Functions — github.com/JingZhang617/cascaded_rgbd

hub / github.com/JingZhang617/cascaded_rgbd_sod / functions

Functions110 in github.com/JingZhang617/cascaded_rgbd_sod

⨍ Functions 110◇ Types & classes 27

↓ 11 callersMethod_make_pred_layer

(self, block, dilation_series, padding_series, NoLabels, input_channel)

model/ResNet_models_combine.py:266

↓ 11 callersMethod_make_pred_layer

(self, block, dilation_series, padding_series, NoLabels, input_channel)

model/ResNet_models_sep.py:266

↓ 8 callersMethodtile

This function is taken form PyTorch forum and mimics the behavior of tf.tile. Source: https://discuss.pytorch.org/t/how-to-tile-a-ten

model/ResNet_models_combine.py:269

↓ 8 callersMethodtile

This function is taken form PyTorch forum and mimics the behavior of tf.tile. Source: https://discuss.pytorch.org/t/how-to-tile-a-ten

model/ResNet_models_sep.py:269

↓ 7 callersMethod__init__

( self, n_feat, kernel_size=3, reduction=16, bias=True, bn=False, act=nn.ReLU(True), res_scale

model/ResNet_models_combine.py:191

↓ 7 callersMethod__init__

( self, n_feat, kernel_size=3, reduction=16, bias=True, bn=False, act=nn.ReLU(True), res_scale

model/ResNet_models_sep.py:191

↓ 7 callersFunctionstructure_loss

(pred, mask)

train.py:51

↓ 6 callersMethod_make_layer

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

model/ResNet.py:111

↓ 2 callersMethod__init__

(self)

model/ResNet.py:84

↓ 2 callersFunctionconv3x3

3x3 convolution with padding

model/ResNet.py:5

↓ 2 callersMethodforward

(self, x)

model/ResNet.py:128

↓ 2 callersMethodkl_divergence

(self, posterior_latent_space, prior_latent_space)

model/ResNet_models_combine.py:80

↓ 2 callersMethodkl_divergence

(self, posterior_latent_space, prior_latent_space)

model/ResNet_models_sep.py:80

↓ 2 callersMethodreparametrize

(self, mu, logvar)

model/ResNet_models_combine.py:84

↓ 2 callersMethodreparametrize

(self, mu, logvar)

model/ResNet_models_sep.py:84

↓ 2 callersMethodrgb_loader

(self, path)

data.py:152

↓ 2 callersMethodrgb_loader

(self, path)

data.py:226

↓ 2 callersFunctiontruncated_normal_

(tensor, mean=0, std=1)

utils.py:18

↓ 1 callersMethod_check_input_dim

(self, x: torch.Tensor)

model/batchrenorm.py:36

↓ 1 callersFunctionadjust_lr

(optimizer, init_lr, epoch, decay_rate=0.1, decay_epoch=5)

utils.py:12

↓ 1 callersMethodbinary_loader

(self, path)

data.py:157

↓ 1 callersFunctioncolorEnhance

(image)

data.py:49

↓ 1 callersFunctioncv_random_flip

(img, label, depth)

data.py:11

↓ 1 callersMethoddefault_conv

(self, in_channels, out_channels, kernel_size, bias=True)

model/ResNet_models_combine.py:205

↓ 1 callersMethoddefault_conv

(self, in_channels, out_channels, kernel_size, bias=True)

model/ResNet_models_sep.py:205

↓ 1 callersMethodfilter_files

(self)

data.py:135

↓ 1 callersFunctiongaussianNoisy

(im, mean=mean, sigma=sigma)

data.py:62

↓ 1 callersFunctionget_loader

(image_root, gt_root, depth_root, batchsize, trainsize, shuffle=True, num_workers=12, pin_memory=True)

data.py:178

↓ 1 callersFunctiongkern

(kernlen=16, nsig=3)

model/HolisticAttention.py:10

↓ 1 callersMethodinitialize_weights

(self)

model/ResNet_models_combine.py:469

↓ 1 callersMethodinitialize_weights

(self)

model/ResNet_models_sep.py:468

↓ 1 callersFunctionlinear_annealing

Linear annealing of a parameter.

train.py:166

↓ 1 callersMethodload_data

(self)

data.py:209

↓ 1 callersFunctionmin_max_norm

(in_)

model/HolisticAttention.py:19

↓ 1 callersFunctionrandomCrop

(image, label, depth)

data.py:27

↓ 1 callersFunctionrandomPeper

(img)

data.py:74

↓ 1 callersFunctionrandomRotation

(image, label, depth)

data.py:39

↓ 1 callersMethodreset

(self)

utils.py:54

↓ 1 callersMethodshow

(self)

utils.py:68

↓ 1 callersMethodupdate

(self, val, n=1)

utils.py:61

↓ 1 callersFunctionvisualize_all_pred

(pred1,pred2,pred3,pred4,pred5,pred6,pred7,pred8)

(self, image_root, gt_root, depth_root, trainsize)

data.py:97

Method__init__

(self, image_root, depth_root, testsize)

data.py:190

Method__init__

(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1)

model/ResNet_models_combine.py:19

Method__init__

(self,dilation_series,padding_series,NoLabels, input_channel)

model/ResNet_models_combine.py:33

Method__init__

(self, input_channels, channels, latent_size)

model/ResNet_models_combine.py:48

Method__init__

(self)

model/ResNet_models_combine.py:141

Method__init__

(self, channel, reduction=16)

model/ResNet_models_combine.py:169

Method__init__

(self, channel,latent_dim)

model/ResNet_models_combine.py:218

Method__init__

(self, channel)

model/ResNet_models_combine.py:347

Method__init__

( self, num_features: int, eps: float = 1e-3, momentum: float = 0.01,

model/batchrenorm.py:8

Method__init__

(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1)

model/ResNet_models_sep.py:19

Method__init__

(self,dilation_series,padding_series,NoLabels, input_channel)

model/ResNet_models_sep.py:33

Method__init__

(self, input_channels, channels, latent_size)

model/ResNet_models_sep.py:48

Method__init__

(self)

model/ResNet_models_sep.py:141

Method__init__

(self, channel, reduction=16)

model/ResNet_models_sep.py:169

Method__init__

(self, channel,latent_dim)

model/ResNet_models_sep.py:218

Method__init__

(self, channel)

model/ResNet_models_sep.py:347

Method__init__

(self)

model/HolisticAttention.py:28

Method__init__

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

model/ResNet.py:14

Method__init__

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

Method_check_input_dim

(self, x: torch.Tensor)

model/batchrenorm.py:84

Method_check_input_dim

(self, x: torch.Tensor)

model/batchrenorm.py:90

Method_check_input_dim

(self, x: torch.Tensor)

model/batchrenorm.py:96

Method_make_pred_layer

(self, block, dilation_series, padding_series, NoLabels, input_channel)

model/ResNet_models_combine.py:397

Method_make_pred_layer

(self, block, dilation_series, padding_series, NoLabels, input_channel)

model/ResNet_models_sep.py:397

Methodbinary_loader

(self, path)

data.py:231

Functionclip_gradient

(optimizer, grad_clip)

utils.py:5

Methoddmax

(self)

model/batchrenorm.py:46

Methodforward

(self, x)

model/ResNet_models_combine.py:27

Methodforward

(self, x)

model/ResNet_models_combine.py:41

Methodforward

(self, rgb_feat, depth_feat)

model/ResNet_models_combine.py:90

Methodforward

inputs : x : input feature maps( B X C X H X W) returns : out : attention value + input featu

model/ResNet_models_combine.py:145

Methodforward

(self, x)

model/ResNet_models_combine.py:181

Methodforward

(self, x)

model/ResNet_models_combine.py:208

Methodforward

(self, x1,x2,x3,x4,z1=None,z2=None,z3=None,z4=None)

model/ResNet_models_combine.py:281

Methodforward

(self, x,depth=None)

model/ResNet_models_combine.py:412

Methodforward

(self, x: torch.Tensor)

model/batchrenorm.py:51

Methodforward

(self, x)

model/ResNet_models_sep.py:27

Methodforward

(self, x)

model/ResNet_models_sep.py:41

Methodforward

(self, rgb_feat, depth_feat)

model/ResNet_models_sep.py:90

Methodforward

inputs : x : input feature maps( B X C X H X W) returns : out : attention value + input featu

model/ResNet_models_sep.py:145

Methodforward

(self, x)

model/ResNet_models_sep.py:181

Methodforward

(self, x)

model/ResNet_models_sep.py:208

Methodforward

(self, x1,x2,x3,x4,z1=None,z2=None,z3=None,z4=None)

model/ResNet_models_sep.py:281

Methodforward

(self, x,depth=None)

model/ResNet_models_sep.py:412

Methodforward

(self, attention, x)

model/HolisticAttention.py:34

Functioninit_weights_orthogonal_normal

(m)

utils.py:33

Functionl2_regularisation

(m)

utils.py:39

FunctionrandomGaussian

(image, mean=0.1, sigma=0.35)

data.py:61

Methodresize

(self, img, gt, depth)

data.py:162

Methodrmax

(self)

model/batchrenorm.py:40

Methodtile

This function is taken form PyTorch forum and mimics the behavior of tf.tile. Source: https://discuss.pytorch.org/t/how-to-tile-a-ten

model/ResNet_models_combine.py:400

next →1–100 of 110, ranked by callers