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"examples/tensorrt_llm/configs/agg_router.yaml" did not exist on "8891aa0cc8d1bad420890a60c5d63d253834ee60"
Commit 495d9ed9 authored by limm's avatar limm
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add part code

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configs/_base_/models/resnet34_gem.py 0 → 100644
View file @ 495d9ed9
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=34,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GeneralizedMeanPooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=512,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/resnet50.py 0 → 100644
View file @ 495d9ed9
# 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),
))
configs/_base_/models/resnet50_cifar.py 0 → 100644
View file @ 495d9ed9
# 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),
))
configs/_base_/models/resnet50_cifar_cutmix.py 0 → 100644
View file @ 495d9ed9
# 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)))
configs/_base_/models/resnet50_cifar_mixup.py 0 → 100644
View file @ 495d9ed9
# 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='Mixup', alpha=1.)),
)
configs/_base_/models/resnet50_cutmix.py 0 → 100644
View file @ 495d9ed9
# 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)))
configs/_base_/models/resnet50_label_smooth.py 0 → 100644
View file @ 495d9ed9
# 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),
))
configs/_base_/models/resnet50_mixup.py 0 → 100644
View file @ 495d9ed9
# 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='Mixup', alpha=0.2)),
)
configs/_base_/models/resnetv1c50.py 0 → 100644
View file @ 495d9ed9
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNetV1c',
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),
))
configs/_base_/models/resnetv1d101.py 0 → 100644
View file @ 495d9ed9
# 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),
))
configs/_base_/models/resnetv1d152.py 0 → 100644
View file @ 495d9ed9
# 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),
))
configs/_base_/models/resnetv1d50.py 0 → 100644
View file @ 495d9ed9
# 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),
))
configs/_base_/models/resnext101_32x4d.py 0 → 100644
View file @ 495d9ed9
# 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),
))
configs/_base_/models/resnext101_32x8d.py 0 → 100644
View file @ 495d9ed9
# 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),
))
configs/_base_/models/resnext152_32x4d.py 0 → 100644
View file @ 495d9ed9
# 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),
))
configs/_base_/models/resnext50_32x4d.py 0 → 100644
View file @ 495d9ed9
# 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),
))
configs/_base_/models/revvit/revvit-base.py 0 → 100644
View file @ 495d9ed9
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='RevVisionTransformer',
arch='deit-base',
img_size=224,
patch_size=16,
out_type='avg_featmap',
),
neck=None,
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=1536,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
),
init_cfg=[
dict(type='TruncNormal', layer='Linear', std=.02),
dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
],
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0)
]),
)
configs/_base_/models/revvit/revvit-small.py 0 → 100644
View file @ 495d9ed9
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='RevVisionTransformer',
arch='deit-small',
img_size=224,
patch_size=16,
out_type='avg_featmap',
),
neck=None,
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=768,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
),
init_cfg=[
dict(type='TruncNormal', layer='Linear', std=.02),
dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
],
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0)
]),
)
configs/_base_/models/seresnet101.py 0 → 100644
View file @ 495d9ed9
# 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),
))
configs/_base_/models/seresnet50.py 0 → 100644
View file @ 495d9ed9
# 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),
))
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