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Functions131 in github.com/ZjjConan/SimAM

⨍ Functions 131◇ Types & classes 31
↓ 17 callersMethodupdate
(self, val, n=1)
util.py:20
↓ 16 callersMethodget_module_name
()
networks/attentions/se_module.py:15
↓ 12 callersFunctionconv1x1
(in_channels, ou_channels, stride=1)
networks/cifar/block.py:11
↓ 8 callersFunction_resnet
(arch, block, layers, **kwargs)
networks/imagenet/resnet.py:293
↓ 8 callersFunctionconv3x3
(in_channels, ou_channels, stride=1)
networks/cifar/block.py:7
↓ 6 callersFunctionconv3x3
3x3 convolution with padding
networks/imagenet/resnet.py:23
↓ 5 callersFunctionPreResNetWrapper
(num_blocks, num_class=10, block=None, attention_module=None)
networks/cifar/preresnet.py:57
↓ 5 callersFunctionResNetWrapper
(num_blocks, num_class=10, block=None, attention_module=None)
networks/cifar/resnet.py:51
↓ 5 callersMethod__init__
(self, in_channels, ou_channels, stride=1, attention_module=None)
networks/cifar/block.py:20
↓ 4 callersMethod__init__
MobileNet V2 main class Args: num_classes (int): Number of classes width_mult (float): Width multiplier - ad
networks/imagenet/mobilenetv2.py:136
↓ 4 callersMethod_make_layer
(self, block, planes, blocks, stride=1, avg_down=False, dilate=False, attention_module=None)
networks/imagenet/resnet.py:240
↓ 4 callersMethod_make_layer
(self, block, planes, blocks, stride=1, dilate=False, attention_module=None)
mmdetection/mmdet/models/backbones/resnet_simam.py:236
↓ 4 callersFunctionaccuracy
Computes the accuracy over the k top predictions for the specified values of k
util.py:48
↓ 4 callersFunctionconv1x1
1x1 convolution
networks/imagenet/resnet.py:29
↓ 4 callersMethodget_message
(self, batch)
util.py:37
↓ 4 callersMethodget_module_name
()
mmdetection/mmdet/models/backbones/attentions/simam_module.py:18
↓ 3 callersMethod__init__
(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=True, bias
networks/attentions/cbam_module.py:7
↓ 3 callersFunction_make_divisible
This function is taken from the original tf repo. It ensures that all layers have a channel number that is divisible by 8 It can be seen
networks/imagenet/mobilenetv2.py:16
↓ 3 callersMethod_make_layer
(self, block, ou_channels, num_blocks, stride)
networks/cifar/preresnet.py:35
↓ 3 callersMethod_make_layer
(self, block, ou_channels, num_blocks, stride)
networks/cifar/resnet.py:32
↓ 3 callersFunctionconv1x1
1x1 convolution
mmdetection/mmdet/models/backbones/resnet_simam.py:19
↓ 3 callersFunctionconv3x3
3x3 convolution with padding
mmdetection/mmdet/models/backbones/resnet_simam.py:13
↓ 3 callersFunctionload_checkpoint
(args, model, optimizer=None, verbose=True)
checkpoint.py:19
↓ 2 callersFunctionWideResNetWrapper
(depth, widen_factor, dropRate=0, num_class=10, attention_module=None)
networks/cifar/wideresnet.py:65
↓ 2 callersMethod__init__
(self, block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=6
networks/imagenet/resnet.py:160
↓ 2 callersMethod__init__
(self, depth, groups=1, width_per_group=64,
mmdetection/mmdet/models/backbones/resnet_simam.py:171
↓ 2 callersFunctionget_attention_module
(attention_type="none")
networks/attentions/__init__.py:5
↓ 2 callersFunctionparse_gpus
(gpu_ids)
util.py:65
↓ 2 callersFunctionsave_checkpoint
(state, is_best, epoch, save_path='./')
checkpoint.py:5
↓ 2 callersMethodtrain
Convert the model into training mode while keep normalization layer freezed.
mmdetection/mmdet/models/backbones/resnet_simam.py:326
↓ 2 callersFunctionvalidate
(val_loader, model, criterion, args)
main_imagenet.py:374
↓ 1 callersMethod__init__
(self, block, depth, widen_factor=1, dropRate=0.0, num_class=10)
networks/cifar/wideresnet.py:21
↓ 1 callersMethod_forward_impl
(self, x: Tensor)
networks/imagenet/mobilenetv2.py:224
↓ 1 callersMethod_forward_impl
(self, x)
networks/imagenet/resnet.py:272
↓ 1 callersMethod_freeze_stages
(self)
mmdetection/mmdet/models/backbones/resnet_simam.py:261
↓ 1 callersMethod_get_batch_fmtstr
(self, num_batches)
util.py:42
↓ 1 callersMethod_make_layer
(self, block, in_planes, out_planes, nb_layers, stride, dropRate)
networks/cifar/wideresnet.py:12
↓ 1 callersMethod_make_layers
(self, block, in_planes)
networks/cifar/mobilenetv2.py:31
↓ 1 callersFunctionadjust_learning_rate
Adjust the learning rate
main_cifar.py:23
↓ 1 callersFunctionadjust_learning_rate
Sets the learning rate to the initial LR decayed by 10 every 30 epochs
main_imagenet.py:428
↓ 1 callersFunctioncreate_net
(args)
networks/imagenet/__init__.py:19
↓ 1 callersFunctioncreate_net
(args)
networks/cifar/__init__.py:47
↓ 1 callersFunctionfind_module_using_name
(module_name)
networks/__init__.py:4
↓ 1 callersFunctionget_block
(block_type="basic")
networks/cifar/__init__.py:26
↓ 1 callersFunctionlogsumexp_2d
(tensor)
networks/attentions/cbam_module.py:62
↓ 1 callersFunctionmain
(args)
main_cifar.py:118
↓ 1 callersFunctionmain
()
main_imagenet.py:94
↓ 1 callersFunctionmain_worker
(gpu, ngpus_per_node, args)
main_imagenet.py:148
↓ 1 callersMethodreset
(self)
util.py:14
↓ 1 callersFunctiontrain
(net, optimizer, epoch, data_loader, args)
main_cifar.py:36
↓ 1 callersFunctiontrain
(train_loader, model, criterion, optimizer, epoch, args)
main_imagenet.py:319
↓ 1 callersFunctionvalidate
(net, epoch, data_loader, args)
main_cifar.py:77
FunctionMobileNetV2Wrapper
(num_class=10, block=None, attention_module=None)
networks/cifar/mobilenetv2.py:51
FunctionPreResNet110
(num_class=10, block=None, attention_module=None)
networks/cifar/preresnet.py:94
FunctionPreResNet164
(num_class=10, block=None, attention_module=None)
networks/cifar/preresnet.py:108
FunctionPreResNet20
(num_class=10, block=None, attention_module=None)
networks/cifar/preresnet.py:65
FunctionPreResNet32
(num_class=10, block=None, attention_module=None)
networks/cifar/preresnet.py:73
FunctionPreResNet56
(num_class=10, block=None, attention_module=None)
networks/cifar/preresnet.py:81
FunctionResNet110
(num_class=10, block=None, attention_module=None)
networks/cifar/resnet.py:87
FunctionResNet164
(num_class=10, block=None, attention_module=None)
networks/cifar/resnet.py:101
FunctionResNet20
(num_class=10, block=None, attention_module=None)
networks/cifar/resnet.py:59
FunctionResNet32
(num_class=10, block=None, attention_module=None)
networks/cifar/resnet.py:66
FunctionResNet56
(num_class=10, block=None, attention_module=None)
networks/cifar/resnet.py:74
FunctionWideResNet28x10
(num_class=10, block=None, attention_module=None)
networks/cifar/wideresnet.py:73
FunctionWideResNet40x10
(num_class=10, block=None, attention_module=None)
networks/cifar/wideresnet.py:83
Method__init__
(self, name, fmt=':f')
util.py:9
Method__init__
(self, num_batches, meters, prefix="")
util.py:32
Method__init__
( self, in_planes: int, out_planes: int, kernel_size: int = 3, stride:
networks/imagenet/mobilenetv2.py:37
Method__init__
( self, inp: int, oup: int, stride: int, expand_ratio: int, no
networks/imagenet/mobilenetv2.py:85
Method__init__
(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm
networks/imagenet/resnet.py:37
Method__init__
(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm
networks/imagenet/resnet.py:101
Method__init__
(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'])
networks/attentions/cbam_module.py:27
Method__init__
(self)
networks/attentions/cbam_module.py:73
Method__init__
(self, gate_channels, reduction=16, pool_types=['avg', 'max'], no_spatial=False)
networks/attentions/cbam_module.py:85
Method__init__
(self, channels = None, e_lambda = 1e-4)
networks/attentions/simam_module.py:6
Method__init__
(self, channel, reduction=16)
networks/attentions/se_module.py:4
Method__init__
(self, in_channels, ou_channels, stride=1, attention_module=None)
networks/cifar/block.py:73
Method__init__
(self, in_channels, ou_channels, stride=1, attention_module=None)
networks/cifar/block.py:134
Method__init__
(self, in_channels, ou_channels, stride=1, attention_module=None)
networks/cifar/block.py:175
Method__init__
(self, in_planes, out_planes, stride, dropRate=0.0, attention_module=None)
networks/cifar/block.py:231
Method__init__
(self, in_planes, out_planes, expansion, stride, attention_module=None)
networks/cifar/block.py:263
Method__init__
(self, block, num_blocks, num_class=10)
networks/cifar/preresnet.py:15
Method__init__
(self, block, num_blocks=0, num_class=10)
networks/cifar/mobilenetv2.py:21
Method__init__
(self, block, num_blocks, num_class=10)
networks/cifar/resnet.py:15
Method__init__
(self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0)
networks/cifar/wideresnet.py:9
Method__init__
(self, inplanes, planes, stride=1, dow
mmdetection/mmdet/models/backbones/resnet_simam.py:27
Method__init__
(self, inplanes, planes, stride=1, dow
mmdetection/mmdet/models/backbones/resnet_simam.py:94
Method__init__
(self, channels = None, e_lambda = 1e-4)
mmdetection/mmdet/models/backbones/attentions/simam_module.py:6
Method__repr__
(self)
networks/attentions/simam_module.py:12
Method__repr__
(self)
mmdetection/mmdet/models/backbones/attentions/simam_module.py:12
Method__str__
(self)
util.py:26
Methodforward
(self, x: Tensor)
networks/imagenet/mobilenetv2.py:128
Methodforward
(self, x: Tensor)
networks/imagenet/mobilenetv2.py:233
Methodforward
(self, x)
networks/imagenet/resnet.py:73
Methodforward
(self, x)
networks/imagenet/resnet.py:135
Methodforward
(self, x)
networks/imagenet/resnet.py:289
Methodforward
(self, x)
networks/attentions/cbam_module.py:14
Methodforward
(self, x)
networks/attentions/cbam_module.py:23
Methodforward
(self, x)
networks/attentions/cbam_module.py:37
Methodforward
(self, x)
networks/attentions/cbam_module.py:69
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