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[cherry-pick] `Fix some typos for Jetson, metrics and result` from release/2.4-typos2 branch and merge. (#6119) 

* [cherry-pick] Fix some typos for Jetson, metrics and result from release/2.4-typos2 branch and merge.
pull/6128/head
Shuangchi He 2022-05-02 14:01:29 +08:00 committed by GitHub
parent e03e52a2a4
commit 55d8af94f5
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13 changed files with 34 additions and 34 deletions
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2
applications/多模态表单识别.md
View File

@ -809,7 +809,7 @@ plt.imshow(img)
```
fout.write(img_path + "\t" + json.dumps(
{
"ser_resule": result,
"ser_result": result,
}, ensure_ascii=False) + "\n")
```

2
doc/doc_ch/add_new_algorithm.md
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@ -246,7 +246,7 @@ class MyMetric(object):
def get_metric(self):
"""
return metircs {
return metrics {
'acc': 0,
'norm_edit_dis': 0,
}

2
doc/doc_en/add_new_algorithm_en.md
View File

@ -237,7 +237,7 @@ class MyMetric(object):
def get_metric(self):
"""
return metircs {
return metrics {
'acc': 0,
'norm_edit_dis': 0,
}

6
notebook/notebook_ch/5.ppocrv2_inference_deployment/PP-OCRv2预测部署实战.ipynb
View File

@ -1876,11 +1876,11 @@
" rec_res)\n",
" filter_boxes, filter_rec_res = [], []\n",
" # 根据识别得分的阈值对结果进行过滤，如果得分小于阈值，就过滤掉\n",
" for box, rec_reuslt in zip(dt_boxes, rec_res):\n",
" text, score = rec_reuslt\n",
" for box, rec_result in zip(dt_boxes, rec_res):\n",
" text, score = rec_result\n",
" if score >= self.drop_score:\n",
" filter_boxes.append(box)\n",
" filter_rec_res.append(rec_reuslt)\n",
" filter_rec_res.append(rec_result)\n",
" return filter_boxes, filter_rec_res\n",
"\n",
"def sorted_boxes(dt_boxes):\n",

6
notebook/notebook_en/5.ppocrv2_inference_deployment/ppocrv2_inference_deployment_practice.ipynb
View File

@ -1886,11 +1886,11 @@
" rec_res)\n",
" filter_boxes, filter_rec_res = [], []\n",
" #Filter the results according to the threshold of the recognition score, if the score is less than the threshold, filter out\n",
" for box, rec_reuslt in zip(dt_boxes, rec_res):\n",
" text, score = rec_reuslt\n",
" for box, rec_result in zip(dt_boxes, rec_res):\n",
" text, score = rec_result\n",
" if score >= self.drop_score:\n",
" filter_boxes.append(box)\n",
" filter_rec_res.append(rec_reuslt)\n",
" filter_rec_res.append(rec_result)\n",
" return filter_boxes, filter_rec_res\n",
"\n",
"def sorted_boxes(dt_boxes):\n",

24
ppocr/metrics/det_metric.py
View File

@ -64,9 +64,9 @@ class DetMetric(object):
}
"""
metircs = self.evaluator.combine_results(self.results)
metrics = self.evaluator.combine_results(self.results)
self.reset()
return metircs
return metrics
def reset(self):
self.results = [] # clear results
@ -127,20 +127,20 @@ class DetFCEMetric(object):
'thr 0.9':'precision: 0 recall: 0 hmean: 0',
}
"""
metircs = {}
metrics = {}
hmean = 0
for score_thr in self.results.keys():
metirc = self.evaluator.combine_results(self.results[score_thr])
# for key, value in metirc.items():
# metircs['{}_{}'.format(key, score_thr)] = value
metirc_str = 'precision:{:.5f} recall:{:.5f} hmean:{:.5f}'.format(
metirc['precision'], metirc['recall'], metirc['hmean'])
metircs['thr {}'.format(score_thr)] = metirc_str
hmean = max(hmean, metirc['hmean'])
metircs['hmean'] = hmean
metric = self.evaluator.combine_results(self.results[score_thr])
# for key, value in metric.items():
# metrics['{}_{}'.format(key, score_thr)] = value
metric_str = 'precision:{:.5f} recall:{:.5f} hmean:{:.5f}'.format(
metric['precision'], metric['recall'], metric['hmean'])
metrics['thr {}'.format(score_thr)] = metric_str
hmean = max(hmean, metric['hmean'])
metrics['hmean'] = hmean
self.reset()
return metircs
return metrics
def reset(self):
self.results = {

4
ppocr/metrics/e2e_metric.py
View File

@ -78,9 +78,9 @@ class E2EMetric(object):
self.results.append(result)
def get_metric(self):
metircs = combine_results(self.results)
metrics = combine_results(self.results)
self.reset()
return metircs
return metrics
def reset(self):
self.results = [] # clear results

4
ppocr/metrics/kie_metric.py
View File

@ -61,9 +61,9 @@ class KIEMetric(object):
def get_metric(self):
metircs = self.combine_results(self.results)
metrics = self.combine_results(self.results)
self.reset()
return metircs
return metrics
def reset(self):
self.results = [] # clear results

4
ppocr/metrics/vqa_token_ser_metric.py
View File

@ -34,13 +34,13 @@ class VQASerTokenMetric(object):
def get_metric(self):
from seqeval.metrics import f1_score, precision_score, recall_score
metircs = {
metrics = {
"precision": precision_score(self.gt_list, self.pred_list),
"recall": recall_score(self.gt_list, self.pred_list),
"hmean": f1_score(self.gt_list, self.pred_list),
}
self.reset()
return metircs
return metrics
def reset(self):
self.pred_list = []

8
test_tipc/docs/jeston_test_train_inference_python.md
View File

@ -1,6 +1,6 @@
# Jeston端基础训练预测功能测试
# Jetson端基础训练预测功能测试
Jeston端基础训练预测功能测试的主程序为`test_inference_inference.sh`，由于Jeston端CPU较差，Jeston只需要测试TIPC关于GPU和TensorRT预测推理的部分即可。
Jetson端基础训练预测功能测试的主程序为`test_inference_inference.sh`，由于Jetson端CPU较差，Jetson只需要测试TIPC关于GPU和TensorRT预测推理的部分即可。
## 1. 测试结论汇总
@ -42,7 +42,7 @@ Jeston端基础训练预测功能测试的主程序为`test_inference_inference.
先运行`prepare.sh`准备数据和模型，然后运行`test_inference_inference.sh`进行测试，最终在```test_tipc/output```目录下生成`python_infer_*.log`格式的日志文件。
`test_inference_inference.sh`仅有一个模式`whole_infer`，在Jeston端，仅需要测试预测推理的模式即可：
`test_inference_inference.sh`仅有一个模式`whole_infer`，在Jetson端，仅需要测试预测推理的模式即可：
```
- 模式3：whole_infer，不训练，全量数据预测，走通开源模型评估、动转静，检查inference model预测时间和精度;
@ -51,7 +51,7 @@ bash test_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_lin
# 用法1:
bash test_tipc/test_inference_inference.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_python_jetson.txt 'whole_infer'
# 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
bash test_tipc/test_inference_jeston.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_python_jetson.txt 'whole_infer' '1'
bash test_tipc/test_inference_jetson.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_python_jetson.txt 'whole_infer' '1'
```
运行相应指令后，在`test_tipc/output`文件夹下自动会保存运行日志。如`whole_infer`模式下，会运行训练+inference的链条，因此，在`test_tipc/output`文件夹有以下文件：

2
tools/infer/utility.py
View File

@ -193,7 +193,7 @@ def create_predictor(args, mode, logger):
gpu_id = get_infer_gpuid()
if gpu_id is None:
logger.warning(
"GPU is not found in current device by nvidia-smi. Please check your device or ignore it if run on jeston."
"GPU is not found in current device by nvidia-smi. Please check your device or ignore it if run on jetson."
)
config.enable_use_gpu(args.gpu_mem, 0)
if args.use_tensorrt:

2
tools/infer_e2e.py
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@ -104,7 +104,7 @@ def main():
preds = model(images)
post_result = post_process_class(preds, shape_list)
points, strs = post_result['points'], post_result['texts']
# write resule
# write result
dt_boxes_json = []
for poly, str in zip(points, strs):
tmp_json = {"transcription": str}

2
tools/infer_vqa_token_ser_re.py
View File

@ -193,7 +193,7 @@ if __name__ == '__main__':
result = result[0]
fout.write(img_path + "\t" + json.dumps(
{
"ser_resule": result,
"ser_result": result,
}, ensure_ascii=False) + "\n")
img_res = draw_re_results(img_path, result)
cv2.imwrite(save_img_path, img_res)