Functions — github.com/SeuTao/TGS-Salt-Identification

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Functions190 in github.com/SeuTao/TGS-Salt-Identification

⨍ Functions 190◇ Types & classes 28

↓ 1 callersFunctionstate_dict_remove_moudle

(moudle_state_dict, model)

utils.py:107

↓ 1 callersMethodtrain_fold

(self, fold_index, aug_list)

train.py:157

↓ 1 callersFunctionvertical_rule1

(img_map, map_img, vertical_mask)

jigsaw/handcraft_ruls_postprocessing.py:116

↓ 1 callersFunctionvertical_rule2

(vertical_mask, img_map, map_img)

jigsaw/handcraft_ruls_postprocessing.py:133

↓ 1 callersFunctionvertical_rule3

(vertical_mask, test_mask_path, img_map, map_img)

jigsaw/handcraft_ruls_postprocessing.py:167

↓ 1 callersFunctionvertical_rule_sub

(img_map, map_img, vertical_mask, semi_vertical_mask, submission_path)

jigsaw/handcraft_ruls_postprocessing.py:669

↓ 1 callersFunctionvertical_rules_combine

(rule)

jigsaw/handcraft_ruls_postprocessing.py:231

↓ 1 callersFunctionvotingAllCycle

(root_dir , model_list, save_name)

predict.py:15

Method__getitem__

(self, index)

data_process/data_loader.py:78

(self, channels, reduction)

model/senet.py:87

Method__init__

(self, inplanes, planes, groups, reduction, stride=1, downsample=None)

model/senet.py:140

Method__init__

(self, inplanes, planes, groups, reduction, stride=1, downsample=None)

model/senet.py:166

Method__init__

(self, inplanes, planes, groups, reduction, stride=1, downsample=None, base_width=4)

model/senet.py:189

Method__init__

Constructor Args: inplanes: input channel dimensionality planes: output channel dimensionality baseWidth:

model/ibnnet.py:39

Method__init__

Constructor Args: baseWidth: baseWidth for ResNeXt. cardinality: number of convolution groups. layers: co

model/ibnnet.py:93

Method__init__

(self, in_, out, kernel_size, norm_type = None)

model/model.py:21

Method__init__

(self, in_channels, n_filters)

model/model.py:56

Method__init__

(self, channel, reduction=16)

model/model.py:100

Method__init__

(self,in_channels, channels, out_channels)

model/model.py:126

Method__init__

(self, inplanes, outplanes)

model/model.py:150

Method__init__

(self,in_channels, channels, out_channels)

model/model.py:191

Method__init__

(self, num_classes=1, mask_class = 2)

model/model.py:209

Method__init__

(self, num_classes=1)

model/model.py:287

Method__init__

(self, num_classes=1)

model/model.py:365

Method__init__

(self, num_classes=1)

model/model.py:464

Method__init__

(self, num_classes=1)

model/model.py:542

Method__init__

(self, num_classes=1)

model/model.py:622

Method__init__

(self, num_classes=1)

model/model.py:721

Method__init__

(self, transform, mode, image_size, fold_index, aug_list, pseudo_csv = None, pseudo_index = -1)

data_process/data_loader.py:15

Method__init__

(self)

loss/lovasz_losses.py:133

Method__init__

(self, optimizer, T_max, T_mult, model, out_dir, take_snapshot, eta_min=0, last_epoch=-1)

loss/cyclic_lr.py:36

Method__init__

(self, smooth=0, eps=1e-7)

loss/bce_losses.py:10

Method__len__

(self)

data_process/data_loader.py:146

Functionbinary_xloss

Binary Cross entropy loss logits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty) labels: [B, H, W] Tensor, bi

loss/lovasz_losses.py:141

Functiondecompose

(labeled)

utils.py:118

Functiondo_brightness_multiply

(image, alpha=1)

data_process/transform.py:120

Functiondo_brightness_shift

(image, alpha=0.125)

data_process/transform.py:114

Functiondo_center_pad_to_factor2

(image, mask, factor=32)

data_process/transform.py:48

Functiondo_flip_transpose2

(image, mask, type=0)

data_process/transform.py:134

Functiondo_gamma

(image, gamma=1.0)

data_process/transform.py:127

Functiondo_horizontal_flip2

(image,mask)

data_process/transform.py:59

Functiondo_invert_intensity

(image)

data_process/transform.py:108

Functiondo_length_encode

(x)

utils.py:58

Functiondo_random_shift_scale_crop_pad2

(image, mask, limit=0.10)

data_process/transform.py:202

Functiondo_resize2

(image, mask, H, W)

data_process/transform.py:5

(self, input, target)

loss/lovasz_losses.py:135

Methodforward

(self, output, target)

loss/bce_losses.py:15

Functionget_crop_pad_sequence

(vertical, horizontal)

utils.py:182

Functionget_list_of_image_predictions

Array of IoU for each (non ignored) class

loss/lovasz_losses.py:53

Functioniou_binary

IoU for foreground class binary: 1 foreground, 0 background

loss/lovasz_losses.py:33

Functionlovasz_softmax

Multi-class Lovasz-Softmax loss probas: [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1) labels: [B, H

loss/lovasz_losses.py:156

Functionmulticlass_dice_loss

Calculate Dice Loss for multiple class output. Args: output (torch.Tensor): Model output of shape (N x C x H x W). target (torch.

loss/bce_losses.py:33

Functionreseed

(augmenter, deterministic=True)

utils.py:206

Functionresize_and_pad_edgeYreflectX

(image, resize_size, factor)

data_process/transform.py:224

Functionresize_and_random_pad_edgeYreflectX

(image, mask, resize_size, factor)

data_process/transform.py:235

Functionresnext152_ibn_a

Construct ResNeXt-152.

model/ibnnet.py:198

Functionresnext50_ibn_a

Construct ResNeXt-50.

model/ibnnet.py:177

Functionrun_length_decoding

Based on https://www.kaggle.com/msl23518/visualize-the-stage1-test-solution and modified Args: mask_rle: run-length as string formatt

utils.py:162

Functionsave_csv_images

(csv_path, save_path)

utils.py:29

Functionsave_id_fea

(predict_dict, save_dir)

utils.py:102

Functionse_resnet101

(num_classes=1000, pretrained='imagenet')

model/senet.py:402

Functionse_resnet50

(num_classes=1000, pretrained='imagenet')

loss/bce_losses.py:62

Functionxloss

Cross entropy loss

loss/lovasz_losses.py:208

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