
 
GIT
	
Overview
	
The GIT model was proposed in GIT: A Generative Image-to-text Transformer for Vision and Language by
Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, Lijuan Wang. GIT is a decoder-only Transformer
that leverages CLIP’s vision encoder to condition the model on vision inputs besides text. The model obtains state-of-the-art results on
image captioning and visual question answering benchmarks.
The abstract from the paper is the following:
In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify vision-language tasks such as image/video captioning and question answering. While generative models provide a consistent network architecture between pre-training and fine-tuning, existing work typically contains complex structures (uni/multi-modal encoder/decoder) and depends on external modules such as object detectors/taggers and optical character recognition (OCR). In GIT, we simplify the architecture as one image encoder and one text decoder under a single language modeling task. We also scale up the pre-training data and the model size to boost the model performance. Without bells and whistles, our GIT establishes new state of the arts on 12 challenging benchmarks with a large margin. For instance, our model surpasses the human performance for the first time on TextCaps (138.2 vs. 125.5 in CIDEr). Furthermore, we present a new scheme of generation-based image classification and scene text recognition, achieving decent performance on standard benchmarks.
Tips:
GIT is implemented in a very similar way to GPT-2, the only difference being that the model is also conditioned on pixel_values.
One can use GitProcessor to prepare images for the model, and the generate method for autoregressive generation.
GIT architecture. Taken from the original paper. 
This model was contributed by nielsr.
The original code can be found here.
Resources
	
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GIT.
Demo notebooks regarding inference + fine-tuning GIT on custom data can be found here.
See also: Causal language modeling task guide
If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we will review it.
The resource should ideally demonstrate something new instead of duplicating an existing resource.
GitVisionConfig
	
class transformers.GitVisionConfig
<
source
>
(
hidden_size = 768
intermediate_size = 3072
num_hidden_layers = 12
num_attention_heads = 12
num_channels = 3
image_size = 224
patch_size = 16
hidden_act = 'quick_gelu'
layer_norm_eps = 1e-05
attention_dropout = 0.0
initializer_range = 0.02
**kwargs
)
Parameters 
hidden_size (int, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (int, optional, defaults to 3072) —
Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (int, optional, defaults to 12) —
Number of hidden layers in the Transformer encoder.
num_attention_heads (int, optional, defaults to 12) —
Number of attention heads for each attention layer in the Transformer encoder.
image_size (int, optional, defaults to 224) —
The size (resolution) of each image.
patch_size (int, optional, defaults to 16) —
The size (resolution) of each patch.
hidden_act (str or function, optional, defaults to "quick_gelu") —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu",
"relu", "selu" and "gelu_new" `"quick_gelu" are supported.
layer_norm_eps (float, optional, defaults to 1e-5) —
The epsilon used by the layer normalization layers.
attention_dropout (float, optional, defaults to 0.0) —
The dropout ratio for the attention probabilities.
initializer_range (float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
This is the configuration class to store the configuration of a GitVisionModel. It is used to instantiate a GIT
vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the vision encoder of the GIT
microsoft/git-base 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 GitVisionConfig, GitVisionModel
 # Initializing a GitVisionConfig with microsoft/git-base style configuration
 configuration = GitVisionConfig()
 # Initializing a GitVisionModel (with random weights) from the microsoft/git-base style configuration
 model = GitVisionModel(configuration)
 # Accessing the model configuration
 configuration = model.config
GitVisionModel
	
class transformers.GitVisionModel
<
source
>
(
config: GitVisionConfig
)
Parameters 
config (GitConfig) — 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 vision model from CLIP, used in GIT, without any head or projection on top.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
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.BaseModelOutput 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.BaseModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutput 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.git.configuration_git.GitVisionConfig'>) 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.
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 GitVisionModel 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, GitVisionModel
 processor = AutoProcessor.from_pretrained("microsoft/git-base")
 model = GitVisionModel.from_pretrained("microsoft/git-base")
 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
GitConfig
	
class transformers.GitConfig
<
source
>
(
vision_config = None
vocab_size = 30522
hidden_size = 768
num_hidden_layers = 6
num_attention_heads = 12
intermediate_size = 3072
hidden_act = 'gelu'
hidden_dropout_prob = 0.1
attention_probs_dropout_prob = 0.1
max_position_embeddings = 1024
initializer_range = 0.02
layer_norm_eps = 1e-12
pad_token_id = 0
position_embedding_type = 'absolute'
use_cache = True
tie_word_embeddings = False
bos_token_id = 101
eos_token_id = 102
num_image_with_embedding = None
**kwargs
)
Parameters 
vision_config (dict, optional) —
Dictionary of configuration options used to initialize GitVisionConfig.
vocab_size (int, optional, defaults to 30522) —
Vocabulary size of the GIT model. Defines the number of different tokens that can be represented by the
inputs_ids passed when calling GitModel.
hidden_size (int, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (int, optional, defaults to 6) —
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” (often named feed-forward) layer in the Transformer encoder.
hidden_act (str or Callable, optional, defaults to "gelu") —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu",
"relu", "silu" and "gelu_new" are supported.
hidden_dropout_prob (float, optional, defaults to 0.1) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (float, optional, defaults to 0.1) —
The dropout ratio for the attention probabilities.
max_position_embeddings (int, optional, defaults to 1024) —
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).
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.
position_embedding_type (str, optional, defaults to "absolute") —
Type of position embedding. Choose one of "absolute", "relative_key", "relative_key_query". For
positional embeddings use "absolute". For more information on "relative_key", please refer to
Self-Attention with Relative Position Representations (Shaw et al.).
For more information on "relative_key_query", please refer to Method 4 in Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.).
use_cache (bool, optional, defaults to True) —
Whether or not the model should return the last key/values attentions (not used by all models).
num_image_with_embedding (int, optional) —
The number of temporal embeddings to add, in case the model is used for video captioning/VQA.
This is the configuration class to store the configuration of a GitModel. It is used to instantiate a GIT 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 GIT
microsoft/git-base architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Examples:
	Copied
 from transformers import GitConfig, GitModel
 # Initializing a GIT microsoft/git-base style configuration
 configuration = GitConfig()
 # Initializing a model (with random weights) from the microsoft/git-base style configuration
 model = GitModel(configuration)
 # Accessing the model configuration
 configuration = model.config
to_dict
<
source
>
(
)
Serializes this instance to a Python dictionary. Override the default to_dict(). Returns:
Dict[str, any]: Dictionary of all the attributes that make up this configuration instance,
GitProcessor
	
class transformers.GitProcessor
<
source
>
(
image_processor
tokenizer
)
Parameters 
image_processor (AutoImageProcessor) —
The image processor is a required input.
tokenizer (AutoTokenizer) —
The tokenizer is a required input.
Constructs a GIT processor which wraps a CLIP image processor and a BERT tokenizer into a single processor.
GitProcessor offers all the functionalities of CLIPImageProcessor and BertTokenizerFast. See the
call() and decode() for more information.
__call__
<
source
>
(
text = None
images = None
return_tensors = None
**kwargs
)
→
BatchEncoding
Parameters 
text (str, List[str], List[List[str]]) —
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
is_split_into_words=True (to lift the ambiguity with a batch of sequences).
images (PIL.Image.Image, np.ndarray, torch.Tensor, List[PIL.Image.Image], List[np.ndarray], List[torch.Tensor]) —
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
number of channels, H and W are image height and width.
return_tensors (str or TensorType, optional) —
If set, will return tensors of a particular framework. Acceptable values are:
'tf': Return TensorFlow tf.constant objects.
'pt': Return PyTorch torch.Tensor objects.
'np': Return NumPy np.ndarray objects.
'jax': Return JAX jnp.ndarray objects.
Returns
BatchEncoding
A BatchEncoding with the following fields:
input_ids — List of token ids to be fed to a model. Returned when text is not None.
attention_mask — List of indices specifying which tokens should be attended to by the model (when
return_attention_mask=True or if “attention_mask” is in self.model_input_names and if text is not
None).
pixel_values — Pixel values to be fed to a model. Returned when images is not None.
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the text
and kwargs arguments to BertTokenizerFast’s call() if text is not None to encode
the text. To prepare the image(s), this method forwards the images and kwrags arguments to
CLIPImageProcessor’s call() if images is not None. Please refer to the doctsring
of the above two methods for more information.
GitModel
	
class transformers.GitModel
<
source
>
(
config
)
Parameters 
config (GitConfig) — 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 GIT Model transformer consisting of a CLIP image encoder and text decoder outputting raw hidden-states without any specific head on top.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forward
<
source
>
(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
pixel_values: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None
use_cache: typing.Optional[bool] = 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.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
attention_mask (torch.FloatTensor 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.
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.
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
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.
past_key_values (tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) —
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).
use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).
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 (GitConfig) 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 GitModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
	Copied
 from transformers import AutoProcessor, AutoModel
 import requests
 from PIL import Image
 processor = AutoProcessor.from_pretrained("microsoft/git-base")
 model = AutoModel.from_pretrained("microsoft/git-base")
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 text = "this is an image of two cats"
 inputs = processor(text, images=image, return_tensors="pt")
 outputs = model(**inputs)
 last_hidden_state = outputs.last_hidden_state
GitForCausalLM
	
class transformers.GitForCausalLM
<
source
>
(
config
)
Parameters 
config (GitConfig) — 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.
GIT Model with a language modeling head on top for autoregressive language modeling.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forward
<
source
>
(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
pixel_values: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
labels: typing.Optional[torch.Tensor] = None
past_key_values: typing.Optional[typing.List[torch.Tensor]] = None
use_cache: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.CausalLMOutputWithPast or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, sequence_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?
attention_mask (torch.FloatTensor 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.
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.
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
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.
labels (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
[-100, 0, ..., config.vocab_size] (see input_ids docstring) Tokens with indices set to -100 are
ignored (masked), the loss is only computed for the tokens with labels n [0, ..., config.vocab_size]
past_key_values (tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) —
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).
use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).
Returns
transformers.modeling_outputs.CausalLMOutputWithPast or tuple(torch.FloatTensor)
A transformers.modeling_outputs.CausalLMOutputWithPast 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 (GitConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head))
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
past_key_values input) to speed up sequential decoding.
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 GitForCausalLM 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:
Image captioning example:
	Copied
 from transformers import AutoProcessor, AutoModelForCausalLM
 import requests
 from PIL import Image
 processor = AutoProcessor.from_pretrained("microsoft/git-base-coco")
 model = AutoModelForCausalLM.from_pretrained("microsoft/git-base-coco")
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 pixel_values = processor(images=image, return_tensors="pt").pixel_values
 generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
 generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
 print(generated_caption)
two cats sleeping on a pink blanket next to remotes.
Visual question answering (VQA) example:
	Copied
 from transformers import AutoProcessor, AutoModelForCausalLM
 from huggingface_hub import hf_hub_download
 from PIL import Image
 processor = AutoProcessor.from_pretrained("microsoft/git-base-textvqa")
 model = AutoModelForCausalLM.from_pretrained("microsoft/git-base-textvqa")
 file_path = hf_hub_download(repo_id="nielsr/textvqa-sample", filename="bus.png", repo_type="dataset")
 image = Image.open(file_path).convert("RGB")
 pixel_values = processor(images=image, return_tensors="pt").pixel_values
 question = "what does the front of the bus say at the top?"
 input_ids = processor(text=question, add_special_tokens=False).input_ids
 input_ids = [processor.tokenizer.cls_token_id] + input_ids
 input_ids = torch.tensor(input_ids).unsqueeze(0)
 generated_ids = model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=50)
 print(processor.batch_decode(generated_ids, skip_special_tokens=True))
['what does the front of the bus say at the top? special']
Video captioning example:
	Copied
 import av
 import numpy as np
 from PIL import Image
 from huggingface_hub import hf_hub_download
 from transformers import AutoProcessor, AutoModelForCausalLM
 processor = AutoProcessor.from_pretrained("microsoft/git-base-vatex")
 model = AutoModelForCausalLM.from_pretrained("microsoft/git-base-vatex")
 # set seed for reproducability
 np.random.seed(45)
 def read_video_pyav(container, indices):
...     '''
...     Decode the video with PyAV decoder.
...     Args:
...         container (`av.container.input.InputContainer`): PyAV container.
...         indices (`List[int]`): List of frame indices to decode.
...     Returns:
...         result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
...     '''
...     frames = []
...     container.seek(0)
...     start_index = indices[0]
...     end_index = indices[-1]
...     for i, frame in enumerate(container.decode(video=0)):
...         if i > end_index:
...             break
...         if i >= start_index and i in indices:
...             frames.append(frame)
...     return np.stack([x.to_ndarray(format="rgb24") for x in frames])
 def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
...     converted_len = int(clip_len * frame_sample_rate)
...     end_idx = np.random.randint(converted_len, seg_len)
...     start_idx = end_idx - converted_len
...     indices = np.linspace(start_idx, end_idx, num=clip_len)
...     indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
...     return indices
 # load video
 file_path = hf_hub_download(
...     repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset"
... )
 container = av.open(file_path)
 # sample frames
 num_frames = model.config.num_image_with_embedding
 indices = sample_frame_indices(
...     clip_len=num_frames, frame_sample_rate=4, seg_len=container.streams.video[0].frames
... )
 frames = read_video_pyav(container, indices)
 pixel_values = processor(images=list(frames), return_tensors="pt").pixel_values
 generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
 print("Generated caption:", processor.batch_decode(generated_ids, skip_special_tokens=True))
Generated caption: ['a woman is sitting at a table and she is talking about the food she is holding.']
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Overview
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GitVisionConfig
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