
 
FLAVA
	
Overview
	
The FLAVA model was proposed in FLAVA: A Foundational Language And Vision Alignment Model by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela and is accepted at CVPR 2022.
The paper aims at creating a single unified foundation model which can work across vision, language
as well as vision-and-language multimodal tasks.
The abstract from the paper is the following:
State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic pretraining for obtaining good performance on a variety
of downstream tasks. Generally, such models are often either cross-modal (contrastive) or multi-modal
(with earlier fusion) but not both; and they often only target specific modalities or tasks. A promising
direction would be to use a single holistic universal model, as a “foundation”, that targets all modalities
at once — a true vision and language foundation model should be good at vision tasks, language tasks, and
cross- and multi-modal vision and language tasks. We introduce FLAVA as such a model and demonstrate
impressive performance on a wide range of 35 tasks spanning these target modalities.
This model was contributed by aps. The original code can be found here.
FlavaConfig
	
class transformers.FlavaConfig
<
source
>
(
image_config: typing.Dict[str, typing.Any] = None
text_config: typing.Dict[str, typing.Any] = None
multimodal_config: typing.Dict[str, typing.Any] = None
image_codebook_config: typing.Dict[str, typing.Any] = None
hidden_size: int = 768
layer_norm_eps: float = 1e-12
projection_dim: int = 768
init_codebook: bool = True
logit_scale_init_value: float = 2.6592
initializer_range: float = 0.02
ce_ignore_index: int = -100
mim_weight: float = 1.0
mlm_weight: float = 1.0
global_contrastive_weight: float = 1.0
itm_weight: float = 1.0
mmm_image_weight: float = 1.0
mmm_text_weight: float = 1.0
global_backprop_contrastive: bool = True
skip_unmasked_multimodal_encoder: bool = True
return_loss: bool = True
**kwargs
)
Parameters 
text_config (dict, optional) —
Dictionary of configuration options used to initialize FlavaTextConfig.
image_config (dict, optional) —
Dictionary of configuration options used to initialize FlavaImageConfig.
multimodal_config (dict, optional) —
Dictionary of configuration options used to initialize FlavaMultimodalConfig.
hidden_size (int, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer.
layer_norm_eps (float, optional, defaults to 1e-12) —
The epsilon used by the layer normalization layers.
projection_dim (int, optional, defaults to 512) —
Dimentionality of text and image 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 FLAVA/CLIP
implementation.
initializer_range (float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
ce_ignore_index (int, optional, defaults to -100) —
Cross entropy index to ignore.
mim_weight (float, optional, defaults to 1.0) —
Weight to be assigned to MIM (Masked Image Modeling) unimodal loss
mlm_weight (float, optional, defaults to 1.0) —
Weight to be assigned to MLM (Masked Language Modeling) unimodal loss
global_contrastive_weight (float, optional, defaults to 1.0) —
Weight to be assigned to global contrastive cross-alignment loss.
itm_weight (float, optional, defaults to 1.0) —
Weight to be assigned to image-text matching multimodal loss.
mmm_image_weight (float, optional, defaults to 1.0) —
Weight to be assigned to MMM loss’s image part.
mmm_text_weight (float, optional, defaults to 1.0) —
Weight to be assigned to MMM loss’s text part.
global_backprop_contrastive (bool, optional, defaults to True) —
Whether to use global backpropgation through all workers in contrastive loss.
skip_unmasked_multimodal_encoder (bool, optional, defaults to True) —
Whether to skip running unmasked multimodal encoder whose outputs are not used by FLAVA losses.
return_loss (bool, optional, defaults to True) —
Whether to return loss or not
kwargs (optional) —
Dictionary of keyword arguments.
FlavaConfig is the configuration class to store the configuration of a FlavaModel. It is used to
instantiate FLAVA model according to the specified arguments, defining the text model, image model, image codebook
and multimodal model configs. Instantiating a configuration with the defaults will yield a similar configuration to
that of the FLAVA facebook/flava-full 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 FlavaConfig, FlavaModel, FlavaForPreTraining
 # Initializing a FlavaConfig with style configuration
 configuration = FlavaConfig()
 # Initializing a FlavaModel and FlavaForPreTraining model (with random weights) from the style configuration
 model = FlavaModel(configuration)
 model_pre = FlavaForPreTraining(configuration)
 # Accessing the model configuration
 configuration = model.config
 configuration_pre = model_pre.config
from_configs
<
source
>
(
image_config: FlavaImageConfig
text_config: FlavaTextConfig
multimodal_config: FlavaMultimodalConfig
image_codebook_config: FlavaImageCodebookConfig
**kwargs
)
→
FlavaConfig
Returns
FlavaConfig
An instance of a configuration object
Instantiate a FlavaConfig (or a derived class) from flava text model configuration, flava image model
configuration, flava multimodal model and flava codebook model configuration.
to_dict
<
source
>
(
)
→
Dict[str, any]
Returns
Dict[str, any]
Dictionary of all the attributes that make up this configuration instance,
Serializes this instance to a Python dictionary. Override the default to_dict().
FlavaTextConfig
	
class transformers.FlavaTextConfig
<
source
>
(
vocab_size: int = 30522
type_vocab_size: int = 2
max_position_embeddings: int = 512
position_embedding_type: str = 'absolute'
hidden_size: int = 768
num_hidden_layers: int = 12
num_attention_heads: int = 12
intermediate_size: int = 3072
hidden_act: str = 'gelu'
hidden_dropout_prob: float = 0.0
attention_probs_dropout_prob: float = 0.0
initializer_range: float = 0.02
layer_norm_eps: float = 1e-12
pad_token_id: int = 0
qkv_bias: bool = True
**kwargs
)
Parameters 
vocab_size (int, optional, defaults to 30522) —
Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
inputs_ids passed when calling FlavaTextModel.
type_vocab_size (int, optional, defaults to 2) —
The vocabulary size of the token_type_ids passed when calling FlavaTextModel. Note that even though
text encoder allows token_type_ids’s value as 2, for text-only pretraining and fine-tuning, only 1 is
used similar to RoBERTa.
max_position_embeddings (int, optional, defaults to 512) —
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). For VL, max_length passed to model is 77.
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.).
hidden_size (int, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer.
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.
intermediate_size (int, optional, defaults to 3072) —
Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
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" are supported.
hidden_dropout_prob (float, optional, defaults to 0.1) —
The dropout probabilitiy 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.
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-12) —
The epsilon used by the layer normalization layers.
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.
num_channels (int, optional, defaults to 3) —
The number of input channels.
qkv_bias (bool, optional, defaults to True) —
Whether to add a bias to the queries, keys and values.
This is the configuration class to store the configuration of a FlavaTextModel. It is used to instantiate an
FLAVA 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 FLAVA
facebook/flava-full 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 FlavaTextConfig, FlavaTextModel
 # Initializing a FlavaTextModel with  style configuration
 configuration = FlavaTextConfig()
 # Initializing a FlavaTextModel model (with random weights) from the style configuration
 model = FlavaTextModel(configuration)
 # Accessing the model configuration
 configuration = model.config
FlavaImageConfig
	
class transformers.FlavaImageConfig
<
source
>
(
hidden_size: int = 768
num_hidden_layers: int = 12
num_attention_heads: int = 12
intermediate_size: int = 3072
hidden_act: int = 'gelu'
hidden_dropout_prob: float = 0.0
attention_probs_dropout_prob: float = 0.0
initializer_range: float = 0.02
layer_norm_eps: float = 1e-12
image_size: int = 224
patch_size: int = 16
num_channels: int = 3
qkv_bias: bool = True
mask_token: bool = True
vocab_size: int = 8192
**kwargs
)
Parameters 
hidden_size (int, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer.
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.
intermediate_size (int, optional, defaults to 3072) —
Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
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" are supported.
hidden_dropout_prob (float, optional, defaults to 0.1) —
The dropout probabilitiy 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.
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-12) —
The epsilon used by the layer normalization layers.
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.
num_channels (int, optional, defaults to 3) —
The number of input channels.
qkv_bias (bool, optional, defaults to True) —
Whether to add a bias to the queries, keys and values.
mask_token (bool, optional, defaults to True) —
Whether to use a mask token or not. Used in MIM (Masked Image Modeling) loss for FLAVA.
vocab_size (int, optional, defaults to 8192) —
Vocabulary size of the FlavaImageCodebook used in conjunction with FlavaImageModel for MIM (Masked
Image Modeling) loss for FLAVA.
This is the configuration class to store the configuration of a FlavaImageModel. It is used to instantiate an
FLAVA 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 FLAVA
facebook/flava-full 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 FlavaImageConfig, FlavaImageModel
 # Initializing a FlavaImageModel with  style configuration
 configuration = FlavaImageConfig()
 # Initializing a FlavaImageModel model (with random weights) from the style configuration
 model = FlavaImageModel(configuration)
 # Accessing the model configuration
 configuration = model.config
FlavaMultimodalConfig
	
class transformers.FlavaMultimodalConfig
<
source
>
(
hidden_size: int = 768
num_hidden_layers: int = 6
num_attention_heads: int = 12
intermediate_size: int = 3072
hidden_act: int = 'gelu'
hidden_dropout_prob: int = 0.0
attention_probs_dropout_prob: int = 0.0
initializer_range: float = 0.02
layer_norm_eps: float = 1e-12
qkv_bias: bool = True
use_cls_token: bool = True
**kwargs
)
Parameters 
hidden_size (int, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer.
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.
intermediate_size (int, optional, defaults to 3072) —
Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
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" are supported.
hidden_dropout_prob (float, optional, defaults to 0.1) —
The dropout probabilitiy 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.
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-12) —
The epsilon used by the layer normalization layers.
qkv_bias (bool, optional, defaults to True) —
Whether to add a bias to the queries, keys and values.
use_cls_token (bool, optional, defaults to True) —
Whether to use an extra CLS token for multimodal settings. Usually needed by the FLAVA model.
This is the configuration class to store the configuration of a FlavaMultimodalModel. It is used to instantiate
an FLAVA 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 FLAVA
facebook/flava-full 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 FlavaMultimodalConfig, FlavaMultimodalModel
 # Initializing a FlavaMultimodalModel with  style configuration
 configuration = FlavaMultimodalConfig()
 # Initializing a FlavaMultimodalModel model (with random weights) from the style configuration
 model = FlavaMultimodalModel(configuration)
 # Accessing the model configuration
 configuration = model.config
FlavaImageCodebookConfig
	
class transformers.FlavaImageCodebookConfig
<
source
>
(
num_groups: int = 4
input_channels: int = 3
num_blocks_per_group: int = 2
hidden_size: int = 256
vocab_size: int = 8192
freeze: int = True
initializer_range: float = 0.02
**kwargs
)
FlavaProcessor
	
class transformers.FlavaProcessor
<
source
>
(
image_processor = None
tokenizer = None
**kwargs
)
Parameters 
image_processor (FlavaImageProcessor) — The image processor is a required input.
tokenizer (BertTokenizerFast) — The tokenizer is a required input.
Constructs a FLAVA processor which wraps a FLAVA image processor and a FLAVA tokenizer into a single processor.
FlavaProcessor offers all the functionalities of FlavaImageProcessor and BertTokenizerFast. See the
__call__() and decode() for more information.
batch_decode
<
source
>
(
*args
**kwargs
)
This method forwards all its arguments to BertTokenizerFast’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 BertTokenizerFast’s decode(). Please refer to
the docstring of this method for more information.
FlavaFeatureExtractor
	
class transformers.FlavaFeatureExtractor
<
source
>
(
*args
**kwargs
)
FlavaImageProcessor
	
class transformers.FlavaImageProcessor
<
source
>
(
do_resize: bool = True
size: typing.Dict[str, int] = None
resample: Resampling = <Resampling.BICUBIC: 3>
do_center_crop: bool = True
crop_size: typing.Dict[str, int] = None
do_rescale: bool = True
rescale_factor: typing.Union[int, float] = 0.00392156862745098
do_normalize: bool = True
image_mean: typing.Union[float, typing.Iterable[float], NoneType] = None
image_std: typing.Union[float, typing.Iterable[float], NoneType] = None
return_image_mask: bool = False
input_size_patches: int = 14
total_mask_patches: int = 75
mask_group_min_patches: int = 16
mask_group_max_patches: typing.Optional[int] = None
mask_group_min_aspect_ratio: float = 0.3
mask_group_max_aspect_ratio: typing.Optional[float] = None
return_codebook_pixels: bool = False
codebook_do_resize: bool = True
codebook_size: bool = None
codebook_resample: int = <Resampling.LANCZOS: 1>
codebook_do_center_crop: bool = True
codebook_crop_size: int = None
codebook_do_rescale: bool = True
codebook_rescale_factor: typing.Union[int, float] = 0.00392156862745098
codebook_do_map_pixels: bool = True
codebook_do_normalize: bool = True
codebook_image_mean: typing.Union[float, typing.Iterable[float], NoneType] = None
codebook_image_std: typing.Union[float, typing.Iterable[float], NoneType] = None
**kwargs
)
Parameters 
do_resize (bool, optional, defaults to True) —
Whether to resize the image’s (height, width) dimensions to the specified size. Can be overridden by the
do_resize parameter in preprocess.
size (Dict[str, int] optional, defaults to {"height" -- 224, "width": 224}):
Size of the image after resizing. Can be overridden by the size parameter in preprocess.
resample (PILImageResampling, optional, defaults to PILImageResampling.BICUBIC) —
Resampling filter to use if resizing the image. Can be overridden by the resample parameter in
preprocess.
do_center_crop (bool, optional, defaults to True) —
Whether to center crop the images. Can be overridden by the do_center_crop parameter in preprocess.
crop_size (Dict[str, int] optional, defaults to {"height" -- 224, "width": 224}):
Size of image after the center crop (crop_size["height"], crop_size["width"]). Can be overridden by the
crop_size parameter in preprocess.
do_rescale (bool, optional, defaults to True) —
Whether to rescale the image by the specified scale rescale_factor. Can be overridden by the do_rescale
parameter in preprocess.
rescale_factor (int or float, optional, defaults to 1/255) —
Scale factor to use if rescaling the image. Can be overridden by the rescale_factor parameter in
preprocess.
do_normalize (bool, optional, defaults to True) —
Whether to normalize the image. Can be overridden by the do_normalize parameter in preprocess.
image_mean (float or List[float], optional, defaults to IMAGENET_STANDARD_MEAN) —
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the image_mean parameter in the preprocess method.
image_std (float or List[float], optional, defaults to IMAGENET_STANDARD_STD) —
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the image_std parameter in the preprocess method.
return_image_mask (bool, optional, defaults to False) —
Whether to return the image mask. Can be overridden by the return_image_mask parameter in preprocess.
input_size_patches (int, optional, defaults to 14) —
Number of patches in the image in height and width direction. 14x14 = 196 total patches. Can be overridden
by the input_size_patches parameter in preprocess.
total_mask_patches (int, optional, defaults to 75) —
Total number of patches that should be masked. Can be overridden by the total_mask_patches parameter in
preprocess.
mask_group_min_patches (int, optional, defaults to 16) —
Minimum number of patches that should be masked. Can be overridden by the mask_group_min_patches
parameter in preprocess.
mask_group_max_patches (int, optional) —
Maximum number of patches that should be masked. Can be overridden by the mask_group_max_patches
parameter in preprocess.
mask_group_min_aspect_ratio (float, optional, defaults to 0.3) —
Minimum aspect ratio of the mask window. Can be overridden by the mask_group_min_aspect_ratio parameter
in preprocess.
mask_group_max_aspect_ratio (float, optional) —
Maximum aspect ratio of the mask window. Can be overridden by the mask_group_max_aspect_ratio parameter
in preprocess.
codebook_do_resize (bool, optional, defaults to True) —
Whether to resize the input for codebook to a certain. Can be overridden by the codebook_do_resize
parameter in preprocess. codebook_size.
codebook_size (Dict[str, int], optional, defaults to {"height" -- 224, "width": 224}):
Resize the input for codebook to the given size. Can be overridden by the codebook_size parameter in
preprocess.
codebook_resample (PILImageResampling, optional, defaults to PILImageResampling.LANCZOS) —
Resampling filter to use if resizing the codebook image. Can be overridden by the codebook_resample
parameter in preprocess.
codebook_do_center_crop (bool, optional, defaults to True) —
Whether to crop the input for codebook at the center. If the input size is smaller than
codebook_crop_size along any edge, the image is padded with 0’s and then center cropped. Can be
overridden by the codebook_do_center_crop parameter in preprocess.
codebook_crop_size (Dict[str, int], optional, defaults to {"height" -- 224, "width": 224}):
Desired output size for codebook input when applying center-cropping. Can be overridden by the
codebook_crop_size parameter in preprocess.
codebook_do_rescale (bool, optional, defaults to True) —
Whether to rescale the input for codebook by the specified scale codebook_rescale_factor. Can be
overridden by the codebook_do_rescale parameter in preprocess.
codebook_rescale_factor (int or float, optional, defaults to 1/255) —
Defines the scale factor to use if rescaling the codebook image. Can be overridden by the
codebook_rescale_factor parameter in preprocess.
codebook_do_map_pixels (bool, optional, defaults to True) —
Whether to map the pixel values of the codebook input to (1 - 2e)x + e. Can be overridden by the
codebook_do_map_pixels parameter in preprocess.
codebook_do_normalize (bool, optional, defaults to True) —
Whether or not to normalize the input for codebook with codebook_image_mean and codebook_image_std. Can
be overridden by the codebook_do_normalize parameter in preprocess.
codebook_image_mean (Optional[Union[float, Iterable[float]]], optional, defaults to [0, 0, 0]) —
The sequence of means for each channel, to be used when normalizing images for codebook. Can be overridden
by the codebook_image_mean parameter in preprocess.
codebook_image_std (Optional[Union[float, Iterable[float]]], optional, defaults to [0.5, 0.5, 0.5]) —
The sequence of standard deviations for each channel, to be used when normalizing images for codebook. Can
be overridden by the codebook_image_std parameter in preprocess.
Constructs a Flava image processor.
preprocess
<
source
>
(
images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]]
do_resize: typing.Optional[bool] = None
size: typing.Dict[str, int] = None
resample: Resampling = None
do_center_crop: typing.Optional[bool] = None
crop_size: typing.Union[typing.Dict[str, int], NoneType] = None
do_rescale: typing.Optional[bool] = None
rescale_factor: typing.Optional[float] = None
do_normalize: typing.Optional[bool] = None
image_mean: typing.Union[float, typing.List[float], NoneType] = None
image_std: typing.Union[float, typing.List[float], NoneType] = None
return_image_mask: typing.Optional[bool] = None
input_size_patches: typing.Optional[int] = None
total_mask_patches: typing.Optional[int] = None
mask_group_min_patches: typing.Optional[int] = None
mask_group_max_patches: typing.Optional[int] = None
mask_group_min_aspect_ratio: typing.Optional[float] = None
mask_group_max_aspect_ratio: typing.Optional[float] = None
return_codebook_pixels: typing.Optional[bool] = None
codebook_do_resize: typing.Optional[bool] = None
codebook_size: typing.Union[typing.Dict[str, int], NoneType] = None
codebook_resample: typing.Optional[int] = None
codebook_do_center_crop: typing.Optional[bool] = None
codebook_crop_size: typing.Union[typing.Dict[str, int], NoneType] = None
codebook_do_rescale: typing.Optional[bool] = None
codebook_rescale_factor: typing.Optional[float] = None
codebook_do_map_pixels: typing.Optional[bool] = None
codebook_do_normalize: typing.Optional[bool] = None
codebook_image_mean: typing.Optional[typing.Iterable[float]] = None
codebook_image_std: typing.Optional[typing.Iterable[float]] = None
return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None
data_format: ChannelDimension = <ChannelDimension.FIRST: 'channels_first'>
**kwargs
)
Parameters 
images (ImageInput) —
Image to preprocess.
do_resize (bool, optional, defaults to self.do_resize) —
Whether to resize the image.
size (Dict[str, int], optional, defaults to self.size) —
Size of the image.
resample (int, optional, defaults to self.resample) —
Resampling filter to use if resizing the image. This can be one of the enum PILImageResampling, Only
has an effect if do_resize is set to True.
do_center_crop (bool, optional, defaults to self.do_center_crop) —
Whether to center crop the image.
crop_size (Dict[str, int], optional, defaults to self.crop_size) —
Size of the center crop. Only has an effect if do_center_crop is set to True.
do_rescale (bool, optional, defaults to self.do_rescale) —
Whether to rescale the image values between [0 - 1].
rescale_factor (float, optional, defaults to self.rescale_factor) —
Rescale factor to rescale the image by if do_rescale is set to True.
do_normalize (bool, optional, defaults to self.do_normalize) —
Whether to normalize the image.
image_mean (float or List[float], optional, defaults to self.image_mean) —
Image mean.
image_std (float or List[float], optional, defaults to self.image_std) —
Image standard deviation.
return_image_mask (bool, optional, defaults to self.return_image_mask) —
Whether to return the image mask.
input_size_patches (int, optional, defaults to self.input_size_patches) —
Size of the patches to extract from the image.
total_mask_patches (int, optional, defaults to self.total_mask_patches) —
Total number of patches to extract from the image.
mask_group_min_patches (int, optional, defaults to self.mask_group_min_patches) —
Minimum number of patches to extract from the image.
mask_group_max_patches (int, optional, defaults to self.mask_group_max_patches) —
Maximum number of patches to extract from the image.
mask_group_min_aspect_ratio (float, optional, defaults to self.mask_group_min_aspect_ratio) —
Minimum aspect ratio of the patches to extract from the image.
mask_group_max_aspect_ratio (float, optional, defaults to self.mask_group_max_aspect_ratio) —
Maximum aspect ratio of the patches to extract from the image.
return_codebook_pixels (bool, optional, defaults to self.return_codebook_pixels) —
Whether to return the codebook pixels.
codebook_do_resize (bool, optional, defaults to self.codebook_do_resize) —
Whether to resize the codebook pixels.
codebook_size (Dict[str, int], optional, defaults to self.codebook_size) —
Size of the codebook pixels.
codebook_resample (int, optional, defaults to self.codebook_resample) —
Resampling filter to use if resizing the codebook pixels. This can be one of the enum
PILImageResampling, Only has an effect if codebook_do_resize is set to True.
codebook_do_center_crop (bool, optional, defaults to self.codebook_do_center_crop) —
Whether to center crop the codebook pixels.
codebook_crop_size (Dict[str, int], optional, defaults to self.codebook_crop_size) —
Size of the center crop of the codebook pixels. Only has an effect if codebook_do_center_crop is set
to True.
codebook_do_rescale (bool, optional, defaults to self.codebook_do_rescale) —
Whether to rescale the codebook pixels values between [0 - 1].
codebook_rescale_factor (float, optional, defaults to self.codebook_rescale_factor) —
Rescale factor to rescale the codebook pixels by if codebook_do_rescale is set to True.
codebook_do_map_pixels (bool, optional, defaults to self.codebook_do_map_pixels) —
Whether to map the codebook pixels values.
codebook_do_normalize (bool, optional, defaults to self.codebook_do_normalize) —
Whether to normalize the codebook pixels.
codebook_image_mean (float or List[float], optional, defaults to self.codebook_image_mean) —
Codebook pixels mean to normalize the codebook pixels by if codebook_do_normalize is set to True.
codebook_image_std (float or List[float], optional, defaults to self.codebook_image_std) —
Codebook pixels standard deviation to normalize the codebook pixels by if codebook_do_normalize is
set to True.
return_tensors (str or TensorType, optional) —
The type of tensors to return. Can be one of:
Unset: Return a list of np.ndarray.
TensorType.TENSORFLOW or 'tf': Return a batch of type tf.Tensor.
TensorType.PYTORCH or 'pt': Return a batch of type torch.Tensor.
TensorType.NUMPY or 'np': Return a batch of type np.ndarray.
TensorType.JAX or 'jax': Return a batch of type jax.numpy.ndarray.
data_format (ChannelDimension or str, optional, defaults to ChannelDimension.FIRST) —
The channel dimension format for the output image. Can be one of:
ChannelDimension.FIRST: image in (num_channels, height, width) format.
ChannelDimension.LAST: image in (height, width, num_channels) format.
Preprocess an image or batch of images.
FlavaForPreTraining
	
class transformers.FlavaForPreTraining
<
source
>
(
config: FlavaConfig
image_codebook: typing.Optional[torch.nn.modules.module.Module] = None
)
Parameters 
config (FlavaConfig) — 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.
image_codebook (nn.Module) — If passed, the image codebook will be set to this. Otherwise. it will
be initialized using the image_codebook_config defined in the config first as the first parameter.
The FLAVA model for pretraining which outputs losses, embeddings, logits and transformer outputs.
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
input_ids_masked: typing.Optional[torch.LongTensor] = None
pixel_values: typing.Optional[torch.FloatTensor] = None
codebook_pixel_values: typing.Optional[torch.FloatTensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
bool_masked_pos: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
image_attention_mask: typing.Optional[torch.Tensor] = None
skip_unmasked_multimodal_encoder: bool = None
mlm_labels: typing.Optional[torch.Tensor] = None
mim_labels: typing.Optional[torch.Tensor] = None
itm_labels: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: bool = True
return_dict: typing.Optional[bool] = None
return_loss: typing.Optional[bool] = None
)
→
transformers.models.flava.modeling_flava.FlavaForPreTrainingOutput or tuple(torch.FloatTensor)
Parameters 
input_ids_masked (torch.LongTensor of shape (batch_size, text_seq_len)) —
Indices of input sequence tokens in the vocabulary. These ones are the masked version of the original task
to be used with MLM. Indices can be obtained using AutoTokenizer along with
DataCollatorForMaskedLanguageModeling. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details. What are input IDs?
input_ids (torch.LongTensor of shape (batch_size, text_seq_len)) —
Indices of input sequence tokens in the vocabulary. Indices can be obtained using AutoTokenizer. See
PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. What are input
IDs?
token_type_ids (torch.LongTensor of shape (batch_size, text_seq_len), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See
FlavaImageProcessor.call() for details.
bool_masked_pos (torch.BoolTensor of shape (batch_size, image_num_patches)) —
Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).
interpolate_pos_encoding (bool, optional) —
Whether to interpolate the pre-trained position encodings.
image_attention_mask (torch.FloatTensor of shape (batch_size, image_num_patches), optional) —
Mask to avoid performing attention on padding token indices specifically for images. Mask values selected
in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
skip_unmasked_multimodal_encoder (bool, optional) —
Skip any calculations for multimodal encoder for unmasked inputs. FLAVA pretraining doesn’t need unmasked
multimodal embeddings or outputs as of now.
mlm_labels (torch.LongTensor of shape (batch_size, text_seq_len), optional) —
Labels for computing the left-to-right language and multimodal masked modeling loss (next word prediction).
Indices should be in [-100, 0, ..., text_config.vocab_size - 1] (see input_ids docstring). Tokens with
indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., text_config.vocab_size - 1].
mim_labels (torch.LongTensor of shape (batch_size, image_num_patches), optional) —
Labels for computing the image and multimodal masked modeling loss. Indices should be in [-100, 0, ..., image_config.vocab_size - 1]. Tokens with indices set to -100 are ignored (masked), the loss is only
computed for the tokens with labels in [0, ..., image_config.vocab_size - 1]. If not passed, they are
generated automatically using the image codebook assigned to the model. By default, it uses
FlavaImageCodebook. See FlavaImageCodebook to understand how to generate mim_labels.
itm_labels (torch.LongTensor of shape (batch_size, 1), optional) —
Labels for computing the image-text matching loss. 0 means the pairs don’t match and 1 means they match.
The pairs with 0 will be skipped for calculation of MMM and global contrastive losses as well.
return_loss (bool, optional, default to None) —
Whether to return calculated loss or not.
attention_mask (torch.FloatTensor of shape (batch_size, text_seq_len), 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?
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
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.
Examples —
Returns
transformers.models.flava.modeling_flava.FlavaForPreTrainingOutput or tuple(torch.FloatTensor)
A transformers.models.flava.modeling_flava.FlavaForPreTrainingOutput 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.flava.configuration_flava.FlavaConfig'>) and inputs.
loss (torch.FloatTensor, optional, returned when return_loss is True) — Total loss calculated for this model.
loss_info (FlavaLosses) — Detailed info for FLAVA Pretraining losses. Check FlavaLosses class description for the information on
the keys.
image_embeddings (torch.FloatTensor of shape (batch_size, output_dim), optional, returned when pixel_values are present) — The image embeddings which are basically the pooled output of FlavaImageModel.
image_output (BaseModelOutputWithPooling, optional, returned when pixel_values are present) — The output of the FlavaImageModel.
text_embeddings (torch.FloatTensor of shape (batch_size, output_dim), optional, returned when input_ids are present) — The text embeddings which are basically the pooled output of FlavaTextModel.
text_output (BaseModelOutputWithPooling, optional, returned when input_ids are present) — The output of the FlavaTextModel.
multimodal_embeddings (torch.FloatTensor of shape (batch_size, output_dim), optional, returned when input_ids and pixel_values are present and skip_unmasked_multimodal_encoder is None or False) — The multimodal embeddings which are basically the pooled output of FlavaTextModel.
multimodal_output (BaseModelOutputWithPooling, returned when input_ids and pixel_values are present and skip_unmasked_multimodal_encoder is None or False) — The output of the FlavaMultimodalModel.
image_masked_embeddings (torch.FloatTensor of shape (batch_size, output_dim), optional, returned when pixel_values are present) — The image embeddings which are basically the pooled output of FlavaImageModel. Uses bool_masked_pos
to create masked images.
image_masked_output (BaseModelOutputWithPooling, optional, returned when pixel_values are present) — The output of the FlavaImageModel. Uses bool_masked_pos to create masked images.
text_masked_embeddings (torch.FloatTensor of shape (batch_size, output_dim), optional, returned when input_ids_masked are present) — The text embeddings which are basically the pooled output of FlavaTextModel.
text_masked_output (BaseModelOutputWithPooling, optional, returned when input_ids_masked are present) — The output of the FlavaTextModel.
multimodal_masked_embeddings (torch.FloatTensor of shape (batch_size, output_dim), optional, returned when input_ids and pixel_values are present) — The multimodal embeddings which are basically the pooled output of FlavaTextModel.
multimodal_masked_output (BaseModelOutputWithPooling, returned when input_ids_masked and pixel_values are present) — The output of the FlavaMultimodalModel.
mim_logits (torch.FloatTensor of shape (batch_size, num_image_patches, image_vocab_size) or of shape (total_masked_patches, image_vocab_size) , optional, returned when pixel_values are present and input_ids_masked are not) — The logits for MIM unimodal loss. Uses book_masked_pos to get masked patches. The flattened output is
returned when bool_masked_pos has some of the patches masked.
mlm_logits (torch.FloatTensor of shape (batch_size, text_seq_length, text_vocab_size) or of shape (total_masked_seq_length, text_vocab_size), optional, returned when input_ids_masked are present and pixel_values are not) — The logits for MLM unimodal loss. The flattened output is returned when input_ids_masked has some of
the tokens masked.
itm_logits (torch.FloatTensor of shape (batch_size, 2), optional, returned when input_ids_masked and pixel_values are present) — The logits for ITM loss. Note that ITM loss is calculated on masked pairs in FLAVA.
mmm_image_logits (torch.FloatTensor of shape (batch_size, num_image_patches, image_vocab_size) or of shape(total_masked_patches, image_vocab_size), optional, returned when pixel_values and input_ids_masked are present) — The logits for MMM image multimodal loss. Uses book_masked_pos to get masked patches. The flattened
output is returned when bool_masked_pos has some of the patches masked.
mmm_text_logits (torch.FloatTensor of shape (batch_size, text_seq_length, text_vocab_size) or of shape ((total_masked_seq_length, text_vocab_size)), *optional*, returned when pixel_valuesandinput_ids_maskedare present) -- The logits for MMM text multimodal loss. The flattened output is returned wheninput_ids_masked` has
some of the tokens masked.
contrastive_logits_per_image (torch.FloatTensor of shape (image_batch_size, text_batch_size)) — The scaled dot product scores between image_embeddings and text_embeddings but passed through FLAVA’s
image_projection and text_projection layers respectively. This represents the image-text similarity
scores. This is calculated on unmasked images and texts.
contrastive_logits_per_text (torch.FloatTensor of shape (text_batch_size, image_batch_size)) — The scaled dot product scores between text_embeddings and image_embeddings but passed through FLAVA’s
text_projection and image_projection layers respectively. This is calculated on unmasked images and
texts.
The FlavaForPreTraining 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.
FlavaModel
	
class transformers.FlavaModel
<
source
>
(
config: FlavaConfig
)
Parameters 
config (FlavaConfig) — 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.
The bare FLAVA Model transformer outputting raw hidden-states without any specific head on top.
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
token_type_ids: typing.Optional[torch.Tensor] = None
bool_masked_pos: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
image_attention_mask: typing.Optional[torch.Tensor] = None
skip_multimodal_encoder: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: bool = True
return_dict: typing.Optional[bool] = None
)
→
transformers.models.flava.modeling_flava.FlavaModelOutput or tuple(torch.FloatTensor)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See
FlavaImageProcessor.call() for details.
bool_masked_pos (torch.BoolTensor of shape (batch_size, image_num_patches)) —
Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).
interpolate_pos_encoding (bool, optional) —
Whether to interpolate the pre-trained position encodings.
input_ids (torch.LongTensor of shape (batch_size, image_num_patches + text_seq_len)) —
Indices of input sequence tokens in the vocabulary. Indices can be obtained using AutoTokenizer. See
PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. What are input
IDs?
token_type_ids (torch.LongTensor of shape (batch_size, image_num_patches + text_seq_len), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
attention_mask (torch.FloatTensor of shape (batch_size, image_num_patches + text_seq_len), 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?
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
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.
skip_multimodal_encoder (bool, optional) —
Skip any calculations for multimodal encoder. Useful if multimodal encoding is not going to be used.
Returns
transformers.models.flava.modeling_flava.FlavaModelOutput or tuple(torch.FloatTensor)
A transformers.models.flava.modeling_flava.FlavaModelOutput 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.flava.configuration_flava.FlavaConfig'>) and inputs.
image_embeddings (torch.FloatTensor of shape (batch_size, output_dim), optional, returned when pixel_values are present) — The image embeddings which are basically the pooled output of FlavaImageModel.
image_output (BaseModelOutputWithPooling, optional, returned when pixel_values are present) — The output of the FlavaImageModel.
text_embeddings (torch.FloatTensor of shape (batch_size, output_dim), optional, returned when input_ids are present) — The text embeddings which are basically the pooled output of FlavaTextModel.
text_output (BaseModelOutputWithPooling, optional, returned when input_ids are present) — The output of the FlavaTextModel.
multimodal_embeddings (torch.FloatTensor of shape (batch_size, output_dim), optional, returned when input_ids and pixel_values are present and skip_multimodal_encoder is None or False) — The multimodal embeddings which are basically the pooled output of FlavaTextModel.
multimodal_output (BaseModelOutputWithPooling, returned when input_ids and pixel_values are present and skip_multimodal_encoder is None or False) — The output of the FlavaMultimodalModel.
The FlavaModel 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, FlavaModel
 model = FlavaModel.from_pretrained("facebook/flava-full")
 processor = AutoProcessor.from_pretrained("facebook/flava-full")
 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"], images=image, return_tensors="pt", padding=True)
 outputs = model(**inputs)
 logits_per_image = outputs.contrastive_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
token_type_ids: 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
)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, text_seq_length)) —
Indices of input sequence tokens in the vocabulary. Indices can be obtained using AutoTokenizer. See
PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. What are input
IDs?
token_type_ids (torch.LongTensor of shape (batch_size, text_seq_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
attention_mask (torch.FloatTensor of shape (batch_size, text_seq_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?
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
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.
The FlavaModel 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.
get_image_features
<
source
>
(
pixel_values: typing.Optional[torch.Tensor] = None
bool_masked_pos: typing.Optional[torch.BoolTensor] = None
interpolate_pos_encoding: typing.Optional[bool] = None
attention_mask: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See
FlavaImageProcessor.call() for details.
bool_masked_pos (torch.BoolTensor of shape (batch_size, image_num_patches)) —
Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).
interpolate_pos_encoding (bool, optional) —
Whether to interpolate the pre-trained position encodings.
attention_mask (torch.FloatTensor of shape (batch_size, image_num_patches), 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?
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
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.
The FlavaModel 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.
FlavaImageCodebook
	
class transformers.FlavaImageCodebook
<
source
>
(
config: FlavaImageCodebookConfig
**kwargs: typing.Any
)
Parameters 
config (FlavaImageCodebookConfig) — 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.
The FLAVA’s image codebook model inspired from DALL-E’s original encoder. Outputs raw hidden states and can be used
to generate image tokens for an image based on DALL-E’s vocab. Used to generate labels for MIM. Use
get_codebook_indices to get image tokens for an image.
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
>
(
pixel_values: FloatTensor
)
get_codebook_indices
<
source
>
(
pixel_values: Tensor
)
get_codebook_probs
<
source
>
(
pixel_values: Tensor
)
FlavaTextModel
	
class transformers.FlavaTextModel
<
source
>
(
config: FlavaTextConfig
add_pooling_layer: bool = True
)
Parameters 
config (FlavaTextConfig) — 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.
The bare FLAVA Text Model transformer outputting raw hidden-states without any specific head on top.
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.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
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, text_seq_length)) —
Indices of input sequence tokens in the vocabulary. Indices can be obtained using AutoTokenizer. See
PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. What are input
IDs?
token_type_ids (torch.LongTensor of shape (batch_size, text_seq_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?
attention_mask (torch.FloatTensor of shape (batch_size, text_seq_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?
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
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 (FlavaTextConfig) 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 FlavaTextModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
	Copied
 from transformers import AutoTokenizer, FlavaTextModel
 import torch
 tokenizer = AutoTokenizer.from_pretrained("facebook/flava-full")
 model = FlavaTextModel.from_pretrained("facebook/flava-full")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
 outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
FlavaImageModel
	
class transformers.FlavaImageModel
<
source
>
(
config: FlavaImageConfig
add_pooling_layer: bool = True
)
Parameters 
config (FlavaImageConfig) — 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.
The bare FLAVA Image Model transformer outputting raw hidden-states without any specific head on top.
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
>
(
pixel_values: typing.Optional[torch.Tensor] = None
bool_masked_pos: typing.Optional[torch.BoolTensor] = None
interpolate_pos_encoding: typing.Optional[bool] = None
attention_mask: typing.Optional[torch.Tensor] = None
head_mask: 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 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See
FlavaImageProcessor.call() for details.
bool_masked_pos (torch.BoolTensor of shape (batch_size, image_num_patches)) —
Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).
interpolate_pos_encoding (bool, optional) —
Whether to interpolate the pre-trained position encodings.
attention_mask (torch.FloatTensor of shape (batch_size, image_num_patches), 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?
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
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 (FlavaImageConfig) 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 FlavaImageModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
	Copied
 from transformers import AutoImageProcessor, FlavaImageModel
 import torch
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 image_processor = AutoImageProcessor.from_pretrained("facebook/flava-full")
 model = FlavaImageModel.from_pretrained("facebook/flava-full")
 inputs = image_processor(image, return_tensors="pt")
 with torch.no_grad():
...     outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
 list(last_hidden_states.shape)
[1, 197, 768]
FlavaMultimodalModel
	
class transformers.FlavaMultimodalModel
<
source
>
(
config: FlavaMultimodalConfig
add_pooling_layer = True
)
Parameters 
config (FlavaMultimodalConfig) — 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.
The bare FLAVA Multimodal Model transformer outputting raw hidden-states without any specific head on top.
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
>
(
hidden_states: Tensor
attention_mask: typing.Optional[torch.Tensor] = None
head_mask: 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 
hidden_states (torch.FloatTensor of shape (batch_size, image_num_patches + text_seq_len, hidden_size)) —
The concatenated hidden states of unimodal encoders.
attention_mask (torch.FloatTensor of shape (batch_size, image_num_patches + text_seq_len), 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?
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
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 (FlavaMultimodalConfig) 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 FlavaMultimodalModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
	Copied
 from transformers import AutoTokenizer, FlavaMultimodalModel
 import torch
 tokenizer = AutoTokenizer.from_pretrained("facebook/flava-full")
 model = FlavaMultimodalModel.from_pretrained("facebook/flava-full")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
 outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
←Donut
GIT→
FLAVA
Overview
FlavaConfig
FlavaTextConfig
FlavaImageConfig
FlavaMultimodalConfig
FlavaImageCodebookConfig
FlavaProcessor
FlavaFeatureExtractor
FlavaImageProcessor
FlavaForPreTraining
FlavaModel
FlavaImageCodebook
FlavaTextModel
FlavaImageModel
FlavaMultimodalModel
