[{"name": "input_ids", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}]- 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() and PreTrainedTokenizer.call() for details.

What are input IDs?

  • attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) -- Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,
    • 0 for tokens that are masked.

    What are attention masks?

  • 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?

  • 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.0transformers.models.clip.modeling_clip.CLIPTextModelOutput or tuple(torch.FloatTensor)A transformers.models.clip.modeling_clip.CLIPTextModelOutput 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 (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 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:

>>> 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[[transformers.CLIPVisionModelWithProjection]]

class transformers.CLIPVisionModelWithProjectiontransformers.CLIPVisionModelWithProjectionhttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/modeling_clip.py#L1099[{"name": "config", "val": ": CLIPVisionConfig"}]- 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 the from_pretrained() method to load the model weights.0

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.

forwardtransformers.CLIPVisionModelWithProjection.forwardhttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/modeling_clip.py#L1117[{"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"}]- 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 CLIPImageProcessor. See CLIPImageProcessor.call() for details (CLIPProcessor uses CLIPImageProcessor 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, defaults to False) -- Whether to interpolate the pre-trained position encodings.0transformers.models.clip.modeling_clip.CLIPVisionModelOutput or tuple(torch.FloatTensor)A transformers.models.clip.modeling_clip.CLIPVisionModelOutput 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 (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 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:

>>> 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[[transformers.CLIPVisionModel]]

class transformers.CLIPVisionModeltransformers.CLIPVisionModelhttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/modeling_clip.py#L769[{"name": "config", "val": ": CLIPVisionConfig"}]- 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 the from_pretrained() method to load the model weights.0

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.

forwardtransformers.CLIPVisionModel.forwardhttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/modeling_clip.py#L783[{"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"}]- 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 CLIPImageProcessor. See CLIPImageProcessor.call() for details (CLIPProcessor uses CLIPImageProcessor 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, defaults to False) -- Whether to interpolate the pre-trained position encodings.0transformers.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)A transformers.modeling_outputs.BaseModelOutputWithPooling or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (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 Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

>>> from 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  # pooled CLS states

CLIPForImageClassification[[transformers.CLIPForImageClassification]]

class transformers.CLIPForImageClassificationtransformers.CLIPForImageClassificationhttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/modeling_clip.py#L1170[{"name": "config", "val": ": CLIPConfig"}]- 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 the from_pretrained() method to load the model weights.0

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.

forwardtransformers.CLIPForImageClassification.forwardhttps://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/clip/modeling_clip.py#L1188[{"name": "pixel_values", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "labels", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}]- 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 using CLIPImageProcessor. See CLIPImageProcessor.call() for details (CLIPProcessor uses CLIPImageProcessor 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), If config.num_labels > 1 a classification loss is computed (Cross-Entropy).

  • 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.0transformers.modeling_outputs.ImageClassifierOutput or tuple(torch.FloatTensor)A transformers.modeling_outputs.ImageClassifierOutput 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 (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 Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

>>> from transformers import 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

>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
...

Xet Storage Details

Size:
88.7 kB
·
Xet hash:
e26dc731a6a661068470bd0554eb50aa3039a8c59861911e039c3a5dc723661e

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.