* Reduce the training iterations in test_fetch_unmerged and test_fuse_bn_act_pass. (#27234)

* Use the single GPU card to execute the test_fuse_bn_act_pass UT.

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -559,7 +559,7 @@ endif()
 set_tests_properties(test_parallel_executor_test_while_train test_parallel_executor_mnist
         test_parallel_executor_feed_persistable_var
         test_buffer_shared_memory_reuse_pass_and_fuse_optimization_op_pass
-        test_data_norm_op test_imperative_using_non_zero_gpu test_fuse_bn_act_pass
+        test_data_norm_op test_imperative_using_non_zero_gpu
         test_dataloader_keep_order
         test_dataloader_unkeep_order
         test_parallel_executor_fetch_isolated_var

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

@@ -28,7 +28,7 @@ class TestFetchUnmerged(unittest.TestCase):
         conv_pool_1 = fluid.nets.simple_img_conv_pool(
             input=img,
             filter_size=5,
-            num_filters=20,
+            num_filters=8,
             pool_size=2,
             pool_stride=2,
             pool_type='max',
@@ -37,12 +37,12 @@ class TestFetchUnmerged(unittest.TestCase):
         conv_pool_2 = fluid.nets.simple_img_conv_pool(
             input=conv_pool_1,
             filter_size=5,
-            num_filters=50,
+            num_filters=16,
             pool_size=2,
             pool_stride=2,
             pool_type='avg',
             act="relu")
-        hidden = fluid.layers.fc(input=conv_pool_2, size=100, act='relu')
+        hidden = fluid.layers.fc(input=conv_pool_2, size=32, act='relu')
         prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
         loss = fluid.layers.cross_entropy(input=prediction, label=label)
         avg_loss = fluid.layers.mean(loss)
@@ -75,8 +75,8 @@ class TestFetchUnmerged(unittest.TestCase):
         binary = fluid.CompiledProgram(main_program).with_data_parallel(
             loss_name=loss.name, build_strategy=build_strategy)
 
-        iters = 3
-        batch_size = 64
+        iters = 2
+        batch_size = 16
         train_reader = paddle.batch(
             paddle.reader.shuffle(
                 paddle.dataset.mnist.train(), buf_size=500),

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

@@ -25,7 +25,7 @@ class TestFuseBatchNormActPass(unittest.TestCase):
             hidden1 = fluid.layers.conv2d(
                 input=x,
                 filter_size=3,
-                num_filters=32,
+                num_filters=16,
                 stride=1,
                 padding=1,
                 act=None,
@@ -43,7 +43,7 @@ class TestFuseBatchNormActPass(unittest.TestCase):
                 bias_attr=bias_attr,
                 act='relu',
                 data_layout='NHWC')
-            hidden3 = fluid.layers.fc(input=hidden2, size=128, act='relu')
+            hidden3 = fluid.layers.fc(input=hidden2, size=32, act='relu')
             hidden4 = fluid.layers.batch_norm(
                 input=hidden3, act='relu', data_layout='NHWC')
             prediction = fluid.layers.fc(input=hidden4, size=10, act='softmax')
@@ -63,7 +63,7 @@ class TestFuseBatchNormActPass(unittest.TestCase):
         startup_program = fluid.Program()
         x, y, loss = self.build_program(main_program, startup_program, use_cuda)
         exe = fluid.Executor(place)
-        iters = 10
+        iters = 8
         batch_size = 16
         feeder = fluid.DataFeeder(feed_list=[x, y], place=place)