o پig"@s2dZddlZddlmZddlmZmZddlmZm Z m Z ddl m Z ddl mZmZdd lmZGd d d ejZGd d d ejZd$ddZd%ddZeeddeddeeddedddZed$defddZed$defddZed$defddZed$defd d!Zed$defd"d#ZdS)&a Selective Kernel Networks (ResNet base) Paper: Selective Kernel Networks (https://arxiv.org/abs/1903.06586) This was inspired by reading 'Compounding the Performance Improvements...' (https://arxiv.org/abs/2001.06268) and a streamlined impl at https://github.com/clovaai/assembled-cnn but I ended up building something closer to the original paper with some modifications of my own to better balance param count vs accuracy. Hacked together by / Copyright 2020 Ross Wightman N)nnIMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STD)SelectiveKernel ConvNormAct create_attn)build_model_with_cfg)register_modelgenerate_default_cfgs)ResNetcsReZdZdZddddddddejejddddffdd ZddZdd Z Z S) SelectiveKernelBasicr N@cstt||p i}t| | d}|dksJd|dks!Jd||}||j}| p-| } t||f|| ||d|||_t||fd| dd ||_t | ||_ | d d |_ ||_ ||_ dS) N act_layer norm_layerr z)BasicBlock only supports cardinality of 1rz0BasicBlock doest not support changing base width)stridedilationaa_layer drop_layerF) kernel_sizer apply_actTinplace)superr__init__dict expansionrconv1rconv2rseact downsample drop_path)selfinplanesplanesrr$ cardinality base_width sk_kwargs reduce_firstrfirst_dilationrr attn_layerr drop_blockr% conv_kwargs first_planes outplanes __class__E/home/ubuntu/.local/lib/python3.10/site-packages/timm/models/sknet.pyrs:     zSelectiveKernelBasic.__init__cC.t|jjdddurtj|jjjdSdSNweight)getattrr!bnrinitzeros_r9r&r5r5r6zero_init_last@z#SelectiveKernelBasic.zero_init_lastcCsj|}||}||}|jdur||}|jdur ||}|jdur*||}||7}||}|SN)r r!r"r%r$r#r&xshortcutr5r5r6forwardDs         zSelectiveKernelBasic.forward __name__ __module__ __qualname__rrReLU BatchNorm2drr?rE __classcell__r5r5r3r6rs&'rcsReZdZdZddddddddejejddddffdd ZddZd d Z Z S) SelectiveKernelBottleneckr Nrcstt||p i}t| | d}tt||d|}||}||j}| p*| } t||fddi||_ t ||f|| |||d|||_ t||fddd||_ t | ||_| dd |_||_||_dS) Nrrrr )rrgroupsrrF)rrTr)rrMrrintmathfloorrrr rr!conv3rr"r#r$r%)r&r'r(rr$r)r*r+r,rr-rrr.rr/r%r0widthr1r2r3r5r6rVs.     z"SelectiveKernelBottleneck.__init__cCr7r8)r:rSr;rr<r=r9r>r5r5r6r?|r@z(SelectiveKernelBottleneck.zero_init_lastcCst|}||}||}||}|jdur||}|jdur%||}|jdur/||}||7}||}|SrA)r r!rSr"r%r$r#rBr5r5r6rEs          z!SelectiveKernelBottleneck.forwardrFr5r5r3r6rMSs&&rMFcKstt||fi|SrA)r r )variant pretrainedkwargsr5r5r6_create_skresnetsrXc Ks|dddddttddd |S) Ni)rrZ)r[g?bicubicr fc) url num_classes input_size pool_sizecrop_pct interpolationmeanstd first_conv classifierr)r^rWr5r5r6_cfgsrhztimm/) hf_hub_idzconv1.0)rf)zskresnet18.ra_in1kzskresnet34.ra_in1kzskresnet50.untrainedzskresnet50d.untrainedzskresnext50_32x4d.ra_in1kreturncKFtdddd}ttgdt|ddd}td |fit|fi|S) zConstructs a Selective Kernel ResNet-18 model. Different from configs in Select Kernel paper or "Compounding the Performance Improvements..." this variation splits the input channels to the selective convolutions to keep param count down. ?Trd_ratio rd_divisor split_input)rrrrrrr+Fblocklayers block_argsr? skresnet18rrrXrVrWr+ model_argsr5r5r6rx rxcKrk) zConstructs a Selective Kernel ResNet-34 model. Different from configs in Select Kernel paper or "Compounding the Performance Improvements..." this variation splits the input channels to the selective convolutions to keep param count down. rlrmTrnrrNrrsFrt skresnet34ryrzr5r5r6rr|rcKsBtdd}ttgdt|ddd}td|fit|fi|S)zConstructs a Select Kernel ResNet-50 model. Different from configs in Select Kernel paper or "Compounding the Performance Improvements..." this variation splits the input channels to the selective convolutions to keep param count down. Trqr}rsFrt skresnet50rrMrXrzr5r5r6rs rc KsHtdd}ttgddddt|ddd}td |fit|fi|S) zConstructs a Select Kernel ResNet-50-D model. Different from configs in Select Kernel paper or "Compounding the Performance Improvements..." this variation splits the input channels to the selective convolutions to keep param count down. Trr} deeprsF)rurv stem_width stem_typeavg_downrwr? skresnet50drrzr5r5r6rs  rcKsJtdddd}ttgdddt|ddd}td |fit|fi|S) zConstructs a Select Kernel ResNeXt50-32x4d model. This should be equivalent to the SKNet-50 model in the Select Kernel Paper g?rFrnr}rNrs)rurvr)r*rwr?skresnext50_32x4drrzr5r5r6rs   r)F)rY)__doc__rQtorchr timm.datarr timm.layersrrr_builderr _registryr r resnetr ModulerrMrXrh default_cfgsrxrrrrr5r5r5r6s>   = =