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a46a0610
编写于
6月 01, 2023
作者:
Z
zhangyubo0722
提交者:
GitHub
6月 01, 2023
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[WIP]Benchmark 2q add PP-OCRv4_rec ultra config (#10067)
* add PP-OCRv4_rec ultra config * modify prepare
上级
0e9c6630
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configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_ampO2_ultra.yml
configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_ampO2_ultra.yml
+140
-0
configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_fp32_ultra.yml
configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_fp32_ultra.yml
+138
-0
configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_hgnet_ampO2_ultra.yml
configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_hgnet_ampO2_ultra.yml
+139
-0
configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_hgnet_fp32_ultra.yml
configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_hgnet_fp32_ultra.yml
+137
-0
test_tipc/configs/ch_PP-OCRv4_mobile_rec_ampO2_ultra/train_infer_python.txt
...ch_PP-OCRv4_mobile_rec_ampO2_ultra/train_infer_python.txt
+61
-0
test_tipc/configs/ch_PP-OCRv4_mobile_rec_fp32_ultra/train_infer_python.txt
.../ch_PP-OCRv4_mobile_rec_fp32_ultra/train_infer_python.txt
+61
-0
test_tipc/configs/ch_PP-OCRv4_server_rec_ampO2_ultra/train_infer_python.txt
...ch_PP-OCRv4_server_rec_ampO2_ultra/train_infer_python.txt
+60
-0
test_tipc/configs/ch_PP-OCRv4_server_rec_fp32_ultra/train_infer_python.txt
.../ch_PP-OCRv4_server_rec_fp32_ultra/train_infer_python.txt
+61
-0
test_tipc/prepare.sh
test_tipc/prepare.sh
+20
-0
未找到文件。
configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_ampO2_ultra.yml
0 → 100644
浏览文件 @
a46a0610
Global
:
debug
:
false
use_gpu
:
true
epoch_num
:
200
log_smooth_window
:
20
print_batch_step
:
10
save_model_dir
:
./output/rec_ppocr_v4
save_epoch_step
:
10
eval_batch_step
:
[
0
,
2000
]
cal_metric_during_train
:
true
pretrained_model
:
checkpoints
:
save_inference_dir
:
use_visualdl
:
false
infer_img
:
doc/imgs_words/ch/word_1.jpg
character_dict_path
:
ppocr/utils/ppocr_keys_v1.txt
max_text_length
:
&max_text_length
25
infer_mode
:
false
use_space_char
:
true
distributed
:
true
save_res_path
:
./output/rec/predicts_ppocrv3.txt
use_amp
:
True
amp_level
:
O2
Optimizer
:
name
:
Adam
beta1
:
0.9
beta2
:
0.999
lr
:
name
:
Cosine
learning_rate
:
0.001
warmup_epoch
:
5
regularizer
:
name
:
L2
factor
:
3.0e-05
Architecture
:
model_type
:
rec
algorithm
:
SVTR_LCNet
Transform
:
Backbone
:
name
:
PPLCNetV3
scale
:
0.95
Head
:
name
:
MultiHead
head_list
:
-
CTCHead
:
Neck
:
name
:
svtr
dims
:
120
depth
:
2
hidden_dims
:
120
kernel_size
:
[
1
,
3
]
use_guide
:
True
Head
:
fc_decay
:
0.00001
-
NRTRHead
:
nrtr_dim
:
384
max_text_length
:
*max_text_length
Loss
:
name
:
MultiLoss
loss_config_list
:
-
CTCLoss
:
-
NRTRLoss
:
PostProcess
:
name
:
CTCLabelDecode
Metric
:
name
:
RecMetric
main_indicator
:
acc
Train
:
dataset
:
name
:
MultiScaleDataSet
ds_width
:
false
data_dir
:
./train_data/
ext_op_transform_idx
:
1
label_file_list
:
-
./train_data/train_list.txt
transforms
:
-
DecodeImage
:
img_mode
:
BGR
channel_first
:
false
-
RecConAug
:
prob
:
0.5
ext_data_num
:
2
image_shape
:
[
48
,
320
,
3
]
max_text_length
:
*max_text_length
-
RecAug
:
-
MultiLabelEncode
:
gtc_encode
:
NRTRLabelEncode
-
KeepKeys
:
keep_keys
:
-
image
-
label_ctc
-
label_gtc
-
length
-
valid_ratio
sampler
:
name
:
MultiScaleSampler
scales
:
[[
320
,
32
],
[
320
,
48
],
[
320
,
64
]]
first_bs
:
&bs
384
fix_bs
:
false
divided_factor
:
[
8
,
16
]
# w, h
is_training
:
True
loader
:
shuffle
:
true
batch_size_per_card
:
*bs
drop_last
:
true
num_workers
:
16
Eval
:
dataset
:
name
:
SimpleDataSet
data_dir
:
./train_data
label_file_list
:
-
./train_data/val_list.txt
transforms
:
-
DecodeImage
:
img_mode
:
BGR
channel_first
:
false
-
MultiLabelEncode
:
gtc_encode
:
NRTRLabelEncode
-
RecResizeImg
:
image_shape
:
[
3
,
48
,
320
]
-
KeepKeys
:
keep_keys
:
-
image
-
label_ctc
-
label_gtc
-
length
-
valid_ratio
loader
:
shuffle
:
false
drop_last
:
false
batch_size_per_card
:
128
num_workers
:
16
configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_fp32_ultra.yml
0 → 100644
浏览文件 @
a46a0610
Global
:
debug
:
false
use_gpu
:
true
epoch_num
:
200
log_smooth_window
:
20
print_batch_step
:
10
save_model_dir
:
./output/rec_ppocr_v4
save_epoch_step
:
10
eval_batch_step
:
[
0
,
2000
]
cal_metric_during_train
:
true
pretrained_model
:
checkpoints
:
save_inference_dir
:
use_visualdl
:
false
infer_img
:
doc/imgs_words/ch/word_1.jpg
character_dict_path
:
ppocr/utils/ppocr_keys_v1.txt
max_text_length
:
&max_text_length
25
infer_mode
:
false
use_space_char
:
true
distributed
:
true
save_res_path
:
./output/rec/predicts_ppocrv3.txt
Optimizer
:
name
:
Adam
beta1
:
0.9
beta2
:
0.999
lr
:
name
:
Cosine
learning_rate
:
0.001
warmup_epoch
:
5
regularizer
:
name
:
L2
factor
:
3.0e-05
Architecture
:
model_type
:
rec
algorithm
:
SVTR_LCNet
Transform
:
Backbone
:
name
:
PPLCNetV3
scale
:
0.95
Head
:
name
:
MultiHead
head_list
:
-
CTCHead
:
Neck
:
name
:
svtr
dims
:
120
depth
:
2
hidden_dims
:
120
kernel_size
:
[
1
,
3
]
use_guide
:
True
Head
:
fc_decay
:
0.00001
-
NRTRHead
:
nrtr_dim
:
384
max_text_length
:
*max_text_length
Loss
:
name
:
MultiLoss
loss_config_list
:
-
CTCLoss
:
-
NRTRLoss
:
PostProcess
:
name
:
CTCLabelDecode
Metric
:
name
:
RecMetric
main_indicator
:
acc
Train
:
dataset
:
name
:
MultiScaleDataSet
ds_width
:
false
data_dir
:
./train_data/
ext_op_transform_idx
:
1
label_file_list
:
-
./train_data/train_list.txt
transforms
:
-
DecodeImage
:
img_mode
:
BGR
channel_first
:
false
-
RecConAug
:
prob
:
0.5
ext_data_num
:
2
image_shape
:
[
48
,
320
,
3
]
max_text_length
:
*max_text_length
-
RecAug
:
-
MultiLabelEncode
:
gtc_encode
:
NRTRLabelEncode
-
KeepKeys
:
keep_keys
:
-
image
-
label_ctc
-
label_gtc
-
length
-
valid_ratio
sampler
:
name
:
MultiScaleSampler
scales
:
[[
320
,
32
],
[
320
,
48
],
[
320
,
64
]]
first_bs
:
&bs
192
fix_bs
:
false
divided_factor
:
[
8
,
16
]
# w, h
is_training
:
True
loader
:
shuffle
:
true
batch_size_per_card
:
*bs
drop_last
:
true
num_workers
:
16
Eval
:
dataset
:
name
:
SimpleDataSet
data_dir
:
./train_data
label_file_list
:
-
./train_data/val_list.txt
transforms
:
-
DecodeImage
:
img_mode
:
BGR
channel_first
:
false
-
MultiLabelEncode
:
gtc_encode
:
NRTRLabelEncode
-
RecResizeImg
:
image_shape
:
[
3
,
48
,
320
]
-
KeepKeys
:
keep_keys
:
-
image
-
label_ctc
-
label_gtc
-
length
-
valid_ratio
loader
:
shuffle
:
false
drop_last
:
false
batch_size_per_card
:
128
num_workers
:
16
configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_hgnet_ampO2_ultra.yml
0 → 100644
浏览文件 @
a46a0610
Global
:
debug
:
false
use_gpu
:
true
epoch_num
:
200
log_smooth_window
:
20
print_batch_step
:
10
save_model_dir
:
./output/rec_ppocr_v4_hgnet
save_epoch_step
:
10
eval_batch_step
:
[
0
,
2000
]
cal_metric_during_train
:
true
pretrained_model
:
checkpoints
:
save_inference_dir
:
use_visualdl
:
false
infer_img
:
doc/imgs_words/ch/word_1.jpg
character_dict_path
:
ppocr/utils/ppocr_keys_v1.txt
max_text_length
:
&max_text_length
25
infer_mode
:
false
use_space_char
:
true
distributed
:
true
save_res_path
:
./output/rec/predicts_ppocrv3.txt
use_amp
:
True
amp_level
:
O2
Optimizer
:
name
:
Adam
beta1
:
0.9
beta2
:
0.999
lr
:
name
:
Cosine
learning_rate
:
0.001
warmup_epoch
:
5
regularizer
:
name
:
L2
factor
:
3.0e-05
Architecture
:
model_type
:
rec
algorithm
:
SVTR_HGNet
Transform
:
Backbone
:
name
:
PPHGNet_small
Head
:
name
:
MultiHead
head_list
:
-
CTCHead
:
Neck
:
name
:
svtr
dims
:
120
depth
:
2
hidden_dims
:
120
kernel_size
:
[
1
,
3
]
use_guide
:
True
Head
:
fc_decay
:
0.00001
-
NRTRHead
:
nrtr_dim
:
384
max_text_length
:
*max_text_length
Loss
:
name
:
MultiLoss
loss_config_list
:
-
CTCLoss
:
-
NRTRLoss
:
PostProcess
:
name
:
CTCLabelDecode
Metric
:
name
:
RecMetric
main_indicator
:
acc
Train
:
dataset
:
name
:
MultiScaleDataSet
ds_width
:
false
data_dir
:
./train_data/
ext_op_transform_idx
:
1
label_file_list
:
-
./train_data/train_list.txt
transforms
:
-
DecodeImage
:
img_mode
:
BGR
channel_first
:
false
-
RecConAug
:
prob
:
0.5
ext_data_num
:
2
image_shape
:
[
48
,
320
,
3
]
max_text_length
:
*max_text_length
-
RecAug
:
-
MultiLabelEncode
:
gtc_encode
:
NRTRLabelEncode
-
KeepKeys
:
keep_keys
:
-
image
-
label_ctc
-
label_gtc
-
length
-
valid_ratio
sampler
:
name
:
MultiScaleSampler
scales
:
[[
320
,
32
],
[
320
,
48
],
[
320
,
64
]]
first_bs
:
&bs
256
fix_bs
:
false
divided_factor
:
[
8
,
16
]
# w, h
is_training
:
True
loader
:
shuffle
:
true
batch_size_per_card
:
*bs
drop_last
:
true
num_workers
:
16
Eval
:
dataset
:
name
:
SimpleDataSet
data_dir
:
./train_data
label_file_list
:
-
./train_data/val_list.txt
transforms
:
-
DecodeImage
:
img_mode
:
BGR
channel_first
:
false
-
MultiLabelEncode
:
gtc_encode
:
NRTRLabelEncode
-
RecResizeImg
:
image_shape
:
[
3
,
48
,
320
]
-
KeepKeys
:
keep_keys
:
-
image
-
label_ctc
-
label_gtc
-
length
-
valid_ratio
loader
:
shuffle
:
false
drop_last
:
false
batch_size_per_card
:
128
num_workers
:
16
configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_hgnet_fp32_ultra.yml
0 → 100644
浏览文件 @
a46a0610
Global
:
debug
:
false
use_gpu
:
true
epoch_num
:
200
log_smooth_window
:
20
print_batch_step
:
10
save_model_dir
:
./output/rec_ppocr_v4_hgnet
save_epoch_step
:
10
eval_batch_step
:
[
0
,
2000
]
cal_metric_during_train
:
true
pretrained_model
:
checkpoints
:
save_inference_dir
:
use_visualdl
:
false
infer_img
:
doc/imgs_words/ch/word_1.jpg
character_dict_path
:
ppocr/utils/ppocr_keys_v1.txt
max_text_length
:
&max_text_length
25
infer_mode
:
false
use_space_char
:
true
distributed
:
true
save_res_path
:
./output/rec/predicts_ppocrv3.txt
Optimizer
:
name
:
Adam
beta1
:
0.9
beta2
:
0.999
lr
:
name
:
Cosine
learning_rate
:
0.001
warmup_epoch
:
5
regularizer
:
name
:
L2
factor
:
3.0e-05
Architecture
:
model_type
:
rec
algorithm
:
SVTR_HGNet
Transform
:
Backbone
:
name
:
PPHGNet_small
Head
:
name
:
MultiHead
head_list
:
-
CTCHead
:
Neck
:
name
:
svtr
dims
:
120
depth
:
2
hidden_dims
:
120
kernel_size
:
[
1
,
3
]
use_guide
:
True
Head
:
fc_decay
:
0.00001
-
NRTRHead
:
nrtr_dim
:
384
max_text_length
:
*max_text_length
Loss
:
name
:
MultiLoss
loss_config_list
:
-
CTCLoss
:
-
NRTRLoss
:
PostProcess
:
name
:
CTCLabelDecode
Metric
:
name
:
RecMetric
main_indicator
:
acc
Train
:
dataset
:
name
:
MultiScaleDataSet
ds_width
:
false
data_dir
:
./train_data/
ext_op_transform_idx
:
1
label_file_list
:
-
./train_data/train_list.txt
transforms
:
-
DecodeImage
:
img_mode
:
BGR
channel_first
:
false
-
RecConAug
:
prob
:
0.5
ext_data_num
:
2
image_shape
:
[
48
,
320
,
3
]
max_text_length
:
*max_text_length
-
RecAug
:
-
MultiLabelEncode
:
gtc_encode
:
NRTRLabelEncode
-
KeepKeys
:
keep_keys
:
-
image
-
label_ctc
-
label_gtc
-
length
-
valid_ratio
sampler
:
name
:
MultiScaleSampler
scales
:
[[
320
,
32
],
[
320
,
48
],
[
320
,
64
]]
first_bs
:
&bs
256
fix_bs
:
false
divided_factor
:
[
8
,
16
]
# w, h
is_training
:
True
loader
:
shuffle
:
true
batch_size_per_card
:
*bs
drop_last
:
true
num_workers
:
16
Eval
:
dataset
:
name
:
SimpleDataSet
data_dir
:
./train_data
label_file_list
:
-
./train_data/val_list.txt
transforms
:
-
DecodeImage
:
img_mode
:
BGR
channel_first
:
false
-
MultiLabelEncode
:
gtc_encode
:
NRTRLabelEncode
-
RecResizeImg
:
image_shape
:
[
3
,
48
,
320
]
-
KeepKeys
:
keep_keys
:
-
image
-
label_ctc
-
label_gtc
-
length
-
valid_ratio
loader
:
shuffle
:
false
drop_last
:
false
batch_size_per_card
:
128
num_workers
:
16
test_tipc/configs/ch_PP-OCRv4_mobile_rec_ampO2_ultra/train_infer_python.txt
0 → 100644
浏览文件 @
a46a0610
===========================train_params===========================
model_name:ch_PP-OCRv4_mobile_rec
python:python
gpu_list:0
Global.use_gpu:True|True
Global.auto_cast:fp32
Global.epoch_num:lite_train_lite_infer=3|whole_train_whole_infer=50
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=128
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./inference/rec_inference
null:null
##
trainer:norm_train
norm_train:tools/train.py -c configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_ampO2_ultra.yml -o Global.cal_metric_during_train=False Global.print_batch_step=1 Train.loader.shuffle=false Train.dataset.data_dir=./train_data/ic15_data Train.dataset.label_file_list=[./train_data/ic15_data/rec_gt_train.txt] Eval.dataset.data_dir=./train_data/ic15_data Eval.dataset.label_file_list=[./train_data/ic15_data/rec_gt_test.txt]
pact_train:null
fpgm_train:null
distill_train:null
to_static_train:Global.to_static=true
null:null
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.checkpoints:
norm_export:tools/export_model.py -c configs/rec/PP-OCRv4/ch_PP-OCRv4_rec.yml -o
quant_export:
fpgm_export:
distill_export:null
export1:null
export2:null
##
infer_model:./inference/ch_PP-OCRv4_rec_infer
infer_export:null
infer_quant:False
inference:tools/infer/predict_rec.py --rec_image_shape="3,48,320"
--use_gpu:True|False
--enable_mkldnn:False
--cpu_threads:6
--rec_batch_num:1
--use_tensorrt:False
--precision:fp32
--rec_model_dir:
--image_dir:./inference/rec_inference
null:null
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,48,320]}]
===========================train_benchmark_params==========================
batch_size:384
fp_items:fp16
epoch:1
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
===========================to_static_train_benchmark_params===========================
to_static_train:Global.to_static=true
test_tipc/configs/ch_PP-OCRv4_mobile_rec_fp32_ultra/train_infer_python.txt
0 → 100644
浏览文件 @
a46a0610
===========================train_params===========================
model_name:ch_PP-OCRv4_mobile_rec
python:python
gpu_list:0
Global.use_gpu:True|True
Global.auto_cast:fp32
Global.epoch_num:lite_train_lite_infer=3|whole_train_whole_infer=50
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=128
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./inference/rec_inference
null:null
##
trainer:norm_train
norm_train:tools/train.py -c configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_fp32_ultra.yml -o Global.cal_metric_during_train=False Global.print_batch_step=1 Train.loader.shuffle=false Train.dataset.data_dir=./train_data/ic15_data Train.dataset.label_file_list=[./train_data/ic15_data/rec_gt_train.txt] Eval.dataset.data_dir=./train_data/ic15_data Eval.dataset.label_file_list=[./train_data/ic15_data/rec_gt_test.txt]
pact_train:null
fpgm_train:null
distill_train:null
to_static_train:Global.to_static=true
null:null
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.checkpoints:
norm_export:tools/export_model.py -c configs/rec/PP-OCRv4/ch_PP-OCRv4_rec.yml -o
quant_export:
fpgm_export:
distill_export:null
export1:null
export2:null
##
infer_model:./inference/ch_PP-OCRv4_rec_infer
infer_export:null
infer_quant:False
inference:tools/infer/predict_rec.py --rec_image_shape="3,48,320"
--use_gpu:True|False
--enable_mkldnn:False
--cpu_threads:6
--rec_batch_num:1
--use_tensorrt:False
--precision:fp32
--rec_model_dir:
--image_dir:./inference/rec_inference
null:null
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,48,320]}]
===========================train_benchmark_params==========================
batch_size:192
fp_items:fp32
epoch:1
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
===========================to_static_train_benchmark_params===========================
to_static_train:Global.to_static=true
test_tipc/configs/ch_PP-OCRv4_server_rec_ampO2_ultra/train_infer_python.txt
0 → 100644
浏览文件 @
a46a0610
===========================train_params===========================
model_name:ch_PP-OCRv4_server_rec
python:python
gpu_list:0
Global.use_gpu:True|True
Global.auto_cast:fp32
Global.epoch_num:lite_train_lite_infer=3|whole_train_whole_infer=50
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=128
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./inference/rec_inference
null:null
##
trainer:norm_train
norm_train:tools/train.py -c configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_hgnet_ampO2_ultra.yml -o Global.cal_metric_during_train=False Global.print_batch_step=1 Train.loader.shuffle=false Train.dataset.data_dir=./train_data/ic15_data Train.dataset.label_file_list=[./train_data/ic15_data/rec_gt_train.txt] Eval.dataset.data_dir=./train_data/ic15_data Eval.dataset.label_file_list=[./train_data/ic15_data/rec_gt_test.txt]
fpgm_train:null
distill_train:null
to_static_train:Global.to_static=true
null:null
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.checkpoints:
norm_export:tools/export_model.py -c configs/rec/PP-OCRv4/ch_PP-OCRv4_rec.yml -o
quant_export:
fpgm_export:
distill_export:null
export1:null
export2:null
##
infer_model:./inference/ch_PP-OCRv4_rec_infer
infer_export:null
infer_quant:False
inference:tools/infer/predict_rec.py --rec_image_shape="3,48,320"
--use_gpu:True|False
--enable_mkldnn:False
--cpu_threads:6
--rec_batch_num:1
--use_tensorrt:False
--precision:fp32
--rec_model_dir:
--image_dir:./inference/rec_inference
null:null
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,48,320]}]
===========================train_benchmark_params==========================
batch_size:256
fp_items:fp16
epoch:1
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
===========================to_static_train_benchmark_params===========================
to_static_train:Global.to_static=true
test_tipc/configs/ch_PP-OCRv4_server_rec_fp32_ultra/train_infer_python.txt
0 → 100644
浏览文件 @
a46a0610
===========================train_params===========================
model_name:ch_PP-OCRv4_server_rec
python:python
gpu_list:0
Global.use_gpu:True|True
Global.auto_cast:fp32
Global.epoch_num:lite_train_lite_infer=3|whole_train_whole_infer=50
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=128
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./inference/rec_inference
null:null
##
trainer:norm_train
norm_train:tools/train.py -c configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_hgnet_fp32_ultra.yml -o Global.cal_metric_during_train=False Global.print_batch_step=1 Train.loader.shuffle=false Train.dataset.data_dir=./train_data/ic15_data Train.dataset.label_file_list=[./train_data/ic15_data/rec_gt_train.txt] Eval.dataset.data_dir=./train_data/ic15_data Eval.dataset.label_file_list=[./train_data/ic15_data/rec_gt_test.txt]
pact_train:null
fpgm_train:null
distill_train:null
to_static_train:Global.to_static=true
null:null
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.checkpoints:
norm_export:tools/export_model.py -c configs/rec/PP-OCRv4/ch_PP-OCRv4_rec.yml -o
quant_export:
fpgm_export:
distill_export:null
export1:null
export2:null
##
infer_model:./inference/ch_PP-OCRv4_rec_infer
infer_export:null
infer_quant:False
inference:tools/infer/predict_rec.py --rec_image_shape="3,48,320"
--use_gpu:True|False
--enable_mkldnn:False
--cpu_threads:6
--rec_batch_num:1
--use_tensorrt:False
--precision:fp32
--rec_model_dir:
--image_dir:./inference/rec_inference
null:null
--benchmark:True
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,48,320]}]
===========================train_benchmark_params==========================
batch_size:256
fp_items:fp32
epoch:1
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
===========================to_static_train_benchmark_params===========================
to_static_train:Global.to_static=true
test_tipc/prepare.sh
浏览文件 @
a46a0610
...
...
@@ -56,6 +56,26 @@ if [ ${MODE} = "benchmark_train" ];then
ln
-s
./icdar2015_benckmark ./icdar2015
cd
../
fi
if
[[
${
model_name
}
=
~
"ch_PP-OCRv4_mobile_rec"
]]
;
then
rm
-rf
./train_data/ic15_data
wget
-nc
-P
./train_data/ https://paddleocr.bj.bcebos.com/dataset/ic15_data_benckmark.tar
--no-check-certificate
cd
./train_data/
&&
tar
xf ic15_data_benckmark.tar
ln
-s
./ic15_data_benckmark ./ic15_data
cd
ic15_data
mv
rec_gt_train4w.txt rec_gt_train.txt
cd
../
cd
../
fi
if
[[
${
model_name
}
=
~
"ch_PP-OCRv4_server_rec"
]]
;
then
rm
-rf
./train_data/ic15_data
wget
-nc
-P
./train_data/ https://paddleocr.bj.bcebos.com/dataset/ic15_data_benckmark.tar
--no-check-certificate
cd
./train_data/
&&
tar
xf ic15_data_benckmark.tar
ln
-s
./ic15_data_benckmark ./ic15_data
cd
ic15_data
mv
rec_gt_train4w.txt rec_gt_train.txt
cd
../
cd
../
fi
if
[[
${
model_name
}
=
~
"ch_ppocr_server_v2_0_det"
||
${
model_name
}
=
~
"ch_PP-OCRv3_det"
]]
;
then
rm
-rf
./train_data/icdar2015
wget
-nc
-P
./train_data/ https://paddleocr.bj.bcebos.com/dataset/icdar2015_benckmark.tar
--no-check-certificate
...
...
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