khJ"dZddlmZddlmZmZmZmZmZm Z ddl Z ddl m Z ddl m cmZddlmZmZmZmZddlmZmZmZmZmZmZmZddlmZmZdd l m!Z!dd l"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)m*Z*dd l+m,Z,m-Z-m.Z.dd l/m0Z0m1Z1dd l2m3Z3m4Z4m5Z5ddgZ6Gdde jnZ8Gdde jnZ9ddZ:ddZ;ddZ<ddZ= ddZ> ddZ?ddZ@ddZA d dZB d dZC d dZDd dZE d dZF dd ZG d d!ZH d d"ZI d d#ZJ d d$ZKdd%ZLd d&ZMd d'ZNd d(ZOd d)ZPdd+ZQe3id,eQd-eQd.eQd/d01d2eQd3eQd4eQd5d01d6eQd7d01d8eQd9eQd:d01d;eQd0eedd?eQd0eedd@eQd0eedAdeQd?d0dddkddddo5 d@eQdAd0dddBddkdjdC idDeQdEd0dddBdkddddoF dGeQdHd0dddBdkddddoF dIeQdJd0dddBdkddddoF dKeQdLd0dMddNOdPeQdQd0dMdZdNddReQdSd0dTddddUeQd0eedZdd[dAdoVdWeQdXd0dZdd[ddYeQd0eedBdZdd[dZd[eQd\d01d]eQd^d01d_eQd`d01daeQdbd01dceQddeQded01dfeQdgd01dheQdid01eQdMdNdjd0keQdldNdmd0keQdndNdod0keQdpdqdrd0keQdsdtdud0keQddAveQddAveQddAveQd0eedAdr?rBstagerr)rI pool_typeN)superr)__init__nnReLU BatchNorm2drrr4rAgrad_checkpointingr conv_stembn1r Sequentialblocksfeatures feature_info stage_endsin_chs conv_headbn2r5head_hidden_sizeIdentityrrC classifierr)selfr3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBrCnorm_act_layerbuilderfhead_chs __class__s T/var/www/teggl/fontify/venv/lib/python3.12/site-packages/timm/models/efficientnet.pyrPzEfficientNet.__init__QsR lD*,( 12>> +J B,}&""'$Y/I&x> ! *8\1hWDN%lDADH8D DD  5[[]DN{{}DH8@ @D  5,=   t//;-H)$/ "$'!Bs% F7cd|j|jg}|j|j|j|j|j |j g|jtj|j|jgtj|S)z3Convert model to sequential for feature extraction.) rUrVextendrXr]r^rCrQDropoutrArarW)rblayerss rh as_sequentialzEfficientNet.as_sequentialsv..$((+ dkk" t~~txx1A1ABC rzz$..14??CD}}f%%coarsec,td|rdnddfdgS)zCreate regex patterns for parameter groups. Args: coarse: Use coarse (stage-level) grouping. Returns: Dictionary mapping group names to regex patterns. z^conv_stem|bn1z^blocks\.(\d+)z^blocks\.(\d+)\.(\d+)N)z conv_head|bn2)i)stemrX)dict)rbros rh group_matcherzEfficientNet.group_matchers*"&,"2JDQ,  rnenablec||_yzEnable or disable gradient checkpointing. Args: enable: Whether to enable gradient checkpointing. NrTrbrts rhset_grad_checkpointingz#EfficientNet.set_grad_checkpointing #)rnc|jS)zGet the classifier module.)ra)rbs rhget_classifierzEfficientNet.get_classifiersrncp||_t|j|j|\|_|_y)zReset the classifier head. Args: num_classes: Number of classes for new classifier. global_pool: Global pooling type. rMN)r4rr5rCra)rbr4rCs rhreset_classifierzEfficientNet.reset_classifiers4',=   t//;-H)$/rnxindicesnorm stop_early output_fmtintermediates_only extra_blockscF|dvsJdg}|r&tt|jdz|\} } nMtt|j|\} } | D cgc]} |j| } } |j| } d} |j |}|j |}| | vr|j |tjjs|s |j} n|jd| } t| dD]Z\} }|jr+tjjs t||}n||}| | vsJ|j |\|r|S| |jdk(r"|j|}|j|}||fScc} w)aForward features that returns intermediates. Args: x: Input image tensor. indices: Take last n blocks if int, all if None, select matching indices if sequence. norm: Apply norm layer to compatible intermediates. stop_early: Stop iterating over blocks when last desired intermediate hit. output_fmt: Shape of intermediate feature outputs. intermediates_only: Only return intermediate features. extra_blocks: Include outputs of all blocks and head conv in output, does not align with feature_info. Returns: List of intermediate features or tuple of (final features, intermediates). )NCHWzOutput shape must be NCHW.rrN)start)r#lenrXr[rUrVappendtorchjit is_scripting enumeraterTr$r]r^)rbrrrrrrr intermediates take_indices max_indexifeat_idxrXblks rhforward_intermediatesz"EfficientNet.forward_intermediatess0Y&D(DD& &:3t{{;Ka;OQX&Y #L)&:3t;OQX&Y #L)8DE1DOOA.ELE 2I NN1  HHQK | #   # 99 ! ! #:[[F[[),F&vQ7 (MHc&&uyy/E/E/G"3*F<'$$Q'  (  tr* *q!A A-9FsF prune_norm prune_headc|r&tt|jdz|\}}n1tt|j|\}}|j|}|jd||_|s|t|jkr2t j |_t j |_|r|jdd|S)aPrune layers not required for specified intermediates. Args: indices: Indices of intermediate layers to keep. prune_norm: Whether to prune normalization layers. prune_head: Whether to prune the classifier head. extra_blocks: Include all blocks in indexing. Returns: List of indices that were kept. rNrr0) r#rrXr[rQr`r]r^r~)rbrrrrrrs rhprune_intermediate_layersz&EfficientNet.prune_intermediate_layerss$ &:3t{{;Ka;OQX&Y #L)&:3t;OQX&Y #L) 2Ikk*9- S%55[[]DN{{}DH   ! !!R (rnc6|j|}|j|}|jr7tjj st |j|d}n|j|}|j|}|j|}|S)z/Forward pass through feature extraction layers.T)flatten) rUrVrTrrrr$rXr]r^rbrs rhforward_featureszEfficientNet.forward_features6st NN1  HHQK  " "599+A+A+Ct{{At>B  !t~~ FAq6DOOA$66rncJ|j|}|j|}|S)z Forward pass.)rrrs rhforwardzEfficientNet.forwardQs'  ! !! $   a rnFT)r2)NFFrFF)rFTF)$__name__ __module__ __qualname____doc__rrintboolstrrrrfloatrPrQrWrmrrignorerr rrsryModuler|r~Tensorrrrrrr __classcell__rgs@rhr)r);s/0 $ $$%"!#-1.2,0,0%3!$&$%U(!U(U( U(  U(  U("U(U(U(U( *U(!+U(y)U(y)U(#U( !U(""#U($%U(& 'U(n&r}}& YY D T#uS$Y?O:O5P  " YY)T)T)) YY  HC Hc Hd H8<$$',!&: ||: eCcN34:  :  :  : !%: :  tELL!5tELL7I)I#JJ K: |./$#!& 3S >*    c > %,, 5<<  7ell 7 7 7%,,rnc!eZdZdZdddddddddddded d fd ed eed fd edededede dedede e de e de e de e de de de f fd Zej j"d de ddfdZdeej(fdZxZS)!r*z EfficientNet Feature Extractor A work-in-progress feature extraction module for EfficientNet, to use as a backbone for segmentation and object detection models. )rrrFr. bottleneckr.r/Fr0Nr1r3 out_indices.feature_locationr6r7r8r9r:r;r<r=r>r?r@rArBc tt| | xstj} | xstj } t | | }| xst} ||_d|_ |s||}t|||d| |_ ||d|_ t|| || | | | || }tj||||_t!|j"||_|j$j'Dcic] }|d|d c}|_t+|d|_|d k7r<|j$j'd }t/||j1|_yycc}w) NFrFrGTrJ) r:r;r@r<r=r>r?rBrrLindexr)module hook_type)keys)rOr*rPrQrRrSrrrArTrrUrVrrWrXr!rYrZ get_dicts_stage_out_idxr feature_hooksr" named_modules)rbr3rrr6r7r8r9r:r;r<r=r>r?r@rArBrcrdrehooksrgs rhrPzEfficientNetFeatures.__init___sc& "D24( 12>> +J B,}""'$Y/I&x6HILR[]TD,t:L0MabM" -(!  #!::lD1gWR^^5g_eOf5g#FG4 L 7J ?, ?E Lrnc Hdgdgdgdgdgdgdgg}tt|}tdt|||| |rd nt d |d d |||j d d xs#tt jfit|t|dd|} t||fi| } | S)z Generate MobileNet-V2 network Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py Paper: https://arxiv.org/abs/1801.04381 ds_r1_k3_s1_c16rir_r3_k3_s2_e6_c32ir_r4_k3_s2_e6_c64ir_r3_k3_s1_e6_c96ir_r3_k3_s2_e6_c160rrrr-r/r=Nr r r) rrrrrrrrQrSrrr) rrr r rrrrr@rrs rh_gen_mobilenet_v2rYs       H>6HIL" -(!  +TD,t:L0M!::lD1gWR^^5g_eOf5g#FG4 L 7J ?, ?E Lrnc dgddggdgdddgdgd gg}tdt|d d tt| |j d dxs#tt j fit|d|}t||fi|}|S)ai FBNet-C Paper: https://arxiv.org/abs/1812.03443 Ref Impl: https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_modeldef.py NOTE: the impl above does not relate to the 'C' variant here, that was derived from paper, it was used to confirm some building block details ir_r1_k3_s1_e1_c16ir_r1_k3_s2_e6_c24ir_r2_k3_s1_e1_c24)ir_r1_k5_s2_e6_c32ir_r1_k5_s1_e3_c32ir_r1_k5_s1_e6_c32ir_r1_k3_s1_e6_c32)ir_r1_k5_s2_e6_c64ir_r1_k5_s1_e3_c64ir_r2_k5_s1_e6_c64ir_r3_k5_s1_e6_c112ir_r1_k5_s1_e3_c112ir_r4_k5_s2_e6_c184ir_r1_k3_s1_e6_c352irr=N)r3r7r5r@r=rrrs rh _gen_fbnetcr)~s  34`J  56  H"8,^8JK::lD1gWR^^5g_eOf5g   L 7J ?, ?E Lrnc dgdgddgddgddgd gd gg}tdt|d tt| |j d dxs#tt j fit|d|}t||fi|}|S)zCreates the Single-Path NAS model from search targeted for Pixel1 phone. Paper: https://arxiv.org/abs/1904.02877 Args: channel_multiplier: multiplier to number of channels per layer. rrir_r1_k5_s2_e6_c40ir_r3_k3_s1_e3_c40ir_r1_k5_s2_e6_c80ir_r3_k3_s1_e3_c80ir_r1_k5_s1_e6_c96ir_r3_k5_s1_e3_c96rrr/rr=Nrrrrs rh _gen_spnasnetr1s ""  34 34 34  %&H "8,^8JK::lD1gWR^^5g_eOf5g   L 7J ?, ?E Lrnc *dgdgdgdgdgdgdgg}tt||}tdt||| |d d |t |d |j d dxs#tt jfit|d|} t||fi| } | S)axCreates an EfficientNet model. Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py Paper: https://arxiv.org/abs/1905.11946 EfficientNet params name: (channel_multiplier, depth_multiplier, resolution, dropout_rate) 'efficientnet-b0': (1.0, 1.0, 224, 0.2), 'efficientnet-b1': (1.0, 1.1, 240, 0.2), 'efficientnet-b2': (1.1, 1.2, 260, 0.3), 'efficientnet-b3': (1.2, 1.4, 300, 0.3), 'efficientnet-b4': (1.4, 1.8, 380, 0.4), 'efficientnet-b5': (1.6, 2.2, 456, 0.4), 'efficientnet-b6': (1.8, 2.6, 528, 0.5), 'efficientnet-b7': (2.0, 3.1, 600, 0.5), 'efficientnet-b8': (2.2, 3.6, 672, 0.5), 'efficientnet-l2': (4.3, 5.3, 800, 0.5), Args: channel_multiplier: multiplier to number of channels per layer depth_multiplier: multiplier to number of repeats per stage ds_r1_k3_s1_e1_c16_se0.25ir_r2_k3_s2_e6_c24_se0.25ir_r2_k5_s2_e6_c40_se0.25ir_r3_k3_s2_e6_c80_se0.25ir_r3_k5_s1_e6_c112_se0.25ir_r4_k5_s2_e6_c192_se0.25ir_r1_k3_s1_e6_c320_se0.25rdivisorr r-r/swishr=Nr3r5r7r@r<r=r rrrrrrrrQrSrr) rrr channel_divisorr rrrr@rrs rh_gen_efficientnetrAs8 %% $% $% $% %& %& %&H>6HRabL"8-=*U!$'!#FG4::lD1gWR^^5g_eOf5g  L 7J ?, ?E Lrnc $dgdgdgdgdgdgg}tt|}tdt||||d d ||j d d xs#tt j fit|t|d d|}t||fi|} | S)z Creates an EfficientNet-EdgeTPU model Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu er_r1_k3_s1_e4_c24_fc24_noskiper_r2_k3_s2_e8_c32er_r4_k3_s2_e8_c48ir_r5_k5_s2_e8_c96ir_r4_k5_s1_e8_c144ir_r2_k5_s2_e8_c192rr<r-r/r=Nrelur3r5r7r@r=r<r rrrrrrrQrSrrr rrr r rrrr@rrs rh_gen_efficientnet_edgerMs **      H>6HIL"8-=*U!$'!::lD1gWR^^5g_eOf5g#FF3  L 7J ?, ?E Lrnc (dgdgdgdgdgdgdgg}tt|}tdt||| |d d ||j d d xs#tt j fit|t|dd|}t||fi|} | S)zCreates an EfficientNet-CondConv model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/condconv r3r4r5r6zir_r3_k5_s1_e6_c112_se0.25_cc4zir_r4_k5_s2_e6_c192_se0.25_cc4zir_r1_k3_s1_e6_c320_se0.25_cc4r)experts_multiplierr-r/r=Nr=rJrrK) rrr rOrrrr@rrs rh_gen_efficientnet_condconvrPs %% $% $% $% )* )* )*H>6HIL"8-=Rde!$'!::lD1gWR^^5g_eOf5g#FG4  L 7J ?, ?E Lrnc dgdgdgdgdgdgdgg}tdt||d d d dtt| t |d |j ddxs#tt jfit|d|}t||fi|}|S)aCreates an EfficientNet-Lite model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite Paper: https://arxiv.org/abs/1905.11946 EfficientNet params name: (channel_multiplier, depth_multiplier, resolution, dropout_rate) 'efficientnet-lite0': (1.0, 1.0, 224, 0.2), 'efficientnet-lite1': (1.0, 1.1, 240, 0.2), 'efficientnet-lite2': (1.1, 1.2, 260, 0.3), 'efficientnet-lite3': (1.2, 1.4, 280, 0.3), 'efficientnet-lite4': (1.4, 1.8, 300, 0.3), Args: channel_multiplier: multiplier to number of channels per layer depth_multiplier: multiplier to number of repeats per stage ds_r1_k3_s1_e1_c16rir_r2_k5_s2_e6_c40ir_r3_k3_s2_e6_c80r$rrT)r r-r/rr r=Nr3r5r7r9r@r<r=r) rrrrrrrrQrSrrrrr rrrrrs rh_gen_efficientnet_literW1s&       H "8-=dS^8JK#FG4::lD1gWR^^5g_eOf5g   L 7J ?, ?E Lrnc &dgdgdgdgdgdgg}tt|d}tdt||| |d d ||j d d xs#tt j fit|t|dd|}t||fi|} | S)z Creates an EfficientNet-V2 base model Ref impl: https://github.com/google/automl/tree/master/efficientnetv2 Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298 cn_r1_k3_s1_e1_c16_skiper_r2_k3_s2_e4_c32er_r2_k3_s2_e4_c48zir_r3_k3_s2_e4_c96_se0.25zir_r5_k3_s1_e6_c112_se0.25zir_r8_k3_s2_e6_c192_se0.25r1r round_limitr<r-r/r=NsilurJrrKrLs rh_gen_efficientnetv2_baser_Zs ##   $% %& %& H>6HVXYL"8-=*U!$'!::lD1gWR^^5g_eOf5g#FF3  L 7J ?, ?E Lrnc Hdgdgdgdgdgdgg}d}|rdg|d <d g|d <d }tt| } tdt|||| |d| |j ddxs#tt j fit|t|dd|} t||fi| } | S)a[ Creates an EfficientNet-V2 Small model Ref impl: https://github.com/google/automl/tree/master/efficientnetv2 Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298 NOTE: `rw` flag sets up 'small' variant to behave like my initial v2 small model, before ref the impl was released. cn_r2_k3_s1_e1_c24_skiper_r4_k3_s2_e4_c48er_r4_k3_s2_e4_c64zir_r6_k3_s2_e4_c128_se0.25zir_r9_k3_s1_e6_c160_se0.25zir_r15_k3_s2_e6_c256_se0.25r-er_r2_k3_s1_e1_c24rzir_r15_k3_s2_e6_c272_se0.25rirr<r=Nr^rJrrK) rrr r rwrrrr5r@rrs rh_gen_efficientnetv2_srgxs ##   %& %& &' HL +, 56  >6HIL"8-=*U!,/!::lD1gWR^^5g_eOf5g#FF3  L 7J ?, ?E Lrnc dgdgdgdgdgdgdgg}tdt|||d d tt| |j d d xs#tt j fit|t|dd|}t||fi|}|S)z Creates an EfficientNet-V2 Medium model Ref impl: https://github.com/google/automl/tree/master/efficientnetv2 Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298 cn_r3_k3_s1_e1_c24_skiper_r5_k3_s2_e4_c48er_r5_k3_s2_e4_c80zir_r7_k3_s2_e4_c160_se0.25zir_r14_k3_s1_e6_c176_se0.25zir_r18_k3_s2_e6_c304_se0.25zir_r5_k3_s1_e6_c512_se0.25r<r-rerr=Nr^rJr rrrrrrrQrSrrr rrr r rrrrrs rh_gen_efficientnetv2_mrns ##   %& &' &' %&H"8-=*U^8JK::lD1gWR^^5g_eOf5g#FF3  L 7J ?, ?E Lrnc dgdgdgdgdgdgdgg}tdt|||d d tt| |j d d xs#tt j fit|t|dd|}t||fi|}|S)z Creates an EfficientNet-V2 Large model Ref impl: https://github.com/google/automl/tree/master/efficientnetv2 Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298 cn_r4_k3_s1_e1_c32_skiper_r7_k3_s2_e4_c64er_r7_k3_s2_e4_c96zir_r10_k3_s2_e4_c192_se0.25zir_r19_k3_s1_e6_c224_se0.25zir_r25_k3_s2_e6_c384_se0.25zir_r7_k3_s1_e6_c640_se0.25r<r-r/rr=Nr^rJrrlrms rh_gen_efficientnetv2_lrs ##   &' &' &' %&H"8-=*U^8JK::lD1gWR^^5g_eOf5g#FF3  L 7J ?, ?E Lrnc dgdgdgdgdgdgdgg}tdt|||d d tt| |j d d xs#tt j fit|t|dd|}t||fi|}|S)z Creates an EfficientNet-V2 Xtra-Large model Ref impl: https://github.com/google/automl/tree/master/efficientnetv2 Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298 rper_r8_k3_s2_e4_c64er_r8_k3_s2_e4_c96zir_r16_k3_s2_e4_c192_se0.25zir_r24_k3_s1_e6_c256_se0.25zir_r32_k3_s2_e6_c512_se0.25zir_r8_k3_s1_e6_c640_se0.25r<r-r/rr=Nr^rJrrlrms rh_gen_efficientnetv2_xlrxrtrnc X |dk(rdgdgdgdgdgdgdgg}ndgd gd gd gdgdgdgg}tt|| } tdt||| | dd| t |d|j ddxs#tt jfit|d|} t||fi| } | S)aCreates an EfficientNet model. Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py Paper: https://arxiv.org/abs/1905.11946 EfficientNet params name: (channel_multiplier, depth_multiplier, resolution, dropout_rate) 'efficientnet-x-b0': (1.0, 1.0, 224, 0.2), 'efficientnet-x-b1': (1.0, 1.1, 240, 0.2), 'efficientnet-x-b2': (1.1, 1.2, 260, 0.3), 'efficientnet-x-b3': (1.2, 1.4, 300, 0.3), 'efficientnet-x-b4': (1.4, 1.8, 380, 0.4), 'efficientnet-x-b5': (1.6, 2.2, 456, 0.4), 'efficientnet-x-b6': (1.8, 2.6, 528, 0.5), 'efficientnet-x-b7': (2.0, 3.1, 600, 0.5), 'efficientnet-x-b8': (2.2, 3.6, 672, 0.5), 'efficientnet-l2': (4.3, 5.3, 800, 0.5), Args: channel_multiplier: multiplier to number of channels per layer depth_multiplier: multiplier to number of repeats per stage rzds_r1_k3_s1_e1_c16_se0.25_d1zer_r2_k3_s2_e6_c24_se0.25_nrezer_r2_k5_s2_e6_c40_se0.25_nrer6r7r8r9zer_r2_k3_s2_e4_c24_se0.25_nrezer_r2_k5_s2_e4_c40_se0.25_nrezir_r3_k3_s2_e4_c80_se0.25r:r<r-r/r^r=Nr>rr?) rrr r@r versionrrrr@rrs rh_gen_efficientnet_xr{s6,!| + , , - , - ( ) ) * ) * ) * , , , - , - ( ) ) * ) * ) * >6HRabL"8-=*U!$'!#FF3::lD1gWR^^5g_eOf5g  L 7J ?, ?E Lrnc dgddgddgddgdd gd d gg}tdt|d d tt||j ddxs#tt j fit|d|}t||fi|}|S)zCreates a MixNet Small model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet Paper: https://arxiv.org/abs/1907.09595 rRzir_r1_k3_a1.1_p1.1_s2_e6_c24zir_r1_k3_a1.1_p1.1_s1_e3_c24z ir_r1_k3.5.7_s2_e6_c40_se0.5_nsw(ir_r3_k3.5_a1.1_p1.1_s1_e6_c40_se0.5_nswz&ir_r1_k3.5.7_p1.1_s2_e6_c80_se0.25_nswz$ir_r2_k3.5_p1.1_s1_e6_c80_se0.25_nswz+ir_r1_k3.5.7_a1.1_p1.1_s1_e6_c120_se0.5_nswz-ir_r2_k3.5.7.9_a1.1_p1.1_s1_e3_c120_se0.5_nswz&ir_r1_k3.5.7.9.11_s2_e6_c200_se0.5_nswz(ir_r2_k3.5.7.9_p1.1_s1_e6_c200_se0.5_nswr(rr=Nr3r5r7r@r=rrrs rh _gen_mixnet_srSs  ')GH +-WX 13YZ 68gh 13]^H"8,^8JK::lD1gWR^^5g_eOf5g   L 7J ?, ?E Lrnc dgddgddgddgdd gd d gg}tdt||d ddtt||j ddxs#tt j fit|d|}t||fi|}|S)zCreates a MixNet Medium-Large model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet Paper: https://arxiv.org/abs/1907.09595 ds_r1_k3_s1_e1_c24z ir_r1_k3.5.7_a1.1_p1.1_s2_e6_c32zir_r1_k3_a1.1_p1.1_s1_e3_c32z"ir_r1_k3.5.7.9_s2_e6_c40_se0.5_nswr}z!ir_r1_k3.5.7_s2_e6_c80_se0.25_nswz-ir_r3_k3.5.7.9_a1.1_p1.1_s1_e6_c80_se0.25_nswzir_r1_k3_s1_e6_c120_se0.5_nswz-ir_r3_k3.5.7.9_a1.1_p1.1_s1_e3_c120_se0.5_nswz#ir_r1_k3.5.7.9_s2_e6_c200_se0.5_nswz(ir_r3_k3.5.7.9_p1.1_s1_e6_c200_se0.5_nswround depth_truncr~rerr=NrrrrVs rh _gen_mixnet_mrts  +-KL -/YZ ,.]^ (*YZ .0Z[H"8-=7S^8JK::lD1gWR^^5g_eOf5g   L 7J ?, ?E Lrnc Fdgdgdgdgdgdgdgg}tdt||d td td |d d d dt t|t |d|j dd xs#t tjfit|d|}t||fi|}|S)zCreates a TinyNet model. r3r4r5r6r7r8r9rrr-rNr/Trr=r=rUr) rrrrrrrrrQrSrr)r model_widthr rrrrrs rh _gen_tinynetrs %%(C'D $%(C'D %&)E(F %& H  "8-=7S~dKDIJ^ D#FG4::lD1gWR^^5g_eOf5g   L 7J ?, ?E Lrnc d|vrgd}d}d}d}t|d} dttdtfd} d |vr d }d }gd } n%d |vrgd} nd|vrgd} d}nd|vr d}d}gd} d}nJ| | |} n'd }d }d}t|d} dgddgddgddgddgdd gd!gg} td&t | ||||t t |"|jd#d$xs#t tjfit|| d%|} t||fi| }|S)'z Based on definitions in: https://github.com/tensorflow/models/tree/d2427a562f401c9af118e47af2f030a0a5599f55/official/projects/edgetpu/vision edgetpu_v2@r-rIchsr c d|dgd|dd|d|dgd|dd|d|dd|dd|d|dgd|d d |d gd |d d |d gd |dd |dgd |dggS)N cn_r1_k1_s1_crer_r1_k3_s2_e8_crer_r1_k3_s1_e4_gs_crFer_r1_k3_s1_e4_cr.ir_r3_k3_s1_e4_cir_r1_k3_s1_e8_crir_r1_k3_s2_e8_crr)rr s rh _arch_defz)_gen_mobilenet_edgetpu.._arch_defs!Q)*#CF8,0A*RPSTUPVx.XY's1vh/' |2c!fX>&s1vh/' |2c!fX> $CF8,0@Q.IJ#CF8,0@Q.IJ#CF8,0@Q.IJ#CF8,-' rn edgetpu_v2_xsr/r.)r(r/0` edgetpu_v2_s)rerrrr edgetpu_v2_m)r/rPrr@i@ edgetpu_v2_lr)r/rrrrricn_r1_k1_s1_c16er_r1_k3_s2_e8_c32er_r3_k3_s1_e4_c32er_r1_k3_s2_e8_c48er_r3_k3_s1_e4_c48ir_r1_k3_s2_e8_c96ir_r3_k3_s1_e4_c96ir_r1_k3_s1_e8_c96_noskipir_r1_k5_s2_e8_c160ir_r3_k5_s1_e4_c160ir_r1_k3_s1_e8_c192rr=N)r3r5r7r8r@r=r<r) rrrrrrrrrrQrSrr)rrr rrr7r8r r5r<rchannelsrrrs rh_gen_mobilenet_edgetpursw   %ff5  49 # . g %I 6H w &7H w &7HL w & J7HL 5Xz2  %ff5   !#7 8 !#7 8 !#7 8 (*> ? 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For more info: https://github.com/DeGirum/pruned-models/releases/tag/efficientnet_v1.0rr+)efficientnet_es_prunedr^rs rhrara. #  q583[e qio qE Lrnc &t ddd|d|}|S)z EfficientNet-Edge-Medium. rr r+)efficientnet_emr^rs rhrdrdr_rnc &t ddd|d|}|S)z EfficientNet-Edge-Large. rrr+)efficientnet_elr^rs rhrfrfr_rnc &t ddd|d|}|S)zw EfficientNet-Edge-Large pruned. For more info: https://github.com/DeGirum/pruned-models/releases/tag/efficientnet_v1.0rrr+)efficientnet_el_prunedr^rs rhrhrhrbrnc &t ddd|d|}|S)' EfficientNet-CondConv-B0 w/ 8 Experts rr+)efficientnet_cc_b0_4erPrs rhrkrks. ' p47#Zd phn pE Lrnc (t dddd|d|}|S)rjrrFrr rOr)efficientnet_cc_b0_8erlrs rhroro0 ' )47#bc )!' )E Lrnc (t dddd|d|}|S)z' EfficientNet-CondConv-B1 w/ 8 Experts rr rFrn)efficientnet_cc_b1_8erlrs rhrrrrrprnc &t ddd|d|}|S) EfficientNet-Lite0 rr+)efficientnet_lite0rWrs rhruru. # m14sWa mek mE Lrnc &t ddd|d|}|S) EfficientNet-Lite1 rr r+)efficientnet_lite1rvrs rhrzrzrwrnc &t ddd|d|}|S) EfficientNet-Lite2 r rr+)efficientnet_lite2rvrs rhr}r}rwrnc &t ddd|d|}|S) EfficientNet-Lite3 rrr+)efficientnet_lite3rvrs rhrrrwrnc &t ddd|d|}|S) EfficientNet-Lite4 rr7r+)efficientnet_lite4rvrs rhrrrwrnc ||jdt|jddd}t|fddd|d|}|S) zc EfficientNet-B1 Pruned. The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf r%r;sameefficientnet_b1_prunedrr Trr prunedrr'r rA)rrrrs rhrr sV h 12 j&)&G  m$'#dWa mek mE Lrnc x|jdt|jddt dddd|d|}|S) zb EfficientNet-B2 Pruned. The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf r%r;rr rTr)efficientnet_b2_prunedrrs rhrr Q h 12 j&)   )583W[ )!' )E Lrnc x|jdt|jddt dddd|d|}|S) zb EfficientNet-B3 Pruned. The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf r%r;rrrTr)efficientnet_b3_prunedrrs rhrr rrnc (t dddd|d|}|S)z; EfficientNet-V2 Tiny (Custom variant, tiny not in paper). 皙?rFrr rfr)efficientnetv2_rw_trgrs rhrr% s1 " x25PUbl xpv xE Lrnc *t ddddd|d|}|S)zR EfficientNet-V2 Tiny w/ Global Context Attn (Custom variant, tiny not in paper). rrFgc)rr rfr?r)gc_efficientnetv2_rw_trrs rhrr- s4 "  B583 4J B:@ BE Lrnc "tdd|d|}|S)z EfficientNet-V2 Small (RW variant). NOTE: This is my initial (pre official code release) w/ some differences. See efficientnetv2_s and tf_efficientnetv2_s for versions that match the official w/ PyTorch vs TF padding T)rfr)efficientnetv2_rw_srrs rhrr6 s " bDZ b[a bE Lrnc (t dddd|d|}|S)z* EfficientNet-V2 Medium (RW variant). r)rrrrr<r<Tr)efficientnetv2_rw_mrrs rhrr@ s1 " )25H_dh )!' )E Lrnc tdd|i|}|S)z EfficientNet-V2 Small. r)efficientnetv2_srrs rhrrJ  " V Vv VE Lrnc tdd|i|}|S)z EfficientNet-V2 Medium. r)efficientnetv2_m)rnrs rhrrQ rrnc tdd|i|}|S)z EfficientNet-V2 Large. r)efficientnetv2_l)rsrs rhrrX rrnc tdd|i|}|S)z EfficientNet-V2 Xtra-Large. r)efficientnetv2_xl)rxrs rhrr_ s # X: XQW XE Lrnc v|jdt|jddt ddd|d|}|S)z1 EfficientNet-B0. Tensorflow compatible variant r%r;rrr+)tf_efficientnet_b0rrs rhrrf O h 12 j&)  m14sWa mek mE Lrnc v|jdt|jddt ddd|d|}|S)z1 EfficientNet-B1. Tensorflow compatible variant r%r;rrr r+)tf_efficientnet_b1rrs rhrrp rrnc v|jdt|jddt ddd|d|}|S)z1 EfficientNet-B2. Tensorflow compatible variant r%r;rr rr+)tf_efficientnet_b2rrs rhrrz rrnc v|jdt|jddt ddd|d|}|S)z0 EfficientNet-B3. Tensorflow compatible variant r%r;rrrr+)tf_efficientnet_b3rrs rhrr rrnc v|jdt|jddt ddd|d|}|S)z0 EfficientNet-B4. Tensorflow compatible variant r%r;rrr7r+)tf_efficientnet_b4rrs rhrr rrnc v|jdt|jddt ddd|d|}|S)z0 EfficientNet-B5. Tensorflow compatible variant r%r;rr<r=r+)tf_efficientnet_b5rrs rhrr rrnc v|jdt|jddt ddd|d|}|S)z0 EfficientNet-B6. Tensorflow compatible variant r%r;rr7rAr+)tf_efficientnet_b6rrs rhrr O h 12 j&)  m14sWa mek mE Lrnc v|jdt|jddt ddd|d|}|S)z0 EfficientNet-B7. Tensorflow compatible variant r%r;rrDrEr+)tf_efficientnet_b7rrs rhrr rrnc v|jdt|jddt ddd|d|}|S)z0 EfficientNet-B8. Tensorflow compatible variant r%r;rr=rHr+)tf_efficientnet_b8rrs rhrr rrnc v|jdt|jddt ddd|d|}|S)z= EfficientNet-L2 NoisyStudent. Tensorflow compatible variant r%r;rrKrLr+)tf_efficientnet_l2rrs rhrr rrnc v|jdt|jddt ddd|d|}|S)z9 EfficientNet-Edge Small. Tensorflow compatible variant r%r;rrr+)tf_efficientnet_esr'r rMrs rhrr O h 12 j&) " m14sWa mek mE Lrnc v|jdt|jddt ddd|d|}|S)z: EfficientNet-Edge-Medium. Tensorflow compatible variant r%r;rrr r+)tf_efficientnet_emrrs rhrr rrnc v|jdt|jddt ddd|d|}|S)z9 EfficientNet-Edge-Large. Tensorflow compatible variant r%r;rrrr+)tf_efficientnet_elrrs rhrr rrnc v|jdt|jddt ddd|d|}|S)zF EfficientNet-CondConv-B0 w/ 4 Experts. Tensorflow compatible variant r%r;rrr+)tf_efficientnet_cc_b0_4er'r rPrs rhrr sO h 12 j&) &" s7:S]g skq sE Lrnc x|jdt|jddt dddd|d|}|S)zF EfficientNet-CondConv-B0 w/ 8 Experts. Tensorflow compatible variant r%r;rrrFrn)tf_efficientnet_cc_b0_8errs rhrr Q h 12 j&) &" )7:Sef )!' )E Lrnc x|jdt|jddt dddd|d|}|S) zF EfficientNet-CondConv-B1 w/ 8 Experts. Tensorflow compatible variant r%r;rrr rFrn)tf_efficientnet_cc_b1_8errs rhrr rrnc v|jdt|jddt ddd|d|}|S)rtr%r;rrr+)tf_efficientnet_lite0r'r rWrs rhrr O h 12 j&) " p47#Zd phn pE Lrnc v|jdt|jddt ddd|d|}|S)ryr%r;rrr r+)tf_efficientnet_lite1rrs rhrr rrnc v|jdt|jddt ddd|d|}|S)r|r%r;rr rr+)tf_efficientnet_lite2rrs rhrr% rrnc v|jdt|jddt ddd|d|}|S)rr%r;rrrr+)tf_efficientnet_lite3rrs rhrr0 rrnc v|jdt|jddt ddd|d|}|S)rr%r;rrr7r+)tf_efficientnet_lite4rrs rhrr; rrnc p|jdt|jddtdd|i|}|S)z7 EfficientNet-V2 Small. Tensorflow compatible variant r%r;rr)tf_efficientnetv2_s)r'r rgrs rhrrF = h 12 j&) ! YJ YRX YE Lrnc p|jdt|jddtdd|i|}|S)z8 EfficientNet-V2 Medium. Tensorflow compatible variant r%r;rr)tf_efficientnetv2_m)r'r rnrs rhrrO rrnc p|jdt|jddtdd|i|}|S)z7 EfficientNet-V2 Large. Tensorflow compatible variant r%r;rr)tf_efficientnetv2_l)r'r rsrs rhrrX rrnc p|jdt|jddtdd|i|}|S)z? EfficientNet-V2 Xtra-Large. Tensorflow compatible variant r%r;rr)tf_efficientnetv2_xl)r'r rxrs rhrra s= h 12 j&) " [j [TZ [E Lrnc p|jdt|jddtdd|i|}|S)z4 EfficientNet-V2-B0. Tensorflow compatible variant r%r;rr)tf_efficientnetv2_b0r'r r_rs rhrrk s= h 12 j&) $ ] ]V\ ]E Lrnc v|jdt|jddt ddd|d|}|S)z4 EfficientNet-V2-B1. Tensorflow compatible variant r%r;rrr r+)tf_efficientnetv2_b1rrs rhrrt O h 12 j&) $ o36Yc ogm oE Lrnc v|jdt|jddt ddd|d|}|S)z4 EfficientNet-V2-B2. Tensorflow compatible variant r%r;rr rr+)tf_efficientnetv2_b2rrs rhrr~ rrnc v|jdt|jddt ddd|d|}|S)z3 EfficientNet-V2-B3. Tensorflow compatible variant r%r;rrrr+)tf_efficientnetv2_b3rrs rhrr rrnc &t ddd|d|}|Sr4r{rs rhefficientnet_x_b3r s.  j.1CT^ jbh jE Lrnc &t ddd|d|}|Sr:rrs rhefficientnet_x_b5r s.  j.1CT^ jbh jE Lrnc (t dddd|d|}|S)r;gQ?r=rF)rr rzrr>rrs rhefficientnet_h_b5r s1  v.2SRS`j vnt vE Lrnc $t dd|d|}|S)z"Creates a MixNet Small model. rrr)mixnet_s)rrs rhrr +  M'*z MEK ME Lrnc $t dd|d|}|S)z#Creates a MixNet Medium model. rr)mixnet_mrrs rhrr rrnc $t dd|d|}|S)z"Creates a MixNet Large model. ?r)mixnet_lrrs rhrr rrnc &t ddd|d|}|S)zgCreates a MixNet Extra-Large model. Not a paper spec, experimental def by RW w/ depth scaling. r<rr+) mixnet_xlrrs rhrr s-  d(+cj d\b dE Lrnc &t ddd|d|}|S)znCreates a MixNet Double Extra Large model. Not a paper spec, experimental def by RW w/ depth scaling. g333333@rr+) mixnet_xxlrrs rhrr s-  e),sz e]c eE Lrnc t|jdt|jddt dd|d|}|S)z@Creates a MixNet Small model. Tensorflow compatible variant r%r;rrr) tf_mixnet_s)r'r rrs rhrr L h 12 j&)  P*-* PHN PE Lrnc t|jdt|jddt dd|d|}|S)zACreates a MixNet Medium model. Tensorflow compatible variant r%r;rrr) tf_mixnet_mr'r rrs rhrr rrnc t|jdt|jddt dd|d|}|S)z@Creates a MixNet Large model. Tensorflow compatible variant r%r;rrr) tf_mixnet_lrrs rhrr rrnc tdd|i|}|S)N) tinynet_arrrrrs rhrr s  P: P PE Lrnc tdd|i|}|S)N) tinynet_brr rrrs rhr r    QJ Q& QE Lrnc tdd|i|}|S)N) tinynet_cHzG?g333333?rrrs rhr r  s  RZ R6 RE Lrnc tdd|i|}|S)N) tinynet_drg= ףp=?rrrs rhrr s  Sj SF SE Lrnc tdd|i|}|S)N) tinynet_egRQ?g333333?rrrs rhrr r rnc tdd|i|}|S)z MobileNet-EdgeTPU-v1 100. r)mobilenet_edgetpu_100rrs rhrr s # \z \U[ \E Lrnc tdd|i|}|S)z# MobileNet-EdgeTPU-v2 Extra Small. r)mobilenet_edgetpu_v2_xsrrs rhrr s # ^ ^W] ^E Lrnc tdd|i|}|S)z MobileNet-EdgeTPU-v2 Small. r)mobilenet_edgetpu_v2_srrs rhrr'  # ] ]V\ ]E Lrnc tdd|i|}|S)z MobileNet-EdgeTPU-v2 Medium. r)mobilenet_edgetpu_v2_mrrs rhrr. rrnc tdd|i|}|S)z MobileNet-EdgeTPU-v2 Large. r)mobilenet_edgetpu_v2_lrrs rhrr5 rrnc tdd|i|}|S)Nr)test_efficientnet)rrs rhr r < s " X: XQW XE Lrnc Bt d|ttdd|}|S)Nrr<rr=)test_efficientnet_gn)rrrrs rhr#r#B s1 " q+5',cdBe qio qE Lrnc ,t d|td|}|S)Nr")test_efficientnet_ln)rrrs rhr%r%I s) " \+5. \TZ \E Lrnc Bt d|ttdd|}|S)Nrr<r")test_efficientnet_evos)rrrrs rhr'r'P s1 "  r-7GKdeDf rjp rE Lrntf_efficientnet_b0_aptf_efficientnet_b1_aptf_efficientnet_b2_aptf_efficientnet_b3_aptf_efficientnet_b4_aptf_efficientnet_b5_aptf_efficientnet_b6_aptf_efficientnet_b7_aptf_efficientnet_b8_aptf_efficientnet_b0_nstf_efficientnet_b1_nstf_efficientnet_b2_nstf_efficientnet_b3_nstf_efficientnet_b4_nstf_efficientnet_b5_nstf_efficientnet_b6_nstf_efficientnet_b7_nsr2r5rr)tf_efficientnet_l2_ns_475tf_efficientnet_l2_nstf_efficientnetv2_s_in21ft1ktf_efficientnetv2_m_in21ft1ktf_efficientnetv2_l_in21ft1ktf_efficientnetv2_xl_in21ft1ktf_efficientnetv2_s_in21ktf_efficientnetv2_m_in21ktf_efficientnetv2_l_in21ktf_efficientnetv2_xl_in21kefficientnet_b2aefficientnet_b3a mnasnet_a1 mnasnet_b1r)rF)rrNFFF)rrNFF)rrrNF)rrNF)rrrF)rrF)rrrNrF)r0)r functoolsrtypingrrrrrr rtorch.nnrQtorch.nn.functional functionalr timm.datar r r r timm.layersrrrrrrr_builderrr_efficientnet_blocksr_efficientnet_builderrrrrrrrr _featuresr!r"r# _manipulater$r% _registryr&r'r(__all__rr)r*rrrrrrr)r1rArMrPrWr_rgrnrsrxr{rrrrrr default_cfgsrrrrrrrrrr r rrrrrrrr#r&r)r,r0r2r5r8r?rBrFrIrMrOrRrTrVrXr[r]rardrfrhrkrorrrurzr}rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr r rrrrrrrr r#r%r'rrrnrhrVs!$J?? rr...G/JJJFF3YY 1 2Z299Zz[&299[&|4!H!H<;>JO!J;>9>"J< HPQ$)/f\aBaf@&T\a>fk%R\aB\aB\aBPQ/4PfBB.Vr0%&TV&TV&T v& TV &tv&t y &t v &tv& t ~ !&(*4 $*@%T,)&2+D $*@%T-3&<*4 $*@m3,=&H I&J  {"K&T U&Vt ~ W&\ !]&b !c&ht ~ i&pD v "q&xd x "y&Dt ~ E&J*4 $*@m3,HK&R*4 $*@ 3&%S ,:S&\t {%S :]&dt ~ FMad fe&l  FMad!fm&t D Hmcf!hu&|' Hsh)X}&B HtQV!XC&H  =Hu"NI&L  =Hu"NM&P  =Hu"NQ&T  =Hu"NU&\#DF]&^&tv_&`)$&a&b#D FM\_%ac&f&t FM\_(ag&j%dfk&nt ~ o&t  FU!Du&|t { Hu F}&F"4 E$G&L"4 E Hu$FM&V&tvW&X&tvY&Z&t}PVaf'g[&^!$ B#_&d#D FU%De&h#D FU%Di&l#D Hu%Fm&p#D Hu%Fq&v"4 { F4:P $Rw&@"4 { F4:P $RA&J"4 { H4:P $RK&V#D A -6\_%aW&^&t G -6\_(a_&f#D E -6\_%ag&n#D D -8^a%co&x!$ -6\_#ay&|!$ -8^a#c}&@!$ -8^a#cA&D"4 -8^a$cE&J%d B '"K&R%d B FU'DS&Z%d B FU'D[&b%d B Hu'Fc&j%d B Hu'Fk&r%d B Hu'Fs&z%d B Hu'F{&B%d B Hu'FC&J)$ F Hu+FK&R%d B Ht'ES&\!$ B $*@]#\]&d!$ B $*@ FU #De&n!$ B $*@ FU #Do&x!$ B $*@ Hu #Fy&B!$ B $*@ Hu #FC&L!$ B $*@ Hu #FM&V!$ B $*@ Hu #FW&`!$ B $*@ Hu #Fa&j!$ B $*@ Hu #Fk&v!$ B Hu#Fw&~!$ B Hu#F&F !$ B Hu#FG &P !$ B #"Q &X !$ B FU#DY &` !$ B FU#Da &h !$ B Hu#Fi &p !$ B Hu#Fq &x !$ D Hu#Fy &@ !$ B Hu#FA &H !$ D Hu#FI &R t A  "S &Z t A FU D[ &b t A FU Dc &j t A Hu Fk &r t A Hu Fs &z t A Hu F{ &D t ~ /  $E &N t ~ / FU  DO &X t ~ / Hu  FY &d $T E $*@&Be &l $T E $*@&Bm &t $T E $*@ FU &Du &@ !$ B / #A &L !$ B / FU #M &Z !$ B / FU #[ &h !$ B / HuT^ #`i &r !$ B / HuT^ #`s &~ ( M / -8^a *c &H ( M / -8^amu *wI &R ( M / -8^amu *wS &\ )$ P / -8^amu +w] &h  F / -8^a !ci &r  F / -8^amu !ws &|  F / -8^amu !w} &H  J /u -8^a "cI&R  J /u -8^amu "wS&\  J /u -8^amu "w]&f!$ M /u -8^amu #wg&r  G -6"Ss&z  G -6\a"c{&B  G -6\a"cC&J)$ $*@ -6\_ks+uK&R  G -6\a"cS&Z!$ $*@e -6\a#c[&d te&j tk&p tq&v xw&|DF}&@ wA&F wG&L wM&T F ` F ` F ` F ` F ` (, 3(0*. 3*0)- 3)037 $*@m43 )- 3)0$( FT$C'+ FT'C'+ / FT'C)- / FT)Cw& D| | | |      < L < < < < < < LLl  <<<<<<<<<<lLl <, << , lllll,,,|,||L L L \ lllllllllllllLLL|||     \\\LLL\l|||\ \ \ \ \  < , , , \     , H '9 '9 '9 '9 ' 9 ' 9 '9 '9 '9 '= '= '= '= '= '= ' =! 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