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Functions166 in github.com/UBC-CIC/COVID19-L3-Net

⨍ Functions 166◇ Types & classes 54

↓ 27 callersFunctionprepare_settings

src/models/base_networks/segmentation_models_pytorch/encoders/timm_efficientnet.py:91

↓ 8 callersFunction_get_pretrained_settings

src/models/base_networks/segmentation_models_pytorch/encoders/efficientnet.py:85

↓ 5 callersMethodrun

(self, img_data, seg_data)

src/datasets/transformers/micnn_augmentor.py:47

↓ 4 callersMethod__init__

(self, in_channels, out_channels, bilinear=True)

src/models/base_networks/unet2d.py:42

↓ 4 callersMethod__init__

(self, dim=None)

src/models/base_networks/segmentation_models_pytorch/base/modules.py:68

↓ 4 callersMethodload_state_dict

(self, state_dict)

src/models/semseg.py:54

↓ 3 callersMethodget_avg_score

src/models/metrics.py:30

↓ 3 callersMethodpredict_on_batch

src/models/semseg.py:135

↓ 2 callersMethod__init__

(self, in_channels, out_channels, pool_size, use_bathcnorm=True)

src/models/base_networks/segmentation_models_pytorch/pspnet/decoder.py:10

↓ 2 callersFunctionconfusion_multi_class

cf = confusion_matrix(y_true=prediction.cpu().numpy().ravel(), y_pred=truth.cpu().numpy().ravel(), labels=labels)

src/models/metrics.py:87

↓ 2 callersMethodget_avg_score

src/models/semseg.py:253

↓ 2 callersMethodget_state_dict

src/models/semseg.py:48

↓ 2 callersFunctionmatch_image_size

(images, logits)

src/models/semseg.py:233

↓ 2 callersMethodval_on_loader

(self, val_loader, savedir_images=None, n_images=0, save_preds=False)

src/models/semseg.py:82

↓ 1 callersMethod__init__

(self, out_channels, depth=5, **kwargs)

src/models/base_networks/segmentation_models_pytorch/encoders/densenet.py:50

↓ 1 callersMethod__init__

(self, in_channels, out_channels, kernel_size=3, activation=None, upsampling=1)

src/models/base_networks/segmentation_models_pytorch/base/heads.py:7

↓ 1 callersMethodadd

(self, score_dict)

src/models/semseg.py:245

↓ 1 callersMethodforward

src/models/base_networks/unet2d.py:53

↓ 1 callersMethodforward

Sequentially pass `x` trough model`s encoder, decoder and heads

src/models/base_networks/segmentation_models_pytorch/base/model.py:13

↓ 1 callersFunctionget_efficientnet_kwargs

Creates an EfficientNet model. Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py Pape

src/models/base_networks/segmentation_models_pytorch/encoders/timm_efficientnet.py:10

↓ 1 callersFunctionget_encoder

(name, in_channels=3, depth=5, weights=None)

src/models/base_networks/segmentation_models_pytorch/encoders/__init__.py:32

↓ 1 callersFunctionget_preprocessing_params

(encoder_name, pretrained="imagenet")

src/models/base_networks/segmentation_models_pytorch/encoders/__init__.py:51

↓ 1 callersMethodget_stages

src/models/base_networks/segmentation_models_pytorch/encoders/xception.py:29

↓ 1 callersMethodget_stages

src/models/base_networks/segmentation_models_pytorch/encoders/inceptionv4.py:56

↓ 1 callersMethodget_stages

Method should be overridden in encoder

src/models/base_networks/segmentation_models_pytorch/encoders/_base.py:31

↓ 1 callersMethodget_stages

src/models/base_networks/segmentation_models_pytorch/encoders/inceptionresnetv2.py:57

↓ 1 callersMethodget_stages

src/models/base_networks/segmentation_models_pytorch/encoders/timm_efficientnet.py:65

↓ 1 callersMethodget_stages

src/models/base_networks/segmentation_models_pytorch/encoders/senet.py:49

↓ 1 callersMethodget_stages

src/models/base_networks/segmentation_models_pytorch/encoders/dpn.py:46

↓ 1 callersMethodget_stages

src/models/base_networks/segmentation_models_pytorch/encoders/densenet.py:61

↓ 1 callersMethodget_stages

src/models/base_networks/segmentation_models_pytorch/encoders/mobilenet.py:41

↓ 1 callersMethodget_stages

src/models/base_networks/segmentation_models_pytorch/encoders/vgg.py:55

↓ 1 callersMethodget_stages

src/models/base_networks/segmentation_models_pytorch/encoders/efficientnet.py:45

↓ 1 callersMethodget_stages

src/models/base_networks/segmentation_models_pytorch/encoders/resnet.py:46

↓ 1 callersMethodinitialize

src/models/base_networks/segmentation_models_pytorch/base/model.py:7

↓ 1 callersMethodload_state_dict

(self, state_dict, **kwargs)

src/models/base_networks/segmentation_models_pytorch/encoders/vgg.py:76

↓ 1 callersMethodset_in_channels

Change first convolution chennels

src/models/base_networks/segmentation_models_pytorch/encoders/_base.py:20

↓ 1 callersFunctiontest

(exp_dict, savedir_base, datadir, num_workers=0, model_path=None, scan_id=None)

↓ 1 callersMethodtrain_on_batch

(self, batch, **extras)

src/models/semseg.py:113

↓ 1 callersMethodtrain_on_loader

(self, train_loader)

src/models/semseg.py:60

↓ 1 callersFunctiontrainval

(exp_dict, savedir_base, datadir, reset=False, num_workers=0)

↓ 1 callersMethodval_on_batch

(self, model, batch)

src/models/metrics.py:13

↓ 1 callersMethodvis_on_batch

(self, batch, savedir_image, save_preds=False)

src/models/semseg.py:144

src/datasets/transformers/trans_utils.py:89

src/datasets/transformers/trans_utils.py:130

src/datasets/transformers/trans_utils.py:148

src/datasets/transformers/trans_utils.py:161

src/datasets/transformers/trans_utils.py:189

src/datasets/transformers/trans_utils.py:222

src/datasets/transformers/trans_utils.py:231

src/datasets/transformers/trans_utils.py:236

src/datasets/transformers/trans_utils.py:241

src/datasets/transformers/trans_utils.py:249

src/datasets/transformers/trans_utils.py:257

src/datasets/transformers/trans_utils.py:267

src/datasets/transformers/trans_utils.py:295

src/datasets/transformers/trans_utils.py:301

src/datasets/transformers/trans_utils.py:319

src/datasets/transformers/trans_utils.py:342

Method__getitem__

src/datasets/open_source.py:66

( self, split, datadir, exp_dict, )

src/datasets/open_source.py:13

(self, cycles=1, scaling=True, rotations=True, elastic_deform=False, mirror=False, brightness

src/datasets/transformers/micnn_augmentor.py:33

(self, img_data, seg_data, seg_label)

src/datasets/transformers/micnn_augmentor.py:153

(self, class_map)

src/datasets/transformers/trans_utils.py:86

src/datasets/transformers/trans_utils.py:127

(self, mean=None, std=None)

src/datasets/transformers/trans_utils.py:136

src/datasets/transformers/trans_utils.py:157

src/datasets/transformers/trans_utils.py:185

(self, min=None, max=None)

src/datasets/transformers/trans_utils.py:218

(self, group_all=False)

src/datasets/transformers/trans_utils.py:246

(self, zoom, order=2)

src/datasets/transformers/trans_utils.py:263

3D linear interpolation Arguments: new_shape {int, list{int}} -- If `new_shape` is an int, the image is reshaped

src/datasets/transformers/trans_utils.py:273

return CxDxWxH Image, uses zero padding if necessary

src/datasets/transformers/trans_utils.py:336

src/models/metrics.py:9

src/models/metrics.py:48

(self, exp_dict, train_set)

src/models/semseg.py:28

src/models/semseg.py:241

(self, in_channels, out_channels)

src/models/base_networks/unet2d.py:10

(self, in_channels, out_channels)

src/models/base_networks/unet2d.py:28

(self, in_channels, out_channels)

src/models/base_networks/unet2d.py:69

(self, n_channels, n_classes, bilinear=True)

src/models/base_networks/unet2d.py:79

(self, out_channels, *args, depth=5, **kwargs)

src/models/base_networks/segmentation_models_pytorch/encoders/xception.py:12

(self, stage_idxs, out_channels, depth=5, **kwargs)

src/models/base_networks/segmentation_models_pytorch/encoders/inceptionv4.py:34

(self, out_channels, depth=5, **kwargs)

src/models/base_networks/segmentation_models_pytorch/encoders/inceptionresnetv2.py:34

(self, stage_idxs, out_channels, depth=5, channel_multiplier=1.0, depth_multiplier=1.0)

src/models/base_networks/segmentation_models_pytorch/encoders/timm_efficientnet.py:54

(self, out_channels, depth=5, **kwargs)

src/models/base_networks/segmentation_models_pytorch/encoders/senet.py:39

(self, stage_idxs, out_channels, depth=5, **kwargs)

src/models/base_networks/segmentation_models_pytorch/encoders/dpn.py:37

src/models/base_networks/segmentation_models_pytorch/encoders/densenet.py:37

(self, out_channels, depth=5, **kwargs)

src/models/base_networks/segmentation_models_pytorch/encoders/mobilenet.py:34

(self, out_channels, config, batch_norm=False, depth=5, **kwargs)

src/models/base_networks/segmentation_models_pytorch/encoders/vgg.py:44

(self, stage_idxs, out_channels, model_name, depth=5)

src/models/base_networks/segmentation_models_pytorch/encoders/efficientnet.py:33

(self, out_channels, depth=5, **kwargs)

src/models/base_networks/segmentation_models_pytorch/encoders/resnet.py:37

(self, in_channels, sizes=(1, 2, 3, 6), use_bathcnorm=True)

src/models/base_networks/segmentation_models_pytorch/pspnet/decoder.py:27

( self, encoder_channels, use_batchnorm=True, out_channels=512

src/models/base_networks/segmentation_models_pytorch/pspnet/decoder.py:42

( self, encoder_name: str = "resnet34", encoder_weights: Optional[str] = "

src/models/base_networks/segmentation_models_pytorch/pspnet/model.py:46

( self, in_channels, out_channels, kernel_size, pa

src/models/base_networks/segmentation_models_pytorch/base/modules.py:11

(self, in_channels, reduction=16)

src/models/base_networks/segmentation_models_pytorch/base/modules.py:51

(self, name, **params)

src/models/base_networks/segmentation_models_pytorch/base/modules.py:78

(self, name, **params)

src/models/base_networks/segmentation_models_pytorch/base/modules.py:107

(self, in_channels, classes, pooling="avg", dropout=0.2, activation=None)

src/models/base_networks/segmentation_models_pytorch/base/heads.py:16

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