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Functions973 in github.com/YtongXie/UniMiSS-code

⨍ Functions 973◇ Types & classes 173
↓ 3 callersMethodmax
(self)
UniMiSSPlus/utils.py:219
↓ 3 callersMethodprocess_plans
(self, plans)
UniMiSS/Downstream/BCV/MiTnnu/training/network_training /nnUNetTrainer.py:325
↓ 3 callersMethodprocess_plans
(self, plans)
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/training/network_training/nnUNetTrainer.py:325
↓ 3 callersFunctionrestore_model
This is a utility function to load any nnUNet trainer from a pkl. It will recursively search nnunet.trainig.network_training for the file tha
UniMiSS/Downstream/BCV/MiTnnu/training/model_restore.py:31
↓ 3 callersFunctionrestore_model
This is a utility function to load any nnUNet trainer from a pkl. It will recursively search nnunet.trainig.network_training for the file tha
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/training/model_restore.py:31
↓ 2 callersFunctionActivation_layer
(activation_cfg, inplace=True)
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /MiT_encoder.py:44
↓ 2 callersFunctionActivation_layer
(activation_cfg, inplace=True)
UniMiSS/models/Pacth_embeds.py:62
↓ 2 callersFunctionActivation_layer
(activation_cfg, inplace=True)
UniMiSSPlus/MiTplus.py:56
↓ 2 callersFunctionNorm_layer
(norm_cfg, inplanes)
UniMiSS/models/Pacth_embeds.py:42
↓ 2 callersFunctionNorm_layer
(norm_cfg, inplanes)
UniMiSSPlus/MiTplus.py:35
↓ 2 callersMethod__init__
(self, params, lr=0, weight_decay=0, momentum=0.9, eta=0.001, weight_decay_filter=None, lars_
UniMiSSPlus/utils.py:618
↓ 2 callersMethod_get_gaussian
(patch_size, sigma_scale=1. / 8)
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /neural_network.py:250
↓ 2 callersMethod_get_gaussian
(patch_size, sigma_scale=1. / 8)
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/network_architecture/neural_network.py:250
↓ 2 callersMethod_internal_maybe_mirror_and_pred_2D
(self, x: Union[np.ndarray, torch.tensor], mirror_axes: tuple, do_m
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /neural_network.py:558
↓ 2 callersMethod_internal_maybe_mirror_and_pred_2D
(self, x: Union[np.ndarray, torch.tensor], mirror_axes: tuple, do_m
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/network_architecture/neural_network.py:558
↓ 2 callersMethod_internal_maybe_mirror_and_pred_3D
(self, x: Union[np.ndarray, torch.tensor], mirror_axes: tuple, do_m
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /neural_network.py:499
↓ 2 callersMethod_internal_maybe_mirror_and_pred_3D
(self, x: Union[np.ndarray, torch.tensor], mirror_axes: tuple, do_m
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/network_architecture/neural_network.py:499
↓ 2 callersMethod_internal_predict_2D_2Dconv_tiled
(self, x: np.ndarray, step_size: float, do_mirroring: bool, mirror_axes: tuple,
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /neural_network.py:601
↓ 2 callersMethod_internal_predict_2D_2Dconv_tiled
(self, x: np.ndarray, step_size: float, do_mirroring: bool, mirror_axes: tuple,
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/network_architecture/neural_network.py:601
↓ 2 callersFunctionbuild_transform_classification
(normalize, crop_size=224, resize=256, mode="train", test_augment=True)
UniMiSSPlus/Downstream/2D/Cls/dataset/my_datasets.py:88
↓ 2 callersFunctionconvert4x4ToTransRot
(matrix_Nx4x4, is_radians=False, eps=np.finfo(float).eps)
UniMiSSPlus/pycuda_drr/pydrr/utils.py:214
↓ 2 callersFunctioncrop_roi
(feature_whole, roi_positions, output_stride)
UniMiSSPlus/utils.py:758
↓ 2 callersMethodcrop_scale_mirror_golbal
(self, image, axes=(0, 1, 2))
UniMiSS/data_loader3D.py:80
↓ 2 callersMethodcrop_scale_mirror_golbal
(self, image, axes=(0, 1, 2))
UniMiSSPlus/data_loader3D2D.py:84
↓ 2 callersMethoddo_split
(self)
UniMiSS/Downstream/BCV/MiTnnu/training/network_training /nnUNetTrainerCascadeFullRes.py:60
↓ 2 callersMethoddo_split
The default split is a 5 fold CV on all available training cases. nnU-Net will create a split (it is seeded, so always the same) and
UniMiSS/Downstream/BCV/MiTnnu/training/network_training /TrainerV2_BCV.py:266
↓ 2 callersMethoddo_split
(self)
UniMiSS/Downstream/BCV/MiTnnu/training/network_training /nnUNetTrainerV2_CascadeFullRes.py:60
↓ 2 callersMethoddo_split
(self)
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/training/network_training/nnUNetTrainerCascadeFullRes.py:60
↓ 2 callersMethoddo_split
The default split is a 5 fold CV on all available training cases. nnU-Net will create a split (it is seeded, so always the same) and
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/training/network_training/TrainerV2_BCV.py:265
↓ 2 callersMethoddo_split
(self)
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/training/network_training/nnUNetTrainerV2_CascadeFullRes.py:60
↓ 2 callersMethodget_basic_generators
(self)
UniMiSS/Downstream/BCV/MiTnnu/training/network_training /nnUNetTrainer.py:396
↓ 2 callersMethodget_basic_generators
(self)
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/training/network_training/nnUNetTrainer.py:396
↓ 2 callersMethodget_kernel
(self, name)
UniMiSSPlus/pycuda_drr/pydrr/KernelModule.py:24
↓ 2 callersMethodget_texture
(self, name, device_obj, interpolation=None)
UniMiSSPlus/pycuda_drr/pydrr/KernelModule.py:51
↓ 2 callersMethodinitialize
- replaced get_default_augmentation with get_moreDA_augmentation - enforce to only run this code once - loss function wrapper
UniMiSS/Downstream/BCV/MiTnnu/training/network_training /TrainerV2_BCV.py:44
↓ 2 callersMethodinitialize
- replaced get_default_augmentation with get_moreDA_augmentation - enforce to only run this code once - loss function wrapper
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/training/network_training/TrainerV2_BCV.py:44
↓ 2 callersMethodinitialize_optimizer_and_scheduler
(self)
UniMiSS/Downstream/BCV/MiTnnu/training/network_training /TrainerV2_BCV.py:144
↓ 2 callersMethodinitialize_optimizer_and_scheduler
(self)
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/training/network_training/TrainerV2_BCV.py:143
↓ 2 callersMethodis_cpu
(self)
UniMiSSPlus/pycuda_drr/pydrr/Detector.py:33
↓ 2 callersMethodis_gpu
(self)
UniMiSSPlus/pycuda_drr/pydrr/VolumeContext.py:62
↓ 2 callersMethodload_best_checkpoint
(self, train=True)
UniMiSS/Downstream/BCV/MiTnnu/training/network_training /network_trainer.py:290
↓ 2 callersMethodload_best_checkpoint
(self, train=True)
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/training/network_training/network_trainer.py:290
↓ 2 callersMethodlog_every
(self, iterable, print_freq, header=None)
UniMiSS/utils.py:235
↓ 2 callersFunctionmodel_plus
(norm_cfg3D='BN3', activation_cfg='ReLU', is_proj1=True, img_size3D=[16, 96, 96], num_classes=3, pretrain=Fals
UniMiSSPlus/Downstream/3D/RICORD/nets/MiTPlus.py:372
↓ 2 callersFunctionmodel_small
(norm_cfg3D='BN3', activation_cfg='ReLU', weight_std=False, img_size3D=[16, 96, 96], num_classes=3, pretrain=F
UniMiSS/Downstream/RICORD/nets/MiT.py:263
↓ 2 callersFunctionnorm_cdf
(x)
UniMiSS/utils.py:447
↓ 2 callersFunctionnorm_cdf
(x)
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /utils.py:447
↓ 2 callersFunctionnorm_cdf
(x)
UniMiSS/Downstream/RICORD/nets/utils.py:10
↓ 2 callersFunctionnorm_cdf
(x)
UniMiSSPlus/utils.py:576
↓ 2 callersFunctionnorm_cdf
(x)
UniMiSSPlus/Downstream/2D/Cls/net/utils.py:465
↓ 2 callersFunctionnorm_cdf
(x)
UniMiSSPlus/Downstream/2D/Seg/net/utils.py:465
↓ 2 callersFunctionnorm_cdf
(x)
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/network_architecture/utils.py:447
↓ 2 callersFunctionnorm_cdf
(x)
UniMiSSPlus/Downstream/3D/RICORD/nets/utils.py:12
↓ 2 callersMethodpredict_preprocessed_data_return_seg_and_softmax
We need to wrap this because we need to enforce self.network.do_ds = False for prediction
UniMiSS/Downstream/BCV/MiTnnu/training/network_training /TrainerV2_BCV.py:179
↓ 2 callersMethodpredict_preprocessed_data_return_seg_and_softmax
:param data: :param do_mirroring: :param mirror_axes: :param use_sliding_window: :param step_size: :p
UniMiSS/Downstream/BCV/MiTnnu/training/network_training /nnUNetTrainer.py:482
↓ 2 callersMethodpredict_preprocessed_data_return_seg_and_softmax
We need to wrap this because we need to enforce self.network.do_ds = False for prediction
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/training/network_training/TrainerV2_BCV.py:178
↓ 2 callersMethodpredict_preprocessed_data_return_seg_and_softmax
:param data: :param do_mirroring: :param mirror_axes: :param use_sliding_window: :param step_size: :p
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/training/network_training/nnUNetTrainer.py:482
↓ 2 callersMethodproject
(self, volume_context, geometry_context, T_Nx4x4)
UniMiSSPlus/pycuda_drr/pydrr/Projector.py:18
↓ 2 callersMethodsetCurrentModule
(self)
UniMiSSPlus/pycuda_drr/pydrr/KernelModule.py:61
↓ 2 callersMethodset_current_kernel
(self, name)
UniMiSSPlus/pycuda_drr/pydrr/KernelManager.py:19
↓ 2 callersMethodset_device
(self, device)
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /neural_network.py:38
↓ 2 callersMethodsetup_DA_params
(self)
UniMiSS/Downstream/BCV/MiTnnu/training/network_training /nnUNetTrainerCascadeFullRes.py:90
↓ 2 callersMethodsetup_DA_params
(self)
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/training/network_training/nnUNetTrainerCascadeFullRes.py:90
↓ 2 callersMethodsynchronize_between_processes
(self)
UniMiSS/utils.py:228
↓ 2 callersMethodsynchronize_between_processes
(self)
UniMiSSPlus/utils.py:290
↓ 2 callersMethodto_gpu
(self)
UniMiSSPlus/pycuda_drr/pydrr/Detector.py:23
↓ 2 callersMethodto_texture
(self, interpolation = 'linear')
UniMiSSPlus/pycuda_drr/pydrr/VolumeContext.py:40
↓ 2 callersFunctiontrain_several_epoch
(student, teacher, teacher_without_ddp, trainloss, data_loader, start_epoch, end_epoch,
UniMiSS/main.py:244
↓ 2 callersFunctiontrain_several_epoch
(student, teacher, teacher_without_ddp, dino_loss, mse_loss, data_loader, start_epoch, end_epoch,
UniMiSSPlus/main_UniMissPlus.py:269
↓ 2 callersMethodupdate
(self, value, n=1)
UniMiSSPlus/Downstream/2D/Cls/net/utils.py:187
↓ 2 callersMethodupdate
(self, value, n=1)
UniMiSSPlus/Downstream/2D/Seg/net/utils.py:187
↓ 2 callersMethodupdate_fold
used to swap between folds for inference (ensemble of models from cross-validation) DO NOT USE DURING TRAINING AS THIS WILL NOT UPDAT
UniMiSS/Downstream/BCV/MiTnnu/training/network_training /nnUNetTrainer.py:134
↓ 2 callersMethodupdate_fold
used to swap between folds for inference (ensemble of models from cross-validation) DO NOT USE DURING TRAINING AS THIS WILL NOT UPDAT
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/training/network_training/nnUNetTrainer.py:134
↓ 1 callersFunctionActivation_layer
(activation_cfg, inplace=True)
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /MiT.py:48
↓ 1 callersFunctionActivation_layer
(activation_cfg, inplace=True)
UniMiSS/Downstream/RICORD/nets/utils.py:69
↓ 1 callersFunctionActivation_layer
(activation_cfg, inplace=True)
UniMiSSPlus/Downstream/2D/Cls/net/MiTPlus_encoder.py:39
↓ 1 callersFunctionActivation_layer
(activation_cfg, inplace=True)
UniMiSSPlus/Downstream/2D/Seg/net/MiTPlus.py:41
↓ 1 callersFunctionActivation_layer
(activation_cfg, inplace=True)
UniMiSSPlus/Downstream/2D/Seg/net/MiTPlus_encoder.py:37
↓ 1 callersFunctionActivation_layer
(activation_cfg, inplace=True)
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/network_architecture/MiTPlus.py:31
↓ 1 callersFunctionActivation_layer
(activation_cfg, inplace=True)
UniMiSSPlus/Downstream/3D/RICORD/nets/MiTPlus.py:31
↓ 1 callersFunctionActivation_layer
(activation_cfg, inplace=True)
UniMiSSPlus/Downstream/3D/RICORD/nets/utils.py:71
↓ 1 callersFunctionFuse_Dice_CE
(predicts, targets)
UniMiSSPlus/Downstream/2D/Seg/main.py:172
↓ 1 callersFunctionJaccard
(pred, mask)
UniMiSSPlus/Downstream/2D/Seg/main.py:142
↓ 1 callersFunctionMiTPlus
(norm_cfg2D='IN2', activation_cfg='LeakyReLU', img_size2D=224, is_proj1=True, **kwargs)
UniMiSSPlus/Downstream/2D/Seg/net/MiTPlus.py:348
↓ 1 callersFunctionMiTPlus_encoder
(norm_cfg2D='IN2', activation_cfg='LeakyReLU', img_size2D=224, is_proj1=True, **kwargs)
UniMiSSPlus/Downstream/2D/Cls/net/MiTPlus_encoder.py:364
↓ 1 callersFunctionMiTPlus_encoder
(norm_cfg3D='BN3', activation_cfg='ReLU', img_size3D=[16, 96, 96], is_proj1=False, **kwargs)
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/network_architecture/MiTPlus.py:645
↓ 1 callersFunctionMiTplus_encoder
(norm_cfg2D='IN2', norm_cfg3D='IN3', activation_cfg='LeakyReLU', img_size2D=224, img_s
UniMiSSPlus/MiTplus.py:1027
↓ 1 callersFunctionNorm_layer
(norm_cfg, inplanes)
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /MiT.py:34
↓ 1 callersFunctionNorm_layer
(norm_cfg, inplanes)
UniMiSS/Downstream/RICORD/nets/utils.py:49
↓ 1 callersFunctionNorm_layer
(norm_cfg, inplanes)
UniMiSSPlus/Downstream/2D/Cls/net/MiTPlus_encoder.py:22
↓ 1 callersFunctionNorm_layer
(norm_cfg, inplanes)
UniMiSSPlus/Downstream/2D/Seg/net/MiTPlus.py:24
↓ 1 callersFunctionNorm_layer
(norm_cfg, inplanes)
UniMiSSPlus/Downstream/2D/Seg/net/MiTPlus_encoder.py:20
↓ 1 callersFunctionNorm_layer
(norm_cfg, inplanes)
UniMiSSPlus/Downstream/3D/BCV/MiTnnu/network_architecture/MiTPlus.py:16
↓ 1 callersFunctionNorm_layer
(norm_cfg, inplanes)
UniMiSSPlus/Downstream/3D/RICORD/nets/MiTPlus.py:11
↓ 1 callersFunctionNorm_layer
(norm_cfg, inplanes)
UniMiSSPlus/Downstream/3D/RICORD/nets/utils.py:51
↓ 1 callersMethod__init__
(self, params, lr=0, weight_decay=0, momentum=0.9, eta=0.001, weight_decay_filter=None, lars_
UniMiSS/utils.py:489
↓ 1 callersMethod__init__
(self, params, lr=0, weight_decay=0, momentum=0.9, eta=0.001, weight_decay_filter=None, lars_
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /utils.py:489
↓ 1 callersMethod__init__
(self)
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /neural_network.py:29
↓ 1 callersMethod__init__
(self, params, lr=0, weight_decay=0, momentum=0.9, eta=0.001, weight_decay_filter=None, lars_
UniMiSSPlus/Downstream/2D/Cls/net/utils.py:507
↓ 1 callersMethod__init__
(self, params, lr=0, weight_decay=0, momentum=0.9, eta=0.001, weight_decay_filter=None, lars_
UniMiSSPlus/Downstream/2D/Seg/net/utils.py:507
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