Functions — github.com/ChunmingHe/WS-SAM

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Functions357 in github.com/ChunmingHe/WS-SAM

⨍ Functions 357◇ Types & classes 99

Methodforward

(self, x,mask=None)

lib/VIT/decoder/decoder_p.py:163

Methodforward

(self,x,side_x,mask)

lib/VIT/decoder/decoder_p.py:180

Methodforward

(self, input1, input2, input3)

lib/VIT/decoder/decoder_p.py:209

Methodforward

(self, E4, E3, E2, E1,shape)

lib/VIT/decoder/decoder_p.py:250

Methodforward

(self, preds, low_feats, high_feats, unlabeled_ROIs)

losses/TreeLoss.py:19

Methodforward

(self, preds, low_feats, high_feats, unlabeled_ROIs)

losses/TreeLoss.py:55

Methodforward

(self, preds, low_feats, high_feats,SAMSegment)

losses/TreeLoss.py:92

Methodforward

(self, cls_score, label, weight=None, avg_

losses/cross_entropy_loss.py:140

Methodforward

(self, guide_in, label=None)

losses/kernels/lib_tree_filter/modules/tree_filter.py:53

Methodforward

(self, feature_in, embed_in, tree, low_tree=True)

losses/kernels/lib_tree_filter/modules/tree_filter.py:133

Methodforward

(ctx, edge_index, max_adj_per_vertex)

losses/kernels/lib_tree_filter/functions/bfs.py:11

Methodforward

(ctx, edge_index, edge_weight, vertex_index)

losses/kernels/lib_tree_filter/functions/mst.py:11

Methodforward

(ctx, feature_in, edge_weight, sorted_index, sorted_parent, sorted_child, low_tree)

losses/kernels/lib_tree_filter/functions/refine.py:11

Methodfreeze_patch_emb

(self)

lib/VIT/encoder/pvtv2_encoder.py:297

Methodget_classifier

(self)

lib/VIT/encoder/pvtv2_encoder.py:304

Functionget_loader

(image_root, gt_root, batchsize, trainsize, shuffle=True, num_workers=4, pin_memory=True)

utils/dataloader.py:73

Functionget_loader

(image_root, gt_root, edge_root, batchsize, trainsize, shuffle=True, num_workers=12, pin_memory=True)

utils/data_val.py:661

Functionget_loader_noEdge

(image_root, gt_root, batchsize, trainsize, shuffle=True, num_workers=12, pin_memory=True)

utils/data_val.py:671

Functionget_loader_noEdge_3326

(image_root, gt_root, imgname_root, batchsize, trainsize, shuffle=True, num_workers=12, pin_memory=True)

utils/data_val.py:681

Functionget_loader_scribble_noEdge

(image_root, gt_root, scribble_root, batchsize, trainsize, shuffle=True, num_workers=12, pin_memory=True)

utils/data_val.py:692

Functionheatmap

(x_show, img, name=None)

utils/heatmap.py:6

Methodinit_weights

(self, pretrained=None)

lib/VIT/encoder/pvtv2_encoder.py:274

Methodinitialize

(self)

lib/VIT/decoder/decoder_p.py:78

Methodinitialize

(self)

lib/VIT/decoder/decoder_p.py:93

Methodinitialize

(self)

lib/VIT/decoder/decoder_p.py:111

Methodinitialize

(self)

lib/VIT/decoder/decoder_p.py:152

Methodinitialize

(self)

lib/VIT/decoder/decoder_p.py:168

Methodinitialize

(self)

lib/VIT/decoder/decoder_p.py:194

Methodinitialize

(self)

lib/VIT/decoder/decoder_p.py:216

Functionkpos_cross_entropy

(pred, label, weight=None, reduction='mean', avg_factor=None)

losses/cross_entropy_loss.py:92

Methodload_data

(self)

utils/dataloader.py:100

Methodno_weight_decay

(self)

lib/VIT/encoder/pvtv2_encoder.py:301

Methodno_weight_decay

(self)

lib/VIT/encoder/swin_encoder.py:556

Methodno_weight_decay_keywords

(self)

lib/VIT/encoder/swin_encoder.py:560

Methodnorm2_distance

(fm_ref, fm_tar)

losses/kernels/lib_tree_filter/modules/tree_filter.py:78

FunctionrandomGaussian

(image, mean=0.1, sigma=0.35)

utils/data_val.py:84

Functionres2net101_v1b

Constructs a Res2Net-50_v1b_26w_4s lib. Args: pretrained (bool): If True, returns a lib pre-trained on ImageNet

lib/Res2Net_v1b.py:177

Functionres2net101_v1b_26w_4s

Constructs a Res2Net-50_v1b_26w_4s lib. Args: pretrained (bool): If True, returns a lib pre-trained on ImageNet

lib/Res2Net_v1b.py:201

Functionres2net152_v1b_26w_4s

Constructs a Res2Net-50_v1b_26w_4s lib. Args: pretrained (bool): If True, returns a lib pre-trained on ImageNet

lib/Res2Net_v1b.py:212

Functionres2net50_v1b

Constructs a Res2Net-50_v1b lib. Res2Net-50 refers to the Res2Net-50_v1b_26w_4s. Args: pretrained (bool): If True, returns a lib pre-t

lib/Res2Net_v1b.py:165

Methodreset_classifier

(self, num_classes, global_pool='')

lib/VIT/encoder/pvtv2_encoder.py:307

Methodreset_drop_path

(self, drop_path_rate)

lib/VIT/encoder/pvtv2_encoder.py:279

Functionresnet101

r"""ResNet-101 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (

lib/resnet.py:275

Functionresnet152

r"""ResNet-152 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (

lib/resnet.py:287

Functionresnet18

r"""ResNet-18 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (b

lib/resnet.py:240

Functionresnet34

r"""ResNet-34 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (b

lib/resnet.py:252

Functionresnet50_v1

r"""ResNet-50 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (b

lib/resnet.py:264

Functionresnext101_32x8d

r"""ResNeXt-101 32x8d model from `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ Args

lib/resnet.py:313

Functionresnext50_32x4d

r"""ResNeXt-50 32x4d model from `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ Args:

utils/cod10k_subclass_split.py:5

Methodupdate

(self, val, n=1)

utils/utils.py:73

Functionweight_init_backbone

(module)

lib/VIT/CamoFormer.py:16

Functionweighted_loss

Create a weighted version of a given loss function. To use this decorator, the loss function must have the signature like `loss_func(pred,

losses/utils.py:55

Functionwide_resnet101_2

r"""Wide ResNet-101-2 model from `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_ The model is the same as ResNet except fo

lib/resnet.py:345

Functionwide_resnet50_2

r"""Wide ResNet-50-2 model from `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_ The model is the same as ResNet except for

lib/resnet.py:327

Functionwrapper

(pred, target, weight=None, reduction='mean',

losses/utils.py:86

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