ó Z[—Yc@s·ddlZddljZddlZdejjfd„ƒYZdejjfd„ƒYZdejjfd„ƒYZdejjfd „ƒYZ d ejjfd „ƒYZ dS( iÿÿÿÿNtTransformerNetcBseZd„Zd„ZRS(c Cs|tt|ƒjƒtddddddƒ|_tdƒ|_tddddddƒ|_tdƒ|_tdd ddddƒ|_ td ƒ|_ t d ƒ|_ t d ƒ|_ t d ƒ|_t d ƒ|_t d ƒ|_td dddddd dƒ|_tdƒ|_tddddddd dƒ|_tdƒ|_tddddddƒ|_tjƒ|_dS( Nii t kernel_sizei tstrideii@ii€tupsample(tsuperRt__init__t ConvLayertconv1tInstanceNormalizationtin1tconv2tin2tconv3tin3t ResidualBlocktres1tres2tres3tres4tres5tUpsampleConvLayertdeconv1tin4tdeconv2tin5tdeconv3tnntReLUtrelu(tself((s`/data/vision/torralba/deepscene/small-projects/fast-neural-style/neural_style/transformer_net.pyRs$$$cCs |}|j|j|j|ƒƒƒ}|j|j|j|ƒƒƒ}|j|j|j|ƒƒƒ}|j|ƒ}|j|ƒ}|j |ƒ}|j |ƒ}|j |ƒ}|j|j |j |ƒƒƒ}|j|j|j|ƒƒƒ}|j|ƒ}|S(N(RR RR R R R RRRRRRRRRR(RtXtin_Xty((s`/data/vision/torralba/deepscene/small-projects/fast-neural-style/neural_style/transformer_net.pytforward#s!!!!!(t__name__t __module__RR!(((s`/data/vision/torralba/deepscene/small-projects/fast-neural-style/neural_style/transformer_net.pyRs RcBseZd„Zd„ZRS(cCs]tt|ƒjƒttj|dƒƒ}tj|ƒ|_tj ||||ƒ|_ dS(Ni( RRRtinttnptfloorRtReflectionPad2dtreflection_padtConv2dtconv2d(Rt in_channelst out_channelsRRtreflection_padding((s`/data/vision/torralba/deepscene/small-projects/fast-neural-style/neural_style/transformer_net.pyR4scCs"|j|ƒ}|j|ƒ}|S(N(R(R*(Rtxtout((s`/data/vision/torralba/deepscene/small-projects/fast-neural-style/neural_style/transformer_net.pyR!:s(R"R#RR!(((s`/data/vision/torralba/deepscene/small-projects/fast-neural-style/neural_style/transformer_net.pyR3s RcBs eZdZd„Zd„ZRS(s‘ResidualBlock introduced in: https://arxiv.org/abs/1512.03385 recommended architecture: http://torch.ch/blog/2016/02/04/resnets.html cCs€tt|ƒjƒt||ddddƒ|_t|ƒ|_t||ddddƒ|_t|ƒ|_t j ƒ|_ dS(NRiRi( RRRRRRR R R RRR(Rtchannels((s`/data/vision/torralba/deepscene/small-projects/fast-neural-style/neural_style/transformer_net.pyRFs cCsM|}|j|j|j|ƒƒƒ}|j|j|ƒƒ}||}|S(N(RR RR R (RR.tresidualR/((s`/data/vision/torralba/deepscene/small-projects/fast-neural-style/neural_style/transformer_net.pyR!Ns ! (R"R#t__doc__RR!(((s`/data/vision/torralba/deepscene/small-projects/fast-neural-style/neural_style/transformer_net.pyR@s RcBs#eZdZdd„Zd„ZRS(sÃUpsampleConvLayer Upsamples the input and then does a convolution. This method gives better results compared to ConvTranspose2d. ref: http://distill.pub/2016/deconv-checkerboard/ cCs‡tt|ƒjƒ||_|r=tjjd|ƒ|_ntt j |dƒƒ}tj |ƒ|_ tj ||||ƒ|_dS(Nt scale_factori(RRRRttorchRtUpsamplingNearest2dtupsample_layerR$R%R&R'R(R)R*(RR+R,RRRR-((s`/data/vision/torralba/deepscene/small-projects/fast-neural-style/neural_style/transformer_net.pyR]s cCsC|}|jr!|j|ƒ}n|j|ƒ}|j|ƒ}|S(N(RR6R(R*(RR.tx_inR/((s`/data/vision/torralba/deepscene/small-projects/fast-neural-style/neural_style/transformer_net.pyR!fs  N(R"R#R2tNoneRR!(((s`/data/vision/torralba/deepscene/small-projects/fast-neural-style/neural_style/transformer_net.pyRVs RcBs,eZdZdd„Zd„Zd„ZRS(srInstanceNormalization Improves convergence of neural-style. ref: https://arxiv.org/pdf/1607.08022.pdf g•Ö&è .>cCs`tt|ƒjƒtjtj|ƒƒ|_tjtj|ƒƒ|_||_ |j ƒdS(N( RRRRt ParameterR4t FloatTensortscaletshifttepst_reset_parameters(RtdimR=((s`/data/vision/torralba/deepscene/small-projects/fast-neural-style/neural_style/transformer_net.pyRus  cCs$|jjjƒ|jjjƒdS(N(R;tdatatuniform_R<tzero_(R((s`/data/vision/torralba/deepscene/small-projects/fast-neural-style/neural_style/transformer_net.pyR>|sc CsE|jdƒ|jdƒ}|j|jdƒ|jdƒ|ƒ}tj|dƒjdƒjdƒj|ƒ}tj|dƒjdƒjdƒj|ƒ|dt|ƒ}|jjdƒjdƒjdƒ}|j|ƒ}|j jdƒjdƒjdƒ}|j|ƒ}||tj ||j ƒ}|||}|S(Niiii( tsizetviewR4tmeant unsqueezet expand_astvartfloatR;R<tsqrtR=( RR.tnttRERHtscale_broadcasttshift_broadcastR/((s`/data/vision/torralba/deepscene/small-projects/fast-neural-style/neural_style/transformer_net.pyR!€s'-?$$(R"R#R2RR>R!(((s`/data/vision/torralba/deepscene/small-projects/fast-neural-style/neural_style/transformer_net.pyRos  ( R4ttorch.nnRtnumpyR%tModuleRRRRR(((s`/data/vision/torralba/deepscene/small-projects/fast-neural-style/neural_style/transformer_net.pyts  -