Update code when get_inference_program is removed.

python/paddle/fluid/contrib/tests/test_quantize_transpiler.py

@@ -177,21 +177,30 @@ class TestQuantizeTranspiler(unittest.TestCase):
         self.residual_block_quant('range_abs_max')
 
     def freeze_program(self, use_cuda):
-        main = fluid.Program()
-        startup = fluid.Program()
-        quant_transpiler = QuantizeTranspiler()
+        def build_program(main, startup, is_test):
+            with fluid.unique_name.guard():
                 with fluid.program_guard(main, startup):
                     img = fluid.layers.data(
                         name='image', shape=[1, 28, 28], dtype='float32')
-            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+                    label = fluid.layers.data(
+                        name='label', shape=[1], dtype='int64')
                     loss = conv_net(img, label)
+                    if not is_test:
                         opt = fluid.optimizer.Adam(learning_rate=0.001)
                         opt.minimize(loss)
-            quant_transpiler.training_transpile(main)
+            return [img, label], loss
 
-        test_program = main.clone()
-        with fluid.program_guard(test_program):
-            test_program = fluid.io.get_inference_program(loss)
+        main = fluid.Program()
+        startup = fluid.Program()
+        test_program = fluid.Program()
+
+        feeds, loss = build_program(main, startup, True)
+        build_program(test_program, startup, True)
+        test_program = test_program.clone(for_test=True)
+
+        quant_transpiler = QuantizeTranspiler()
+        quant_transpiler.training_transpile(main)
+        quant_transpiler.training_transpile(test_program)
 
         place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
         exe = fluid.Executor(place)
@@ -206,7 +215,7 @@ class TestQuantizeTranspiler(unittest.TestCase):
             batch_size=batch_size)
         test_reader = paddle.batch(
             paddle.dataset.mnist.test(), batch_size=batch_size)
-        feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
+        feeder = fluid.DataFeeder(feed_list=feeds, place=place)
 
         with fluid.program_guard(main):
             for _ in range(iter):