def test_forward_backward_single_tensor_output(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[2], dtype='float32')
x.stop_gradient = False # For test gradient
mask = layers.data(name='mask', shape=[1], dtype='int32')
out = program.current_block().create_var(
dtype='float32', type=core.VarDesc.VarType.LOD_TENSOR)
select_output(x, out, mask)
y = select_input(out, mask)
mean = layers.mean(y)
append_backward(mean)
place = fluid.CUDAPlace(
0) if core.is_compiled_with_cuda() else fluid.CPUPlace()
exe = Executor(place)
feed_x = np.asarray([1.3, -1.4]).astype(np.float32)
feed_mask = np.asarray([0]).astype(np.int32)
ret = exe.run(program,
feed={
'x': feed_x,
'mask': feed_mask
},
fetch_list=[y.name, x.grad_name])
x_grad = np.asarray([0.5, 0.5]).astype(np.float32)
self.assertTrue(np.allclose(np.asarray(ret[0]), feed_x))
self.assertTrue(np.allclose(np.asarray(ret[1]), x_grad))
def test_outputs_type():
select_output(in1, out1, mask=mask_int32)
def test_mask_type():
select_output(in1, [out1], mask=1)
def test_mask_dtype():
select_output(in1, [out1], mask=mask_float32)
def test_input_type():
select_output(1, [out1], mask_int32)