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Functions204 in github.com/YaoZhang93/mmFormer

⨍ Functions 204◇ Types & classes 60
↓ 11 callersMethod__init__
(self)
mmformer/mmformer.py:247
↓ 9 callersMethod__init__
(self, in_channel=64)
mmformer/layers.py:101
↓ 6 callersFunctiondice
(output, target,eps =1e-5)
mmformer/utils/criterions.py:72
↓ 4 callersMethodupdate
(self, val, n=1)
mmformer/predict.py:225
↓ 3 callersMethodsample
(self, *shape)
mmformer/data/transforms.py:257
↓ 3 callersMethodstrip
(self)
mmformer/utils/parser.py:46
↓ 3 callersFunctionsup_128
(xmin, xmax)
mmformer/preprocess.py:15
↓ 2 callersFunctionflatten
Flattens a given tensor such that the channel axis is first. The shapes are transformed as follows: (N, C, D, H, W) -> (C, N * D * H * W)
mmformer/utils/criterions.py:160
↓ 2 callersMethodmerge
(self, other)
mmformer/utils/parser.py:35
↓ 2 callersFunctionnormalization
(planes, norm='bn')
mmformer/layers.py:4
↓ 2 callersMethodsave_cfg
(self, fname)
mmformer/utils/parser.py:127
↓ 2 callersFunctionsetup
(args, log)
mmformer/utils/parser.py:80
↓ 2 callersFunctiontest_softmax
( test_loader, model, dataname = 'BRATS2020', feature_mask=None,
mmformer/predict.py:121
↓ 2 callersMethodtf
(self, img, k=0)
mmformer/data/transforms.py:16
↓ 2 callersMethodtf
(self, img, k=0)
mmformer/data/transforms.py:267
↓ 1 callersFunctionJs_div
(feat1, feat2, KLDivLoss)
mmformer/utils/lr_scheduler.py:46
↓ 1 callersMethod__init__
(self, base_lr, num_epochs, mode='poly')
mmformer/utils/lr_scheduler.py:7
↓ 1 callersMethod__iter__
(self)
mmformer/utils/lr_scheduler.py:83
↓ 1 callersMethod_adjust_learning_rate
(self, optimizer, lr)
mmformer/utils/lr_scheduler.py:18
↓ 1 callersMethodadd_args
(self, args)
mmformer/utils/parser.py:102
↓ 1 callersMethodadd_cfg
(self, cfg, args=None, update=False)
mmformer/utils/parser.py:109
↓ 1 callersMethodcast
(d)
mmformer/utils/parser.py:54
↓ 1 callersFunctioncrop
(vol)
mmformer/preprocess.py:26
↓ 1 callersFunctionexpand_target
Converts NxDxHxW label image to NxCxDxHxW, where each label is stored in a separate channel :param input: 4D input image (NxDxHxW)
mmformer/utils/criterions.py:138
↓ 1 callersMethodgetdir
(self)
mmformer/utils/parser.py:131
↓ 1 callersFunctionload
(fname)
mmformer/utils/parser.py:74
↓ 1 callersFunctionmain
()
mmformer/train.py:66
↓ 1 callersFunctionmulti_data_generator
(data_iters, index_data, n, size)
mmformer/data/sampler.py:27
↓ 1 callersFunctionnormalize
(vol)
mmformer/preprocess.py:44
↓ 1 callersFunctionparse
(d)
mmformer/utils/parser.py:60
↓ 1 callersMethodreset
(self)
mmformer/predict.py:219
↓ 1 callersMethodsample
(self, *shape)
mmformer/data/transforms.py:13
↓ 1 callersMethodsample
(self)
mmformer/data/rand.py:8
↓ 1 callersFunctionsingle_data_generator
(data_iter, n)
mmformer/data/sampler.py:58
↓ 1 callersFunctionsoftmax_output_dice_class4
(output, target)
mmformer/predict.py:18
↓ 1 callersFunctionsoftmax_output_dice_class5
(output, target)
mmformer/predict.py:66
FunctionFocalLoss
(output, target, alpha=0.25, gamma=2.0)
mmformer/utils/criterions.py:51
FunctionGeneralizedDiceLoss
Generalised Dice : 'Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations'
mmformer/utils/criterions.py:101
Method__call__
(self, optimizer, epoch)
mmformer/utils/lr_scheduler.py:12
Method__call__
(self, img, dim=3, reuse=False)
mmformer/data/transforms.py:19
Method__getattr__
(self, name)
mmformer/utils/parser.py:15
Method__getitem__
(self, index)
mmformer/data/datasets.py:27
Method__getitem__
(self, index)
mmformer/data/datasets.py:78
Method__getitem__
(self, index)
mmformer/data/datasets.py:113
Method__getitem__
(self, index)
mmformer/data/datasets_nii.py:74
Method__getitem__
(self, index)
mmformer/data/datasets_nii.py:146
Method__getitem__
(self, index)
mmformer/data/datasets_nii.py:191
Method__init__
(self)
mmformer/predict.py:216
Method__init__
(self, in_ch, out_ch, k_size=3, stride=1, padding=1, pad_type='zeros', norm='in', is_training=True, act_type='
mmformer/layers.py:18
Method__init__
(self, in_ch, out_ch, k_size=3, stride=1, padding=1, pad_type='zeros', norm='in', is_training=True, act_type='
mmformer/layers.py:27
Method__init__
(self, in_ch, out_ch, k_size=3, stride=1, padding=1, pad_type='zeros', norm='in', is_training=True, act_type='
mmformer/layers.py:45
Method__init__
(self, in_channel=64, norm='in', num_cls=4)
mmformer/layers.py:63
Method__init__
(self, in_channel=64, norm='in', num_cls=4)
mmformer/layers.py:81
Method__init__
(self, in_channel=64, num_cls=4)
mmformer/layers.py:126
Method__init__
(self, in_channel=64, num_cls=4)
mmformer/layers.py:139
Method__init__
(self, in_channel=64, num_cls=4)
mmformer/layers.py:151
Method__init__
(self, in_channel=64, norm='in', num_cls=4)
mmformer/layers.py:162
Method__init__
(self)
mmformer/mmformer.py:16
Method__init__
(self, num_cls=4)
mmformer/mmformer.py:58
Method__init__
(self, num_cls=4)
mmformer/mmformer.py:108
Method__init__
( self, dim, heads=8, qkv_bias=False, qk_scale=None, dropout_rate=0.0 )
mmformer/mmformer.py:180
Method__init__
(self, fn)
mmformer/mmformer.py:217
Method__init__
(self, dim, fn)
mmformer/mmformer.py:226
Method__init__
(self, dim, dropout_rate, fn)
mmformer/mmformer.py:236
Method__init__
(self, dim, hidden_dim, dropout_rate)
mmformer/mmformer.py:254
Method__init__
(self, embedding_dim, depth, heads, mlp_dim, dropout_rate=0.1, n_levels=1, n_points=4)
mmformer/mmformer.py:269
Method__init__
(self)
mmformer/mmformer.py:303
Method__init__
(self, num_cls=4)
mmformer/mmformer.py:315
Method__init__
(self, cfg_name='')
mmformer/utils/parser.py:97
Method__init__
(self, *args, **kwargs)
mmformer/utils/lr_scheduler.py:68
Method__init__
(self, sampler)
mmformer/utils/lr_scheduler.py:81
Method__init__
(self, axes=(0, 1))
mmformer/data/transforms.py:43
Method__init__
(self,angle_spectrum=10)
mmformer/data/transforms.py:86
Method__init__
(self, axis=0)
mmformer/data/transforms.py:123
Method__init__
(self,axis=0)
mmformer/data/transforms.py:134
Method__init__
(self, prob=0.5, tf=None)
mmformer/data/transforms.py:158
Method__init__
(self, size)
mmformer/data/transforms.py:187
Method__init__
(self,factor)
mmformer/data/transforms.py:232
Method__init__
(self, pad)
mmformer/data/transforms.py:253
Method__init__
(self, dim, sigma=0.1, channel=True, num=-1)
mmformer/data/transforms.py:277
Method__init__
(self, dim, sigma=Constant(1.5), app=-1)
mmformer/data/transforms.py:300
Method__init__
(self, num=-1)
mmformer/data/transforms.py:332
Method__init__
(self, num=-1)
mmformer/data/transforms.py:345
Method__init__
(self, types, num=-1)
mmformer/data/transforms.py:359
Method__init__
(self, types, num=-1)
mmformer/data/transforms.py:375
Method__init__
(self, mean=0.0, std=1.0, num=-1)
mmformer/data/transforms.py:391
Method__init__
(self, ops)
mmformer/data/transforms.py:408
Method__init__
(self, transforms='', root=None, settype='train', split='split1')
mmformer/data/datasets.py:16
Method__init__
(self, transforms='', root=None, settype='train', split='split1')
mmformer/data/datasets.py:68
Method__init__
(self, transforms='', root=None, settype='train', split='split1')
mmformer/data/datasets.py:99
Method__init__
(self, a, b)
mmformer/data/rand.py:4
Method__init__
(self, mean, std)
mmformer/data/rand.py:12
Method__init__
(self, val)
mmformer/data/rand.py:20
Method__init__
(self, data)
mmformer/data/sampler.py:9
Method__init__
(self, batch_sizes, sizes, num_samples=None, num_iters=None)
mmformer/data/sampler.py:36
Method__init__
(self, size, num_samples=None, num_epochs=0)
mmformer/data/sampler.py:65
Method__init__
(self, data_source, state=None, seed=None)
mmformer/data/sampler.py:76
Method__init__
Yao
mmformer/data/datasets_nii.py:31
Method__init__
Yao
mmformer/data/datasets_nii.py:106
Method__init__
(self, transforms='', root=None, settype='train', modal='all')
mmformer/data/datasets_nii.py:169
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