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Functions575 in github.com/HiLab-git/SSL4MIS

⨍ Functions 575◇ Types & classes 123

basically more flexible than v1, architecture is the same Does this look complicated? Nah bro. Functionality > usability Th

code/networks/nnunet.py:203

(self, in_channels, out_channels, dilation, padding)

code/networks/pnet.py:18

(self, in_channels, out_channels)

code/networks/pnet.py:46

(self, in_channels, out_channels)

code/networks/pnet.py:66

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

code/networks/efficient_encoder.py:71

(self, in_size, gate_size, inter_size, nonlocal_mode, sub_sample_factor)

code/networks/attention_unet.py:114

(self, in_channels, r)

code/networks/VoxResNet.py:10

(self, in_channels, out_channels)

code/networks/VoxResNet.py:47

(self, in_channels, out_channels)

code/networks/VoxResNet.py:67

(self, in_chns=1, feature_chns=64, class_num=2)

code/networks/VoxResNet.py:80

(self, config, img_size=224, num_classes=21843, zero_head=False, vis=False)

code/networks/vision_transformer.py:25

(self, img, percent=1)

code/dataloaders/brats_proprecessing.py:82

( self, base_dir=None, split="train", num=None, transform=None,

code/dataloaders/dataset.py:21

(self, output_size, cta)

code/dataloaders/dataset.py:107

(self, output_size)

code/dataloaders/dataset.py:148

(self, output_size)

code/dataloaders/dataset.py:177

(self, primary_indices, secondary_indices, batch_size, secondary_batch_size)

code/dataloaders/dataset.py:214

(self, base_dir=None, split='train', num=None, transform=None)

code/dataloaders/brats2019.py:14

(self, output_size)

code/dataloaders/brats2019.py:49

(self, output_size, with_sdf=False)

code/dataloaders/brats2019.py:87

(self, mu=0, sigma=0.1)

code/dataloaders/brats2019.py:151

(self, num_classes)

code/dataloaders/brats2019.py:165

(self, primary_indices, secondary_indices, batch_size, secondary_batch_size)

code/dataloaders/brats2019.py:199

code/utils/util.py:157

code/dataloaders/dataset.py:223

code/dataloaders/brats2019.py:208

code/utils/util.py:160

code/dataloaders/dataset.py:54

code/dataloaders/dataset.py:234

code/dataloaders/brats2019.py:34

code/dataloaders/brats2019.py:218

Method_concatenation

code/networks/grid_attention_layer.py:84

Method_concatenation

code/networks/grid_attention_layer.py:294

Method_concatenation_debug

code/networks/grid_attention_layer.py:109

Method_concatenation_residual

code/networks/grid_attention_layer.py:135

Method_init_weights

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:728

Functionadjust_learning_rate

Sets the learning rate to a fixed number

code/networks/networks_other.py:90

Methodapply_argmax_softmax

code/networks/unet_3D.py:97

Methodapply_argmax_softmax

code/networks/unet_3D_dv_semi.py:109

Methodapply_argmax_softmax

code/networks/attention_unet.py:107

Functionautocontrast

code/augmentations/ctaugment.py:118

Functionbenchmark_fp_bp_time

(model, x, y, n_trial=1000)

code/networks/networks_other.py:226

code/augmentations/ctaugment.py:165

Functionbrightness

(x, brightness)

code/augmentations/ctaugment.py:123

Functioncal_dice

(prediction, label, num=2)

code/test_urpc_util.py:138

Functioncal_dice

(prediction, label, num=2)

code/test_3D_util.py:132

Functioncal_dice

(prediction, label, num=2)

code/utils/metrics.py:13

Functioncal_metric

code/test_urpc_util.py:87

Functioncal_metric

code/test_3D_util.py:82

Functioncalculate_metric_percase

code/utils/metrics.py:27

code/augmentations/ctaugment.py:128

Methodcompute_approx_vram_consumption

This only applies for num_conv_per_stage and convolutional_upsampling=True not real vram consumption. just a constant term to which t

code/networks/nnunet.py:437

Functioncompute_kl_loss

code/utils/losses.py:208

Functioncompute_sdf

compute the signed distance map of binary mask input: segmentation, shape = (batch_size, x, y, z) output: the Signed Distance Map (SDM)

code/utils/util.py:205

Functioncontrast

code/augmentations/ctaugment.py:133

Functioncosine_rampdown

Cosine rampdown from https://arxiv.org/abs/1608.03983

code/utils/ramps.py:38

Functioncross_entropy2d

(logit, target, ignore_index=255, weight=None, size_average=True, batch_average=True)

code/dataloaders/utils.py:123

Functioncta_apply

code/augmentations/__init__.py:23

Apply cutout to pil_img at the specified level.

code/augmentations/ctaugment.py:178

Functiondecode_seg_map_sequence

(label_masks, dataset='pascal')

code/dataloaders/utils.py:72

Functiondefine_D

(input_nc, ndf, which_model_netD, n_layers_D=3, norm='batch', use_sigmoid=False, init_type='norma

code/networks/networks_other.py:167

Functiondefine_G

(input_nc, output_nc, ngf, which_model_netG, norm='batch', use_dropout=False, init_type='normal', gpu_ids=[])

code/networks/networks_other.py:143

Functiondeinterleave

code/augmentations/__init__.py:61

Functiondeserialize

code/augmentations/__init__.py:31

(input, target, ignore_index=None)

code/utils/metrics.py:36

Functiondistributed_setup

(rank, world_size)

code/utils/util.py:240

Functionencode_segmap

Encode segmentation label images as pascal classes Args: mask (np.ndarray): raw segmentation label image of dimension (M, N, 3),

code/dataloaders/utils.py:55

Functionentropy_loss

code/utils/losses.py:30

Functionentropy_loss_map

code/utils/losses.py:59

Functionentropy_map

code/utils/losses.py:202

Functionentropy_minmization

code/utils/losses.py:195

Functionequalize

code/augmentations/ctaugment.py:138

Methodextra_repr

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:152

Methodextra_repr

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:290

Methodextra_repr

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:348

Methodextra_repr

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:474

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:155

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:294

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:351

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:590

code/networks/swin_transformer_unet_skip_expand_decoder_sys.py:795

(self, input, target)

code/utils/losses.py:130

(self, inputs, target, weight=None, softmax=False)

code/utils/losses.py:179

code/networks/encoder_tool.py:80

code/networks/unet.py:46

code/networks/unet.py:61

code/networks/unet.py:81

code/networks/unet.py:110

(self, feature)

code/networks/unet.py:141

(self, feature, shape)

code/networks/unet.py:186

(self, feature, shape)

code/networks/unet.py:240

code/networks/unet.py:299

code/networks/unet.py:318

code/networks/unet.py:340

code/networks/unet.py:365

code/networks/unet.py:386

code/networks/unet_3D.py:71

code/networks/enet.py:58

code/networks/enet.py:193

code/networks/enet.py:308

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