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Functions36 in github.com/Closed11/Unsupervised-Image-Classification

⨍ Functions 36◇ Types & classes 8
↓ 15 callersMethodformat
(self, record)
UIC/util.py:26
↓ 14 callersMethodupdate
(self, val, n=1)
UIC/util.py:16
↓ 5 callersFunctionconv1x1
1x1 convolution
UIC/models/resnet50.py:49
↓ 4 callersMethod_make_layer
(self, block, planes, blocks, stride=1, dilate=False)
UIC/models/resnet50.py:223
↓ 3 callersMethod__init__
(self, kernel_size=23, channel=3, sigma_range=(0.1, 2.0), dim=2)
UIC/models/resnet50.py:13
↓ 3 callersFunctionconv3x3
3x3 convolution with padding
UIC/models/resnet50.py:44
↓ 2 callersFunctionaccuracy
Computes the precision@k for the specified values of k
eval_linear.py:160
↓ 2 callersFunctioncreate_logger
Create a logger. Use a different log file for each process.
UIC/util.py:39
↓ 1 callersFunction_resnet
(block, layers, out=1000, dim=3, **kwargs)
UIC/models/resnet50.py:287
↓ 1 callersFunctioncolor_distortion
A strong data transformation following SimCLR
UIC/transform.py:3
↓ 1 callersFunctioncompute_labels
Pre-generate pseudo labels via network forward or uniformly assignment
main.py:240
↓ 1 callersMethodgenerate_indexes_epoch
(self)
UIC/sampler.py:16
↓ 1 callersFunctionlistdir
Output the path of files in the folder with specific suffix
UIC/dataset.py:6
↓ 1 callersFunctionmain
()
eval_linear.py:39
↓ 1 callersFunctionmain
()
main.py:48
↓ 1 callersMethodreset
(self)
UIC/util.py:10
↓ 1 callersFunctiontrain
(train_loader, model, criterion, optimizer, epoch)
eval_linear.py:175
↓ 1 callersFunctiontrain
(loader, model, crit, opt, epoch, labels, label_update=True)
main.py:194
↓ 1 callersFunctionvalidate
(val_loader, model, criterion)
eval_linear.py:222
Method__getitem__
(self, index)
UIC/dataset.py:37
Method__init__
(self, root, labels=None, transform=None, suffix='.jpg')
UIC/dataset.py:17
Method__init__
(self, N, images_lists)
UIC/sampler.py:11
Method__init__
(self)
UIC/util.py:7
Method__init__
(self)
UIC/util.py:23
Method__init__
(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm
UIC/models/resnet50.py:56
Method__init__
(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm
UIC/models/resnet50.py:101
Method__init__
(self, block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=6
UIC/models/resnet50.py:142
Method__iter__
(self)
UIC/sampler.py:31
Method__len__
(self)
UIC/dataset.py:49
Method__len__
(self)
UIC/sampler.py:34
Methodforward
(self, x, p=0.5)
UIC/models/resnet50.py:28
Methodforward
(self, x)
UIC/models/resnet50.py:80
Methodforward
(self, x)
UIC/models/resnet50.py:118
Methodforward
(self, x)
UIC/models/resnet50.py:248
Functionreset_time
()
UIC/util.py:67
Functionresnet50
(out=1000, extra_mlp=False, random_gblur=False, linear_eval=False, dim=3, **kwargs)
UIC/models/resnet50.py:291