Functions — github.com/MichaelRamamonjisoa/SharpNet

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Functions105 in github.com/MichaelRamamonjisoa/SharpNet

⨍ Functions 105◇ Types & classes 24

↓ 11 callersFunctioncompute_depth_metrics

(input, target, mask=None)

eval.py:53

↓ 7 callersFunctioncompute_depth_boundary_error

(edges_gt, pred, mask=None, low_thresh=0.15, high_thresh=0.3)

eval.py:105

↓ 6 callersMethod__init__

(self, size_average=True, use_logs=True, sigma=1)

loss.py:151

↓ 4 callersMethod__init__

(self, data)

representations.py:221

↓ 4 callersMethod_make_layer

(self, block, planes, blocks, stride=1)

resnet.py:134

↓ 4 callersMethod_make_res_layer

(self, block, planes, blocks, stride=1, expansion=4, dilation=1)

sharpnet_model.py:218

↓ 4 callersFunctionconv1x1

1x1 convolution

resnet.py:30

↓ 4 callersMethodnormalize

(self, mean, std)

representations.py:114

↓ 4 callersFunctionpad_constant

(image, top, bottom, left, right, value)

data_transforms.py:65

↓ 4 callersFunctionpad_image

(mode, image, top, bottom, left, right, value=0)

data_transforms.py:80

↓ 4 callersFunctionpad_reflection

(image, top, bottom, left, right)

data_transforms.py:35

↓ 3 callersMethodcrop

(self, x1, y1, tw, th)

representations.py:164

↓ 3 callersMethodformat_data

(self, image=None, mask_valid=None, depth=None, no

dataset_manager.py:36

↓ 3 callersMethodfreeze

(self)

sharpnet_model.py:71

↓ 3 callersFunctionfreeze_model_decoders

(model, freeze_decoders)

utils.py:117

↓ 3 callersFunctionget_params

(model)

utils.py:110

↓ 2 callersMethod__init__

(self, block, layers, num_classes=1000)

resnet.py:111

↓ 2 callersMethod__init__

(self, img_list, root_dir='', img_size=480, transforms=None, use_boundary=False,

dataset_manager.py:14

↓ 2 callersFunctionconv3x3

3x3 convolution with padding

resnet.py:24

↓ 2 callersMethodfliplr

(self)

representations.py:167

↓ 2 callersFunctionget_gt_sample

(dataloader, loader_iter, args)

utils.py:126

↓ 2 callersFunctionget_pred_from_input

(image_pil, args)

demo.py:16

↓ 2 callersFunctionget_tensor_preds

(input, model, args)

utils.py:204

↓ 2 callersFunctionround_down

(num, divisor)

utils.py:11

↓ 2 callersFunctionround_down

(num, divisor)

demo.py:13

↓ 2 callersFunctionsave_preds

(outpath, preds, img_name)

demo.py:114

↓ 2 callersMethodscale

(self, ratio)

representations.py:183

↓ 2 callersMethodset_data

(self, data)

representations.py:19

↓ 2 callersFunctionwrite_loss_components

(tb_writer, iteration, epoch, dataset_size, args, depth_loss_meter=None, depth_loss=

utils.py:172

↓ 1 callersMethod__init__

(self, inplanes, in_channels=[1024, 512, 256, 64, 16], out_channels=1, layers_nums=[2, 2, 2,

sharpnet_model.py:8

↓ 1 callersMethod_make_decoder

(self, in_channels, out_channels, in_layers_nums, kernel_size=3, bias=True, out_activati

sharpnet_model.py:31

↓ 1 callersFunctionadjust_learning_rate

Sets the learning rate to the initial LR decayed by 10 every 30 epochs

utils.py:228

↓ 1 callersMethodgamma

(self, gamma_ratio)

representations.py:111

↓ 1 callersFunctionget_trainval_splits

(args)

train.py:191

↓ 1 callersFunctionmain

()

train.py:260

↓ 1 callersFunctionnormals_loss

(input, target, mask=None)

loss.py:171

↓ 1 callersFunctionpredict_depth

(model, image)

eval.py:44

↓ 1 callersMethodrotate

(self, angle, cval=0)

representations.py:224

↓ 1 callersMethodshape

(self)

representations.py:84

↓ 1 callersMethodto_tensor

(self)

representations.py:187

↓ 1 callersFunctiontrain_epoch

(train_loader, val_loader, model, criterion, optimizer, epoch, train_writer, val_writer,

train.py:20

Method__call__

(self, image, labels=None)

data_transforms.py:17

Method__call__

(self, pic, labels=None)

data_transforms.py:273

Method__call__

(self, *args)

data_transforms.py:323

Method__getitem__

(self, idx)

dataset_manager.py:103

Method__getitem__

(self, idx)

dataset_manager.py:135

Method__getitem__

(self, idx)

dataset_manager.py:197

Method__init__

(self, beta, gamma, sigma)

loss.py:11

Method__init__

(self, lamb, mu, use_depth=False, use_normals=False, use_boundary=False, use_geo_consensus=Fa

loss.py:46

Method__init__

(self, size_average=True, use_logs=True, clamp_val=1e-9)

loss.py:122

Method__init__

(self, kernel_size=3, use_logs=True, clamp_value=1e-7, size_average=False, smooth_error=True,

loss.py:190

Method__init__

(self, kernel_size=3, use_logs=True, clamp_value=1e-7, size_average=False)

loss.py:267

Method__init__

(self, kernel_size=3, clamp_value=1e-7, size_average=False)

loss.py:318

Method__init__

(self, mean, std)

data_transforms.py:13

Method__init__

(self, transforms)

data_transforms.py:320

Method__init__

(self, data=None, name=None)

representations.py:15

Method__init__

(self, data)

representations.py:61

Method__init__

(self, data)

representations.py:139

Method__init__

(self, data)

representations.py:180

Method__init__

(self, data)

representations.py:196

Method__init__

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

resnet.py:38

Method__init__

(self, inplanes, planes, stride=1, downsample=None, expansion=4, dilation=(1, 1))

resnet.py:70

Method__init__

(self, block, layers_encoder, layers_decoders, use_normals=False, use_depth=

sharpnet_model.py:141

Method__init__

(self, img_list, root_dir='', img_size=480, transforms=None, use_boundary=False,

dataset_manager.py:120

Method__init__

(self, dataset_path, split_type='train', root_dir='', img_size=480, transforms=None, use_boun

dataset_manager.py:178

Method__len__

(self)

dataset_manager.py:33

Method__len__

(self)

dataset_manager.py:132

Method__len__

(self)

dataset_manager.py:194

Methodcrop

(self, x1, y1, tw, th)

representations.py:43

Methodcrop

(self, x1, y1, tw, th)

representations.py:108

Methodfliplr

(self)

representations.py:46

Methodfliplr

(self)

representations.py:105

(self, mask_gt, d_pred=None, d_gt=None, n_pred=None, n_gt=None,

loss.py:72

Methodforward

(self, input, target, mask)

loss.py:128

Methodforward

(self, input, target, mask=None)

loss.py:156

Methodforward

(self, input, target, mask=None)

loss.py:205

Methodforward

(self, depth, boundary, mask=None)

loss.py:274

Methodforward

(self, normals, depth, boundary)

(self, resized_resnet_outputs, input_image)

sharpnet_model.py:75

Methodforward

(self, x_img)

sharpnet_model.py:240

Functionget_np_preds

(image_pil, model, device, args)

utils.py:15

Functionget_random_bbox

(data, tw, th)

data_transforms.py:216

Functionget_random_crop

(data, tw, th)

data_transforms.py:97

Methodnormalize

(self)

representations.py:52

Functionresnet101

Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

resnet.py:206

Functionresnet152

Constructs a ResNet-152 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

resnet.py:217

Functionresnet18

Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

resnet.py:169

Functionresnet34

Constructs a ResNet-34 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

resnet.py:180

Functionresnet50

Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

resnet.py:191

Methodrotate

(self, angle, cval=0)

representations.py:25

Methodrotate

(self, angle, cval=0)

representations.py:87

Methodrotate

(self, angle, cval=0)

representations.py:152

(self, ratio, interpolation='NEAREST')

representations.py:28

Methodscale

(self, ratio)

representations.py:93

Methodscale

(self, ratio)

representations.py:145

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