^j"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 ddZB ddZC ddZDddZE ddZF dd ZG dd!ZH dd"ZI dd#ZJ dd$ZKdd%ZLdd&ZMdd'ZNdd(ZOdd)ZPd d+ZQe3id,eQd-eQd.eQd/d01d2eQd3eQd4eQd5d01d6eQd7d01d8eQd9eQd:d01d;eQd0eedd?eQd0eedd@eQd0eedAdd0dddkddddo: d?eQd@d0ddddkdjd7idAeQdBd0dddkddddo: dCeQdDd0dddkddddo: dEeQdFd0dddGddkdjdH dIeQdJd0dddGdkddddoK dLeQdMd0dddGdkddddoK dNeQdOd0dddGdkddddoK dPeQdQd0dRddSTdUeQdVd0dRdZdSddWeQdXd0dYddddZeQd0eedZdd[dAdo[d\eQd]d0dZdd[dd^eQd0eedGdZdd[d_d`eQdad01dbeQdcd01ddeQded01dfeQdgd01dheQidieQdjd01dkeQdld01dmeQdnd01doeQdRdSdpd0qdreQdsdSdtd0qdueQdvdSdwd0qdxeQdydzd{d0qd|eQd}d~dd0qdeQddAdeQddAdeQddAdeQd0eedAdr?rBstagerrrL pool_typeN)super__init__nnReLU BatchNorm2drrr4r6rAgrad_checkpointingr conv_stembn1r Sequentialblocksfeatures feature_info stage_endsin_chs conv_headbn2r5head_hidden_sizeIdentityrrC classifierr)selfr3r4r5r6r7r8r9r:r;r<r=r>r?r@rArBrCrGrHddnorm_act_layerbuilderfhead_chs __class__s c/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/timm/models/efficientnet.pyrRzEfficientNet.__init__QsV  /( 12>> +J B,}&  ""'$Y/I&x> ! *8\1]h]Z\]DN%lGDGBGDH8D DD  5[[]DN{{}DH8@ @D  5,=      - "-  - )$/ "$')Bs0 Gcd|j|jg}|j|j|j|j|j |j g|jtj|j|jgtj|S)z3Convert model to sequential for feature extraction.) rWrXextendrZr_r`rCrSDropoutrArcrY)rdlayerss rk 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)stemrZ)dict)rdrrs rk group_matcherzEfficientNet.group_matchers*"&,"2JDQ,  rqenablec||_yzEnable or disable gradient checkpointing. Args: enable: Whether to enable gradient checkpointing. NrVrdrws rkset_grad_checkpointingz#EfficientNet.set_grad_checkpointing #)rqc|jS)zGet the classifier module.)rc)rds rkget_classifierzEfficientNet.get_classifiersrqcp||_t|j|j|\|_|_y)zReset the classifier head. Args: num_classes: Number of classes for new classifier. global_pool: Global pooling type. )rON)r4rr5rCrc)rdr4rCs rkreset_classifierzEfficientNet.reset_classifiers4',=   t//;-H)$/rqxindicesnorm stop_early output_fmtintermediates_only extra_blockscF|dvsJdg}|r&tt|jdz|\} } nMtt|j|\} } | D cgc]} |j| } } |j| } d} |j |}|j |}| | vr|j |tjjs|s |j} n|jd| } t| dD]Z\} }|jr+tjjs t||}n||}| | vsJ|j |\|r|S| |jdk(r"|j|}|j|}||fScc} w)aForward features that returns intermediates. Args: x: Input image tensor. indices: Take last n blocks if int, all if None, select matching indices if sequence. norm: Apply norm layer to compatible intermediates. stop_early: Stop iterating over blocks when last desired intermediate hit. output_fmt: Shape of intermediate feature outputs. intermediates_only: Only return intermediate features. extra_blocks: Include outputs of all blocks and head conv in output, does not align with feature_info. Returns: List of intermediate features or tuple of (final features, intermediates). )NCHWzOutput shape must be NCHW.rrN)start)r#lenrZr]rWrXappendtorchjit is_scripting enumeraterVr$r_r`)rdrrrrrrr intermediates take_indices max_indexifeat_idxrZblks rkforward_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<'$$Q'  (  tr* *q!A A-9FsF prune_norm prune_headc|r&tt|jdz|\}}n1tt|j|\}}|j|}|jd||_|s|t|jkr2t j |_t j |_|r|jdd|S)aPrune layers not required for specified intermediates. Args: indices: Indices of intermediate layers to keep. prune_norm: Whether to prune normalization layers. prune_head: Whether to prune the classifier head. extra_blocks: Include all blocks in indexing. Returns: List of indices that were kept. rNrr0) r#rrZr]rSrbr_r`r)rdrrrrrrs rkprune_intermediate_layersz&EfficientNet.prune_intermediate_layers s$ &:3t{{;Ka;OQX&Y #L)&:3t;OQX&Y #L) 2Ikk*9- S%55[[]DN{{}DH   ! !!R (rqc6|j|}|j|}|jr7tjj st |j|d}n|j|}|j|}|j|}|S)z/Forward pass through feature extraction layers.T)flatten) rWrXrVrrrr$rZr_r`rdrs rkforward_featureszEfficientNet.forward_features?st NN1  HHQK  " "599+A+A+Ct{{At>B  !t~~ FAq6DOOA$66rqcJ|j|}|j|}|S)z Forward pass.)rrrs rkforwardzEfficientNet.forwardZs'  ! !! $   a rqFT)r2)NFFrFF)rFTF)$__name__ __module__ __qualname____doc__rrintboolstrrrrfloatrRrSrYrprrignorerr rrvr|ModulerrTensorrrrrrr __classcell__rjs@rkr)r);s50 $ $$%"!#-1.2,0,0%3!$&$)^(!^(^( ^(  ^(  ^("^(^(^(^( *^(!+^(y)^(y)^(#^( !^(""#^($%^(* +^(@&r}}& YY D T#uS$Y?O:O5P  " YY)T)T)) YY  HC Hc Hd H8<$$',!&: ||: eCcN34:  :  :  : !%: :  tELL!5tELL7I)I#JJ K: |./$#!& 3S >*    c > %,, 5<<  7ell 7 7 7%,,rqc!eZdZdZdddddddddddded d ddfd ed eed fd edededede dedede e de e de e de e de de de f fd Zej j"d de ddfdZdeej(fdZxZS)!r*z EfficientNet Feature Extractor A work-in-progress feature extraction module for EfficientNet, to use as a backbone for segmentation and object detection models. )rrrIr. bottleneckr.r/Fr0Nr1r3 out_indices.feature_locationr6r7r8r9r:r;r<r=r>r?r@rArBc t|||d}| xstj} | xstj} t | | }| xst } ||_||_d|_ |s||}t|||fd| d||_ ||fddi||_ td || || | | | ||d |}tj||||_t!|j"||_|j$j'Dcic] }|d|d  c}|_t+|d|_|d k7r<|j$j'd }t/||j1|_yycc}w)NrFFrIrJrMT) r:r;r@r<r=r>r?rBrrNindexr)module hook_type)keysrP)rQrRrSrTrUrrr6rArVrrWrXrrYrZr!r[r\ get_dicts_stage_out_idxr feature_hooksr" named_modules)rdr3rrr6r7r8r9r:r;r<r=r>r?r@rArBrGrHrerfrgrhhooksrjs rkrRzEfficientNetFeatures.__init__hs*  /( 12>> +J B,}  ""'$Y/I&x6HILR[]TD,t:L0MabM" -(!  #!::lD1gWR^^5g_eOf5g#FG4 L 7J ?, ?E Lrqc 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_c160rrr r-r/r=Nr rrP) rrrurrrrSrUrrr) rrr r rrrrr@rrs rk_gen_mobilenet_v2rhs       H>6HIL" -(!  +TD,t:L0M!::lD1gWR^^5g_eOf5g#FG4 L 7J ?, ?E Lrqc 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=rPrrs rk _gen_fbnetcr*s  34`J  56  H"8,^8JK::lD1gWR^^5g_eOf5g   L 7J ?, ?E Lrqc 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=NrrPrrs rk _gen_spnasnetr2s ""  34 34 34  %&H "8,^8JK::lD1gWR^^5g_eOf5g   L 7J ?, ?E Lrqc *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=rP rrrurrrrSrUrr) rrr channel_divisorr rrrr@rrs rk_gen_efficientnetrBs8 %% $% $% $% %& %& %&H>6HRabL"8-=*U!$'!#FG4::lD1gWR^^5g_eOf5g  L 7J ?, ?E Lrqc $dgdgdgdgdgdgg}tt|}tdt||||d d ||j d d xs#tt j fit|t|d d|}t||fi|} | S)z Creates an EfficientNet-EdgeTPU model Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu er_r1_k3_s1_e4_c24_fc24_noskiper_r2_k3_s2_e8_c32er_r4_k3_s2_e8_c48ir_r5_k5_s2_e8_c96ir_r4_k5_s1_e8_c144ir_r2_k5_s2_e8_c192rr=r-r/r=Nrelur3r5r7r@r=r<rP rrrurrrSrUrrr rrr r rrrr@rrs rk_gen_efficientnet_edgerNs **      H>6HIL"8-=*U!$'!::lD1gWR^^5g_eOf5g#FF3  L 7J ?, ?E Lrqc (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 r4r5r6r7zir_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>rKrPrL) rrr rPrrrr@rrs rk_gen_efficientnet_condconvrQ s %% $% $% $% )* )* )*H>6HIL"8-=Rde!$'!::lD1gWR^^5g_eOf5g#FG4  L 7J ?, ?E Lrqc 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=rP) rurrrrrrSrUrrrrr rrrrrs rk_gen_efficientnet_literX@s&       H "8-=dS^8JK#FG4::lD1gWR^^5g_eOf5g   L 7J ?, ?E Lrqc &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=NsilurKrPrLrMs rk_gen_efficientnetv2_baser`is ##   $% %& %& H>6HVXYL"8-=*U!$'!::lD1gWR^^5g_eOf5g#FF3  L 7J ?, ?E Lrqc 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_rKrPrL) rrr r rwrrrr5r@rrs rk_gen_efficientnetv2_srhs ##   %& %& &' HL +, 56  >6HIL"8-=*U!,/!::lD1gWR^^5g_eOf5g#FF3  L 7J ?, ?E Lrqc 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-rfrr=Nr_rKrP rurrrrrSrUrrr rrr r rrrrrs rk_gen_efficientnetv2_mros ##   %& &' &' %&H"8-=*U^8JK::lD1gWR^^5g_eOf5g#FF3  L 7J ?, ?E Lrqc 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_rKrPrmrns rk_gen_efficientnetv2_lrt ##   &' &' &' %&H"8-=*U^8JK::lD1gWR^^5g_eOf5g#FF3  L 7J ?, ?E Lrqc 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 rqer_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_rKrPrmrns rk_gen_efficientnetv2_xlryrurqc 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_nrer7r8r9r:zer_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?rPr@) rrr rAr versionrrrr@rrs rk_gen_efficientnet_xr|s6,!| + , , - , - ( ) ) * ) * ) * , , , - , - ( ) ) * ) * ) * >6HRabL"8-=*U!$'!#FF3::lD1gWR^^5g_eOf5g  L 7J ?, ?E Lrqc dgddgddgddgdd gd d gg}tdt|d d tt||j ddxs#tt j fit|d|}t||fi|}|S)zCreates a MixNet Small model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet Paper: https://arxiv.org/abs/1907.09595 rSzir_r1_k3_a1.1_p1.1_s2_e6_c24zir_r1_k3_a1.1_p1.1_s1_e3_c24z ir_r1_k3.5.7_s2_e6_c40_se0.5_nsw(ir_r3_k3.5_a1.1_p1.1_s1_e6_c40_se0.5_nswz&ir_r1_k3.5.7_p1.1_s2_e6_c80_se0.25_nswz$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=rPrrs rk _gen_mixnet_srbs  ')GH +-WX 13YZ 68gh 13]^H"8,^8JK::lD1gWR^^5g_eOf5g   L 7J ?, ?E Lrqc 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_truncrrfrr=NrrPrrWs rk _gen_mixnet_mrs  +-KL -/YZ ,.]^ (*YZ .0Z[H"8-=7S^8JK::lD1gWR^^5g_eOf5g   L 7J ?, ?E Lrqc 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. r4r5r6r7r8r9r:rrr-rNr/Trr>r=rVrP) rurrrrrrrSrUrr)r model_widthr rrrrrs rk _gen_tinynetrs %%(C'D $%(C'D %&)E(F %& H  "8-=7S~dKDIJ^ D#FG4::lD1gWR^^5g_eOf5g   L 7J ?, ?E Lrqc d|vrgd}d}d}d}t|d} dttdtfd} d |vr d }d }gd } n%d |vrgd} nd|vrgd} d}nd|vr d}d}gd} d}nJ| | |} n'd }d }d}t|d} dgddgddgddgddgdd gd!gg} td&t | ||||t t |"|jd#d$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-rJchsr 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_crIer_r1_k3_s1_e4_cr.ir_r3_k3_s1_e4_cir_r1_k3_s1_e8_crir_r1_k3_s2_e8_crrP)rr s rk _arch_defz)_gen_mobilenet_edgetpu.._arch_defs!Q)*#CF8,0A*RPSTUPVx.XY's1vh/' |2c!fX>&s1vh/' |2c!fX> $CF8,0@Q.IJ#CF8,0@Q.IJ#CF8,0@Q.IJ#CF8,-' rq edgetpu_v2_xsr/r.)r)r/0` edgetpu_v2_s)rfrrrr edgetpu_v2_m)r/rPrr@i@ edgetpu_v2_lr)r/rrrrricn_r1_k1_s1_c16er_r1_k3_s2_e8_c32er_r3_k3_s1_e4_c32er_r1_k3_s2_e8_c48er_r3_k3_s1_e4_c48ir_r1_k3_s2_e8_c96ir_r3_k3_s1_e4_c96ir_r1_k3_s1_e8_c96_noskipir_r1_k5_s2_e8_c160ir_r3_k5_s1_e4_c160ir_r1_k3_s1_e8_c192rr=N)r3r5r7r8r@r=r<rP) rrrrurrrrrSrUrr)rrr rrr7r8r r5r<rchannelsrrrs rk_gen_mobilenet_edgetpursw   %ff5  49 # . g %I 6H w &7H w &7HL w & J7HL 5Xz2  %ff5   !#7 8 !#7 8 !#7 8 (*> ? 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For more info: https://github.com/DeGirum/pruned-models/releases/tag/efficientnet_v1.0rr.)efficientnet_es_prunedr_rs rkrbrb. #  q583[e qio qE Lrqc &t ddd|d|}|S)z EfficientNet-Edge-Medium. rr#r.)efficientnet_emr_rs rkrerer`rqc &t ddd|d|}|S)z EfficientNet-Edge-Large. r rr.)efficientnet_elr_rs rkrgrgr`rqc &t ddd|d|}|S)zw EfficientNet-Edge-Large pruned. For more info: https://github.com/DeGirum/pruned-models/releases/tag/efficientnet_v1.0r rr.)efficientnet_el_prunedr_rs rkrirircrqc &t ddd|d|}|S)' EfficientNet-CondConv-B0 w/ 8 Experts rr.)efficientnet_cc_b0_4erQrs rkrlrls. ' p47#Zd phn pE Lrqc (t dddd|d|}|S)rkrrIrr rPr)efficientnet_cc_b0_8ermrs rkrprp0 ' )47#bc )!' )E Lrqc (t dddd|d|}|S)z' EfficientNet-CondConv-B1 w/ 8 Experts rr#rIro)efficientnet_cc_b1_8ermrs rkrsrsrqrqc &t ddd|d|}|S) EfficientNet-Lite0 rr.)efficientnet_lite0rXrs rkrvrv. # m14sWa mek mE Lrqc &t ddd|d|}|S) EfficientNet-Lite1 rr#r.)efficientnet_lite1rwrs rkr{r{rxrqc &t ddd|d|}|S) EfficientNet-Lite2 r#r r.)efficientnet_lite2rwrs rkr~r~ rxrqc &t ddd|d|}|S) EfficientNet-Lite3 r rr.)efficientnet_lite3rwrs rkrr rxrqc &t ddd|d|}|S) EfficientNet-Lite4 rr:r.)efficientnet_lite4rwrs rkrr rxrqc ||jdt|jddd}t|fddd|d|}|S) zc EfficientNet-B1 Pruned. The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf r(r;sameefficientnet_b1_prunedrr#Trr prunedrr*r rB)rrrrs rkrr sV h 12 j&)&G  m$'#dWa mek mE Lrqc 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#r Tr)efficientnet_b2_prunedrrs rkrr( Q h 12 j&)   )583W[ )!' )E Lrqc 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;rr rTr)efficientnet_b3_prunedrrs rkrr3 rrqc (t dddd|d|}|S)z; EfficientNet-V2 Tiny (Custom variant, tiny not in paper). 皙?rFrr rgr)efficientnetv2_rw_trhrs rkrr> s1 " x25PUbl xpv xE Lrqc *t ddddd|d|}|S)zR EfficientNet-V2 Tiny w/ Global Context Attn (Custom variant, tiny not in paper). rrFgc)rr rgr?r)gc_efficientnetv2_rw_trrs rkrrF s4 "  B583 4J B:@ BE Lrqc "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)rgr)efficientnetv2_rw_srrs rkrrO s " bDZ b[a bE Lrqc (t dddd|d|}|S)z* EfficientNet-V2 Medium (RW variant). r )r r r r r>r>Tr)efficientnetv2_rw_mrrs rkrrY s1 " )25H_dh )!' )E Lrqc tdd|i|}|S)z EfficientNet-V2 Small. r)efficientnetv2_srrs rkrrc  " V Vv VE Lrqc tdd|i|}|S)z EfficientNet-V2 Medium. r)efficientnetv2_m)rors rkrrj rrqc tdd|i|}|S)z EfficientNet-V2 Large. r)efficientnetv2_l)rtrs rkrrq rrqc tdd|i|}|S)z EfficientNet-V2 Xtra-Large. r)efficientnetv2_xl)ryrs rkrrx s # X: XQW XE Lrqc v|jdt|jddt ddd|d|}|S)z1 EfficientNet-B0. Tensorflow compatible variant r(r;rrr.)tf_efficientnet_b0rrs rkrr O h 12 j&)  m14sWa mek mE Lrqc v|jdt|jddt ddd|d|}|S)z1 EfficientNet-B1. Tensorflow compatible variant r(r;rrr#r.)tf_efficientnet_b1rrs rkrr rrqc v|jdt|jddt ddd|d|}|S)z1 EfficientNet-B2. Tensorflow compatible variant r(r;rr#r r.)tf_efficientnet_b2rrs rkrr rrqc v|jdt|jddt ddd|d|}|S)z0 EfficientNet-B3. Tensorflow compatible variant r(r;rr rr.)tf_efficientnet_b3rrs rkrr rrqc v|jdt|jddt ddd|d|}|S)z0 EfficientNet-B4. Tensorflow compatible variant r(r;rrr:r.)tf_efficientnet_b4rrs rkrr rrqc v|jdt|jddt ddd|d|}|S)z0 EfficientNet-B5. Tensorflow compatible variant r(r;rr>r?r.)tf_efficientnet_b5rrs rkrr rrqc v|jdt|jddt ddd|d|}|S)z0 EfficientNet-B6. Tensorflow compatible variant r(r;rr:rBr.)tf_efficientnet_b6rrs rkrr O h 12 j&)  m14sWa mek mE Lrqc v|jdt|jddt ddd|d|}|S)z0 EfficientNet-B7. Tensorflow compatible variant r(r;rrErFr.)tf_efficientnet_b7rrs rkrr rrqc v|jdt|jddt ddd|d|}|S)z0 EfficientNet-B8. Tensorflow compatible variant r(r;rr?rIr.)tf_efficientnet_b8rrs rkrr rrqc v|jdt|jddt ddd|d|}|S)z= EfficientNet-L2 NoisyStudent. Tensorflow compatible variant r(r;rrLrMr.)tf_efficientnet_l2rrs rkrr rrqc v|jdt|jddt ddd|d|}|S)z9 EfficientNet-Edge Small. Tensorflow compatible variant r(r;rrr.)tf_efficientnet_esr*r rNrs rkrr O h 12 j&) " m14sWa mek mE Lrqc v|jdt|jddt ddd|d|}|S)z: EfficientNet-Edge-Medium. Tensorflow compatible variant r(r;rrr#r.)tf_efficientnet_emrrs rkrr rrqc v|jdt|jddt ddd|d|}|S)z9 EfficientNet-Edge-Large. Tensorflow compatible variant r(r;rr rr.)tf_efficientnet_elrrs rkrr rrqc 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 rQrs rkrr sO h 12 j&) &" s7:S]g skq sE Lrqc x|jdt|jddt dddd|d|}|S)zF EfficientNet-CondConv-B0 w/ 8 Experts. Tensorflow compatible variant r(r;rrrIro)tf_efficientnet_cc_b0_8errs rkrr Q h 12 j&) &" )7:Sef )!' )E Lrqc x|jdt|jddt dddd|d|}|S) zF EfficientNet-CondConv-B1 w/ 8 Experts. Tensorflow compatible variant r(r;rrr#rIro)tf_efficientnet_cc_b1_8errs rkrr rrqc v|jdt|jddt ddd|d|}|S)rur(r;rrr.)tf_efficientnet_lite0r*r rXrs rkrr( O h 12 j&) " p47#Zd phn pE Lrqc v|jdt|jddt ddd|d|}|S)rzr(r;rrr#r.)tf_efficientnet_lite1rrs rkrr3 rrqc v|jdt|jddt ddd|d|}|S)r}r(r;rr#r r.)tf_efficientnet_lite2rrs rkrr> rrqc v|jdt|jddt ddd|d|}|S)rr(r;rr rr.)tf_efficientnet_lite3rrs rkrrI rrqc v|jdt|jddt ddd|d|}|S)rr(r;rrr:r.)tf_efficientnet_lite4rrs rkrrT rrqc p|jdt|jddtdd|i|}|S)z7 EfficientNet-V2 Small. Tensorflow compatible variant r(r;rr)tf_efficientnetv2_s)r*r rhrs rkrr_ = h 12 j&) ! YJ YRX YE Lrqc p|jdt|jddtdd|i|}|S)z8 EfficientNet-V2 Medium. Tensorflow compatible variant r(r;rr)tf_efficientnetv2_m)r*r rors rkrrh rrqc p|jdt|jddtdd|i|}|S)z7 EfficientNet-V2 Large. Tensorflow compatible variant r(r;rr)tf_efficientnetv2_l)r*r rtrs rkrrq rrqc p|jdt|jddtdd|i|}|S)z? EfficientNet-V2 Xtra-Large. Tensorflow compatible variant r(r;rr)tf_efficientnetv2_xl)r*r ryrs rkrrz s= h 12 j&) " [j [TZ [E Lrqc p|jdt|jddtdd|i|}|S)z4 EfficientNet-V2-B0. Tensorflow compatible variant r(r;rr)tf_efficientnetv2_b0r*r r`rs rkrr s= h 12 j&) $ ] ]V\ ]E Lrqc v|jdt|jddt ddd|d|}|S)z4 EfficientNet-V2-B1. Tensorflow compatible variant r(r;rrr#r.)tf_efficientnetv2_b1rrs rkrr O h 12 j&) $ o36Yc ogm oE Lrqc v|jdt|jddt ddd|d|}|S)z4 EfficientNet-V2-B2. Tensorflow compatible variant r(r;rr#r r.)tf_efficientnetv2_b2rrs rkrr rrqc v|jdt|jddt ddd|d|}|S)z3 EfficientNet-V2-B3. Tensorflow compatible variant r(r;rr rr.)tf_efficientnetv2_b3rrs rkrr rrqc &t ddd|d|}|S)r7r rr.)efficientnet_x_b3r|rs rkrr s.  l03cV` ldj lE Lrqc &t ddd|d|}|S)r=r>r?r.)efficientnet_x_b5rrs rkrr s.  l03cV` ldj lE Lrqc (t dddd|d|}|S)r=gQ?r?rI)rr r{r)efficientnet_h_b5rrs rkrr s1  x04sTUbl xpv xE Lrqc $t dd|d|}|S)z"Creates a MixNet Small model. rrr)mixnet_s)rrs rkrr +  M'*z MEK ME Lrqc $t dd|d|}|S)z#Creates a MixNet Medium model. rr)mixnet_mrrs rkrr rrqc $t dd|d|}|S)z"Creates a MixNet Large model. ?r)mixnet_lrrs rkrr rrqc &t ddd|d|}|S)zgCreates a MixNet Extra-Large model. Not a paper spec, experimental def by RW w/ depth scaling. r>r r.) mixnet_xlrrs rkrr s-  d(+cj d\b dE Lrqc &t ddd|d|}|S)znCreates a MixNet Double Extra Large model. Not a paper spec, experimental def by RW w/ depth scaling. g333333@rr.) mixnet_xxlrrs rkrr s-  e),sz e]c eE Lrqc t|jdt|jddt dd|d|}|S)z@Creates a MixNet Small model. Tensorflow compatible variant r(r;rrr) tf_mixnet_s)r*r rrs rkrr L h 12 j&)  P*-* PHN PE Lrqc t|jdt|jddt dd|d|}|S)zACreates a MixNet Medium model. Tensorflow compatible variant r(r;rrr) tf_mixnet_mr*r rrs rkrr rrqc t|jdt|jddt dd|d|}|S)z@Creates a MixNet Large model. Tensorflow compatible variant r(r;rrr) tf_mixnet_lrrs rkrr rrqc tdd|i|}|S)N) tinynet_arr rrrs rkrr s  P: P PE Lrqc tdd|i|}|S)N) tinynet_brr#rr rs rkr r    QJ Q& QE Lrqc tdd|i|}|S)N) tinynet_cHzG?g333333?rr rs rkrr s  RZ R6 RE Lrqc tdd|i|}|S)N) tinynet_drg= ףp=?rr rs rkrr& s  Sj SF SE Lrqc tdd|i|}|S)N) tinynet_egRQ?g333333?rr rs rkrr, r rqc tdd|i|}|S)z MobileNet-EdgeTPU-v1 100. r)mobilenet_edgetpu_100rrs rkrr2 s # \z \U[ \E Lrqc tdd|i|}|S)z# MobileNet-EdgeTPU-v2 Extra Small. r)mobilenet_edgetpu_v2_xsrrs rkrr9 s # ^ ^W] ^E Lrqc tdd|i|}|S)z MobileNet-EdgeTPU-v2 Small. r)mobilenet_edgetpu_v2_srrs rkrr@  # ] ]V\ ]E Lrqc tdd|i|}|S)z MobileNet-EdgeTPU-v2 Medium. r)mobilenet_edgetpu_v2_mrrs rkrrG rrqc tdd|i|}|S)z MobileNet-EdgeTPU-v2 Large. r)mobilenet_edgetpu_v2_lrrs rkrrN rrqc tdd|i|}|S)Nr)test_efficientnet)rrs rkr!r!U s " X: XQW XE Lrqc bt d||jdttdd|}|S)Nr=rr=rr=)test_efficientnet_gn)rrrrrs rkr$r$[ s? # ::lGLQ,OP    E Lrqc Lt d||jdtd|}|S)Nr=r#)test_efficientnet_ln)rrrrs rkr&r&g s6 " ::lN;    E Lrqc bt d||jdttdd|}|S)Nr=rr=r#)test_efficientnet_evos)rrrrrs rkr(r(r s= "  ::lGKA,NO    E Lrqtf_efficientnet_b0_aptf_efficientnet_b1_aptf_efficientnet_b2_aptf_efficientnet_b3_aptf_efficientnet_b4_aptf_efficientnet_b5_aptf_efficientnet_b6_aptf_efficientnet_b7_aptf_efficientnet_b8_aptf_efficientnet_b0_nstf_efficientnet_b1_nstf_efficientnet_b2_nstf_efficientnet_b3_nstf_efficientnet_b4_nstf_efficientnet_b5_nstf_efficientnet_b6_nstf_efficientnet_b7_nsr5r8rr)tf_efficientnet_l2_ns_475tf_efficientnet_l2_nstf_efficientnetv2_s_in21ft1ktf_efficientnetv2_m_in21ft1ktf_efficientnetv2_l_in21ft1ktf_efficientnetv2_xl_in21ft1ktf_efficientnetv2_s_in21ktf_efficientnetv2_m_in21ktf_efficientnetv2_l_in21ktf_efficientnetv2_xl_in21kefficientnet_b2aefficientnet_b3a mnasnet_a1 mnasnet_b1r)rF)rrNFFF)rrNFF)rrrNF)rrNF)rrrF)rrF)rrrNrF)r0)r functoolsrtypingrrrrrr rtorch.nnrStorch.nn.functional functionalr timm.datar r r r timm.layersrrrrrrr_builderrr_efficientnet_blocksr_efficientnet_builderrrrrrrrr _featuresr!r"r# _manipulater$r% _registryr&r'r(__all__rr)r*rrrrrrr*r2rBrNrQrXr`rhrortryr|rrrrrr default_cfgsrrrrrrrrr r rrrrrrrr"r&r)r,r/r3r5r8r;r@rCrGrJrNrPrSrUrWrYr\r^rbrergrirlrprsrvr{r~rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr rrrrrrrrr!r$r&r(rrPrqrkrWs"$J?? rr...G/JJJFF3YY 1 2c299cL `&299`&F6!H!H<;>JO!J;>9>"J< HPQ$)/f\aBaf@&T\a>fk%R\aB\aB\aBPQ/4PfBB.Vr0%I &TVI &TVI &T vI & TV I &tvI &t y I &t v I &tvI & t ~ !I &(*4 $*@%T,)I &2+D $*@%T-3I &<*4 $*@m3,=I &H II &J  {"KI &T UI &Vt ~ WI &\ !]I &b !cI &ht ~ iI &pD v "qI &xd x "yI &Dt ~ EI &J*4 $*@m3,HKI &R*4 $*@ 3&%S ,:SI &\t {%S :]I &dt ~ FMad feI &l  FMad!fmI &t D Hmcf!huI &|' Hsh)X}I &B HtQV!XCI &H  =Hu"NII &L  =Hu"NMI &P  =Hu"NQI &T  =Hu"NUI &\#DF]I &^&tv_I &`)$&aI &b#D FM\_%acI &f&t FM\_(agI &j%dfkI &l*4 HsM,;mI &t"4 =FT$KuI &x*4 HsM,;yI &Bt ~ CI &H  FU!DII &Pt { Hu FQI &Z"4 E$[I &`"4 E Hu$FaI &j&tvkI &l&tvmI &n&t}PVaf'goI &r!$ B#sI &x#D FU%DyI &|#D FU%D}I &@#D Hu%FAI &D#D Hu%FEI &J"4 { F4:P $RKI &T"4 { F4:P $RUI &^"4 { H4:P $R_I &j#D A -6\_%akI &r&t G -6\_(asI &z#D E -6\_%a{I &B#D D -8^a%cCI &L!$ -6\_#aMI &P!$ -8^a#cQI &T!$ -8^a#cUI &X"4 -8^a$cYI &^%d B '"_I &f%d B FU'DgI &n%d B FU'DoI &v%d B Hu'FwI &~%d B Hu'FI &F%d B Hu'FGI &N%d B Hu'FOI &V%d B Hu'FWI &^)$ F Hu+F_I &f%d B Ht'EgI &p!$ B $*@]#\qI &x!$ B $*@ FU #DyI &B!$ B $*@ FU #DCI &L!$ B $*@ Hu #FMI &V!$ B $*@ Hu #FWI &`!$ B $*@ Hu #FaI &j!$ B $*@ Hu #FkI &t!$ B $*@ Hu #FuI &~!$ B $*@ Hu #FI &J !$ B Hu#FK I &R !$ B Hu#FS I &Z !$ B Hu#F[ I &d !$ B #"e I &l !$ B FU#Dm I &t !$ B FU#Du I &| !$ B Hu#F} I &D !$ B Hu#FE I &L !$ D Hu#FM I &T !$ B Hu#FU I &\ !$ D Hu#F] I &f t A  "g I &n t A FU Do I &v t A FU Dw I &~ t A Hu F I &F t A Hu FG I &N t A Hu FO I &X t ~ /  $Y I &b t ~ / FU  Dc I &l t ~ / Hu  Fm I &x $T E $*@&By I &@ $T E $*@&BA I &H $T E $*@ FU &DI I &T !$ B / #U I &` !$ B / FU #a I &n !$ B / FU #o I &| !$ B / HuT^ #`} I &F !$ B / HuT^ #`G I &R ( M / -8^a *cS I &\ ( M / -8^amu *w] I &f ( M / -8^amu *wg I &p )$ P / -8^amu +wq I &|  F / -8^a !c} I &F F / -8^amu !wGI &P F / -8^amu !wQI &\  J /u -8^a "c]I &f  J /u -8^amu "wgI &p  J /u -8^amu "wqI &z!$ M /u -8^amu #w{I &F  G -6"SGI &N  G -6\a"cOI &V  G -6\a"cWI &^)$ $*@ -6\_ks+u_I &f  G -6\a"cgI &n!$ $*@e -6\a#coI &x tyI &~ tI &D tEI &J xKI &PDFQI &T wUI &Z w[I &` waI &hd F `iI &pd F `qI &xd F `yI &@d F `AI &Hd F `II &R&t 3(0SI &X( 3*0YI &^' 3)0_I &d1$ $*@m43eI &n' 3)0oI &v"4 FT$CwI &|%d FT'C}I &B%d / FT'CCI &J' / FT)CKI &I  X| | | |      < L < < < < < < LLl  <<<<<<<<<<lLl <, << , lllll,,,|,||L L L \ lllllllllllllLLL|||     \\\LLL\l|||\ \ \ \ \  < , , , \   ,H '9 '9 '9 '9 ' 9 ' 9 '9 '9 '9 '= '= '= '= '= '= ' =! 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