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Commit 85529f35 authored by unknown's avatar unknown
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添加openmmlab测试用例

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openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/resnet18_cifar.py 0 → 100644
View file @ 85529f35
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=18,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=10,
in_channels=512,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
))
openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/resnet34.py 0 → 100644
View file @ 85529f35
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=34,
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),
))
openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/resnet34_cifar.py 0 → 100644
View file @ 85529f35
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=34,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=10,
in_channels=512,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
))
openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/resnet50.py 0 → 100644
View file @ 85529f35
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
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/resnet50_cifar.py 0 → 100644
View file @ 85529f35
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=50,
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/resnet50_cifar_cutmix.py 0 → 100644
View file @ 85529f35
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=50,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='MultiLabelLinearClsHead',
num_classes=10,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0, use_soft=True)),
train_cfg=dict(
augments=dict(type='BatchCutMix', alpha=1.0, num_classes=10,
prob=1.0)))
openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/resnet50_cifar_mixup.py 0 → 100644
View file @ 85529f35
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=50,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='MultiLabelLinearClsHead',
num_classes=10,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0, use_soft=True)),
train_cfg=dict(
augments=dict(type='BatchMixup', alpha=1., num_classes=10, prob=1.)))
openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/resnet50_cutmix.py 0 → 100644
View file @ 85529f35
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='MultiLabelLinearClsHead',
num_classes=1000,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0, use_soft=True)),
train_cfg=dict(
augments=dict(
type='BatchCutMix', alpha=1.0, num_classes=1000, prob=1.0)))
openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/resnet50_label_smooth.py 0 → 100644
View file @ 85529f35
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
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, loss_weight=1.0),
topk=(1, 5),
))
openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/resnet50_mixup.py 0 → 100644
View file @ 85529f35
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='MultiLabelLinearClsHead',
num_classes=1000,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0, use_soft=True)),
train_cfg=dict(
augments=dict(type='BatchMixup', alpha=0.2, num_classes=1000,
prob=1.)))
openmmlab_test/mmclassification-speed-benchmark/configs/_base_/models/resnetv1d101.py 0 → 100644
View file @ 85529f35
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNetV1d',
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/resnetv1d152.py 0 → 100644
View file @ 85529f35
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNetV1d',
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/resnetv1d50.py 0 → 100644
View file @ 85529f35
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNetV1d',
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/resnext101_32x4d.py 0 → 100644
View file @ 85529f35
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNeXt',
depth=101,
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/resnext101_32x8d.py 0 → 100644
View file @ 85529f35
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNeXt',
depth=101,
num_stages=4,
out_indices=(3, ),
groups=32,
width_per_group=8,
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/resnext152_32x4d.py 0 → 100644
View file @ 85529f35
# 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 0 → 100644
View file @ 85529f35
# 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 0 → 100644
View file @ 85529f35
# 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 0 → 100644
View file @ 85529f35
# 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 0 → 100644
View file @ 85529f35
# 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),
))
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