Update test_pe_mnist threshold (#12348)

* update test_pe_mnist threshold

* clean code

python/paddle/fluid/tests/unittests/test_parallel_executor_mnist.py

@@ -107,44 +107,24 @@ class TestMNIST(TestParallelExecutorBase):
         label = np.ones(shape=[32, 1], dtype='int64')
         return img, label
 
-    # simple_fc
-    def check_simple_fc_convergence(self, use_cuda, use_reduce=False):
+    def _compare_reduce_and_allreduce(self, model, use_cuda, random_data=True):
         if use_cuda and not core.is_compiled_with_cuda():
             return
-        self.check_network_convergence(simple_fc_net, use_cuda=use_cuda)
         self.check_network_convergence(
-            simple_fc_net, use_cuda=use_cuda, allow_op_delay=True)
-
-        img, label = self._init_data()
-
+            model, use_cuda=use_cuda, use_reduce=True)
         self.check_network_convergence(
-            simple_fc_net,
-            feed_dict={"image": img,
-                       "label": label},
-            use_cuda=use_cuda,
-            use_reduce=use_reduce)
+            model, use_cuda=use_cuda, allow_op_delay=True, use_reduce=True)
 
-    def check_simple_fc_convergence_with_Reduce(self, use_cuda):
-        if use_cuda and not core.is_compiled_with_cuda():
-            return
-        self.check_network_convergence(
-            simple_fc_net, use_cuda=use_cuda, use_reduce=True)
-        self.check_network_convergence(
-            simple_fc_net,
-            use_cuda=use_cuda,
-            allow_op_delay=True,
-            use_reduce=True)
-
-        img, label = self._init_data()
+        img, label = self._init_data(random_data)
 
         all_reduce_first_loss, all_reduce_last_loss = self.check_network_convergence(
-            simple_fc_net,
+            model,
             feed_dict={"image": img,
                        "label": label},
             use_cuda=use_cuda,
             use_reduce=False)
         reduce_first_loss, reduce_last_loss = self.check_network_convergence(
-            simple_fc_net,
+            model,
             feed_dict={"image": img,
                        "label": label},
             use_cuda=use_cuda,
@@ -153,7 +133,24 @@ class TestMNIST(TestParallelExecutorBase):
         for loss in zip(all_reduce_first_loss, reduce_first_loss):
             self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
         for loss in zip(all_reduce_last_loss, reduce_last_loss):
-            self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
+            self.assertAlmostEquals(loss[0], loss[1], delta=1e-4)
+
+    # simple_fc
+    def check_simple_fc_convergence(self, use_cuda, use_reduce=False):
+        if use_cuda and not core.is_compiled_with_cuda():
+            return
+        self.check_network_convergence(simple_fc_net, use_cuda=use_cuda)
+        self.check_network_convergence(
+            simple_fc_net, use_cuda=use_cuda, allow_op_delay=True)
+
+        img, label = self._init_data()
+
+        self.check_network_convergence(
+            simple_fc_net,
+            feed_dict={"image": img,
+                       "label": label},
+            use_cuda=use_cuda,
+            use_reduce=use_reduce)
 
     def test_simple_fc(self):
         # use_cuda
@@ -162,8 +159,8 @@ class TestMNIST(TestParallelExecutorBase):
 
     def test_simple_fc_with_new_strategy(self):
         # use_cuda, use_reduce
-        self.check_simple_fc_convergence_with_Reduce(True)
-        self.check_simple_fc_convergence_with_Reduce(False)
+        self._compare_reduce_and_allreduce(simple_fc_net, True)
+        self._compare_reduce_and_allreduce(simple_fc_net, False)
 
     def check_simple_fc_parallel_accuracy(self, use_cuda):
         if use_cuda and not core.is_compiled_with_cuda():
@@ -209,39 +206,13 @@ class TestMNIST(TestParallelExecutorBase):
                        "label": label},
             use_cuda=use_cuda)
 
-    def check_batchnorm_fc_convergence_use_reduce(self, use_cuda):
-        if use_cuda and not core.is_compiled_with_cuda():
-            return
-        self.check_network_convergence(
-            fc_with_batchnorm, use_cuda=use_cuda, use_reduce=True)
-
-        img, label = self._init_data()
-
-        all_reduce_first_loss, all_reduce_last_loss = self.check_network_convergence(
-            fc_with_batchnorm,
-            feed_dict={"image": img,
-                       "label": label},
-            use_cuda=use_cuda,
-            use_reduce=False)
-        reduce_first_loss, reduce_last_loss = self.check_network_convergence(
-            fc_with_batchnorm,
-            feed_dict={"image": img,
-                       "label": label},
-            use_cuda=use_cuda,
-            use_reduce=True)
-
-        for loss in zip(all_reduce_first_loss, reduce_first_loss):
-            self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
-        for loss in zip(all_reduce_last_loss, reduce_last_loss):
-            self.assertAlmostEquals(loss[0], loss[1], delta=1e-4)
-
     def test_batchnorm_fc(self):
         self.check_batchnorm_fc_convergence(True)
         self.check_batchnorm_fc_convergence(False)
 
     def test_batchnorm_fc_with_new_strategy(self):
-        self.check_batchnorm_fc_convergence_use_reduce(True)
-        self.check_batchnorm_fc_convergence_use_reduce(False)
+        self._compare_reduce_and_allreduce(fc_with_batchnorm, True)
+        self._compare_reduce_and_allreduce(fc_with_batchnorm, False)
 
 
 if __name__ == '__main__':