Merge branch 'add_Recommendation' into 'main'

添加openmmlab测试用例

See merge request dcutoolkit/deeplearing/dlexamples_new!32

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/resnext152_32x4d.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ResNeXt',
+        depth=152,
+        num_stages=4,
+        out_indices=(3, ),
+        groups=32,
+        width_per_group=4,
+        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),
+    ))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/resnext50_32x4d.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ResNeXt',
+        depth=50,
+        num_stages=4,
+        out_indices=(3, ),
+        groups=32,
+        width_per_group=4,
+        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),
+    ))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/seresnet101.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='SEResNet',
+        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),
+    ))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/seresnet50.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='SEResNet',
+        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='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/seresnext101_32x4d.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='SEResNeXt',
+        depth=101,
+        num_stages=4,
+        out_indices=(3, ),
+        groups=32,
+        width_per_group=4,
+        se_ratio=16,
+        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),
+    ))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/seresnext50_32x4d.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='SEResNeXt',
+        depth=50,
+        num_stages=4,
+        out_indices=(3, ),
+        groups=32,
+        width_per_group=4,
+        se_ratio=16,
+        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),
+    ))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/shufflenet_v1_1x.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='ShuffleNetV1', groups=3),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=960,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/shufflenet_v2_1x.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='ShuffleNetV2', widen_factor=1.0),
+    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),
+    ))

openmmlab_test/mmclassification-speed-benchmark/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),
+    ))

openmmlab_test/mmclassification-speed-benchmark/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),
+    ))

openmmlab_test/mmclassification-speed-benchmark/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),
+    ))

openmmlab_test/mmclassification-speed-benchmark/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),
+    ))

openmmlab_test/mmclassification-speed-benchmark/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),
+    ))

openmmlab_test/mmclassification-speed-benchmark/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),
+    ))

openmmlab_test/mmclassification-speed-benchmark/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),
+    ))

openmmlab_test/mmclassification-speed-benchmark/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),
+    ))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/vit_base_patch16_224_finetune.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='VisionTransformer',
+        num_layers=12,
+        embed_dim=768,
+        num_heads=12,
+        img_size=224,
+        patch_size=16,
+        in_channels=3,
+        feedforward_channels=3072,
+        drop_rate=0.1),
+    neck=None,
+    head=dict(
+        type='VisionTransformerClsHead',
+        num_classes=1000,
+        in_channels=768,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/vit_base_patch16_224_pretrain.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='VisionTransformer',
+        num_layers=12,
+        embed_dim=768,
+        num_heads=12,
+        img_size=224,
+        patch_size=16,
+        in_channels=3,
+        feedforward_channels=3072,
+        drop_rate=0.1,
+        attn_drop_rate=0.),
+    neck=None,
+    head=dict(
+        type='VisionTransformerClsHead',
+        num_classes=1000,
+        in_channels=768,
+        hidden_dim=3072,
+        loss=dict(type='LabelSmoothLoss', label_smooth_val=0.1),
+        topk=(1, 5),
+    ),
+    train_cfg=dict(
+        augments=dict(type='BatchMixup', alpha=0.2, num_classes=1000,
+                      prob=1.)))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/vit_base_patch16_384_finetune.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='VisionTransformer',
+        num_layers=12,
+        embed_dim=768,
+        num_heads=12,
+        img_size=384,
+        patch_size=16,
+        in_channels=3,
+        feedforward_channels=3072,
+        drop_rate=0.1),
+    neck=None,
+    head=dict(
+        type='VisionTransformerClsHead',
+        num_classes=1000,
+        in_channels=768,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/vit_base_patch32_384_finetune.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='VisionTransformer',
+        num_layers=12,
+        embed_dim=768,
+        num_heads=12,
+        img_size=384,
+        patch_size=32,
+        in_channels=3,
+        feedforward_channels=3072,
+        drop_rate=0.1),
+    neck=None,
+    head=dict(
+        type='VisionTransformerClsHead',
+        num_classes=1000,
+        in_channels=768,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))