SYMBOL INDEX (1024 symbols across 75 files) FILE: config/augmentation/online_aug.py function data_transform_2d (line 6) | def data_transform_2d(): function data_normalize_2d (line 30) | def data_normalize_2d(mean, std): function data_transform_aerial_lanenet (line 39) | def data_transform_aerial_lanenet(H, W): function data_transform_3d (line 47) | def data_transform_3d(normalization): FILE: config/dataset_config/dataset_cfg.py function dataset_cfg (line 4) | def dataset_cfg(dataet_name): FILE: config/eval_config/eval.py function evaluate (line 7) | def evaluate(y_scores, y_true, interval=0.02): function evaluate_multi (line 36) | def evaluate_multi(y_scores, y_true): FILE: config/ramps/ramps.py function sigmoid_rampup (line 4) | def sigmoid_rampup(current, rampup_length): function linear_rampup (line 14) | def linear_rampup(current, rampup_length): function cosine_rampdown (line 23) | def cosine_rampdown(current, rampdown_length): FILE: config/train_test_config/train_test_config.py function print_train_loss_sup (line 8) | def print_train_loss_sup(train_loss, num_batches, print_num, print_num_m... function print_train_loss_MT (line 15) | def print_train_loss_MT(train_loss_sup_1, train_loss_cps, train_loss, nu... function print_train_loss_ConResNet (line 25) | def print_train_loss_ConResNet(train_loss_seg, train_loss_res, train_los... function print_train_loss_EM (line 36) | def print_train_loss_EM(train_loss_sup_1, train_loss_cps, train_loss, nu... function print_train_loss_XNet (line 48) | def print_train_loss_XNet(train_loss_sup_1, train_loss_sup_2, train_loss... function print_val_loss_sup (line 59) | def print_val_loss_sup(val_loss, num_batches, print_num, print_num_minus): function print_val_loss (line 66) | def print_val_loss(val_loss_sup_1, val_loss_sup_2, num_batches, print_nu... function print_val_loss_ConResNet (line 74) | def print_val_loss_ConResNet(val_loss_seg, val_loss_res, num_batches, pr... function print_train_eval_sup (line 82) | def print_train_eval_sup(num_classes, score_list_train, mask_list_train,... function print_train_eval_XNet (line 103) | def print_train_eval_XNet(num_classes, score_list_train1, score_list_tra... function print_val_eval_sup (line 126) | def print_val_eval_sup(num_classes, score_list_val, mask_list_val, print... function print_val_eval (line 143) | def print_val_eval(num_classes, score_list_val1, score_list_val2, mask_l... function save_val_best_sup_2d (line 164) | def save_val_best_sup_2d(num_classes, best_list, model, score_list_val, ... function save_val_best_sup_3d (line 200) | def save_val_best_sup_3d(num_classes, best_list, model, score_list_val, ... function save_val_best_2d (line 216) | def save_val_best_2d(num_classes, best_model, best_list, best_result, mo... function save_val_best_3d (line 278) | def save_val_best_3d(num_classes, best_model, best_list, best_result, mo... function draw_pred_sup (line 310) | def draw_pred_sup(num_classes, mask_train_sup, mask_val, pred_train_sup,... function draw_pred_XNet (line 335) | def draw_pred_XNet(num_classes, mask_train, mask_val, pred_train_sup1, p... function draw_pred_MT (line 374) | def draw_pred_MT(num_classes, mask_train, mask_val, pred_train_sup1, out... function print_best_sup (line 407) | def print_best_sup(num_classes, best_val_list, print_num): function print_best (line 419) | def print_best(num_classes, best_val_list, best_model, best_result, path... function print_test_eval (line 439) | def print_test_eval(num_classes, score_list_test, mask_list_test, print_... function save_test_2d (line 456) | def save_test_2d(num_classes, score_list_test, name_list_test, threshold... function save_test_3d (line 482) | def save_test_3d(num_classes, score_test, name_test, threshold, path_seg... FILE: config/visdom_config/visual_visdom.py function visdom_initialization_sup (line 4) | def visdom_initialization_sup(env, port): function visualization_sup (line 12) | def visualization_sup(vis, epoch, train_loss, train_m_jc, val_loss, val_... function visual_image_sup (line 18) | def visual_image_sup(vis, mask_train, pred_train, mask_val, pred_val): function visdom_initialization_XNet (line 26) | def visdom_initialization_XNet(env, port): function visualization_XNet (line 34) | def visualization_XNet(vis, epoch, train_loss, train_loss_sup1, train_lo... function visual_image_XNet (line 40) | def visual_image_XNet(vis, mask_train, pred_train1, pred_train2, mask_va... function visdom_initialization_MT (line 51) | def visdom_initialization_MT(env, port): function visualization_MT (line 59) | def visualization_MT(vis, epoch, train_loss, train_loss_sup1, train_loss... function visual_image_MT (line 65) | def visual_image_MT(vis, mask_train, pred_train1, mask_val, pred_val1, p... function visdom_initialization_EM (line 74) | def visdom_initialization_EM(env, port): function visualization_EM (line 82) | def visualization_EM(vis, epoch, train_loss, train_loss_sup1, train_loss... function visdom_initialization_ConResNet (line 89) | def visdom_initialization_ConResNet(env, port): function visualization_ConResNet (line 97) | def visualization_ConResNet(vis, epoch, train_loss, train_loss_seg, trai... FILE: config/warmup_config/warmup.py class GradualWarmupScheduler (line 5) | class GradualWarmupScheduler(_LRScheduler): method __init__ (line 15) | def __init__(self, optimizer, multiplier, total_epoch, after_scheduler... method get_lr (line 24) | def get_lr(self): method step_ReduceLROnPlateau (line 38) | def step_ReduceLROnPlateau(self, metrics, epoch=None): method step (line 52) | def step(self, epoch=None, metrics=None): FILE: dataload/dataset_2d.py class dataset_itn (line 9) | class dataset_itn(Dataset): method __init__ (line 10) | def __init__(self, data_dir, input1, augmentation_1, normalize_1, sup=... method __getitem__ (line 57) | def __getitem__(self, index): method __len__ (line 89) | def __len__(self): function imagefloder_itn (line 93) | def imagefloder_itn(data_dir, input1, data_transform_1, data_normalize_1... class dataset_iitnn (line 105) | class dataset_iitnn(Dataset): method __init__ (line 106) | def __init__(self, data_dir, input1, input2, augmentation1, normalize_... method __getitem__ (line 165) | def __getitem__(self, index): method __len__ (line 210) | def __len__(self): function imagefloder_iitnn (line 214) | def imagefloder_iitnn(data_dir, input1, input2, data_transform_1, data_n... class dataset_wds (line 227) | class dataset_wds(Dataset): method __init__ (line 228) | def __init__(self, data_dir, augmentation1, normalize_LL, normalize_LH... method __getitem__ (line 268) | def __getitem__(self, index): method __len__ (line 315) | def __len__(self): function imagefloder_wds (line 319) | def imagefloder_wds(data_dir, data_transform_1, data_normalize_LL, data_... class dataset_aerial_lanenet (line 330) | class dataset_aerial_lanenet(Dataset): method __init__ (line 331) | def __init__(self, data_dir, augmentation1, normalize_1, normalize_l1,... method __getitem__ (line 357) | def __getitem__(self, index): method __len__ (line 395) | def __len__(self): function imagefloder_aerial_lanenet (line 399) | def imagefloder_aerial_lanenet(data_dir, data_transform, data_normalize,... FILE: dataload/dataset_3d.py class dataset_it (line 12) | class dataset_it(Dataset): method __init__ (line 13) | def __init__(self, data_dir, input1, transform_1, queue_length=20, sam... class dataset_it_dtc (line 60) | class dataset_it_dtc(Dataset): method __init__ (line 61) | def __init__(self, data_dir, input1, num_classes, transform_1, queue_l... class dataset_iit (line 124) | class dataset_iit(Dataset): method __init__ (line 125) | def __init__(self, data_dir, input1, input2, transform_1, queue_length... class dataset_iit_conresnet (line 175) | class dataset_iit_conresnet(Dataset): method __init__ (line 176) | def __init__(self, data_dir, input1, input2, transform_1, queue_length... FILE: loss/loss_function.py class MixSoftmaxCrossEntropyLoss (line 9) | class MixSoftmaxCrossEntropyLoss(nn.CrossEntropyLoss): method __init__ (line 10) | def __init__(self, aux=True, aux_weight=0.2, ignore_index=-1, **kwargs): method _aux_forward (line 15) | def _aux_forward(self, output, target, **kwargs): method forward (line 24) | def forward(self, output, target): class BinaryDiceLoss (line 32) | class BinaryDiceLoss(nn.Module): method __init__ (line 47) | def __init__(self, smooth=1, p=2, reduction='mean'): method forward (line 53) | def forward(self, predict, target, valid_mask): class DiceLoss (line 74) | class DiceLoss(nn.Module): method __init__ (line 77) | def __init__(self, weight=None, aux=False, aux_weight=0.4, ignore_inde... method _base_forward (line 85) | def _base_forward(self, predict, target, valid_mask): method _aux_forward (line 102) | def _aux_forward(self, output, target, **kwargs): method forward (line 112) | def forward(self, output, target): function softmax_mse_loss (line 123) | def softmax_mse_loss(input_logits, target_logits, sigmoid=False): function entropy_loss (line 142) | def entropy_loss(p, C=2): class BCELossBoud (line 149) | class BCELossBoud(nn.Module): method __init__ (line 150) | def __init__(self, num_classes, weight=None, ignore_index=None, **kwar... method weighted_BCE_cross_entropy (line 158) | def weighted_BCE_cross_entropy(self, output, target, weights = None): method forward (line 168) | def forward(self, predict, target): class CustomKLLoss (line 187) | class CustomKLLoss(_Loss): method __init__ (line 193) | def __init__(self, *args, **kwargs): method forward (line 196) | def forward(self, mean, std): function segmentation_loss (line 201) | def segmentation_loss(loss='CE', aux=False, **kwargs): FILE: models/getnetwork.py function get_network (line 5) | def get_network(network, in_channels, num_classes, **kwargs): FILE: models/networks_2d/aerial_lanenet.py class basic_block (line 10) | class basic_block(nn.Module): method __init__ (line 11) | def __init__(self, ch_in, ch_out): method forward (line 16) | def forward(self, x): class Aerial_LaneNet (line 20) | class Aerial_LaneNet(nn.Module): method __init__ (line 21) | def __init__(self, in_channels, num_classes): method forward (line 96) | def forward(self, x, x_wavelet_1, x_wavelet_2, x_wavelet_3, x_wavelet_4): FILE: models/networks_2d/hrnet.py function load_url (line 37) | def load_url(url, model_dir='./pretrained', map_location=None): function conv3x3 (line 47) | def conv3x3(in_planes, out_planes, stride=1): class BasicBlock (line 53) | class BasicBlock(nn.Module): method __init__ (line 56) | def __init__(self, inplanes, planes, stride=1, downsample=None): method forward (line 66) | def forward(self, x): class Bottleneck (line 85) | class Bottleneck(nn.Module): method __init__ (line 88) | def __init__(self, inplanes, planes, stride=1, downsample=None): method forward (line 103) | def forward(self, x): class HighResolutionModule (line 126) | class HighResolutionModule(nn.Module): method __init__ (line 127) | def __init__(self, num_branches, blocks, num_blocks, num_inchannels, method _check_branches (line 144) | def _check_branches(self, num_branches, blocks, num_blocks, method _make_one_branch (line 164) | def _make_one_branch(self, branch_index, block, num_blocks, num_channels, method _make_branches (line 188) | def _make_branches(self, num_branches, block, num_blocks, num_channels): method _make_fuse_layers (line 197) | def _make_fuse_layers(self): method get_num_inchannels (line 243) | def get_num_inchannels(self): method forward (line 246) | def forward(self, x): class HighResolutionNet (line 279) | class HighResolutionNet(nn.Module): method __init__ (line 281) | def __init__(self, in_channels, extra, num_classes,**kwargs): method _make_transition_layer (line 349) | def _make_transition_layer( method _make_layer (line 385) | def _make_layer(self, block, inplanes, planes, blocks, stride=1): method _make_stage (line 402) | def _make_stage(self, layer_config, num_inchannels, method forward (line 431) | def forward(self, x): method init_weights (line 485) | def init_weights(self, pretrained='', ): function init_weights (line 553) | def init_weights(net, init_type='normal', gain=0.02): function hrnet18 (line 592) | def hrnet18(in_channels, num_classes): function hrnet32 (line 597) | def hrnet32(in_channels, num_classes): function hrnet48 (line 602) | def hrnet48(in_channels, num_classes): function hrnet64 (line 607) | def hrnet64(in_channels, num_classes): FILE: models/networks_2d/mwcnn.py function default_conv (line 9) | def default_conv(in_channels, out_channels, kernel_size, bias=True, dila... function default_conv1 (line 15) | def default_conv1(in_channels, out_channels, kernel_size, bias=True, gro... function channel_shuffle (line 23) | def channel_shuffle(x, groups): function pixel_down_shuffle (line 40) | def pixel_down_shuffle(x, downsacale_factor): function sp_init (line 54) | def sp_init(x): function dwt_init (line 65) | def dwt_init(x): function iwt_init (line 80) | def iwt_init(x): class Channel_Shuffle (line 102) | class Channel_Shuffle(nn.Module): method __init__ (line 103) | def __init__(self, conv_groups): method forward (line 108) | def forward(self, x): class SP (line 112) | class SP(nn.Module): method __init__ (line 113) | def __init__(self): method forward (line 117) | def forward(self, x): class Pixel_Down_Shuffle (line 121) | class Pixel_Down_Shuffle(nn.Module): method __init__ (line 122) | def __init__(self): method forward (line 126) | def forward(self, x): class DWT (line 130) | class DWT(nn.Module): method __init__ (line 131) | def __init__(self): method forward (line 135) | def forward(self, x): class IWT (line 139) | class IWT(nn.Module): method __init__ (line 140) | def __init__(self): method forward (line 144) | def forward(self, x): class MeanShift (line 148) | class MeanShift(nn.Conv2d): method __init__ (line 149) | def __init__(self, rgb_range, rgb_mean, rgb_std, sign=-1): class MeanShift2 (line 162) | class MeanShift2(nn.Conv2d): method __init__ (line 163) | def __init__(self, rgb_range, rgb_mean, rgb_std, sign=-1): class BasicBlock (line 175) | class BasicBlock(nn.Sequential): method __init__ (line 176) | def __init__( class BBlock (line 189) | class BBlock(nn.Module): method __init__ (line 190) | def __init__( method forward (line 202) | def forward(self, x): class DBlock_com (line 207) | class DBlock_com(nn.Module): method __init__ (line 208) | def __init__( method forward (line 225) | def forward(self, x): class DBlock_inv (line 230) | class DBlock_inv(nn.Module): method __init__ (line 231) | def __init__( method forward (line 248) | def forward(self, x): class DBlock_com1 (line 253) | class DBlock_com1(nn.Module): method __init__ (line 254) | def __init__( method forward (line 271) | def forward(self, x): class DBlock_inv1 (line 276) | class DBlock_inv1(nn.Module): method __init__ (line 277) | def __init__( method forward (line 294) | def forward(self, x): class DBlock_com2 (line 299) | class DBlock_com2(nn.Module): method __init__ (line 300) | def __init__( method forward (line 317) | def forward(self, x): class DBlock_inv2 (line 322) | class DBlock_inv2(nn.Module): method __init__ (line 323) | def __init__( method forward (line 340) | def forward(self, x): class ShuffleBlock (line 345) | class ShuffleBlock(nn.Module): method __init__ (line 346) | def __init__( method forward (line 359) | def forward(self, x): class DWBlock (line 364) | class DWBlock(nn.Module): method __init__ (line 365) | def __init__( method forward (line 382) | def forward(self, x): class ResBlock (line 387) | class ResBlock(nn.Module): method __init__ (line 388) | def __init__( method forward (line 402) | def forward(self, x): class Block (line 409) | class Block(nn.Module): method __init__ (line 410) | def __init__( method forward (line 424) | def forward(self, x): class Upsampler (line 431) | class Upsampler(nn.Sequential): method __init__ (line 432) | def __init__(self, conv, scale, n_feat, bn=False, act=False, bias=True): class MWCNN (line 452) | class MWCNN(nn.Module): method __init__ (line 453) | def __init__(self, in_channels, num_classes, conv=default_conv): method forward (line 501) | def forward(self, x): method set_scale (line 512) | def set_scale(self, scale_idx): function mwcnn (line 516) | def mwcnn(in_channels, num_classes): FILE: models/networks_2d/resunet.py function init_weights (line 5) | def init_weights(net, init_type='normal', gain=0.02): class ResidualConv (line 28) | class ResidualConv(nn.Module): method __init__ (line 29) | def __init__(self, input_dim, output_dim, stride, padding): method forward (line 47) | def forward(self, x): class Upsample (line 52) | class Upsample(nn.Module): method __init__ (line 53) | def __init__(self, input_dim, output_dim, kernel, stride): method forward (line 60) | def forward(self, x): class ResUnet (line 64) | class ResUnet(nn.Module): method __init__ (line 65) | def __init__(self, in_channels, num_classes, filters=[64, 128, 256, 51... method forward (line 94) | def forward(self, x): function res_unet (line 122) | def res_unet(in_channels, num_classes): FILE: models/networks_2d/resunet_plusplus.py function init_weights (line 5) | def init_weights(net, init_type='normal', gain=0.02): class ResidualConv (line 28) | class ResidualConv(nn.Module): method __init__ (line 29) | def __init__(self, input_dim, output_dim, stride, padding): method forward (line 47) | def forward(self, x): class Upsample (line 52) | class Upsample(nn.Module): method __init__ (line 53) | def __init__(self, input_dim, output_dim, kernel, stride): method forward (line 60) | def forward(self, x): class Squeeze_Excite_Block (line 63) | class Squeeze_Excite_Block(nn.Module): method __init__ (line 64) | def __init__(self, channel, reduction=16): method forward (line 74) | def forward(self, x): class ASPP (line 80) | class ASPP(nn.Module): method __init__ (line 81) | def __init__(self, in_dims, out_dims, rate=[6, 12, 18]): method forward (line 109) | def forward(self, x): method _init_weights (line 116) | def _init_weights(self): class Upsample_ (line 124) | class Upsample_(nn.Module): method __init__ (line 125) | def __init__(self, scale=2): method forward (line 130) | def forward(self, x): class AttentionBlock (line 133) | class AttentionBlock(nn.Module): method __init__ (line 134) | def __init__(self, input_encoder, input_decoder, output_dim): method forward (line 156) | def forward(self, x1, x2): class ResUnetPlusPlus (line 163) | class ResUnetPlusPlus(nn.Module): method __init__ (line 164) | def __init__(self, in_channels, num_classes, filters=[32, 64, 128, 256... method forward (line 207) | def forward(self, x): function res_unet_plusplus (line 241) | def res_unet_plusplus(in_channels, num_classes): FILE: models/networks_2d/swinunet.py class Mlp (line 25) | class Mlp(nn.Module): method __init__ (line 26) | def __init__(self, in_features, hidden_features=None, out_features=Non... method forward (line 35) | def forward(self, x): function window_partition (line 44) | def window_partition(x, window_size): function window_reverse (line 59) | def window_reverse(windows, window_size, H, W): class WindowAttention (line 76) | class WindowAttention(nn.Module): method __init__ (line 89) | def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scal... method forward (line 127) | def forward(self, x, mask=None): method extra_repr (line 164) | def extra_repr(self) -> str: method flops (line 167) | def flops(self, N): class SwinTransformerBlock (line 181) | class SwinTransformerBlock(nn.Module): method __init__ (line 199) | def __init__(self, dim, input_resolution, num_heads, window_size=7, sh... method forward (line 255) | def forward(self, x): method extra_repr (line 301) | def extra_repr(self) -> str: method flops (line 305) | def flops(self): class PatchMerging (line 320) | class PatchMerging(nn.Module): method __init__ (line 328) | def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): method forward (line 335) | def forward(self, x): method extra_repr (line 358) | def extra_repr(self) -> str: method flops (line 361) | def flops(self): class PatchExpand (line 368) | class PatchExpand(nn.Module): method __init__ (line 369) | def __init__(self, input_resolution, dim, dim_scale=2, norm_layer=nn.L... method forward (line 377) | def forward(self, x): class FinalPatchExpand_X4 (line 395) | class FinalPatchExpand_X4(nn.Module): method __init__ (line 396) | def __init__(self, input_resolution, dim, dim_scale=4, norm_layer=nn.L... method forward (line 405) | def forward(self, x): class BasicLayer (line 422) | class BasicLayer(nn.Module): method __init__ (line 441) | def __init__(self, dim, input_resolution, depth, num_heads, window_size, method forward (line 472) | def forward(self, x): method extra_repr (line 482) | def extra_repr(self) -> str: method flops (line 485) | def flops(self): class BasicLayer_up (line 494) | class BasicLayer_up(nn.Module): method __init__ (line 513) | def __init__(self, dim, input_resolution, depth, num_heads, window_size, method forward (line 544) | def forward(self, x): class PatchEmbed (line 555) | class PatchEmbed(nn.Module): method __init__ (line 565) | def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=9... method forward (line 586) | def forward(self, x): method flops (line 596) | def flops(self): class SwinTransformerSys (line 605) | class SwinTransformerSys(nn.Module): method __init__ (line 630) | def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes... method _init_weights (line 733) | def _init_weights(self, m): method no_weight_decay (line 743) | def no_weight_decay(self): method no_weight_decay_keywords (line 747) | def no_weight_decay_keywords(self): method forward_features (line 751) | def forward_features(self, x): method forward_up_features (line 767) | def forward_up_features(self, x, x_downsample): method up_x4 (line 780) | def up_x4(self, x): method forward (line 793) | def forward(self, x): method flops (line 800) | def flops(self): class SwinUnet (line 811) | class SwinUnet(nn.Module): method __init__ (line 812) | def __init__(self, num_classes, img_size, zero_head=False, vis=False): method forward (line 833) | def forward(self, x): method load_from (line 839) | def load_from(self, config): function swinunet (line 877) | def swinunet(num_classes, img_size): FILE: models/networks_2d/u2net.py function init_weights (line 6) | def init_weights(net, init_type='normal', gain=0.02): class REBNCONV (line 30) | class REBNCONV(nn.Module): method __init__ (line 31) | def __init__(self,in_ch=3,out_ch=3,dirate=1): method forward (line 38) | def forward(self,x): function _upsample_like (line 46) | def _upsample_like(src,tar): class RSU7 (line 54) | class RSU7(nn.Module):#UNet07DRES(nn.Module): method __init__ (line 56) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3): method forward (line 87) | def forward(self,x): class RSU6 (line 131) | class RSU6(nn.Module):#UNet06DRES(nn.Module): method __init__ (line 133) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3): method forward (line 160) | def forward(self,x): class RSU5 (line 200) | class RSU5(nn.Module):#UNet05DRES(nn.Module): method __init__ (line 202) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3): method forward (line 225) | def forward(self,x): class RSU4 (line 258) | class RSU4(nn.Module):#UNet04DRES(nn.Module): method __init__ (line 260) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3): method forward (line 279) | def forward(self,x): class RSU4F (line 306) | class RSU4F(nn.Module):#UNet04FRES(nn.Module): method __init__ (line 308) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3): method forward (line 323) | def forward(self,x): class U2NET (line 343) | class U2NET(nn.Module): method __init__ (line 345) | def __init__(self,in_ch=3,out_ch=1): method forward (line 381) | def forward(self,x): class U2NETP (line 448) | class U2NETP(nn.Module): method __init__ (line 450) | def __init__(self,in_ch=3,out_ch=1): method forward (line 486) | def forward(self,x): function u2net (line 552) | def u2net(in_channels, num_classes): function u2net_small (line 557) | def u2net_small(in_channels, num_classes): FILE: models/networks_2d/unet.py function init_weights (line 7) | def init_weights(net, init_type='normal', gain=0.02): class conv_block (line 31) | class conv_block(nn.Module): method __init__ (line 32) | def __init__(self, ch_in, ch_out): method forward (line 43) | def forward(self, x): class up_conv (line 48) | class up_conv(nn.Module): method __init__ (line 49) | def __init__(self, ch_in, ch_out): method forward (line 58) | def forward(self, x): class Recurrent_block (line 63) | class Recurrent_block(nn.Module): method __init__ (line 64) | def __init__(self, ch_out, t=2): method forward (line 74) | def forward(self, x): class RRCNN_block (line 84) | class RRCNN_block(nn.Module): method __init__ (line 85) | def __init__(self, ch_in, ch_out, t=2): method forward (line 93) | def forward(self, x): class single_conv (line 99) | class single_conv(nn.Module): method __init__ (line 100) | def __init__(self, ch_in, ch_out): method forward (line 108) | def forward(self, x): class Attention_block (line 113) | class Attention_block(nn.Module): method __init__ (line 114) | def __init__(self, F_g, F_l, F_int): method forward (line 134) | def forward(self, g, x): class U_Net (line 143) | class U_Net(nn.Module): method __init__ (line 144) | def __init__(self, in_channels=3, num_classes=1): method forward (line 169) | def forward(self, x): class R2U_Net (line 210) | class R2U_Net(nn.Module): method __init__ (line 211) | def __init__(self, in_channels=3, num_classes=1, t=2): method forward (line 241) | def forward(self, x): class AttU_Net (line 281) | class AttU_Net(nn.Module): method __init__ (line 282) | def __init__(self, in_channels=3, num_classes=1): method forward (line 311) | def forward(self, x): class R2AttU_Net (line 355) | class R2AttU_Net(nn.Module): method __init__ (line 356) | def __init__(self, in_channels=3, num_classes=1, t=2): method forward (line 390) | def forward(self, x): function unet (line 434) | def unet(in_channels, num_classes): function r2_unet (line 439) | def r2_unet(in_channels, num_classes): function attention_unet (line 444) | def attention_unet(in_channels, num_classes): function r2_attention_unet (line 449) | def r2_attention_unet(in_channels, num_classes): FILE: models/networks_2d/unet_3plus.py function weights_init_normal (line 8) | def weights_init_normal(m): function weights_init_xavier (line 20) | def weights_init_xavier(m): function weights_init_kaiming (line 32) | def weights_init_kaiming(m): function weights_init_orthogonal (line 44) | def weights_init_orthogonal(m): function init_weights (line 56) | def init_weights(net, init_type='normal'): class unetConv2 (line 70) | class unetConv2(nn.Module): method __init__ (line 71) | def __init__(self, in_size, out_size, is_batchnorm, n=2, ks=3, stride=... method forward (line 98) | def forward(self, inputs): class UNet_3Plus (line 110) | class UNet_3Plus(nn.Module): method __init__ (line 112) | def __init__(self, in_channels, num_classes): method forward (line 294) | def forward(self, inputs): class UNet_3Plus_DeepSup (line 352) | class UNet_3Plus_DeepSup(nn.Module): method __init__ (line 353) | def __init__(self, in_channels=3, num_classes=1, feature_scale=4, is_d... method forward (line 543) | def forward(self, inputs): class UNet_3Plus_DeepSup_CGM (line 613) | class UNet_3Plus_DeepSup_CGM(nn.Module): method __init__ (line 615) | def __init__(self, in_channels=3, n_classes=1, feature_scale=4, is_dec... method dotProduct (line 811) | def dotProduct(self, seg, cls): method forward (line 818) | def forward(self, inputs): function unet_3plus (line 891) | def unet_3plus(in_channels, num_classes): function unet_3plus_ds (line 895) | def unet_3plus_ds(in_channels, num_classes): function unet_3plus_ds_cgm (line 899) | def unet_3plus_ds_cgm(in_channels, num_classes): FILE: models/networks_2d/unet_cct.py function init_weights (line 7) | def init_weights(net, init_type='normal', gain=0.02): class ConvBlock (line 30) | class ConvBlock(nn.Module): method __init__ (line 33) | def __init__(self, in_channels, out_channels, dropout_p): method forward (line 45) | def forward(self, x): class DownBlock (line 49) | class DownBlock(nn.Module): method __init__ (line 52) | def __init__(self, in_channels, out_channels, dropout_p): method forward (line 60) | def forward(self, x): class UpBlock (line 64) | class UpBlock(nn.Module): method __init__ (line 67) | def __init__(self, in_channels1, in_channels2, out_channels, dropout_p, method forward (line 80) | def forward(self, x1, x2): class Encoder (line 87) | class Encoder(nn.Module): method __init__ (line 88) | def __init__(self, params): method forward (line 108) | def forward(self, x): class Decoder (line 116) | class Decoder(nn.Module): method __init__ (line 117) | def __init__(self, params): method forward (line 138) | def forward(self, feature): function Dropout (line 153) | def Dropout(x, p=0.3): function FeatureDropout (line 158) | def FeatureDropout(x): class FeatureNoise (line 169) | class FeatureNoise(nn.Module): method __init__ (line 170) | def __init__(self, uniform_range=0.3): method feature_based_noise (line 174) | def feature_based_noise(self, x): method forward (line 180) | def forward(self, x): class UNet_CCT (line 184) | class UNet_CCT(nn.Module): method __init__ (line 185) | def __init__(self, in_chns, class_num): method forward (line 200) | def forward(self, x): function unet_cct (line 211) | def unet_cct(in_channels, num_classes): FILE: models/networks_2d/unet_plusplus.py function init_weights (line 5) | def init_weights(net, init_type='normal', gain=0.02): class VGGBlock (line 29) | class VGGBlock(nn.Module): method __init__ (line 30) | def __init__(self, in_channels, middle_channels, out_channels): method forward (line 38) | def forward(self, x): class NestedUNet (line 50) | class NestedUNet(nn.Module): method __init__ (line 51) | def __init__(self, num_classes, input_channels=3, deep_supervision=Fal... method forward (line 90) | def forward(self, input): function unet_plusplus (line 129) | def unet_plusplus(in_channels, num_classes): FILE: models/networks_2d/unet_urpc.py function init_weights (line 8) | def init_weights(net, init_type='normal', gain=0.02): class ConvBlock (line 31) | class ConvBlock(nn.Module): method __init__ (line 34) | def __init__(self, in_channels, out_channels, dropout_p): method forward (line 46) | def forward(self, x): class Encoder (line 49) | class Encoder(nn.Module): method __init__ (line 50) | def __init__(self, params): method forward (line 70) | def forward(self, x): class DownBlock (line 78) | class DownBlock(nn.Module): method __init__ (line 81) | def __init__(self, in_channels, out_channels, dropout_p): method forward (line 89) | def forward(self, x): class UpBlock (line 93) | class UpBlock(nn.Module): method __init__ (line 96) | def __init__(self, in_channels1, in_channels2, out_channels, dropout_p, method forward (line 109) | def forward(self, x1, x2): class FeatureNoise (line 116) | class FeatureNoise(nn.Module): method __init__ (line 117) | def __init__(self, uniform_range=0.3): method feature_based_noise (line 121) | def feature_based_noise(self, x): method forward (line 127) | def forward(self, x): function Dropout (line 131) | def Dropout(x, p=0.3): function FeatureDropout (line 136) | def FeatureDropout(x): class Decoder_URPC (line 146) | class Decoder_URPC(nn.Module): method __init__ (line 147) | def __init__(self, params): method forward (line 177) | def forward(self, feature, shape): class UNet_URPC (line 213) | class UNet_URPC(nn.Module): method __init__ (line 214) | def __init__(self, in_chns, class_num): method forward (line 226) | def forward(self, x): function unet_urpc (line 233) | def unet_urpc(in_channels, num_classes): FILE: models/networks_2d/wavesnet.py class My_DownSampling_SC (line 11) | class My_DownSampling_SC(nn.Module): method __init__ (line 12) | def __init__(self, in_channel, out_channel, kernel_size = (1,1), strid... method forward (line 16) | def forward(self, input): class My_DownSampling_MP (line 20) | class My_DownSampling_MP(nn.Module): method __init__ (line 21) | def __init__(self, stride = 2, kernel_size = 2): method forward (line 25) | def forward(self, input): class My_UpSampling_SC (line 29) | class My_UpSampling_SC(nn.Module): method __init__ (line 30) | def __init__(self, in_channel, out_channel, kernel_size = (1,1), strid... method forward (line 34) | def forward(self, input, feature_map): class My_DownSampling_DWT (line 38) | class My_DownSampling_DWT(nn.Module): method __init__ (line 39) | def __init__(self, wavename = 'haar'): method forward (line 43) | def forward(self, input): class My_UpSampling_IDWT (line 48) | class My_UpSampling_IDWT(nn.Module): method __init__ (line 49) | def __init__(self, wavename = 'haar'): method forward (line 53) | def forward(self, LL, LH, HL, HH, feature_map): class My_Sequential (line 58) | class My_Sequential(Module): method __init__ (line 65) | def __init__(self, *args): method _get_item_by_idx (line 74) | def _get_item_by_idx(self, iterator, idx): method __getitem__ (line 83) | def __getitem__(self, idx): method __setitem__ (line 89) | def __setitem__(self, idx, module): method __delitem__ (line 93) | def __delitem__(self, idx): method __len__ (line 101) | def __len__(self): method __dir__ (line 104) | def __dir__(self): method forward (line 109) | def forward(self, input): class My_Sequential_re (line 123) | class My_Sequential_re(Module): method __init__ (line 130) | def __init__(self, *args): method _get_item_by_idx (line 140) | def _get_item_by_idx(self, iterator, idx): method __getitem__ (line 149) | def __getitem__(self, idx): method __setitem__ (line 155) | def __setitem__(self, idx, module): method __delitem__ (line 159) | def __delitem__(self, idx): method __len__ (line 167) | def __len__(self): method __dir__ (line 170) | def __dir__(self): method forward (line 175) | def forward(self, *input): class DWTFunction_1D (line 207) | class DWTFunction_1D(Function): method forward (line 209) | def forward(ctx, input, matrix_Low, matrix_High): method backward (line 215) | def backward(ctx, grad_L, grad_H): class IDWTFunction_1D (line 221) | class IDWTFunction_1D(Function): method forward (line 223) | def forward(ctx, input_L, input_H, matrix_L, matrix_H): method backward (line 228) | def backward(ctx, grad_output): class DWTFunction_2D (line 235) | class DWTFunction_2D(Function): method forward (line 237) | def forward(ctx, input, matrix_Low_0, matrix_Low_1, matrix_High_0, mat... method backward (line 247) | def backward(ctx, grad_LL, grad_LH, grad_HL, grad_HH): class DWTFunction_2D_tiny (line 255) | class DWTFunction_2D_tiny(Function): method forward (line 257) | def forward(ctx, input, matrix_Low_0, matrix_Low_1, matrix_High_0, mat... method backward (line 263) | def backward(ctx, grad_LL): class IDWTFunction_2D (line 270) | class IDWTFunction_2D(Function): method forward (line 272) | def forward(ctx, input_LL, input_LH, input_HL, input_HH, method backward (line 280) | def backward(ctx, grad_output): class DWTFunction_3D (line 291) | class DWTFunction_3D(Function): method forward (line 293) | def forward(ctx, input, method backward (line 315) | def backward(ctx, grad_LLL, grad_LLH, grad_LHL, grad_LHH, class IDWTFunction_3D (line 328) | class IDWTFunction_3D(Function): method forward (line 330) | def forward(ctx, input_LLL, input_LLH, input_LHL, input_LHH, method backward (line 345) | def backward(ctx, grad_output): class DWT_1D (line 364) | class DWT_1D(Module): method __init__ (line 370) | def __init__(self, wavename): method get_matrix (line 384) | def get_matrix(self): method forward (line 413) | def forward(self, input): class IDWT_1D (line 421) | class IDWT_1D(Module): method __init__ (line 427) | def __init__(self, wavename): method get_matrix (line 443) | def get_matrix(self): method forward (line 472) | def forward(self, L, H): class DWT_2D (line 480) | class DWT_2D(Module): method __init__ (line 488) | def __init__(self, wavename): method get_matrix (line 502) | def get_matrix(self): method forward (line 547) | def forward(self, input): class DWT_2D_tiny (line 557) | class DWT_2D_tiny(Module): method __init__ (line 562) | def __init__(self, wavename): method get_matrix (line 576) | def get_matrix(self): method forward (line 621) | def forward(self, input): class IDWT_2D (line 630) | class IDWT_2D(Module): method __init__ (line 638) | def __init__(self, wavename): method get_matrix (line 654) | def get_matrix(self): method forward (line 698) | def forward(self, LL, LH, HL, HH): class DWT_3D (line 707) | class DWT_3D(Module): method __init__ (line 719) | def __init__(self, wavename): method get_matrix (line 733) | def get_matrix(self): method forward (line 786) | def forward(self, input): class IDWT_3D (line 797) | class IDWT_3D(Module): method __init__ (line 809) | def __init__(self, wavename): method get_matrix (line 825) | def get_matrix(self): method forward (line 878) | def forward(self, LLL, LLH, LHL, LHH, HLL, HLH, HHL, HHH): class SegNet_VGG (line 918) | class SegNet_VGG(nn.Module): method __init__ (line 919) | def __init__(self, features, num_classes = 21, init_weights = True, wa... method forward (line 931) | def forward(self, x): method _initialize_weights (line 938) | def _initialize_weights(self): method __str__ (line 955) | def __str__(self): class WSegNet_VGG (line 959) | class WSegNet_VGG(nn.Module): method __init__ (line 960) | def __init__(self, features, num_classes, init_weights = True, wavenam... method forward (line 972) | def forward(self, x): method _initialize_weights (line 979) | def _initialize_weights(self): method __str__ (line 996) | def __str__(self): function make_layers (line 1000) | def make_layers(cfg, batch_norm = False): function make_w_layers (line 1037) | def make_w_layers(cfg, in_channels, batch_norm = False, wavename = 'haar'): function segnet_vgg11 (line 1080) | def segnet_vgg11(pretrained = False, **kwargs): function segnet_vgg11_bn (line 1091) | def segnet_vgg11_bn(pretrained=False, **kwargs): function segnet_vgg13 (line 1102) | def segnet_vgg13(pretrained=False, **kwargs): function segnet_vgg13_bn (line 1113) | def segnet_vgg13_bn(pretrained=False, **kwargs): function segnet_vgg16 (line 1124) | def segnet_vgg16(pretrained=False, **kwargs): function segnet_vgg16_bn (line 1135) | def segnet_vgg16_bn(pretrained=False, **kwargs): function segnet_vgg19 (line 1146) | def segnet_vgg19(pretrained=False, **kwargs): function segnet_vgg19_bn (line 1157) | def segnet_vgg19_bn(pretrained=False, **kwargs): function wsegnet_vgg11 (line 1169) | def wsegnet_vgg11(pretrained = False, wavename = 'haar', **kwargs): function wsegnet_vgg11_bn (line 1180) | def wsegnet_vgg11_bn(pretrained=False, wavename = 'haar', **kwargs): function wsegnet_vgg13 (line 1191) | def wsegnet_vgg13(pretrained=False, wavename = 'haar', **kwargs): function wsegnet_vgg13_bn (line 1202) | def wsegnet_vgg13_bn(pretrained=False, wavename = 'haar', **kwargs): function wsegnet_vgg16 (line 1213) | def wsegnet_vgg16(pretrained=False, wavename = 'haar', **kwargs): function wsegnet_vgg16_bn (line 1224) | def wsegnet_vgg16_bn(in_channels, num_classes, pretrained=False, wavenam... function wsegnet_vgg19 (line 1235) | def wsegnet_vgg19(pretrained=False, wavename = 'haar', **kwargs): function wsegnet_vgg19_bn (line 1246) | def wsegnet_vgg19_bn(pretrained=False, wavename = 'haar', **kwargs): FILE: models/networks_2d/wds.py class basic_block (line 9) | class basic_block(nn.Module): method __init__ (line 10) | def __init__(self, ch_in, ch_out): method forward (line 15) | def forward(self, x): class WDS (line 19) | class WDS(nn.Module): method __init__ (line 20) | def __init__(self, in_channels, num_classes): method forward (line 92) | def forward(self, LL, LH, HL, HH): FILE: models/networks_2d/xnet.py function conv1x1 (line 15) | def conv1x1(in_planes, out_planes, stride=1): function conv3x3 (line 19) | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): class up_conv (line 23) | class up_conv(nn.Module): method __init__ (line 24) | def __init__(self, ch_in, ch_out): method forward (line 33) | def forward(self, x): class down_conv (line 37) | class down_conv(nn.Module): method __init__ (line 38) | def __init__(self, ch_in, ch_out): method forward (line 45) | def forward(self, x): class same_conv (line 49) | class same_conv(nn.Module): method __init__ (line 50) | def __init__(self, ch_in, ch_out): method forward (line 56) | def forward(self, x): class transition_conv (line 60) | class transition_conv(nn.Module): method __init__ (line 61) | def __init__(self, ch_in, ch_out): method forward (line 67) | def forward(self, x): class BasicBlock (line 71) | class BasicBlock(nn.Module): method __init__ (line 74) | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, method forward (line 92) | def forward(self, x): class DoubleBasicBlock (line 110) | class DoubleBasicBlock(nn.Module): method __init__ (line 111) | def __init__(self, inplanes, planes, downsample=None): method forward (line 119) | def forward(self, x): class XNet (line 124) | class XNet(nn.Module): method __init__ (line 125) | def __init__(self, in_channels, num_classes): method forward (line 226) | def forward(self, input1, input2): class XNet_1_1_m (line 322) | class XNet_1_1_m(nn.Module): method __init__ (line 323) | def __init__(self, in_channels, num_classes): method forward (line 412) | def forward(self, input1, input2): class XNet_1_2_m (line 494) | class XNet_1_2_m(nn.Module): method __init__ (line 495) | def __init__(self, in_channels, num_classes): method forward (line 590) | def forward(self, input1, input2): class XNet_2_1_m (line 680) | class XNet_2_1_m(nn.Module): method __init__ (line 681) | def __init__(self, in_channels, num_classes): method forward (line 776) | def forward(self, input1, input2): class XNet_2_3_m (line 865) | class XNet_2_3_m(nn.Module): method __init__ (line 866) | def __init__(self, in_channels, num_classes): method forward (line 975) | def forward(self, input1, input2): class XNet_3_2_m (line 1082) | class XNet_3_2_m(nn.Module): method __init__ (line 1083) | def __init__(self, in_channels, num_classes): method forward (line 1192) | def forward(self, input1, input2): class XNet_3_3_m (line 1300) | class XNet_3_3_m(nn.Module): method __init__ (line 1301) | def __init__(self, in_channels, num_classes): method forward (line 1418) | def forward(self, input1, input2): class XNet_sb (line 1533) | class XNet_sb(nn.Module): method __init__ (line 1534) | def __init__(self, in_channels, num_classes): method forward (line 1597) | def forward(self, input1): FILE: models/networks_3d/conresnet.py function init_weights (line 8) | def init_weights(net, init_type='normal', gain=0.02): class Conv3d (line 32) | class Conv3d(nn.Conv3d): method __init__ (line 34) | def __init__(self, in_channels, out_channels, kernel_size, stride=(1,1... method forward (line 37) | def forward(self, x): function conv3x3x3 (line 45) | def conv3x3x3(in_planes, out_planes, kernel_size=(3, 3, 3), stride=(1, 1... class ConResAtt (line 56) | class ConResAtt(nn.Module): method __init__ (line 57) | def __init__(self, in_channels, in_planes, out_planes, kernel_size=(3,... method _res (line 95) | def _res(self, x): # bs, channel, D, W, H method forward (line 106) | def forward(self, input): class NoBottleneck (line 146) | class NoBottleneck(nn.Module): method __init__ (line 147) | def __init__(self, inplanes, planes, stride=(1, 1, 1), dilation=(1, 1,... method forward (line 165) | def forward(self, x): class ConResNet (line 183) | class ConResNet(nn.Module): method __init__ (line 184) | def __init__(self, in_channels, num_classes, shape, block, layers, wei... method _make_layer (line 240) | def _make_layer(self, block, inplanes, outplanes, blocks, stride=(1, 1... method forward (line 261) | def forward(self, x, x_res): function conresnet (line 310) | def conresnet(in_channels, num_classes, **kwargs): FILE: models/networks_3d/cotr.py class PositionEmbeddingSine (line 10) | class PositionEmbeddingSine(nn.Module): method __init__ (line 16) | def __init__(self, num_pos_feats=[64, 64, 64], temperature=10000, norm... method forward (line 27) | def forward(self, x): function build_position_encoding (line 63) | def build_position_encoding(mode, hidden_dim): function ms_deform_attn_core_pytorch_3D (line 77) | def ms_deform_attn_core_pytorch_3D(value, value_spatial_shapes, sampling... class MSDeformAttn (line 93) | class MSDeformAttn(nn.Module): method __init__ (line 94) | def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4): method _reset_parameters (line 121) | def _reset_parameters(self): method forward (line 137) | def forward(self, query, reference_points, input_flatten, input_spatia... function _get_clones (line 168) | def _get_clones(module, N): function _get_activation_fn (line 172) | def _get_activation_fn(activation): class DeformableTransformerEncoderLayer (line 182) | class DeformableTransformerEncoderLayer(nn.Module): method __init__ (line 183) | def __init__(self, method with_pos_embed (line 203) | def with_pos_embed(tensor, pos): method forward_ffn (line 206) | def forward_ffn(self, src): method forward (line 212) | def forward(self, src, pos, reference_points, spatial_shapes, level_st... class DeformableTransformerEncoder (line 224) | class DeformableTransformerEncoder(nn.Module): method __init__ (line 225) | def __init__(self, encoder_layer, num_layers): method get_reference_points (line 231) | def get_reference_points(spatial_shapes, valid_ratios, device): method forward (line 249) | def forward(self, src, spatial_shapes, level_start_index, valid_ratios... class DeformableTransformer (line 258) | class DeformableTransformer(nn.Module): method __init__ (line 259) | def __init__(self, d_model=256, nhead=8, num_encoder_layers=6, dim_fee... method _reset_parameters (line 272) | def _reset_parameters(self): method get_valid_ratio (line 281) | def get_valid_ratio(self, mask): method forward (line 293) | def forward(self, srcs, masks, pos_embeds): class Conv3d_wd (line 323) | class Conv3d_wd(nn.Conv3d): method __init__ (line 325) | def __init__(self, in_channels, out_channels, kernel_size, stride=(1, ... method forward (line 328) | def forward(self, x): function conv3x3x3 (line 338) | def conv3x3x3(in_planes, out_planes, kernel_size, stride=(1, 1, 1), padd... function Norm_layer (line 347) | def Norm_layer(norm_cfg, inplanes): function Activation_layer (line 360) | def Activation_layer(activation_cfg, inplace=True): class ResBlock (line 368) | class ResBlock(nn.Module): method __init__ (line 370) | def __init__(self, inplanes, planes, norm_cfg, activation_cfg, stride=... method forward (line 378) | def forward(self, x): class Backbone (line 392) | class Backbone(nn.Module): method __init__ (line 395) | def __init__(self, depth, in_channels=1, norm_cfg='BN', activation_cfg... method _make_layer (line 422) | def _make_layer(self, block, planes, blocks, stride=(1, 1, 1), norm_cf... method init_weights (line 441) | def init_weights(self): method forward (line 451) | def forward(self, x): class Conv3dBlock (line 467) | class Conv3dBlock(nn.Module): method __init__ (line 468) | def __init__(self, in_channels, out_channels, norm_cfg, activation_cfg... method forward (line 474) | def forward(self, x): class ResBlock_ (line 481) | class ResBlock_(nn.Module): method __init__ (line 483) | def __init__(self, inplanes, planes, norm_cfg, activation_cfg, weight_... method forward (line 488) | def forward(self, x): class U_ResTran3D (line 497) | class U_ResTran3D(nn.Module): method __init__ (line 498) | def __init__(self, in_channels, num_classes, norm_cfg='BN', activation... method posi_mask (line 539) | def posi_mask(self, x): method forward (line 552) | def forward(self, inputs): function cotr (line 587) | def cotr(in_channels, num_classes): FILE: models/networks_3d/dmfnet.py function normalization (line 6) | def normalization(planes, norm='bn'): class Conv3d_Block (line 17) | class Conv3d_Block(nn.Module): method __init__ (line 18) | def __init__(self,num_in,num_out,kernel_size=1,stride=1,g=1,padding=No... method forward (line 26) | def forward(self, x): # BN + Relu + Conv class DilatedConv3DBlock (line 32) | class DilatedConv3DBlock(nn.Module): method __init__ (line 33) | def __init__(self, num_in, num_out, kernel_size=(1,1,1), stride=1, g=1... method forward (line 45) | def forward(self, x): class MFunit (line 51) | class MFunit(nn.Module): method __init__ (line 52) | def __init__(self, num_in, num_out, g=1, stride=1, d=(1,1),norm=None): method forward (line 77) | def forward(self, x): class DMFUnit (line 92) | class DMFUnit(nn.Module): method __init__ (line 94) | def __init__(self, num_in, num_out, g=1, stride=1,norm=None,dilation=N... method forward (line 125) | def forward(self, x): class MFNet (line 138) | class MFNet(nn.Module): # method __init__ (line 142) | def __init__(self,in_channels, num_classes, n=32, channels=128, groups... method forward (line 184) | def forward(self, x): class DMFNet (line 208) | class DMFNet(MFNet): # softmax method __init__ (line 210) | def __init__(self,in_channels, num_classes, n=32,channels=128, groups=... function dmfnet (line 225) | def dmfnet(in_channels, num_classes): FILE: models/networks_3d/espnet3d.py class CBR (line 9) | class CBR(nn.Module): method __init__ (line 10) | def __init__(self, nIn, nOut, kSize, stride=1): method forward (line 17) | def forward(self, input): class CB (line 24) | class CB(nn.Module): method __init__ (line 25) | def __init__(self, nIn, nOut, kSize, stride=1): method forward (line 31) | def forward(self, input): class C (line 37) | class C(nn.Module): method __init__ (line 38) | def __init__(self, nIn, nOut, kSize, stride=1, groups=1): method forward (line 43) | def forward(self, input): class DownSamplerA (line 48) | class DownSamplerA(nn.Module): method __init__ (line 49) | def __init__(self, nIn, nOut): method forward (line 53) | def forward(self, input): class DownSamplerB (line 58) | class DownSamplerB(nn.Module): method __init__ (line 59) | def __init__(self, nIn, nOut): method forward (line 71) | def forward(self, input): class BR (line 89) | class BR(nn.Module): method __init__ (line 90) | def __init__(self, nOut): method forward (line 95) | def forward(self, input): class CDilated (line 101) | class CDilated(nn.Module): method __init__ (line 102) | def __init__(self, nIn, nOut, kSize, stride=1, d=1, groups=1): method forward (line 108) | def forward(self, input): class InputProjectionA (line 113) | class InputProjectionA(nn.Module): method __init__ (line 120) | def __init__(self, samplingTimes): method forward (line 130) | def forward(self, input): class DilatedParllelResidualBlockB1 (line 140) | class DilatedParllelResidualBlockB1(nn.Module): # with k=4 method __init__ (line 141) | def __init__(self, nIn, nOut, stride=1): method forward (line 153) | def forward(self, input): class ASPBlock (line 170) | class ASPBlock(nn.Module): # with k=4 method __init__ (line 171) | def __init__(self, nIn, nOut, stride=1): method forward (line 179) | def forward(self, input): class UpSampler (line 192) | class UpSampler(nn.Module): method __init__ (line 196) | def __init__(self, nIn, nOut): method forward (line 200) | def forward(self, inp): class PSPDec (line 204) | class PSPDec(nn.Module): method __init__ (line 209) | def __init__(self, nIn, nOut, downSize): method forward (line 213) | def forward(self, x): class ESPNet (line 220) | class ESPNet(nn.Module): method __init__ (line 221) | def __init__(self, in_channels, num_classes): method forward (line 305) | def forward(self, input1, inp_res=(128, 128, 128), inpSt2=False): function espnet3d (line 365) | def espnet3d(in_channels, num_classes): FILE: models/networks_3d/res_unet3d.py function init_weights (line 6) | def init_weights(net, init_type='normal', gain=0.02): class UNet (line 30) | class UNet(nn.Module): method __init__ (line 35) | def __init__(self, in_channels, n_classes, base_n_filter=8): method conv_norm_lrelu (line 88) | def conv_norm_lrelu(self, feat_in, feat_out): method norm_lrelu_conv (line 94) | def norm_lrelu_conv(self, feat_in, feat_out): method lrelu_conv (line 100) | def lrelu_conv(self, feat_in, feat_out): method norm_lrelu_upscale_conv_norm_lrelu (line 105) | def norm_lrelu_upscale_conv_norm_lrelu(self, feat_in, feat_out): method forward (line 115) | def forward(self, x): function res_unet3d (line 212) | def res_unet3d(in_channels, num_classes): FILE: models/networks_3d/transbts.py function init_weights (line 7) | def init_weights(net, init_type='normal', gain=0.02): function normalization (line 30) | def normalization(planes, norm='gn'): class InitConv (line 41) | class InitConv(nn.Module): method __init__ (line 42) | def __init__(self, in_channels=4, out_channels=16, dropout=0.2): method forward (line 48) | def forward(self, x): class EnBlock (line 54) | class EnBlock(nn.Module): method __init__ (line 55) | def __init__(self, in_channels, norm='gn'): method forward (line 66) | def forward(self, x): class EnDown (line 77) | class EnDown(nn.Module): method __init__ (line 78) | def __init__(self, in_channels, out_channels): method forward (line 82) | def forward(self, x): class Unet (line 87) | class Unet(nn.Module): method __init__ (line 88) | def __init__(self, in_channels=4, base_channels=16): method forward (line 108) | def forward(self, x): class FixedPositionalEncoding (line 129) | class FixedPositionalEncoding(nn.Module): method __init__ (line 130) | def __init__(self, embedding_dim, max_length=512): method forward (line 141) | def forward(self, x): class LearnedPositionalEncoding (line 146) | class LearnedPositionalEncoding(nn.Module): method __init__ (line 147) | def __init__(self, max_position_embeddings, embedding_dim): method forward (line 152) | def forward(self, x): class IntermediateSequential (line 157) | class IntermediateSequential(nn.Sequential): method __init__ (line 158) | def __init__(self, *args, return_intermediate=True): method forward (line 162) | def forward(self, input): class SelfAttention (line 173) | class SelfAttention(nn.Module): method __init__ (line 174) | def __init__( method forward (line 187) | def forward(self, x): class Residual (line 201) | class Residual(nn.Module): method __init__ (line 202) | def __init__(self, fn): method forward (line 206) | def forward(self, x): class PreNorm (line 209) | class PreNorm(nn.Module): method __init__ (line 210) | def __init__(self, dim, fn): method forward (line 215) | def forward(self, x): class PreNormDrop (line 219) | class PreNormDrop(nn.Module): method __init__ (line 220) | def __init__(self, dim, dropout_rate, fn): method forward (line 226) | def forward(self, x): class FeedForward (line 230) | class FeedForward(nn.Module): method __init__ (line 231) | def __init__(self, dim, hidden_dim, dropout_rate): method forward (line 241) | def forward(self, x): class TransformerModel (line 244) | class TransformerModel(nn.Module): method __init__ (line 245) | def __init__(self,dim,depth,heads,mlp_dim,dropout_rate=0.1,attn_dropou... method forward (line 255) | def forward(self, x): class TransformerBTS (line 259) | class TransformerBTS(nn.Module): method __init__ (line 260) | def __init__( method encode (line 312) | def encode(self, x): method forward (line 347) | def forward(self, x, auxillary_output_layers=[1, 2, 3, 4]): method _reshape_output (line 371) | def _reshape_output(self, x): class BTS (line 384) | class BTS(TransformerBTS): method __init__ (line 385) | def __init__(self, method decode (line 426) | def decode(self, x1_1, x2_1, x3_1, x, intmd_x, intmd_layers=[1, 2, 3, ... class EnBlock1 (line 455) | class EnBlock1(nn.Module): method __init__ (line 456) | def __init__(self, in_channels, ): method forward (line 466) | def forward(self, x): class EnBlock2 (line 476) | class EnBlock2(nn.Module): method __init__ (line 477) | def __init__(self, in_channels): method forward (line 487) | def forward(self, x): class DeUp_Cat (line 498) | class DeUp_Cat(nn.Module): method __init__ (line 499) | def __init__(self, in_channels, out_channels): method forward (line 505) | def forward(self, x, prev): class DeBlock (line 513) | class DeBlock(nn.Module): method __init__ (line 514) | def __init__(self, in_channels): method forward (line 524) | def forward(self, x): function transbts (line 536) | def transbts(in_channels, num_classes, **kwargs): FILE: models/networks_3d/unet3d.py function init_weights (line 7) | def init_weights(net, init_type='normal', gain=0.02): class UNet3D (line 31) | class UNet3D(nn.Module): method __init__ (line 32) | def __init__(self, in_channels=1, out_channels=3, init_features=64): method forward (line 72) | def forward(self, x): method _block (line 96) | def _block(in_channels, features, name): class UNet3D_min (line 129) | class UNet3D_min(nn.Module): method __init__ (line 130) | def __init__(self, in_channels=1, out_channels=3, init_features=32): method forward (line 170) | def forward(self, x): method _block (line 194) | def _block(in_channels, features, name): function unet3d (line 226) | def unet3d(in_channels, num_classes): function unet3d_min (line 231) | def unet3d_min(in_channels, num_classes): FILE: models/networks_3d/unet3d_cct.py function init_weights (line 8) | def init_weights(net, init_type='normal', gain=0.02): class FeatureNoise (line 31) | class FeatureNoise(nn.Module): method __init__ (line 32) | def __init__(self, uniform_range=0.3): method feature_based_noise (line 36) | def feature_based_noise(self, x): method forward (line 41) | def forward(self, x): function Dropout (line 45) | def Dropout(x, p=0.3): function FeatureDropout (line 49) | def FeatureDropout(x): class Decoder (line 58) | class Decoder(nn.Module): method __init__ (line 59) | def __init__(self, features, out_channels): method forward (line 73) | def forward(self, x5, x4, x3, x2, x1): method _block (line 92) | def _block(in_channels, features, name): class UNet3D_CCT (line 124) | class UNet3D_CCT(nn.Module): method __init__ (line 125) | def __init__(self, in_channels=1, out_channels=3, init_features=64): method forward (line 151) | def forward(self, x): method _block (line 168) | def _block(in_channels, features, name): class UNet3D_CCT_min (line 200) | class UNet3D_CCT_min(nn.Module): method __init__ (line 201) | def __init__(self, in_channels=1, out_channels=3, init_features=32): method forward (line 227) | def forward(self, x): method _block (line 244) | def _block(in_channels, features, name): function unet3d_cct (line 276) | def unet3d_cct(in_channels, num_classes): function unet3d_cct_min (line 281) | def unet3d_cct_min(in_channels, num_classes): FILE: models/networks_3d/unet3d_dtc.py function init_weights (line 8) | def init_weights(net, init_type='normal', gain=0.02): class UNet3D_DTC (line 32) | class UNet3D_DTC(nn.Module): method __init__ (line 33) | def __init__(self, in_channels=1, out_channels=3, init_features=64): method forward (line 68) | def forward(self, x): method _block (line 94) | def _block(in_channels, features, name): function unet3d_dtc (line 127) | def unet3d_dtc(in_channels, num_classes): FILE: models/networks_3d/unet3d_urpc.py function init_weights (line 8) | def init_weights(net, init_type='normal', gain=0.02): class UnetConv3 (line 31) | class UnetConv3(nn.Module): method __init__ (line 32) | def __init__(self, in_size, out_size, is_batchnorm, kernel_size=(3,3,1... method forward (line 52) | def forward(self, inputs): class UnetUp3 (line 57) | class UnetUp3(nn.Module): method __init__ (line 58) | def __init__(self, in_size, out_size, is_deconv, is_batchnorm=True): method forward (line 72) | def forward(self, inputs1, inputs2): class UnetUp3_CT (line 80) | class UnetUp3_CT(nn.Module): method __init__ (line 81) | def __init__(self, in_size, out_size, is_batchnorm=True): method forward (line 91) | def forward(self, inputs1, inputs2): class UnetDsv3 (line 99) | class UnetDsv3(nn.Module): method __init__ (line 100) | def __init__(self, in_size, out_size, scale_factor): method forward (line 105) | def forward(self, input): class unet_3D_dv_semi (line 108) | class unet_3D_dv_semi(nn.Module): method __init__ (line 110) | def __init__(self, in_channels=3, n_classes=21, feature_scale=4, is_de... method forward (line 168) | def forward(self, inputs): method apply_argmax_softmax (line 204) | def apply_argmax_softmax(pred): function unet3d_urpc (line 209) | def unet3d_urpc(in_channels, num_classes): FILE: models/networks_3d/unetr.py function init_weights (line 8) | def init_weights(net, init_type='normal', gain=0.02): class SingleDeconv3DBlock (line 32) | class SingleDeconv3DBlock(nn.Module): method __init__ (line 33) | def __init__(self, in_planes, out_planes): method forward (line 37) | def forward(self, x): class SingleConv3DBlock (line 41) | class SingleConv3DBlock(nn.Module): method __init__ (line 42) | def __init__(self, in_planes, out_planes, kernel_size): method forward (line 47) | def forward(self, x): class Conv3DBlock (line 51) | class Conv3DBlock(nn.Module): method __init__ (line 52) | def __init__(self, in_planes, out_planes, kernel_size=3): method forward (line 60) | def forward(self, x): class Deconv3DBlock (line 64) | class Deconv3DBlock(nn.Module): method __init__ (line 65) | def __init__(self, in_planes, out_planes, kernel_size=3): method forward (line 74) | def forward(self, x): class SelfAttention (line 78) | class SelfAttention(nn.Module): method __init__ (line 79) | def __init__(self, num_heads, embed_dim, dropout): method transpose_for_scores (line 97) | def transpose_for_scores(self, x): method forward (line 102) | def forward(self, hidden_states): class PositionwiseFeedForward (line 141) | class PositionwiseFeedForward(nn.Module): method __init__ (line 142) | def __init__(self, d_model=786, d_ff=2048, dropout=0.1): method forward (line 149) | def forward(self, x): class Embeddings (line 153) | class Embeddings(nn.Module): method __init__ (line 154) | def __init__(self, input_dim, embed_dim, cube_size, patch_size, dropout): method forward (line 164) | def forward(self, x): class TransformerBlock (line 173) | class TransformerBlock(nn.Module): method __init__ (line 174) | def __init__(self, embed_dim, num_heads, dropout, cube_size, patch_size): method forward (line 182) | def forward(self, x): class Transformer (line 198) | class Transformer(nn.Module): method __init__ (line 199) | def __init__(self, input_dim, embed_dim, cube_size, patch_size, num_he... method forward (line 209) | def forward(self, x): class UNETR (line 222) | class UNETR(nn.Module): method __init__ (line 223) | def __init__(self, input_dim=4, output_dim=3, img_shape=(128, 128, 128... method forward (line 306) | def forward(self, x): function unertr (line 326) | def unertr(in_channels, num_classes, **kwargs): FILE: models/networks_3d/vnet.py function init_weights (line 8) | def init_weights(net, init_type='normal', gain=0.02): function passthrough (line 33) | def passthrough(x, **kwargs): function ELUCons (line 37) | def ELUCons(elu, nchan): class LUConv (line 44) | class LUConv(nn.Module): method __init__ (line 45) | def __init__(self, nchan, elu): method forward (line 52) | def forward(self, x): function _make_nConv (line 57) | def _make_nConv(nchan, depth, elu): class InputTransition (line 64) | class InputTransition(nn.Module): method __init__ (line 65) | def __init__(self, in_channels, elu): method forward (line 76) | def forward(self, x): class DownTransition (line 84) | class DownTransition(nn.Module): method __init__ (line 85) | def __init__(self, inChans, nConvs, elu, dropout=False): method forward (line 98) | def forward(self, x): class UpTransition (line 106) | class UpTransition(nn.Module): method __init__ (line 107) | def __init__(self, inChans, outChans, nConvs, elu, dropout=False): method forward (line 120) | def forward(self, x, skipx): class OutputTransition (line 130) | class OutputTransition(nn.Module): method __init__ (line 131) | def __init__(self, in_channels, classes, elu): method forward (line 140) | def forward(self, x): class VNet (line 147) | class VNet(nn.Module): method __init__ (line 152) | def __init__(self, in_channels=1, classes=1, elu=True): method forward (line 169) | def forward(self, x): function vnet (line 182) | def vnet(in_channels, num_classes): FILE: models/networks_3d/vnet_cct.py function init_weights (line 10) | def init_weights(net, init_type='normal', gain=0.02): class FeatureNoise (line 33) | class FeatureNoise(nn.Module): method __init__ (line 34) | def __init__(self, uniform_range=0.3): method feature_based_noise (line 38) | def feature_based_noise(self, x): method forward (line 43) | def forward(self, x): function Dropout (line 47) | def Dropout(x, p=0.3): function FeatureDropout (line 51) | def FeatureDropout(x): function passthrough (line 62) | def passthrough(x, **kwargs): function ELUCons (line 66) | def ELUCons(elu, nchan): class LUConv (line 73) | class LUConv(nn.Module): method __init__ (line 74) | def __init__(self, nchan, elu): method forward (line 81) | def forward(self, x): function _make_nConv (line 86) | def _make_nConv(nchan, depth, elu): class InputTransition (line 93) | class InputTransition(nn.Module): method __init__ (line 94) | def __init__(self, in_channels, elu): method forward (line 105) | def forward(self, x): class DownTransition (line 113) | class DownTransition(nn.Module): method __init__ (line 114) | def __init__(self, inChans, nConvs, elu, dropout=False): method forward (line 127) | def forward(self, x): class UpTransition (line 135) | class UpTransition(nn.Module): method __init__ (line 136) | def __init__(self, inChans, outChans, nConvs, elu, dropout=False): method forward (line 149) | def forward(self, x, skipx): class OutputTransition (line 159) | class OutputTransition(nn.Module): method __init__ (line 160) | def __init__(self, in_channels, classes, elu): method forward (line 169) | def forward(self, x): class Decoder (line 176) | class Decoder(nn.Module): method __init__ (line 177) | def __init__(self, out_channels, elu): method forward (line 186) | def forward(self, out256, out128, out64, out32, out16): class VNet_CCT (line 195) | class VNet_CCT(nn.Module): method __init__ (line 200) | def __init__(self, in_channels=1, classes=1, elu=True): method forward (line 218) | def forward(self, x): function vnet_cct (line 233) | def vnet_cct(in_channels, num_classes): FILE: models/networks_3d/vnet_dtc.py function init_weights (line 9) | def init_weights(net, init_type='normal', gain=0.02): function passthrough (line 34) | def passthrough(x, **kwargs): function ELUCons (line 38) | def ELUCons(elu, nchan): class LUConv (line 45) | class LUConv(nn.Module): method __init__ (line 46) | def __init__(self, nchan, elu): method forward (line 53) | def forward(self, x): function _make_nConv (line 58) | def _make_nConv(nchan, depth, elu): class InputTransition (line 65) | class InputTransition(nn.Module): method __init__ (line 66) | def __init__(self, in_channels, elu): method forward (line 77) | def forward(self, x): class DownTransition (line 85) | class DownTransition(nn.Module): method __init__ (line 86) | def __init__(self, inChans, nConvs, elu, dropout=False): method forward (line 99) | def forward(self, x): class UpTransition (line 107) | class UpTransition(nn.Module): method __init__ (line 108) | def __init__(self, inChans, outChans, nConvs, elu, dropout=False): method forward (line 121) | def forward(self, x, skipx): class OutputTransition (line 131) | class OutputTransition(nn.Module): method __init__ (line 132) | def __init__(self, in_channels, classes, elu): method forward (line 141) | def forward(self, x): class VNet_DTC (line 148) | class VNet_DTC(nn.Module): method __init__ (line 153) | def __init__(self, in_channels=1, classes=1, elu=True): method forward (line 176) | def forward(self, x): function vnet_dtc (line 192) | def vnet_dtc(in_channels, num_classes): FILE: models/networks_3d/xnet3d.py function conv1x1 (line 20) | def conv1x1(in_planes, out_planes, stride=1): function conv3x3 (line 24) | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): class up_conv (line 28) | class up_conv(nn.Module): method __init__ (line 29) | def __init__(self, ch_in, ch_out): method forward (line 38) | def forward(self, x): class down_conv (line 42) | class down_conv(nn.Module): method __init__ (line 43) | def __init__(self, ch_in, ch_out): method forward (line 50) | def forward(self, x): class same_conv (line 54) | class same_conv(nn.Module): method __init__ (line 55) | def __init__(self, ch_in, ch_out): method forward (line 62) | def forward(self, x): class transition_conv (line 66) | class transition_conv(nn.Module): method __init__ (line 67) | def __init__(self, ch_in, ch_out): method forward (line 74) | def forward(self, x): class BasicBlock (line 78) | class BasicBlock(nn.Module): method __init__ (line 81) | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, method forward (line 99) | def forward(self, x): class DoubleBasicBlock (line 117) | class DoubleBasicBlock(nn.Module): method __init__ (line 118) | def __init__(self, inplanes, planes, downsample=None): method forward (line 126) | def forward(self, x): class XNet3D (line 130) | class XNet3D(nn.Module): method __init__ (line 131) | def __init__(self, in_channels, num_classes): method forward (line 234) | def forward(self, input1, input2): function xnet3d (line 329) | def xnet3d(in_channels, num_classes): FILE: test.py function init_seeds (line 22) | def init_seeds(seed): FILE: test_3d.py function init_seeds (line 23) | def init_seeds(seed): FILE: test_ConResNet.py function init_seeds (line 23) | def init_seeds(seed): FILE: test_DTC.py function init_seeds (line 23) | def init_seeds(seed): FILE: test_xnet.py function init_seeds (line 22) | def init_seeds(seed): FILE: test_xnet3d.py function init_seeds (line 23) | def init_seeds(seed): FILE: tools/Atrial/postprocess.py function save_max_objects (line 8) | def save_max_objects(image): FILE: tools/LiTS/postprocess.py function save_max_objects (line 8) | def save_max_objects(image): FILE: tools/eval.py function eval_distance (line 10) | def eval_distance(mask_list, seg_result_list, num_classes): function eval_pixel (line 77) | def eval_pixel(mask_list, seg_result_list, num_classes): FILE: train_semi_CCT.py function init_seeds (line 32) | def init_seeds(seed): FILE: train_semi_CCT_3d.py function init_seeds (line 31) | def init_seeds(seed): FILE: train_semi_CPS.py function init_seeds (line 32) | def init_seeds(seed): FILE: train_semi_CPS_3d.py function init_seeds (line 29) | def init_seeds(seed): FILE: train_semi_CT.py function init_seeds (line 33) | def init_seeds(seed): FILE: train_semi_CT_3d.py function init_seeds (line 29) | def init_seeds(seed): FILE: train_semi_DTC.py function init_seeds (line 31) | def init_seeds(seed): FILE: train_semi_EM.py function init_seeds (line 33) | def init_seeds(seed): FILE: train_semi_EM_3d.py function init_seeds (line 31) | def init_seeds(seed): FILE: train_semi_MT.py function update_ema_variables (line 32) | def update_ema_variables(model, ema_model, alpha, global_step): function init_seeds (line 38) | def init_seeds(seed): FILE: train_semi_MT_3d.py function update_ema_variables (line 30) | def update_ema_variables(model, ema_model, alpha, global_step): function init_seeds (line 36) | def init_seeds(seed): FILE: train_semi_UAMT.py function update_ema_variables (line 34) | def update_ema_variables(model, ema_model, alpha, global_step): function init_seeds (line 41) | def init_seeds(seed): FILE: train_semi_UAMT_3d.py function update_ema_variables (line 31) | def update_ema_variables(model, ema_model, alpha, global_step): function init_seeds (line 37) | def init_seeds(seed): FILE: train_semi_URPC.py function init_seeds (line 33) | def init_seeds(seed): FILE: train_semi_URPC_3d.py function init_seeds (line 31) | def init_seeds(seed): FILE: train_semi_XNet.py function init_seeds (line 32) | def init_seeds(seed): FILE: train_semi_XNet3d.py function init_seeds (line 31) | def init_seeds(seed): FILE: train_sup.py function init_seeds (line 29) | def init_seeds(seed): FILE: train_sup_3d.py function init_seeds (line 29) | def init_seeds(seed): FILE: train_sup_ConResNet.py function init_seeds (line 31) | def init_seeds(seed): FILE: train_sup_XNet.py function init_seeds (line 30) | def init_seeds(seed): FILE: train_sup_XNet3d.py function init_seeds (line 29) | def init_seeds(seed): FILE: train_sup_XNet_sb.py function init_seeds (line 30) | def init_seeds(seed): FILE: train_sup_alnet.py function init_seeds (line 29) | def init_seeds(seed): FILE: train_sup_wds.py function init_seeds (line 29) | def init_seeds(seed):