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Functions932 in github.com/SLDGroup/MERIT

↓ 41 callersFunction_assert
(condition: bool, message: str)
lib/models_timm/layers/trace_utils.py:4
↓ 38 callersFunction_cfg
(url='', **kwargs)
lib/maxxvit_4out.py:63
↓ 38 callersFunction_cfg
(url='', **kwargs)
lib/models_timm/maxxvit.py:62
↓ 38 callersFunction_create_maxxvit
(variant, cfg_variant=None, pretrained=False, **kwargs)
lib/maxxvit_4out.py:1736
↓ 38 callersFunction_create_maxxvit
(variant, cfg_variant=None, pretrained=False, **kwargs)
lib/models_timm/maxxvit.py:1718
↓ 32 callersMethodget
Get value by key at specified index (indices) if idx == None, returns value for key at each output index if idx is an integer, return
lib/models_timm/features.py:36
↓ 28 callersFunction_cfg
(url='', **kwargs)
lib/models_timm/convnext.py:30
↓ 28 callersFunction_create_convnext
(variant, pretrained=False, **kwargs)
lib/models_timm/convnext.py:453
↓ 26 callersFunction_cfg
(url='', **kwargs)
lib/models_timm/mlp_mixer.py:54
↓ 26 callersFunction_create_mixer
(variant, pretrained=False, **kwargs)
lib/models_timm/mlp_mixer.py:380
↓ 22 callersFunctioncreate_conv2d
Select a 2d convolution implementation based on arguments Creates and returns one of torch.nn.Conv2d, Conv2dSame, MixedConv2d, or CondConv2d.
lib/models_timm/layers/create_conv2d.py:11
↓ 20 callersFunctionget_act_layer
Activation Layer Factory Fetching activation layers by name with this function allows export or torch script friendly functions to be returne
lib/models_timm/layers/create_act.py:125
↓ 20 callersFunctionnamed_apply
(fn: Callable, module: nn.Module, name='', depth_first=True, include_root=False)
lib/models_timm/helpers.py:605
↓ 17 callersFunctiontrunc_normal_
r"""Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distributi
lib/models_timm/layers/weight_init.py:43
↓ 16 callersFunctioncreate_act_layer
(name: Union[nn.Module, str], inplace=None, **kwargs)
lib/models_timm/layers/create_act.py:144
↓ 16 callersFunctionmake_divisible
(v, divisor=8, min_value=None, round_limit=.9)
lib/models_timm/layers/helpers.py:25
↓ 15 callersMethod__init__
( self, in_chs: int, out_chs: int, kernel_size: int = 3,
lib/maxxvit_4out.py:1557
↓ 15 callersMethod__init__
( self, in_chs: int, out_chs: int, kernel_size: int = 3,
lib/models_timm/maxxvit.py:1556
↓ 13 callersFunction_cfg
(url='', **kwargs)
lib/models_timm/vision_transformer_relpos.py:27
↓ 13 callersFunction_create_vision_transformer_relpos
(variant, pretrained=False, **kwargs)
lib/models_timm/vision_transformer_relpos.py:508
↓ 13 callersFunction_rw_max_cfg
( stride_mode='dw', pool_type='avg2', conv_output_bias=False, conv_attn_ratio=
lib/maxxvit_4out.py:290
↓ 13 callersFunction_rw_max_cfg
( stride_mode='dw', pool_type='avg2', conv_output_bias=False, conv_attn_ratio=
lib/models_timm/maxxvit.py:289
↓ 13 callersFunctionget_norm_layer
(norm_layer)
lib/models_timm/layers/create_norm.py:32
↓ 10 callersFunction_rw_coat_cfg
( stride_mode='pool', pool_type='avg2', conv_output_bias=False, conv_attn_earl
lib/maxxvit_4out.py:238
↓ 10 callersFunction_rw_coat_cfg
( stride_mode='pool', pool_type='avg2', conv_output_bias=False, conv_attn_earl
lib/models_timm/maxxvit.py:237
↓ 9 callersMethod__init__
(self, n_class=1, img_size=224, model_scale='small')
lib/networks.py:63
↓ 8 callersMethod__init__
( self, img_size=224, patch_size=16, in_chans=3, n
lib/models_timm/levit.py:400
↓ 8 callersMethod__init__
(self, num_features, apply_act=True, momentum=0.1, eps=1e-3, **_)
lib/models_timm/layers/evo_norm.py:100
↓ 8 callersMethod__init__
(self, inplace: bool = False)
lib/models_timm/layers/activations.py:39
↓ 8 callersFunctionget_norm_act_layer
(norm_layer, act_layer=None)
lib/models_timm/layers/create_norm_act.py:52
↓ 7 callersFunctionis_fast_norm
()
lib/models_timm/layers/fast_norm.py:25
↓ 7 callersFunctionload_pretrained_weights
(img_size, model_scale)
lib/networks.py:30
↓ 7 callersFunctionpad_same
(x, k: List[int], s: List[int], d: List[int] = (1, 1), value: float = 0)
lib/models_timm/layers/padding.py:28
↓ 6 callersMethod__init__
(self,ch_in,ch_out,kernel_size=3, stride=1, padding=1, groups=1)
lib/decoders.py:24
↓ 6 callersMethod__init__
( self, window_size, num_heads=8, hidden_dim=128,
lib/models_timm/vision_transformer_relpos.py:139
↓ 6 callersFunction_cfg
(url='', **kwargs)
lib/models_timm/levit.py:41
↓ 6 callersFunctionbuild_model_with_cfg
Build model with specified default_cfg and optional model_cfg This helper fn aids in the construction of a model including: * handling def
lib/models_timm/helpers.py:505
↓ 6 callersFunctioncreate_levit
(variant, pretrained=False, distilled=True, **kwargs)
lib/models_timm/levit.py:582
↓ 5 callersMethod__init__
(self, dim)
lib/models_timm/mlp_mixer.py:168
↓ 5 callersMethod__init__
( self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1, spatial_kernel_size=
lib/models_timm/layers/cbam.py:83
↓ 5 callersFunctioncheckpoint_seq
r"""A helper function for checkpointing sequential models. Sequential models execute a list of modules/functions in order (sequentially). The
lib/models_timm/helpers.py:722
↓ 5 callersFunctionget_padding
(kernel_size: int, stride: int = 1, dilation: int = 1, **_)
lib/models_timm/layers/padding.py:12
↓ 5 callersFunctionget_padding_value
(padding, kernel_size, **kwargs)
lib/models_timm/layers/padding.py:36
↓ 5 callersFunctionhas_hf_hub
(necessary=False)
lib/models_timm/hub.py:57
↓ 5 callersFunctiontrunc_normal_tf_
r"""Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distributi
lib/models_timm/layers/weight_init.py:70
↓ 5 callersMethodupdate
(self, val, n=1)
utils/utils.py:64
↓ 4 callersMethod__init__
(self, inplace: bool = False)
lib/models_timm/layers/activations_jit.py:41
↓ 4 callersMethod__init__
(self, inplace: bool = False)
lib/models_timm/layers/activations_me.py:92
↓ 4 callersMethod__init__
(self, num_channels, num_groups=32, eps=1e-5, affine=True)
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
lib/models_timm/layers/norm_act.py:181
↓ 4 callersFunction_next_cfg
( stride_mode='dw', pool_type='avg2', conv_norm_layer='layernorm2d', conv_norm
lib/maxxvit_4out.py:334
↓ 4 callersFunction_next_cfg
( stride_mode='dw', pool_type='avg2', conv_norm_layer='layernorm2d', conv_norm
lib/models_timm/maxxvit.py:333
↓ 4 callersFunction_ntuple
(n)
lib/models_timm/layers/helpers.py:10
↓ 4 callersFunctioncreate_attn
(attn_type, channels, **kwargs)
lib/models_timm/layers/create_attn.py:84
↓ 4 callersFunctionfast_layer_norm
( x: torch.Tensor, normalized_shape: List[int], weight: Optional[torch.Tensor] = None, bias: O
lib/models_timm/layers/fast_norm.py:56
↓ 4 callersFunctionget_rel_pos_cls
(cfg: MaxxVitTransformerCfg, window_size)
lib/maxxvit_4out.py:1162
↓ 4 callersFunctionget_rel_pos_cls
(cfg: MaxxVitTransformerCfg, window_size)
lib/models_timm/maxxvit.py:1161
↓ 4 callersFunctiongroup_std
(x, groups: int = 32, eps: float = 1e-5, flatten: bool = False)
lib/models_timm/layers/evo_norm.py:62
↓ 4 callersFunctionis_exportable
()
lib/models_timm/layers/config.py:44
↓ 4 callersFunctionis_no_jit
()
lib/models_timm/layers/config.py:25
↓ 4 callersFunctionset_layer
(model, layer, val)
lib/models_timm/helpers.py:341
↓ 4 callersMethodtrain
(self, mode=True)
lib/models_timm/levit.py:267
↓ 3 callersMethod__init__
(self, feature_info: List[Dict], out_indices: Tuple[int])
lib/models_timm/features.py:22
↓ 3 callersMethod__init__
( self, in_channel, out_channels, kernel_size, stride=1, padding=None, dilation=1, gro
lib/models_timm/layers/std_conv.py:32
↓ 3 callersMethod__init__
(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=0.)
lib/models_timm/layers/mlp.py:13
↓ 3 callersMethod__init__
(self, output_size=1)
lib/models_timm/layers/adaptive_avgmax_pool.py:62
↓ 3 callersFunction_get_feature_info
(net, out_indices)
lib/models_timm/features.py:135
↓ 3 callersFunctionadaptive_avgmax_pool2d
(x, output_size=1)
lib/models_timm/layers/adaptive_avgmax_pool.py:24
↓ 3 callersFunctionapply_rot_embed
(x: torch.Tensor, sin_emb, cos_emb)
lib/models_timm/layers/pos_embed.py:148
↓ 3 callersFunctionfast_group_norm
( x: torch.Tensor, num_groups: int, weight: Optional[torch.Tensor] = None, bias: Optional[torc
lib/models_timm/layers/fast_norm.py:34
↓ 3 callersFunctionmanual_var
(x, dim: Union[int, Sequence[int]], diff_sqm: bool = False)
lib/models_timm/layers/evo_norm.py:52
↓ 3 callersFunctionpixel_freq_bands
( num_bands: int, max_freq: float = 224., linear_bands: bool = True, dtype: to
lib/models_timm/layers/pos_embed.py:8
↓ 3 callersFunctionrot
(x)
lib/models_timm/layers/pos_embed.py:144
↓ 2 callersMethod__init__
( self, in_chans=3, num_classes=1000, global_pool='avg',
lib/models_timm/convnext.py:269
↓ 2 callersMethod__init__
(self, in_channels, use_scale=True, rd_ratio=1/8, rd_channels=None, rd_divisor=8, **kwargs)
lib/models_timm/layers/non_local_attn.py:23
↓ 2 callersMethod__init__
( self, channels, rd_ratio=1. / 16, rd_channels=None, rd_divisor=8, add_maxpool=False,
lib/models_timm/layers/squeeze_excite.py:28
↓ 2 callersMethod__init__
(self, embed_len_decoder: int)
lib/models_timm/layers/ml_decoder.py:93
↓ 2 callersMethod__init__
(self, block_size=4)
lib/models_timm/layers/space_to_depth.py:6
↓ 2 callersMethod_collect
(self, x)
lib/models_timm/features.py:200
↓ 2 callersFunction_create_fc
(num_features, num_classes, use_conv=False)
lib/models_timm/layers/classifier.py:22
↓ 2 callersFunction_create_pool
(num_features, num_classes, pool_type='avg', use_conv=False)
lib/models_timm/layers/classifier.py:11
↓ 2 callersFunction_download_from_hf
(model_id: str, filename: str)
lib/models_timm/hub.py:80
↓ 2 callersFunction_module_list
(module, flatten_sequential=False)
lib/models_timm/features.py:121
↓ 2 callersFunction_resolve_pretrained_source
(pretrained_cfg)
lib/models_timm/helpers.py:134
↓ 2 callersFunction_split_channels
(num_chan, num_groups)
lib/models_timm/layers/mixed_conv2d.py:14
↓ 2 callersFunction_trunc_normal_
(tensor, mean, std, a, b)
lib/models_timm/layers/weight_init.py:8
↓ 2 callersFunctionadaptive_catavgmax_pool2d
(x, output_size=1)
lib/models_timm/layers/adaptive_avgmax_pool.py:30
↓ 2 callersMethodbackward
(ctx, grad_output)
lib/models_timm/layers/activations_me.py:82
↓ 2 callersFunctionbuild_fourier_pos_embed
( feat_shape: List[int], bands: Optional[torch.Tensor] = None, num_bands: int = 64,
lib/models_timm/layers/pos_embed.py:72
↓ 2 callersFunctioncalculate_dice_percase
(pred, gt)
utils/utils.py:160
↓ 2 callersFunctionclean_state_dict
(state_dict)
lib/models_timm/helpers.py:36
↓ 2 callersFunctionconv2d_same
( x, weight: torch.Tensor, bias: Optional[torch.Tensor] = None, stride: Tuple[int, int] = (1, 1),
lib/models_timm/layers/conv2d_same.py:13
↓ 2 callersFunctioncreate_conv2d_pad
(in_chs, out_chs, kernel_size, **kwargs)
lib/models_timm/layers/conv2d_same.py:33
↓ 2 callersFunctioncreate_feature_extractor
(model: nn.Module, return_nodes: Union[Dict[str, str], List[str]])
lib/models_timm/fx_features.py:58
↓ 2 callersFunctionextend_tuple
(x, n)
lib/models_timm/layers/helpers.py:34
↓ 2 callersMethodforward_features
(self, x)
lib/models_timm/levit.py:517
↓ 2 callersFunctiongen_relative_position_index
( q_size: Tuple[int, int], k_size: Tuple[int, int] = None, class_token: bool = False)
lib/models_timm/vision_transformer_relpos.py:70
↓ 2 callersMethodget_attention_biases
(self, device: torch.device)
lib/models_timm/levit.py:272
↓ 2 callersMethodget_attention_biases
(self, device: torch.device)
lib/models_timm/levit.py:359
↓ 2 callersMethodget_bias
(self)
lib/models_timm/vision_transformer_relpos.py:187
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