Uh dZddlZddlZddlmZddlmZmZm Z ddl Z ddl Z ddl m Z ddl mZddlmZmZdd lmZdd lmZmZmZdd lmZmZmZd d lmZej<eZ eGddeZ!dZ"dZ#dDde jHde%de&de jHfdZ'Gdde jPZ)Gdde jPZ*Gdde jPZ+Gdde jPZ,Gdd e jPZ-Gd!d"e jPZ.Gd#d$e jPZ/Gd%d&e jPZ0Gd'd(e jPZ1Gd)d*e jPZ2Gd+d,e jPZ3Gd-d.e jPZ4Gd/d0e jPZ5eGd1d2eZ6eGd3d4e6Z7Gd5d6e jPZ8Gd7d8e jPZ9Gd9d:e jPZ:Gd;de jPZ<ed?@GdAdBe6Z=gdCZ>y)Ez"PyTorch Swin2SR Transformer model.N) dataclass)OptionalTupleUnion)nn)ACT2FN)BaseModelOutputImageSuperResolutionOutput)PreTrainedModel) find_pruneable_heads_and_indicesmeshgridprune_linear_layer) ModelOutputauto_docstringlogging) Swin2SRConfigceZdZUdZdZeejed<dZ ee ejed<dZ ee ejed<y)Swin2SREncoderOutputa Swin2SR encoder's outputs, with potential hidden states and attentions. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nlast_hidden_state hidden_states attentions) __name__ __module__ __qualname____doc__rrtorch FloatTensor__annotations__rrr~/var/www/catia.catastroantioquia-mas.com/valormas/lib/python3.12/site-packages/transformers/models/swin2sr/modeling_swin2sr.pyrr%sS&6:x 1 1298r?floor_div)r8r9r: keep_probr* random_tensoroutputs r# drop_pathrGXsCx I I [[^  Q 7 7E 5 ELL YYM YYy !M 1F Mr"cxeZdZdZd deeddffd ZdejdejfdZ de fdZ xZ S) Swin2SRDropPathzXDrop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).Nr9r;c0t|||_yN)super__init__r9)selfr9 __class__s r#rMzSwin2SRDropPath.__init__ps "r"rcDt||j|jSrK)rGr9r:rNrs r#forwardzSwin2SRDropPath.forwardts FFr"c8dj|jS)Nzp={})formatr9rNs r# extra_reprzSwin2SRDropPath.extra_reprws}}T^^,,r"rK) rrrrrfloatrMrTensorrRstrrV __classcell__rOs@r#rIrImsGb#(5/#T#GU\\GellG-C-r"rIcfeZdZdZfdZdeejdeejfdZ xZ S)Swin2SREmbeddingsz? Construct the patch and optional position embeddings. cxt|t||_|jj}|j r=t jtjd|dz|j|_ nd|_ t j|j|_|j|_y)Nr)rLrMSwin2SRPatchEmbeddingspatch_embeddings num_patchesuse_absolute_embeddingsr Parameterrzeros embed_dimposition_embeddingsDropouthidden_dropout_probdropoutr/)rNconfigrarOs r#rMzSwin2SREmbeddings.__init__s  6v >++77  ) )')||EKK;QR?TZTdTd4e'fD $'+D $zz&"<"<= !--r" pixel_valuesr;c|j|\}}|j||jz}|j|}||fSrK)r`rfri)rNrk embeddingsoutput_dimensionss r#rRzSwin2SREmbeddings.forwardsN(,(=(=l(K% %  # # /#d&>&>>J\\*- ,,,r") rrrrrMrrrrrXrRrZr[s@r#r]r]{s4 .-HU->->$?-E%,,DW-r"r]cneZdZdfd Zdeej deejee ffdZ xZ S)r_ct||j}|j|j}}t |t jjr|n||f}t |t jjr|n||f}|d|dz|d|dzg}||_ |d|dz|_ tj||j|||_ |r%tj|j|_yd|_y)Nrr) kernel_sizestride)rLrMre image_size patch_size isinstance collectionsabcIterablepatches_resolutionrarConv2d projection LayerNorm layernorm)rNrjnormalize_patchesr3rsrtryrOs r#rMzSwin2SRPatchEmbeddings.__init__s '' !'!2!2F4E4EJ #-j+//:R:R#SZZdfpYq #-j+//:R:R#SZZdfpYq (mz!}\]"4-a03Ea3HH))L&2B2BPZcmn;Lf&6&67RVr"rmr;c|j|}|j\}}}}||f}|jdjdd}|j|j |}||fS)Nr%r)r{r*flatten transposer})rNrm_r1r2rns r#rRzSwin2SRPatchEmbeddings.forwardso__Z0 (..1fe#UO''*44Q: >> % 3J,,,r")T) rrrrMrrrrrXintrRrZr[s@r#r_r_s< W -(5+<+<"= -% V[\_V`H`Ba -r"r_c(eZdZdZfdZdZxZS)Swin2SRPatchUnEmbeddingszImage to Patch UnembeddingcDt||j|_yrK)rLrMre)rNrjrOs r#rMz!Swin2SRPatchUnEmbeddings.__init__s ))r"c|j\}}}|jddj||j|d|d}|S)Nrr%r)r*rr+re)rNrmx_sizer0 height_widthr3s r#rRz Swin2SRPatchUnEmbeddings.forwardsO1;1A1A. L,))!Q/44ZQWXYQZ\bcd\ef r"rrrrrMrRrZr[s@r#rrs%* r"rc eZdZdZej fdeededejddffd Z dZ d e jd eeefde jfd Z xZS) Swin2SRPatchMerginga' Patch Merging Layer. Args: input_resolution (`Tuple[int]`): Resolution of input feature. dim (`int`): Number of input channels. norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`): Normalization layer class. input_resolutiondim norm_layerr;Nct|||_||_t j d|zd|zd|_|d|z|_y)Nr&r%Fbias)rLrMrrrLinear reductionnorm)rNrrrrOs r#rMzSwin2SRPatchMerging.__init__sI  01s7AG%@q3w' r"c|dzdk(xs|dzdk(}|r.ddd|dzd|dzf}tjj||}|S)Nr%rr)r functionalpad)rNr.r1r2 should_pad pad_valuess r# maybe_padzSwin2SRPatchMerging.maybe_padsUqjAo:519> Q519a!|tj@tjB|dzztEj@dz }|jGtI|j,jKjL}|jOd|d tj.|jd} tj.|jd} tj4t7| | gd} tjP| d} | dddddf| dddddfz } | j9dddj;} | dddddfxx|jddz z cc<| dddddfxx|jddz z cc<| dddddfxxd|jdzdz zcc<| jSd}|jOd|d tj(|j |j |jT |_+tj(|j |j d |_,tj(|j |j |jT |_-tj\|j^|_0y)NrzThe hidden size (z6) is not a multiple of the number of attention heads () rr%iTrinplaceFr>ij)indexing?relative_coords_table persistentr(relative_position_index)1rLrM ValueErrornum_attention_headsrattention_head_size all_head_sizerurvrwrxr/pretrained_window_sizerrcrlogones logit_scale SequentialrReLUcontinuous_position_bias_mlparangeint64rWstackrr,r- unsqueezesignlog2absmathtonext parametersr>register_bufferrsumqkv_biasquerykeyvaluergattention_probs_dropout_probri)rNrjr num_headsr/rrelative_coords_hrelative_coords_wrcoords_hcoords_wcoordscoords_flattenrelative_coordsrrOs r#rMzSwin2SRSelfAttention.__init__s  ?a #C5(^_h^iijk $- #&sY#7 !558P8PP%k;??3K3KLKS^`kRl '=#<< "uzz9aQRBS7T2T(UV,.MM IIa4 ("''$*?3PY`eAf- ) "LL4+;+;A+>+B)CTEUEUVWEX`e`k`klrrt!LL4+;+;A+>+B)CTEUEUVWEX`e`k`klrrt KK"35F!GRVW X WQ1  Z\ Yq\  "! $q ( !!Q1* -1G1JQ1N N - !!Q1* -1G1JQ1N N - 1_ !!Q1* -1A1A!1Dq1H H - !!Q1* -1A1A!1Dq1H H -" JJ, - 599EZ;[^a;a0b beienenopeq q !6 8 8d>_>_>j>j>l9m9s9s t 46KX]^<< 0 0 34<< 0 0 34Xx&:TJKvq1(At4~aqj7QQ)11!Q:EEG1a D$4$4Q$7!$;; 1a D$4$4Q$7!$;; 1a A(8(8(;$;a$?? 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z&3&9&9#:y! ??Q $)JJ}tFVY]YhYhXhEipv$w !$1 !!11FHXHX Y 5 : :2t?O?ORVRbRb?bdl m&&z9MDWDW&X  ! %:%A%ABI NN !9iK\+ -Q/,11"d6F6FHXHXZbc():D>-#@@ ((7 {{<0 $t~~d6J6J<6X'YY @Q'8';< YeWf r")r=rrNF)rrrrMrrr$r@rrrXrrrrRrZr[s@r#r r sqrL2'eTYZ]_bZbTcejknpsksetTtNu' 8)26,1 8||8 S/8E--. 8 $D> 8 u||U\\) * 8r"r c eZdZdZd fd Z d dej deeefde ejde e deej f dZ xZ S) Swin2SRStagezh This corresponds to the Residual Swin Transformer Block (RSTB) in the original implementation. c t |||_||_t j t |Dcgc]*}t|||||dzdk(rdn|jdz|,c}|_ |jdk(rt j||ddd|_ n|jdk(rt jt j||dzdddt jd d t j|dz|dzdddt jd d t j|dz|ddd|_ t|d |_t#||_ycc}w)Nr%r)rjrrrr%r1convrr3convr&皙?Tnegative_sloperF)r~)rLrMrjrr ModuleListranger r/layersresi_connectionrzconvr LeakyReLUr_ patch_embedr patch_unembed) rNrjrrdepthrrGrirOs r#rMzSwin2SRStage.__init__XsI  mmu !%5'%&UaZqf6H6HA6M+A       ! !W , #sAq!4DI  # #w .  #saxAq1 C> #(C1HaA6 C> #(CAq1 DI2&ER5f=7 s/E.rrrrr;c |}|\}}t|jD]\}} |||nd} | ||| |} | d} ||||f} |j||}|j|}|j |\}} ||z}|| f}|r| ddz }|Sr) enumerater_rdrarc)rNrrrrresidualr1r2rf layer_modulelayer_head_maskrSrnr stage_outputss r#rRzSwin2SRStage.forwardzs!( (5 -OA|.7.CilO(8H/[lmM)!,M  -$UFE:**=:JK  -0 ++M: q%0 &(9:  ]12. .Mr"rrT)rrrrrMrrXrrrrrrRrZr[s@r#rVrVSso >L26,1 || S/E--.  $D>  u||  r"rVceZdZfdZ d dej deeefdeejdee dee dee de ee ffd Z xZS) Swin2SREncoderczt|t|j|_||_t jd|jt|jdDcgc]}|j}}tjt|jDcgc]r}t||j|d|df|j||j ||t|jd|t|jd|dzdtc}|_d|_ycc}wcc}w)Nrcpu)r?r)rjrrrerrGrF)rLrMr depths num_stagesrjrlinspacer,ritemrr]r^rVrerstagesgradient_checkpointing)rNrj grid_sizerdpr stage_idxrOs r#rMzSwin2SREncoder.__init__s fmm, !&63H3H#fmmJ\ej!klAqvvxllmm"'t!7 !((&/lIaL%A -- 2$..y9!#fmmJY&?"@3v}}UdW`cdWdGeCfg+,    ',#!m s )D3(A7D8rrrroutput_hidden_states return_dictr;cd}|rdnd}|rdnd} |r||fz }t|jD]~\} } ||| nd} |jr,|jr |j | j ||| |} n | ||| |} | d}| d}|d|df}||fz }|r||fz }|sw| | ddz } |st d||| fDSt||| S) Nr!rrrr(r%c3&K|] }|| ywrKr!).0vs r# z)Swin2SREncoder.forward..smq_`_lmsrrr)rhrurvr:_gradient_checkpointing_func__call__tupler)rNrrrrrzr{all_input_dimensionsall_hidden_statesall_self_attentionsrf stage_modulerkrSrns r#rRzSwin2SREncoder.forwards6 ""6BD$5b4  -!1 1 (5 9OA|.7.CilO**t}} $ A A ))=:JO]n! 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Args: scale (`int`): Scale factor. Supported scales: 2^n and 3. num_features (`int`): Channel number of intermediate features. c t|||_||dz zdk(rtt t j |dD]Y}|jd|tj|d|zddd|jd|tjd[y|dk(r.> aQRTU$V!,,t4 > !#<9P9PRSUVXY!Zr"c|j|}|j|}|j|}|j|}|SrK)rrrr)rNrrs r#rRzPixelShuffleUpsampler.forwardsC  % %o 6 OOA  MM!   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