ViTMAE · Hugging Face
This model was published in HF papers on 2021-11-11 and contributed to Hugging Face Transformers on 2022-01-18.
is a self-supervised vision model that is pretrained by masking large portions of an image (~75%). An encoder processes the visible image patches and a decoder reconstructs the missing pixels from the encoded patches and mask tokens. After pretraining, the encoder can be reused for downstream tasks like image classification or object detection — often outperforming models trained with supervised learning.
You can find all the original ViTMAE checkpoints under the
organization.
Click on the ViTMAE models in the right sidebar for more examples of how to apply ViTMAE to vision tasks.
The example below demonstrates how to reconstruct the missing pixels with the
class.
AutoModel
import requests import torch from PIL import Image from transformers import ViTImageProcessor, ViTMAEForPreTraining url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg" image = Image.open(requests.get(url, stream=True).raw) processor = ViTImageProcessor.from_pretrained("facebook/vit-mae-base") inputs = processor(image, return_tensors="pt").to(model.device) inputs = {k: v.to(model.device) for k, v in inputs.items()} model = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base", attn_implementation="sdpa", device_map="auto") with torch.no_grad(): outputs = model(**inputs) reconstruction = outputs.logitsNotes
ViTMAE is typically used in two stages. Self-supervised pretraining with
, and then discarding the decoder and fine-tuning the encoder. After fine-tuning, the weights can be plugged into a model like
.
Use
for input preparation.
Resources
Refer to this
to learn how to visualize the reconstructed pixels from
.
ViTMAEConfig
classtransformers.ViTMAEConfig
(transformers_version: str | None = Nonearchitectures: list[str] | None = Noneoutput_hidden_states: bool | None = Falsereturn_dict: bool | None = Truedtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = Nonechunk_size_feed_forward: int = 0is_encoder_decoder: bool = Falseid2label: dict[int, str] | dict[str, str] | None = Nonelabel2id: dict[str, int] | dict[str, str] | None = Noneproblem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = Nonehidden_size: int = 768num_hidden_layers: int = 12num_attention_heads: int = 12intermediate_size: int = 3072hidden_act: str = 'gelu'hidden_dropout_prob: float | int = 0.0attention_probs_dropout_prob: float | int = 0.0initializer_range: float = 0.02layer_norm_eps: float = 1e-12image_size: int | list[int] | tuple[int, int] = 224patch_size: int | list[int] | tuple[int, int] = 16num_channels: int = 3qkv_bias: bool = Truedecoder_num_attention_heads: int = 16decoder_hidden_size: int = 512decoder_num_hidden_layers: int = 8decoder_intermediate_size: int = 2048mask_ratio: float = 0.75norm_pix_loss: bool = False)
Parameters
hidden_size (int, optional, defaults to 768) — Dimension of the hidden representations.
num_hidden_layers (int, optional, defaults to 12) — Number of hidden layers in the Transformer decoder.
num_attention_heads (int, optional, defaults to 12) — Number of attention heads for each attention layer in the Transformer decoder.
intermediate_size (int, optional, defaults to 3072) — Dimension of the MLP representations.
hidden_act (str, optional, defaults to gelu) — The non-linear activation function (function or string) in the decoder. For example, "gelu", "relu", "silu", etc.
hidden_dropout_prob (Union[float, int], optional, defaults to 0.0) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (Union[float, int], 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.
layer_norm_eps (float, optional, defaults to 1e-12) — The epsilon used by the layer normalization layers.
image_size (Union[int, list[int], tuple[int, int]], optional, defaults to 224) — The size (resolution) of each image.
patch_size (Union[int, list[int], tuple[int, 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.
decoder_num_attention_heads (int, optional, defaults to 16) — Number of attention heads for each attention layer in the Transformer decoder.
decoder_hidden_size (int, optional, defaults to 512) — Dimension of the hidden representations.
decoder_num_hidden_layers (int, optional, defaults to 8) — Number of hidden layers in the decoder.
decoder_intermediate_size (int, optional, defaults to 2048) — Dimension of the MLP representations.
mask_ratio (float, optional, defaults to 0.75) — The ratio of the number of masked tokens in the input sequence.
norm_pix_loss (bool, optional, defaults to False) — Whether or not to train with normalized pixels (see Table 3 in the paper). Using normalized pixels improved representation quality in the experiments of the authors.
This is the configuration class to store the configuration of a ViTMAEModel. It is used to instantiate a Vit Mae 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
Configuration objects inherit from
and can be used to control the model outputs. Read the documentation from
for more information.
Example:
>>> from transformers import ViTMAEConfig, ViTMAEModel >>> >>> configuration = ViTMAEConfig() >>> >>> model = ViTMAEModel(configuration) >>> >>> configuration = model.configViTMAEModel
classtransformers.ViTMAEModel
(config: ViTMAEConfig)
Parameters
config (
) — Configuration for the model.
The bare Vit Mae Model outputting raw hidden-states without any specific head on top.
This model inherits from
. 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
subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
(pixel_values: typing.Optional[torch.Tensor] = Nonenoise: typing.Optional[torch.Tensor] = Noneinterpolate_pos_encoding: bool | None = Noneattention_mask: typing.Optional[torch.Tensor] = None**kwargs: Unpack)→ViTMAEModelOutput or tuple(torch.FloatTensor)
Parameters
pixel_values (torch.Tensor of shape (batch_size, num_channels, image_size, image_size), optional) — The tensors corresponding to the input images. Pixel values can be obtained using
. See ViTImageProcessor.__call__() for details (processor_class uses
for processing images).
noise (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mainly used for testing purposes to control randomness and maintain the reproducibility
interpolate_pos_encoding (bool, optional, default False) — Whether to interpolate the pre-trained position encodings. This is mainly used to use the model on higher resolution images.
attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]: 1 for tokens that are not masked,
0 for tokens that are masked.
Returns
ViTMAEModelOutput or tuple(torch.FloatTensor)
A ViTMAEModelOutput 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 (
) and inputs.
The
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.
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the model.
mask (torch.FloatTensor of shape (batch_size, sequence_length)) — Tensor indicating which patches are masked (1) and which are not (0).
ids_restore (torch.LongTensor of shape (batch_size, sequence_length)) — Tensor containing the original index of the (shuffled) masked patches.
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.
Examples:
>>> from transformers import AutoImageProcessor, ViTMAEModel >>> from PIL import Image >>> import httpx >>> from io import BytesIO >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg">>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())) >>> image_processor = AutoImageProcessor.from_pretrained("facebook/vit-mae-base") >>> model = ViTMAEModel.from_pretrained("facebook/vit-mae-base") >>> inputs = image_processor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_stateViTMAEForPreTraining
classtransformers.ViTMAEForPreTraining
(config: ViTMAEConfig)
Parameters
config (
) — 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
method to load the model weights.
The ViTMAE Model transformer with the decoder on top for self-supervised pre-training.
Note that we provide a script to pre-train this model on custom data in our
.
This model inherits from
. 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
subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
(pixel_values: typing.Optional[torch.Tensor] = Nonenoise: typing.Optional[torch.Tensor] = Noneinterpolate_pos_encoding: bool | None = Noneattention_mask: typing.Optional[torch.Tensor] = None**kwargs: Unpack)→ViTMAEForPreTrainingOutput or tuple(torch.FloatTensor)
Parameters
pixel_values (torch.Tensor of shape (batch_size, num_channels, image_size, image_size), optional) — The tensors corresponding to the input images. Pixel values can be obtained using
. See ViTImageProcessor.__call__() for details (processor_class uses
for processing images).
noise (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mainly used for testing purposes to control randomness and maintain the reproducibility
interpolate_pos_encoding (bool, optional, default False) — Whether to interpolate the pre-trained position encodings. This is mainly used to use the model on higher resolution images.
attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]: 1 for tokens that are not masked,
0 for tokens that are masked.
Returns
ViTMAEForPreTrainingOutput or tuple(torch.FloatTensor)
A ViTMAEForPreTrainingOutput 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 (
) and inputs.
The
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.
loss (torch.FloatTensor of shape (1,)) — Pixel reconstruction loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, patch_size ** 2 * num_channels)) — Pixel reconstruction logits.
mask (torch.FloatTensor of shape (batch_size, sequence_length)) — Tensor indicating which patches are masked (1) and which are not (0).
ids_restore (torch.LongTensor of shape (batch_size, sequence_length)) — Tensor containing the original index of the (shuffled) masked patches.
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.
Examples:
>>> from transformers import AutoImageProcessor, ViTMAEForPreTraining >>> from PIL import Image >>> import httpx >>> from io import BytesIO >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg">>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())).convert("RGB") >>> image_processor = AutoImageProcessor.from_pretrained("facebook/vit-mae-base") >>> model = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base") >>> inputs = image_processor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> loss = outputs.loss >>> mask = outputs.mask >>> ids_restore = outputs.ids_restore