a  d.@stddlZddlmZddlmmZddlmZGdddejZ GdddejZ e GdddejZ dS) N) ARCH_REGISTRYcs2eZdZdZd fdd ZddZddZZS) SeqConv3x3aThe re-parameterizable block used in the ECBSR architecture. Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices Ref git repo: https://github.com/xindongzhang/ECBSR Args: seq_type (str): Sequence type, option: conv1x1-conv3x3 | conv1x1-sobelx | conv1x1-sobely | conv1x1-laplacian. in_channels (int): Channel number of input. out_channels (int): Channel number of output. depth_multiplier (int): Width multiplier in the expand-and-squeeze conv. Default: 1. c s<tt|||_||_||_|jdkrt|||_tj j |j|jddd}|j |_ |j |_tj j |j|jdd}|j |_|j |_n|jdkrtj j |j|jddd}|j |_ |j |_tj|jdddfdd }t ||_t|jd }t||jf}t ||_ tj|jdddftjd |_t|jD]r} d |j| dddf<d |j| dddf<d |j| dd df<d|j| ddd f<d|j| ddd f<d|j| dd d f<q6t j|jdd|_nx|jdkrtj j |j|jddd}|j |_ |j |_tj|jdddfdd }t t||_t|jd }t||jf}t t||_ tj|jdddftjd |_t|jD]r} d |j| dddf<d |j| dddf<d |j| ddd f<d|j| dd df<d|j| dd df<d|j| dd d f<qxt j|jdd|_n6|jdkr0tj j |j|jddd}|j |_ |j |_tj|jdddfdd }t t||_t|jd }t||jf}t t||_ tj|jdddftjd |_t|jD]`} d |j| dddf<d |j| dddf<d |j| ddd f<d |j| dd df<d|j| dddf<qt j|jdd|_ntddS)Nconv1x1-conv3x3rr kernel_sizepadding)rconv1x1-sobelx)sizegMbP?)Zdtype?@ggF)dataZ requires_gradconv1x1-sobelyconv1x1-laplaciangz%The type of seqconv is not supported!)superr__init__seq_type in_channels out_channelsint mid_planestorchnnConv2dweightk0biasb0k1b1Zrandn ParameterscaleZreshapezerosfloat32maskrangeZ FloatTensor ValueError) selfrrrdepth_multiplierZconv0Zconv1r#ri __class__a/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/basicsr/archs/ecbsr_arch.pyrs       zSeqConv3x3.__init__cCs|jdkrtj||j|jdd}t|ddd}|jdddd}||ddddddddf<||ddddddddf<||ddddddddf<||ddddddddf<tj||j|jdd}ntj||j|jdd}t|ddd}|jdddd}||ddddddddf<||ddddddddf<||ddddddddf<||ddddddddf<tj||j |j |j d|j d}|S) Nrr)inputrrstride)rrrrZconstantr)r0rrr1groups) rFconv2drrpadviewr r!r#r&rr)r)xZy0Zb0_pady1r.r.r/forwardhs$          zSeqConv3x3.forwardcCsP|j}|dkrd}|jdkrtj|j|jddddd}tjd|j dd|d|j dddd}tj||jd d|j }n|j |j}tj|j|jddf|d}t|jD]0}||dddddf|||ddddf<q|j}tj||jddddd}tjd|jdd|d|j dddd}tj||d d|}||fS) Nrrrrr )r0rdevicer2)r get_devicerr4r5r ZpermuterZonesrrr7r!r#r&r$rr'r)r)r< rep_weightrep_biastmpr r+r!r.r.r/ rep_paramss   ( .(zSeqConv3x3.rep_params)r__name__ __module__ __qualname____doc__rr:rA __classcell__r.r.r,r/rs Srcs2eZdZdZd fdd ZddZdd ZZS) ECBaQThe ECB block used in the ECBSR architecture. Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices Ref git repo: https://github.com/xindongzhang/ECBSR Args: in_channels (int): Channel number of input. out_channels (int): Channel number of output. depth_multiplier (int): Width multiplier in the expand-and-squeeze conv. Default: 1. act_type (str): Activation type. Option: prelu | relu | rrelu | softplus | linear. Default: prelu. with_idt (bool): Whether to use identity connection. Default: False. preluFcs.tt|||_||_||_||_|r>|j|jkr>d|_nd|_tj j |j|jddd|_ t d|j|j|j|_ t d|j|j|_t d|j|j|_t d |j|j|_|jd krt j|jd |_nd|jd krt jdd |_nJ|jdkrt jddd|_n.|jdkrt |_n|jdkr"ntddS)NTFr rrrr rrrI)Znum_parametersZrelu)ZinplaceZrrelugg?)lowerupperZsoftpluslinearz&The type of activation if not support!)rrHrr*rract_typewith_idtrrrconv3x3r conv1x1_3x3 conv1x1_sbx conv1x1_sby conv1x1_lplZPReLUactZReLUZRReLUZSoftplusr()r)rrr*rMrNr,r.r/rs0      z ECB.__init__cCs|jrH||||||||||}|jrh||7}n |\}}tj |||ddd}|j dkr|| |}|S)Nr)r0rrr1rrL) ZtrainingrOrPrQrRrSrNrAr4r5rMrT)r)r8yr>r?r.r.r/r:s2    z ECB.forwardcCs|jj|jj}}|j\}}|j\}}|j\}}|j\} } ||||| ||||| } } |jr| } | dkrd} t j |j |j dd| d}t |j D]}d|||ddf<qd}| || |} } | | fS)Nrr r;r rg)rOrrrPrArQrRrSrNr=rr$rr')r)Zweight0Zbias0Zweight1Zbias1Zweight2Zbias2Zweight3Zbias3Zweight4Zbias4r>r?r<Z weight_idtr+Zbias_idtr.r.r/rAs$zECB.rep_params)rIFrBr.r.r,r/rHs   rHcs(eZdZdZfddZddZZS)ECBSRaECBSR architecture. Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices Ref git repo: https://github.com/xindongzhang/ECBSR Args: num_in_ch (int): Channel number of inputs. num_out_ch (int): Channel number of outputs. num_block (int): Block number in the trunk network. num_channel (int): Channel number. with_idt (bool): Whether use identity in convolution layers. act_type (str): Activation type. scale (int): Upsampling factor. c stt|||_||_g}|t||d||dg7}t|D]} |t||d||dg7}q>|t||||dd|dg7}tj||_ t ||_ dS)Nr )r*rMrNrL) rrVr num_in_chr#rHr'rZ SequentialbackboneZ PixelShuffle upsampler) r)rWZ num_out_chZ num_blockZ num_channelrNrMr#rX_r,r.r/rs  zECBSR.__init__cCsD|jdkr$tj||j|jdd}n|}|||}||}|S)Nr)Zdim)rWrZrepeat_interleaver#rXrY)r)r8ZshortcutrUr.r.r/r: s   z ECBSR.forward)rCrDrErFrr:rGr.r.r,r/rVs rV) rZtorch.nnrZtorch.nn.functionalZ functionalr4Zbasicsr.utils.registryrModulerrHregisterrVr.r.r.r/s  O