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Args: input_resolution (tuple[int]): Resolution of input feature. dim (int): Number of input channels. norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm csBtƒ ¡||_||_tjd|d|dd|_|d|ƒ|_dS)Nr0r/FrA)rrrmrEr#r$Ú reductionÚnorm)rrmrErurrrras  zPatchMerging.__init__c Cs6|j\}}|j\}}}|||ks*Jdƒ‚|ddkrB|ddksXJd|›d|›dƒ‚| ||||¡}|dd…ddd…ddd…dd…f}|dd…ddd…ddd…dd…f}|dd…ddd…ddd…dd…f} |dd…ddd…ddd…dd…f} t ||| | gd ¡}| |d d |¡}| |¡}| |¡}|S) z x: b, h*w, c zinput feature has wrong sizer/rzx size (Ú*z) are not even.Nrr2r0)rmr r3r Úcatr€r) rrr8r9r7Zseq_lenr:Zx0Úx1Zx2Zx3rrrrhs  .$$$$  zPatchMerging.forwardr`cCsd|j›d|j›S)Nzinput_resolution=z, dim=©rmrErerrrrfszPatchMerging.extra_reprcCsD|j\}}|||j}||d|dd|jd|j7}|S)Nr/r0r„©rr8r9rgrrrrg‚s (zPatchMerging.flops© rrrr r#r}rrrhrfrgr!rrrrr~Xs r~c sTeZdZdZddddddejddf ‡fdd„ Zd d „Zed œd d „Z dd„Z ‡Z S)Ú BasicLayera/ A basic Swin Transformer layer for one stage. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resolution. depth (int): Number of blocks. num_heads (int): Number of attention heads. window_size (int): Local window size. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0 drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. rkTNrFc sxtƒ ¡ˆ|_ˆ|_||_||_t ‡‡‡‡‡‡‡‡‡‡ ‡ f dd„t|ƒDƒ¡|_ | durn| ˆˆˆd|_ nd|_ dS)NcsRg|]J}tˆˆˆˆ |ddkr"dnˆ dˆˆ ˆˆˆtˆtƒrDˆ|nˆˆd ‘qS)r/r) rErmrFr6rnrorTrUr)rPrru)riÚ isinstanceÚlist)Ú.0Úi© rPrEr)rrmrorurFrUrTr6rrÚ ´s ôôz'BasicLayer.__init__..)rEru) rrrErmÚdepthÚuse_checkpointr#Ú ModuleListÚrangeÚblocksÚ downsample)rrErmrŽrFr6rorTrUr)rPrrur“rrrŒrrs " ó zBasicLayer.__init__cCsB|jD]"}|jrt ||¡}q|||ƒ}q|jdur>| |¡}|Sr)r’rÚ checkpointr“)rrrxÚblkrrrrÊs    zBasicLayer.forwardr`cCsd|j›d|j›d|j›S)Nrbr|z, depth=)rErmrŽrerrrrfÔszBasicLayer.extra_reprcCs8d}|jD]}|| ¡7}q |jdur4||j ¡7}|S©Nr)r’rgr“)rrgr•rrrrg×s   zBasicLayer.flopsr†rrrrr‡‰sò- r‡c sLeZdZdZddddddejddddd f ‡fd d „ Zd d „Zdd„Z‡Z S)ÚRSTBa¹Residual Swin Transformer Block (RSTB). Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resolution. depth (int): Number of blocks. num_heads (int): Number of attention heads. window_size (int): Local window size. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. drop (float, optional): Dropout rate. Default: 0.0 attn_drop (float, optional): Attention dropout rate. Default: 0.0 drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. img_size: Input image size. patch_size: Patch size. resi_connection: The convolutional block before residual connection. rkTNrFéàr0Ú1convcsôtt|ƒ ¡||_||_t||||||||| | | | | |d|_|dkr^t ||ddd¡|_ nj|dkrÈt  t ||dddd¡tj ddd t |d|dddd ¡tj ddd t |d|ddd¡¡|_ t ||d |dd |_ t||d |dd |_dS) N)rErmrŽrFr6rorTrUr)rPrrur“rr™r.rÚ3convr0çš™™™™™É?T©Znegative_slopeÚinplacer©Úimg_sizeÚ patch_sizeÚin_chansÚ embed_dimru)rr—rrErmr‡Úresidual_groupr#ÚConv2dÚconvÚ SequentialÚ LeakyReLUÚ PatchEmbedÚ patch_embedÚ PatchUnEmbedÚ patch_unembed)rrErmrŽrFr6rorTrUr)rPrrur“rrŸr Úresi_connectionrrrr÷sBò $ý ÿ ÿz RSTB.__init__c Cs$| | | | ||¡|¡¡¡|Sr)r©r¥r«r£©rrrxrrrr-sz RSTB.forwardcCsXd}||j ¡7}|j\}}||||j|jd7}||j ¡7}||j ¡7}|S)Nré )r£rgrmrEr©r«©rrgr8r9rrrrg0s z RSTB.flops) rrrr r#r}rrrgr!rrrrr—às ï6r—cs2eZdZdZd ‡fdd„ Zd d „Zd d „Z‡ZS)r¨ax Image to Patch Embedding Args: img_size (int): Image size. Default: 224. patch_size (int): Patch token size. Default: 4. in_chans (int): Number of input image channels. Default: 3. embed_dim (int): Number of linear projection output channels. Default: 96. norm_layer (nn.Module, optional): Normalization layer. Default: None r˜r0r.é`Ncsˆtƒ ¡t|ƒ}t|ƒ}|d|d|d|dg}||_||_||_|d|d|_||_||_|dur~||ƒ|_ nd|_ dS©Nrr) rrrrŸr Úpatches_resolutionÚ num_patchesr¡r¢r€©rrŸr r¡r¢rur²rrrrFs   zPatchEmbed.__init__cCs*| d¡ dd¡}|jdur&| |¡}|S)Nr/r)rLrWr€rrrrrXs  zPatchEmbed.forwardcCs.d}|j\}}|jdur*||||j7}|Sr–)rŸr€r¢r¯rrrrg^s   zPatchEmbed.flops)r˜r0r.r°N©rrrr rrrgr!rrrrr¨;s r¨cs2eZdZdZd ‡fdd„ Zd d „Zd d „Z‡ZS)rªaz Image to Patch Unembedding Args: img_size (int): Image size. Default: 224. patch_size (int): Patch token size. Default: 4. in_chans (int): Number of input image channels. Default: 3. embed_dim (int): Number of linear projection output channels. Default: 96. norm_layer (nn.Module, optional): Normalization layer. Default: None r˜r0r.r°Ncsntƒ ¡t|ƒ}t|ƒ}|d|d|d|dg}||_||_||_|d|d|_||_||_dSr±) rrrrŸr r²r³r¡r¢r´rrrrqs  zPatchUnEmbed.__init__cCs,| dd¡ |jd|j|d|d¡}|S)Nrr/r)rWr3r r¢r­rrrr~s(zPatchUnEmbed.forwardcCsd}|Sr–r)rrgrrrrg‚szPatchUnEmbed.flops)r˜r0r.r°Nrµrrrrrªfs  rªcs eZdZdZ‡fdd„Z‡ZS)ÚUpsamplez¡Upsample module. Args: scale (int): Scale factor. Supported scales: 2^n and 3. num_feat (int): Channel number of intermediate features. c s¶g}||d@dkr\ttt |d¡ƒƒD]0}| t |d|ddd¡¡| t d¡¡q(nF|dkr’| t |d|ddd¡¡| t d¡¡ntd|›dƒ‚t t |ƒj |ŽdS) Nrrr/r0r.r®zscale z/ is not supported. Supported scales: 2^n and 3.) r‘r=ÚmathÚlogÚappendr#r¤Ú PixelShuffleÚ ValueErrorrr¶r)rrGÚnum_featÚmr{rrrrszUpsample.__init__)rrrr rr!rrrrr¶‡sr¶cs*eZdZdZd‡fdd„ Zdd„Z‡ZS)ÚUpsampleOneStepa*UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) Used in lightweight SR to save parameters. Args: scale (int): Scale factor. Supported scales: 2^n and 3. num_feat (int): Channel number of intermediate features. Nc sT||_||_g}| t ||d|ddd¡¡| t |¡¡tt|ƒj|ŽdS)Nr/r.r) r¼rmr¹r#r¤rºrr¾r)rrGr¼Ú num_out_chrmr½rrrr§s  zUpsampleOneStep.__init__cCs$|j\}}|||jdd}|S)Nr.r®)rmr¼r…rrrrg¯s zUpsampleOneStep.flops)N)rrrr rrgr!rrrrr¾s r¾csŽeZdZdZddddddddd d d d d ejd d d ddddf‡fdd„ Zdd„Zej j dd„ƒZ ej j dd„ƒZ dd„Z dd„Zdd„Z‡ZS) ÚSwinIRaØ SwinIR A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer. Args: img_size (int | tuple(int)): Input image size. Default 64 patch_size (int | tuple(int)): Patch size. Default: 1 in_chans (int): Number of input image channels. Default: 3 embed_dim (int): Patch embedding dimension. Default: 96 depths (tuple(int)): Depth of each Swin Transformer layer. num_heads (tuple(int)): Number of attention heads in different layers. window_size (int): Window size. Default: 7 mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None drop_rate (float): Dropout rate. Default: 0 attn_drop_rate (float): Attention dropout rate. Default: 0 drop_path_rate (float): Stochastic depth rate. Default: 0.1 norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. ape (bool): If True, add absolute position embedding to the patch embedding. Default: False patch_norm (bool): If True, add normalization after patch embedding. Default: True use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction img_range: Image range. 1. or 255. upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None resi_connection: The convolutional block before residual connection. 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