fix use cache file, test=develop (#22240)

python/paddle/fluid/contrib/slim/tests/test_post_training_quantization_mobilenetv1.py

@@ -236,6 +236,7 @@ class TestPostTrainingQuantization(unittest.TestCase):
 
     def generate_quantized_model(self,
                                  model_path,
+                                 quantizable_op_type,
                                  algo="KL",
                                  is_full_quantize=False,
                                  is_use_cache_file=False):
@@ -250,9 +251,6 @@ class TestPostTrainingQuantization(unittest.TestCase):
         exe = fluid.Executor(place)
         scope = fluid.global_scope()
         val_reader = val()
-        quantizable_op_type = [
-            "conv2d", "depthwise_conv2d", "mul", "pool2d", "elementwise_add"
-        ]
 
         ptq = PostTrainingQuantization(
             executor=exe,
@@ -265,8 +263,8 @@ class TestPostTrainingQuantization(unittest.TestCase):
         ptq.quantize()
         ptq.save_quantized_model(self.int8_model)
 
-    def run_test(self, model, algo, data_urls, data_md5s, is_full_quantize,
-                 is_use_cache_file):
+    def run_test(self, model, algo, data_urls, data_md5s, quantizable_op_type,
+                 is_full_quantize, is_use_cache_file):
         infer_iterations = self.infer_iterations
         batch_size = self.batch_size
         sample_iterations = self.sample_iterations
@@ -280,7 +278,8 @@ class TestPostTrainingQuantization(unittest.TestCase):
 
         print("Start INT8 post training quantization for {0} on {1} images ...".
               format(model, sample_iterations * batch_size))
-        self.generate_quantized_model(model_cache_folder + "/model", algo,
+        self.generate_quantized_model(model_cache_folder + "/model",
+                                      quantizable_op_type, algo,
                                       is_full_quantize, is_use_cache_file)
 
         print("Start INT8 inference for {0} on {1} images ...".format(
@@ -308,10 +307,13 @@ class TestPostTrainingForMobilenetv1(TestPostTrainingQuantization):
             'http://paddle-inference-dist.bj.bcebos.com/int8/mobilenetv1_int8_model.tar.gz'
         ]
         data_md5s = ['13892b0716d26443a8cdea15b3c6438b']
+        quantizable_op_type = [
+            "conv2d", "depthwise_conv2d", "mul", "pool2d", "elementwise_add"
+        ]
         is_full_quantize = True
         is_use_cache_file = False
-        self.run_test(model, algo, data_urls, data_md5s, is_full_quantize,
-                      is_use_cache_file)
+        self.run_test(model, algo, data_urls, data_md5s, quantizable_op_type,
+                      is_full_quantize, is_use_cache_file)
 
 
 if __name__ == '__main__':

python/paddle/fluid/contrib/slim/tests/test_post_training_quantization_resnet50.py

@@ -25,10 +25,11 @@ class TestPostTrainingForResnet50(TestPostTrainingQuantization):
             'http://paddle-inference-dist.bj.bcebos.com/int8/resnet50_int8_model.tar.gz'
         ]
         data_md5s = ['4a5194524823d9b76da6e738e1367881']
+        quantizable_op_type = ["conv2d", "mul"]
         is_full_quantize = False
-        is_use_cache_file = True
-        self.run_test(model, algo, data_urls, data_md5s, is_full_quantize,
-                      is_use_cache_file)
+        is_use_cache_file = False
+        self.run_test(model, algo, data_urls, data_md5s, quantizable_op_type,
+                      is_full_quantize, is_use_cache_file)
 
 
 if __name__ == '__main__':