def execute():
startup_program = fluid.Program()
main_program = fluid.Program()
with fluid.program_guard(main_program, startup_program):
# prepare input
np_x = np.array([1, 2, 3, 4, 5], dtype='int32')
# add feed var
x = data_layer_not_check(name='x',
shape=list(np_x.shape),
dtype=str(np_x.dtype))
# build net
result = static_func(x)
print(main_program)
# prepare exe
place = fluid.CPUPlace()
exe = fluid.Executor(place)
# run
# exe.run(startup_program)
out, = exe.run(main_program, feed={'x': np_x}, fetch_list=[result])
print(out[0])
def test_feed_mismatch_shape(self):
main_program = fluid.Program()
with fluid.program_guard(main_program):
d = data_layer_not_check(name="d", shape=(1, 2, 3))
feed_in_data = np.random.uniform(size=[1, 2, 4]).astype(np.float32)
place = fluid.CUDAPlace(
0) if fluid.is_compiled_with_cuda() else fluid.CPUPlace()
exe = fluid.Executor(place)
ret = exe.run(main_program,
feed={d.name: feed_in_data},
fetch_list=[d.name])
self.assertTrue(np.allclose(ret, feed_in_data))
def _add_feed_layers(self, args, kwargs):
"""
Adds `fluid.data` if the input `numpy.ndarray` is converted into `Variable`
by `to_variable()`, it makes program to be executed dynamically.
"""
if not self._feed_name_to_idx:
self._feed_name_to_idx = self._get_name_to_idx(self._forward_func)
with framework.program_guard(self._main_program,
self._startup_program):
for feed_name, idx in self.feed_name_to_idx.items():
batch_data = args[idx]
assert isinstance(
batch_data, numpy.ndarray
), "Input {} should be numpy.ndarray, but received {}.".format(
feed_name, type(batch_data))
feed_layer = data_layer_not_check(name=feed_name,
shape=list(batch_data.shape),
dtype=str(batch_data.dtype))
self._inputs.append(feed_layer)
def test_create_none_shape(self):
main_program = fluid.Program()
with fluid.program_guard(main_program):
d = data_layer_not_check(name="d", shape=(None, -1, 3))
self.assertEqual(d.shape, (-1, -1, 3))
self.assertEqual(d.name, "d")