
 
Swin2SR
	
Overview
	
The Swin2SR model was proposed in Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte.
Swin2R improves the SwinIR model by incorporating Swin Transformer v2 layers which mitigates issues such as training instability, resolution gaps between pre-training
and fine-tuning, and hunger on data.
The abstract from the paper is the following:
Compression plays an important role on the efficient transmission and storage of images and videos through band-limited systems such as streaming services, virtual reality or videogames. However, compression unavoidably leads to artifacts and the loss of the original information, which may severely degrade the visual quality. For these reasons, quality enhancement of compressed images has become a popular research topic. While most state-of-the-art image restoration methods are based on convolutional neural networks, other transformers-based methods such as SwinIR, show impressive performance on these tasks.
In this paper, we explore the novel Swin Transformer V2, to improve SwinIR for image super-resolution, and in particular, the compressed input scenario. Using this method we can tackle the major issues in training transformer vision models, such as training instability, resolution gaps between pre-training and fine-tuning, and hunger on data. We conduct experiments on three representative tasks: JPEG compression artifacts removal, image super-resolution (classical and lightweight), and compressed image super-resolution. Experimental results demonstrate that our method, Swin2SR, can improve the training convergence and performance of SwinIR, and is a top-5 solution at the “AIM 2022 Challenge on Super-Resolution of Compressed Image and Video”.
Swin2SR architecture. Taken from the original paper.
This model was contributed by nielsr.
The original code can be found here.
Resources
	
Demo notebooks for Swin2SR can be found here.
A demo Space for image super-resolution with SwinSR can be found here.
Swin2SRImageProcessor
	
class transformers.Swin2SRImageProcessor
<
source
>
(
do_rescale: bool = True
rescale_factor: typing.Union[int, float] = 0.00392156862745098
do_pad: bool = True
pad_size: int = 8
**kwargs
)
Parameters 
do_rescale (bool, optional, defaults to True) —
Whether to rescale the image by the specified scale rescale_factor. Can be overridden by the do_rescale
parameter in the preprocess method.
rescale_factor (int or float, optional, defaults to 1/255) —
Scale factor to use if rescaling the image. Can be overridden by the rescale_factor parameter in the
preprocess method.
Constructs a Swin2SR image processor.
preprocess
<
source
>
(
images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]]
do_rescale: typing.Optional[bool] = None
rescale_factor: typing.Optional[float] = None
do_pad: typing.Optional[bool] = None
pad_size: typing.Optional[int] = None
return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None
data_format: typing.Union[str, transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'>
**kwargs
)
Parameters 
images (ImageInput) —
Image to preprocess.
do_rescale (bool, optional, defaults to self.do_rescale) —
Whether to rescale the image values between [0 - 1].
rescale_factor (float, optional, defaults to self.rescale_factor) —
Rescale factor to rescale the image by if do_rescale is set to True.
do_pad (bool, optional, defaults to True) —
Whether to pad the image to make the height and width divisible by window_size.
pad_size (int, optional, defaults to 32) —
The size of the sliding window for the local attention.
return_tensors (str or TensorType, optional) —
The type of tensors to return. Can be one of:
Unset: Return a list of np.ndarray.
TensorType.TENSORFLOW or 'tf': Return a batch of type tf.Tensor.
TensorType.PYTORCH or 'pt': Return a batch of type torch.Tensor.
TensorType.NUMPY or 'np': Return a batch of type np.ndarray.
TensorType.JAX or 'jax': Return a batch of type jax.numpy.ndarray.
data_format (ChannelDimension or str, optional, defaults to ChannelDimension.FIRST) —
The channel dimension format for the output image. Can be one of:
"channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
"channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
Unset: Use the channel dimension format of the input image.
Preprocess an image or batch of images.
Swin2SRConfig
	
class transformers.Swin2SRConfig
<
source
>
(
image_size = 64
patch_size = 1
num_channels = 3
embed_dim = 180
depths = [6, 6, 6, 6, 6, 6]
num_heads = [6, 6, 6, 6, 6, 6]
window_size = 8
mlp_ratio = 2.0
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
upscale = 2
img_range = 1.0
resi_connection = '1conv'
upsampler = 'pixelshuffle'
**kwargs
)
Parameters 
image_size (int, optional, defaults to 64) —
The size (resolution) of each image.
patch_size (int, optional, defaults to 1) —
The size (resolution) of each patch.
num_channels (int, optional, defaults to 3) —
The number of input channels.
embed_dim (int, optional, defaults to 180) —
Dimensionality of patch embedding.
depths (list(int), optional, defaults to [6, 6, 6, 6, 6, 6]) —
Depth of each layer in the Transformer encoder.
num_heads (list(int), optional, defaults to [6, 6, 6, 6, 6, 6]) —
Number of attention heads in each layer of the Transformer encoder.
window_size (int, optional, defaults to 8) —
Size of windows.
mlp_ratio (float, optional, defaults to 2.0) —
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.
upscale (int, optional, defaults to 2) —
The upscale factor for the image. 2/3/4/8 for image super resolution, 1 for denoising and compress artifact
reduction
img_range (float, optional, defaults to 1.) —
The range of the values of the input image.
resi_connection (str, optional, defaults to "1conv") —
The convolutional block to use before the residual connection in each stage.
upsampler (str, optional, defaults to "pixelshuffle") —
The reconstruction reconstruction module. Can be ‘pixelshuffle’/‘pixelshuffledirect’/‘nearest+conv’/None.
This is the configuration class to store the configuration of a Swin2SRModel. 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
caidas/swin2sr-classicalsr-x2-64 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 Swin2SRConfig, Swin2SRModel
 # Initializing a Swin2SR caidas/swin2sr-classicalsr-x2-64 style configuration
 configuration = Swin2SRConfig()
 # Initializing a model (with random weights) from the caidas/swin2sr-classicalsr-x2-64 style configuration
 model = Swin2SRModel(configuration)
 # Accessing the model configuration
 configuration = model.config
Swin2SRModel
	
class transformers.Swin2SRModel
<
source
>
(
config
)
Parameters 
config (Swin2SRConfig) — 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 Swin2SR 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
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.modeling_outputs.BaseModelOutput 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
Swin2SRImageProcessor.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.
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 (Swin2SRConfig) 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 Swin2SRModel 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, Swin2SRModel
 import torch
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 image_processor = AutoImageProcessor.from_pretrained("caidas/swin2SR-classical-sr-x2-64")
 model = Swin2SRModel.from_pretrained("caidas/swin2SR-classical-sr-x2-64")
 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, 180, 488, 648]
Swin2SRForImageSuperResolution
	
class transformers.Swin2SRForImageSuperResolution
<
source
>
(
config
)
Parameters 
config (Swin2SRConfig) — 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.
Swin2SR Model transformer with an upsampler head on top for image super resolution and restoration.
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.modeling_outputs.ImageSuperResolutionOutput 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
Swin2SRImageProcessor.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.
Returns
transformers.modeling_outputs.ImageSuperResolutionOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.ImageSuperResolutionOutput 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 (Swin2SRConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Reconstruction loss.
reconstruction (torch.FloatTensor of shape (batch_size, num_channels, height, width)) — Reconstructed images, possibly upscaled.
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 stage) of shape (batch_size, sequence_length, hidden_size). Hidden-states
(also called feature maps) of the model at the output of each stage.
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, patch_size, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The Swin2SRForImageSuperResolution 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
 import torch
 import numpy as np
 from PIL import Image
 import requests
 from transformers import AutoImageProcessor, Swin2SRForImageSuperResolution
 processor = AutoImageProcessor.from_pretrained("caidas/swin2SR-classical-sr-x2-64")
 model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64")
 url = "https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/butterfly.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 # prepare image for the model
 inputs = processor(image, return_tensors="pt")
 # forward pass
 with torch.no_grad():
...     outputs = model(**inputs)
 output = outputs.reconstruction.data.squeeze().float().cpu().clamp_(0, 1).numpy()
 output = np.moveaxis(output, source=0, destination=-1)
 output = (output * 255.0).round().astype(np.uint8)  # float32 to uint8
 # you can visualize `output` with `Image.fromarray`
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Swin2SR
Overview
Resources
Swin2SRImageProcessor
Swin2SRConfig
Swin2SRModel
Swin2SRForImageSuperResolution
