Functions — github.com/ChongQingNoSubway/SelfReg-UNet

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Functions3,840 in github.com/ChongQingNoSubway/SelfReg-UNet

⨍ Functions 3,840◇ Types & classes 731

Creates a hardcorenas model Ref impl: https://github.com/Alibaba-MIIL/HardCoReNAS Paper: https://arxiv.org/abs/2102.11646

src/train_acdc/lib/models_timm/hardcorenas.py:34

↓ 6 callersFunction_gen_mixnet_m

Creates a MixNet Medium-Large model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet Paper: https://ar

src/train_acdc/lib/models_timm/efficientnet.py:1178

↓ 6 callersFunction_nfreg_cfg

(depths, channels=(48, 104, 208, 440))

src/train_acdc/lib/models_timm/nfnet.py:168

↓ 6 callersFunctioncreate_levit

(variant, pretrained=False, distilled=True, **kwargs)

src/train_acdc/lib/models_timm/levit.py:582

↓ 6 callersFunctionreshape_post_pool

( x, num_heads: int, cls_tok: Optional[torch.Tensor] = None )

src/train_acdc/lib/models_timm/mvitv2.py:197

↓ 6 callersFunctionreshape_pre_pool

( x, feat_size: List[int], has_cls_token: bool = True )

src/train_acdc/lib/models_timm/mvitv2.py:182

↓ 5 callersMethod__init__

(self, n_class=1)

src/train_acdc/lib/networks.py:31

↓ 5 callersMethod__init__

( self, levels, channels, output_stride=32, num_classes=1000, in_chans=3, global_pool='avg',

src/train_acdc/lib/models_timm/dla.py:264

↓ 5 callersMethod__init__

( self, in_chans=3, num_classes=1000, global_pool='avg',

src/train_acdc/lib/models_timm/edgenext.py:336

↓ 5 callersMethod__init__

( self, layers, channels=(256, 512, 1024, 2048), num_classes=1000, in_chans=3, global_

src/train_acdc/lib/models_timm/resnetv2.py:356

↓ 5 callersMethod__init__

Parameters ---------- block (nn.Module): Bottleneck class. - For SENet154: SEBottleneck - For SE-ResN

src/train_acdc/lib/models_timm/senet.py:219

↓ 5 callersMethod__init__

( self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=(0, 0, 0, 0),

src/train_acdc/lib/models_timm/coat.py:330

↓ 5 callersMethod__init__

(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None)

src/train_acdc/lib/models_timm/swin_transformer_v2_cr.py:492

↓ 5 callersMethod__init__

( self, dim_in=3, dim_out=768, kernel=(7, 7), stri

src/train_acdc/lib/models_timm/mvitv2.py:157

↓ 5 callersMethod__init__

(self, dim)

src/train_acdc/lib/models_timm/mlp_mixer.py:168

↓ 5 callersMethod__init__

( self, in_chs, out_chs, kernel_size, stride=1, dilation=1, group_size=0, pad_type='',

src/train_acdc/lib/models_timm/efficientnet_blocks.py:62

↓ 5 callersMethod__init__

( self, dim, num_heads, mlp_ratio=4., drop=0., attn_drop=0., drop_path=0., act_layer=n

src/train_acdc/lib/models_timm/twins.py:201

↓ 5 callersMethod__init__

( self, dim, num_heads, mlp_ratio=4., sr_ratio=1, linear_attn=False, qkv_bias=False, d

src/train_acdc/lib/models_timm/pvt_v2.py:156

↓ 5 callersMethod__init__

( self, dim, num_heads, mlp_ratio=4., qkv_bias=Fal

src/train_acdc/lib/models_timm/vision_transformer.py:242

↓ 5 callersMethod__init__

( self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1, spatial_kernel_size=

src/train_acdc/lib/models_timm/layers/cbam.py:83

↓ 5 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/edgenext.py:29

↓ 5 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/xception_aligned.py:22

↓ 5 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/selecsls.py:26

↓ 5 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/sknet.py:22

↓ 5 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/poolformer.py:33

↓ 5 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/gcvit.py:41

↓ 5 callersFunction_cfg_coat

(url='', **kwargs)

src/train_acdc/lib/models_timm/coat.py:34

↓ 5 callersFunction_create_coat

(variant, pretrained=False, default_cfg=None, **kwargs)

src/train_acdc/lib/models_timm/coat.py:636

↓ 5 callersFunction_create_edgenext

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/edgenext.py:513

↓ 5 callersFunction_create_gcvit

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/gcvit.py:537

↓ 5 callersFunction_create_mnv3

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/mobilenetv3.py:279

↓ 5 callersFunction_create_mvitv2

(variant, cfg_variant=None, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/mvitv2.py:964

↓ 5 callersFunction_create_poolformer

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/poolformer.py:265

↓ 5 callersFunction_create_selecsls

(variant, pretrained, **kwargs)

src/train_acdc/lib/models_timm/selecsls.py:215

↓ 5 callersFunction_create_skresnet

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/sknet.py:136

↓ 5 callersFunction_gen_lcnet

LCNet Essentially a MobileNet-V3 crossed with a MobileNet-V1 Paper: `PP-LCNet: A Lightweight CPU Convolutional Neural Network` - https://arx

src/train_acdc/lib/models_timm/mobilenetv3.py:493

↓ 5 callersFunction_gen_tinynet

Creates a TinyNet model.

src/train_acdc/lib/models_timm/efficientnet.py:1211

↓ 5 callersFunction_parse_ksize

(ss)

src/train_acdc/lib/models_timm/efficientnet_builder.py:69

↓ 5 callersFunction_xception

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/xception_aligned.py:256

↓ 5 callersMethodbackward

(ctx, grad_output)

src/train_acdc/lib/models_timm/layers/activations_me.py:82

↓ 5 callersFunctioncreate_shortcut

(downsample_type, layers: LayerFn, in_chs, out_chs, stride, dilation, **kwargs)

src/train_acdc/lib/models_timm/byobnet.py:947

↓ 5 callersFunctionefficientnet_init_weights

(model: nn.Module, init_fn=None)

src/train_acdc/lib/models_timm/efficientnet_builder.py:473

↓ 5 callersFunctionget_padding

(kernel_size: int, stride: int = 1, dilation: int = 1, **_)

src/train_acdc/lib/models_timm/layers/padding.py:12

↓ 5 callersFunctionget_padding_value

(padding, kernel_size, **kwargs)

src/train_acdc/lib/models_timm/layers/padding.py:36

↓ 5 callersFunctionhas_hf_hub

(necessary=False)

src/train_acdc/lib/models_timm/hub.py:57

↓ 5 callersFunctionlecun_normal_

(tensor)

src/train_acdc/lib/models_timm/layers/weight_init.py:124

↓ 5 callersFunctionpretrained_cfg_for_features

(pretrained_cfg)

src/train_acdc/lib/models_timm/helpers.py:428

↓ 4 callersMethod__init__

( self, base_dim, depth, heads, mlp_ratio, pool=None, drop_rate=.0, attn_drop_rate=.0, drop_path_p

src/train_acdc/lib/models_timm/pit.py:78

↓ 4 callersMethod__init__

( self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, global_pool='token',

src/train_acdc/lib/models_timm/cait.py:204

↓ 4 callersMethod__init__

( self, in_chs, out_chs, stride=1, dilation=1, pad_type='', start_with_relu=True, no_s

src/train_acdc/lib/models_timm/xception_aligned.py:110

↓ 4 callersMethod__init__

( self, img_size=224, img_scale=(1.0, 1.0), patch_size=(8, 16), in_chans=3, num_classes=1000,

src/train_acdc/lib/models_timm/crossvit.py:289

↓ 4 callersMethod__init__

( self, dim, out_dim, input_resolution, depth, num_heads=4, head_dim=None, window_size

src/train_acdc/lib/models_timm/swin_transformer.py:390

↓ 4 callersMethod__init__

Args: img_size (int, tuple): input image size in_chans (int): number of input channels patch_size (int):

src/train_acdc/lib/models_timm/nest.py:225

↓ 4 callersMethod__init__

( self, dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4., qkv_bias=T

src/train_acdc/lib/models_timm/swin_transformer_v2.py:420

↓ 4 callersMethod__init__

(self, inplace: bool = False)

src/train_acdc/lib/models_timm/layers/activations_jit.py:41

↓ 4 callersMethod__init__

(self, inplace: bool = False)

src/train_acdc/lib/models_timm/layers/activations_me.py:92

↓ 4 callersMethod__init__

(self, num_channels, num_groups=32, eps=1e-5, affine=True)

src/train_acdc/lib/models_timm/layers/norm.py:16

↓ 4 callersMethod__init__

( self, num_channels, num_groups=32, eps=1e-5, affine=True, group_size=None, apply_act

src/train_acdc/lib/models_timm/layers/norm_act.py:181

↓ 4 callersFunction_cfg

(url='', **kwargs)

src/train_acdc/lib/models_timm/inception_v3.py:16

↓ 4 callersFunction_create_inception_v3

(variant, pretrained=False, **kwargs)

src/train_acdc/lib/models_timm/inception_v3.py:430

↓ 4 callersFunction_gen_efficientnetv2_base

Creates an EfficientNet-V2 base model Ref impl: https://github.com/google/automl/tree/master/efficientnetv2 Paper: `EfficientNetV2: Smaller

src/train_acdc/lib/models_timm/efficientnet.py:987

↓ 4 callersFunction_gen_efficientnetv2_l

Creates an EfficientNet-V2 Large model Ref impl: https://github.com/google/automl/tree/master/efficientnetv2 Paper: `EfficientNetV2: Smaller

src/train_acdc/lib/models_timm/efficientnet.py:1085

↓ 4 callersFunction_gen_efficientnetv2_m

Creates an EfficientNet-V2 Medium model Ref impl: https://github.com/google/automl/tree/master/efficientnetv2 Paper: `EfficientNetV2: Smalle

src/train_acdc/lib/models_timm/efficientnet.py:1055

↓ 4 callersFunction_gen_mnasnet_a1

Creates a mnasnet-a1 model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet Paper: https://arxiv.org/pdf/1807

src/train_acdc/lib/models_timm/efficientnet.py:639

↓ 4 callersFunction_gen_mnasnet_b1

Creates a mnasnet-b1 model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet Paper: https://arxiv.org/pdf/1807

src/train_acdc/lib/models_timm/efficientnet.py:675

↓ 4 callersMethod_make_layer

(self, block, planes, blocks, groups, reduction, stride=1, downsample_kernel_size=1, downs

src/train_acdc/lib/models_timm/senet.py:343

↓ 4 callersMethod_make_layer

(self, block, planes, blocks, stride=1, use_se=True, aa_layer=None)

src/train_acdc/lib/models_timm/tresnet.py:230

↓ 4 callersFunction_next_cfg

( stride_mode='dw', pool_type='avg2', conv_norm_layer='layernorm2d', conv_norm

src/train_acdc/lib/maxxvit_4out.py:333

↓ 4 callersFunction_next_cfg

( stride_mode='dw', pool_type='avg2', conv_norm_layer='layernorm2d', conv_norm

src/train_acdc/lib/models_timm/maxxvit.py:333

↓ 4 callersFunction_ntuple

(n)

src/train_acdc/lib/models_timm/layers/helpers.py:10

↓ 4 callersFunction_pad

(x, crop_size)

src/train_acdc/utils/misc.py:155

↓ 4 callersFunction_pad

(x, crop_size)

src/train_synase/utils/misc.py:155

↓ 4 callersFunctionadapt_input_conv

(in_chans, conv_weight)

src/train_acdc/lib/models_timm/helpers.py:215

↓ 4 callersFunctionfast_layer_norm

( x: torch.Tensor, normalized_shape: List[int], weight: Optional[torch.Tensor] = None, bias: O

src/train_acdc/lib/models_timm/layers/fast_norm.py:56

↓ 4 callersMethodget_dicts

return info dicts for specified keys (or all if None) at specified indices (or out_indices if None)

src/train_acdc/lib/models_timm/features.py:49

↓ 4 callersFunctionget_files

(folder)

src/train_acdc/utils/lesion/make_dataset.py:18

↓ 4 callersFunctionget_files

(folder)

src/train_synase/utils/lesion/make_dataset.py:18

↓ 4 callersFunctionget_rel_pos_cls

(cfg: MaxxVitTransformerCfg, window_size)

src/train_acdc/lib/maxxvit_4out.py:1161

↓ 4 callersFunctionget_rel_pos_cls

(cfg: MaxxVitTransformerCfg, window_size)

src/train_acdc/lib/models_timm/maxxvit.py:1161

↓ 4 callersFunctiongroup_std

(x, groups: int = 32, eps: float = 1e-5, flatten: bool = False)

src/train_acdc/lib/models_timm/layers/evo_norm.py:62

↓ 4 callersMethodinit_weights

(self)

src/train_acdc/lib/models_timm/vision_transformer.py:301

↓ 4 callersFunctioninsert_cls

Insert CLS token.

src/train_acdc/lib/models_timm/coat.py:612

↓ 4 callersFunctionis_exportable

()

src/train_acdc/lib/models_timm/layers/config.py:44

↓ 4 callersFunctionis_no_jit

()

src/train_acdc/lib/models_timm/layers/config.py:25

↓ 4 callersFunctionmake_div

(v, divisor=8)

src/train_acdc/lib/models_timm/resnetv2.py:137

↓ 4 callersFunctionmaybe_mkdir_p

(directory: str)

src/visualize_cam.py:39

↓ 4 callersFunctionnum_groups

(group_size, channels)

src/train_acdc/lib/models_timm/efficientnet_blocks.py:17

↓ 4 callersFunctionresize_pos_embed

(posemb, posemb_new, num_prefix_tokens=1, gs_new=())

src/train_acdc/lib/models_timm/vision_transformer.py:691

↓ 4 callersFunctionset_layer

(model, layer, val)

src/train_acdc/lib/models_timm/helpers.py:341

↓ 3 callersMethod__init__

(self, optimizer, curr_iter, max_iter, lr_decay)

src/train_acdc/utils/misc.py:101

↓ 3 callersMethod__init__

(self, in_features, hidden_features=None, out_features=None, act='relu', drop_path=0.0)

src/train_acdc/lib/pyramid_vig.py:41

↓ 3 callersMethod__init__

VovNet (v2)

src/train_acdc/lib/models_timm/vovnet.py:270

↓ 3 callersMethod__init__

(self, feature_info: List[Dict], out_indices: Tuple[int])

src/train_acdc/lib/models_timm/features.py:22

↓ 3 callersMethod__init__

( self, small=False, num_init_features=64, k_r=96, groups=32, global_pool='avg', b=Fal

src/train_acdc/lib/models_timm/dpn.py:169

↓ 3 callersMethod__init__

( self, growth_rate=32, block_config=(6, 12, 24, 16), num_classes=1000, in_chans=3, global_pool='a

src/train_acdc/lib/models_timm/densenet.py:167

↓ 3 callersMethod__init__

( self, dim, num_heads, head_dim_ratio=1., mlp_ratio=4., drop=0., attn_drop=0., drop_p

src/train_acdc/lib/models_timm/visformer.py:117

↓ 3 callersMethod__init__

( self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, global_pool='avg',

src/train_acdc/lib/models_timm/beit.py:261

↓ 3 callersMethod__init__

(self, cfg, num_classes=1000, in_chans=3, drop_rate=0.0, global_pool='avg')

src/train_acdc/lib/models_timm/selecsls.py:156

↓ 3 callersMethod__init__

(self, act_type='relu', gamma: float = 1.0, inplace=False)

src/train_acdc/lib/models_timm/nfnet.py:261

↓ 3 callersMethod__init__

( self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, global_pool='token',

src/train_acdc/lib/models_timm/tnt.py:158

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