Merge branch 'sun_22.10' into 'main'

Sun 22.10

See merge request dcutoolkit/deeplearing/dlexamples_new!51

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/mlp_mixer_base_patch16.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='MlpMixer',
+        arch='b',
+        img_size=224,
+        patch_size=16,
+        drop_rate=0.1,
+        init_cfg=[
+            dict(
+                type='Kaiming',
+                layer='Conv2d',
+                mode='fan_in',
+                nonlinearity='linear')
+        ]),
+    neck=dict(type='GlobalAveragePooling', dim=1),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=768,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ),
+)

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/mlp_mixer_large_patch16.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='MlpMixer',
+        arch='l',
+        img_size=224,
+        patch_size=16,
+        drop_rate=0.1,
+        init_cfg=[
+            dict(
+                type='Kaiming',
+                layer='Conv2d',
+                mode='fan_in',
+                nonlinearity='linear')
+        ]),
+    neck=dict(type='GlobalAveragePooling', dim=1),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1024,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ),
+)

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/mobilenet_v3_large_imagenet.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='MobileNetV3', arch='large'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='StackedLinearClsHead',
+        num_classes=1000,
+        in_channels=960,
+        mid_channels=[1280],
+        dropout_rate=0.2,
+        act_cfg=dict(type='HSwish'),
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        init_cfg=dict(
+            type='Normal', layer='Linear', mean=0., std=0.01, bias=0.),
+        topk=(1, 5)))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/mobilenet_v3_small_cifar.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='MobileNetV3', arch='small'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='StackedLinearClsHead',
+        num_classes=10,
+        in_channels=576,
+        mid_channels=[1280],
+        act_cfg=dict(type='HSwish'),
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5)))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/mobilenet_v3_small_imagenet.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='MobileNetV3', arch='small'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='StackedLinearClsHead',
+        num_classes=1000,
+        in_channels=576,
+        mid_channels=[1024],
+        dropout_rate=0.2,
+        act_cfg=dict(type='HSwish'),
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        init_cfg=dict(
+            type='Normal', layer='Linear', mean=0., std=0.01, bias=0.),
+        topk=(1, 5)))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/mvit/mvitv2-base.py

+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='MViT', arch='base', drop_path_rate=0.3),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        in_channels=768,
+        num_classes=1000,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+    ),
+    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='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/mvit/mvitv2-large.py

+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='MViT',
+        arch='large',
+        drop_path_rate=0.5,
+        dim_mul_in_attention=False),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        in_channels=1152,
+        num_classes=1000,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+    ),
+    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='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/mvit/mvitv2-small.py

+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='MViT', arch='small', drop_path_rate=0.1),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        in_channels=768,
+        num_classes=1000,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+    ),
+    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='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/mvit/mvitv2-tiny.py

+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='MViT', arch='tiny', drop_path_rate=0.1),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        in_channels=768,
+        num_classes=1000,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+    ),
+    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='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/poolformer/poolformer_m36.py

+# Model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='PoolFormer',
+        arch='m36',
+        drop_path_rate=0.1,
+        init_cfg=[
+            dict(
+                type='TruncNormal',
+                layer=['Conv2d', 'Linear'],
+                std=.02,
+                bias=0.),
+            dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.),
+        ]),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=768,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/poolformer/poolformer_m48.py

+# Model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='PoolFormer',
+        arch='m48',
+        drop_path_rate=0.1,
+        init_cfg=[
+            dict(
+                type='TruncNormal',
+                layer=['Conv2d', 'Linear'],
+                std=.02,
+                bias=0.),
+            dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.),
+        ]),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=768,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/poolformer/poolformer_s12.py

+# Model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='PoolFormer',
+        arch='s12',
+        drop_path_rate=0.1,
+        init_cfg=[
+            dict(
+                type='TruncNormal',
+                layer=['Conv2d', 'Linear'],
+                std=.02,
+                bias=0.),
+            dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.),
+        ]),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=512,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/poolformer/poolformer_s24.py

+# Model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='PoolFormer',
+        arch='s24',
+        drop_path_rate=0.1,
+        init_cfg=[
+            dict(
+                type='TruncNormal',
+                layer=['Conv2d', 'Linear'],
+                std=.02,
+                bias=0.),
+            dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.),
+        ]),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=512,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))

openmmlab_test/mmclassification-0.24.1/configs/_base_/models/poolformer/poolformer_s36.py

+# Model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='PoolFormer',
+        arch='s36',
+        drop_path_rate=0.1,
+        init_cfg=[
+            dict(
+                type='TruncNormal',
+                layer=['Conv2d', 'Linear'],
+                std=.02,
+                bias=0.),
+            dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.),
+        ]),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=512,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+    ))