[LatencyPredictor] Add some ops for ocr and tinypose. (PaddlePaddle#991)

467718584 · Feb 16, 2022 · 0706349 · 0706349
1 parent f89fc93
commit 0706349
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Showing 6 changed files with 199 additions and 86 deletions.
diff --git a/docs/zh_cn/tutorials/analysis/dygraph/latency_predictor.md b/docs/zh_cn/tutorials/analysis/dygraph/latency_predictor.md
@@ -37,9 +37,9 @@ tar -xf mobilenetv1.tar
 ### 2.2 预估推理延时
 构造 TableLatencyPredictor 类实例，并调用 predict 函数预估推理模型的延时。
 ```
-import paddleslim
+from paddleslim.analysis import TableLatencyPredictor
 
-predictor = paddleslim.TableLatencyPredictor(table_file='SD710')
+predictor = TableLatencyPredictor(table_file='SD710')
 latency = predictor.predict(model_file='mobilenetv1_fp32.pdmodel', param_file='mobilenetv1_fp32.pdiparams, data_type='fp32')
 print('predicted latency = {}ms'.format(latency))
 ```
@@ -58,9 +58,9 @@ print('predicted latency = {}ms'.format(latency))
 ### 3.2 支持预估 INT8 模型
 延时预估器支持对 INT8 量化模型进行延时预估，仅需提供 INT8 量化保存的推理模型文件，并将在调用 predict 函数时，设置 data_type='int8'，如下所示：
 ```
-import paddleslim
+from paddleslim.analysis import TableLatencyPredictor
 
-predictor = paddleslim.TableLatencyPredictor(table_file='SD710')
+predictor = TableLatencyPredictor(table_file='SD710')
 predictor.predict(model_file='mobilenetv1_int8.pdmodel', param_file='mobilenetv1_int8.pdiparams, data_type='int8')
 ```
 

diff --git a/paddleslim/analysis/_utils.py b/paddleslim/analysis/_utils.py
@@ -31,7 +31,7 @@ def opt_model(opt="paddle_lite_opt",
               optimize_out_type='protobuf',
               valid_targets='arm'):
     assert os.path.exists(model_file) and os.path.exists(
-        param_file), f'{model_file} or {param_file} is not existed.'
+        param_file), f'{model_file} or {param_file} does not exist.'
     save_dir = f'./opt_models_tmp/{os.getpid()}'
     if not os.path.exists(save_dir):
         os.makedirs(save_dir)
@@ -225,11 +225,11 @@ def distance(x, y):
     return latency[idx]
 
 
-def dowload_predictor(op_dir, op):
-    """Dowload op predictors' model file
+def download_predictor(op_dir, op):
+    """Download op predictors' model file
         
         Args:
-            op_dir(str): the dowload path of op predictor. Actually, it's the hardware information. 
+            op_dir(str): the path to op predictor. Actually, it's the hardware information. 
             op(str): the op type.
         Returns:
             op_path: The path of the file.
@@ -252,7 +252,7 @@ def load_predictor(op_type, op_dir, data_type='fp32'):
     elif 'matmul' in op_type:
         op = 'matmul'
 
-    op_path = dowload_predictor(op_dir, op)
+    op_path = download_predictor(op_dir, op)
     with open(op_path, 'rb') as f:
         model = pickle.load(f)