spandrel.architectures.Swin2SR.Swin2SR

class documentation

class Swin2SR(nn.Module): (source)

Constructor: Swin2SR(img_size, patch_size, in_chans, embed_dim, ...)

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Swin2SR
A PyTorch impl of : Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration.

Parameters
img_sizeInput image size. Default 64
patch_sizePatch size. Default: 1
in_chansNumber of input image channels. Default: 3
embed_dimPatch embedding dimension. Default: 96
depthsDepth of each Swin Transformer layer.
num_headsNumber of attention heads in different layers.
window_sizeWindow size. Default: 7
mlp_ratioRatio of mlp hidden dim to embedding dim. Default: 4
qkv_biasIf True, add a learnable bias to query, key, value. Default: True
drop_rateDropout rate. Default: 0
attn_drop_rateAttention dropout rate. Default: 0
drop_path_rateStochastic depth rate. Default: 0.1
norm_layerNormalization layer. Default: nn.LayerNorm.
apeIf True, add absolute position embedding to the patch embedding. Default: False
patch_normIf True, add normalization after patch embedding. Default: True
use_checkpointWhether to use checkpointing to save memory. Default: False
upscaleUpscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
img_rangeImage range. 1. or 255.
upsamplerThe reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
resi_connectionThe convolutional block before residual connection. '1conv'/'3conv'
Method __init__ Undocumented
Method check_image_size Undocumented
Method flops Undocumented
Method forward Undocumented
Method forward_features Undocumented
Method forward_features_hf Undocumented
Method no_weight_decay Undocumented
Method no_weight_decay_keywords Undocumented
Class Variable hyperparameters Undocumented
Instance Variable absolute_pos_embed Undocumented
Instance Variable ape Undocumented
Instance Variable conv_after_body Undocumented
Instance Variable conv_before_upsample Undocumented
Instance Variable conv_first Undocumented
Instance Variable conv_last Undocumented
Instance Variable embed_dim Undocumented
Instance Variable img_range Undocumented
Instance Variable layers Undocumented
Instance Variable layers_hf Undocumented
Instance Variable mean Undocumented
Instance Variable mlp_ratio Undocumented
Instance Variable norm Undocumented
Instance Variable num_features Undocumented
Instance Variable num_layers Undocumented
Instance Variable patch_embed Undocumented
Instance Variable patch_norm Undocumented
Instance Variable patch_unembed Undocumented
Instance Variable patches_resolution Undocumented
Instance Variable pos_drop Undocumented
Instance Variable upsample Undocumented
Instance Variable upsampler Undocumented
Instance Variable upscale Undocumented
Instance Variable window_size Undocumented
Method _init_weights Undocumented
def __init__(self, *, img_size=64, patch_size=1, in_chans=3, embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6], window_size=7, mlp_ratio=4.0, qkv_bias=True, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1, norm_layer=nn.LayerNorm, ape=False, patch_norm=True, use_checkpoint=False, upscale=2, img_range=1.0, upsampler='', resi_connection='1conv'): (source)

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def check_image_size(self, x): (source)

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def flops(self): (source)

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def forward(self, x): (source)

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def forward_features(self, x): (source)

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def forward_features_hf(self, x): (source)

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@torch.jit.ignore
def no_weight_decay(self): (source)

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@torch.jit.ignore
def no_weight_decay_keywords(self): (source)

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hyperparameters: dict = (source)

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absolute_pos_embed = (source)

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ape: bool = (source)

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conv_after_body = (source)

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conv_before_upsample = (source)

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conv_first = (source)

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conv_last = (source)

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embed_dim: int = (source)

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img_range = (source)

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layers = (source)

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layers_hf = (source)

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mean = (source)

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mlp_ratio: float = (source)

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norm = (source)

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num_features = (source)

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num_layers = (source)

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patch_embed = (source)

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patch_norm: bool = (source)

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patch_unembed = (source)

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patches_resolution = (source)

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pos_drop = (source)

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upsample = (source)

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upsampler = (source)

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upscale = (source)

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window_size: int = (source)

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def _init_weights(self, m): (source)

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