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68d5bb03
编写于
7月 09, 2021
作者:
C
cuicheng01
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add MixNet and ReXNet configs
上级
2d049aea
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
1058 addition
and
9 deletion
+1058
-9
ppcls/arch/backbone/model_zoo/mixnet.py
ppcls/arch/backbone/model_zoo/mixnet.py
+2
-9
ppcls/configs/ImageNet/MixNet/MixNet_L.yaml
ppcls/configs/ImageNet/MixNet/MixNet_L.yaml
+132
-0
ppcls/configs/ImageNet/MixNet/MixNet_M.yaml
ppcls/configs/ImageNet/MixNet/MixNet_M.yaml
+132
-0
ppcls/configs/ImageNet/MixNet/MixNet_S.yaml
ppcls/configs/ImageNet/MixNet/MixNet_S.yaml
+132
-0
ppcls/configs/ImageNet/ReXNet/ReXNet_1_0.yaml
ppcls/configs/ImageNet/ReXNet/ReXNet_1_0.yaml
+132
-0
ppcls/configs/ImageNet/ReXNet/ReXNet_1_3.yaml
ppcls/configs/ImageNet/ReXNet/ReXNet_1_3.yaml
+132
-0
ppcls/configs/ImageNet/ReXNet/ReXNet_1_5.yaml
ppcls/configs/ImageNet/ReXNet/ReXNet_1_5.yaml
+132
-0
ppcls/configs/ImageNet/ReXNet/ReXNet_2_0.yaml
ppcls/configs/ImageNet/ReXNet/ReXNet_2_0.yaml
+132
-0
ppcls/configs/ImageNet/ReXNet/ReXNet_3_0.yaml
ppcls/configs/ImageNet/ReXNet/ReXNet_3_0.yaml
+132
-0
未找到文件。
ppcls/arch/backbone/model_zoo/mixnet.py
浏览文件 @
68d5bb03
...
...
@@ -780,13 +780,6 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
def
MixNet_S
(
pretrained
=
False
,
use_ssld
=
False
,
**
kwargs
):
model
=
InceptionV4DY
(
**
kwargs
)
_load_pretrained
(
pretrained
,
model
,
MODEL_URLS
[
"InceptionV4"
],
use_ssld
=
use_ssld
)
return
model
def
MixNet_S
(
**
kwargs
):
"""
MixNet-S model from 'MixConv: Mixed Depthwise Convolutional Kernels,'
https://arxiv.org/abs/1907.09595.
...
...
@@ -798,7 +791,7 @@ def MixNet_S(**kwargs):
return
model
def
MixNet_M
(
**
kwargs
):
def
MixNet_M
(
pretrained
=
False
,
use_ssld
=
False
,
**
kwargs
):
"""
MixNet-M model from 'MixConv: Mixed Depthwise Convolutional Kernels,'
https://arxiv.org/abs/1907.09595.
...
...
@@ -810,7 +803,7 @@ def MixNet_M(**kwargs):
return
model
def
MixNet_L
(
**
kwargs
):
def
MixNet_L
(
pretrained
=
False
,
use_ssld
=
False
,
**
kwargs
):
"""
MixNet-S model from 'MixConv: Mixed Depthwise Convolutional Kernels,'
https://arxiv.org/abs/1907.09595.
...
...
ppcls/configs/ImageNet/MixNet/MixNet_L.yaml
0 → 100644
浏览文件 @
68d5bb03
# global configs
Global
:
checkpoints
:
null
pretrained_model
:
null
output_dir
:
./output/
device
:
gpu
save_interval
:
1
eval_during_train
:
True
eval_interval
:
1
epochs
:
120
print_batch_step
:
10
use_visualdl
:
False
# used for static mode and model export
image_shape
:
[
3
,
224
,
224
]
save_inference_dir
:
./inference
# training model under @to_static
to_static
:
False
# model architecture
Arch
:
name
:
MixNet_L
class_num
:
1000
# loss function config for traing/eval process
Loss
:
Train
:
-
CELoss
:
weight
:
1.0
Eval
:
-
CELoss
:
weight
:
1.0
Optimizer
:
name
:
Momentum
momentum
:
0.9
lr
:
name
:
Piecewise
learning_rate
:
0.1
decay_epochs
:
[
30
,
60
,
90
]
values
:
[
0.1
,
0.01
,
0.001
,
0.0001
]
regularizer
:
name
:
'
L2'
coeff
:
0.0001
# data loader for train and eval
DataLoader
:
Train
:
dataset
:
name
:
ImageNetDataset
image_root
:
./dataset/ILSVRC2012/
cls_label_path
:
./dataset/ILSVRC2012/train_list.txt
transform_ops
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
RandCropImage
:
size
:
224
-
RandFlipImage
:
flip_code
:
1
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
sampler
:
name
:
DistributedBatchSampler
batch_size
:
64
drop_last
:
False
shuffle
:
True
loader
:
num_workers
:
4
use_shared_memory
:
True
Eval
:
dataset
:
name
:
ImageNetDataset
image_root
:
./dataset/ILSVRC2012/
cls_label_path
:
./dataset/ILSVRC2012/val_list.txt
transform_ops
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
ResizeImage
:
resize_short
:
256
-
CropImage
:
size
:
224
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
sampler
:
name
:
DistributedBatchSampler
batch_size
:
64
drop_last
:
False
shuffle
:
False
loader
:
num_workers
:
4
use_shared_memory
:
True
Infer
:
infer_imgs
:
docs/images/whl/demo.jpg
batch_size
:
10
transforms
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
ResizeImage
:
resize_short
:
256
-
CropImage
:
size
:
224
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
-
ToCHWImage
:
PostProcess
:
name
:
Topk
topk
:
5
class_id_map_file
:
ppcls/utils/imagenet1k_label_list.txt
Metric
:
Train
:
-
TopkAcc
:
topk
:
[
1
,
5
]
Eval
:
-
TopkAcc
:
topk
:
[
1
,
5
]
ppcls/configs/ImageNet/MixNet/MixNet_M.yaml
0 → 100644
浏览文件 @
68d5bb03
# global configs
Global
:
checkpoints
:
null
pretrained_model
:
null
output_dir
:
./output/
device
:
gpu
save_interval
:
1
eval_during_train
:
True
eval_interval
:
1
epochs
:
120
print_batch_step
:
10
use_visualdl
:
False
# used for static mode and model export
image_shape
:
[
3
,
224
,
224
]
save_inference_dir
:
./inference
# training model under @to_static
to_static
:
False
# model architecture
Arch
:
name
:
MixNet_M
class_num
:
1000
# loss function config for traing/eval process
Loss
:
Train
:
-
CELoss
:
weight
:
1.0
Eval
:
-
CELoss
:
weight
:
1.0
Optimizer
:
name
:
Momentum
momentum
:
0.9
lr
:
name
:
Piecewise
learning_rate
:
0.1
decay_epochs
:
[
30
,
60
,
90
]
values
:
[
0.1
,
0.01
,
0.001
,
0.0001
]
regularizer
:
name
:
'
L2'
coeff
:
0.0001
# data loader for train and eval
DataLoader
:
Train
:
dataset
:
name
:
ImageNetDataset
image_root
:
./dataset/ILSVRC2012/
cls_label_path
:
./dataset/ILSVRC2012/train_list.txt
transform_ops
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
RandCropImage
:
size
:
224
-
RandFlipImage
:
flip_code
:
1
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
sampler
:
name
:
DistributedBatchSampler
batch_size
:
64
drop_last
:
False
shuffle
:
True
loader
:
num_workers
:
4
use_shared_memory
:
True
Eval
:
dataset
:
name
:
ImageNetDataset
image_root
:
./dataset/ILSVRC2012/
cls_label_path
:
./dataset/ILSVRC2012/val_list.txt
transform_ops
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
ResizeImage
:
resize_short
:
256
-
CropImage
:
size
:
224
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
sampler
:
name
:
DistributedBatchSampler
batch_size
:
64
drop_last
:
False
shuffle
:
False
loader
:
num_workers
:
4
use_shared_memory
:
True
Infer
:
infer_imgs
:
docs/images/whl/demo.jpg
batch_size
:
10
transforms
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
ResizeImage
:
resize_short
:
256
-
CropImage
:
size
:
224
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
-
ToCHWImage
:
PostProcess
:
name
:
Topk
topk
:
5
class_id_map_file
:
ppcls/utils/imagenet1k_label_list.txt
Metric
:
Train
:
-
TopkAcc
:
topk
:
[
1
,
5
]
Eval
:
-
TopkAcc
:
topk
:
[
1
,
5
]
ppcls/configs/ImageNet/MixNet/MixNet_S.yaml
0 → 100644
浏览文件 @
68d5bb03
# global configs
Global
:
checkpoints
:
null
pretrained_model
:
null
output_dir
:
./output/
device
:
gpu
save_interval
:
1
eval_during_train
:
True
eval_interval
:
1
epochs
:
120
print_batch_step
:
10
use_visualdl
:
False
# used for static mode and model export
image_shape
:
[
3
,
224
,
224
]
save_inference_dir
:
./inference
# training model under @to_static
to_static
:
False
# model architecture
Arch
:
name
:
MixNet_S
class_num
:
1000
# loss function config for traing/eval process
Loss
:
Train
:
-
CELoss
:
weight
:
1.0
Eval
:
-
CELoss
:
weight
:
1.0
Optimizer
:
name
:
Momentum
momentum
:
0.9
lr
:
name
:
Piecewise
learning_rate
:
0.1
decay_epochs
:
[
30
,
60
,
90
]
values
:
[
0.1
,
0.01
,
0.001
,
0.0001
]
regularizer
:
name
:
'
L2'
coeff
:
0.0001
# data loader for train and eval
DataLoader
:
Train
:
dataset
:
name
:
ImageNetDataset
image_root
:
./dataset/ILSVRC2012/
cls_label_path
:
./dataset/ILSVRC2012/train_list.txt
transform_ops
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
RandCropImage
:
size
:
224
-
RandFlipImage
:
flip_code
:
1
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
sampler
:
name
:
DistributedBatchSampler
batch_size
:
64
drop_last
:
False
shuffle
:
True
loader
:
num_workers
:
4
use_shared_memory
:
True
Eval
:
dataset
:
name
:
ImageNetDataset
image_root
:
./dataset/ILSVRC2012/
cls_label_path
:
./dataset/ILSVRC2012/val_list.txt
transform_ops
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
ResizeImage
:
resize_short
:
256
-
CropImage
:
size
:
224
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
sampler
:
name
:
DistributedBatchSampler
batch_size
:
64
drop_last
:
False
shuffle
:
False
loader
:
num_workers
:
4
use_shared_memory
:
True
Infer
:
infer_imgs
:
docs/images/whl/demo.jpg
batch_size
:
10
transforms
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
ResizeImage
:
resize_short
:
256
-
CropImage
:
size
:
224
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
-
ToCHWImage
:
PostProcess
:
name
:
Topk
topk
:
5
class_id_map_file
:
ppcls/utils/imagenet1k_label_list.txt
Metric
:
Train
:
-
TopkAcc
:
topk
:
[
1
,
5
]
Eval
:
-
TopkAcc
:
topk
:
[
1
,
5
]
ppcls/configs/ImageNet/ReXNet/ReXNet_1_0.yaml
0 → 100644
浏览文件 @
68d5bb03
# global configs
Global
:
checkpoints
:
null
pretrained_model
:
null
output_dir
:
./output/
device
:
gpu
save_interval
:
1
eval_during_train
:
True
eval_interval
:
1
epochs
:
120
print_batch_step
:
10
use_visualdl
:
False
# used for static mode and model export
image_shape
:
[
3
,
224
,
224
]
save_inference_dir
:
./inference
# training model under @to_static
to_static
:
False
# model architecture
Arch
:
name
:
ReXNet_1_0
class_num
:
1000
# loss function config for traing/eval process
Loss
:
Train
:
-
CELoss
:
weight
:
1.0
Eval
:
-
CELoss
:
weight
:
1.0
Optimizer
:
name
:
Momentum
momentum
:
0.9
lr
:
name
:
Piecewise
learning_rate
:
0.1
decay_epochs
:
[
30
,
60
,
90
]
values
:
[
0.1
,
0.01
,
0.001
,
0.0001
]
regularizer
:
name
:
'
L2'
coeff
:
0.0001
# data loader for train and eval
DataLoader
:
Train
:
dataset
:
name
:
ImageNetDataset
image_root
:
./dataset/ILSVRC2012/
cls_label_path
:
./dataset/ILSVRC2012/train_list.txt
transform_ops
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
RandCropImage
:
size
:
224
-
RandFlipImage
:
flip_code
:
1
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
sampler
:
name
:
DistributedBatchSampler
batch_size
:
64
drop_last
:
False
shuffle
:
True
loader
:
num_workers
:
4
use_shared_memory
:
True
Eval
:
dataset
:
name
:
ImageNetDataset
image_root
:
./dataset/ILSVRC2012/
cls_label_path
:
./dataset/ILSVRC2012/val_list.txt
transform_ops
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
ResizeImage
:
resize_short
:
256
-
CropImage
:
size
:
224
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
sampler
:
name
:
DistributedBatchSampler
batch_size
:
64
drop_last
:
False
shuffle
:
False
loader
:
num_workers
:
4
use_shared_memory
:
True
Infer
:
infer_imgs
:
docs/images/whl/demo.jpg
batch_size
:
10
transforms
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
ResizeImage
:
resize_short
:
256
-
CropImage
:
size
:
224
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
-
ToCHWImage
:
PostProcess
:
name
:
Topk
topk
:
5
class_id_map_file
:
ppcls/utils/imagenet1k_label_list.txt
Metric
:
Train
:
-
TopkAcc
:
topk
:
[
1
,
5
]
Eval
:
-
TopkAcc
:
topk
:
[
1
,
5
]
ppcls/configs/ImageNet/ReXNet/ReXNet_1_3.yaml
0 → 100644
浏览文件 @
68d5bb03
# global configs
Global
:
checkpoints
:
null
pretrained_model
:
null
output_dir
:
./output/
device
:
gpu
save_interval
:
1
eval_during_train
:
True
eval_interval
:
1
epochs
:
120
print_batch_step
:
10
use_visualdl
:
False
# used for static mode and model export
image_shape
:
[
3
,
224
,
224
]
save_inference_dir
:
./inference
# training model under @to_static
to_static
:
False
# model architecture
Arch
:
name
:
ReXNet_1_3
class_num
:
1000
# loss function config for traing/eval process
Loss
:
Train
:
-
CELoss
:
weight
:
1.0
Eval
:
-
CELoss
:
weight
:
1.0
Optimizer
:
name
:
Momentum
momentum
:
0.9
lr
:
name
:
Piecewise
learning_rate
:
0.1
decay_epochs
:
[
30
,
60
,
90
]
values
:
[
0.1
,
0.01
,
0.001
,
0.0001
]
regularizer
:
name
:
'
L2'
coeff
:
0.0001
# data loader for train and eval
DataLoader
:
Train
:
dataset
:
name
:
ImageNetDataset
image_root
:
./dataset/ILSVRC2012/
cls_label_path
:
./dataset/ILSVRC2012/train_list.txt
transform_ops
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
RandCropImage
:
size
:
224
-
RandFlipImage
:
flip_code
:
1
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
sampler
:
name
:
DistributedBatchSampler
batch_size
:
64
drop_last
:
False
shuffle
:
True
loader
:
num_workers
:
4
use_shared_memory
:
True
Eval
:
dataset
:
name
:
ImageNetDataset
image_root
:
./dataset/ILSVRC2012/
cls_label_path
:
./dataset/ILSVRC2012/val_list.txt
transform_ops
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
ResizeImage
:
resize_short
:
256
-
CropImage
:
size
:
224
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
sampler
:
name
:
DistributedBatchSampler
batch_size
:
64
drop_last
:
False
shuffle
:
False
loader
:
num_workers
:
4
use_shared_memory
:
True
Infer
:
infer_imgs
:
docs/images/whl/demo.jpg
batch_size
:
10
transforms
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
ResizeImage
:
resize_short
:
256
-
CropImage
:
size
:
224
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
-
ToCHWImage
:
PostProcess
:
name
:
Topk
topk
:
5
class_id_map_file
:
ppcls/utils/imagenet1k_label_list.txt
Metric
:
Train
:
-
TopkAcc
:
topk
:
[
1
,
5
]
Eval
:
-
TopkAcc
:
topk
:
[
1
,
5
]
ppcls/configs/ImageNet/ReXNet/ReXNet_1_5.yaml
0 → 100644
浏览文件 @
68d5bb03
# global configs
Global
:
checkpoints
:
null
pretrained_model
:
null
output_dir
:
./output/
device
:
gpu
save_interval
:
1
eval_during_train
:
True
eval_interval
:
1
epochs
:
120
print_batch_step
:
10
use_visualdl
:
False
# used for static mode and model export
image_shape
:
[
3
,
224
,
224
]
save_inference_dir
:
./inference
# training model under @to_static
to_static
:
False
# model architecture
Arch
:
name
:
ReXNet_1_5
class_num
:
1000
# loss function config for traing/eval process
Loss
:
Train
:
-
CELoss
:
weight
:
1.0
Eval
:
-
CELoss
:
weight
:
1.0
Optimizer
:
name
:
Momentum
momentum
:
0.9
lr
:
name
:
Piecewise
learning_rate
:
0.1
decay_epochs
:
[
30
,
60
,
90
]
values
:
[
0.1
,
0.01
,
0.001
,
0.0001
]
regularizer
:
name
:
'
L2'
coeff
:
0.0001
# data loader for train and eval
DataLoader
:
Train
:
dataset
:
name
:
ImageNetDataset
image_root
:
./dataset/ILSVRC2012/
cls_label_path
:
./dataset/ILSVRC2012/train_list.txt
transform_ops
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
RandCropImage
:
size
:
224
-
RandFlipImage
:
flip_code
:
1
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
sampler
:
name
:
DistributedBatchSampler
batch_size
:
64
drop_last
:
False
shuffle
:
True
loader
:
num_workers
:
4
use_shared_memory
:
True
Eval
:
dataset
:
name
:
ImageNetDataset
image_root
:
./dataset/ILSVRC2012/
cls_label_path
:
./dataset/ILSVRC2012/val_list.txt
transform_ops
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
ResizeImage
:
resize_short
:
256
-
CropImage
:
size
:
224
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
sampler
:
name
:
DistributedBatchSampler
batch_size
:
64
drop_last
:
False
shuffle
:
False
loader
:
num_workers
:
4
use_shared_memory
:
True
Infer
:
infer_imgs
:
docs/images/whl/demo.jpg
batch_size
:
10
transforms
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
ResizeImage
:
resize_short
:
256
-
CropImage
:
size
:
224
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
-
ToCHWImage
:
PostProcess
:
name
:
Topk
topk
:
5
class_id_map_file
:
ppcls/utils/imagenet1k_label_list.txt
Metric
:
Train
:
-
TopkAcc
:
topk
:
[
1
,
5
]
Eval
:
-
TopkAcc
:
topk
:
[
1
,
5
]
ppcls/configs/ImageNet/ReXNet/ReXNet_2_0.yaml
0 → 100644
浏览文件 @
68d5bb03
# global configs
Global
:
checkpoints
:
null
pretrained_model
:
null
output_dir
:
./output/
device
:
gpu
save_interval
:
1
eval_during_train
:
True
eval_interval
:
1
epochs
:
120
print_batch_step
:
10
use_visualdl
:
False
# used for static mode and model export
image_shape
:
[
3
,
224
,
224
]
save_inference_dir
:
./inference
# training model under @to_static
to_static
:
False
# model architecture
Arch
:
name
:
ReXNet_2_0
class_num
:
1000
# loss function config for traing/eval process
Loss
:
Train
:
-
CELoss
:
weight
:
1.0
Eval
:
-
CELoss
:
weight
:
1.0
Optimizer
:
name
:
Momentum
momentum
:
0.9
lr
:
name
:
Piecewise
learning_rate
:
0.1
decay_epochs
:
[
30
,
60
,
90
]
values
:
[
0.1
,
0.01
,
0.001
,
0.0001
]
regularizer
:
name
:
'
L2'
coeff
:
0.0001
# data loader for train and eval
DataLoader
:
Train
:
dataset
:
name
:
ImageNetDataset
image_root
:
./dataset/ILSVRC2012/
cls_label_path
:
./dataset/ILSVRC2012/train_list.txt
transform_ops
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
RandCropImage
:
size
:
224
-
RandFlipImage
:
flip_code
:
1
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
sampler
:
name
:
DistributedBatchSampler
batch_size
:
64
drop_last
:
False
shuffle
:
True
loader
:
num_workers
:
4
use_shared_memory
:
True
Eval
:
dataset
:
name
:
ImageNetDataset
image_root
:
./dataset/ILSVRC2012/
cls_label_path
:
./dataset/ILSVRC2012/val_list.txt
transform_ops
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
ResizeImage
:
resize_short
:
256
-
CropImage
:
size
:
224
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
sampler
:
name
:
DistributedBatchSampler
batch_size
:
64
drop_last
:
False
shuffle
:
False
loader
:
num_workers
:
4
use_shared_memory
:
True
Infer
:
infer_imgs
:
docs/images/whl/demo.jpg
batch_size
:
10
transforms
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
ResizeImage
:
resize_short
:
256
-
CropImage
:
size
:
224
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
-
ToCHWImage
:
PostProcess
:
name
:
Topk
topk
:
5
class_id_map_file
:
ppcls/utils/imagenet1k_label_list.txt
Metric
:
Train
:
-
TopkAcc
:
topk
:
[
1
,
5
]
Eval
:
-
TopkAcc
:
topk
:
[
1
,
5
]
ppcls/configs/ImageNet/ReXNet/ReXNet_3_0.yaml
0 → 100644
浏览文件 @
68d5bb03
# global configs
Global
:
checkpoints
:
null
pretrained_model
:
null
output_dir
:
./output/
device
:
gpu
save_interval
:
1
eval_during_train
:
True
eval_interval
:
1
epochs
:
120
print_batch_step
:
10
use_visualdl
:
False
# used for static mode and model export
image_shape
:
[
3
,
224
,
224
]
save_inference_dir
:
./inference
# training model under @to_static
to_static
:
False
# model architecture
Arch
:
name
:
ReXNet_3_0
class_num
:
1000
# loss function config for traing/eval process
Loss
:
Train
:
-
CELoss
:
weight
:
1.0
Eval
:
-
CELoss
:
weight
:
1.0
Optimizer
:
name
:
Momentum
momentum
:
0.9
lr
:
name
:
Piecewise
learning_rate
:
0.1
decay_epochs
:
[
30
,
60
,
90
]
values
:
[
0.1
,
0.01
,
0.001
,
0.0001
]
regularizer
:
name
:
'
L2'
coeff
:
0.0001
# data loader for train and eval
DataLoader
:
Train
:
dataset
:
name
:
ImageNetDataset
image_root
:
./dataset/ILSVRC2012/
cls_label_path
:
./dataset/ILSVRC2012/train_list.txt
transform_ops
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
RandCropImage
:
size
:
224
-
RandFlipImage
:
flip_code
:
1
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
sampler
:
name
:
DistributedBatchSampler
batch_size
:
64
drop_last
:
False
shuffle
:
True
loader
:
num_workers
:
4
use_shared_memory
:
True
Eval
:
dataset
:
name
:
ImageNetDataset
image_root
:
./dataset/ILSVRC2012/
cls_label_path
:
./dataset/ILSVRC2012/val_list.txt
transform_ops
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
ResizeImage
:
resize_short
:
256
-
CropImage
:
size
:
224
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
sampler
:
name
:
DistributedBatchSampler
batch_size
:
64
drop_last
:
False
shuffle
:
False
loader
:
num_workers
:
4
use_shared_memory
:
True
Infer
:
infer_imgs
:
docs/images/whl/demo.jpg
batch_size
:
10
transforms
:
-
DecodeImage
:
to_rgb
:
True
channel_first
:
False
-
ResizeImage
:
resize_short
:
256
-
CropImage
:
size
:
224
-
NormalizeImage
:
scale
:
1.0/255.0
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
-
ToCHWImage
:
PostProcess
:
name
:
Topk
topk
:
5
class_id_map_file
:
ppcls/utils/imagenet1k_label_list.txt
Metric
:
Train
:
-
TopkAcc
:
topk
:
[
1
,
5
]
Eval
:
-
TopkAcc
:
topk
:
[
1
,
5
]
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