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.
>>> 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 probabilitiesTips:
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 768) — 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:
>>> 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
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 theinputs_idspassed 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 (
strorCallable, 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 1) — The vocabulary size of thetoken_type_idspassed when calling AltCLIPTextModel - 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 0.02) — A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). - layer_norm_eps (
float, optional, defaults to 1e-05) — The epsilon used by the layer normalization layers. - pad_token_id (
int, optional, defaults to 1) — The id of the padding token. - bos_token_id (
int, optional, defaults to 0) — The id of the beginning-of-sequence token. - eos_token_id (
Union[int, List[int]], optional, defaults to 2) — The id of the end-of-sequence token. Optionally, use a list to set multiple end-of-sequence tokens. - 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 toTrue) — Whether or not the model should return the last key/values attentions (not used by all models). Only relevant ifconfig.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:
>>> 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.configAltCLIPVisionConfig
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. - projection_dim (
int, optional, defaults to 512) — Dimentionality 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 (
strorfunction, optional, defaults to"quick_gelu") — The non-linear activation function (function or string) in the encoder and pooler. If string,"gelu","relu","selu"and"gelu_new"`"quick_gelu"are supported. - layer_norm_eps (
float, optional, defaults to 1e-05) — The epsilon used by the layer normalization layers. - attention_dropout (
float, optional, defaults to 0.0) — The dropout ratio for the attention probabilities. - initializer_range (
float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices. - initializer_factor (
float, optional, defaults to 1.0) — A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing).
This is the configuration class to store the configuration of a 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:
>>> 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.configAltCLIPProcessor
class transformers.AltCLIPProcessor
< source >( image_processor = None tokenizer = None **kwargs )
Parameters
- image_processor (CLIPImageProcessor, optional) — The image processor is a required input.
- tokenizer (XLMRobertaTokenizerFast, optional) — 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.
This method forwards all its arguments to XLMRobertaTokenizerFast’s batch_decode(). Please refer to the docstring of this method for more information.
This method forwards all its arguments to XLMRobertaTokenizerFast’s decode(). Please refer to the docstring of this method for more information.
AltCLIPModel
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: typing.Optional[torch.Tensor] = 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.LongTensorof 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.
- attention_mask (
torch.Tensorof 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.
- position_ids (
torch.LongTensorof 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]. - pixel_values (
torch.FloatTensorof 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. Seeattentionsunder returned tensors for more detail. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder 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.FloatTensorof shape(1,), optional, returned whenreturn_lossisTrue) — Contrastive loss for image-text similarity. - logits_per_image:(
torch.FloatTensorof shape(image_batch_size, text_batch_size)) — The scaled dot product scores betweenimage_embedsandtext_embeds. This represents the image-text similarity scores. - logits_per_text:(
torch.FloatTensorof shape(text_batch_size, image_batch_size)) — The scaled dot product scores betweentext_embedsandimage_embeds. This represents the text-image similarity scores. - text_embeds(
torch.FloatTensorof shape(batch_size, output_dim) — The text embeddings obtained by applying the projection layer to the pooled output of AltCLIPTextModel. - image_embeds(
torch.FloatTensorof 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:
>>> 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 probabilitiesget_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.LongTensorof 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.
- attention_mask (
torch.Tensorof 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.
- position_ids (
torch.LongTensorof 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]. - output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder 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:
>>> 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.FloatTensorof 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. Seeattentionsunder returned tensors for more detail. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder 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:
>>> 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
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.LongTensorof 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.
- attention_mask (
torch.Tensorof 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.
- position_ids (
torch.LongTensorof 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]. - output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder 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.FloatTensorof shape(batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model. -
pooler_output (
torch.FloatTensorof 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 whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.FloatTensorof 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:
>>> 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 statesAltCLIPVisionModel
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.FloatTensorof 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. Seeattentionsunder returned tensors for more detail. - output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder 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.FloatTensorof shape(batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model. -
pooler_output (
torch.FloatTensorof 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 whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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:
>>> 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