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"fix dtype test in ci" #10667

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merged 3 commits into from
May 16, 2018
Merged

"fix dtype test in ci" #10667

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9e6c283
"fix ci"
dzhwinter May 15, 2018
164f992
"fix network config"
dzhwinter May 16, 2018
ba06af6
"rerun ci"
dzhwinter May 16, 2018
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35 changes: 15 additions & 20 deletions python/paddle/fluid/tests/unittests/test_network_with_dtype.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,48 +24,43 @@


class TestNetWithDtype(unittest.TestCase):
def set_network(self):
def setUp(self):
self.dtype = "float64"
self.init_dtype()
main = fluid.Program()
with fluid.program_guard(main):
self.x = fluid.layers.data(name='x', shape=[13], dtype=self.dtype)
self.y = fluid.layers.data(name='y', shape=[1], dtype=self.dtype)
y_predict = fluid.layers.fc(input=self.x, size=1, act=None)

cost = fluid.layers.square_error_cost(input=y_predict, label=self.y)
def run_net_on_place(self, place):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
x = fluid.layers.data(name='x', shape=[13], dtype=self.dtype)
y = fluid.layers.data(name='y', shape=[1], dtype=self.dtype)
y_predict = fluid.layers.fc(input=x, size=1, act=None)
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
self.program = main
self.fetch_list = [avg_cost]
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)

sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)

def run_net_on_place(self, place):
fetch_list = [avg_cost]
train_reader = paddle.batch(
paddle.dataset.uci_housing.train(), batch_size=BATCH_SIZE)
feeder = fluid.DataFeeder(place=place, feed_list=[self.x, self.y])
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
exe.run(startup)
for data in train_reader():
exe.run(self.program,
feed=feeder.feed(data),
fetch_list=self.fetch_list)
exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)
# the main program is runable, the datatype is fully supported
break

def init_dtype(self):
pass

def test_cpu(self):
self.set_network()
place = fluid.CPUPlace()
self.run_net_on_place(place)

def test_gpu(self):
if not core.is_compiled_with_cuda():
return
self.set_network()
place = fluid.CUDAPlace(0)
self.run_net_on_place(place)

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