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Functions277 in github.com/Junjue-Wang/LoveNAS

⨍ Functions 277◇ Types & classes 68

(self, loss_config)

module/loss.py:92

(self, module, tta_config)

module/tta.py:28

module/tta.py:64

module/tta.py:80

module/tta.py:93

module/tta.py:106

(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, bias=True, pa

module/nas/operations.py:9

(self, output_size, in_channels, out_channels, bn=True, relu=True)

module/nas/operations.py:41

(self, in_channels, out_channels, ops_list = [0, 1, 2, 3],

module/nas/operations.py:65

(self, connect_map, ops_map, in_strides:tuple, in_channels, channels=256, cell_fn=parallel_block, fusion_op='c

module/nas/nasdecoder.py:73

(self, in_channels, out_channels, in_feat_output_strides=(4

module/nas/lovenas.py:15

module/baseline/factseg.py:12

module/baseline/unet.py:8

module/baseline/unet.py:40

module/baseline/unet.py:103

module/baseline/unet.py:133

module/baseline/unet.py:164

module/baseline/unet.py:194

module/baseline/unet.py:226

module/baseline/unet.py:288

(self, in_channels)

module/baseline/hrnet.py:14

(self, k, channels, multiplier=0.5)

module/baseline/lrn.py:6

(self, k, padding, stride)

module/baseline/lrn.py:31

(self, channels, k=7, stride=1, m=8, padding=3)

module/baseline/lrn.py:58

(self, in_channels, out_channels, in_feat_output_strides=(4

module/baseline/base.py:7

(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1,

module/baseline/base.py:49

(self, in_channels, out_channels)

module/baseline/base.py:178

(self, scene_embedding_channels, in_channels_list, out_chan

module/baseline/base.py:200

module/baseline/farsegv1.py:15

module/baseline/semantic_fpn.py:15

(self, in_channels, channels, norm_layer=nn.BatchNorm2d, **kwargs)

module/baseline/fcn8s.py:65

(self, features, out_features=1024, sizes=(1, 2, 3, 6))

module/baseline/pspnet.py:10

(self, in_channels, out_channels)

module/baseline/pspnet.py:30

(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm

module/baseline/base_resnet/_resnets.py:35

(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm

module/baseline/base_resnet/_resnets.py:75

module/baseline/base_resnet/resnet.py:45

(self, embed_dims, num_heads, window_size,

module/baseline/swin/swin_transformer.py:147

(self, embed_dims, num_heads, feedforward_channels,

module/baseline/swin/swin_transformer.py:309

(self, embed_dims, num_heads, feedforward_channels,

module/baseline/swin/swin_transformer.py:404

(self, pretrain_img_size=224, in_channels=3, embed_dims=96,

module/baseline/swin/swin_transformer.py:510

(self, kernel_size=1, stride=1, dilation=1, padding='corner')

module/baseline/swin/embed.py:42

(self, in_channels, out_channels, kernel_size=2,

module/baseline/swin/embed.py:229

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

module/baseline/base_hrnet/_hrnet.py:221

(self, num_branches, blocks, num_blocks, num_inchannels, num_channels, fuse_method, multi_sca

module/baseline/base_hrnet/_hrnet.py:260

(self, extra, norm_eval=True, zero_init_residual=False,

module/baseline/base_hrnet/_hrnet.py:407

(self, config=defalut_config)

module/baseline/base_hrnet/hrnet_encoder.py:29

(self, image_dir, mask_dir, transforms=None)

data/loveda.py:53

data/loveda.py:101

(self, size_divisor=32, value=0, mask_value=-1,

data/transforms.py:11

(self, image_dir, mask_dir, transforms=None)

data/floodnet.py:37

data/floodnet.py:80

data/loveda.py:95

data/floodnet.py:74

Method_function

module/baseline/base_resnet/resnet.py:134

Method_inner_forward

module/baseline/swin/swin_transformer.py:355

Method_nostride_dilate

(self, m, dilate)

module/baseline/base_resnet/resnet.py:192

Methodapply_to_mask

(self, img, **params)

data/transforms.py:33

Methodarch_params

module/nas/nasdecoder.py:63

Functionevaluate_cls_fn

(self, test_dataloader, config=None)

train_floodnet.py:12

Functionevaluate_cls_fn

(self, test_dataloader, config=None)

train_loveda.py:14

(self, y_pred, y_true: torch.Tensor)

module/loss.py:96

module/tta.py:36

(self, x: torch.Tensor)

module/nas/operations.py:49

module/nas/operations.py:87

(self, features_list)

module/nas/nasdecoder.py:49

(self, features_list)

module/nas/nasdecoder.py:120

(self, feat_list: list)

module/nas/lovenas.py:47

(self, x, y=None)

module/nas/lovenas.py:102

(self, x, y=None)

module/baseline/factseg.py:27

(self, x, y=None)

module/baseline/unet.py:17

(self, x, y=None)

module/baseline/unet.py:60

(self, x, y=None)

module/baseline/unet.py:112

(self, x, y=None)

module/baseline/unet.py:142

(self, x, y=None)

module/baseline/unet.py:173

(self, x, y=None)

module/baseline/unet.py:203

(self, x, y=None)

module/baseline/unet.py:244

(self, x, y=None)

module/baseline/unet.py:297

(self, feat_list)

module/baseline/hrnet.py:22

(self, x, y=None)

module/baseline/hrnet.py:45

module/baseline/lrn.py:15

module/baseline/lrn.py:26

module/baseline/lrn.py:36

module/baseline/lrn.py:72

(self, feat_list: list)

module/baseline/base.py:38

Arguments: x (list[Tensor]): feature maps for each feature level. Returns: results (tuple[Tensor]): feature m

module/baseline/base.py:131

module/baseline/base.py:169

module/baseline/base.py:187

(self, scene_feature, features: list)

module/baseline/base.py:243

(self, x, y=None)

module/baseline/farsegv1.py:25

(self, x, y=None)

module/baseline/semantic_fpn.py:29

(self, x, y=None)

module/baseline/fcn8s.py:28

module/baseline/fcn8s.py:76

module/baseline/pspnet.py:22

module/baseline/pspnet.py:38

(self, x, y=None)

module/baseline/pspnet.py:62

module/baseline/base_resnet/_resnets.py:53

module/baseline/base_resnet/_resnets.py:92

module/baseline/base_resnet/_resnets.py:214

module/baseline/base_resnet/resnet.py:140

Args: x (tensor): input features with shape of (num_windows*B, N, C) mask (tensor | None, Optional): mask with shape

module/baseline/swin/swin_transformer.py:79

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