Update test_cross_entropy_loss.py

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

@@ -848,30 +848,6 @@ class CrossEntropyLoss(unittest.TestCase):
         label_np = np.random.randint(0, C, size=(N)).astype(np.int64)
         label_np[0] = 255
         weight_np = np.random.random([C]).astype(self.dtype)
-        paddle.enable_static()
-        prog = fluid.Program()
-        startup_prog = fluid.Program()
-        place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
-        ) else fluid.CPUPlace()
-        with fluid.program_guard(prog, startup_prog):
-            input = fluid.data(name='input', shape=[N, C], dtype=self.dtype)
-            label = fluid.data(name='label', shape=[N], dtype='int64')
-            weight = fluid.data(
-                name='weight', shape=[C],
-                dtype=self.dtype)  #weight for each class
-            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
-                weight=weight, ignore_index=255)
-            ret = cross_entropy_loss(input, label)
-
-            exe = fluid.Executor(place)
-            static_ret = exe.run(prog,
-                                 feed={
-                                     'input': input_np,
-                                     'label': label_np,
-                                     "weight": weight_np
-                                 },
-                                 fetch_list=[ret])
-            self.assertIsNotNone(static_ret)
 
         with fluid.dygraph.guard():
             cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
@@ -886,8 +862,6 @@ class CrossEntropyLoss(unittest.TestCase):
         expected = cross_entropy_loss_1d(
             input_np, label_np, weight=weight_np, ignore_index=255)[0]
 
-        self.assertTrue(np.allclose(static_ret, dy_ret_value))
-        self.assertTrue(np.allclose(static_ret, expected))
         self.assertTrue(np.allclose(dy_ret_value, expected))
 
     def test_cross_entropy_loss_1d_with_weight_mean(self):
@@ -1214,31 +1188,6 @@ class CrossEntropyLoss(unittest.TestCase):
         label_np = np.random.randint(0, C, size=(N, H, W)).astype(np.int64)
         label_np[0, 0, 0] = 255
         weight_np = np.random.random([C]).astype(self.dtype)
-        paddle.enable_static()
-        prog = fluid.Program()
-        startup_prog = fluid.Program()
-        place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
-        ) else fluid.CPUPlace()
-        with fluid.program_guard(prog, startup_prog):
-            input = fluid.data(
-                name='input', shape=[N, H, W, C], dtype=self.dtype)
-            label = fluid.data(name='label', shape=[N, H, W], dtype='int64')
-            weight = fluid.data(
-                name='weight', shape=[C],
-                dtype=self.dtype)  #weight for each class
-            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
-                weight=weight, ignore_index=255)
-            ret = cross_entropy_loss(input, label)
-
-            exe = fluid.Executor(place)
-            static_ret = exe.run(prog,
-                                 feed={
-                                     'input': input_np,
-                                     'label': label_np,
-                                     "weight": weight_np
-                                 },
-                                 fetch_list=[ret])
-            self.assertIsNotNone(static_ret)
 
         with fluid.dygraph.guard():
             cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(