Functions — github.com/SLDGroup/MERIT

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Functions932 in github.com/SLDGroup/MERIT

⨍ Functions 932◇ Types & classes 209

Functioncoatnet_3_224

(pretrained=False, **kwargs)

lib/maxxvit_4out.py:1830

Functioncoatnet_3_224

(pretrained=False, **kwargs)

lib/models_timm/maxxvit.py:1812

Functioncoatnet_3_rw_224

(pretrained=False, **kwargs)

lib/maxxvit_4out.py:1770

Functioncoatnet_3_rw_224

(pretrained=False, **kwargs)

lib/models_timm/maxxvit.py:1752

Functioncoatnet_4_224

(pretrained=False, **kwargs)

lib/maxxvit_4out.py:1835

Functioncoatnet_4_224

(pretrained=False, **kwargs)

lib/models_timm/maxxvit.py:1817

Functioncoatnet_5_224

(pretrained=False, **kwargs)

lib/maxxvit_4out.py:1840

Functioncoatnet_5_224

(pretrained=False, **kwargs)

lib/models_timm/maxxvit.py:1822

Functioncoatnet_bn_0_rw_224

(pretrained=False, **kwargs)

lib/maxxvit_4out.py:1775

Functioncoatnet_bn_0_rw_224

(pretrained=False, **kwargs)

lib/models_timm/maxxvit.py:1757

Functioncoatnet_nano_cc_224

(pretrained=False, **kwargs)

lib/maxxvit_4out.py:1805

Functioncoatnet_nano_cc_224

(pretrained=False, **kwargs)

lib/models_timm/maxxvit.py:1787

Functioncoatnet_nano_rw_224

(pretrained=False, **kwargs)

lib/maxxvit_4out.py:1750

Functioncoatnet_nano_rw_224

(pretrained=False, **kwargs)

lib/models_timm/maxxvit.py:1732

Functioncoatnet_pico_rw_224

(pretrained=False, **kwargs)

lib/maxxvit_4out.py:1745

Functioncoatnet_pico_rw_224

(pretrained=False, **kwargs)

lib/models_timm/maxxvit.py:1727

Functioncoatnet_rmlp_0_rw_224

(pretrained=False, **kwargs)

lib/maxxvit_4out.py:1785

Functioncoatnet_rmlp_0_rw_224

(pretrained=False, **kwargs)

lib/models_timm/maxxvit.py:1767

Functioncoatnet_rmlp_1_rw_224

(pretrained=False, **kwargs)

lib/maxxvit_4out.py:1790

Functioncoatnet_rmlp_1_rw_224

(pretrained=False, **kwargs)

lib/models_timm/maxxvit.py:1772

Functioncoatnet_rmlp_2_rw_224

(pretrained=False, **kwargs)

lib/maxxvit_4out.py:1795

Functioncoatnet_rmlp_2_rw_224

(pretrained=False, **kwargs)

lib/models_timm/maxxvit.py:1777

Functioncoatnet_rmlp_3_rw_224

(pretrained=False, **kwargs)

lib/maxxvit_4out.py:1800

Functioncoatnet_rmlp_3_rw_224

(pretrained=False, **kwargs)

lib/models_timm/maxxvit.py:1782

Functioncoatnet_rmlp_nano_rw_224

(pretrained=False, **kwargs)

lib/maxxvit_4out.py:1780

Functioncoatnet_rmlp_nano_rw_224

(pretrained=False, **kwargs)

lib/models_timm/maxxvit.py:1762

Functioncoatnext_nano_rw_224

(pretrained=False, **kwargs)

lib/maxxvit_4out.py:1810

Functioncoatnext_nano_rw_224

(pretrained=False, **kwargs)

lib/models_timm/maxxvit.py:1792

Functioncondconv_initializer

CondConv initializer function.

lib/models_timm/layers/cond_conv2d.py:22

Functionconvert_splitbn_model

Recursively traverse module and its children to replace all instances of ``torch.nn.modules.batchnorm._BatchNorm`` with `SplitBatchnorm2d`.

lib/models_timm/layers/split_batchnorm.py:41

Functionconvert_sync_batchnorm

(module, process_group=None)

lib/models_timm/layers/norm_act.py:130

Functionconvnext_atto

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:463

Functionconvnext_atto_ols

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:472

Functionconvnext_base

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:558

Functionconvnext_base_384_in22ft1k

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:621

Functionconvnext_base_in22ft1k

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:586

Functionconvnext_base_in22k

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:656

Functionconvnext_femto

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:481

Functionconvnext_femto_ols

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:490

Functionconvnext_large

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:565

Functionconvnext_large_384_in22ft1k

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:628

Functionconvnext_large_in22ft1k

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:593

Functionconvnext_large_in22k

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:663

Functionconvnext_nano

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:517

Functionconvnext_nano_ols

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:526

Functionconvnext_pico

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:499

Functionconvnext_pico_ols

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:508

Functionconvnext_small

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:551

Functionconvnext_small_384_in22ft1k

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:614

Functionconvnext_small_in22ft1k

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:579

Functionconvnext_small_in22k

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:649

Functionconvnext_tiny

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:544

Functionconvnext_tiny_384_in22ft1k

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:607

Functionconvnext_tiny_hnf

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:535

Functionconvnext_tiny_in22ft1k

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:572

Functionconvnext_tiny_in22k

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:642

Functionconvnext_xlarge_384_in22ft1k

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:635

Functionconvnext_xlarge_in22ft1k

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:600

Functionconvnext_xlarge_in22k

(pretrained=False, **kwargs)

lib/models_timm/convnext.py:670

Functioncreate_classifier

(num_features, num_classes, pool_type='avg', use_conv=False)

lib/models_timm/layers/classifier.py:32

Functioncreate_model

Create a model Args: model_name (str): name of model to instantiate pretrained (bool): load pretrained ImageNet-1k weights if tru

lib/models_timm/factory.py:30

Functioncreate_norm_act_layer

(layer_name, num_features, act_layer=None, apply_act=True, jit=False, **kwargs)

lib/models_timm/layers/create_norm_act.py:44

Functioncreate_norm_layer

(layer_name, num_features, act_layer=None, apply_act=True, **kwargs)

lib/models_timm/layers/create_norm.py:26

Functioncreate_pool2d

(pool_type, kernel_size, stride=None, **kwargs)

lib/models_timm/layers/pool2d_same.py:56

Methodextra_repr

(self)

lib/models_timm/layers/drop.py:168

Functionflatten_modules

(named_modules, depth=1, prefix='', module_types='sequential')

lib/models_timm/helpers.py:792

Functionforward

(_x)

lib/models_timm/helpers.py:767

Methodforward

(self, inputs, target, weight=None, softmax=False)

utils/utils.py:129

Methodforward

(self, x, shared_rel_pos: Optional[torch.Tensor] = None)

lib/maxxvit_4out.py:713

Methodforward

(self, x, shared_rel_pos: Optional[torch.Tensor] = None)

lib/maxxvit_4out.py:759

Methodforward

(self, x)

lib/maxxvit_4out.py:785

Methodforward

(self, x)

lib/maxxvit_4out.py:796

Methodforward

(self, x)

lib/maxxvit_4out.py:832

Methodforward

(self, x, shared_rel_pos: Optional[torch.Tensor] = None)

lib/maxxvit_4out.py:921

Methodforward

(self, x)

lib/maxxvit_4out.py:1026

Methodforward

(self, x)

lib/maxxvit_4out.py:1107

Methodforward

(self, x)

lib/maxxvit_4out.py:1228

Methodforward

(self, x)

lib/maxxvit_4out.py:1299

Methodforward

(self, x)

lib/maxxvit_4out.py:1399

Methodforward

(self, x)

lib/maxxvit_4out.py:1435

Methodforward

(self, x)

lib/maxxvit_4out.py:1478

Methodforward

(self, x)

lib/maxxvit_4out.py:1547

Methodforward

(self, x)

lib/maxxvit_4out.py:1581

Methodforward

(self, x)

lib/maxxvit_4out.py:1730

(self, x, im1_size=(256,256), im2_size=(224,224))

lib/networks.py:678

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