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Functions240 in github.com/WanderRainy/OARENet

⨍ Functions 240◇ Types & classes 64

(self, y_true, y_pred)

Method__getitem__

Method__getitem__

Method__getitem__

(self, batch=True)

(self,numclass)

test_metric.py:66

(self, net, loss, lr=2e-4, evalmode = False)

:param trainlist: :param root:

(self, root, type=None)

(self, root, type=None)

(self,numclass)

large_test.py:62

Constructor Args: inplanes: input channel dimensionality planes: output channel dimensionality stride: co

networks/dla.py:32

Constructor Args: inplanes: input channel dimensionality planes: output channel dimensionality stride: co

networks/dla.py:115

(self, in_channels, out_channels, kernel_size, residual)

networks/dla.py:195

(self, levels, block, in_channels, out_channels, stride=1, level_root=False, root_dim=0, root

networks/dla.py:216

(self, in_channels, n_filters, cov_size)

networks/decoder.py:6

(self, in_channels, filters)

networks/decoder.py:108

(self,filters = [256, 512, 1024, 2048])

networks/decoder.py:184

networks/unet.py:5

Constructor Args: inplanes: input channel dimensionality planes: output channel dimensionality baseWidth:

networks/res2next.py:17

(self, in_channels, n_filters, cov_size)

networks/intersection.py:6

Constructor Args: inplanes: input channel dimensionality planes: output channel dimensionality stride: co

networks/res2net.py:24

(self, gate_channel, reduction_ratio=16, num_layers=1)

networks/bam.py:10

(self, gate_channel, reduction_ratio=16, dilation_conv_num=2, dilation_val=4)

networks/bam.py:28

(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.)

networks/swin_transformer.py:110

(self, dim, num_heads, window_size=7, shift_size=0, mlp_ratio=4., qkv_bias=True, qk_scale=Non

networks/swin_transformer.py:197

(self, dim, norm_layer=nn.LayerNorm)

networks/swin_transformer.py:288

(self, dim, depth, num_heads, window_size=

networks/swin_transformer.py:343

(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None)

networks/swin_transformer.py:438

(self, pretrain_img_size=512, patch_size=4, in_chans=3,

networks/swin_transformer.py:501

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

networks/cabm_resnet.py:18

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

networks/cabm_resnet.py:57

(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=True, bias

networks/cbam.py:7

(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'])

networks/cbam.py:27

networks/cbam.py:73

networks/testNet.py:10

networks/testNet.py:76

networks/testNet.py:108

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

networks/backbone.py:19

networks/dunet.py:12

( self, inplanes: int, planes: int, stride: int = 1, downsample:

networks/resnet.py:40

( self, inplanes: int, planes: int, stride: int = 1, downsample:

networks/resnet.py:95

(self, in_channels, n_filters)

networks/dinknet.py:20

(self, channel)

networks/dinknet.py:47

(self, num_classes=1)

networks/dinknet.py:69

(self, num_classes=1)

networks/dinknet.py:129

(self, num_classes=1)

networks/dinknet.py:188

(self, num_classes=1)

networks/dinknet.py:238

(self, num_classes=1)

networks/dinknet.py:312

(self, num_classes=1)

networks/dinknet.py:361

(self, num_classes=1)

networks/dinknet.py:446

Method_init_weight

networks/decoder.py:125

Method_init_weight

networks/intersection.py:119

Method_init_weights

networks/swin_transformer.py:605

Method_make_level

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

networks/dla.py:310

Functionbuild_backbone

Build backbone.

networks/builder.py:36

Functionbuild_head

networks/builder.py:46

Functionbuild_loss

networks/builder.py:51

Functionbuild_neck

networks/builder.py:41

Functionbuild_segmentor

Build segmentor.

networks/builder.py:56

Functionconv3x3

3x3 convolution with padding

networks/backbone.py:11

Functiondefault_loader

(id, root ,type=None)

(self, x, residual=None)

networks/dla.py:75

(self, x, residual=None)

networks/dla.py:159

networks/dla.py:204

(self, x, residual=None, children=None)

networks/dla.py:255

networks/dla.py:339

networks/decoder.py:45

(self, x, inp = False)

networks/decoder.py:117

(self, e1, e2, e3, e4)

networks/decoder.py:165

(self, e1, e2, e3, e4)

networks/decoder.py:212

networks/unet.py:74

networks/res2next.py:59

networks/res2next.py:150

networks/intersection.py:38

networks/intersection.py:111

networks/res2net.py:64

networks/res2net.py:144

networks/bam.py:7

(self, in_tensor)

networks/bam.py:23

(self, in_tensor)

networks/bam.py:40

(self,in_tensor)

networks/bam.py:47

networks/swin_transformer.py:55

Forward function. Args: x: input features with shape of (num_windows*B, N, C) mask: (0/-inf) mask with shape of (num

networks/swin_transformer.py:144

Forward function. Args: x: Input feature, tensor size (B, H*W, C). H, W: Spatial resolution of the input feature.

networks/swin_transformer.py:221

Forward function. Args: x: Input feature, tensor size (B, H*W, C). H, W: Spatial resolution of the input feature.

networks/swin_transformer.py:294

Forward function. Args: x: Input feature, tensor size (B, H*W, C). H, W: Spatial resolution of the input feature.

networks/swin_transformer.py:385

Forward function.

networks/swin_transformer.py:452

Forward function.

networks/swin_transformer.py:623

networks/cabm_resnet.py:33

networks/cabm_resnet.py:75

networks/cabm_resnet.py:160

networks/cbam.py:14

networks/cbam.py:23

networks/cbam.py:37

networks/cbam.py:69

networks/cbam.py:78

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