U ʹj]@sdZddlmZddlmZmZmZddlZddlm Z ddl m m Z ddl mZmZddlmZmZmZmZmZmZddlmZdd lmZdd lmZmZd gZGd d d e jZ Gddde jZ!Gddde jZ"Gddde jZ#Gddde jZ$Gdd d e jZ%dDddZ&dEddZ'ee'dde'dde'dde'dde'dde'dde'ddd dd!d"e'ddd dd!d"e'ddd dd!d"e'ddd dd!d"e'dd!d#e'ddd#d$ Z(e%d%d&d'Z)dFd)d*Z*edGe%d+d,d-Z+edHe%d+d.d/Z,edIe%d+d0d1Z-edJe%d+d2d3Z.edKe%d+d4d5Z/edLe%d+d6d7Z0edMe%d+d8d9Z1edNe%d+d:d;Z2edOe%d+dd?Z4edQe%d+d@dAZ5edRe%d+dBdCZ6dS)Sa[ FocalNet As described in `Focal Modulation Networks` - https://arxiv.org/abs/2203.11926 Significant modifications and refactoring from the original impl at https://github.com/microsoft/FocalNet This impl is/has: * fully convolutional, NCHW tensor layout throughout, seemed to have minimal performance impact but more flexible * re-ordered downsample / layer so that striding always at beginning of layer (stage) * no input size constraints or input resolution/H/W tracking through the model * torchscript fixed and a number of quirks cleaned up * feature extraction support via `features_only=True` )partial)CallableOptionalTupleNIMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STD)MlpDropPath LayerNorm2d trunc_normal_ClassifierHeadNormMlpClassifierHead)build_model_with_cfg) named_apply)generate_default_cfgsregister_modelFocalNetc sFeZdZdddddefeeeeeeeedfdd Zdd Z Z S) FocalModulationTF)dim focal_level focal_factorbias use_post_normnormalize_modulator proj_drop norm_layerc s&t||_||_||_||_||_||_|||jdg|_t j |d||jdd|d|_ t j ||d|d|_ t |_t j ||dd|_t ||_t |_g|_t|jD]N} |j| |j} |jt t j ||| || dddt |j| q|jr| |nt |_dS)Nrr) kernel_sizer)r F)r groupspaddingr)super__init__r focal_windowrrrr input_splitnnConv2dfhGELUactprojZDropoutr ModuleList focal_layersZ kernel_sizesrangeappend SequentialIdentitynorm) selfrr%rrrrrrrkr  __class__S/var/www/html/bwave/python/venv/lib/python3.8/site-packages/timm/models/focalnet.pyr$%s. 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Frin_chsout_chsstrideoverlaprcs<g|]4}t ttr.|nd qS)) rrXrr%rrYrrZrr[r)rU isinstancelist.0i r[rr%rZrXrrrjrrrYr9r: sz*FocalNetStage.__init__..N) r#r$rrkgrad_checkpointing Downsamplerlr'r3r.r0blocks)r5rrjrkrXrlrr%rmrrYrrZrr[rr7rxr:r$s #   "zFocalNetStage.__init__cCs ||_dSrN)rz)r5enabler9r9r:set_grad_checkpointing sz$FocalNetStage.set_grad_checkpointingcCs@||}|jD]*}|jr2tjs2t||}q||}q|SrN)rlr|rzr<jitZ is_scripting checkpoint)r5r@Zblkr9r9r:rDs    zFocalNetStage.forward)T)rErFrGrgr rHrJrIrr$r<rignorer~rDrKr9r9r7r:rhsBD rhcs8eZdZd eeeeeedfdd ZddZZ S) r{FNrncs~t||_d}|}|rJ|dks(t|dkr:d\}}n|dkrJd\}}tj|||||d|_|dk rp||nt|_dS) a Args: in_chs: Number of input image channels. out_chs: Number of linear projection output channels. stride: Downsample stride. overlap: Use overlapping convolutions if True. norm_layer: Normalization layer. r)rrr)rr)r;r)r rqr"N) r#r$rqAssertionErrorr'r(r-r3r4)r5rorprqrrrr"r r7r9r:r$s   zDownsample.__init__cCs||}||}|SrN)r-r4r5r@r9r9r:rD;s  zDownsample.forward)rFN) rErFrGrHrIrrr$rDrKr9r9r7r:r{sr{cseZdZdZdddddddd d d d d d d d d d deeddfeeeeeedfe eedfeedfe e e e e ee e e e e e e dfdd Z ejjddZejjd'ddZejjd(ddZejjddZd)ddZd d!Zd*e d"d#d$Zd%d&ZZS)+rz," Focal Modulation Networks (FocalNets) r;avg`)rrrrV)rrrr)r;r;r;r;FN?rg?rM)eps.)in_chans num_classes global_pool embed_dimdepthsrX focal_levels focal_windowsrmrrYrhead_hidden_sizehead_init_scalerZ drop_rateproj_drop_ratedrop_path_ratercstt||_fddt|jD||_|_d|_g|_t |d| |d|_ d}ddt d|t |D}g}t|jD]}|}t||||||dk||||| | | | |||t |d|t |d|d|d }|}||g7}|jt|d d |d |d g7_qtj||_| rdt|_t|j|| |||d|_n ||j|_t|j|||d|_ttt|d|dS)a` Args: in_chans: Number of input image channels. num_classes: Number of classes for classification head. embed_dim: Patch embedding dimension. depths: Depth of each Focal Transformer layer. mlp_ratio: Ratio of mlp hidden dim to embedding dim. focal_levels: How many focal levels at all stages. 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