Functions — github.com/ChongQingNoSubway/SelfReg-UNet

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

⨍ Functions 3,840◇ Types & classes 731

↓ 12 callersMethodmax

(self)

src/train_acdc/levit/utils.py:65

↓ 12 callersFunctionnp2th

Possibly convert HWIO to OIHW.

src/train_acdc/networks_trans/vit_seg_modeling_resnet_skip.py:11

↓ 12 callersFunctionnp2th

Possibly convert HWIO to OIHW.

src/train_synase/networks_trans/vit_seg_modeling_resnet_skip.py:11

↓ 11 callersMethod__init__

(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.)

src/train_acdc/lib/pvtv2.py:15

↓ 11 callersMethod__init__

(self, )

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

↓ 11 callersFunction_cfg

(url='')

src/train_acdc/lib/models_timm/vovnet.py:139

↓ 11 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/crossvit.py:44

↓ 11 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/beit.py:56

↓ 11 callersFunction_cfg

(url='', **kwargs)

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

↓ 11 callersFunction_create_beit

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/beit.py:394

↓ 11 callersFunction_create_crossvit

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/crossvit.py:423

↓ 11 callersFunction_create_volo

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/volo.py:647

↓ 11 callersFunction_create_vovnet

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/vovnet.py:358

↓ 11 callersFunctionconv2d_iabn

(ni, nf, stride, kernel_size=3, groups=1, act_layer="leaky_relu", act_param=1e-2)

src/train_acdc/lib/models_timm/tresnet.py:63

↓ 10 callersMethod__init__

(self, m, drop)

src/train_acdc/levit/LeViTUNet128s.py:141

↓ 10 callersMethod__init__

(self, config)

src/train_acdc/networks_trans/vit_seg_modeling.py:98

↓ 10 callersMethod__init__

(self, m, drop)

src/train_synase/levit/LeViTUNet128s.py:141

↓ 10 callersMethod__init__

(self, config)

src/train_synase/networks_trans/vit_seg_modeling.py:98

↓ 10 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/cait.py:26

↓ 10 callersFunction_create_cait

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/cait.py:343

↓ 10 callersFunction_gen_efficientnet_lite

Creates an EfficientNet-Lite model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite Paper: https://

src/train_acdc/lib/models_timm/efficientnet.py:946

↓ 10 callersFunction_rw_coat_cfg

( stride_mode='pool', pool_type='avg2', conv_output_bias=False, conv_attn_earl

src/train_acdc/lib/maxxvit_4out.py:237

↓ 10 callersFunction_rw_coat_cfg

( stride_mode='pool', pool_type='avg2', conv_output_bias=False, conv_attn_earl

src/train_acdc/lib/models_timm/maxxvit.py:237

↓ 10 callersFunctionbatched_index_select

r"""fetches neighbors features from a given neighbor idx Args: x (Tensor): input feature Tensor :math:`\mathbf{X} \in \ma

src/train_acdc/lib/gcn_lib/torch_nn.py:81

↓ 10 callersFunctionprod

(iterable)

src/train_acdc/lib/models_timm/mvitv2.py:148

↓ 9 callersMethod__init__

(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.)

src/train_acdc/network/swin_transformer_unet_skip_expand_decoder_sys.py:9

↓ 9 callersMethod__init__

(self)

src/train_acdc/lib/models_timm/inception_v4.py:44

↓ 9 callersMethod__init__

(self, in_channels, out_channels, kernel_size, stride=1, padding='')

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

↓ 9 callersMethod__init__

(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.)

src/train_synase/networks/swin_transformer_unet_skip_expand_decoder_sys.py:9

↓ 9 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/senet.py:29

↓ 9 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/hrnet.py:29

↓ 9 callersFunction_cfg

(url='')

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

↓ 9 callersFunction_create_densenet

(variant, growth_rate, block_config, pretrained, **kwargs)

src/train_acdc/lib/models_timm/densenet.py:300

↓ 9 callersFunction_create_hrnet

(variant, pretrained, **model_kwargs)

src/train_acdc/lib/models_timm/hrnet.py:796

↓ 9 callersFunction_create_senet

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/senet.py:400

↓ 9 callersFunction_nfres_cfg

( depths, channels=(256, 512, 1024, 2048), group_size=None, act_layer='relu', attn_layer=None, attn_kw

src/train_acdc/lib/models_timm/nfnet.py:159

↓ 9 callersFunctioncreate_attn

(attn_type, channels, **kwargs)

src/train_acdc/lib/models_timm/layers/create_attn.py:84

↓ 9 callersMethodupsample

Feature map up-sampling.

src/train_acdc/lib/models_timm/coat.py:267

↓ 8 callersMethod__init__

( self, img_size=224, patch_size=16, in_chans=3, n

src/train_acdc/lib/models_timm/levit.py:400

↓ 8 callersMethod__init__

(self, in_channels, pool_features, conv_block=None)

src/train_acdc/lib/models_timm/inception_v3.py:54

↓ 8 callersMethod__init__

( self, in_chs, out_chs, kernel_size=3, stride=4, pool='maxpool', num_rep=3, num_act=N

src/train_acdc/lib/models_timm/byobnet.py:1276

↓ 8 callersMethod__init__

( self, in_chs: int = 3, out_chs: int = 96, act_layer: Callabl

src/train_acdc/lib/models_timm/gcvit.py:162

↓ 8 callersMethod__init__

( self, layers, img_size=224, in_chans=3, num_clas

src/train_acdc/lib/models_timm/volo.py:390

↓ 8 callersMethod__init__

(self, num_features, apply_act=True, momentum=0.1, eps=1e-3, **_)

src/train_acdc/lib/models_timm/layers/evo_norm.py:100

↓ 8 callersMethod__init__

(self, inplace: bool = False)

src/train_acdc/lib/models_timm/layers/activations.py:39

↓ 8 callersMethod__init__

(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.)

src/train_acdc/modelsHiFormer/utils.py:22

↓ 8 callersMethod__init__

(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.)

src/train_synase/modelsHiFormer/utils.py:22

↓ 8 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/resnest.py:19

↓ 8 callersFunction_cfg

(url='', **kwargs)

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

↓ 8 callersFunction_cfg

(url='')

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

↓ 8 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/tresnet.py:20

↓ 8 callersFunction_cfg

(url='', **kwargs)

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

↓ 8 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/mvitv2.py:33

↓ 8 callersFunction_create_pit

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/pit.py:280

↓ 8 callersFunction_create_resnest

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/resnest.py:138

↓ 8 callersFunction_create_rexnet

(variant, pretrained, **kwargs)

src/train_acdc/lib/models_timm/rexnet.py:208

↓ 8 callersFunction_create_tresnet

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/tresnet.py:279

↓ 8 callersFunction_create_vgg

(variant: str, pretrained: bool, **kwargs: Any)

src/train_acdc/lib/models_timm/vgg.py:197

↓ 8 callersFunction_gen_efficientnet_edge

Creates an EfficientNet-EdgeTPU model Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu

src/train_acdc/lib/models_timm/efficientnet.py:884

↓ 8 callersFunction_gen_efficientnetv2_s

Creates an EfficientNet-V2 Small model Ref impl: https://github.com/google/automl/tree/master/efficientnetv2 Paper: `EfficientNetV2: Smaller

src/train_acdc/lib/models_timm/efficientnet.py:1016

↓ 8 callersFunction_log_info_if

(msg, condition)

src/train_acdc/lib/models_timm/efficientnet_builder.py:64

↓ 8 callersFunction_rep_vgg_bcfg

(d=(4, 6, 16, 1), wf=(1., 1., 1., 1.), groups=0)

src/train_acdc/lib/models_timm/byobnet.py:211

↓ 8 callersFunction_thresh

(img)

src/train_acdc/utils/lesion/helpers.py:12

↓ 8 callersFunction_thresh

(img)

src/train_synase/utils/lesion/helpers.py:12

↓ 8 callersFunctionconv_bn

(in_chs, out_chs, k=3, stride=1, padding=None, dilation=1)

src/train_acdc/lib/models_timm/selecsls.py:100

↓ 8 callersFunctionnum_groups

(group_size, channels)

src/train_acdc/lib/models_timm/byobnet.py:912

↓ 8 callersMethodreduction

feature reduction (output stride) accessor

src/train_acdc/lib/models_timm/features.py:67

↓ 8 callersFunctionremove_cls

Remove CLS token.

src/train_acdc/lib/models_timm/coat.py:619

↓ 7 callersMethod__init__

(self, scale=1.0, no_relu=False)

src/train_acdc/lib/models_timm/inception_resnet_v2.py:199

↓ 7 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/res2net.py:18

↓ 7 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/pvt_v2.py:34

↓ 7 callersFunction_create_pvt2

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/pvt_v2.py:410

↓ 7 callersFunction_create_res2net

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/res2net.py:135

↓ 7 callersFunction_dm_nfnet_cfg

(depths, channels=(256, 512, 1536, 1536), act_layer='gelu', skipinit=True)

src/train_acdc/lib/models_timm/nfnet.py:189

↓ 7 callersFunction_gen_mobilenet_v2

Generate MobileNet-V2 network Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py Paper:

src/train_acdc/lib/models_timm/efficientnet.py:740

↓ 7 callersFunction_mobilevitv2_cfg

(multiplier=1.0)

src/train_acdc/lib/models_timm/mobilevit.py:131

↓ 7 callersFunctionconv3x3

3x3 convolution + batch norm

src/train_acdc/lib/models_timm/xcit.py:138

↓ 7 callersMethodinter_fd

(self,f_s, f_t)

src/train_acdc/train_unetKD.py:43

↓ 7 callersMethodinter_fd

(self,f_s, f_t)

src/train_synase/trainer_unet.py:26

↓ 7 callersMethodintra_fd

(self,f_s)

src/train_acdc/train_unetKD.py:60

↓ 7 callersMethodintra_fd

(self,f_s)

src/train_synase/trainer_unet.py:43

↓ 7 callersFunctionis_fast_norm

()

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

↓ 7 callersFunctionpad_same

(x, k: List[int], s: List[int], d: List[int] = (1, 1), value: float = 0)

src/train_acdc/lib/models_timm/layers/padding.py:28

↓ 7 callersMethodreset_parameters

(self)

src/train_acdc/lib/models_timm/layers/halo_attn.py:162

↓ 7 callersMethodtrain

(self, mode=True)

src/train_acdc/levit/LeViTUNet128s.py:196

↓ 6 callersMethod__init__

(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type='', is_reduction=Fals

src/train_acdc/lib/models_timm/pnasnet.py:188

↓ 6 callersMethod__init__

(self, in_features, out_features=None, act_layer=nn.GELU, kernel_size=3)

src/train_acdc/lib/models_timm/xcit.py:192

↓ 6 callersMethod__init__

( self, cfg: CspModelCfg, in_chans=3, num_classes=1000,

src/train_acdc/lib/models_timm/cspnet.py:864

↓ 6 callersMethod__init__

( self, input_size: int, hidden_size: int, num_layers: int = 1, bias: bool = True, bid

src/train_acdc/lib/models_timm/sequencer.py:193

↓ 6 callersMethod__init__

( self, window_size, num_heads=8, hidden_dim=128,

src/train_acdc/lib/models_timm/vision_transformer_relpos.py:139

↓ 6 callersMethod__init__

(self, in_channels, kernel_size=9, dilation=1, conv='edge', act='relu', norm=None, bias=True,

src/train_acdc/lib/gcn_lib/torch_vertex.py:139

↓ 6 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/levit.py:41

↓ 6 callersFunction_cfg

(url='', **kwargs)

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

↓ 6 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/nest.py:38

↓ 6 callersFunction_cfg

(url='', **kwargs)

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

↓ 6 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/twins.py:31

↓ 6 callersFunction_create_dpn

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/dpn.py:287

↓ 6 callersFunction_create_nest

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/nest.py:421

↓ 6 callersFunction_create_twins

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/twins.py:396

↓ 6 callersFunction_gen_efficientnet_condconv

Creates an EfficientNet-CondConv model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/condconv

src/train_acdc/lib/models_timm/efficientnet.py:915

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