a =¼Ÿejã@srddlZddlmZmZddlmZddlmZGdd„dejƒZGdd„dejƒZ Gd d „d ejƒZ d d „Z dS) éN)ÚnnÚTensor)Ú _DenseBlock)Ú center_cropcs<eZdZdZd eeedœ‡fdd„ Zeedœdd„Z‡ZS) Ú TransitionUpz; Scale the resolution up by transposed convolution é)Ú in_channelsÚ out_channelsÚscalec srtƒ ¡|dkr,tj||ddddd|_nB|dkrnt tj||dddddt |¡tj||ddddd¡|_dS)NrérT)rr Ú kernel_sizeÚstrideÚpaddingÚbiaséF)ÚsuperÚ__init__rÚConvTranspose2dÚ convTransÚ SequentialÚ BatchNorm2d)Úselfrr r ©Ú __class__©ú{/Users/opencvuniversity/Desktop/blogs/adas-stereo-vision/repositories/stereo-transformer/module/feat_extractor_tokenizer.pyrs$ þ þþûzTransitionUp.__init__)ÚxÚskipcCs6| |¡}t|| d¡| d¡ƒ}t ||gd¡}|S)Nrr é)rrÚsizeÚtorchÚcat)rrrÚoutrrrÚforward"s zTransitionUp.forward)r) Ú__name__Ú __module__Ú __qualname__Ú__doc__Úintrrr#Ú __classcell__rrrrr srcs0eZdZdZeedœ‡fdd„ Zdd„Z‡ZS)Ú DoubleConvz$ Two conv2d-bn-relu modules )rr c sdtt|ƒ ¡t tj||ddddt |¡tjddtj||ddddt |¡tjdd¡|_dS)Nr rF)r rrT)Úinplace) rr*rrrÚConv2drÚReLUÚ double_conv)rrr rrrr.s  úzDoubleConv.__init__cCs | |¡S)N)r.)rrrrrr#:szDoubleConv.forward)r$r%r&r'r(rr#r)rrrrr*)s r*cs:eZdZdZeeeedœ‡fdd„ Zedœdd„Z‡ZS)Ú Tokenizerz@ Expanding path of feature descriptor using DenseBlocks )Ú block_configÚbackbone_feat_channelÚ hidden_dimÚ growth_ratec stt|ƒ ¡| ¡| ¡t|ƒ|_||_||_t|d|ddd|d|_ g}g}||d}t |jƒD]Š}||jdkr¨|  t ||dƒ¡|  t |d|ƒ¡qn|  t ||ƒ¡|||d} |  t||d| dd|d¡|||d}qnt |¡|_t |¡|_dS)Nrrg)Ú drop_rater3rr )rr/rÚreverseÚlenÚnum_resolutionr0r3rÚ bottle_neckÚrangeÚappendrr*rÚ ModuleListÚupÚ dense_block) rr0r1r2r3r<r=Zprev_block_channelsÚiZcur_channels_countrrrrCs0 ÿ ÿ zTokenizer.__init__)ÚfeaturescCs¦| ¡| |d¡}|dd…|jd|j d…f}t|jƒD]^}|j||||dƒ}|j||ƒ}||jdkrB|dd…|j|d|j d…f}qB|S)aO :param features: list containing feature descriptors at different spatial resolution 0: [2N, 3, H, W] 1: [2N, C0, H//4, W//4] 2: [2N, C1, H//8, W//8] 3: [2N, C2, H//16, W//16] :return: feature descriptor at full resolution [2N,C,H,W] rNr)r5r8r0r3r9r7r<r=)rr?Úoutputr>Úhsrrrr#`s "(zTokenizer.forward) r$r%r&r'Úlistr(rr#r)rrrrr/>sr/cCsd}gd¢}t|||j|ƒS)Nr)rrrr)r/Ú channel_dim)ÚargsÚ layer_channelr3r0rrrÚbuild_tokenizerysrF) r rrZtorchvision.models.densenetrÚutilities.miscrÚModulerr*r/rFrrrrÚs  ;