complete test_async_ssa_graph_executor_mnist test=develop

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

@@ -18,60 +18,61 @@ import os
 import unittest
 
 import numpy
+import time
 import paddle
 import paddle.fluid as fluid
 
 BATCH_SIZE = 64
 
 
-def loss_net(hidden, label):
-    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)
-    acc = fluid.layers.accuracy(input=prediction, label=label)
-    return prediction, avg_loss, acc
-
-
-def convolutional_neural_network(img, label):
-    conv_pool_1 = fluid.nets.simple_img_conv_pool(
-        input=img,
-        filter_size=5,
-        num_filters=20,
-        pool_size=2,
-        pool_stride=2,
-        act="relu")
-    conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
-    conv_pool_2 = fluid.nets.simple_img_conv_pool(
-        input=conv_pool_1,
-        filter_size=5,
-        num_filters=50,
-        pool_size=2,
-        pool_stride=2,
-        act="relu")
-    return loss_net(conv_pool_2, label)
-
-
-def train(use_cuda, thread_num, cpu_num):
-    if use_cuda and not fluid.core.is_compiled_with_cuda():
-        print("paddle is not compiled with cuda, exit!")
-        return
-
-    img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
-    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
-
-    py_reader = fluid.layers.create_py_reader_by_data(
-        capacity=64,
-        feed_list=[img, label],
-        name='py_reader',
-        use_double_buffer=True)
-    img, label = fluid.layers.read_file(py_reader)
-
-    prediction, avg_loss, acc = convolutional_neural_network(img, label)
+def convolutional_neural_network(use_py_reader):
+    with fluid.unique_name.guard():
+        img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+        py_reader = None
+        if use_py_reader:
+            py_reader = fluid.layers.create_py_reader_by_data(
+                capacity=64,
+                feed_list=[img, label],
+                name='py_reader',
+                use_double_buffer=True)
+            img, label = fluid.layers.read_file(py_reader)
+
+        conv_pool_1 = fluid.nets.simple_img_conv_pool(
+            input=img,
+            filter_size=5,
+            num_filters=20,
+            pool_size=2,
+            pool_stride=2,
+            act="relu")
+        conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
+        conv_pool_2 = fluid.nets.simple_img_conv_pool(
+            input=conv_pool_1,
+            filter_size=5,
+            num_filters=50,
+            pool_size=2,
+            pool_stride=2,
+            act="relu")
+
+        prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
+        loss = fluid.layers.cross_entropy(input=prediction, label=label)
+        avg_loss = fluid.layers.mean(loss)
+        acc = fluid.layers.accuracy(input=prediction, label=label)
+
+        return img, label, prediction, avg_loss, acc, py_reader
+
+
+def test():
+    place = fluid.CPUPlace()
+    exe = fluid.Executor(place)
 
-    test_program = fluid.default_main_program().clone(for_test=True)
+    test_reader = paddle.batch(
+        paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
 
-    optimizer = fluid.optimizer.Adam(learning_rate=0.001)
-    optimizer.minimize(avg_loss)
+    img, label, prediction, avg_loss, acc, py_reader = convolutional_neural_network(
+        use_py_reader=False)
+    feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
 
     def train_test(train_test_program, train_test_feed, train_test_reader):
         acc_set = []
@@ -87,16 +88,33 @@ def train(use_cuda, thread_num, cpu_num):
         avg_loss_val_mean = numpy.array(avg_loss_set).mean()
         return avg_loss_val_mean, acc_val_mean
 
-    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    # test for epoch
+    avg_loss_val, acc_val = train_test(
+        train_test_program=fluid.default_main_program(),
+        train_test_reader=test_reader,
+        train_test_feed=feeder)
+
+    print("Test: avg_cost: %s, acc: %s" % (avg_loss_val, acc_val))
+    assert acc_val > 0.96
+
+
+def train(use_cuda, thread_num, cpu_num):
+    if use_cuda and not fluid.core.is_compiled_with_cuda():
+        print("paddle is not compiled with cuda, exit!")
+        return
+
+    img, label, prediction, avg_loss, acc, py_reader = convolutional_neural_network(
+        use_py_reader=True)
+
+    optimizer = fluid.optimizer.Adam(learning_rate=0.001)
+    optimizer.minimize(avg_loss)
 
     train_reader = paddle.batch(
         paddle.reader.shuffle(
             paddle.dataset.mnist.train(), buf_size=500),
         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)
 
+    place = fluid.CPUPlace()
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
 
@@ -106,11 +124,11 @@ def train(use_cuda, thread_num, cpu_num):
     print("thread_num:" + str(thread_num))
 
     build_strategy = fluid.BuildStrategy()
-    build_strategy.async_mode = True  # enable async mode
+    build_strategy.async_mode = True
 
     exec_strategy = fluid.ExecutionStrategy()
     exec_strategy.num_threads = thread_num
-    exec_strategy.num_iteration_per_run = 1
+    exec_strategy.num_iteration_per_run = 10
 
     main_program = fluid.default_main_program()
     pe = fluid.ParallelExecutor(
@@ -126,37 +144,39 @@ def train(use_cuda, thread_num, cpu_num):
     step = 0
     try:
         while True:
-            print("step %d in" % step)
             loss_val = pe.run(fetch_list=[avg_loss.name])
             loss_val = numpy.mean(loss_val)
-            if step % 1 == 0:
+            if step % 100 == 0:
                 print("Batch %d, Cost %f, queue size %d" %
                       (step, loss_val, py_reader.queue.size()))
             step += 1
     except fluid.core.EOFException:
+        print("train end")
         py_reader.reset()
-    """
-    step = 0
-    for step_id, data in enumerate(train_reader()):
-        loss_val = pe.run(feed=feeder.feed(data), fetch_list=[avg_loss.name])
-        loss_val = numpy.mean(loss_val)
-        if step % 100 == 0:
-            print("Batch %d, Cost %f" % (step, loss_val))
-        step += 1
-    """
-
-    # test for epoch
-    avg_loss_val, acc_val = train_test(
-        train_test_program=test_program,
-        train_test_reader=test_reader,
-        train_test_feed=feeder)
 
-    print("Test: avg_cost: %s, acc: %s" % (avg_loss_val, acc_val))
+    return step
 
 
 class TestAsyncSSAGraphExecutor(unittest.TestCase):
     def test_check_async_ssa_exe_train(self):
-        train(use_cuda=False, thread_num=2, cpu_num=2)
+        step_list = []
+        for cpu_num in [1, 2, 4]:
+            scope = fluid.core.Scope()
+            with fluid.scope_guard(scope):
+                with fluid.program_guard(
+                        fluid.Program(), startup_program=fluid.Program()):
+                    start_time = time.time()
+                    step = train(
+                        use_cuda=False, thread_num=cpu_num, cpu_num=cpu_num)
+                    end_time = time.time()
+                    step_list.append(step)
+                print("cpu_num -> " + str(cpu_num) + " step -> " + str(step) +
+                      " time -> " + str(end_time - start_time))
+                with fluid.program_guard(
+                        fluid.Program(), startup_program=fluid.Program()):
+                    test()
+        assert step_list[0] / 2 == step_list[1]
+        assert step_list[1] / 2 == step_list[2]
 
 
 if __name__ == "__main__":