 with GroupViT is by checking the example notebooks (which showcase zero-shot segmentation inference).
One can also check out the HuggingFace Spaces demo to play with GroupViT. 
GroupViTConfig
	
class transformers.GroupViTConfig
<
source
>
(
text_config = None
vision_config = None
projection_dim = 256
projection_intermediate_dim = 4096
logit_scale_init_value = 2.6592
**kwargs
)
Parameters 
text_config (dict, optional) —
Dictionary of configuration options used to initialize GroupViTTextConfig.
vision_config (dict, optional) —
Dictionary of configuration options used to initialize GroupViTVisionConfig.
projection_dim (int, optional, defaults to 256) —
Dimentionality of text and vision projection layers.
projection_intermediate_dim (int, optional, defaults to 4096) —
Dimentionality of intermediate layer of text and vision projection layers.
logit_scale_init_value (float, optional, defaults to 2.6592) —
The inital value of the logit_scale parameter. Default is used as per the original GroupViT
implementation.
kwargs (optional) —
Dictionary of keyword arguments.
GroupViTConfig is the configuration class to store the configuration of a GroupViTModel. It is used to
instantiate a GroupViT 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 GroupViT
nvidia/groupvit-gcc-yfcc architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
from_text_vision_configs
<
source
>
(
text_config: GroupViTTextConfig
vision_config: GroupViTVisionConfig
**kwargs
)
→
GroupViTConfig
Returns
GroupViTConfig
An instance of a configuration object
Instantiate a GroupViTConfig (or a derived class) from groupvit text model configuration and groupvit
vision model configuration.
GroupViTTextConfig
	
class transformers.GroupViTTextConfig
<
source
>
(
vocab_size = 49408
hidden_size = 256
intermediate_size = 1024
num_hidden_layers = 12
num_attention_heads = 4
max_position_embeddings = 77
hidden_act = 'quick_gelu'
layer_norm_eps = 1e-05
dropout = 0.0
attention_dropout = 0.0
initializer_range = 0.02
initializer_factor = 1.0
pad_token_id = 1
bos_token_id = 0
eos_token_id = 2
**kwargs
)
Parameters 
vocab_size (int, optional, defaults to 49408) —
Vocabulary size of the GroupViT text model. Defines the number of different tokens that can be represented
by the inputs_ids passed when calling GroupViTModel.
hidden_size (int, optional, defaults to 256) —
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (int, optional, defaults to 1024) —
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 4) —
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-5) —
The epsilon used by the layer normalization layers.
attention_dropout (float, optional, defaults to 0.0) —
The dropout ratio for the attention probabilities.
dropout (float, optional, defaults to 0.0) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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 GroupViTTextModel. It is used to instantiate an
GroupViT 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 GroupViT
nvidia/groupvit-gcc-yfcc 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 GroupViTTextConfig, GroupViTTextModel
 # Initializing a GroupViTTextModel with nvidia/groupvit-gcc-yfcc style configuration
 configuration = GroupViTTextConfig()
 model = GroupViTTextModel(configuration)
 # Accessing the model configuration
 configuration = model.config
GroupViTVisionConfig
	
class transformers.GroupViTVisionConfig
<
source
>
(
hidden_size = 384
intermediate_size = 1536
depths = [6, 3, 3]
num_hidden_layers = 12
num_group_tokens = [64, 8, 0]
num_output_groups = [64, 8, 8]
num_attention_heads = 6
image_size = 224
patch_size = 16
num_channels = 3
hidden_act = 'gelu'
layer_norm_eps = 1e-05
dropout = 0.0
attention_dropout = 0.0
initializer_range = 0.02
initializer_factor = 1.0
assign_eps = 1.0
assign_mlp_ratio = [0.5, 4]
**kwargs
)
Parameters 
hidden_size (int, optional, defaults to 384) —
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (int, optional, defaults to 1536) —
Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
depths (List[int], optional, defaults to [6, 3, 3]) —
The number of layers in each encoder block.
num_group_tokens (List[int], optional, defaults to [64, 8, 0]) —
The number of group tokens for each stage.
num_output_groups (List[int], optional, defaults to [64, 8, 8]) —
The number of output groups for each stage, 0 means no group.
num_attention_heads (int, optional, defaults to 6) —
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 16) —
The size (resolution) of each patch.
hidden_act (str or function, optional, defaults to "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.
dropout (float, optional, defaults to 0.0) —
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
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 GroupViTVisionModel. It is used to instantiate
an GroupViT 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 GroupViT
nvidia/groupvit-gcc-yfcc 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 GroupViTVisionConfig, GroupViTVisionModel
 # Initializing a GroupViTVisionModel with nvidia/groupvit-gcc-yfcc style configuration
 configuration = GroupViTVisionConfig()
 model = GroupViTVisionModel(configuration)
 # Accessing the model configuration
 configuration = model.config
GroupViTModel
	
class transformers.GroupViTModel
<
source
>
(
config: GroupViTConfig
)
Parameters 
config (GroupViTConfig) — 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.
This model is a PyTorch torch.nn.Module subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
forward
<
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
return_loss: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
output_segmentation: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.models.groupvit.modeling_groupvit.GroupViTModelOutput 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 CLIPTokenizer. 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. 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.groupvit.modeling_groupvit.GroupViTModelOutput or tuple(torch.FloatTensor)
A transformers.models.groupvit.modeling_groupvit.GroupViTModelOutput 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.groupvit.configuration_groupvit.GroupViTConfig'>) 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.
segmentation_logits (torch.FloatTensor of shape (batch_size, config.num_labels, logits_height, logits_width)) — Classification scores for each pixel.
The logits returned do not necessarily have the same size as the pixel_values passed as inputs. This is
to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
original image size as post-processing. You should always check your logits shape and resize as needed.
text_embeds (torch.FloatTensor of shape (batch_size, output_dim) — The text embeddings obtained by applying the projection layer to the pooled output of
GroupViTTextModel.
image_embeds (torch.FloatTensor of shape (batch_size, output_dim) — The image embeddings obtained by applying the projection layer to the pooled output of
GroupViTVisionModel.
text_model_output (BaseModelOutputWithPooling) — The output of the GroupViTTextModel.
vision_model_output (BaseModelOutputWithPooling) — The output of the GroupViTVisionModel.
The GroupViTModel 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, GroupViTModel
 model = GroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
 processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
 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
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 CLIPTokenizer. 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 GroupViTTextModel.
The GroupViTModel 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 CLIPTokenizer, GroupViTModel
 model = GroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
 tokenizer = CLIPTokenizer.from_pretrained("nvidia/groupvit-gcc-yfcc")
 inputs = tokenizer(["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 GroupViTVisionModel.
The GroupViTModel 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, GroupViTModel
 model = GroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
 processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
 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)
GroupViTTextModel
	
class transformers.GroupViTTextModel
<
source
>
(
config: GroupViTTextConfig
)
forward
<
source
>
(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = 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 
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 CLIPTokenizer. 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.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.groupvit.configuration_groupvit.GroupViTTextConfig'>) 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 GroupViTTextModel 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 CLIPTokenizer, GroupViTTextModel
 tokenizer = CLIPTokenizer.from_pretrained("nvidia/groupvit-gcc-yfcc")
 model = GroupViTTextModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
 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
GroupViTVisionModel
	
class transformers.GroupViTVisionModel
<
source
>
(
config: GroupViTVisionConfig
)
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.groupvit.configuration_groupvit.GroupViTVisionConfig'>) 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 GroupViTVisionModel 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, GroupViTVisionModel
 processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
 model = GroupViTVisionModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
 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
TFGroupViTModel
	
class transformers.TFGroupViTModel
<
source
>
(
*args
**kwargs
)
Parameters 
config (GroupViTConfig) — 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.
This model inherits from TFPreTrainedModel. 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 tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
TF 2.0 models accepts two formats as inputs:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using tf.keras.Model.fit method which currently requires having all the
tensors in the first argument of the model call function: model(inputs).
If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the
first positional argument :
a single Tensor with input_ids only and nothing else: model(input_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids})
call
<
source
>
(
input_ids: TFModelInputType | None = None
pixel_values: TFModelInputType | None = None
attention_mask: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
return_loss: Optional[bool] = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
output_segmentation: Optional[bool] = None
return_dict: Optional[bool] = None
training: bool = False
)
→
transformers.models.groupvit.modeling_tf_groupvit.TFGroupViTModelOutput or tuple(tf.Tensor)
Parameters 
input_ids (np.ndarray, tf.Tensor, List[tf.Tensor] `Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
pixel_values (np.ndarray, tf.Tensor, List[tf.Tensor] Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See
CLIPImageProcessor.call() for details.
attention_mask (np.ndarray or tf.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 (np.ndarray or tf.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.max_position_embeddings - 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. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (bool, optional, defaults to `False“) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
Returns
transformers.models.groupvit.modeling_tf_groupvit.TFGroupViTModelOutput or tuple(tf.Tensor)
A transformers.models.groupvit.modeling_tf_groupvit.TFGroupViTModelOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (<class 'transformers.models.groupvit.configuration_groupvit.GroupViTConfig'>) and inputs.
loss (tf.Tensor of shape (1,), optional, returned when return_loss is True) — Contrastive loss for image-text similarity.
logits_per_image (tf.Tensor 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 (tf.Tensor 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.
segmentation_logits (tf.Tensor of shape (batch_size, config.num_labels, logits_height, logits_width)) — Classification scores for each pixel.
The logits returned do not necessarily have the same size as the pixel_values passed as inputs. This is
to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
original image size as post-processing. You should always check your logits shape and resize as needed.
text_embeds (tf.Tensor of shape (batch_size, output_dim) — The text embeddings obtained by applying the projection layer to the pooled output of
TFGroupViTTextModel.
image_embeds (tf.Tensor of shape (batch_size, output_dim) — The image embeddings obtained by applying the projection layer to the pooled output of
TFGroupViTVisionModel.
text_model_output (TFBaseModelOutputWithPooling) — The output of the TFGroupViTTextModel.
vision_model_output (TFBaseModelOutputWithPooling) — The output of the TFGroupViTVisionModel.
The TFGroupViTModel 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, TFGroupViTModel
 import tensorflow as tf
 model = TFGroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
 processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
 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="tf", padding=True
... )
 outputs = model(**inputs)
 logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
 probs = tf.math.softmax(logits_per_image, axis=1)  # we can take the softmax to get the label probabilities
get_text_features
<
source
>
(
input_ids: TFModelInputType | None = None
attention_mask: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
training: bool = False
)
→
text_features (tf.Tensor of shape (batch_size, output_dim)
Parameters 
input_ids (np.ndarray, tf.Tensor, List[tf.Tensor] `Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
attention_mask (np.ndarray or tf.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 (np.ndarray or tf.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.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. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (bool, optional, defaults to `False“) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
Returns
text_features (tf.Tensor of shape (batch_size, output_dim)
The text embeddings obtained by applying
the projection layer to the pooled output of TFGroupViTTextModel.
The TFGroupViTModel 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 CLIPTokenizer, TFGroupViTModel
 model = TFGroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
 tokenizer = CLIPTokenizer.from_pretrained("nvidia/groupvit-gcc-yfcc")
 inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="tf")
 text_features = model.get_text_features(**inputs)
get_image_features
<
source
>
(
pixel_values: TFModelInputType | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
training: bool = False
)
→
image_features (tf.Tensor of shape (batch_size, output_dim)
Parameters 
pixel_values (np.ndarray, tf.Tensor, List[tf.Tensor], Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, num_channels, height, width)) —
Pixel values. 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. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (bool, optional, defaults to `False“) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
Returns
image_features (tf.Tensor of shape (batch_size, output_dim)
The image embeddings obtained by applying
the projection layer to the pooled output of TFGroupViTVisionModel.
The TFGroupViTModel 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, TFGroupViTModel
 model = TFGroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
 processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 inputs = processor(images=image, return_tensors="tf")
 image_features = model.get_image_features(**inputs)
TFGroupViTTextModel
	
class transformers.TFGroupViTTextModel
<
source
>
(
*args
**kwargs
)
call
<
source
>
(
input_ids: TFModelInputType | None = None
attention_mask: np.ndarray | tf.Tensor | None = None
position_ids: np.ndarray | tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
training: bool = False
)
→
transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling or tuple(tf.Tensor)
Parameters 
input_ids (np.ndarray, tf.Tensor, List[tf.Tensor] `Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?
attention_mask (np.ndarray or tf.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 (np.ndarray or tf.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.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. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (bool, optional, defaults to `False“) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
Returns
transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (<class 'transformers.models.groupvit.configuration_groupvit.GroupViTTextConfig'>) and inputs.
last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
pooler_output (tf.Tensor of shape (batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.
This output is usually not a good summary of the semantic content of the input, you’re often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + 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 initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (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 TFGroupViTTextModel 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 CLIPTokenizer, TFGroupViTTextModel
 tokenizer = CLIPTokenizer.from_pretrained("nvidia/groupvit-gcc-yfcc")
 model = TFGroupViTTextModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
 inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="tf")
 outputs = model(**inputs)
 last_hidden_state = outputs.last_hidden_state
 pooled_output = outputs.pooler_output  # pooled (EOS token) states
TFGroupViTVisionModel
	
class transformers.TFGroupViTVisionModel
<
source
>
(
*args
**kwargs
)
call
<
source
>
(
pixel_values: TFModelInputType | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
training: bool = False
)
→
transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling or tuple(tf.Tensor)
Parameters 
pixel_values (np.ndarray, tf.Tensor, List[tf.Tensor], Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, num_channels, height, width)) —
Pixel values. 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. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (bool, optional, defaults to `False“) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
Returns
transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (<class 'transformers.models.groupvit.configuration_groupvit.GroupViTVisionConfig'>) and inputs.
last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
pooler_output (tf.Tensor of shape (batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.
This output is usually not a good summary of the semantic content of the input, you’re often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + 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 initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (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 TFGroupViTVisionModel 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, TFGroupViTVisionModel
 processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
 model = TFGroupViTVisionModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 inputs = processor(images=image, return_tensors="tf")
 outputs = model(**inputs)
 last_hidden_state = outputs.last_hidden_state
 pooled_output = outputs.pooler_output  # pooled CLS states
←GIT
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GroupViT
Overview
Resources
GroupViTConfig
GroupViTTextConfig
GroupViTVisionConfig
GroupViTModel
GroupViTTextModel
GroupViTVisionModel
TFGroupViTModel
TFGroupViTTextModel
TFGroupViTVisionModel
