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⨍ Functions 575◇ Types & classes 123

↓ 1 callersFunctionupdate_ema_variables

(model, ema_model, alpha, global_step)

code/train_mean_teacher_2D.py:85

↓ 1 callersFunctionupdate_ema_variables

(model, ema_model, alpha, global_step)

code/train_interpolation_consistency_training_2D.py:87

↓ 1 callersFunctionupdate_ema_variables

(model, ema_model, alpha, global_step)

code/train_mean_teacher_3D.py:72

↓ 1 callersFunctionupdate_ema_variables

(model, ema_model, alpha, global_step)

code/train_fixmatch_standard_augs.py:123

↓ 1 callersFunctionupdate_ema_variables

(model, ema_model, alpha, global_step)

code/train_interpolation_consistency_training_3D.py:76

↓ 1 callersFunctionupdate_ema_variables

(model, ema_model, alpha, global_step)

code/train_uncertainty_aware_mean_teacher_2D.py:85

↓ 1 callersFunctionwindow_reverse

Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:45

↓ 1 callersFunctionxavier_normal_init_weight

code/train_regularized_dropout_3D.py:91

↓ 1 callersFunctionxavier_normal_init_weight

code/train_cross_pseudo_supervision_3D.py:89

(self, input, target_is_real)

code/networks/networks_other.py:292

code/networks/nnunet.py:34

(self, sample, ops_weak, ops_strong)

code/dataloaders/dataset.py:111

code/dataloaders/dataset.py:151

code/dataloaders/dataset.py:180

code/dataloaders/brats2019.py:52

code/dataloaders/brats2019.py:91

code/dataloaders/brats2019.py:138

code/dataloaders/brats2019.py:155

code/dataloaders/brats2019.py:168

code/dataloaders/brats2019.py:180

Method__enter__

code/networks/neural_network.py:26

code/networks/neural_network.py:29

Method__getitem__

code/dataloaders/dataset.py:57

Method__getitem__

code/dataloaders/brats2019.py:37

(self, depth=2, th=0.85, decay=0.99)

code/augmentations/ctaugment.py:40

(self, gamma=2, alpha=None, size_average=True)

code/utils/losses.py:120

(self, N, images_lists)

code/utils/util.py:137

code/utils/util.py:167

code/utils/util.py:195

(self, stage_idxs, out_channels, model_name, depth=5)

code/networks/encoder_tool.py:68

(self, in_channels, out_channels, dropout_p)

code/networks/unet.py:34

(self, in_channels, out_channels, dropout_p)

code/networks/unet.py:53

(self, in_channels1, in_channels2, out_channels, dropout_p, bilinear=True)

code/networks/unet.py:68

code/networks/unet.py:90

code/networks/unet.py:120

code/networks/unet.py:157

code/networks/unet.py:210

(self, uniform_range=0.3)

code/networks/unet.py:289

(self, in_chns, class_num)

code/networks/unet.py:325

(self, in_chns, class_num)

code/networks/unet.py:353

(self, in_chns, class_num)

code/networks/unet.py:374

(self, feature_scale=4, n_classes=21, is_deconv=True, in_channels=3, is_batchnorm=True)

code/networks/unet_3D.py:22

(self, in_channels, out_channels, bias=False,

code/networks/enet.py:26

(self, channels, internal_ratio=4, kernel_size=3,

code/networks/enet.py:106

(self, in_channels, out_channels, internal_ratio=4,

code/networks/enet.py:238

(self, in_channels, out_channels, internal_ratio=4,

code/networks/enet.py:370

(self, feature_scale=4, n_classes=21, is_deconv=True, in_channels=3, is_batchnorm=True)

code/networks/unet_3D_dv_semi.py:15

(self, num_classes, ndf=64, n_channel=1)

code/networks/discriminator.py:8

(self, in_channels, n_filters, k_size, stride, padding, bias=True)

code/networks/utils.py:9

(self, in_channels, n_filters, k_size, stride, padding, bias=True)

code/networks/utils.py:22

(self, in_channels, n_filters, k_size, stride, padding, bias=True)

code/networks/utils.py:35

(self, in_channels, n_filters, k_size, stride, padding, bias=True)

code/networks/utils.py:49

(self, in_size, out_size, is_batchnorm, n=2, ks=3, stride=1, padding=1)

code/networks/utils.py:63

(self, in_size, out_size, is_batchnorm, kernel_size=(3,3,1), padding_size=(1,1,0), init_stride=(1,1,1))

code/networks/utils.py:100

(self, in_size, out_size, is_batchnorm, kernel_size=(3,3,1), padding_size=(1,1,0), init_stride=(1,1,1))

code/networks/utils.py:127

(self, in_size, out_size, is_batchnorm)

code/networks/utils.py:160

(self, in_size, out_size, kernel_size=(1,1,1), is_batchnorm=True)

code/networks/utils.py:193

(self, in_size, out_size, is_deconv)

code/networks/utils.py:216

(self, in_size, out_size, is_deconv, is_batchnorm=True)

code/networks/utils.py:238

(self, in_size, out_size, is_batchnorm=True)

code/networks/utils.py:261

(self, in_size, out_size, is_deconv, is_batchnorm)

code/networks/utils.py:304

(self, in_channels, n_filters, stride=1, downsample=None)

code/networks/utils.py:332

(self, in_channels, n_filters, stride=1, downsample=None)

code/networks/utils.py:358

(self, submodule, extracted_layers)

code/networks/utils.py:386

(self, submodule, layername, upscale=False)

code/networks/utils.py:402

(self, in_size, out_size, scale_factor)

code/networks/utils.py:456

(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, gpu_ids=[], padd

code/networks/networks_other.py:302

(self, dim, padding_type, norm_layer, use_dropout, use_bias)

code/networks/networks_other.py:355

(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False,

code/networks/networks_other.py:401

(self, outer_nc, inner_nc, input_nc=None, submodule=None, outermost=False, innermost=False, n

code/networks/networks_other.py:428

(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, gpu_ids=[])

code/networks/networks_other.py:482

code/networks/neural_network.py:71

( self, in_channels, skip_channels, out_channels,

code/networks/efficientunet.py:28

(self, in_channels, out_channels, use_batchnorm=True)

code/networks/efficientunet.py:66

( self, encoder_channels, decoder_channels, n_blocks=5,

code/networks/efficientunet.py:85

(self, in_channels, gating_channels, inter_channels=None, dimension=3, mode='concatenation',

code/networks/grid_attention_layer.py:8

(self, in_channels, gating_channels, inter_channels=None, mode='concatenation', sub_sample_fa

code/networks/grid_attention_layer.py:174

(self, in_channels, gating_channels, inter_channels=None, dimension=3, mode='concatenation',

code/networks/grid_attention_layer.py:184

(self, in_channels, gating_channels, inter_channels=None, mode='concatenation', sub_sample_fa

code/networks/grid_attention_layer.py:360

(self, in_channels, gating_channels, inter_channels=None, mode='concatenation', sub_sample_fa

code/networks/grid_attention_layer.py:378

( self, in_channels, out_channels, kernel_size, pa

code/networks/attention.py:10

(self, in_channels, reduction=16)

code/networks/attention.py:52

(self, name, **params)

code/networks/attention.py:69

(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.)

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:77

(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, mlp_ratio=4., qkv_bias=

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:188

(self, input_resolution, dim, norm_layer=nn.LayerNorm)

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:318

(self, input_resolution, dim, dim_scale=2, norm_layer=nn.LayerNorm)

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:359

(self, input_resolution, dim, dim_scale=4, norm_layer=nn.LayerNorm)

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:386

(self, dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4., qkv_bias=True, qk_

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:433

(self, dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4., qkv_bias=True, qk_

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:506

(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None)

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:559

(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000, embed_dim=96, depths=[2, 2,

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:625

(self, n_stages, n_filters_in, n_filters_out, normalization='none')

code/networks/vnet.py:6

(self, n_stages, n_filters_in, n_filters_out, normalization='none')

code/networks/vnet.py:35

(self, n_filters_in, n_filters_out, stride=2, normalization='none')

code/networks/vnet.py:68

(self, n_filters_in, n_filters_out, stride=2, normalization='none')

code/networks/vnet.py:95

(self, n_filters_in, n_filters_out, stride=2, normalization='none')

code/networks/vnet.py:122

(self, neg_slope=1e-2)

code/networks/nnunet.py:31

(self, input_channels, output_channels, conv_op=nn.Conv2d, conv_kwargs=None,

code/networks/nnunet.py:47

stacks ConvDropoutNormLReLU layers. initial_stride will only be applied to first layer in the stack. The other parameters affect all layers

code/networks/nnunet.py:98

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