[auto-compression] solve prediction problems and fix docs. (#1142)

* solve prediction problems.

* fix docs.

demo/auto_compression/detection/README.md

@@ -45,7 +45,8 @@ pip install paddlepaddle-gpu
 
 安装paddleslim：
 ```shell
-pip install paddleslim
+https://github.com/PaddlePaddle/PaddleSlim.git
+python setup.py install
 ```
 
 安装paddledet：

demo/auto_compression/image_classification/README.md

@@ -43,7 +43,8 @@ pip install paddlepaddle-gpu
 
 安装paddleslim：
 ```shell
-pip install paddleslim
+https://github.com/PaddlePaddle/PaddleSlim.git
+python setup.py install
 ```
 
 #### 3.2 准备数据集

demo/auto_compression/nlp/README.md

@@ -56,7 +56,8 @@ pip install paddlepaddle-gpu
 
 安装paddleslim：
 ```shell
-pip install paddleslim
+https://github.com/PaddlePaddle/PaddleSlim.git
+python setup.py install
 ```
 
 安装paddlenlp：

demo/auto_compression/semantic_segmentation/README.md

@@ -48,7 +48,8 @@ pip install paddlepaddle-gpu
 
 安装paddleslim：
 ```shell
-pip install paddleslim
+https://github.com/PaddlePaddle/PaddleSlim.git
+python setup.py install
 ```
 
 安装paddleseg

paddleslim/auto_compression/utils/predict.py

@@ -27,9 +27,9 @@ def predict_compressed_model(model_dir,
         latency_dict(dict): The latency latency of the model under various compression strategies.
     """
     local_rank = paddle.distributed.get_rank()
-    quant_model_path = f'quant_model/rank_{local_rank}'
-    prune_model_path = f'prune_model/rank_{local_rank}'
-    sparse_model_path = f'sparse_model/rank_{local_rank}'
+    quant_model_path = f'quant_model_rank_{local_rank}_tmp'
+    prune_model_path = f'prune_model_rank_{local_rank}_tmp'
+    sparse_model_path = f'sparse_model_rank_{local_rank}_tmp'
 
     latency_dict = {}
 
@@ -116,7 +116,7 @@ def predict_compressed_model(model_dir,
             model_dir=sparse_model_path,
             model_filename=model_filename,
             params_filename=params_filename,
-            save_model_path='quant_model',
+            save_model_path=quant_model_path,
             quantizable_op_type=["conv2d", "depthwise_conv2d", "mul"],
             is_full_quantize=False,
             activation_bits=8,
@@ -131,10 +131,10 @@ def predict_compressed_model(model_dir,
         latency_dict.update({f'sparse_{sparse_ratio}_int8': latency})
 
     # NOTE: Delete temporary model files
-    if os.path.exists('quant_model'):
-        shutil.rmtree('quant_model', ignore_errors=True)
-    if os.path.exists('prune_model'):
-        shutil.rmtree('prune_model', ignore_errors=True)
-    if os.path.exists('sparse_model'):
-        shutil.rmtree('sparse_model', ignore_errors=True)
+    if os.path.exists(quant_model_path):
+        shutil.rmtree(quant_model_path, ignore_errors=True)
+    if os.path.exists(prune_model_path):
+        shutil.rmtree(prune_model_path, ignore_errors=True)
+    if os.path.exists(sparse_model_path):
+        shutil.rmtree(sparse_model_path, ignore_errors=True)
     return latency_dict

paddleslim/auto_compression/utils/prune_model.py

@@ -122,7 +122,7 @@ def get_prune_model(model_file, param_file, ratio, save_path):
     main_prog = static.Program()
     startup_prog = static.Program()
     place = paddle.CPUPlace()
-    exe = paddle.static.Executor()
+    exe = paddle.static.Executor(place)
     scope = static.global_scope()
     exe.run(startup_prog)