Method__init__(self, root, list_path, crop_size_3D=(64, 256, 256), data_type='3D_Modal')
UniMiSS/data_loader3D.py:15
Method__init__(self, in_channels, out_channels, kernel_size, stride=(1,1,1), padding=(0,0,0), dilation=(1,1,1), groups=1, bi
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /MiT_encoder.py:12
Method__init__(self, in_channels, out_channels, norm_cfg, activation_cfg, kernel_size, stride=(1, 1, 1),
pa
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /MiT_encoder.py:54
Method__init__(self, in_channels, out_channels, norm_cfg, activation_cfg, kernel_size, stride=(1, 1, 1),
pa
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /MiT_encoder.py:73
Method__init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1)
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /MiT_encoder.py:145
Method__init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
dr
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /MiT_encoder.py:191
Method__init__(self, norm_cfg='BN', activation_cfg='ReLU', weight_std=False, img_size=[16, 96, 96], patch_size=[16, 16, 16],
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /MiT_encoder.py:215
Method__init__(self, norm_cfg='BN', activation_cfg='ReLU', weight_std=False, img_size=[48, 192, 192], in_chans=1, num_classe
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /MiT_encoder.py:234
Method__init__(self, in_channels, out_channels, kernel_size, stride=(1,1,1), padding=(0,0,0), dilation=(1,1,1), groups=1, bi
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /MiT.py:17
Method__init__(self, in_channels, out_channels, norm_cfg, activation_cfg, kernel_size, stride=(1, 1, 1),
pa
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /MiT.py:58
Method__init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1)
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /MiT.py:115
Method__init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
dr
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /MiT.py:161
Method__init__(self, img_size=[16, 96, 96], patch_size=[16, 16, 16], in_chans=1, embed_dim=768)
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /MiT.py:184
Method__init__(self, norm_cfg='BN', activation_cfg='ReLU', weight_std=False, img_size=[48, 192, 192], num_classes=None, in_c
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /MiT.py:202
Method__init__(self, norm_cfg='BN', activation_cfg='ReLU', weight_std=False, img_size=None, num_classes=None, in_chans=1,
UniMiSS/Downstream/BCV/MiTnnu/network_architecture /MiT.py:349
Method__init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None,
UniMiSS/Downstream/BCV/MiTnnu/training/network_training /nnUNetTrainerCascadeFullRes.py:37
Method__init__(self, plans_file, fold, norm_cfg, activation_cfg, epochs, pre_train, pre_path, output_folder=None, dataset_di
UniMiSS/Downstream/BCV/MiTnnu/training/network_training /TrainerV2_BCV.py:25
Method__init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None,
UniMiSS/Downstream/BCV/MiTnnu/training/network_training /nnUNetTrainerV2_CascadeFullRes.py:41
Method__init__(self, in_channels, out_channels, kernel_size, stride=(1,1,1), padding=(0,0,0), dilation=(1,1,1), groups=1, bi
UniMiSS/Downstream/RICORD/nets/utils.py:104
Method__init__(self,in_channels,out_channels,norm_cfg,activation_cfg,kernel_size,stride=(1, 1, 1),padding=(0, 0, 0),dilation
UniMiSS/Downstream/RICORD/nets/utils.py:122
Method__init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1)
UniMiSS/Downstream/RICORD/nets/utils.py:154
Method__init__(self, norm_cfg='BN', activation_cfg='ReLU', weight_std=False, img_size=[16, 96, 96],
patch_s
UniMiSS/Downstream/RICORD/nets/utils.py:204
Method__init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
dr
UniMiSS/Downstream/RICORD/nets/utils.py:224
Method__init__(self, num_classes=2, norm_cfg3D='BN3', activation_cfg='ReLU', weight_std=False, img_size3D=[16, 96, 96], in_c
UniMiSS/Downstream/RICORD/nets/MiT.py:14
Method__init__(self, root, list_path, crop_size_3D=(64, 64, 64), max_iters=None)
UniMiSS/Downstream/RICORD/dataset/mydataset3D.py:15
Method__init__(self, root, list_path, crop_size_3D=(64, 64, 64))
UniMiSS/Downstream/RICORD/dataset/mydataset3D.py:100
Method__init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1,
UniMiSS/models/MiT_encoder.py:54
Method__init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
dr
UniMiSS/models/MiT_encoder.py:115
Method__init__(self, norm_cfg='BN', activation_cfg='ReLU', weight_std=False, img_size=224, patch_size=16, in_chans=3,
UniMiSS/models/MiT_encoder.py:139
Method__init__(self, norm_cfg='BN', activation_cfg='ReLU', weight_std=False, img_size=[16, 96, 96],
patch_s
UniMiSS/models/MiT_encoder.py:161
Method__init__(self, norm_cfg2D='BN2', norm_cfg3D='BN3', activation_cfg='ReLU', weight_std=False, img_size2D=224,
UniMiSS/models/MiT_encoder.py:182
Method__init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1,
UniMiSS/models/MiT.py:54
Method__init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
dr
UniMiSS/models/MiT.py:115
Method__init__(self, img_size=[16, 96, 96], patch_size=[16, 16, 16], in_chans=1, embed_dim=768)
UniMiSS/models/MiT.py:159
Method__init__(self, norm_cfg2D='BN2', norm_cfg3D='BN3', activation_cfg='ReLU', weight_std=False,
img_size2
UniMiSS/models/MiT.py:177
Method__init__(self, in_dim, out_dim, use_bn=False, norm_last_layer=True, nlayers=3, hidden_dim=2048,
bottl
UniMiSS/models/MiT.py:518
Method__init__(self, in_channels, out_channels, kernel_size, stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=
UniMiSS/models/Pacth_embeds.py:19