add part code

configs/_base_/models/tinyvit/tinyvit-21m.py

+# Model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='TinyViT',
+        arch='21m',
+        img_size=(224, 224),
+        window_size=[7, 7, 14, 7],
+        out_indices=(3, ),
+        drop_path_rate=0.2,
+        gap_before_final_norm=True,
+        init_cfg=[
+            dict(
+                type='TruncNormal',
+                layer=['Conv2d', 'Linear'],
+                std=.02,
+                bias=0.),
+            dict(type='Constant', layer=['LayerNorm'], val=1., bias=0.),
+        ]),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=576,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

configs/_base_/models/tinyvit/tinyvit-5m.py

+# Model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='TinyViT',
+        arch='5m',
+        img_size=(224, 224),
+        window_size=[7, 7, 14, 7],
+        out_indices=(3, ),
+        drop_path_rate=0.0,
+        gap_before_final_norm=True,
+        init_cfg=[
+            dict(
+                type='TruncNormal',
+                layer=['Conv2d', 'Linear'],
+                std=.02,
+                bias=0.),
+            dict(type='Constant', layer=['LayerNorm'], val=1., bias=0.),
+        ]),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=320,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

configs/_base_/models/tnt_s_patch16_224.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='TNT',
+        arch='s',
+        img_size=224,
+        patch_size=16,
+        in_channels=3,
+        ffn_ratio=4,
+        qkv_bias=False,
+        drop_rate=0.,
+        attn_drop_rate=0.,
+        drop_path_rate=0.1,
+        first_stride=4,
+        num_fcs=2,
+        init_cfg=[
+            dict(type='TruncNormal', layer='Linear', std=.02),
+            dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+        ]),
+    neck=None,
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=384,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        topk=(1, 5),
+        init_cfg=dict(type='TruncNormal', layer='Linear', std=.02)))

configs/_base_/models/twins_pcpvt_base.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='PCPVT',
+        arch='base',
+        in_channels=3,
+        out_indices=(3, ),
+        qkv_bias=True,
+        norm_cfg=dict(type='LN', eps=1e-06),
+        norm_after_stage=[False, False, False, True],
+        drop_rate=0.0,
+        attn_drop_rate=0.,
+        drop_path_rate=0.3),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=512,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='Mixup', alpha=0.8),
+        dict(type='CutMix', alpha=1.0)
+    ]),
+)

configs/_base_/models/twins_svt_base.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='SVT',
+        arch='base',
+        in_channels=3,
+        out_indices=(3, ),
+        qkv_bias=True,
+        norm_cfg=dict(type='LN'),
+        norm_after_stage=[False, False, False, True],
+        drop_rate=0.0,
+        attn_drop_rate=0.,
+        drop_path_rate=0.3),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=768,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='Mixup', alpha=0.8),
+        dict(type='CutMix', alpha=1.0)
+    ]),
+)

configs/_base_/models/van/van_base.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VAN', arch='base', drop_path_rate=0.1),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=512,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False))

configs/_base_/models/van/van_large.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VAN', arch='large', drop_path_rate=0.2),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=512,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False))

configs/_base_/models/van/van_small.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VAN', arch='small', drop_path_rate=0.1),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=512,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='Mixup', alpha=0.8),
+        dict(type='CutMix', alpha=1.0)
+    ]),
+)

configs/_base_/models/van/van_tiny.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VAN', arch='tiny', drop_path_rate=0.1),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=256,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='Mixup', alpha=0.8),
+        dict(type='CutMix', alpha=1.0)
+    ]),
+)

configs/_base_/models/vgg11.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VGG', depth=11, num_classes=1000),
+    neck=None,
+    head=dict(
+        type='ClsHead',
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/vgg11bn.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='VGG', depth=11, norm_cfg=dict(type='BN'), num_classes=1000),
+    neck=None,
+    head=dict(
+        type='ClsHead',
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/vgg13.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VGG', depth=13, num_classes=1000),
+    neck=None,
+    head=dict(
+        type='ClsHead',
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/vgg13bn.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='VGG', depth=13, norm_cfg=dict(type='BN'), num_classes=1000),
+    neck=None,
+    head=dict(
+        type='ClsHead',
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/vgg16.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VGG', depth=16, num_classes=1000),
+    neck=None,
+    head=dict(
+        type='ClsHead',
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/vgg16bn.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='VGG', depth=16, norm_cfg=dict(type='BN'), num_classes=1000),
+    neck=None,
+    head=dict(
+        type='ClsHead',
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/vgg19.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VGG', depth=19, num_classes=1000),
+    neck=None,
+    head=dict(
+        type='ClsHead',
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/vgg19bn.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='VGG', depth=19, norm_cfg=dict(type='BN'), num_classes=1000),
+    neck=None,
+    head=dict(
+        type='ClsHead',
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/vig/pyramid_vig_base.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='PyramidVig',
+        arch='base',
+        k=9,
+        act_cfg=dict(type='GELU'),
+        norm_cfg=dict(type='BN'),
+        graph_conv_type='mr',
+        graph_conv_bias=True,
+        epsilon=0.2,
+        use_stochastic=False,
+        drop_path=0.1,
+        norm_eval=False,
+        frozen_stages=0),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='VigClsHead',
+        num_classes=1000,
+        in_channels=1024,
+        hidden_dim=1024,
+        act_cfg=dict(type='GELU'),
+        dropout=0.,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ),
+    train_cfg=dict(augments=[
+        dict(type='Mixup', alpha=0.8),
+        dict(type='CutMix', alpha=1.0)
+    ]),
+)

configs/_base_/models/vig/pyramid_vig_medium.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='PyramidVig',
+        arch='medium',
+        k=9,
+        act_cfg=dict(type='GELU'),
+        norm_cfg=dict(type='BN'),
+        graph_conv_type='mr',
+        graph_conv_bias=True,
+        epsilon=0.2,
+        use_stochastic=False,
+        drop_path=0.1,
+        norm_eval=False,
+        frozen_stages=0),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='VigClsHead',
+        num_classes=1000,
+        in_channels=768,
+        hidden_dim=1024,
+        act_cfg=dict(type='GELU'),
+        dropout=0.,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ),
+    train_cfg=dict(augments=[
+        dict(type='Mixup', alpha=0.8),
+        dict(type='CutMix', alpha=1.0)
+    ]),
+)

configs/_base_/models/vig/pyramid_vig_small.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='PyramidVig',
+        arch='small',
+        k=9,
+        act_cfg=dict(type='GELU'),
+        norm_cfg=dict(type='BN'),
+        graph_conv_type='mr',
+        graph_conv_bias=True,
+        epsilon=0.2,
+        use_stochastic=False,
+        drop_path=0.1,
+        norm_eval=False,
+        frozen_stages=0),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='VigClsHead',
+        num_classes=1000,
+        in_channels=640,
+        hidden_dim=1024,
+        act_cfg=dict(type='GELU'),
+        dropout=0.,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ),
+    train_cfg=dict(augments=[
+        dict(type='Mixup', alpha=0.8),
+        dict(type='CutMix', alpha=1.0)
+    ]),
+)