
 
AltCLIP
	
Overview
	
The AltCLIP model was proposed in AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities by Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu. AltCLIP
(Altering the Language Encoder in CLIP) is a neural network trained on a variety of image-text and text-text pairs. By switching CLIP’s
text encoder with a pretrained multilingual text encoder XLM-R, we could obtain very close performances with CLIP on almost all tasks, and extended original CLIP’s capabilities such as multilingual understanding.
The abstract from the paper is the following:
In this work, we present a conceptually simple and effective method to train a strong bilingual multimodal representation model.
Starting from the pretrained multimodal representation model CLIP released by OpenAI, we switched its text encoder with a pretrained
multilingual text encoder XLM-R, and aligned both languages and image representations by a two-stage training schema consisting of
teacher learning and contrastive learning. We validate our method through evaluations of a wide range of tasks. We set new state-of-the-art
performances on a bunch of tasks including ImageNet-CN, Flicker30k- CN, and COCO-CN. Further, we obtain very close performances with
CLIP on almost all tasks, suggesting that one can simply alter the text encoder in CLIP for extended capabilities such as multilingual understanding.
Usage
	
The usage of AltCLIP is very similar to the CLIP. the difference between CLIP is the text encoder. Note that we use bidirectional attention instead of casual attention
and we take the [CLS] token in XLM-R to represent text embedding.
AltCLIP is a multi-modal vision and language model. It can be used for image-text similarity and for zero-shot image
classification. AltCLIP uses a ViT like transformer to get visual features and a bidirectional language model to get the text
features. Both the text and visual features are then projected to a latent space with identical dimension. The dot
product between the projected image and text features is then used as a similar score.
To feed images to the Transformer encoder, each image is split into a sequence of fixed-size non-overlapping patches,
which are then linearly embedded. A [CLS] token is added to serve as representation of an entire image. The authors
also add absolute position embeddings, and feed the resulting sequence of vectors to a standard Transformer encoder.
The CLIPImageProcessor can be used to resize (or rescale) and normalize images for the model.
The AltCLIPProcessor wraps a CLIPImageProcessor and a XLMRobertaTokenizer into a single instance to both
encode the text and prepare the images. The following example shows how to get the image-text similarity scores using
AltCLIPProcessor and AltCLIPModel.
	Copied
 from PIL import Image
 import requests
 from transformers import AltCLIPModel, AltCLIPProcessor
 model = AltCLIPModel.from_pretrained("BAAI/AltCLIP")
 processor = AltCLIPProcessor.from_pretrained("BAAI/AltCLIP")
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
 outputs = model(**inputs)
 logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
 probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities
Tips:
This model is build on CLIPModel, so use it like a original CLIP. 
This model was contributed by jongjyh.
AltCLIPConfig
	
class transformers.AltCLIPConfig
<
source
>
(
text_config = None
vision_config = None
projection_dim = 768
logit_scale_init_value = 2.6592
**kwargs
)
Parameters 
text_config (dict, optional) —
Dictionary of configuration options used to initialize AltCLIPTextConfig.
vision_config (dict, optional) —
Dictionary of configuration options used to initialize AltCLIPVisionConfig.
projection_dim (int, optional, defaults to 512) —
Dimentionality of text and vision projection layers.
logit_scale_init_value (float, optional, defaults to 2.6592) —
The inital value of the logit_scale paramter. Default is used as per the original CLIP implementation.
kwargs (optional) —
Dictionary of keyword arguments.
This is the configuration class to store the configuration of a AltCLIPModel. It is used to instantiate an
AltCLIP model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the AltCLIP
BAAI/AltCLIP architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Example:
	Copied
 from transformers import AltCLIPConfig, AltCLIPModel
 # Initializing a AltCLIPConfig with BAAI/AltCLIP style configuration
 configuration = AltCLIPConfig()
 # Initializing a AltCLIPModel (with random weights) from the BAAI/AltCLIP style configuration
 model = AltCLIPModel(configuration)
 # Accessing the model configuration
 configuration = model.config
 # We can also initialize a AltCLIPConfig from a AltCLIPTextConfig and a AltCLIPVisionConfig
 # Initializing a AltCLIPText and AltCLIPVision configuration
 config_text = AltCLIPTextConfig()
 config_vision = AltCLIPVisionConfig()
 config = AltCLIPConfig.from_text_vision_configs(config_text, config_vision)
from_text_vision_configs
<
source
>
(
text_config: AltCLIPTextConfig
vision_config: AltCLIPVisionConfig
**kwargs
)
→
AltCLIPConfig
Returns
AltCLIPConfig
An instance of a configuration object
Instantiate a AltCLIPConfig (or a derived class) from altclip text model configuration and altclip vision
model configuration.
AltCLIPTextConfig
	
class transformers.AltCLIPTextConfig
<
source
>
(
vocab_size = 250002
hidden_size = 1024
num_hidden_layers = 24
num_attention_heads = 16
intermediate_size = 4096
hidden_act = 'gelu'
hidden_dropout_prob = 0.1
attention_probs_dropout_prob = 0.1
max_position_embeddings = 514
type_vocab_size = 1
initializer_range = 0.02
initializer_factor = 0.02
layer_norm_eps = 1e-05
pad_token_id = 1
bos_token_id = 0
eos_token_id = 2
position_embedding_type = 'absolute'
use_cache = True
project_dim = 768
**kwargs
)
Parameters 
vocab_size (int, optional, defaults to 250002) —
Vocabulary size of the AltCLIP model. Defines the number of different tokens that can be represented by the
inputs_ids passed when calling AltCLIPTextModel.
hidden_size (int, optional, defaults to 1024) —
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (int, optional, defaults to 24) —
Number of hidden layers in the Transformer encoder.
num_attention_heads (int, optional, defaults to 16) —
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (int, optional, defaults to 4096) —
Dimensionality of the “intermediate” (often named feed-forward) layer in the Transformer encoder.
hidden_act (str or Callable, optional, defaults to "gelu") —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu",
"relu", "silu" and "gelu_new" are supported.
hidden_dropout_prob (float, optional, defaults to 0.1) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (float, optional, defaults to 0.1) —
The dropout ratio for the attention probabilities.
max_position_embeddings (int, optional, defaults to 514) —
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (int, optional, defaults to 2) —
The vocabulary size of the token_type_ids passed when calling AltCLIPTextModel
initializer_range (float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (float, optional, defaults to 1e-5) —
The epsilon used by the layer normalization layers.
position_embedding_type (str, optional, defaults to "absolute") —
Type of position embedding. Choose one of "absolute", "relative_key", "relative_key_query". For
positional embeddings use "absolute". For more information on "relative_key", please refer to
Self-Attention with Relative Position Representations (Shaw et al.).
For more information on "relative_key_query", please refer to Method 4 in Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.).
use_cache (bool, optional, defaults to True) —
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if config.is_decoder=True.
project_dim (int, optional, defaults to 768) —
The dimentions of the teacher model before the mapping layer.
This is the configuration class to store the configuration of a AltCLIPTextModel. It is used to instantiate a
AltCLIP text model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the AltCLIP
BAAI/AltCLIP architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Examples:
	Copied
 from transformers import AltCLIPTextModel, AltCLIPTextConfig
 # Initializing a AltCLIPTextConfig with BAAI/AltCLIP style configuration
 configuration = AltCLIPTextConfig()
 # Initializing a AltCLIPTextModel (with random weights) from the BAAI/AltCLIP style configuration
 model = AltCLIPTextModel(configuration)
 # Accessing the model configuration
 configuration = model.config
AltCLIPVisionConfig
	
class transformers.AltCLIPVisionConfig
<
source
>
(
hidden_size = 768
intermediate_size = 3072
projection_dim = 512
num_hidden_layers = 12
num_attention_heads = 12
num_channels = 3
image_size = 224
patch_size = 32
hidden_act = 'quick_gelu'
layer_norm_eps = 1e-05
attention_dropout = 0.0
initializer_range = 0.02
initializer_factor = 1.0
**kwargs
)
Parameters 
hidden_size (int, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (int, optional, defaults to 3072) —
Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
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.
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-5) —
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) —
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 AltCLIPModel. It is used to instantiate an
AltCLIP model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the AltCLIP
BAAI/AltCLIP architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Example:
	Copied
 from transformers import AltCLIPVisionConfig, AltCLIPVisionModel
 # Initializing a AltCLIPVisionConfig with BAAI/AltCLIP style configuration
 configuration = AltCLIPVisionConfig()
 # Initializing a AltCLIPVisionModel (with random weights) from the BAAI/AltCLIP style configuration
 model = AltCLIPVisionModel(configuration)
 # Accessing the model configuration
 configuration = model.config
AltCLIPProcessor
	
class transformers.AltCLIPProcessor
<
source
>
(
image_processor = None
tokenizer = None
**kwargs
)
Parameters 
image_processor (CLIPImageProcessor) —
The image processor is a required input.
tokenizer (XLMRobertaTokenizerFast) —
The tokenizer is a required input.
Constructs a AltCLIP processor which wraps a CLIP image processor and a XLM-Roberta tokenizer into a single
processor.
AltCLIPProcessor offers all the functionalities of CLIPImageProcessor and XLMRobertaTokenizerFast. See
the __call__() and decode() for more information.
batch_decode
<
source
>
(
*args
**kwargs
)
This method forwards all its arguments to XLMRobertaTokenizerFast’s batch_decode().
Please refer to the docstring of this method for more information.
decode
<
source
>
(
*args
**kwargs
)
This method forwards all its arguments to XLMRobertaTokenizerFast’s decode(). Please
refer to the docstring of this method for more information.
AltCLIPModel
	
class transformers.AltCLIPModel
<
source
>
(
config: AltCLIPConfig
)
forward
<
source
>
(
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
token_type_ids = None
return_loss: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.models.altclip.modeling_altclip.AltCLIPOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
AutoImageProcessor. See CLIPImageProcessor.call() for details.
return_loss (bool, optional) —
Whether or not to return the contrastive loss.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.models.altclip.modeling_altclip.AltCLIPOutput or tuple(torch.FloatTensor)
A transformers.models.altclip.modeling_altclip.AltCLIPOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (<class 'transformers.models.altclip.configuration_altclip.AltCLIPConfig'>) 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 AltCLIPTextModel.
image_embeds(torch.FloatTensor of shape (batch_size, output_dim) — The image embeddings obtained by applying the projection layer to the pooled output of
AltCLIPVisionModel.
text_model_output(BaseModelOutputWithPooling):
The output of the AltCLIPTextModel.
vision_model_output(BaseModelOutputWithPooling):
The output of the AltCLIPVisionModel.
The AltCLIPModel 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:
	Copied
 from PIL import Image
 import requests
 from transformers import AutoProcessor, AltCLIPModel
 model = AltCLIPModel.from_pretrained("BAAI/AltCLIP")
 processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 inputs = processor(
...     text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True
... )
 outputs = model(**inputs)
 logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
 probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities
get_text_features
<
source
>
(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
token_type_ids = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
text_features (torch.FloatTensor of shape (batch_size, output_dim)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
text_features (torch.FloatTensor of shape (batch_size, output_dim)
The text embeddings obtained by
applying the projection layer to the pooled output of AltCLIPTextModel.
The AltCLIPModel 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:
	Copied
 from transformers import AutoProcessor, AltCLIPModel
 model = AltCLIPModel.from_pretrained("BAAI/AltCLIP")
 processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")
 inputs = processor(text=["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
 text_features = model.get_text_features(**inputs)
get_image_features
<
source
>
(
pixel_values: typing.Optional[torch.FloatTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
image_features (torch.FloatTensor of shape (batch_size, output_dim)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
AutoImageProcessor. See CLIPImageProcessor.call() for details.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
image_features (torch.FloatTensor of shape (batch_size, output_dim)
The image embeddings obtained by
applying the projection layer to the pooled output of AltCLIPVisionModel.
The AltCLIPModel 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:
	Copied
 from PIL import Image
 import requests
 from transformers import AutoProcessor, AltCLIPModel
 model = AltCLIPModel.from_pretrained("BAAI/AltCLIP")
 processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 inputs = processor(images=image, return_tensors="pt")
 image_features = model.get_image_features(**inputs)
AltCLIPTextModel
	
class transformers.AltCLIPTextModel
<
source
>
(
config
)
forward
<
source
>
(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
encoder_hidden_states: typing.Optional[torch.Tensor] = None
encoder_attention_mask: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.BaseModelOutputWithPoolingAndProjection or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_outputs.BaseModelOutputWithPoolingAndProjection or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutputWithPoolingAndProjection or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (<class 'transformers.models.altclip.configuration_altclip.AltCLIPTextConfig'>) 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.
projection_state (tuple(torch.FloatTensor), returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor of shape (batch_size,config.project_dim).
Text embeddings before the projection layer, used to mimic the last hidden state of the teacher encoder.
The AltCLIPTextModel 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:
	Copied
 from transformers import AutoProcessor, AltCLIPTextModel
 model = AltCLIPTextModel.from_pretrained("BAAI/AltCLIP")
 processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")
 texts = ["it's a cat", "it's a dog"]
 inputs = processor(text=texts, padding=True, return_tensors="pt")
 outputs = model(**inputs)
 last_hidden_state = outputs.last_hidden_state
 pooled_output = outputs.pooler_output  # pooled CLS states
AltCLIPVisionModel
	
class transformers.AltCLIPVisionModel
<
source
>
(
config: AltCLIPVisionConfig
)
forward
<
source
>
(
pixel_values: typing.Optional[torch.FloatTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
AutoImageProcessor. See CLIPImageProcessor.call() for details.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutputWithPooling or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (<class 'transformers.models.altclip.configuration_altclip.AltCLIPVisionConfig'>) 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 AltCLIPVisionModel 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:
	Copied
 from PIL import Image
 import requests
 from transformers import AutoProcessor, AltCLIPVisionModel
 model = AltCLIPVisionModel.from_pretrained("BAAI/AltCLIP")
 processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")
 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
←ALIGN
BLIP→
AltCLIP
Overview
Usage
AltCLIPConfig
AltCLIPTextConfig
AltCLIPVisionConfig
AltCLIPProcessor
AltCLIPModel
AltCLIPTextModel
AltCLIPVisionModel
