upload mmpretrain

configs/_base_/models/replknet-31B_in1k.py

+from mmpretrain.models import build_classifier
+
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='RepLKNet',
+        arch='31B',
+        out_indices=(3, ),
+    ),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1024,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))
+
+if __name__ == '__main__':
+    # model.pop('type')
+    model = build_classifier(model)
+    model.eval()
+    print('------------------- training-time model -------------')
+    for i in model.state_dict().keys():
+        print(i)

configs/_base_/models/replknet-31L_in1k.py

+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='RepLKNet',
+        arch='31L',
+        out_indices=(3, ),
+    ),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1536,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/replknet-XL_in1k.py

+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='RepLKNet',
+        arch='XL',
+        out_indices=(3, ),
+    ),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/repmlp-base_224.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='RepMLPNet',
+        arch='B',
+        img_size=224,
+        out_indices=(3, ),
+        reparam_conv_kernels=(1, 3),
+        deploy=False),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=768,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/repvgg-A0_in1k.py

+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='RepVGG',
+        arch='A0',
+        out_indices=(3, ),
+    ),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1280,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/repvgg-B3_lbs-mixup_in1k.py

+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='RepVGG',
+        arch='B3',
+        out_indices=(3, ),
+    ),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2560,
+        loss=dict(
+            type='LabelSmoothLoss',
+            loss_weight=1.0,
+            label_smooth_val=0.1,
+            mode='classy_vision',
+            num_classes=1000),
+        topk=(1, 5),
+    ),
+    train_cfg=dict(augments=dict(type='Mixup', alpha=0.2)),
+)

configs/_base_/models/res2net101-w26-s4.py

+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='Res2Net',
+        depth=101,
+        scales=4,
+        base_width=26,
+        deep_stem=False,
+        avg_down=False,
+    ),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/res2net50-w14-s8.py

+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='Res2Net',
+        depth=50,
+        scales=8,
+        base_width=14,
+        deep_stem=False,
+        avg_down=False,
+    ),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/res2net50-w26-s4.py

+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='Res2Net',
+        depth=50,
+        scales=4,
+        base_width=26,
+        deep_stem=False,
+        avg_down=False,
+    ),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/res2net50-w26-s6.py

+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='Res2Net',
+        depth=50,
+        scales=6,
+        base_width=26,
+        deep_stem=False,
+        avg_down=False,
+    ),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/res2net50-w26-s8.py

+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='Res2Net',
+        depth=50,
+        scales=8,
+        base_width=26,
+        deep_stem=False,
+        avg_down=False,
+    ),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/res2net50-w48-s2.py

+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='Res2Net',
+        depth=50,
+        scales=2,
+        base_width=48,
+        deep_stem=False,
+        avg_down=False,
+    ),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/resnest101.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ResNeSt',
+        depth=101,
+        num_stages=4,
+        stem_channels=128,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(
+            type='LabelSmoothLoss',
+            label_smooth_val=0.1,
+            num_classes=1000,
+            reduction='mean',
+            loss_weight=1.0),
+        topk=(1, 5),
+        cal_acc=False),
+    train_cfg=dict(augments=dict(type='Mixup', alpha=0.2)),
+)

configs/_base_/models/resnest200.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ResNeSt',
+        depth=200,
+        num_stages=4,
+        stem_channels=128,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(
+            type='LabelSmoothLoss',
+            label_smooth_val=0.1,
+            num_classes=1000,
+            reduction='mean',
+            loss_weight=1.0),
+        topk=(1, 5),
+        cal_acc=False),
+    train_cfg=dict(augments=dict(type='Mixup', alpha=0.2)),
+)

configs/_base_/models/resnest269.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ResNeSt',
+        depth=269,
+        num_stages=4,
+        stem_channels=128,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(
+            type='LabelSmoothLoss',
+            label_smooth_val=0.1,
+            num_classes=1000,
+            reduction='mean',
+            loss_weight=1.0),
+        topk=(1, 5),
+        cal_acc=False),
+    train_cfg=dict(augments=dict(type='Mixup', alpha=0.2)),
+)

configs/_base_/models/resnest50.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ResNeSt',
+        depth=50,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(
+            type='LabelSmoothLoss',
+            label_smooth_val=0.1,
+            num_classes=1000,
+            reduction='mean',
+            loss_weight=1.0),
+        topk=(1, 5),
+        cal_acc=False),
+    train_cfg=dict(augments=dict(type='Mixup', alpha=0.2)),
+)

configs/_base_/models/resnet101.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ResNet',
+        depth=101,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/resnet101_cifar.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ResNet_CIFAR',
+        depth=101,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=10,
+        in_channels=2048,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

configs/_base_/models/resnet152.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ResNet',
+        depth=152,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2048,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

configs/_base_/models/resnet152_cifar.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ResNet_CIFAR',
+        depth=152,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=10,
+        in_channels=2048,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))