def test_static_graph(self):
startup_program = fluid.Program()
train_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
inputs = fluid.data(name='input', dtype='int64', shape=[7])
weights = fluid.data(name='weights', dtype='int64', shape=[7])
output = paddle.bincount(inputs, weights=weights)
place = fluid.CPUPlace()
if fluid.core.is_compiled_with_cuda():
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(startup_program)
img = np.array([0, 1, 1, 3, 2, 1, 7]).astype(np.int64)
w = np.array([0, 1, 1, 2, 2, 1, 0]).astype(np.int64)
res = exe.run(train_program,
feed={
'input': img,
'weights': w
},
fetch_list=[output])
actual = np.array(res[0])
expected = np.bincount(img, weights=w)
self.assertTrue(
(actual == expected).all(),
msg='bincount output is wrong, out =' + str(actual))
def test_dygraph(self):
with fluid.dygraph.guard():
inputs_np = np.array([0, 1, 1, 3, 2, 1, 7]).astype(np.int64)
inputs = fluid.dygraph.to_variable(inputs_np)
actual = paddle.bincount(inputs)
expected = np.bincount(inputs)
self.assertTrue(
(actual.numpy() == expected).all(),
msg='bincount output is wrong, out =' + str(actual.numpy()))
def net_func():
input_value = paddle.to_tensor([1, 2, 3, 4, 5])
paddle.bincount(input_value, minlength=-1)
def net_func():
input_value = paddle.to_tensor([[1, 2, 3], [4, 5, 6]])
paddle.bincount(input_value)
def net_func():
input_value = paddle.to_tensor([1, 2, 3, 4, -5])
paddle.bincount(input_value)
def net_func():
input_value = paddle.to_tensor([1, 2, 3, 4, 5])
weights = paddle.to_tensor([1, 1, 1, 1, 1, 1])
paddle.bincount(input_value, weights=weights)
def net_func():
input_value = paddle.to_tensor([1., 2., 3., 4., 5.])
paddle.bincount(input_value)
