#
Swin Transformer V2

Swin Transformer V2 is a 3B parameter model that focuses on how to scale a vision model to billions of parameters. It introduces techniques like residual-post-norm combined with cosine attention for improved training stability, log-spaced continuous position bias to better handle varying image resolutions between pre-training and fine-tuning, and a new pre-training method (SimMIM) to reduce the need for large amounts of labeled data. These improvements enable efficiently training very large models (up to 3 billion parameters) capable of processing high-resolution images.

You can find official Swin Transformer V2 checkpoints under the Microsoft organization.

Click on the Swin Transformer V2 models in the right sidebar for more examples of how to apply Swin Transformer V2 to vision tasks.

Pipeline
AutoModel

    import torch
    from transformers import pipeline

    pipeline = pipeline(
        task="image-classification",
        model="microsoft/swinv2-tiny-patch4-window8-256",
        torch_dtype=torch.float16,
        device=0
    )
    pipeline(images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")

##
Notes

- Swin Transformer V2 can pad the inputs for any input height and width divisible by `32`.
- Swin Transformer V2 can be used as a backbone. When `output_hidden_states = True`, it outputs both `hidden_states` and `reshaped_hidden_states`. The `reshaped_hidden_states` have a shape of `(batch, num_channels, height, width)` rather than `(batch_size, sequence_length, num_channels)`.

##
Swinv2Config

###
class transformers.Swinv2Config

< source >

( image_size = 224 patch_size = 4 num_channels = 3 embed_dim = 96 depths = [2, 2, 6, 2] num_heads = [3, 6, 12, 24] window_size = 7 pretrained_window_sizes = [0, 0, 0, 0] mlp_ratio = 4.0 qkv_bias = True hidden_dropout_prob = 0.0 attention_probs_dropout_prob = 0.0 drop_path_rate = 0.1 hidden_act = 'gelu' use_absolute_embeddings = False initializer_range = 0.02 layer_norm_eps = 1e-05 encoder_stride = 32 out_features = None out_indices = None **kwargs )

Parameters

- image_size (`int`, optional, defaults to 224) — The size (resolution) of each image.
- patch_size (`int`, optional, defaults to 4) — The size (resolution) of each patch.
- num_channels (`int`, optional, defaults to 3) — The number of input channels.
- embed_dim (`int`, optional, defaults to 96) — Dimensionality of patch embedding.
- depths (`list(int)`, optional, defaults to `[2, 2, 6, 2]`) — Depth of each layer in the Transformer encoder.
- num_heads (`list(int)`, optional, defaults to `[3, 6, 12, 24]`) — Number of attention heads in each layer of the Transformer encoder.
- window_size (`int`, optional, defaults to 7) — Size of windows.
- pretrained_window_sizes (`list(int)`, optional, defaults to `[0, 0, 0, 0]`) — Size of windows during pretraining.
- mlp_ratio (`float`, optional, defaults to 4.0) — Ratio of MLP hidden dimensionality to embedding dimensionality.
- qkv_bias (`bool`, optional, defaults to `True`) — Whether or not a learnable bias should be added to the queries, keys and values.
- hidden_dropout_prob (`float`, optional, defaults to 0.0) — The dropout probability for all fully connected layers in the embeddings and encoder.
- attention_probs_dropout_prob (`float`, optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
- drop_path_rate (`float`, optional, defaults to 0.1) — Stochastic depth rate.
- hidden_act (`str` or `function`, optional, defaults to `"gelu"`) — The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
- use_absolute_embeddings (`bool`, optional, defaults to `False`) — Whether or not to add absolute position embeddings to the patch embeddings.
- 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-05) — The epsilon used by the layer normalization layers.
- encoder_stride (`int`, optional, defaults to 32) — Factor to increase the spatial resolution by in the decoder head for masked image modeling.
- out_features (`List[str]`, optional) — If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc. (depending on how many stages the model has). If unset and `out_indices` is set, will default to the corresponding stages. If unset and `out_indices` is unset, will default to the last stage.
- out_indices (`List[int]`, optional) — If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how many stages the model has). If unset and `out_features` is set, will default to the corresponding stages. If unset and `out_features` is unset, will default to the last stage.

This is the configuration class to store the configuration of a Swinv2Model. It is used to instantiate a Swin Transformer v2 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 Swin Transformer v2 microsoft/swinv2-tiny-patch4-window8-256 architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

    >>> from transformers import Swinv2Config, Swinv2Model

    >>> # Initializing a Swinv2 microsoft/swinv2-tiny-patch4-window8-256 style configuration
    >>> configuration = Swinv2Config()

    >>> # Initializing a model (with random weights) from the microsoft/swinv2-tiny-patch4-window8-256 style configuration
    >>> model = Swinv2Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config

##
Swinv2Model

###
class transformers.Swinv2Model

< source >

( config add_pooling_layer = True use_mask_token = False )

Parameters

- config (Swinv2Model) — 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.
- add_pooling_layer (`bool`, optional, defaults to `True`) — Whether or not to apply pooling layer.
- use_mask_token (`bool`, optional, defaults to `False`) — Whether or not to create and apply mask tokens in the embedding layer.

The bare Swinv2 Model 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 >

( pixel_values: typing.Optional[torch.FloatTensor] = None bool_masked_pos: typing.Optional[torch.BoolTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None interpolate_pos_encoding: bool = False return_dict: typing.Optional[bool] = None ) →
`transformers.models.swinv2.modeling_swinv2.Swinv2ModelOutput` or `tuple(torch.FloatTensor)`

Parameters

- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, optional) — The tensors corresponding to the input images. Pixel values can be obtained using `{image_processor_class}`. See `{image_processor_class}.__call__` for details (`{processor_class}` uses `{image_processor_class}` for processing images).
- bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, optional) — Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).
- 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.
- interpolate_pos_encoding (`bool`, defaults to `False`) — Whether to interpolate the pre-trained position encodings.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

`transformers.models.swinv2.modeling_swinv2.Swinv2ModelOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.swinv2.modeling_swinv2.Swinv2ModelOutput` 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 (Swinv2Config) 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)`, optional, returned when `add_pooling_layer=True` is passed) — Average pooling of the last layer hidden-state.

- 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 + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each stage) 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.

- reshaped_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 + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions.

The Swinv2Model 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:

##
Swinv2ForMaskedImageModeling

###
class transformers.Swinv2ForMaskedImageModeling

< source >

( config )

Parameters

- config (Swinv2ForMaskedImageModeling) — 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.

Swinv2 Model with a decoder on top for masked image modeling, as proposed in SimMIM.

Note that we provide a script to pre-train this model on custom data in our examples directory.

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 bool_masked_pos: typing.Optional[torch.BoolTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None interpolate_pos_encoding: bool = False return_dict: typing.Optional[bool] = None ) →
`transformers.models.swinv2.modeling_swinv2.Swinv2MaskedImageModelingOutput` or `tuple(torch.FloatTensor)`

Parameters

- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, optional) — The tensors corresponding to the input images. Pixel values can be obtained using `{image_processor_class}`. See `{image_processor_class}.__call__` for details (`{processor_class}` uses `{image_processor_class}` for processing images).
- bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`) — Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).
- 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.
- interpolate_pos_encoding (`bool`, defaults to `False`) — Whether to interpolate the pre-trained position encodings.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

`transformers.models.swinv2.modeling_swinv2.Swinv2MaskedImageModelingOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.swinv2.modeling_swinv2.Swinv2MaskedImageModelingOutput` 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 (Swinv2Config) and inputs.

- loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `bool_masked_pos` is provided) — Masked image modeling (MLM) loss.

- reconstruction (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) — Reconstructed pixel values.

- 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 + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each stage) 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.

- reshaped_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 + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions.

The Swinv2ForMaskedImageModeling forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

    >>> from transformers import AutoImageProcessor, Swinv2ForMaskedImageModeling
    >>> import torch
    >>> from PIL import Image
    >>> import requests

    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    >>> image = Image.open(requests.get(url, stream=True).raw)

    >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256")
    >>> model = Swinv2ForMaskedImageModeling.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256")

    >>> num_patches = (model.config.image_size // model.config.patch_size) ** 2
    >>> pixel_values = image_processor(images=image, return_tensors="pt").pixel_values
    >>> # create random boolean mask of shape (batch_size, num_patches)
    >>> bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool()

    >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
    >>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
    >>> list(reconstructed_pixel_values.shape)
    [1, 3, 256, 256]

##
Swinv2ForImageClassification

###
class transformers.Swinv2ForImageClassification

< source >

( config )

Parameters

- config (Swinv2ForImageClassification) — 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.

Swinv2 Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet.

Note that it’s possible to fine-tune SwinV2 on higher resolution images than the ones it has been trained on, by setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained position embeddings to the higher resolution.

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 head_mask: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None interpolate_pos_encoding: bool = False return_dict: typing.Optional[bool] = None ) →
`transformers.models.swinv2.modeling_swinv2.Swinv2ImageClassifierOutput` or `tuple(torch.FloatTensor)`

Parameters

- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, optional) — The tensors corresponding to the input images. Pixel values can be obtained using `{image_processor_class}`. See `{image_processor_class}.__call__` for details (`{processor_class}` uses `{image_processor_class}` for processing images).
- 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.
- labels (`torch.LongTensor` of shape `(batch_size,)`, optional) — Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
- 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.
- interpolate_pos_encoding (`bool`, defaults to `False`) — Whether to interpolate the pre-trained position encodings.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

`transformers.models.swinv2.modeling_swinv2.Swinv2ImageClassifierOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.swinv2.modeling_swinv2.Swinv2ImageClassifierOutput` 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 (Swinv2Config) and inputs.

- loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided) — Classification (or regression if config.num_labels==1) loss.

- logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`) — Classification (or regression if config.num_labels==1) scores (before SoftMax).

- 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 + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each stage) 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.

- reshaped_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 + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions.

The Swinv2ForImageClassification 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:

    >>> from transformers import AutoImageProcessor, Swinv2ForImageClassification
    >>> import torch
    >>> from datasets import load_dataset

    >>> dataset = load_dataset("huggingface/cats-image", trust_remote_code=True)
    >>> image = dataset["test"]["image"][0]

    >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256")
    >>> model = Swinv2ForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256")

    >>> inputs = image_processor(image, return_tensors="pt")

    >>> with torch.no_grad():
    ...     logits = model(**inputs).logits

    >>> # model predicts one of the 1000 ImageNet classes
    >>> predicted_label = logits.argmax(-1).item()
    >>> print(model.config.id2label[predicted_label])
    ...

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