MCPcopy Create free account

hub / github.com/MontaEllis/Pytorch-Medical-Segmentation / functions

Functions168 in github.com/MontaEllis/Pytorch-Medical-Segmentation

⨍ Functions 168◇ Types & classes 69
Methodforward
(self, inputs, target, weight=None, softmax=False)
loss_function.py:124
Methodforward
(self, x)
utils/dilation.py:39
Methodforward
(self, x)
utils/convolution.py:74
Methodforward
(self, x)
utils/convolution.py:85
Methodforward
From the original ResNet paper, page 4: "When the dimensions increase, we consider two options: (A) The shortcut still perfor
utils/residual.py:57
Methodforward
(self, x)
models/two_d/unet.py:23
Methodforward
(self, x)
models/two_d/unet.py:33
Methodforward
(self, x)
models/two_d/unet.py:46
Methodforward
(self, x1, x2)
models/two_d/unet.py:62
Methodforward
(self, x)
models/two_d/unet.py:80
Methodforward
(self, x)
models/two_d/unet.py:103
Methodforward
(self, x)
models/two_d/segnet.py:75
Methodforward
(self, x)
models/two_d/unetpp.py:50
Methodforward
(self, x)
models/two_d/unetpp.py:76
Methodforward
(self, x)
models/two_d/unetpp.py:187
Methodforward
(self, input)
models/two_d/miniseg.py:14
Methodforward
(self, input)
models/two_d/miniseg.py:27
Methodforward
(self, input)
models/two_d/miniseg.py:53
Methodforward
(self, input)
models/two_d/miniseg.py:83
Methodforward
(self, input)
models/two_d/miniseg.py:182
Methodforward
(self, x)
models/two_d/fcn.py:107
Methodforward
(self, x)
models/two_d/deeplab.py:39
Methodforward
(self, x, start_module=1, end_module=5)
models/two_d/deeplab.py:100
Methodforward
(self, x)
models/two_d/deeplab.py:158
Methodforward
(self, input)
models/two_d/deeplab.py:207
Methodforward
(self, x)
models/two_d/highresnet.py:111
Methodforward
(self, x)
models/two_d/pspnet.py:28
Methodforward
(self, x)
models/two_d/pspnet.py:63
Methodforward
(self, x)
models/two_d/pspnet.py:124
Methodforward
(self, feats)
models/two_d/pspnet.py:152
Methodforward
(self, x)
models/two_d/pspnet.py:168
Methodforward
(self, x)
models/two_d/pspnet.py:198
Methodforward
(self, x)
models/three_d/unetr.py:13
Methodforward
(self, x)
models/three_d/unetr.py:23
Methodforward
(self, x)
models/three_d/unetr.py:36
Methodforward
(self, x)
models/three_d/unetr.py:50
Methodforward
(self, hidden_states)
models/three_d/unetr.py:78
Methodforward
(self, x)
models/three_d/unetr.py:109
Methodforward
(self, x)
models/three_d/unetr.py:124
Methodforward
(self, x)
models/three_d/unetr.py:139
Methodforward
(self, x)
models/three_d/unetr.py:157
Methodforward
(self, x)
models/three_d/unetr.py:182
Methodforward
(self, x)
models/three_d/unetr.py:277
Methodforward
(self, x)
models/three_d/residual_unet3d.py:109
Methodforward
(self, x)
models/three_d/unet3d.py:49
Methodforward
(self, x)
models/three_d/vnet3d.py:29
Methodforward
(self, x)
models/three_d/vnet3d.py:53
Methodforward
(self, x)
models/three_d/vnet3d.py:75
Methodforward
(self, x, skipx)
models/three_d/vnet3d.py:97
Methodforward
(self, x)
models/three_d/vnet3d.py:117
Methodforward
(self, x)
models/three_d/vnet3d.py:146
Methodforward
(self, x)
models/three_d/densenet3d.py:22
Methodforward
(self, x)
models/three_d/densenet3d.py:135
Methodforward
(self, x)
models/three_d/highresnet.py:111
Methodforward
(self, x)
models/three_d/densevoxelnet3d.py:29
Methodforward
(self, x)
models/three_d/densevoxelnet3d.py:59
Methodforward
(self, x)
models/three_d/densevoxelnet3d.py:116
Methodforward
(self, x)
models/three_d/fcn3d.py:108
Methodget_receptive_field_world
(self, spacing=1)
models/two_d/highresnet.py:134
Methodget_receptive_field_world
(self, spacing=1)
models/three_d/highresnet.py:134
Functioninit_weights
The weights were randomly initialized with a Gaussian distribution (µ = 0, σ = 0.01)
models/three_d/densevoxelnet3d.py:7
Functionmake_one_hot
Convert class index tensor to one hot encoding tensor. Args: input: A tensor of shape [N, 1, *] num_classes: An int of number of
loss_function.py:46
Methodnum_parameters
(self)
models/two_d/highresnet.py:115
Methodnum_parameters
(self)
models/three_d/highresnet.py:115
Functionpassthrough
(x, **kwargs)
models/three_d/vnet3d.py:10
Methodreceptive_field
B: number of convolutional layers per residual block N: number of residual blocks per dilation factor D: number of different
models/two_d/highresnet.py:120
Methodreceptive_field
B: number of convolutional layers per residual block N: number of residual blocks per dilation factor D: number of different
models/three_d/highresnet.py:120
Methodrequire_encoder_grad
(self, requires_grad)
models/two_d/unetpp.py:176
← previous101–168 of 168, ranked by callers