Functions — github.com/black0017/MedicalZooPytorch

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Functions419 in github.com/black0017/MedicalZooPytorch

⨍ Functions 419◇ Types & classes 94

Method__init__

(self, transforms=[], p=0.9)

lib/augment3D/__init__.py:49

Method__init__

(self, min_percentage=0.8, max_percentage=1.1)

lib/augment3D/random_rescale.py:21

Method__init__

(self)

lib/augment3D/random_flip.py:28

Method__init__

(self, alpha=1, sigma=20, c_val=0.0, method="linear")

lib/augment3D/elastic_deform.py:78

Method__init__

(self, mean=0, std=0.001)

lib/augment3D/gaussian_noise.py:11

Method__init__

(self, min_angle=-10, max_angle=10)

lib/augment3D/random_rotate.py:24

Method__init__

(self, max_percentage=0.2)

lib/augment3D/random_shift.py:20

Method__init__

(self, classes=4, skip_index_after=None, weight=None, sigmoid_normalization=True )

lib/losses3D/dice.py:11

Method__init__

(self, ignore_index=-1)

lib/losses3D/weight_cross_entropy.py:10

Method__init__

(self, delta_var=0.5, delta_dist=1.5, norm='fro', alpha=1., beta=1., gamma=0.001)

lib/losses3D/ContrastiveLoss.py:13

Method__init__

(self, weight=None, sigmoid_normalization=True)

lib/losses3D/BaseClass.py:15

Method__init__

(self, class_weights=None, ignore_index=None)

lib/losses3D/pixel_wise_cross_entropy.py:7

Method__init__

(self, loss, squeeze_channel=False)

lib/losses3D/__init__.py:63

Method__init__

(self, classes, epsilon=1e-5, sigmoid_normalization=True)

lib/losses3D/Dice2D.py:7

Method__init__

(self, alpha=1, beta=1, classes=4)

lib/losses3D/BCE_dice.py:10

Method__init__

(self, tags_coefficients=[1.0, 0.8, 0.5], classes=4)

lib/losses3D/tags_angular_loss.py:9

Method__init__

(self, classes=4, sigmoid_normalization=True, skip_index_after=None, epsilon=1e-6,)

lib/losses3D/generalized_dice.py:12

Method__init__

(self, threshold=0, initial_weight=0.1, apply_below_threshold=True,classes=4)

lib/losses3D/weight_smooth_l1.py:8

Method__init__

(self, *keys, writer=None, mode='/')

lib/utils/covid_utils.py:35

Method__init__

:param dataset_path: the extracted path that contains the desired images :param voxels_space: for reshampling the voxel space

lib/medloaders/ixi_t1_t2.py:18

Method__init__

(self, mode, n_classes=3, dataset_path='./datasets', dim=(224, 224))

lib/medloaders/COVIDxdataset.py:18

Method__init__

Args: txt_path (string): Path to the txt file with annotations. root_dir (string): Directory with all the images.

lib/medloaders/covid_ct_dataset.py:10

Method__init__

:param mode: 'train','val','test' :param dataset_path: root dataset folder :param crop_dim: subvolume tuple :param sp

lib/medloaders/brats2020.py:19

Method__init__

(self, mode, sub_task='lung', split=0.2, fold=0, n_classes=3, samples=10, dataset_path='../datasets',

lib/medloaders/Covid_Segmentation_dataset.py:26

Method__init__

:param mode: 'train','val' :param image_paths: image dataset paths :param label_paths: label dataset paths :param cro

lib/medloaders/miccai_2019_pathology.py:19

Method__init__

:param mode: 'train','val','test' :param dataset_path: root dataset folder :param crop_dim: subvolume tuple :param fo

lib/medloaders/iseg2017.py:19

Method__init__

:param mode: 'train','val','test' :param dataset_path: root dataset folder :param crop_dim: subvolume tuple :param sp

lib/medloaders/brats2018.py:19

Method__init__

(self, args, mode, dataset_path='../datasets', classes=4, dim=(32, 32, 32), split_id=0, samples=1000,

lib/medloaders/mrbrains2018.py:14

Method__init__

:param mode: 'train','val','test' :param dataset_path: root dataset folder :param crop_dim: subvolume tuple :param fo

lib/medloaders/iseg2019.py:19

Method__init__

:param mode: 'train','val','test' :param dataset_path: root dataset folder :param crop_dim: subvolume tuple :param sp

lib/medloaders/brats2019.py:19

Method__init__

(self, args)

lib/visual3D_temp/BaseWriter.py:22

Method__init__

(self, in_channels, n_classes, base_n_filter=8)

lib/medzoo/Unet3D.py:13

Method__init__

(self, in_planes, planes, stride=1, dilation=1, downsample=None)

lib/medzoo/ResNet3DMedNet.py:93

Method__init__

(self, in_channels, classes)

lib/medzoo/ResNet3DMedNet.py:139

Method__init__

(self, in_channels=3, classes=10, block=BasicBlock, layers=[1, 1, 1, 1],

lib/medzoo/ResNet3DMedNet.py:177

Method__init__

(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate)

lib/medzoo/SkipDenseNet3D.py:38

Method__init__

(self, num_input_features, num_output_features)

lib/medzoo/SkipDenseNet3D.py:46

Method__init__

(self, in_channels=2, classes=4, growth_rate=16, block_config=(4, 4, 4, 4), num_init_features=32, drop_rate=0.

lib/medzoo/SkipDenseNet3D.py:74

Method__init__

(self, in_channels)

lib/medzoo/HighResNet3D.py:12

Method__init__

(self, in_channels)

lib/medzoo/HighResNet3D.py:46

Method__init__

(self, in_channels)

lib/medzoo/HighResNet3D.py:61

Method__init__

(self, in_channels, classes)

lib/medzoo/HighResNet3D.py:77

Method__init__

(self, in_channels=1, classes=4, shortcut_type="A", dropout_layer=True)

lib/medzoo/HighResNet3D.py:93

Method__init__

(self, num_input_features, drop_rate)

lib/medzoo/Densenet3D.py:37

Method__init__

(self, num_input_features, drop_rate)

lib/medzoo/Densenet3D.py:56

Method__init__

(self, in_channels, classes=4, drop_rate=0.1, return_logits=True, early_fusion=False)

lib/medzoo/Densenet3D.py:75

Method__init__

2-stream and 3-stream implementation with late fusion :param in_channels: 2 or 3 (dual or triple path based on paper specifications).

lib/medzoo/Densenet3D.py:146

Method__init__

:param input_channels: 2 or 3 (dual or triple path based on paper specifications). Channels are the input modalities i.e T1,T2 etc..

lib/medzoo/Densenet3D.py:238

Method__init__

(self, nchan, elu)

lib/medzoo/Vnet.py:26

Method__init__

(self, in_channels, elu)

lib/medzoo/Vnet.py:46

Method__init__

(self, inChans, nConvs, elu, dropout=False)

lib/medzoo/Vnet.py:66

Method__init__

(self, inChans, outChans, nConvs, elu, dropout=False)

lib/medzoo/Vnet.py:88

Method__init__

(self, in_channels, classes, elu)

lib/medzoo/Vnet.py:112

Method__init__

(self, elu=True, in_channels=1, classes=4)

lib/medzoo/Vnet.py:178

Method__init__

(self, in_channels, out_channels=32, norm="group")

lib/medzoo/ResNet3D_VAE.py:13

Method__init__

(self, in_channels, out_channels)

lib/medzoo/ResNet3D_VAE.py:44

Method__init__

(self, in_channels, out_channels=32)

lib/medzoo/ResNet3D_VAE.py:55

Method__init__

(self, in_channels, out_channels)

lib/medzoo/ResNet3D_VAE.py:69

Method__init__

(self, in_channels, out_channels)

lib/medzoo/ResNet3D_VAE.py:80

Method__init__

(self, in_channels, start_channels=32)

lib/medzoo/ResNet3D_VAE.py:98

Method__init__

(self, in_channels=256, classes=4)

lib/medzoo/ResNet3D_VAE.py:152

Method__init__

(self, in_channels=2, classes=4, max_conv_channels=256, dim=(64, 64, 64))

lib/medzoo/ResNet3D_VAE.py:254

Method__init__

(self, in_ch, out_ch)

lib/medzoo/Unet2D.py:11

Method__init__

(self, in_ch, out_ch)

lib/medzoo/Unet2D.py:27

Method__init__

(self, in_ch, out_ch)

lib/medzoo/Unet2D.py:37

Method__init__

(self, in_ch, out_ch)

lib/medzoo/Unet2D.py:74

Method__init__

(self, in_channels, classes)

lib/medzoo/Unet2D.py:84

Method__init__

(self, n_input, n_out)

lib/medzoo/COVIDNet.py:13

Method__init__

(self, model='large', n_classes=3)

lib/medzoo/COVIDNet.py:49

Method__init__

(self, in_channels=2, classes=4)

lib/medzoo/HyperDensenet.py:274

Method__init__

(self, in_channels=3, classes=4)

lib/medzoo/HyperDensenet.py:425

Method__init__

(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate=0.2)

lib/medzoo/DenseVoxelNet.py:51

Method__init__

(self, num_input_features, num_output_features)

lib/medzoo/DenseVoxelNet.py:59

Method__init__

(self, input_features, out_features)

lib/medzoo/DenseVoxelNet.py:81

Method__init__

(self, in_channels=1, classes=3)

lib/medzoo/DenseVoxelNet.py:105

Method__init__

(self)

lib/medzoo/BaseModelClass.py:17

Method__init__

(self, args, model, criterion, optimizer, train_data_loader, valid_data_loader=None, lr_sched

lib/train/trainer.py:13

Method__init__

(self, model, criterion, metric_ftns, optimizer, config)

lib/train/BaseTrainer.py:11

Method__init__

(self, batch=1, dim=64, classes=10)

tests/test_dataloaders.py:10

Method__init__

(self, batch=1, dim=64, classes=10)

tests/test_losses3D.py:18

Method__len__

(self)

lib/medloaders/ixi_t1_t2.py:55

Method__len__

(self)

lib/medloaders/COVIDxdataset.py:35

Method__len__

(self)

lib/medloaders/covid_ct_dataset.py:66

Method__len__

(self)

lib/medloaders/brats2020.py:104

Method__len__

(self)

lib/medloaders/Covid_Segmentation_dataset.py:78

Method__len__

(self)

lib/medloaders/miccai_2019_pathology.py:64

Method__len__

(self)

lib/medloaders/iseg2017.py:102

Method__len__

(self)

lib/medloaders/brats2018.py:102

Method__len__

(self)

lib/medloaders/mrbrains2018.py:73

Method__len__

(self)

lib/medloaders/iseg2019.py:90

Method__len__

(self)

lib/medloaders/brats2019.py:102

Method_downsample_basic_block

(self, x, planes, stride)

lib/medzoo/ResNet3DMedNet.py:232

Functionadd_conf_matrix

(target, pred, conf_matrix)

lib/visual3D_temp/conf_matrix.py:54

Functionadjust_opt

(optAlg, optimizer, epoch)

lib/utils/general.py:92

Methodavg_Acc

(self, key)

lib/utils/covid_utils.py:58

Functionavrgpool0125

()

lib/medzoo/HyperDensenet.py:182

Functionavrgpool025

()

lib/medzoo/HyperDensenet.py:177

Functionavrgpool05

()

lib/medzoo/HyperDensenet.py:172

FunctionclassificationNet

(D_in)

lib/medzoo/HyperDensenet.py:221

FunctionconvBatch

(nin, nout, kernel_size=3, stride=1, padding=1, bias=False, layer=nn.Conv2d, dilation=1)

lib/medzoo/HyperDensenet.py:264

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