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The C and D variants are also discussed in the 'Bag of Tricks' paper: https://arxiv.org/pdf/1812.01187. The B variant is equivalent to torchvision default. ResNet variants (the same modifications can be used in SE/ResNeXt models as well): * normal, b - 7x7 stem, stem_width = 64, same as torchvision ResNet, NVIDIA ResNet 'v1.5', Gluon v1b * c - 3 layer deep 3x3 stem, stem_width = 32 (32, 32, 64) * d - 3 layer deep 3x3 stem, stem_width = 32 (32, 32, 64), average pool in downsample * e - 3 layer deep 3x3 stem, stem_width = 64 (64, 64, 128), average pool in downsample * s - 3 layer deep 3x3 stem, stem_width = 64 (64, 64, 128) * t - 3 layer deep 3x3 stem, stem width = 32 (24, 48, 64), average pool in downsample * tn - 3 layer deep 3x3 stem, stem width = 32 (24, 32, 64), average pool in downsample ResNeXt * normal - 7x7 stem, stem_width = 64, standard cardinality and base widths * same c,d, e, s variants as ResNet can be enabled SE-ResNeXt * normal - 7x7 stem, stem_width = 64 * same c, d, e, s variants as ResNet can be enabled SENet-154 - 3 layer deep 3x3 stem (same as v1c-v1s), stem_width = 64, cardinality=64, reduction by 2 on width of first bottleneck convolution, 3x3 downsample convs after first block r+rwavgrr*FNrpTcstt||pt}|dvs$J||_||_d|_t|}t|}d| v}|rZ| dnd}|r| | f}d| vrd| d| f}t j t j ||d ddd dd ||d |d d t j |d |d dd d dd ||d |d d t j |d |dd d dd g|_ nt j ||ddddd|_ |||_ |d d |_t|dddg|_| rt j tdt j ||d|rdd ndd dd |durt||ddnd|||d d g|_n\|durt|t jr|d|_n$t j t jdd d d||ddg|_nt jddd d|_gd}t||||f|||| || |||||d |\}}|D]}|j|q<|j|d|j|_t|j|j|d\|_|_|j|ddS)a Args: block (nn.Module): class for the residual block. Options are BasicBlock, Bottleneck. layers (List[int]) : number of layers in each block num_classes (int): number of classification classes (default 1000) in_chans (int): number of input (color) channels. (default 3) output_stride (int): output stride of the network, 32, 16, or 8. (default 32) global_pool (str): Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax' (default 'avg') cardinality (int): number of convolution groups for 3x3 conv in Bottleneck. (default 1) base_width (int): bottleneck channels factor. `planes * base_width / 64 * cardinality` (default 64) stem_width (int): number of channels in stem convolutions (default 64) stem_type (str): The type of stem (default ''): * '', default - a single 7x7 conv with a width of stem_width * 'deep' - three 3x3 convolution layers of widths stem_width, stem_width, stem_width * 2 * 'deep_tiered' - three 3x3 conv layers of widths stem_width//4 * 3, stem_width, stem_width * 2 replace_stem_pool (bool): replace stem max-pooling layer with a 3x3 stride-2 convolution block_reduce_first (int): Reduction factor for first convolution output width of residual blocks, 1 for all archs except senets, where 2 (default 1) down_kernel_size (int): kernel size of residual block downsample path, 1x1 for most, 3x3 for senets (default: 1) avg_down (bool): use avg pooling for projection skip connection between stages/downsample (default False) act_layer (str, nn.Module): activation layer norm_layer (str, 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This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels in the deep stem and ECA attn. rrwrTecarGrrrrrr ecaresnet26trrrrrrLs   rLc Ks<ttgddddtddd}td|fit|fi|S) z-Constructs a ResNet-50-D model with eca. r!rwrTrIrJrK ecaresnet50drrrrrrMs rMc Ks@ttgddddtddd}td|fd dit|fi|S) zConstructs a ResNet-50-D model pruned with eca. The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf r!rwrTrIrJrKecaresnet50d_prunedprunedrrrrrrNs rNc Ks<ttgddddtddd}td|fit|fi|S) zConstructs an ECA-ResNet-50-T model. Like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels in the deep stem and ECA attn. r!rwrTrIrJrK ecaresnet50trrrrrrPs   rPcKs:ttgdddtddd}td|fit|fi|S)z3Constructs a ResNet-50-D light model with eca. )rr r+rwTrIrJ)rrrrrecaresnetlightrrrrrrRs  rRc Ks<ttgddddtddd}td|fit|fi|S) z.Constructs a ResNet-101-D model with eca. r-rwrTrIrJrK ecaresnet101drrrrrrSs rSc Ks@ttgddddtddd}td|fd dit|fi|S) zConstructs a ResNet-101-D model pruned with eca. The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf r-rwrTrIrJrKecaresnet101d_prunedrOrrrrrrTs rTc Ks<ttgddddtddd}td|fit|fi|S) z.Constructs a ResNet-200-D model with ECA. r9rwrTrIrJrK ecaresnet200drrrrrrUs rUc Ks<ttgddddtddd}td|fit|fi|S) z.Constructs a ResNet-269-D model with ECA. r+0rrwrTrIrJrK ecaresnet269drrrrrrYs rYc Ks@ttgddddddtddd}td |fit|fi|S) zConstructs an ECA-ResNeXt-26-T model. This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels in the deep stem. This model replaces SE module with the ECA module rrwr_rTrIrJrrrArBrrrrecaresnext26t_32x4drrrrrr['s  r[c Ks@ttgddddddtddd}td |fit|fi|S) zConstructs an ECA-ResNeXt-50-T model. This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels in the deep stem. This model replaces SE module with the ECA module rrwr_rTrIrJrZecaresnext50t_32x4drrrrrr\3s  r\cKs6ttgdtddd}td|fit|fi|S)Nrr:rJrrr seresnet18rrrrrr^?sr^cKs6ttgdtddd}td|fit|fi|S)Nr!r:rJr] seresnet34rrrrrr_Esr_cKs6ttgdtddd}td|fit|fi|S)Nr!r:rJr] seresnet50rrrrrr`Ksr`c Ks<ttgddddtddd}td|fit|fi|S) Nr!rwrTr:rJrK seresnet50trrrrrraQs   racKs6ttgdtddd}td|fit|fi|S)Nr-r:rJr] seresnet101rrrrrrbYsrbcKs6ttgdtddd}td|fit|fi|S)Nr3r:rJr] seresnet152rrrrrrc_srcc Ks<ttgddddtddd}td|fit|fi|S) Nr3rwrTr:rJrK seresnet152drrrrrrdes   rdc Ks<ttgddddtddd}td|fit|fi|S) z2Constructs a ResNet-200-D model with SE attn. r9rwrTr:rJrK seresnet200drrrrrrems   rec Ks<ttgddddtddd}td|fit|fi|S) z2Constructs a ResNet-269-D model with SE attn. rVrwrTr:rJrK seresnet269drrrrrrfws   rfc Ks@ttgddddddtddd}td |fit|fi|S) zConstructs a SE-ResNeXt-26-D model.` This is technically a 28 layer ResNet, using the 'D' modifier from Gluon / bag-of-tricks for combination of deep stem and avg_pool in downsample. rrwr_rTr:rJrZseresnext26d_32x4drrrrrrgs  rgc Ks@ttgddddddtddd}td |fit|fi|S) zConstructs a SE-ResNet-26-T model. This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels in the deep stem. rrwr_rTr:rJrZseresnext26t_32x4drrrrrrhs  rhcKstfd|i|S)zConstructs a SE-ResNeXt-26-T model. NOTE I deprecated previous 't' model defs and replaced 't' with 'tn', this was the only tn model of note so keeping this def for backwards compat with any uses out there. Old 't' model is lost. r)rh)rrrrrseresnext26tn_32x4dsricKs:ttgdddtddd}td|fit|fi|S)Nr!rwr_r:rJrrrArBrseresnext50_32x4drrrrrrks  rkcKs:ttgdddtddd}td|fit|fi|S)Nr-rwr_r:rJrjseresnext101_32x4drrrrrrls  rlcKs:ttgdddtddd}td|fit|fi|S)Nr-rwrr:rJrjseresnext101_32x8drrrrrrms  rmc Ks@ttgddddddtddd}td |fit|fi|S) Nr-rwrrTr:rJrZseresnext101d_32x8drrrrrrns  rncKs:ttgdddtddd}td|fit|fi|S)Nr-r*r_r:rJrjseresnext101_64x4drrrrrros  roc Ks@ttgddddddtddd }td |fit|fi|S) Nr3r*r_rr+rr:rJ)rrrArBrrrrsenet154rrrrrrps  rpcKs0ttgdtd}td|fit|fi|S)z9Constructs a ResNet-18 model with blur anti-aliasing rrrr' resnetblur18)r}rr rrrrrrrsrrcKs0ttgdtd}td|fit|fi|S)z9Constructs a ResNet-50 model with blur anti-aliasing r!rq resnetblur50r}rr rrrrrrssrscKs6ttgdtdddd}td|fit|fi|S)z;Constructs a ResNet-50-D model with blur anti-aliasing r!rwrTrrr'rrr resnetblur50drtrrrrrvs  rvcKs6ttgdtdddd}td|fit|fi|S)zConstructs a ResNet-50-D model with avgpool anti-aliasing r!rwrTru resnetaa50drzrrrrr{s  r{cKs8ttgdtjdddd}td|fit|fi|S)z?Constructs a ResNet-101-D model with avgpool anti-aliasing r-rwrTru resnetaa101drzrrrrr|s  r|c Ks@ttgdtjdddtddd}td|fit|fi|S) zAConstructs a SE=ResNet-50-D model with avgpool anti-aliasing r!rwrTr:rJ)rrr'rrrr seresnetaa50drzrrrrr}s  r}c KsDttgddddddtjtddd }td |fit|fi|S) zIConstructs a SE=ResNeXt-101-D 32x8d model with avgpool anti-aliasing r-rwrrTr:rJ) rrrArBrrrr'rseresnextaa101d_32x8drzrrrrr~&s   r~c KsNttddd}ttgdddddt|dd }td |fit|fi|S) zConstructs a ResNet-RS-50 model. Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579 Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs r:rrZrd_ratior!rwrTrJrrrrrrr resnetrs50rr r}rrrrrGrrrrr1s  rc KsNttddd}ttgdddddt|dd }td |fit|fi|S) zConstructs a ResNet-RS-101 model. Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579 Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs r:rrrr-rwrTrJr resnetrs101rrrrrr>s  rc KsNttddd}ttgdddddt|dd }td |fit|fi|S) zConstructs a ResNet-RS-152 model. Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579 Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs r:rrrr3rwrTrJr resnetrs152rrrrrrKs  rc KsNttddd}ttgdddddt|dd }td |fit|fi|S) zConstructs a ResNet-RS-200 model. Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579 Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs r:rrrr9rwrTrJr resnetrs200rrrrrrXs  rc KsNttddd}ttgdddddt|dd }td |fit|fi|S) zConstructs a ResNet-RS-270 model. Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579 Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs r:rrr)r_5r_rwrTrJr resnetrs270rrrrrres  rc KsNttddd}ttgdddddt|dd }td |fit|fi|S) zConstructs a ResNet-RS-350 model. Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579 Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs r:rrr)r_r4Hr_rwrTrJr resnetrs350rrrrrrss  rc KsNttddd}ttgdddddt|dd }td |fit|fi|S) zConstructs a ResNet-RS-420 model Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579 Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs r:rrr)r_,Wr_rwrTrJr resnetrs420rrrrrrs  rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr r r r r rrrrr)*Z tv_resnet34Z tv_resnet50Z tv_resnet101Z tv_resnet152Ztv_resnext50_32x4dZig_resnext101_32x8dZig_resnext101_32x16dZig_resnext101_32x32dZig_resnext101_32x48dZ ssl_resnet18Z ssl_resnet50Zssl_resnext50_32x4dZssl_resnext101_32x4dZssl_resnext101_32x8dZssl_resnext101_32x16dZ swsl_resnet18Z swsl_resnet50Zswsl_resnext50_32x4dZswsl_resnext101_32x4dZswsl_resnext101_32x8dZswsl_resnext101_32x16dZgluon_resnet18_v1bZgluon_resnet34_v1bZgluon_resnet50_v1bZgluon_resnet101_v1bZgluon_resnet152_v1bZgluon_resnet50_v1cZgluon_resnet101_v1cZgluon_resnet152_v1cZgluon_resnet50_v1dZgluon_resnet101_v1dZgluon_resnet152_v1dZgluon_resnet50_v1sZgluon_resnet101_v1sZgluon_resnet152_v1sZgluon_resnext50_32x4dZgluon_resnext101_32x4dZgluon_resnext101_64x4dZgluon_seresnext50_32x4dZgluon_seresnext101_32x4dZgluon_seresnext101_64x4dZgluon_senet154)r)rT)rrNN)rrNN)rp)rrwrFrprp)F)r)r)r)r)r)r)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)F)rrb functoolsrrZtorch.nnr#Ztorch.nn.functionalZ functionalrZ timm.datarrZ timm.layersrrrr r r r r rrZ_builderrZ _manipulater _registryrrr__all__r r)ModulerrrlrorvrrrrrrrrrZ default_cfgsrrrr r"r#r$r%r&r'r)r*r+r,r/r0r1r2r5r6r7r8r;r<r>r?r@rBrCrDrErFrGrHrLrMrNrPrRrSrTrUrYr[r\r^r_r`rarbrcrdrerfrgrhrirkrlrmrnrorprrrsrvrwrxryr{r|r}r~rrrrrrrrYrrrrs   0    PZ    B^         a