添加openmmlab测试用例

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/default_runtime.py

+# checkpoint saving
+checkpoint_config = dict(interval=1)
+# yapf:disable
+log_config = dict(
+    interval=100,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        # dict(type='TensorboardLoggerHook')
+    ])
+# yapf:enable
+
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/AlexNet_1x.py

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

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/mobilenet_v2_1x.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='MobileNetV2', widen_factor=1.0),
+    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),
+    ))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/regnet/regnetx_1.6gf.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='RegNet', arch='regnetx_1.6gf'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=912,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/regnet/regnetx_12gf.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='RegNet', arch='regnetx_12gf'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=2240,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/regnet/regnetx_3.2gf.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='RegNet', arch='regnetx_3.2gf'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1008,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/regnet/regnetx_4.0gf.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='RegNet', arch='regnetx_4.0gf'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1360,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/regnet/regnetx_400mf.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='RegNet', arch='regnetx_400mf'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=384,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/regnet/regnetx_6.4gf.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='RegNet', arch='regnetx_6.4gf'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1624,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/regnet/regnetx_8.0gf.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='RegNet', arch='regnetx_8.0gf'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1920,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/regnet/regnetx_800mf.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='RegNet', arch='regnetx_800mf'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=672,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-speed-benchmark/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='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-speed-benchmark/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='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-speed-benchmark/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='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

openmmlab_test/mmclassification-speed-benchmark/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='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))

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

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

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

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

openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/resnet18.py

+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ResNet',
+        depth=18,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=512,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5),
+    ))