mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2026-09-24 23:33:08 +08:00
Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into doc
This commit is contained in:
WenmuZhou
@@ -0,0 +1,99 @@
|
||||
Global:
|
||||
debug: false
|
||||
use_gpu: true
|
||||
epoch_num: 100
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec_ppocr_v3_rotnet
|
||||
save_epoch_step: 3
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: true
|
||||
pretrained_model: null
|
||||
checkpoints: null
|
||||
save_inference_dir: null
|
||||
use_visualdl: false
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
|
||||
max_text_length: 25
|
||||
infer_mode: false
|
||||
use_space_char: true
|
||||
save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txt
|
||||
Optimizer:
|
||||
name: Adam
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.001
|
||||
regularizer:
|
||||
name: L2
|
||||
factor: 1.0e-05
|
||||
Architecture:
|
||||
model_type: cls
|
||||
algorithm: CLS
|
||||
Transform: null
|
||||
Backbone:
|
||||
name: MobileNetV1Enhance
|
||||
scale: 0.5
|
||||
last_conv_stride: [1, 2]
|
||||
last_pool_type: avg
|
||||
Neck:
|
||||
Head:
|
||||
name: ClsHead
|
||||
class_dim: 4
|
||||
|
||||
Loss:
|
||||
name: ClsLoss
|
||||
main_indicator: acc
|
||||
|
||||
PostProcess:
|
||||
name: ClsPostProcess
|
||||
|
||||
Metric:
|
||||
name: ClsMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/train_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- RecAug:
|
||||
use_tia: False
|
||||
- RandAugment:
|
||||
- SSLRotateResize:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys: ["image", "label"]
|
||||
loader:
|
||||
collate_fn: "SSLRotateCollate"
|
||||
shuffle: true
|
||||
batch_size_per_card: 32
|
||||
drop_last: true
|
||||
num_workers: 8
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data
|
||||
label_file_list:
|
||||
- ./train_data/val_list.txt
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
img_mode: BGR
|
||||
channel_first: false
|
||||
- SSLRotateResize:
|
||||
image_shape: [3, 48, 320]
|
||||
- KeepKeys:
|
||||
keep_keys: ["image", "label"]
|
||||
loader:
|
||||
collate_fn: "SSLRotateCollate"
|
||||
shuffle: false
|
||||
drop_last: false
|
||||
batch_size_per_card: 64
|
||||
num_workers: 8
|
||||
profiler_options: null
|
||||
@@ -0,0 +1,117 @@
|
||||
Global:
|
||||
use_gpu: True
|
||||
epoch_num: 20
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 10
|
||||
save_model_dir: ./output/rec/svtr/
|
||||
save_epoch_step: 1
|
||||
# evaluation is run every 2000 iterations after the 0th iteration
|
||||
eval_batch_step: [0, 2000]
|
||||
cal_metric_during_train: True
|
||||
pretrained_model:
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_words_en/word_10.png
|
||||
# for data or label process
|
||||
character_dict_path:
|
||||
character_type: en
|
||||
max_text_length: 25
|
||||
infer_mode: False
|
||||
use_space_char: False
|
||||
save_res_path: ./output/rec/predicts_svtr_tiny.txt
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: AdamW
|
||||
beta1: 0.9
|
||||
beta2: 0.99
|
||||
epsilon: 0.00000008
|
||||
weight_decay: 0.05
|
||||
no_weight_decay_name: norm pos_embed
|
||||
one_dim_param_no_weight_decay: true
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.0005
|
||||
warmup_epoch: 2
|
||||
|
||||
Architecture:
|
||||
model_type: rec
|
||||
algorithm: SVTR
|
||||
Transform:
|
||||
name: STN_ON
|
||||
tps_inputsize: [32, 64]
|
||||
tps_outputsize: [32, 100]
|
||||
num_control_points: 20
|
||||
tps_margins: [0.05,0.05]
|
||||
stn_activation: none
|
||||
Backbone:
|
||||
name: SVTRNet
|
||||
img_size: [32, 100]
|
||||
out_char_num: 25
|
||||
out_channels: 192
|
||||
patch_merging: 'Conv'
|
||||
embed_dim: [64, 128, 256]
|
||||
depth: [3, 6, 3]
|
||||
num_heads: [2, 4, 8]
|
||||
mixer: ['Local','Local','Local','Local','Local','Local','Global','Global','Global','Global','Global','Global']
|
||||
local_mixer: [[7, 11], [7, 11], [7, 11]]
|
||||
last_stage: True
|
||||
prenorm: false
|
||||
Neck:
|
||||
name: SequenceEncoder
|
||||
encoder_type: reshape
|
||||
Head:
|
||||
name: CTCHead
|
||||
|
||||
Loss:
|
||||
name: CTCLoss
|
||||
|
||||
PostProcess:
|
||||
name: CTCLabelDecode
|
||||
|
||||
Metric:
|
||||
name: RecMetric
|
||||
main_indicator: acc
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/training/
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
character_dict_path:
|
||||
image_shape: [3, 64, 256]
|
||||
padding: False
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: True
|
||||
batch_size_per_card: 512
|
||||
drop_last: True
|
||||
num_workers: 4
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: LMDBDataSet
|
||||
data_dir: ./train_data/data_lmdb_release/validation/
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- CTCLabelEncode: # Class handling label
|
||||
- RecResizeImg:
|
||||
character_dict_path:
|
||||
image_shape: [3, 64, 256]
|
||||
padding: False
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 256
|
||||
num_workers: 2
|
||||
+4
-2
@@ -22,9 +22,11 @@ PP-OCR has supported muti deployment schemes. Click the link to get the specific
|
||||
|
||||
- [Python Inference](../doc/doc_en/inference_ppocr_en.md)
|
||||
- [C++ Inference](./cpp_infer/readme.md)
|
||||
- [Serving](./pdserving/README.md)
|
||||
- [Paddle-Lite](./lite/readme.md)
|
||||
- [Serving (Python/C++)](./pdserving/README.md)
|
||||
- [Paddle-Lite (ARM CPU/OpenCL ARM GPU/Metal ARM GPU)](./lite/readme.md)
|
||||
- [Paddle.js](./paddlejs/README.md)
|
||||
- [Jetson Inference]()
|
||||
- [XPU Inference]()
|
||||
- [Paddle2ONNX](./paddle2onnx/readme.md)
|
||||
|
||||
If you need the deployment tutorial of academic algorithm models other than PP-OCR, please directly enter the main page of corresponding algorithms, [entrance](../doc/doc_en/algorithm_overview_en.md)。
|
||||
+5
-3
@@ -22,9 +22,11 @@ PP-OCR模型已打通多种场景部署方案，点击链接获取具体的使
|
||||
|
||||
- [Python 推理](../doc/doc_ch/inference_ppocr.md)
|
||||
- [C++ 推理](./cpp_infer/readme_ch.md)
|
||||
- [Serving 服务化部署](./pdserving/README_CN.md)
|
||||
- [Paddle-Lite 端侧部署](./lite/readme_ch.md)
|
||||
- [Paddle.js 服务化部署](./paddlejs/README_ch.md)
|
||||
- [Serving 服务化部署（Python/C++）](./pdserving/README_CN.md)
|
||||
- [Paddle-Lite 端侧部署（ARM CPU/OpenCL ARM GPU/Metal ARM GPU）](./lite/readme_ch.md)
|
||||
- [Paddle.js 部署](./paddlejs/README_ch.md)
|
||||
- [Jetson 推理]()
|
||||
- [XPU 推理]()
|
||||
- [Paddle2ONNX 推理](./paddle2onnx/readme_ch.md)
|
||||
|
||||
需要PP-OCR以外的学术算法模型的推理部署，请直接进入相应算法主页面，[入口](../doc/doc_ch/algorithm_overview.md)。
|
||||
@@ -35,17 +35,7 @@ from ppocr.metrics import build_metric
|
||||
import tools.program as program
|
||||
from paddleslim.dygraph.quant import QAT
|
||||
from ppocr.data import build_dataloader
|
||||
|
||||
|
||||
def export_single_model(quanter, model, infer_shape, save_path, logger):
|
||||
quanter.save_quantized_model(
|
||||
model,
|
||||
save_path,
|
||||
input_spec=[
|
||||
paddle.static.InputSpec(
|
||||
shape=[None] + infer_shape, dtype='float32')
|
||||
])
|
||||
logger.info('inference QAT model is saved to {}'.format(save_path))
|
||||
from tools.export_model import export_single_model
|
||||
|
||||
|
||||
def main():
|
||||
@@ -84,17 +74,54 @@ def main():
|
||||
config['Global'])
|
||||
|
||||
# build model
|
||||
# for rec algorithm
|
||||
if hasattr(post_process_class, 'character'):
|
||||
char_num = len(getattr(post_process_class, 'character'))
|
||||
if config['Architecture']["algorithm"] in ["Distillation",
|
||||
]: # distillation model
|
||||
for key in config['Architecture']["Models"]:
|
||||
config['Architecture']["Models"][key]["Head"][
|
||||
'out_channels'] = char_num
|
||||
if config['Architecture']['Models'][key]['Head'][
|
||||
'name'] == 'MultiHead': # for multi head
|
||||
if config['PostProcess'][
|
||||
'name'] == 'DistillationSARLabelDecode':
|
||||
char_num = char_num - 2
|
||||
# update SARLoss params
|
||||
assert list(config['Loss']['loss_config_list'][-1].keys())[
|
||||
0] == 'DistillationSARLoss'
|
||||
config['Loss']['loss_config_list'][-1][
|
||||
'DistillationSARLoss']['ignore_index'] = char_num + 1
|
||||
out_channels_list = {}
|
||||
out_channels_list['CTCLabelDecode'] = char_num
|
||||
out_channels_list['SARLabelDecode'] = char_num + 2
|
||||
config['Architecture']['Models'][key]['Head'][
|
||||
'out_channels_list'] = out_channels_list
|
||||
else:
|
||||
config['Architecture']["Models"][key]["Head"][
|
||||
'out_channels'] = char_num
|
||||
elif config['Architecture']['Head'][
|
||||
'name'] == 'MultiHead': # for multi head
|
||||
if config['PostProcess']['name'] == 'SARLabelDecode':
|
||||
char_num = char_num - 2
|
||||
# update SARLoss params
|
||||
assert list(config['Loss']['loss_config_list'][1].keys())[
|
||||
0] == 'SARLoss'
|
||||
if config['Loss']['loss_config_list'][1]['SARLoss'] is None:
|
||||
config['Loss']['loss_config_list'][1]['SARLoss'] = {
|
||||
'ignore_index': char_num + 1
|
||||
}
|
||||
else:
|
||||
config['Loss']['loss_config_list'][1]['SARLoss'][
|
||||
'ignore_index'] = char_num + 1
|
||||
out_channels_list = {}
|
||||
out_channels_list['CTCLabelDecode'] = char_num
|
||||
out_channels_list['SARLabelDecode'] = char_num + 2
|
||||
config['Architecture']['Head'][
|
||||
'out_channels_list'] = out_channels_list
|
||||
else: # base rec model
|
||||
config['Architecture']["Head"]['out_channels'] = char_num
|
||||
|
||||
if config['PostProcess']['name'] == 'SARLabelDecode': # for SAR model
|
||||
config['Loss']['ignore_index'] = char_num - 1
|
||||
|
||||
model = build_model(config['Architecture'])
|
||||
|
||||
# get QAT model
|
||||
@@ -120,21 +147,22 @@ def main():
|
||||
for k, v in metric.items():
|
||||
logger.info('{}:{}'.format(k, v))
|
||||
|
||||
infer_shape = [3, 32, 100] if model_type == "rec" else [3, 640, 640]
|
||||
|
||||
save_path = config["Global"]["save_inference_dir"]
|
||||
|
||||
arch_config = config["Architecture"]
|
||||
|
||||
arch_config = config["Architecture"]
|
||||
|
||||
if arch_config["algorithm"] in ["Distillation", ]: # distillation model
|
||||
archs = list(arch_config["Models"].values())
|
||||
for idx, name in enumerate(model.model_name_list):
|
||||
model.model_list[idx].eval()
|
||||
sub_model_save_path = os.path.join(save_path, name, "inference")
|
||||
export_single_model(quanter, model.model_list[idx], infer_shape,
|
||||
sub_model_save_path, logger)
|
||||
export_single_model(model.model_list[idx], archs[idx],
|
||||
sub_model_save_path, logger, quanter)
|
||||
else:
|
||||
save_path = os.path.join(save_path, "inference")
|
||||
model.eval()
|
||||
export_single_model(quanter, model, infer_shape, save_path, logger)
|
||||
export_single_model(model, arch_config, save_path, logger, quanter)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
@@ -112,10 +112,48 @@ def main(config, device, logger, vdl_writer):
|
||||
if config['Architecture']["algorithm"] in ["Distillation",
|
||||
]: # distillation model
|
||||
for key in config['Architecture']["Models"]:
|
||||
config['Architecture']["Models"][key]["Head"][
|
||||
'out_channels'] = char_num
|
||||
if config['Architecture']['Models'][key]['Head'][
|
||||
'name'] == 'MultiHead': # for multi head
|
||||
if config['PostProcess'][
|
||||
'name'] == 'DistillationSARLabelDecode':
|
||||
char_num = char_num - 2
|
||||
# update SARLoss params
|
||||
assert list(config['Loss']['loss_config_list'][-1].keys())[
|
||||
0] == 'DistillationSARLoss'
|
||||
config['Loss']['loss_config_list'][-1][
|
||||
'DistillationSARLoss']['ignore_index'] = char_num + 1
|
||||
out_channels_list = {}
|
||||
out_channels_list['CTCLabelDecode'] = char_num
|
||||
out_channels_list['SARLabelDecode'] = char_num + 2
|
||||
config['Architecture']['Models'][key]['Head'][
|
||||
'out_channels_list'] = out_channels_list
|
||||
else:
|
||||
config['Architecture']["Models"][key]["Head"][
|
||||
'out_channels'] = char_num
|
||||
elif config['Architecture']['Head'][
|
||||
'name'] == 'MultiHead': # for multi head
|
||||
if config['PostProcess']['name'] == 'SARLabelDecode':
|
||||
char_num = char_num - 2
|
||||
# update SARLoss params
|
||||
assert list(config['Loss']['loss_config_list'][1].keys())[
|
||||
0] == 'SARLoss'
|
||||
if config['Loss']['loss_config_list'][1]['SARLoss'] is None:
|
||||
config['Loss']['loss_config_list'][1]['SARLoss'] = {
|
||||
'ignore_index': char_num + 1
|
||||
}
|
||||
else:
|
||||
config['Loss']['loss_config_list'][1]['SARLoss'][
|
||||
'ignore_index'] = char_num + 1
|
||||
out_channels_list = {}
|
||||
out_channels_list['CTCLabelDecode'] = char_num
|
||||
out_channels_list['SARLabelDecode'] = char_num + 2
|
||||
config['Architecture']['Head'][
|
||||
'out_channels_list'] = out_channels_list
|
||||
else: # base rec model
|
||||
config['Architecture']["Head"]['out_channels'] = char_num
|
||||
|
||||
if config['PostProcess']['name'] == 'SARLabelDecode': # for SAR model
|
||||
config['Loss']['ignore_index'] = char_num - 1
|
||||
model = build_model(config['Architecture'])
|
||||
|
||||
pre_best_model_dict = dict()
|
||||
@@ -137,7 +175,7 @@ def main(config, device, logger, vdl_writer):
|
||||
config['Optimizer'],
|
||||
epochs=config['Global']['epoch_num'],
|
||||
step_each_epoch=len(train_dataloader),
|
||||
parameters=model.parameters())
|
||||
model=model)
|
||||
|
||||
# resume PACT training process
|
||||
if config["Global"]["checkpoints"] is not None:
|
||||
|
||||
@@ -25,8 +25,8 @@
|
||||
|
||||
|模型|骨干网络|配置文件|precision|recall|Hmean|下载链接|
|
||||
| --- | --- | --- | --- | --- | --- | --- |
|
||||
|DB|ResNet50_vd|configs/det/det_r50_vd_db.yml|86.41%|78.72%|82.38%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
|
||||
|DB|MobileNetV3|configs/det/det_mv3_db.yml|77.29%|73.08%|75.12%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
|
||||
|DB|ResNet50_vd|[configs/det/det_r50_vd_db.yml](../../configs/det/det_r50_vd_db.yml)|86.41%|78.72%|82.38%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
|
||||
|DB|MobileNetV3|[configs/det/det_mv3_db.yml](../../configs/det/det_mv3_db.yml)|77.29%|73.08%|75.12%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
|
||||
|
||||
|
||||
<a name="2"></a>
|
||||
|
||||
@@ -27,7 +27,7 @@
|
||||
|
||||
#### 2、CDLA数据集
|
||||
- **数据来源**：https://github.com/buptlihang/CDLA
|
||||
- **数据简介**：publaynet数据集的训练集合中包含5000张图像，验证集合中包含1000张图像。总共包含10个类别，分别是： `Text, Title, Figure, Figure caption, Table, Table caption, Header, Footer, Reference, Equation`。部分图像以及标注框可视化如下所示。
|
||||
- **数据简介**：CDLA据集的训练集合中包含5000张图像，验证集合中包含1000张图像。总共包含10个类别，分别是： `Text, Title, Figure, Figure caption, Table, Table caption, Header, Footer, Reference, Equation`。部分图像以及标注框可视化如下所示。
|
||||
|
||||
<div align="center">
|
||||
<img src="../datasets/CDLA_demo/val_0633.jpg" width="500">
|
||||
|
||||
@@ -17,6 +17,8 @@
|
||||
|
||||
PP-OCR是PaddleOCR自研的实用的超轻量OCR系统。在实现[前沿算法](algorithm.md)的基础上，考虑精度与速度的平衡，进行**模型瘦身**和**深度优化**，使其尽可能满足产业落地需求。
|
||||
|
||||
#### PP-OCR
|
||||
|
||||
PP-OCR是一个两阶段的OCR系统，其中文本检测算法选用[DB](algorithm_det_db.md)，文本识别算法选用[CRNN](algorithm_rec_crnn.md)，并在检测和识别模块之间添加[文本方向分类器](angle_class.md)，以应对不同方向的文本识别。
|
||||
|
||||
PP-OCR系统pipeline如下：
|
||||
@@ -28,9 +30,13 @@ PP-OCR系统pipeline如下：
|
||||
|
||||
PP-OCR系统在持续迭代优化，目前已发布PP-OCR和PP-OCRv2两个版本：
|
||||
|
||||
[1] PP-OCR从骨干网络选择和调整、预测头部的设计、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型自动裁剪量化8个方面，采用19个有效策略，对各个模块的模型进行效果调优和瘦身(如绿框所示)，最终得到整体大小为3.5M的超轻量中英文OCR和2.8M的英文数字OCR。更多细节请参考PP-OCR技术方案 https://arxiv.org/abs/2009.09941
|
||||
PP-OCR从骨干网络选择和调整、预测头部的设计、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型自动裁剪量化8个方面，采用19个有效策略，对各个模块的模型进行效果调优和瘦身(如绿框所示)，最终得到整体大小为3.5M的超轻量中英文OCR和2.8M的英文数字OCR。更多细节请参考PP-OCR技术方案 https://arxiv.org/abs/2009.09941
|
||||
|
||||
[2] PP-OCRv2在PP-OCR的基础上，进一步在5个方面重点优化，检测模型采用CML协同互学习知识蒸馏策略和CopyPaste数据增广策略；识别模型采用LCNet轻量级骨干网络、UDML 改进知识蒸馏策略和[Enhanced CTC loss](./doc/doc_ch/enhanced_ctc_loss.md)损失函数改进（如上图红框所示），进一步在推理速度和预测效果上取得明显提升。更多细节请参考PP-OCRv2[技术报告](https://arxiv.org/abs/2109.03144)。
|
||||
#### PP-OCRv2
|
||||
|
||||
PP-OCRv2在PP-OCR的基础上，进一步在5个方面重点优化，检测模型采用CML协同互学习知识蒸馏策略和CopyPaste数据增广策略；识别模型采用LCNet轻量级骨干网络、UDML 改进知识蒸馏策略和[Enhanced CTC loss](./doc/doc_ch/enhanced_ctc_loss.md)损失函数改进（如上图红框所示），进一步在推理速度和预测效果上取得明显提升。更多细节请参考PP-OCRv2[技术报告](https://arxiv.org/abs/2109.03144)。
|
||||
|
||||
#### PP-OCRv3
|
||||
|
||||
|
||||
<a name="2"></a>
|
||||
|
||||
@@ -25,8 +25,8 @@ On the ICDAR2015 dataset, the text detection result is as follows:
|
||||
|
||||
|Model|Backbone|Configuration|Precision|Recall|Hmean|Download|
|
||||
| --- | --- | --- | --- | --- | --- | --- |
|
||||
|DB|ResNet50_vd|configs/det/det_r50_vd_db.yml|86.41%|78.72%|82.38%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
|
||||
|DB|MobileNetV3|configs/det/det_mv3_db.yml|77.29%|73.08%|75.12%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
|
||||
|DB|ResNet50_vd|[configs/det/det_r50_vd_db.yml](../../configs/det/det_r50_vd_db.yml)|86.41%|78.72%|82.38%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
|
||||
|DB|MobileNetV3|[configs/det/det_mv3_db.yml](../../configs/det/det_mv3_db.yml)|77.29%|73.08%|75.12%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
|
||||
|
||||
|
||||
<a name="2"></a>
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 201 KiB After Width: | Height: | Size: 200 KiB |
@@ -72,6 +72,7 @@ def build_dataloader(config, mode, device, logger, seed=None):
|
||||
use_shared_memory = loader_config['use_shared_memory']
|
||||
else:
|
||||
use_shared_memory = True
|
||||
|
||||
if mode == "Train":
|
||||
# Distribute data to multiple cards
|
||||
batch_sampler = DistributedBatchSampler(
|
||||
|
||||
@@ -56,3 +56,17 @@ class ListCollator(object):
|
||||
for idx in to_tensor_idxs:
|
||||
data_dict[idx] = paddle.to_tensor(data_dict[idx])
|
||||
return list(data_dict.values())
|
||||
|
||||
|
||||
class SSLRotateCollate(object):
|
||||
"""
|
||||
bach: [
|
||||
[(4*3xH*W), (4,)]
|
||||
[(4*3xH*W), (4,)]
|
||||
...
|
||||
]
|
||||
"""
|
||||
|
||||
def __call__(self, batch):
|
||||
output = [np.concatenate(d, axis=0) for d in zip(*batch)]
|
||||
return output
|
||||
|
||||
@@ -23,7 +23,8 @@ from .random_crop_data import EastRandomCropData, RandomCropImgMask
|
||||
from .make_pse_gt import MakePseGt
|
||||
|
||||
from .rec_img_aug import RecAug, RecConAug, RecResizeImg, ClsResizeImg, \
|
||||
SRNRecResizeImg, NRTRRecResizeImg, SARRecResizeImg, PRENResizeImg
|
||||
SRNRecResizeImg, NRTRRecResizeImg, SARRecResizeImg, PRENResizeImg, SVTRRecResizeImg
|
||||
from .ssl_img_aug import SSLRotateResize
|
||||
from .randaugment import RandAugment
|
||||
from .copy_paste import CopyPaste
|
||||
from .ColorJitter import ColorJitter
|
||||
|
||||
@@ -113,14 +113,14 @@ class BaseRecLabelEncode(object):
|
||||
dict_character = list(self.character_str)
|
||||
self.lower = True
|
||||
else:
|
||||
self.character_str = ""
|
||||
self.character_str = []
|
||||
with open(character_dict_path, "rb") as fin:
|
||||
lines = fin.readlines()
|
||||
for line in lines:
|
||||
line = line.decode('utf-8').strip("\n").strip("\r\n")
|
||||
self.character_str += line
|
||||
self.character_str.append(line)
|
||||
if use_space_char:
|
||||
self.character_str += " "
|
||||
self.character_str.append(" ")
|
||||
dict_character = list(self.character_str)
|
||||
dict_character = self.add_special_char(dict_character)
|
||||
self.dict = {}
|
||||
|
||||
@@ -16,6 +16,7 @@ import math
|
||||
import cv2
|
||||
import numpy as np
|
||||
import random
|
||||
import copy
|
||||
from PIL import Image
|
||||
from .text_image_aug import tia_perspective, tia_stretch, tia_distort
|
||||
|
||||
@@ -81,7 +82,7 @@ class ClsResizeImg(object):
|
||||
|
||||
def __call__(self, data):
|
||||
img = data['image']
|
||||
norm_img = resize_norm_img(img, self.image_shape)
|
||||
norm_img, _ = resize_norm_img(img, self.image_shape)
|
||||
data['image'] = norm_img
|
||||
return data
|
||||
|
||||
@@ -206,6 +207,25 @@ class PRENResizeImg(object):
|
||||
return data
|
||||
|
||||
|
||||
class SVTRRecResizeImg(object):
|
||||
def __init__(self,
|
||||
image_shape,
|
||||
infer_mode=False,
|
||||
character_dict_path='./ppocr/utils/ppocr_keys_v1.txt',
|
||||
padding=True,
|
||||
**kwargs):
|
||||
self.image_shape = image_shape
|
||||
self.infer_mode = infer_mode
|
||||
self.character_dict_path = character_dict_path
|
||||
self.padding = padding
|
||||
|
||||
def __call__(self, data):
|
||||
img = data['image']
|
||||
norm_img = resize_norm_img_svtr(img, self.image_shape, self.padding)
|
||||
data['image'] = norm_img
|
||||
return data
|
||||
|
||||
|
||||
def resize_norm_img_sar(img, image_shape, width_downsample_ratio=0.25):
|
||||
imgC, imgH, imgW_min, imgW_max = image_shape
|
||||
h = img.shape[0]
|
||||
@@ -324,6 +344,58 @@ def resize_norm_img_srn(img, image_shape):
|
||||
return np.reshape(img_black, (c, row, col)).astype(np.float32)
|
||||
|
||||
|
||||
def resize_norm_img_svtr(img, image_shape, padding=False):
|
||||
imgC, imgH, imgW = image_shape
|
||||
h = img.shape[0]
|
||||
w = img.shape[1]
|
||||
if not padding:
|
||||
if h > 2.0 * w:
|
||||
image = Image.fromarray(img)
|
||||
image1 = image.rotate(90, expand=True)
|
||||
image2 = image.rotate(-90, expand=True)
|
||||
img1 = np.array(image1)
|
||||
img2 = np.array(image2)
|
||||
else:
|
||||
img1 = copy.deepcopy(img)
|
||||
img2 = copy.deepcopy(img)
|
||||
|
||||
resized_image = cv2.resize(
|
||||
img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
|
||||
resized_image1 = cv2.resize(
|
||||
img1, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
|
||||
resized_image2 = cv2.resize(
|
||||
img2, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
|
||||
resized_w = imgW
|
||||
else:
|
||||
ratio = w / float(h)
|
||||
if math.ceil(imgH * ratio) > imgW:
|
||||
resized_w = imgW
|
||||
else:
|
||||
resized_w = int(math.ceil(imgH * ratio))
|
||||
resized_image = cv2.resize(img, (resized_w, imgH))
|
||||
resized_image = resized_image.astype('float32')
|
||||
resized_image1 = resized_image1.astype('float32')
|
||||
resized_image2 = resized_image2.astype('float32')
|
||||
if image_shape[0] == 1:
|
||||
resized_image = resized_image / 255
|
||||
resized_image = resized_image[np.newaxis, :]
|
||||
else:
|
||||
resized_image = resized_image.transpose((2, 0, 1)) / 255
|
||||
resized_image1 = resized_image1.transpose((2, 0, 1)) / 255
|
||||
resized_image2 = resized_image2.transpose((2, 0, 1)) / 255
|
||||
resized_image -= 0.5
|
||||
resized_image /= 0.5
|
||||
resized_image1 -= 0.5
|
||||
resized_image1 /= 0.5
|
||||
resized_image2 -= 0.5
|
||||
resized_image2 /= 0.5
|
||||
padding_im = np.zeros((3, imgC, imgH, imgW), dtype=np.float32)
|
||||
padding_im[0, :, :, 0:resized_w] = resized_image
|
||||
padding_im[1, :, :, 0:resized_w] = resized_image1
|
||||
padding_im[2, :, :, 0:resized_w] = resized_image2
|
||||
return padding_im
|
||||
|
||||
|
||||
def srn_other_inputs(image_shape, num_heads, max_text_length):
|
||||
|
||||
imgC, imgH, imgW = image_shape
|
||||
|
||||
@@ -0,0 +1,60 @@
|
||||
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import math
|
||||
import cv2
|
||||
import numpy as np
|
||||
import random
|
||||
from PIL import Image
|
||||
|
||||
from .rec_img_aug import resize_norm_img
|
||||
|
||||
|
||||
class SSLRotateResize(object):
|
||||
def __init__(self,
|
||||
image_shape,
|
||||
padding=False,
|
||||
select_all=True,
|
||||
mode="train",
|
||||
**kwargs):
|
||||
self.image_shape = image_shape
|
||||
self.padding = padding
|
||||
self.select_all = select_all
|
||||
self.mode = mode
|
||||
|
||||
def __call__(self, data):
|
||||
img = data["image"]
|
||||
|
||||
data["image_r90"] = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)
|
||||
data["image_r180"] = cv2.rotate(data["image_r90"],
|
||||
cv2.ROTATE_90_CLOCKWISE)
|
||||
data["image_r270"] = cv2.rotate(data["image_r180"],
|
||||
cv2.ROTATE_90_CLOCKWISE)
|
||||
|
||||
images = []
|
||||
for key in ["image", "image_r90", "image_r180", "image_r270"]:
|
||||
images.append(
|
||||
resize_norm_img(
|
||||
data.pop(key),
|
||||
image_shape=self.image_shape,
|
||||
padding=self.padding)[0])
|
||||
data["image"] = np.stack(images, axis=0)
|
||||
data["label"] = np.array(list(range(4)))
|
||||
if not self.select_all:
|
||||
data["image"] = data["image"][0::2] # just choose 0 and 180
|
||||
data["label"] = data["label"][0:2] # label needs to be continuous
|
||||
if self.mode == "test":
|
||||
data["image"] = data["image"][0]
|
||||
data["label"] = data["label"][0]
|
||||
return data
|
||||
@@ -296,47 +296,49 @@ class PatchEmbed(nn.Layer):
|
||||
if sub_num == 2:
|
||||
self.proj = nn.Sequential(
|
||||
ConvBNLayer(
|
||||
in_channels,
|
||||
embed_dim // 2,
|
||||
3,
|
||||
2,
|
||||
1,
|
||||
in_channels=in_channels,
|
||||
out_channels=embed_dim // 2,
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
act=nn.GELU,
|
||||
bias_attr=None),
|
||||
ConvBNLayer(
|
||||
embed_dim // 2,
|
||||
embed_dim,
|
||||
3,
|
||||
2,
|
||||
1,
|
||||
in_channels=embed_dim // 2,
|
||||
out_channels=embed_dim,
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
act=nn.GELU,
|
||||
bias_attr=None))
|
||||
if sub_num == 3:
|
||||
self.proj = nn.Sequential(
|
||||
ConvBNLayer(
|
||||
in_channels,
|
||||
embed_dim // 4,
|
||||
3,
|
||||
2,
|
||||
1,
|
||||
in_channels=in_channels,
|
||||
out_channels=embed_dim // 4,
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
act=nn.GELU,
|
||||
bias_attr=None),
|
||||
ConvBNLayer(
|
||||
embed_dim // 4,
|
||||
embed_dim // 2,
|
||||
3,
|
||||
2,
|
||||
1,
|
||||
in_channels=embed_dim // 4,
|
||||
out_channels=embed_dim // 2,
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
act=nn.GELU,
|
||||
bias_attr=None),
|
||||
ConvBNLayer(
|
||||
embed_dim // 2,
|
||||
embed_dim,
|
||||
3,
|
||||
2,
|
||||
1,
|
||||
in_channels=embed_dim // 2,
|
||||
out_channels=embed_dim,
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
act=nn.GELU,
|
||||
bias_attr=None), )
|
||||
bias_attr=None))
|
||||
|
||||
def forward(self, x):
|
||||
B, C, H, W = x.shape
|
||||
@@ -455,7 +457,7 @@ class SVTRNet(nn.Layer):
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
drop=drop_rate,
|
||||
act_layer=nn.Swish,
|
||||
act_layer=eval(act),
|
||||
attn_drop=attn_drop_rate,
|
||||
drop_path=dpr[0:depth[0]][i],
|
||||
norm_layer=norm_layer,
|
||||
|
||||
@@ -128,6 +128,8 @@ class STN_ON(nn.Layer):
|
||||
self.out_channels = in_channels
|
||||
|
||||
def forward(self, image):
|
||||
if len(image.shape)==5:
|
||||
image = image.reshape([0, image.shape[-3], image.shape[-2], image.shape[-1]])
|
||||
stn_input = paddle.nn.functional.interpolate(
|
||||
image, self.tps_inputsize, mode="bilinear", align_corners=True)
|
||||
stn_img_feat, ctrl_points = self.stn_head(stn_input)
|
||||
|
||||
@@ -138,9 +138,9 @@ class TPSSpatialTransformer(nn.Layer):
|
||||
assert source_control_points.shape[2] == 2
|
||||
batch_size = paddle.shape(source_control_points)[0]
|
||||
|
||||
self.padding_matrix = paddle.expand(
|
||||
padding_matrix = paddle.expand(
|
||||
self.padding_matrix, shape=[batch_size, 3, 2])
|
||||
Y = paddle.concat([source_control_points, self.padding_matrix], 1)
|
||||
Y = paddle.concat([source_control_points, padding_matrix], 1)
|
||||
mapping_matrix = paddle.matmul(self.inverse_kernel, Y)
|
||||
source_coordinate = paddle.matmul(self.target_coordinate_repr,
|
||||
mapping_matrix)
|
||||
|
||||
@@ -30,7 +30,7 @@ def build_lr_scheduler(lr_config, epochs, step_each_epoch):
|
||||
return lr
|
||||
|
||||
|
||||
def build_optimizer(config, epochs, step_each_epoch, parameters):
|
||||
def build_optimizer(config, epochs, step_each_epoch, model):
|
||||
from . import regularizer, optimizer
|
||||
config = copy.deepcopy(config)
|
||||
# step1 build lr
|
||||
@@ -43,6 +43,8 @@ def build_optimizer(config, epochs, step_each_epoch, parameters):
|
||||
if not hasattr(regularizer, reg_name):
|
||||
reg_name += 'Decay'
|
||||
reg = getattr(regularizer, reg_name)(**reg_config)()
|
||||
elif 'weight_decay' in config:
|
||||
reg = config.pop('weight_decay')
|
||||
else:
|
||||
reg = None
|
||||
|
||||
@@ -57,4 +59,4 @@ def build_optimizer(config, epochs, step_each_epoch, parameters):
|
||||
weight_decay=reg,
|
||||
grad_clip=grad_clip,
|
||||
**config)
|
||||
return optim(parameters), lr
|
||||
return optim(model), lr
|
||||
|
||||
@@ -42,13 +42,13 @@ class Momentum(object):
|
||||
self.weight_decay = weight_decay
|
||||
self.grad_clip = grad_clip
|
||||
|
||||
def __call__(self, parameters):
|
||||
def __call__(self, model):
|
||||
opt = optim.Momentum(
|
||||
learning_rate=self.learning_rate,
|
||||
momentum=self.momentum,
|
||||
weight_decay=self.weight_decay,
|
||||
grad_clip=self.grad_clip,
|
||||
parameters=parameters)
|
||||
parameters=model.parameters())
|
||||
return opt
|
||||
|
||||
|
||||
@@ -75,7 +75,7 @@ class Adam(object):
|
||||
self.name = name
|
||||
self.lazy_mode = lazy_mode
|
||||
|
||||
def __call__(self, parameters):
|
||||
def __call__(self, model):
|
||||
opt = optim.Adam(
|
||||
learning_rate=self.learning_rate,
|
||||
beta1=self.beta1,
|
||||
@@ -85,7 +85,7 @@ class Adam(object):
|
||||
grad_clip=self.grad_clip,
|
||||
name=self.name,
|
||||
lazy_mode=self.lazy_mode,
|
||||
parameters=parameters)
|
||||
parameters=model.parameters())
|
||||
return opt
|
||||
|
||||
|
||||
@@ -117,7 +117,7 @@ class RMSProp(object):
|
||||
self.weight_decay = weight_decay
|
||||
self.grad_clip = grad_clip
|
||||
|
||||
def __call__(self, parameters):
|
||||
def __call__(self, model):
|
||||
opt = optim.RMSProp(
|
||||
learning_rate=self.learning_rate,
|
||||
momentum=self.momentum,
|
||||
@@ -125,7 +125,7 @@ class RMSProp(object):
|
||||
epsilon=self.epsilon,
|
||||
weight_decay=self.weight_decay,
|
||||
grad_clip=self.grad_clip,
|
||||
parameters=parameters)
|
||||
parameters=model.parameters())
|
||||
return opt
|
||||
|
||||
|
||||
@@ -148,7 +148,7 @@ class Adadelta(object):
|
||||
self.grad_clip = grad_clip
|
||||
self.name = name
|
||||
|
||||
def __call__(self, parameters):
|
||||
def __call__(self, model):
|
||||
opt = optim.Adadelta(
|
||||
learning_rate=self.learning_rate,
|
||||
epsilon=self.epsilon,
|
||||
@@ -156,7 +156,7 @@ class Adadelta(object):
|
||||
weight_decay=self.weight_decay,
|
||||
grad_clip=self.grad_clip,
|
||||
name=self.name,
|
||||
parameters=parameters)
|
||||
parameters=model.parameters())
|
||||
return opt
|
||||
|
||||
|
||||
@@ -165,31 +165,55 @@ class AdamW(object):
|
||||
learning_rate=0.001,
|
||||
beta1=0.9,
|
||||
beta2=0.999,
|
||||
epsilon=1e-08,
|
||||
epsilon=1e-8,
|
||||
weight_decay=0.01,
|
||||
multi_precision=False,
|
||||
grad_clip=None,
|
||||
no_weight_decay_name=None,
|
||||
one_dim_param_no_weight_decay=False,
|
||||
name=None,
|
||||
lazy_mode=False,
|
||||
**kwargs):
|
||||
**args):
|
||||
super().__init__()
|
||||
self.learning_rate = learning_rate
|
||||
self.beta1 = beta1
|
||||
self.beta2 = beta2
|
||||
self.epsilon = epsilon
|
||||
self.learning_rate = learning_rate
|
||||
self.grad_clip = grad_clip
|
||||
self.weight_decay = 0.01 if weight_decay is None else weight_decay
|
||||
self.grad_clip = grad_clip
|
||||
self.name = name
|
||||
self.lazy_mode = lazy_mode
|
||||
self.multi_precision = multi_precision
|
||||
self.no_weight_decay_name_list = no_weight_decay_name.split(
|
||||
) if no_weight_decay_name else []
|
||||
self.one_dim_param_no_weight_decay = one_dim_param_no_weight_decay
|
||||
|
||||
def __call__(self, parameters):
|
||||
def __call__(self, model):
|
||||
parameters = model.parameters()
|
||||
|
||||
self.no_weight_decay_param_name_list = [
|
||||
p.name for n, p in model.named_parameters() if any(nd in n for nd in self.no_weight_decay_name_list)
|
||||
]
|
||||
|
||||
if self.one_dim_param_no_weight_decay:
|
||||
self.no_weight_decay_param_name_list += [
|
||||
p.name for n, p in model.named_parameters() if len(p.shape) == 1
|
||||
]
|
||||
|
||||
opt = optim.AdamW(
|
||||
learning_rate=self.learning_rate,
|
||||
beta1=self.beta1,
|
||||
beta2=self.beta2,
|
||||
epsilon=self.epsilon,
|
||||
parameters=parameters,
|
||||
weight_decay=self.weight_decay,
|
||||
multi_precision=self.multi_precision,
|
||||
grad_clip=self.grad_clip,
|
||||
name=self.name,
|
||||
lazy_mode=self.lazy_mode,
|
||||
parameters=parameters)
|
||||
apply_decay_param_fun=self._apply_decay_param_fun)
|
||||
return opt
|
||||
|
||||
def _apply_decay_param_fun(self, name):
|
||||
return name not in self.no_weight_decay_param_name_list
|
||||
@@ -27,7 +27,7 @@ from .sast_postprocess import SASTPostProcess
|
||||
from .fce_postprocess import FCEPostProcess
|
||||
from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, \
|
||||
DistillationCTCLabelDecode, TableLabelDecode, NRTRLabelDecode, SARLabelDecode, \
|
||||
SEEDLabelDecode, PRENLabelDecode
|
||||
SEEDLabelDecode, PRENLabelDecode, SVTRLabelDecode
|
||||
from .cls_postprocess import ClsPostProcess
|
||||
from .pg_postprocess import PGPostProcess
|
||||
from .vqa_token_ser_layoutlm_postprocess import VQASerTokenLayoutLMPostProcess
|
||||
@@ -42,7 +42,7 @@ def build_post_process(config, global_config=None):
|
||||
'DistillationDBPostProcess', 'NRTRLabelDecode', 'SARLabelDecode',
|
||||
'SEEDLabelDecode', 'VQASerTokenLayoutLMPostProcess',
|
||||
'VQAReTokenLayoutLMPostProcess', 'PRENLabelDecode',
|
||||
'DistillationSARLabelDecode'
|
||||
'DistillationSARLabelDecode', 'SVTRLabelDecode'
|
||||
]
|
||||
|
||||
if config['name'] == 'PSEPostProcess':
|
||||
|
||||
@@ -17,17 +17,26 @@ import paddle
|
||||
class ClsPostProcess(object):
|
||||
""" Convert between text-label and text-index """
|
||||
|
||||
def __init__(self, label_list, **kwargs):
|
||||
def __init__(self, label_list=None, key=None, **kwargs):
|
||||
super(ClsPostProcess, self).__init__()
|
||||
self.label_list = label_list
|
||||
self.key = key
|
||||
|
||||
def __call__(self, preds, label=None, *args, **kwargs):
|
||||
if self.key is not None:
|
||||
preds = preds[self.key]
|
||||
|
||||
label_list = self.label_list
|
||||
if label_list is None:
|
||||
label_list = {idx: idx for idx in range(preds.shape[-1])}
|
||||
|
||||
if isinstance(preds, paddle.Tensor):
|
||||
preds = preds.numpy()
|
||||
|
||||
pred_idxs = preds.argmax(axis=1)
|
||||
decode_out = [(self.label_list[idx], preds[i, idx])
|
||||
decode_out = [(label_list[idx], preds[i, idx])
|
||||
for i, idx in enumerate(pred_idxs)]
|
||||
if label is None:
|
||||
return decode_out
|
||||
label = [(self.label_list[idx], 1.0) for idx in label]
|
||||
label = [(label_list[idx], 1.0) for idx in label]
|
||||
return decode_out, label
|
||||
|
||||
@@ -752,3 +752,40 @@ class PRENLabelDecode(BaseRecLabelDecode):
|
||||
return text
|
||||
label = self.decode(label)
|
||||
return text, label
|
||||
|
||||
|
||||
class SVTRLabelDecode(BaseRecLabelDecode):
|
||||
""" Convert between text-label and text-index """
|
||||
|
||||
def __init__(self, character_dict_path=None, use_space_char=False,
|
||||
**kwargs):
|
||||
super(SVTRLabelDecode, self).__init__(character_dict_path,
|
||||
use_space_char)
|
||||
|
||||
def __call__(self, preds, label=None, *args, **kwargs):
|
||||
if isinstance(preds, tuple):
|
||||
preds = preds[-1]
|
||||
if isinstance(preds, paddle.Tensor):
|
||||
preds = preds.numpy()
|
||||
preds_idx = preds.argmax(axis=-1)
|
||||
preds_prob = preds.max(axis=-1)
|
||||
|
||||
text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
|
||||
return_text = []
|
||||
for i in range(0, len(text), 3):
|
||||
text0 = text[i]
|
||||
text1 = text[i + 1]
|
||||
text2 = text[i + 2]
|
||||
|
||||
text_pred = [text0[0], text1[0], text2[0]]
|
||||
text_prob = [text0[1], text1[1], text2[1]]
|
||||
id_max = text_prob.index(max(text_prob))
|
||||
return_text.append((text_pred[id_max], text_prob[id_max]))
|
||||
if label is None:
|
||||
return return_text
|
||||
label = self.decode(label)
|
||||
return return_text, label
|
||||
|
||||
def add_special_char(self, dict_character):
|
||||
dict_character = ['blank'] + dict_character
|
||||
return dict_character
|
||||
@@ -21,7 +21,7 @@ l
|
||||
8
|
||||
.
|
||||
j
|
||||
p
|
||||
p
|
||||
ಗ
|
||||
ು
|
||||
ಣ
|
||||
|
||||
@@ -22,7 +22,7 @@ l
|
||||
8
|
||||
.
|
||||
j
|
||||
p
|
||||
p
|
||||
ப
|
||||
ூ
|
||||
த
|
||||
|
||||
+53
@@ -0,0 +1,53 @@
|
||||
===========================train_params===========================
|
||||
model_name:ch_PPOCRv2_det
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
Global.use_gpu:True|True
|
||||
Global.auto_cast:amp
|
||||
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
|
||||
Global.save_model_dir:./output/
|
||||
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
|
||||
Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
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/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o
|
||||
quant_export:null
|
||||
fpgm_export:
|
||||
distill_export:null
|
||||
export1:null
|
||||
export2:null
|
||||
inference_dir:Student
|
||||
infer_model:./inference/ch_PP-OCRv2_det_infer/
|
||||
infer_export:null
|
||||
infer_quant:False
|
||||
inference:tools/infer/predict_det.py
|
||||
--use_gpu:True|False
|
||||
--enable_mkldnn:True|False
|
||||
--cpu_threads:1|6
|
||||
--rec_batch_num:1
|
||||
--use_tensorrt:False|True
|
||||
--precision:fp32|fp16|int8
|
||||
--det_model_dir:
|
||||
--image_dir:./inference/ch_det_data_50/all-sum-510/
|
||||
null:null
|
||||
--benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}]
|
||||
+53
@@ -0,0 +1,53 @@
|
||||
===========================train_params===========================
|
||||
model_name:ch_PPOCRv2_det_PACT
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
Global.use_gpu:True|True
|
||||
Global.auto_cast:amp
|
||||
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
|
||||
Global.save_model_dir:./output/
|
||||
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
|
||||
Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
|
||||
null:null
|
||||
##
|
||||
trainer:pact_train
|
||||
norm_train:null
|
||||
pact_train:deploy/slim/quantization/quant.py -c configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:null
|
||||
null:null
|
||||
##
|
||||
===========================infer_params===========================
|
||||
Global.save_inference_dir:./output/
|
||||
Global.checkpoints:
|
||||
norm_export:null
|
||||
quant_export:deploy/slim/quantization/export_model.py -c configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o
|
||||
fpgm_export:
|
||||
distill_export:null
|
||||
export1:null
|
||||
export2:null
|
||||
inference_dir:Student
|
||||
infer_model:./inference/ch_PP-OCRv2_det_infer/
|
||||
infer_export:null
|
||||
infer_quant:False
|
||||
inference:tools/infer/predict_det.py
|
||||
--use_gpu:True|False
|
||||
--enable_mkldnn:True|False
|
||||
--cpu_threads:1|6
|
||||
--rec_batch_num:1
|
||||
--use_tensorrt:False|True
|
||||
--precision:fp32|fp16|int8
|
||||
--det_model_dir:
|
||||
--image_dir:./inference/ch_det_data_50/all-sum-510/
|
||||
null:null
|
||||
--benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}]
|
||||
+53
@@ -0,0 +1,53 @@
|
||||
===========================train_params===========================
|
||||
model_name:PPOCRv2_ocr_rec
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
Global.use_gpu:True|True
|
||||
Global.auto_cast:amp
|
||||
Global.epoch_num:lite_train_lite_infer=3|whole_train_whole_infer=300
|
||||
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 test_tipc/configs/ch_PP-OCRv2_rec/ch_PP-OCRv2_rec_distillation.yml -o
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:null
|
||||
null:null
|
||||
##
|
||||
===========================infer_params===========================
|
||||
Global.save_inference_dir:./output/
|
||||
Global.checkpoints:
|
||||
norm_export:tools/export_model.py -c test_tipc/configs/ch_PP-OCRv2_rec/ch_PP-OCRv2_rec_distillation.yml -o
|
||||
quant_export:
|
||||
fpgm_export:
|
||||
distill_export:null
|
||||
export1:null
|
||||
export2:null
|
||||
inference_dir:Student
|
||||
infer_model:./inference/ch_PP-OCRv2_rec_infer
|
||||
infer_export:null
|
||||
infer_quant:False
|
||||
inference:tools/infer/predict_rec.py
|
||||
--use_gpu:True|False
|
||||
--enable_mkldnn:True|False
|
||||
--cpu_threads:1|6
|
||||
--rec_batch_num:1|6
|
||||
--use_tensorrt:False|True
|
||||
--precision:fp32|int8
|
||||
--rec_model_dir:
|
||||
--image_dir:./inference/rec_inference
|
||||
null:null
|
||||
--benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,32,320]}]
|
||||
+53
@@ -0,0 +1,53 @@
|
||||
===========================train_params===========================
|
||||
model_name:ch_PPOCRv2_rec_PACT
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
Global.use_gpu:True|True
|
||||
Global.auto_cast:amp
|
||||
Global.epoch_num:lite_train_lite_infer=3|whole_train_whole_infer=300
|
||||
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:pact_train
|
||||
norm_train:null
|
||||
pact_train:deploy/slim/quantization/quant.py -c test_tipc/configs/ch_PP-OCRv2_rec/ch_PP-OCRv2_rec_distillation.yml -o
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:null
|
||||
null:null
|
||||
##
|
||||
===========================infer_params===========================
|
||||
Global.save_inference_dir:./output/
|
||||
Global.checkpoints:
|
||||
norm_export:null
|
||||
quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/ch_PP-OCRv2_rec/ch_PP-OCRv2_rec_distillation.yml -o
|
||||
fpgm_export: null
|
||||
distill_export:null
|
||||
export1:null
|
||||
export2:null
|
||||
inference_dir:Student
|
||||
infer_model:./inference/ch_PP-OCRv2_rec_slim_quant_infer
|
||||
infer_export:null
|
||||
infer_quant:True
|
||||
inference:tools/infer/predict_rec.py
|
||||
--use_gpu:True|False
|
||||
--enable_mkldnn:True|False
|
||||
--cpu_threads:1|6
|
||||
--rec_batch_num:1|6
|
||||
--use_tensorrt:False|True
|
||||
--precision:fp32|int8
|
||||
--rec_model_dir:
|
||||
--image_dir:./inference/rec_inference
|
||||
null:null
|
||||
--benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,32,320]}]
|
||||
@@ -1,126 +0,0 @@
|
||||
Global:
|
||||
use_gpu: false
|
||||
epoch_num: 5
|
||||
log_smooth_window: 20
|
||||
print_batch_step: 1
|
||||
save_model_dir: ./output/db_mv3/
|
||||
save_epoch_step: 1200
|
||||
# evaluation is run every 2000 iterations
|
||||
eval_batch_step: [0, 400]
|
||||
cal_metric_during_train: False
|
||||
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
|
||||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_en/img_10.jpg
|
||||
save_res_path: ./output/det_db/predicts_db.txt
|
||||
|
||||
Architecture:
|
||||
model_type: det
|
||||
algorithm: DB
|
||||
Transform:
|
||||
Backbone:
|
||||
name: MobileNetV3
|
||||
scale: 0.5
|
||||
model_name: large
|
||||
disable_se: False
|
||||
Neck:
|
||||
name: DBFPN
|
||||
out_channels: 256
|
||||
Head:
|
||||
name: DBHead
|
||||
k: 50
|
||||
|
||||
Loss:
|
||||
name: DBLoss
|
||||
balance_loss: true
|
||||
main_loss_type: DiceLoss
|
||||
alpha: 5
|
||||
beta: 10
|
||||
ohem_ratio: 3
|
||||
|
||||
Optimizer:
|
||||
name: Adam #Momentum
|
||||
#momentum: 0.9
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
lr:
|
||||
learning_rate: 0.001
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
factor: 0
|
||||
|
||||
PostProcess:
|
||||
name: DBPostProcess
|
||||
thresh: 0.3
|
||||
box_thresh: 0.6
|
||||
max_candidates: 1000
|
||||
unclip_ratio: 1.5
|
||||
|
||||
Metric:
|
||||
name: DetMetric
|
||||
main_indicator: hmean
|
||||
|
||||
Train:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
|
||||
ratio_list: [1.0]
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- Resize:
|
||||
size: [640, 640]
|
||||
- MakeBorderMap:
|
||||
shrink_ratio: 0.4
|
||||
thresh_min: 0.3
|
||||
thresh_max: 0.7
|
||||
- MakeShrinkMap:
|
||||
shrink_ratio: 0.4
|
||||
min_text_size: 8
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1
|
||||
num_workers: 0
|
||||
use_shared_memory: False
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: SimpleDataSet
|
||||
data_dir: ./train_data/icdar2015/text_localization/
|
||||
label_file_list:
|
||||
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
|
||||
transforms:
|
||||
- DecodeImage: # load image
|
||||
img_mode: BGR
|
||||
channel_first: False
|
||||
- DetLabelEncode: # Class handling label
|
||||
- DetResizeForTest:
|
||||
image_shape: [736, 1280]
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: 'hwc'
|
||||
- ToCHWImage:
|
||||
- KeepKeys:
|
||||
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
|
||||
loader:
|
||||
shuffle: False
|
||||
drop_last: False
|
||||
batch_size_per_card: 1 # must be 1
|
||||
num_workers: 0
|
||||
use_shared_memory: False
|
||||
+12
-10
@@ -1,10 +1,10 @@
|
||||
===========================train_params===========================
|
||||
model_name:ocr_det
|
||||
model_name:ch_ppocr_mobile_v2.0_det
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
Global.use_gpu:True|True
|
||||
Global.auto_cast:amp
|
||||
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
|
||||
Global.epoch_num:lite_train_lite_infer=100|whole_train_whole_infer=300
|
||||
Global.save_model_dir:./output/
|
||||
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
|
||||
Global.pretrained_model:null
|
||||
@@ -12,10 +12,10 @@ train_model_name:latest
|
||||
train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train|pact_train|fpgm_train
|
||||
norm_train:tools/train.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
|
||||
pact_train:deploy/slim/quantization/quant.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o
|
||||
fpgm_train:deploy/slim/prune/sensitivity_anal.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
@@ -26,10 +26,10 @@ null:null
|
||||
##
|
||||
===========================infer_params===========================
|
||||
Global.save_inference_dir:./output/
|
||||
Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o
|
||||
quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o
|
||||
fpgm_export:deploy/slim/prune/export_prune_model.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o
|
||||
Global.checkpoints:
|
||||
norm_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
export1:null
|
||||
export2:null
|
||||
@@ -49,3 +49,5 @@ inference:tools/infer/predict_det.py
|
||||
null:null
|
||||
--benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}]
|
||||
+1
-1
@@ -1,5 +1,5 @@
|
||||
===========================train_params===========================
|
||||
model_name:ocr_det
|
||||
model_name:ch_ppocr_mobile_v2.0_det_FPGM
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
Global.use_gpu:True|True
|
||||
+53
@@ -0,0 +1,53 @@
|
||||
===========================train_params===========================
|
||||
model_name:ch_ppocr_mobile_v2.0_det_FPGM
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
Global.use_gpu:True|True
|
||||
Global.auto_cast:amp
|
||||
Global.epoch_num:lite_train_lite_infer=5|whole_train_whole_infer=300
|
||||
Global.save_model_dir:./output/
|
||||
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
|
||||
Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
|
||||
null:null
|
||||
##
|
||||
trainer:fpgm_train
|
||||
norm_train:null
|
||||
pact_train:null
|
||||
fpgm_train:deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:null
|
||||
null:null
|
||||
##
|
||||
===========================infer_params===========================
|
||||
Global.save_inference_dir:./output/
|
||||
Global.checkpoints:
|
||||
norm_export:null
|
||||
quant_export:null
|
||||
fpgm_export:deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
|
||||
distill_export:null
|
||||
export1:null
|
||||
export2:null
|
||||
inference_dir:null
|
||||
train_model:null
|
||||
infer_export:null
|
||||
infer_quant:False
|
||||
inference:tools/infer/predict_det.py
|
||||
--use_gpu:True|False
|
||||
--enable_mkldnn:True|False
|
||||
--cpu_threads:1|6
|
||||
--rec_batch_num:1
|
||||
--use_tensorrt:False|True
|
||||
--precision:fp32|fp16|int8
|
||||
--det_model_dir:
|
||||
--image_dir:./inference/ch_det_data_50/all-sum-510/
|
||||
null:null
|
||||
--benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}]
|
||||
+53
@@ -0,0 +1,53 @@
|
||||
===========================train_params===========================
|
||||
model_name:ch_ppocr_mobile_v2.0_det_PACT
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
Global.use_gpu:True|True
|
||||
Global.auto_cast:amp
|
||||
Global.epoch_num:lite_train_lite_infer=20|whole_train_whole_infer=300
|
||||
Global.save_model_dir:./output/
|
||||
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
|
||||
Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
|
||||
null:null
|
||||
##
|
||||
trainer:pact_train
|
||||
norm_train:null
|
||||
pact_train:deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:null
|
||||
null:null
|
||||
##
|
||||
===========================infer_params===========================
|
||||
Global.save_inference_dir:./output/
|
||||
Global.checkpoints:
|
||||
norm_export:null
|
||||
quant_export:deploy/slim/quantization/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
export1:null
|
||||
export2:null
|
||||
inference_dir:null
|
||||
train_model:./inference/ch_ppocr_mobile_v2.0_det_prune_infer/
|
||||
infer_export:null
|
||||
infer_quant:False
|
||||
inference:tools/infer/predict_det.py
|
||||
--use_gpu:True|False
|
||||
--enable_mkldnn:True|False
|
||||
--cpu_threads:1|6
|
||||
--rec_batch_num:1
|
||||
--use_tensorrt:False|True
|
||||
--precision:fp32|fp16|int8
|
||||
--det_model_dir:
|
||||
--image_dir:./inference/ch_det_data_50/all-sum-510/
|
||||
null:null
|
||||
--benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}]
|
||||
+53
@@ -0,0 +1,53 @@
|
||||
===========================train_params===========================
|
||||
model_name:ch_ppocr_mobile_v2.0_rec
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
Global.use_gpu:True|True
|
||||
Global.auto_cast:amp
|
||||
Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=300
|
||||
Global.save_model_dir:./output/
|
||||
Train.loader.batch_size_per_card:lite_train_lite_infer=128|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/rec_icdar15_train.yml -o
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c configs/rec/rec_icdar15_train.yml -o
|
||||
null:null
|
||||
##
|
||||
===========================infer_params===========================
|
||||
Global.save_inference_dir:./output/
|
||||
Global.checkpoints:
|
||||
norm_export:tools/export_model.py -c configs/rec/rec_icdar15_train.yml -o
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
export1:null
|
||||
export2:null
|
||||
##
|
||||
train_model:./inference/ch_ppocr_mobile_v2.0_rec_train/best_accuracy
|
||||
infer_export:tools/export_model.py -c configs/rec/rec_icdar15_train.yml -o
|
||||
infer_quant:False
|
||||
inference:tools/infer/predict_rec.py
|
||||
--use_gpu:True|False
|
||||
--enable_mkldnn:True|False
|
||||
--cpu_threads:1|6
|
||||
--rec_batch_num:1|6
|
||||
--use_tensorrt:True|False
|
||||
--precision:fp32|int8
|
||||
--rec_model_dir:
|
||||
--image_dir:./inference/rec_inference
|
||||
--save_log_path:./test/output/
|
||||
--benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,32,100]}]
|
||||
+53
@@ -0,0 +1,53 @@
|
||||
===========================train_params===========================
|
||||
model_name:ch_ppocr_mobile_v2.0_rec_FPGM
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
Global.use_gpu:True|True
|
||||
Global.auto_cast:amp
|
||||
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
|
||||
Global.save_model_dir:./output/
|
||||
Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
|
||||
Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./train_data/ic15_data/test/word_1.png
|
||||
null:null
|
||||
##
|
||||
trainer:fpgm_train
|
||||
norm_train:null
|
||||
pact_train:null
|
||||
fpgm_train:deploy/slim/prune/sensitivity_anal.py -c test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./pretrain_models/ch_ppocr_mobile_v2.0_rec_train/best_accuracy
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:null
|
||||
null:null
|
||||
##
|
||||
===========================infer_params===========================
|
||||
Global.save_inference_dir:./output/
|
||||
Global.checkpoints:
|
||||
norm_export:null
|
||||
quant_export:null
|
||||
fpgm_export:deploy/slim/prune/export_prune_model.py -c test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/rec_chinese_lite_train_v2.0.yml -o
|
||||
distill_export:null
|
||||
export1:null
|
||||
export2:null
|
||||
inference_dir:null
|
||||
train_model:null
|
||||
infer_export:null
|
||||
infer_quant:False
|
||||
inference:tools/infer/predict_rec.py
|
||||
--use_gpu:True|False
|
||||
--enable_mkldnn:True|False
|
||||
--cpu_threads:1|6
|
||||
--rec_batch_num:1
|
||||
--use_tensorrt:False|True
|
||||
--precision:fp32|int8
|
||||
--rec_model_dir:
|
||||
--image_dir:./inference/rec_inference
|
||||
null:null
|
||||
--benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,32,320]}]
|
||||
+53
@@ -0,0 +1,53 @@
|
||||
===========================train_params===========================
|
||||
model_name:ch_ppocr_mobile_v2.0_rec_PACT
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
Global.use_gpu:True|True
|
||||
Global.auto_cast:amp
|
||||
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
|
||||
Global.save_model_dir:./output/
|
||||
Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
|
||||
Global.checkpoints:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./train_data/ic15_data/test/word_1.png
|
||||
null:null
|
||||
##
|
||||
trainer:pact_train
|
||||
norm_train:null
|
||||
pact_train:deploy/slim/quantization/quant.py -c test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/rec_chinese_lite_train_v2.0.yml -o
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:null
|
||||
null:null
|
||||
##
|
||||
===========================infer_params===========================
|
||||
Global.save_inference_dir:./output/
|
||||
Global.checkpoints:
|
||||
norm_export:null
|
||||
quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/rec_chinese_lite_train_v2.0.yml -o
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
export1:null
|
||||
export2:null
|
||||
inference_dir:null
|
||||
infer_model:./inference/ch_ppocr_mobile_v2.0_rec_slim_infer/
|
||||
infer_export:null
|
||||
infer_quant:False
|
||||
inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ppocr_keys_v1.txt --rec_image_shape="3,32,100"
|
||||
--use_gpu:True|False
|
||||
--enable_mkldnn:True|False
|
||||
--cpu_threads:1|6
|
||||
--rec_batch_num:1|6
|
||||
--use_tensorrt:False|True
|
||||
--precision:fp32|int8
|
||||
--rec_model_dir:
|
||||
--image_dir:./inference/rec_inference
|
||||
--save_log_path:./test/output/
|
||||
--benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,32,320]}]
|
||||
+53
@@ -0,0 +1,53 @@
|
||||
===========================train_params===========================
|
||||
model_name:ch_ppocr_server_v2.0_det
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
Global.use_gpu:True|True
|
||||
Global.auto_cast:amp
|
||||
Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=300
|
||||
Global.save_model_dir:./output/
|
||||
Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_lite_infer=4
|
||||
Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c test_tipc/configs/ch_ppocr_server_v2.0_det/det_r50_vd_db.yml -o
|
||||
quant_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c test_tipc/configs/ch_ppocr_server_v2.0_det/det_r50_vd_db.yml -o
|
||||
null:null
|
||||
##
|
||||
===========================infer_params===========================
|
||||
Global.save_inference_dir:./output/
|
||||
Global.checkpoints:
|
||||
norm_export:tools/export_model.py -c test_tipc/configs/ch_ppocr_server_v2.0_det/det_r50_vd_db.yml -o
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
export1:null
|
||||
export2:null
|
||||
##
|
||||
train_model:./inference/ch_ppocr_server_v2.0_det_train/best_accuracy
|
||||
infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml -o
|
||||
infer_quant:False
|
||||
inference:tools/infer/predict_det.py
|
||||
--use_gpu:True|False
|
||||
--enable_mkldnn:True|False
|
||||
--cpu_threads:1|6
|
||||
--rec_batch_num:1
|
||||
--use_tensorrt:False|True
|
||||
--precision:fp32|fp16|int8
|
||||
--det_model_dir:
|
||||
--image_dir:./inference/ch_det_data_50/all-sum-510/
|
||||
--save_log_path:null
|
||||
--benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,640,640]}];[{float32,[3,960,960]}]
|
||||
+53
@@ -0,0 +1,53 @@
|
||||
===========================train_params===========================
|
||||
model_name:ch_ppocr_server_v2.0_rec
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
Global.use_gpu:True|True
|
||||
Global.auto_cast:amp
|
||||
Global.epoch_num:lite_train_lite_infer=5|whole_train_whole_infer=100
|
||||
Global.save_model_dir:./output/
|
||||
Train.loader.batch_size_per_card:lite_train_lite_infer=128|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 test_tipc/configs/ch_ppocr_server_v2.0_rec/rec_icdar15_train.yml -o
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c test_tipc/configs/ch_ppocr_server_v2.0_rec/rec_icdar15_train.yml -o
|
||||
null:null
|
||||
##
|
||||
===========================infer_params===========================
|
||||
Global.save_inference_dir:./output/
|
||||
Global.checkpoints:
|
||||
norm_export:tools/export_model.py -c test_tipc/configs/ch_ppocr_server_v2.0_rec/rec_icdar15_train.yml -o
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
export1:null
|
||||
export2:null
|
||||
##
|
||||
train_model:./inference/ch_ppocr_server_v2.0_rec_train/best_accuracy
|
||||
infer_export:tools/export_model.py -c test_tipc/configs/ch_ppocr_server_v2.0_rec/rec_icdar15_train.yml -o
|
||||
infer_quant:False
|
||||
inference:tools/infer/predict_rec.py
|
||||
--use_gpu:True|False
|
||||
--enable_mkldnn:True|False
|
||||
--cpu_threads:1|6
|
||||
--rec_batch_num:1|6
|
||||
--use_tensorrt:True|False
|
||||
--precision:fp32|int8
|
||||
--rec_model_dir:
|
||||
--image_dir:./inference/rec_inference
|
||||
--save_log_path:./test/output/
|
||||
--benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,32,100]}]
|
||||
+4
-2
@@ -74,9 +74,11 @@ def main():
|
||||
|
||||
model = build_model(config['Architecture'])
|
||||
extra_input_models = ["SRN", "NRTR", "SAR", "SEED", "SVTR"]
|
||||
extra_input = False
|
||||
if config['Architecture']['algorithm'] == 'Distillation':
|
||||
extra_input = config['Architecture']['Models']['Teacher'][
|
||||
'algorithm'] in extra_input_models
|
||||
for key in config['Architecture']["Models"]:
|
||||
extra_input = extra_input or config['Architecture']['Models'][key][
|
||||
'algorithm'] in extra_input_models
|
||||
else:
|
||||
extra_input = config['Architecture']['algorithm'] in extra_input_models
|
||||
if "model_type" in config['Architecture'].keys():
|
||||
|
||||
+10
-3
@@ -31,7 +31,7 @@ from ppocr.utils.logging import get_logger
|
||||
from tools.program import load_config, merge_config, ArgsParser
|
||||
|
||||
|
||||
def export_single_model(model, arch_config, save_path, logger):
|
||||
def export_single_model(model, arch_config, save_path, logger, quanter=None):
|
||||
if arch_config["algorithm"] == "SRN":
|
||||
max_text_length = arch_config["Head"]["max_text_length"]
|
||||
other_shape = [
|
||||
@@ -61,6 +61,11 @@ def export_single_model(model, arch_config, save_path, logger):
|
||||
paddle.static.InputSpec(
|
||||
shape=[None, 3, 48, -1], dtype="float32"),
|
||||
]
|
||||
else:
|
||||
other_shape = [
|
||||
paddle.static.InputSpec(
|
||||
shape=[None, 3, 64, 256], dtype="float32"),
|
||||
]
|
||||
model = to_static(model, input_spec=other_shape)
|
||||
elif arch_config["algorithm"] == "PREN":
|
||||
other_shape = [
|
||||
@@ -90,7 +95,10 @@ def export_single_model(model, arch_config, save_path, logger):
|
||||
shape=[None] + infer_shape, dtype="float32")
|
||||
])
|
||||
|
||||
paddle.jit.save(model, save_path)
|
||||
if quanter is None:
|
||||
paddle.jit.save(model, save_path)
|
||||
else:
|
||||
quanter.save_quantized_model(model, save_path)
|
||||
logger.info("inference model is saved to {}".format(save_path))
|
||||
return
|
||||
|
||||
@@ -120,7 +128,6 @@ def main():
|
||||
char_num = char_num - 2
|
||||
out_channels_list['CTCLabelDecode'] = char_num
|
||||
out_channels_list['SARLabelDecode'] = char_num + 2
|
||||
loss_list = config['Loss']['loss_config_list']
|
||||
config['Architecture']['Models'][key]['Head'][
|
||||
'out_channels_list'] = out_channels_list
|
||||
else:
|
||||
|
||||
@@ -131,6 +131,17 @@ class TextRecognizer(object):
|
||||
padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
|
||||
padding_im[:, :, 0:resized_w] = resized_image
|
||||
return padding_im
|
||||
|
||||
def resize_norm_img_svtr(self, img, image_shape):
|
||||
|
||||
imgC, imgH, imgW = image_shape
|
||||
resized_image = cv2.resize(
|
||||
img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
|
||||
resized_image = resized_image.astype('float32')
|
||||
resized_image = resized_image.transpose((2, 0, 1)) / 255
|
||||
resized_image -= 0.5
|
||||
resized_image /= 0.5
|
||||
return resized_image
|
||||
|
||||
def resize_norm_img_srn(self, img, image_shape):
|
||||
imgC, imgH, imgW = image_shape
|
||||
@@ -263,12 +274,8 @@ class TextRecognizer(object):
|
||||
wh_ratio = w * 1.0 / h
|
||||
max_wh_ratio = max(max_wh_ratio, wh_ratio)
|
||||
for ino in range(beg_img_no, end_img_no):
|
||||
if self.rec_algorithm != "SRN" and self.rec_algorithm != "SAR":
|
||||
norm_img = self.resize_norm_img(img_list[indices[ino]],
|
||||
max_wh_ratio)
|
||||
norm_img = norm_img[np.newaxis, :]
|
||||
norm_img_batch.append(norm_img)
|
||||
elif self.rec_algorithm == "SAR":
|
||||
|
||||
if self.rec_algorithm == "SAR":
|
||||
norm_img, _, _, valid_ratio = self.resize_norm_img_sar(
|
||||
img_list[indices[ino]], self.rec_image_shape)
|
||||
norm_img = norm_img[np.newaxis, :]
|
||||
@@ -276,7 +283,7 @@ class TextRecognizer(object):
|
||||
valid_ratios = []
|
||||
valid_ratios.append(valid_ratio)
|
||||
norm_img_batch.append(norm_img)
|
||||
else:
|
||||
elif self.rec_algorithm == "SRN":
|
||||
norm_img = self.process_image_srn(
|
||||
img_list[indices[ino]], self.rec_image_shape, 8, 25)
|
||||
encoder_word_pos_list = []
|
||||
@@ -288,6 +295,16 @@ class TextRecognizer(object):
|
||||
gsrm_slf_attn_bias1_list.append(norm_img[3])
|
||||
gsrm_slf_attn_bias2_list.append(norm_img[4])
|
||||
norm_img_batch.append(norm_img[0])
|
||||
elif self.rec_algorithm == "SVTR":
|
||||
norm_img = self.resize_norm_img_svtr(
|
||||
img_list[indices[ino]], self.rec_image_shape)
|
||||
norm_img = norm_img[np.newaxis, :]
|
||||
norm_img_batch.append(norm_img)
|
||||
else:
|
||||
norm_img = self.resize_norm_img(img_list[indices[ino]],
|
||||
max_wh_ratio)
|
||||
norm_img = norm_img[np.newaxis, :]
|
||||
norm_img_batch.append(norm_img)
|
||||
norm_img_batch = np.concatenate(norm_img_batch)
|
||||
norm_img_batch = norm_img_batch.copy()
|
||||
if self.benchmark:
|
||||
|
||||
@@ -271,9 +271,10 @@ def create_predictor(args, mode, logger):
|
||||
elif mode == "rec":
|
||||
if args.rec_algorithm != "CRNN":
|
||||
use_dynamic_shape = False
|
||||
min_input_shape = {"x": [1, 3, 32, 10]}
|
||||
max_input_shape = {"x": [args.rec_batch_num, 3, 32, 1536]}
|
||||
opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]}
|
||||
imgH = int(args.rec_image_shape.split(',')[-2])
|
||||
min_input_shape = {"x": [1, 3, imgH, 10]}
|
||||
max_input_shape = {"x": [args.rec_batch_num, 3, imgH, 1536]}
|
||||
opt_input_shape = {"x": [args.rec_batch_num, 3, imgH, 320]}
|
||||
elif mode == "cls":
|
||||
min_input_shape = {"x": [1, 3, 48, 10]}
|
||||
max_input_shape = {"x": [args.rec_batch_num, 3, 48, 1024]}
|
||||
@@ -300,8 +301,8 @@ def create_predictor(args, mode, logger):
|
||||
# enable memory optim
|
||||
config.enable_memory_optim()
|
||||
config.disable_glog_info()
|
||||
|
||||
config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
|
||||
config.delete_pass("matmul_transpose_reshape_fuse_pass")
|
||||
if mode == 'table':
|
||||
config.delete_pass("fc_fuse_pass") # not supported for table
|
||||
config.switch_use_feed_fetch_ops(False)
|
||||
|
||||
@@ -57,6 +57,8 @@ def main():
|
||||
continue
|
||||
elif op_name == 'KeepKeys':
|
||||
op[op_name]['keep_keys'] = ['image']
|
||||
elif op_name == "SSLRotateResize":
|
||||
op[op_name]["mode"] = "test"
|
||||
transforms.append(op)
|
||||
global_config['infer_mode'] = True
|
||||
ops = create_operators(transforms, global_config)
|
||||
|
||||
+4
-2
@@ -202,9 +202,11 @@ def train(config,
|
||||
|
||||
use_srn = config['Architecture']['algorithm'] == "SRN"
|
||||
extra_input_models = ["SRN", "NRTR", "SAR", "SEED", "SVTR"]
|
||||
extra_input = False
|
||||
if config['Architecture']['algorithm'] == 'Distillation':
|
||||
extra_input = config['Architecture']['Models']['Teacher'][
|
||||
'algorithm'] in extra_input_models
|
||||
for key in config['Architecture']["Models"]:
|
||||
extra_input = extra_input or config['Architecture']['Models'][key][
|
||||
'algorithm'] in extra_input_models
|
||||
else:
|
||||
extra_input = config['Architecture']['algorithm'] in extra_input_models
|
||||
try:
|
||||
|
||||
+1
-1
@@ -129,7 +129,7 @@ def main(config, device, logger, vdl_writer):
|
||||
config['Optimizer'],
|
||||
epochs=config['Global']['epoch_num'],
|
||||
step_each_epoch=len(train_dataloader),
|
||||
parameters=model.parameters())
|
||||
model=model)
|
||||
|
||||
# build metric
|
||||
eval_class = build_metric(config['Metric'])
|
||||
|
||||
Reference in New Issue
Block a user