Functions — github.com/ChongQingNoSubway/SelfReg-UNet

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Functions3,840 in github.com/ChongQingNoSubway/SelfReg-UNet

⨍ Functions 3,840◇ Types & classes 731

↓ 257 callersFunctionnorm_layer

(norm, nc)

src/train_acdc/lib/gcn_lib/torch_nn.py:30

↓ 124 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/efficientnet.py:58

↓ 90 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/resnet.py:25

↓ 90 callersFunction_create_resnet

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/resnet.py:735

↓ 90 callersFunctiontrunc_normal_

r"""Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distributi

src/train_acdc/lib/models_timm/layers/weight_init.py:43

↓ 89 callersFunctionprint

(*args, **kwargs)

src/train_acdc/levit/utils.py:180

↓ 86 callersFunctionact_layer

(act, inplace=False, neg_slope=0.2, n_prelu=1)

src/train_acdc/lib/gcn_lib/torch_nn.py:11

↓ 69 callersMethodget

Get value by key at specified index (indices) if idx == None, returns value for key at each output index if idx is an integer, return

src/train_acdc/lib/models_timm/features.py:36

↓ 66 callersFunctionbuild_model_with_cfg

Build model with specified default_cfg and optional model_cfg This helper fn aids in the construction of a model including: * handling def

src/train_acdc/lib/models_timm/helpers.py:505

↓ 63 callersFunctioncreate_conv2d

Select a 2d convolution implementation based on arguments Creates and returns one of torch.nn.Conv2d, Conv2dSame, MixedConv2d, or CondConv2d.

src/train_acdc/lib/models_timm/layers/create_conv2d.py:11

↓ 62 callersFunction_assert

(condition: bool, message: str)

src/train_acdc/lib/models_timm/layers/trace_utils.py:4

↓ 52 callersFunctionprint

(*args, **kwargs)

src/train_synase/levit/utils.py:180

↓ 48 callersMethodinterpolate

Feature map interpolation.

src/train_acdc/lib/models_timm/coat.py:275

↓ 47 callersFunction_create_vision_transformer

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/vision_transformer.py:781

↓ 46 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/vision_transformer.py:42

↓ 46 callersFunction_gen_efficientnet

Creates an EfficientNet model. Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py Pap

src/train_acdc/lib/models_timm/efficientnet.py:835

↓ 44 callersFunctionmake_divisible

(v, divisor=8, min_value=None, round_limit=.9)

src/train_acdc/lib/models_timm/layers/helpers.py:25

↓ 42 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/xcit.py:30

↓ 42 callersFunction_create_xcit

(variant, pretrained=False, default_cfg=None, **kwargs)

src/train_acdc/lib/models_timm/xcit.py:502

↓ 38 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/maxxvit_4out.py:62

↓ 38 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/maxxvit.py:62

↓ 38 callersFunction_create_maxxvit

(variant, cfg_variant=None, pretrained=False, **kwargs)

src/train_acdc/lib/maxxvit_4out.py:1735

↓ 38 callersFunction_create_maxxvit

(variant, cfg_variant=None, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/maxxvit.py:1718

↓ 35 callersFunction_create_normfreenet

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/nfnet.py:588

↓ 35 callersFunction_dcfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/nfnet.py:36

↓ 34 callersFunctioncreate_classifier

(num_features, num_classes, pool_type='avg', use_conv=False)

src/train_acdc/lib/models_timm/layers/classifier.py:32

↓ 33 callersFunction_create_byobnet

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/byobnet.py:1582

↓ 33 callersMethoddownsample

Feature map down-sampling.

src/train_acdc/lib/models_timm/coat.py:271

↓ 31 callersFunctionget_act_layer

Activation Layer Factory Fetching activation layers by name with this function allows export or torch script friendly functions to be returne

src/train_acdc/lib/models_timm/layers/create_act.py:125

↓ 31 callersFunctionnamed_apply

(fn: Callable, module: nn.Module, name='', depth_first=True, include_root=False)

src/train_acdc/lib/models_timm/helpers.py:605

↓ 30 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/regnet.py:108

↓ 30 callersFunction_create_regnet

(variant, pretrained, **kwargs)

src/train_acdc/lib/models_timm/regnet.py:517

↓ 30 callersFunctioncreate_pool2d

(pool_type, kernel_size, stride=None, **kwargs)

src/train_acdc/lib/models_timm/layers/pool2d_same.py:56

↓ 28 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/convnext.py:30

↓ 28 callersFunction_create_convnext

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/convnext.py:453

↓ 28 callersFunction_n2p

(w, t=True)

src/train_acdc/lib/models_timm/vision_transformer.py:612

↓ 26 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/resnetv2.py:45

↓ 26 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/mlp_mixer.py:54

↓ 26 callersFunction_create_mixer

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/mlp_mixer.py:380

↓ 26 callersFunctioncheckpoint_seq

r"""A helper function for checkpointing sequential models. Sequential models execute a list of modules/functions in order (sequentially). The

src/train_acdc/lib/models_timm/helpers.py:722

↓ 26 callersFunctioninterleave_blocks

interleave 2 block types in stack

src/train_acdc/lib/models_timm/byobnet.py:220

↓ 25 callersFunctionnp2th

Possibly convert HWIO to OIHW.

src/train_acdc/networks_trans/vit_seg_modeling.py:36

↓ 25 callersFunctionnp2th

Possibly convert HWIO to OIHW.

src/train_synase/networks_trans/vit_seg_modeling.py:36

↓ 24 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/deit.py:25

↓ 24 callersFunction_create_deit

(variant, pretrained=False, distilled=False, **kwargs)

src/train_acdc/lib/models_timm/deit.py:181

↓ 24 callersFunctiondecode_arch_def

Decode block architecture definition strings -> block kwargs Args: arch_def: architecture definition strings, list of list of strings

src/train_acdc/lib/models_timm/efficientnet_builder.py:230

↓ 24 callersFunctionresolve_bn_args

(kwargs)

src/train_acdc/lib/models_timm/efficientnet_builder.py:42

↓ 23 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/cspnet.py:33

↓ 23 callersFunction_create_cspnet

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/cspnet.py:957

↓ 22 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/mobilenetv3.py:28

↓ 21 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/gluon_resnet.py:14

↓ 21 callersFunction_cfgr

(url='', **kwargs)

src/train_acdc/lib/models_timm/byobnet.py:55

↓ 21 callersFunction_create_resnet

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/gluon_resnet.py:60

↓ 20 callersFunctioncreate_act_layer

(name: Union[nn.Module, str], inplace=None, **kwargs)

src/train_acdc/lib/models_timm/layers/create_act.py:144

↓ 20 callersFunctionget_norm_act_layer

(norm_layer, act_layer=None)

src/train_acdc/lib/models_timm/layers/create_norm_act.py:52

↓ 20 callersFunctionresolve_act_layer

(kwargs, default='relu')

src/train_acdc/lib/models_timm/efficientnet_builder.py:53

↓ 18 callersFunction_create_effnet

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/efficientnet.py:621

↓ 18 callersMethodupdate

(self, **kwargs)

src/train_acdc/levit/utils.py:86

↓ 17 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/vision_transformer_hybrid.py:30

↓ 17 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/mobilevit.py:33

↓ 17 callersFunction_create_mobilevit

(variant, cfg_variant=None, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/mobilevit.py:595

↓ 17 callersFunction_create_vision_transformer_hybrid

(variant, backbone, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/vision_transformer_hybrid.py:152

↓ 16 callersFunctionget_attn

(attn_type)

src/train_acdc/lib/models_timm/layers/create_attn.py:21

↓ 16 callersFunctionget_norm_layer

(norm_layer)

src/train_acdc/lib/models_timm/layers/create_norm.py:32

↓ 15 callersMethod__init__

( self, in_chs: int, out_chs: int, kernel_size: int = 3,

src/train_acdc/lib/maxxvit_4out.py:1556

↓ 15 callersMethod__init__

( self, in_chs: int, out_chs: int, kernel_size: int = 3,

src/train_acdc/lib/models_timm/maxxvit.py:1556

↓ 15 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/byoanet.py:23

↓ 15 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/swin_transformer_v2_cr.py:50

↓ 15 callersFunction_create_byoanet

(variant, cfg_variant=None, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/byoanet.py:327

↓ 15 callersFunction_create_swin_transformer_v2_cr

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/swin_transformer_v2_cr.py:829

↓ 15 callersMethodresize

(self, img, gt)

src/train_acdc/utils/dataloader.py:124

↓ 15 callersMethodresize

(self, img, gt)

src/train_synase/utils/dataloader.py:124

↓ 15 callersFunctiont2p

Possibly convert HWIO to OIHW.

src/train_acdc/lib/models_timm/resnetv2.py:471

↓ 14 callersFunction_create_resnetv2_bit

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/resnetv2.py:514

↓ 14 callersMethodinter_fd

(self,f_s, f_t)

src/train_acdc/train.py:42

↓ 14 callersMethodinter_fd

(self,f_s, f_t)

src/train_synase/trainer.py:26

↓ 14 callersMethodintra_fd

(self,f_s)

src/train_acdc/train.py:59

↓ 14 callersMethodintra_fd

(self,f_s)

src/train_synase/trainer.py:43

↓ 14 callersFunctiontrunc_normal_tf_

r"""Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distributi

src/train_acdc/lib/models_timm/layers/weight_init.py:70

↓ 13 callersMethod__init__

(self,ch_in,ch_out,kernel_size=3, stride=1, padding=1, groups=1, activation='relu')

src/train_acdc/lib/decoders.py:48

↓ 13 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/swin_transformer.py:37

↓ 13 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/vision_transformer_relpos.py:27

↓ 13 callersFunction_create_resnetv2

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/resnetv2.py:505

↓ 13 callersFunction_create_swin_transformer

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/swin_transformer.py:573

↓ 13 callersFunction_create_vision_transformer_relpos

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/vision_transformer_relpos.py:508

↓ 13 callersFunction_gen_mobilenet_v3

Creates a MobileNet-V3 model. Ref impl: ? Paper: https://arxiv.org/abs/1905.02244 Args: channel_multiplier: multiplier to number o

src/train_acdc/lib/models_timm/mobilenetv3.py:335

↓ 13 callersFunction_nfnet_cfg

( depths, channels=(256, 512, 1536, 1536), group_size=128, bottle_ratio=0.5, feat_mult=2., act

src/train_acdc/lib/models_timm/nfnet.py:177

↓ 13 callersFunction_resnetv2

ResNet-V2 backbone helper

src/train_acdc/lib/models_timm/vision_transformer_hybrid.py:158

↓ 13 callersFunction_rw_max_cfg

( stride_mode='dw', pool_type='avg2', conv_output_bias=False, conv_attn_ratio=

src/train_acdc/lib/maxxvit_4out.py:289

↓ 13 callersFunction_rw_max_cfg

( stride_mode='dw', pool_type='avg2', conv_output_bias=False, conv_attn_ratio=

src/train_acdc/lib/models_timm/maxxvit.py:289

↓ 13 callersMethodstages

(self, x)

src/train_acdc/lib/models_timm/hrnet.py:709

↓ 12 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/dla.py:23

↓ 12 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/byobnet.py:45

↓ 12 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/swin_transformer_v2.py:30

↓ 12 callersFunction_create_dla

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/dla.py:373

↓ 12 callersFunction_create_swin_transformer_v2

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/swin_transformer_v2.py:632

↓ 12 callersFunction_cs3_cfg

( width_multiplier=1.0, depth_multiplier=1.0, avg_down=False, act_layer='silu'

src/train_acdc/lib/models_timm/cspnet.py:175

↓ 12 callersFunction_inverted_residual_block

(d, c, s, br=4.0)

src/train_acdc/lib/models_timm/mobilevit.py:97

↓ 12 callersFunction_mobilevit_block

(d, c, s, transformer_dim, transformer_depth, patch_size=4, br=4.0)

src/train_acdc/lib/models_timm/mobilevit.py:104

↓ 12 callersFunction_pad_arg

(x, n)

src/train_acdc/lib/models_timm/cspnet.py:117

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