khBdZddlmZddlmZddlmZmZmZm Z m Z m Z m Z ddl Z ddlmZddlmZmZddlmZmZmZmZmZmZmZmZmZmZmZmZm Z dd l!m"Z"dd l#m$Z$dd l%m&Z&m'Z'm(Z(dd l)m*Z*m+Z+m,Z,d gZ-Gddej\Z/Gddej\Z0Gddej\Z1Gddej\Z2Gddej\Z3Gddej\Z4de5de6fdZ7dddeeed !fd"e8d#e8de5d$e6d%ed&edejrfd'Z:Gd(d ej\Z;dwd)ej\d*e5d+e6ddfd,Z<e jzdxd-ej\d.e5d/e5fd0Z>dyd1e5d2e6d3ede;fd4Z?dyd1e5d2e6d3ede;fd5Z@dzd6e5d3edee5effd7ZAe*id8eAd9d:d;d<eAd9d:d;d=eAd9d>d?d@d:dAdBeAd9dCdDd@dEdFeAd9dCdDd@dEdGeAd9dCdDd@dEdHeAd9dCdDd@dEdIeAd9dCdDd@dEdJeAd9dKdLd@dEdMeAd9dNdOdPeAd9dNdOdQeAd9dNdOdReAd9dNdOdSeAd9dNdOdTeAd9dNdOdUeAd9d:dVdWd@XdYeAd9d:dVdWd@dZ[eAd9d:dVdWd@XeAd9d:dVdWd@dZ[eAd9d@d>d?dCd:dZ\eAd9d:d]dWd@XeAd:dZ^eAd:dZ^eAd9d:d]dWd@XeAd:dZ^eAd:_eAd:dZ^eAd9d:dZd]dWd@`eAd9d:dZd]dWd@`eAd:dZ^da ZBe+dyd2e6d3ede;fdbZCe+dyd2e6d3ede;fdcZDe+dyd2e6d3ede;fddZEe+dyd2e6d3ede;fdeZFe+dyd2e6d3ede;fdfZGe+dyd2e6d3ede;fdgZHe+dyd2e6d3ede;fdhZIe+dyd2e6d3ede;fdiZJe+dyd2e6d3ede;fdjZKe+dyd2e6d3ede;fdkZLe+dyd2e6d3ede;fdlZMe+dyd2e6d3ede;fdmZNe+dyd2e6d3ede;fdnZOe+dyd2e6d3ede;fdoZPe+dyd2e6d3ede;fdpZQe+dyd2e6d3ede;fdqZRe+dyd2e6d3ede;fdrZSe+dyd2e6d3ede;fdsZTe+dyd2e6d3ede;fdtZUe+dyd2e6d3ede;fduZVe,eWdBdFdGdHdIdJdMdPdQdRdSdTd8d '38,9 G7r@rBrArErGrTx_preactshortcuts rJforwardzPreActBasic.forwardssn::a= ?? &x0H JJx  JJtzz!} % NN1 8|rK) N?rrNrNNNNrLN__name__ __module__ __qualname____doc__intr floatrr=rRtorchTensorrZ __classcell__rIs@rJr'r'2sK &*"%,0,0-1-1-1$&8[8[c]8[ 8[  8[  8[%SM8[8[ )8[!*8[!*8[!*8["8[t*%,,rKr'ceZdZdZ ddedeededededeeded eed eed eed eed effd ZddZ de jde jfdZ xZ S)PreActBottleneckaPre-activation (v2) bottleneck block. Follows the implementation of "Identity Mappings in Deep Residual Networks": https://github.com/KaimingHe/resnet-1k-layers/blob/master/resnet-pre-act.lua Except it puts the stride on 3x3 conv when available. r(r)r*r+r,r-r.r/r0r1r2r3c t||xs|}| xst} | xsttd} |xs|}t ||z} | | |||||d| | |_nd|_| ||_| || d|_| | |_ | | | d||||_ | | |_ | | |d|_ | d kDrt| |_ytj|_y) abInitialize PreActBottleneck block. Args: in_chs: Input channels. out_chs: Output channels. bottle_ratio: Bottleneck ratio. stride: Stride for convolution. dilation: Dilation rate. first_dilation: First dilation rate. groups: Group convolution size. act_layer: Activation layer type. conv_layer: Convolution layer type. norm_layer: Normalization layer type. proj_layer: Projection/downsampling layer type. drop_path_rate: Stochastic depth drop rate. r5r6NTr8rr:r;r)r<r=rrrrr>r?r@rArBnorm3conv3rrCrDrErFs rJr=zPreActBottleneck.__init__s> '38,9 G7r@rBrArmrlrErWs rJrZzPreActBottleneck.forwards::a= ?? &x0H JJx  JJtzz!} % JJtzz!} % NN1 8|rK N?rrNrNNNNr\r]r^rhs@rJrjrjs&*"&,0,0-1-1-1$&:[:[c]:[ :[  :[  :[%SM:[:[ ):[!*:[!*:[!*:[":[x*%,,rKrjceZdZdZ ddedeededededeeded eed eed eed eed effd ZddZ de jde jfdZ xZ S) BottleneckzUNon Pre-activation bottleneck block, equiv to V1.5/V1b Bottleneck. Used for ViT. r(r)r*r+r,r-r.r/r0r1r2r3c t||xs|}|xstj}| xst} | xst t d} |xs|}t||z} | | ||||d| | |_nd|_| || d|_ | | |_ | | | d||||_ | | |_ | | |d|_ | |d|_| d kDr t| ntj |_|d |_y) Nr5r6F)r+r,r9r0r1rr:r; apply_actrT)inplace)r<r=rCReLUrrrrr>r@r?rBrArmrlrrDrEact3rFs rJr=zBottleneck.__init__s '38( ,9 G7r@r?rBrArmrlrEry)rGrTrYs rJrZzBottleneck.forward s ?? &q)H JJqM JJqM JJqM JJqM JJqM JJqM NN1  IIa(l #rKrpr]r^rhs@rJrsrss &*"&,0,0-1-1-1$&+,+,c]+, +,  +,  +,%SM+,+, )+,!*+,!*+,!*+,"+,Z. %,,rKrsceZdZdZ ddededededeededeed eeffd Zd e jd e jfd Z xZ S)DownsampleConvz$1x1 convolution downsampling module.r(r)r+r,r-r9r0r1c tt| |||d||_|rt j |_y||d|_y)Nrr+Fru)r<r~r=convrCrDnorm) rGr(r)r+r,r-r9r0r1rIs rJr=zDownsampleConv.__init__=s@ nd,.vw&A %+BKKM Gu1U rKrTrLcB|j|j|SztForward pass. Args: x: Input tensor. Returns: Downsampled tensor. )rrrGrTs rJrZzDownsampleConv.forwardLsyy1&&rKrrNTNN r_r`rarbrcr boolrr=rerfrZrgrhs@rJr~r~:s. ,0-1-1 V V V V  V %SM V V!* V!* V ' '%,, 'rKr~ceZdZdZ ddededededeededeed eeffd Zd e jd e jfd Z xZ S) DownsampleAvgz/AvgPool downsampling as in 'D' ResNet variants.r(r)r+r,r-r9r0r1c ^tt| |dk(r|nd} |dkDs|dkDr2| dk(r |dkDrtntj } | d| dd|_nt j|_|||dd|_|rt j|_ y||d|_ y)NrTF) ceil_modecount_include_padrru) r<rr=rrC AvgPool2dpoolrDrr) rGr(r)r+r,r-r9r0r1 avg_stride avg_pool_fnrIs rJr=zDownsampleAvg.__init__[s mT+-'1}V! A:A+5?x!|-QSQ]Q]K#AzTUZ[DI DIvw!< %+BKKM Gu1U rKrTrLc`|j|j|j|Sr)rrrrs rJrZzDownsampleAvg.forwardps$yy499Q<011rKrrrhs@rJrrXs9 ,0-1-1VVV V  V %SM VV!*V!*V* 2 2%,, 2rKrceZdZdZddddedddfdededed ed ed ed ed edee ede dee dee dee de ffd Z de jde jfdZxZS) ResNetStagez ResNet Stage.rqrFNr(r)r+r,depthr*r.avg_down block_dprblock_fnr/r0r1 block_kwargsc ttt| d|_|dvrdnd}t | | | }|rt nt }|}tj|_ t|D]Q}| r| |nd}|dk(r|nd}|jjt|| ||f|||||||d|||}|}d}Sy) NF)rrrr)r/r0r1r\r)r+r,r*r.r-r2r3) r<rr=grad_checkpointingdictrr~rC Sequentialblocksrange add_modulestr)rGr(r)r+r,rr*r.rrrr/r0r1rr- layer_kwargsr2prev_chs block_idxr3rIs rJr=zResNetStage.__init__~s" k4)+"'&&0aiJS]^ &.]N mmo u I5>Yy1BN(A~V1F KK " "3y>8 4!)-%- 4 4 4 H%NJ% rKrTrLc|jr6tjjst |j |}|S|j |}|S)zForward pass through all blocks in the stage. Args: x: Input tensor. Returns: Output tensor. )rrejit is_scriptingrrrs rJrZzResNetStage.forwardsG  " "599+A+A+Ct{{A.A AArK)r_r`rarbrjrcrdrr rrrr=rerfrZrgrhs@rJrr|s#'"/3!1,0-1-1+++ +  +  + +++ U ,++ )+!*+!*+ +Z  %,, rKr stem_typerLcBtdDcgc]}||vc}Scc}w)zCheck if stem type is deep (has multiple convolutions). Args: stem_type: Type of stem to check. Returns: True if stem is deep, False otherwise. )deeptiered)any)rss rJ is_stem_deeprs" (:;1Y; <<;s @Tr5r6r(r)r9r0r1cnt}|dvsJt|rd|vrd|zdz|dzf}n |dz|dzf}|||ddd|d<||d|d <||d|d dd |d <||d |d <||d |dd |d <|s(|||d<n|||dd|d<|s |||d<d|vr5tjd d|d<tjddd|d<n2d|vrt dddd|d<ntjddd |d<tj |S)N)rfixedsamer deep_fixed deep_samerrr:rr) kernel_sizer+r@r?rrBrArmrlrrrr\pad)rr+paddingrrmax)rrrC ConstantPad2d MaxPool2drr)r(r)rr9r0r1stemstem_chss rJcreate_resnetv2_stemrsu =D Z ZZ ZI y G q('Q,7H1 gl3H"68A;AaPW "8A;/W "8A; STUW "8A;/W "8A;QqQW &w/DM"&'qKV %g.DL)&&q"-U ||!QGV 9 $U!VTV ||!QGV == rKc'eZdZdZdddddddd d d d d ej eed eddd fde e de e dfde de de de de de de de de de dedededed ed!ed"e f&fd# Zej$j&d>d"e d$d%fd&Zej$j'd?d'e d(e d$d%fd)Zej$j&d@d*e d$ee effd+Zej$j&d>d,e d$d%fd-Zej$j&d$ej4fd.ZdAde dee d$d%fd/Z dBd0ej<d1eee e e fd2e d3e d4e d5e d$ee ej<e ej<e ej<fffd6Z dCd1ee e e fd7e d8e fd9Z!d0ej<d$ej<fd:Z"d@d0ej<d;e d$ej<fd<Z#d0ej<d$ej<fd=Z$xZ%S)Dr%z7Implementation of Pre-activation (v2) ResNet mode. )iir:avgr5rrrFTrqr6r\layerschannels. num_classesin_chans global_pool output_stride width_factorrrrr9basicr*r/r1r0 drop_rater3rRct#|||_||_|}t ||}t |}g|_t||z}t||| | |||_ | rt| rdndnd}|j jt|d||}d}d }tjd |t|j!|Dcgc]}|j#}}| r| rt$nt&}n | rJt(}t+j,|_t1t3|||D]\}\}}}t||z} |d k(rd nd}!||k\r||!z}d }!t5|| |!||| | ||||| }"| }||!z}|xj t||d |gz c_|j.j7t9||"|x|_|_| r||j:nt+j>|_ tC|j:|||jd |_"|jG|ycc}w)a Args: layers (List[int]) : number of layers in each block channels (List[int]) : number of channels in each block: num_classes (int): number of classification classes (default 1000) in_chans (int): number of input (color) channels. (default 3) global_pool (str): Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax' (default 'avg') output_stride (int): output stride of the network, 32, 16, or 8. (default 32) width_factor (int): channel (width) multiplication factor stem_chs (int): stem width (default: 64) stem_type (str): stem type (default: '' == 7x7) avg_down (bool): average pooling in residual downsampling (default: False) preact (bool): pre-activation (default: True) act_layer (Union[str, nn.Module]): activation layer norm_layer (Union[str, nn.Module]): normalization layer conv_layer (nn.Module): convolution module drop_rate: classifier dropout rate (default: 0.) drop_path_rate: stochastic depth rate (default: 0.) zero_init_last: zero-init last weight in residual path (default: False) )r/)r0r1z stem.conv3 stem.convz stem.normr)num_chs reductionmodulerr) r+r,rr*rr/r0r1rrzstages.T) pool_typeruse_convrRN)$r<r=rrrr feature_inforrrrappendrrelinspacesumsplittolistr'rjrsrCrstages enumerateziprrr num_featureshead_hidden_sizerDrrhead init_weights)$rGrrrrrrrrrrr9rr*r/r1r0rr3rRwf stem_featr curr_strider,rT block_dprsr stage_idxdcbdprr)r+stagerIs$ rJr=zResNetV2.__init__sUT &" ' iH !), !(R-0(    !!   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N)rrreset)rGrrs rJreset_classifierzResNetV2.reset_classifiers'  [1rKrTindicesr stop_early output_fmtintermediates_onlyc|dvsJdg}td|\}} d} |jdd\} } |jD]'} | |}|jdd| dz| dzfk(s&|})| |vr|jt |j }t jjs|s |j }n|j d| }t|d D]O\} }||}| |vs| |k(r'|r|j|n|}|j|?|j|Q|r|S| |k(r|j|}||fS) a Forward features that returns intermediates. Args: x: Input image tensor indices: Take last n blocks if int, all if None, select matching indices if sequence norm: Apply norm layer to compatible intermediates stop_early: Stop iterating over blocks when last desired intermediate hit output_fmt: Shape of intermediate feature outputs intermediates_only: Only return intermediate features Returns: )NCHWzOutput shape must be NCHW.rNrr)start) rshaperrlenrrerrrr)rGrTrrrrr intermediates take_indices max_indexfeat_idxHWrx_downlast_idxrrx_inters rJforward_intermediateszResNetV2.forward_intermediatessc*Y&D(DD& "6q'"B iwwrs|1II DQAwwrs|Aq1u~-  | #   (t{{# 99 ! ! #:[[F[[),F(q9 ,OHeaA<'x'.2diilG!((1!((+ ,  x  ! A-rK prune_norm prune_headctd|\}}|jd||_|rtj|_|r|j dd|S)z@ Prune layers not required for specified intermediates. r Nrr)rrrCrDrr)rGrrrrrs rJprune_intermediate_layersz"ResNetV2.prune_intermediate_layerssP#7q'"B ikk*9-  DI   ! !!R (rKcl|j|}|j|}|j|}|S)zForward pass through feature extraction layers. Args: x: Input tensor. Returns: Feature tensor. )rrrrs rJforward_featureszResNetV2.forward_featuress0 IIaL KKN IIaLrK pre_logitscN|r|j||S|j|S)zForward pass through classifier head. Args: x: Input features. pre_logits: Return features before final linear layer. Returns: Classification logits or features. )r)r)rGrTrs rJ forward_headzResNetV2.forward_heads(7Atyyzy2RdiiPQlRrKcJ|j|}|j|}|S)zoForward pass. Args: x: Input tensor. Returns: Output logits. )rr rs rJrZzResNetV2.forwards)  ! !! $   a rK)Tzresnet/F)N)NFFrF)rFT)&r_r`rarbrCrxrrrrrcr rrrdrr=rerignorerrrrrrModulerr rrfr rrrr rZrgrhs@rJr%r%sl )?#$!# !""&"$''#**  %,, 5<<  Sell S S S  %,, rKrnamerRcrt|tjsd|vrpt|tjrVtjj |j ddtjj|jyt|tjrctjj|j dd|j*tjj|jyyt|tjtjtjfrStjj|j tjj|jy|rt|d r|jyyy) zInitialize module weights. Args: module: PyTorch module to initialize. name: Module name. zero_init_last: Zero-initialize last layer weights. head.fcr\g{Gz?)meanstdfan_outrelu)mode nonlinearityNrR) isinstancerCLinearConv2drNnormal_rPrObiaskaiming_normal_ BatchNorm2d LayerNorm GroupNormones_hasattrrR)rr&rRs rJrrs &"))$d):z&RTR[R[?\  CT: v{{# FBII &  IFS ;; " GGNN6;; ' # FR^^R\\2<<H I  fmm$ v{{# GF,<=>rKmodelrrc ddl}d}|j|}t|jjj j d|||d}|jjj j||jj j|||d|jjj|||dtt|jddtjr|jjj j d||dj d k(rv|jjj j|||d|jjjj|||d t!|j"j%D]]\}\}} t!| j&j%D]-\} \} } d } |d |dzd | dzdd}| j(j j|||d| d| j*j j|||d| d| j,j j|||d| d| j.j j|||d| j0j j|||d| j2j j|||d| j.jj|||d| j0jj|||d| j2jj|||d| j4||d| d}| j4jj j||0`y)Nrcp|jdk(r|jgd}tj|S)zPossibly convert HWIO to OIHW.r)r:rrr)ndim transposere from_numpy) conv_weightss rJt2pz_load_weights..t2ps1    !'11,?L --rKrz%root_block/standardized_conv2d/kernelzgroup_norm/gammazgroup_norm/betarzhead/conv2d/kernelzhead/conv2d/biasstandardized_conv2dblockz/unit02d/za/z/kernelzb/zc/za/group_norm/gammazb/group_norm/gammazc/group_norm/gammaza/group_norm/betazb/group_norm/betazc/group_norm/betaza/proj/)numpyloadr!rrrPr copy_rr3r/r{rrCr1rrrnamed_childrenrr@rBrmr?rArlr>)r:rrnprAweights stem_conv_wisnamerjbnamerDcname block_prefixws rJrrs. ggo&G" $$Q'Wx?d5e-f)giK JJOO  - JJC6(2B(C DEF JJOO#g&@ABC'%**dD1299= JJMM & &q )Wx?Q5R-S-Y-YZ\-] ] ""3w&9K/L'M#NO   Wx7G-H%I!JK&u||'B'B'DE;>E5!*5<<+F+F+H!I ; A~u)E$XU1q5'q1uSkCL KK   $ $SL>E7'1R)S%T U KK   $ $SL>E7'1R)S%T U KK   $ $SL>E7'1R)S%T U KK   $ $SL>AS1T)U%V W KK   $ $SL>AS1T)U%V W KK   $ $SL>AS1T)U%V W KK   " "3w,?P/Q'R#S T KK   " "3w,?P/Q'R#S T KK   " "3w,?P/Q'R#S T+|nGE7'BC  %%,,223q6: ;;rKvariant pretrainedkwargsc Btd}tt||fd|i|S)zCreate a ResNetV2 model. Args: variant: Model variant name. pretrained: Load pretrained weights. **kwargs: Additional model arguments. Returns: ResNetV2 model instance. T)flatten_sequential feature_cfg)rrr%)rUrVrWrZs rJ_create_resnetv2r[=s4$/K ':    rKc @t|f|dttdd|S)zCreate a ResNetV2 model with BiT weights. Args: variant: Model variant name. pretrained: Load pretrained weights. **kwargs: Additional model arguments. 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