Make Fit a line a normal unittest

python/paddle/v2/fluid/tests/book/test_fit_a_line.py

@@ -12,44 +12,74 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
+import contextlib
+import unittest
 
-x = fluid.layers.data(name='x', shape=[13], dtype='float32')
 
-y_predict = fluid.layers.fc(input=x, size=1, act=None)
+def main(use_cuda):
+    if use_cuda and not fluid.core.is_compiled_with_cuda():
+        return
 
-y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+    x = fluid.layers.data(name='x', shape=[13], dtype='float32')
 
-cost = fluid.layers.square_error_cost(input=y_predict, label=y)
-avg_cost = fluid.layers.mean(x=cost)
+    y_predict = fluid.layers.fc(input=x, size=1, act=None)
 
-sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
-sgd_optimizer.minimize(avg_cost)
+    y = fluid.layers.data(name='y', shape=[1], dtype='float32')
 
-BATCH_SIZE = 20
+    cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+    avg_cost = fluid.layers.mean(x=cost)
 
-train_reader = paddle.batch(
-    paddle.reader.shuffle(
-        paddle.dataset.uci_housing.train(), buf_size=500),
-    batch_size=BATCH_SIZE)
+    sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+    sgd_optimizer.minimize(avg_cost)
 
-place = fluid.CPUPlace()
-feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
-exe = fluid.Executor(place)
+    BATCH_SIZE = 20
 
-exe.run(fluid.default_startup_program())
+    train_reader = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.uci_housing.train(), buf_size=500),
+        batch_size=BATCH_SIZE)
 
-PASS_NUM = 100
-for pass_id in range(PASS_NUM):
-    fluid.io.save_persistables(exe, "./fit_a_line.model/")
-    fluid.io.load_persistables(exe, "./fit_a_line.model/")
-    for data in train_reader():
-        avg_loss_value, = exe.run(fluid.default_main_program(),
-                                  feed=feeder.feed(data),
-                                  fetch_list=[avg_cost])
-        print(avg_loss_value)
-        if avg_loss_value[0] < 10.0:
-            exit(0)  # if avg cost less than 10.0, we think our code is good.
-exit(1)
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
+    exe = fluid.Executor(place)
+
+    exe.run(fluid.default_startup_program())
+
+    PASS_NUM = 100
+    for pass_id in range(PASS_NUM):
+        fluid.io.save_persistables(exe, "./fit_a_line.model/")
+        fluid.io.load_persistables(exe, "./fit_a_line.model/")
+        for data in train_reader():
+            avg_loss_value, = exe.run(fluid.default_main_program(),
+                                      feed=feeder.feed(data),
+                                      fetch_list=[avg_cost])
+            print(avg_loss_value)
+            if avg_loss_value[0] < 10.0:
+                return
+    raise AssertionError("Fit a line cost is too large, {0:2.2}".format(
+        avg_loss_value[0]))
+
+
+class TestFitALine(unittest.TestCase):
+    def test_cpu(self):
+        with self.program_scope_guard():
+            main(use_cuda=False)
+
+    def test_cuda(self):
+        with self.program_scope_guard():
+            main(use_cuda=True)
+
+    @contextlib.contextmanager
+    def program_scope_guard(self):
+        prog = fluid.Program()
+        startup_prog = fluid.Program()
+        scope = fluid.core.Scope()
+        with fluid.scope_guard(scope):
+            with fluid.program_guard(prog, startup_prog):
+                yield
+
+
+if __name__ == '__main__':
+    unittest.main()