
 
Swin Transformer V2
	
Overview
	
The Swin Transformer V2 model was proposed in Swin Transformer V2: Scaling Up Capacity and Resolution by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
The abstract from the paper is the following:
Large-scale NLP models have been shown to significantly improve the performance on language tasks with no signs of saturation. They also demonstrate amazing few-shot capabilities like that of human beings. This paper aims to explore large-scale models in computer vision. We tackle three major issues in training and application of large vision models, including training instability, resolution gaps between pre-training and fine-tuning, and hunger on labelled data. Three main techniques are proposed: 1) a residual-post-norm method combined with cosine attention to improve training stability; 2) A log-spaced continuous position bias method to effectively transfer models pre-trained using low-resolution images to downstream tasks with high-resolution inputs; 3) A self-supervised pre-training method, SimMIM, to reduce the needs of vast labeled images. Through these techniques, this paper successfully trained a 3 billion-parameter Swin Transformer V2 model, which is the largest dense vision model to date, and makes it capable of training with images of up to 1,536×1,536 resolution. It set new performance records on 4 representative vision tasks, including ImageNet-V2 image classification, COCO object detection, ADE20K semantic segmentation, and Kinetics-400 video action classification. Also note our training is much more efficient than that in Google’s billion-level visual models, which consumes 40 times less labelled data and 40 times less training time.
Tips:
One can use the AutoImageProcessor API to prepare images for the model.
This model was contributed by nandwalritik.
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 Swin Transformer v2.
Image Classification
Swinv2ForImageClassification is supported by this example script and notebook.
See also: Image classification task guide
Besides that:
Swinv2ForMaskedImageModeling is supported by this example script.
If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
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
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
**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.
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-12) —
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.
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:
	Copied
 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 (Swinv2Config) — 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 Swinv2 Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch torch.nn.Module sub-class. 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
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, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See ViTImageProcessor.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.
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.
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).
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:
	Copied
 from transformers import AutoImageProcessor, Swinv2Model
 import torch
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 image_processor = AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256")
 model = Swinv2Model.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256")
 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, 64, 768]
Swinv2ForMaskedImageModeling
	
class transformers.Swinv2ForMaskedImageModeling
<
source
>
(
config
)
Parameters 
config (Swinv2Config) — 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 is a PyTorch torch.nn.Module sub-class. 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
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, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See ViTImageProcessor.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.
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.
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).
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:
	Copied
 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 (Swinv2Config) — 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.
This model is a PyTorch torch.nn.Module sub-class. 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
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, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See ViTImageProcessor.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.
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,), 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).
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:
	Copied
 from transformers import AutoImageProcessor, Swinv2ForImageClassification
 import torch
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 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])
Egyptian cat
←Swin Transformer
Swin2SR→
Swin Transformer V2
Overview
Resources
Swinv2Config
Swinv2Model
Swinv2ForMaskedImageModeling
Swinv2ForImageClassification
