Fix some typos for Jetson, metrics and result. (#6118)

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,
             }

ppocr/metrics/det_metric.py

@@ -65,9 +65,9 @@ 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

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

@@ -59,9 +59,9 @@ 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

ppstructure/vqa/infer_ser_e2e.py

@@ -149,7 +149,7 @@ if __name__ == "__main__":
             result, _ = ser_engine(img)
             fout.write(img_path + "\t" + json.dumps(
                 {
-                    "ser_resule": result,
+                    "ser_result": result,
                 }, ensure_ascii=False) + "\n")
 
             img_res = draw_ser_results(img, result)

ppstructure/vqa/train_re.py

@@ -145,7 +145,7 @@ def train(args):
     global_step = 0
     model.clear_gradients()
     train_dataloader_len = len(train_dataloader)
-    best_metirc = {'f1': 0}
+    best_metric = {'f1': 0}
     model.train()
 
     train_reader_cost = 0.0
@@ -192,8 +192,8 @@ def train(args):
                 # Log metrics
                 # Only evaluate when single GPU otherwise metrics may not average well
                 results = evaluate(model, eval_dataloader, logger)
-                if results['f1'] >= best_metirc['f1']:
-                    best_metirc = results
+                if results['f1'] >= best_metric['f1']:
+                    best_metric = results
                     output_dir = os.path.join(args.output_dir, "best_model")
                     os.makedirs(output_dir, exist_ok=True)
                     if distributed:
@@ -206,7 +206,7 @@ def train(args):
                     logger.info("Saving model checkpoint to {}".format(
                         output_dir))
                 logger.info("eval results: {}".format(results))
-                logger.info("best_metirc: {}".format(best_metirc))
+                logger.info("best_metric: {}".format(best_metric))
             reader_start = time.time()
 
         if rank == 0:
@@ -220,7 +220,7 @@ def train(args):
             tokenizer.save_pretrained(output_dir)
             paddle.save(args, os.path.join(output_dir, "training_args.bin"))
             logger.info("Saving model checkpoint to {}".format(output_dir))
-    logger.info("best_metirc: {}".format(best_metirc))
+    logger.info("best_metric: {}".format(best_metric))
 
 
 if __name__ == "__main__":

test_tipc/docs/jeston_test_train_inference_python.md

@@ -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：

tools/infer/predict_system.py

@@ -93,11 +93,11 @@ class TextSystem(object):
             self.draw_crop_rec_res(self.args.crop_res_save_dir, img_crop_list,
                                    rec_res)
         filter_boxes, filter_rec_res = [], []
-        for box, rec_reuslt in zip(dt_boxes, rec_res):
-            text, score = rec_reuslt
+        for box, rec_result in zip(dt_boxes, rec_res):
+            text, score = rec_result
             if score >= self.drop_score:
                 filter_boxes.append(box)
-                filter_rec_res.append(rec_reuslt)
+                filter_rec_res.append(rec_result)
         return filter_boxes, filter_rec_res
 
 

tools/infer/utility.py

@@ -187,7 +187,7 @@ 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_cls.py

@@ -73,8 +73,8 @@ def main():
         images = paddle.to_tensor(images)
         preds = model(images)
         post_result = post_process_class(preds)
-        for rec_reuslt in post_result:
-            logger.info('\t result: {}'.format(rec_reuslt))
+        for rec_result in post_result:
+            logger.info('\t result: {}'.format(rec_result))
     logger.info("success!")
 
 

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}