^jh<dZddlZddlmZmZmZmZddlZddlm cm Z ddlm Z ddl m Z mZmZmZmZmZddlmZddlmZdd lmZmZmZdd lmZmZmZmZm Z m!Z!dd l"m#Z$gZ%dgd Z&dhdZ'didZ(djdZ)e*ee&dddde&dddde'ddddddde'ddddd dde'dddddddfddddd d!"ee&dd#d$e&ddd$e'dddddd%e'dddd&d d%e'dddd'dd%fddddd d("ee&dd#d$e&ddd$e'dddd'dd%e'dd)dd*d d%e'dd+dd,dd%fddddd d-"ee&dd#d$e&ddd$e'dddd'dd%e'dd)dd*d d%e'dd+dd,dd%fddddd.e*d/0d-1e)de)d2e)d3e)de)d4e)d5e)d6 Z+eGd7d8e jXZ-Gd9d:e jXZ.Gd;de jXZ0ed?e-ed@e0dkdAZ1dkdBZ2dldCZ3eidDe3dEFdGe3dEFdHe3dEFdIe3dEdJKdLe3dEdJKdMe3dEdJKdNe3dEdJKdOe3dEdJKdPe3dEdJKdQe3dEdJKdRe3dEdJKdSe3dEdJKdTe3dEdJKdUe3dEdVdWdXdYe3dEdVdWdXdZe3dEdVdWdXZ4edmd[efd\Z5edmd[efd]Z6edmd[efd^Z7edmd[efd_Z8edmd[efd`Z9edmd[efdaZ:edmd[efdbZ;edmd[efdcZee?dRdSdTdUdYdZdfy)na  MobileViT Paper: V1: `MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer` - https://arxiv.org/abs/2110.02178 V2: `Separable Self-attention for Mobile Vision Transformers` - https://arxiv.org/abs/2206.02680 MobileVitBlock and checkpoints adapted from https://github.com/apple/ml-cvnets (original copyright below) License: https://github.com/apple/ml-cvnets/blob/main/LICENSE (Apple open source) Rest of code, ByobNet, and Transformer block hacked together by / Copyright 2022, Ross Wightman N)CallableTupleOptionalType)nn) to_2tuplemake_divisible GroupNorm1ConvMlpDropPath is_exportable)build_model_with_cfg)register_notrace_module)register_modelgenerate_default_cfgsregister_model_deprecations)register_block ByoBlockCfg ByoModelCfgByobNetLayerFn num_groups)Blockc <td|||d|tddS)NbottlerT) bottle_in linear_out)typedcsgsbr block_kwargs)rdictr r!r"r$s `/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/timm/models/mobilevit.py_inverted_residual_blockr)!s&  a1rDT: <<c Xt||||tdd|dt|||fS)Nr' mobilevitr)transformer_dimtransformer_depth patch_size)rr r!r"r%r)rr&r r!r"r.r/r0r$s r(_mobilevit_blockr3(s> !1Q26Q! /"3%'   r*@?c Zt||||tdd|d|dt||fS)Nr' mobilevit2r)r/r0)rr r!r"r$r#r%r1)r r!r"r/r0r$transformer_brs r(_mobilevitv2_blockr:6s? !1Q26a1A"3%'  r*?c Ld}|dk7r%t|Dcgc]}t||zc}}ttd|dddtd|dddt d|dddt d|d dd t d|d dd ftd |zd d d d}|Scc}w)N)@ir;rrr5r'r4)r r!r"r/r+ 3x3silu)blocksstem_chs stem_type stem_pool downsample act_layer)tupleintrr)r:) multiplierchsr!cfgs r(_mobilevitv2_cfgrQCs "CS#6QSZ(67  $qCFaC @ $qCFaC @ c!fQ G c!fQ G c!fQ G  R*_% C J7sB!r'rA0r=r2P`rCrDrEi@)rFrGrHrIrJrK num_featuresrB)r r!r")r r!r"r.r/r0xr@r>iseg?)rd_ratio)rFrGrHrIrJ attn_layer attn_kwargsrWg?g?g?g?) mobilevit_xxs mobilevit_xs mobilevit_s semobilevit_smobilevitv2_050mobilevitv2_075mobilevitv2_125mobilevitv2_100mobilevitv2_150mobilevitv2_175mobilevitv2_200c&eZdZdZdddddddddd d d d d d dej ddfd edeededededeede eefdedeedededededede dede de ejf$fd Zd ej d!ej fd"ZxZS)#MobileVitBlockzS MobileViT block Paper: https://arxiv.org/abs/2110.02178?context=cs.LG NrArr;rrr5r4r+Fin_chsout_chs kernel_sizestride bottle_ratio group_sizedilation mlp_ratior.r/r0 num_heads attn_dropdrop no_fusiondrop_path_ratelayerstransformer_norm_layerc ||d}t||xs t}t||}|xs|}| xst ||z} |j ||f||||dd||_tj|| fddd||_ tjt| Dcgc]!}t| f|| d| |||j|d|#c}|_|| fi||_|j | |fddd ||_|rd|_n|j ||z|f|dd ||_t%| |_|j&d|j&dz|_ycc}w) NdevicedtyperrsrtgroupsrwrFrsbiasT)rxryqkv_biasrz proj_drop drop_pathrK norm_layerrsrt)super__init__rrr conv_norm_actconv_kxkrConv2dconv_1x1 SequentialrangeTransformerBlockact transformernorm conv_proj conv_fusionrr0 patch_area)selfrqrrrsrtrurvrwrxr.r/r0ryrzr{r|r}r~rrrkwargsddr_ __class__s r(rzMobileVitBlock.__init__s0 / $79J/#V)R^L6GG9 FM>&'ggbcl# m \ ) X W X ) XX ==''(;!);<##$7T^^a%78 XXq$..1qX9 s ==''(:;;?==;M;M;Y##DNNQ$67_c 5b17.33D3IffUmn66u==mmC mmC  r*cN||j|S|j||S)N)r)rr)rrrs r(rzLinearSelfAttention.forwards. >**1- -++Af+= =r*)rprpTNNN)rrrrrMrrrrrrjitignorerrrrrs@r(rrs, #"... .  . .<ELLU\\0 YY(U\\(8ELL;Q(]b]i]i((T>>x /E>QVQ]Q]>r*rceZdZdZ ddedededededeeejd eeejd dffd Z dd e jd ee jd e jfdZ xZS)LinearTransformerBlockaF This class defines the pre-norm transformer encoder with linear self-attention in `MobileViTv2 paper <>`_ Args: embed_dim (int): :math:`C_{in}` from an expected input of size :math:`(B, C_{in}, P, N)` mlp_ratio (float): Inner dimension ratio of the FFN relative to embed_dim drop (float): Dropout rate. Default: 0.0 attn_drop (float): Dropout rate for attention in multi-head attention. Default: 0.0 drop_path (float): Stochastic depth rate Default: 0.0 norm_layer (Callable): Normalization layer. Default: layer_norm_2d Shape: - Input: :math:`(B, C_{in}, P, N)` where :math:`B` is batch size, :math:`C_{in}` is input embedding dim, :math:`P` is number of pixels in a patch, and :math:`N` is number of patches, - Output: same shape as the input Nrrxr{rzrrKrrc J|| d} t ||xstj}|xst}||fi| |_t d|||d| |_t||_ ||fi| |_ td|t||z||d| |_ t||_y)Nr)rrzr) in_featureshidden_featuresrKr{r)rrrSiLUr norm1rattnr drop_path1norm2r rMmlp drop_path2) rrrxr{rzrrKrrrrrs r(rzLinearTransformerBlock.__init__s / ( -:  0R0 'g)y\`gdfg "9- 0R0 ! I 56    #9-r*rrcF|3||j|j|j|z}n9|}|j|}|j||}|j||z}||j|j |j |z}|Sr)rrrrrr)rrrress r(rzLinearTransformerBlock.forwards >DOODIIdjjm$<==AC 1 A !V$A"S(A A 78 8r*)r5rprprpNNNNr)rrrrrMrrrrrrrrrrrs@r(rrs $ #""3748... .  .  . RYY0.!bii1. .<  x /E QVQ]Q] r*rc eZdZdZddddddddd d d d deddfd ed eed ededeedeeefdedeedededededede de e jffd Z dejdejfdZxZS)MobileVitV2Blockz8 This class defines the `MobileViTv2 block <>`_ NrAr;rrnr5r4rorprqrrrsrurvrwrxr.r/r0rzr{r}r~rc ||d}t||xs t}t||}|xs|}|xst ||z}|j ||f|d||dd||_tj||fddd||_ tjt| Dcgc]}t|f|| | | |j|d|!c}|_||fi||_|j ||fdddd||_t#| |_|j$d|j$dz|_t)|_ycc}w) NrrrrFr)rxrzr{rrKr)rsrt apply_act)rrrrr rrrrrrrrrrrrrr0rr coreml_exportable)rrqrrrsrurvrwrxr.r/r0rzr{r}r~rrrrrrrrs r(rzMobileVitV2Block.__init__s~* / $79J/#V)R^L6 * -8*W%j>UD1    r*c dtt||f|s t|nt|tdd|Srrrs r(_create_mobilevit2r"Gr r*c $|ddddddddd d d d |S) Ni)rAr?r?)rorog?bicubic)rprprp)r;r;r;z stem.convzhead.fcFzcvnets-license) url num_classes input_size pool_sizecrop_pct interpolationmeanstd first_conv classifierfixed_input_sizelicenser)r%rs r(_cfgr1Os54}SY)\!!#    r*zmobilevit_xxs.cvnets_in1kztimm/) hf_hub_idzmobilevit_xs.cvnets_in1kzmobilevit_s.cvnets_in1kzmobilevitv2_050.cvnets_in1kg"~j?)r2r)zmobilevitv2_075.cvnets_in1kzmobilevitv2_100.cvnets_in1kzmobilevitv2_125.cvnets_in1kzmobilevitv2_150.cvnets_in1kzmobilevitv2_175.cvnets_in1kzmobilevitv2_200.cvnets_in1kz$mobilevitv2_150.cvnets_in22k_ft_in1kz$mobilevitv2_175.cvnets_in22k_ft_in1kz$mobilevitv2_200.cvnets_in22k_ft_in1kz(mobilevitv2_150.cvnets_in22k_ft_in1k_384)rAr@r@) r3)r2r'r(r)z(mobilevitv2_175.cvnets_in22k_ft_in1k_384z(mobilevitv2_200.cvnets_in22k_ft_in1k_384rc tdd|i|S)Nr)rarrrs r(raras  N Nv NNr*c tdd|i|S)Nr)rbr5r6s r(rbrbs  M Mf MMr*c tdd|i|S)Nr)rcr5r6s r(rcrcs  Lz LV LLr*c tdd|i|S)Nr)rer5r6s r(rere  P: P PPr*c tdd|i|S)Nr)rfr5r6s r(rfrfr:r*c tdd|i|S)Nr)rhr5r6s r(rhrhr:r*c tdd|i|S)Nr)rgr5r6s r(rgrgr:r*c tdd|i|S)Nr)rir5r6s r(ririr:r*c tdd|i|S)Nr)rjr5r6s r(rjrjr:r*c tdd|i|S)Nr)rkr5r6s r(rkrkr:r*)mobilevitv2_150_in22ft1kmobilevitv2_175_in22ft1kmobilevitv2_200_in22ft1kmobilevitv2_150_384_in22ft1kmobilevitv2_175_384_in22ft1kmobilevitv2_200_384_in22ft1k)@)r+rG)r4r5r6)r;)NF)rD)F)@rrtypingrrrrrtorch.nn.functionalr functionalr timm.layersrr r r r r _builderr _features_fxr _registryrrrbyobnetrrrrrrvision_transformerrr__all__r)r3r:rQr&rrrmrrr rr"r1 default_cfgsrarbrcrerfrhrgrirjrkrrr*r(rSs  22 __*1YY[[9 <  * $qB! < $qB! < qB!RSTabgj k qB!RSTabgj k qB!RSTabgj k    $qB! 4 $qB! 4 qB!RSTab c qB!STUbc d qB!STUbc d    $qB! 4 $qB! 4 qB!STUbc d qC1cUVcd e qC1cUVcd e    $qB! 4 $qB! 4 qB!STUbc d qC1cUVcd e qC1cUVcd e  #&"%S)$S)$T*$S)$S)$T*$S)QI XqRYYqqhy>"))y>x;RYY;|dryyddN{N+|-. %.&!8.&w 7.&tg6.& "4$ .&"4$.&"4$.&"4$.&""4$#.&("4$).&."4$/.&6+D-7.&<+D-=.&B+D-C.&J/ Hs1DK.&P/ Hs1DQ.&V/ Hs1DW.&. bOOONNNMwMMQ7QQQ7QQQ7QQQ7QQQ7QQQ7QQQ7QQH F F F$N$N$N'r*