[cherry-pick] `Fix some typos for Jetson, metrics and result` from...

[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.

applications/多模态表单识别.md

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

doc/doc_ch/add_new_algorithm.md

@@ -246,7 +246,7 @@ class MyMetric(object):
 
     def get_metric(self):
         """
-        return metircs {
+        return metrics {
                  'acc': 0,
                  'norm_edit_dis': 0,
             }

doc/doc_en/add_new_algorithm_en.md

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

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

@@ -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",

notebook/notebook_en/5.ppocrv2_inference_deployment/ppocrv2_inference_deployment_practice.ipynb

@@ -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",

ppocr/metrics/det_metric.py

@@ -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 = {

ppocr/metrics/e2e_metric.py

@@ -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

ppocr/metrics/kie_metric.py

@@ -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

ppocr/metrics/vqa_token_ser_metric.py

@@ -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 = []

test_tipc/docs/jeston_test_train_inference_python.md

-# 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：

tools/infer/utility.py

@@ -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:

tools/infer_e2e.py

@@ -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}

tools/infer_vqa_token_ser_re.py

@@ -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)