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PaddleDetection
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PaddleDetection
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8fbdf1cb
编写于
8月 18, 2022
作者:
W
Wenyu
提交者:
GitHub
8月 18, 2022
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电子邮件补丁
差异文件
add ppyoloe plus cfgs (#6686)
上级
0f75cb6c
变更
5
隐藏空白更改
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并排
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5 changed file
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160 addition
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+160
-0
deploy/auto_compression/configs/ppyoloe_plus_l_qat_dis.yaml
deploy/auto_compression/configs/ppyoloe_plus_l_qat_dis.yaml
+33
-0
deploy/auto_compression/configs/ppyoloe_plus_m_qat_dis.yaml
deploy/auto_compression/configs/ppyoloe_plus_m_qat_dis.yaml
+33
-0
deploy/auto_compression/configs/ppyoloe_plus_reader.yml
deploy/auto_compression/configs/ppyoloe_plus_reader.yml
+28
-0
deploy/auto_compression/configs/ppyoloe_plus_s_qat_dis.yaml
deploy/auto_compression/configs/ppyoloe_plus_s_qat_dis.yaml
+33
-0
deploy/auto_compression/configs/ppyoloe_plus_x_qat_dis.yaml
deploy/auto_compression/configs/ppyoloe_plus_x_qat_dis.yaml
+33
-0
未找到文件。
deploy/auto_compression/configs/ppyoloe_plus_l_qat_dis.yaml
0 → 100644
浏览文件 @
8fbdf1cb
Global
:
reader_config
:
configs/ppyoloe_plus_reader.yml
input_list
:
[
'
image'
,
'
scale_factor'
]
arch
:
YOLO
Evaluation
:
True
model_dir
:
./ppyoloe_plus_crn_l_80e_coco
model_filename
:
model.pdmodel
params_filename
:
model.pdiparams
Distillation
:
alpha
:
1.0
loss
:
soft_label
Quantization
:
use_pact
:
true
activation_quantize_type
:
'
moving_average_abs_max'
quantize_op_types
:
-
conv2d
-
depthwise_conv2d
TrainConfig
:
train_iter
:
5000
eval_iter
:
1000
learning_rate
:
type
:
CosineAnnealingDecay
learning_rate
:
0.00003
T_max
:
6000
optimizer_builder
:
optimizer
:
type
:
SGD
weight_decay
:
4.0e-05
deploy/auto_compression/configs/ppyoloe_plus_m_qat_dis.yaml
0 → 100644
浏览文件 @
8fbdf1cb
Global
:
reader_config
:
configs/ppyoloe_plus_reader.yml
input_list
:
[
'
image'
,
'
scale_factor'
]
arch
:
YOLO
Evaluation
:
True
model_dir
:
./ppyoloe_plus_crn_m_80e_coco
model_filename
:
model.pdmodel
params_filename
:
model.pdiparams
Distillation
:
alpha
:
1.0
loss
:
soft_label
Quantization
:
use_pact
:
true
activation_quantize_type
:
'
moving_average_abs_max'
quantize_op_types
:
-
conv2d
-
depthwise_conv2d
TrainConfig
:
train_iter
:
5000
eval_iter
:
1000
learning_rate
:
type
:
CosineAnnealingDecay
learning_rate
:
0.00003
T_max
:
6000
optimizer_builder
:
optimizer
:
type
:
SGD
weight_decay
:
4.0e-05
deploy/auto_compression/configs/ppyoloe_plus_reader.yml
0 → 100644
浏览文件 @
8fbdf1cb
metric
:
COCO
num_classes
:
80
# Datset configuration
TrainDataset
:
!COCODataSet
image_dir
:
train2017
anno_path
:
annotations/instances_train2017.json
dataset_dir
:
dataset/coco/
EvalDataset
:
!COCODataSet
image_dir
:
val2017
anno_path
:
annotations/instances_val2017.json
dataset_dir
:
dataset/coco/
worker_num
:
0
# preprocess reader in test
EvalReader
:
sample_transforms
:
-
Decode
:
{}
-
Resize
:
{
target_size
:
[
640
,
640
],
keep_ratio
:
False
,
interp
:
2
}
-
NormalizeImage
:
{
mean
:
[
0.
,
0.
,
0.
],
std
:
[
1.
,
1.
,
1.
],
is_scale
:
True
}
-
Permute
:
{}
batch_size
:
4
deploy/auto_compression/configs/ppyoloe_plus_s_qat_dis.yaml
0 → 100644
浏览文件 @
8fbdf1cb
Global
:
reader_config
:
configs/ppyoloe_plus_reader.yml
input_list
:
[
'
image'
,
'
scale_factor'
]
arch
:
YOLO
Evaluation
:
True
model_dir
:
./ppyoloe_plus_crn_s_80e_coco
model_filename
:
model.pdmodel
params_filename
:
model.pdiparams
Distillation
:
alpha
:
1.0
loss
:
soft_label
Quantization
:
use_pact
:
true
activation_quantize_type
:
'
moving_average_abs_max'
quantize_op_types
:
-
conv2d
-
depthwise_conv2d
TrainConfig
:
train_iter
:
5000
eval_iter
:
1000
learning_rate
:
type
:
CosineAnnealingDecay
learning_rate
:
0.00003
T_max
:
6000
optimizer_builder
:
optimizer
:
type
:
SGD
weight_decay
:
4.0e-05
deploy/auto_compression/configs/ppyoloe_plus_x_qat_dis.yaml
0 → 100644
浏览文件 @
8fbdf1cb
Global
:
reader_config
:
configs/ppyoloe_plus_reader.yml
input_list
:
[
'
image'
,
'
scale_factor'
]
arch
:
YOLO
Evaluation
:
True
model_dir
:
./ppyoloe_plus_crn_x_80e_coco
model_filename
:
model.pdmodel
params_filename
:
model.pdiparams
Distillation
:
alpha
:
1.0
loss
:
soft_label
Quantization
:
use_pact
:
true
activation_quantize_type
:
'
moving_average_abs_max'
quantize_op_types
:
-
conv2d
-
depthwise_conv2d
TrainConfig
:
train_iter
:
5000
eval_iter
:
1000
learning_rate
:
type
:
CosineAnnealingDecay
learning_rate
:
0.00003
T_max
:
6000
optimizer_builder
:
optimizer
:
type
:
SGD
weight_decay
:
4.0e-05
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