def _test_concat_with_three_tensor_backward(test_case, device):
input1 = flow.Tensor(
np.random.randn(2, 6, 5, 3),
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
input2 = flow.Tensor(
np.random.randn(2, 6, 5, 3),
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
input3 = flow.Tensor(
np.random.randn(2, 6, 5, 3),
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
of_out = flow.cat([input1, input2, input3], dim=1)
of_out = of_out.sum()
of_out.backward()
test_case.assertTrue(
np.allclose(input1.grad.numpy(), np.ones((2, 6, 5, 3)), 1e-4, 1e-4))
test_case.assertTrue(
np.allclose(input2.grad.numpy(), np.ones((2, 6, 5, 3)), 1e-4, 1e-4))
test_case.assertTrue(
np.allclose(input3.grad.numpy(), np.ones((2, 6, 5, 3)), 1e-4, 1e-4))
def test_concat_with_axis_one(test_case):
input1 = flow.Tensor(np.random.randn(2, 6, 5, 3), dtype=flow.float32)
input2 = flow.Tensor(np.random.randn(2, 6, 5, 3), dtype=flow.float32)
of_out = flow.cat([input1, input2], dim=1)
np_out = np.concatenate((input1.numpy(), input2.numpy()), axis=1)
test_case.assertTrue(np.array_equal(of_out.numpy(), np_out))
def _test_concat_origin(test_case, device):
input1 = flow.Tensor(np.random.randn(2, 6, 5, 3),
dtype=flow.float32,
device=flow.device(device))
input2 = flow.Tensor(np.random.randn(2, 6, 5, 3),
dtype=flow.float32,
device=flow.device(device))
of_out = flow.cat([input1, input2], dim=0)
np_out = np.concatenate((input1.numpy(), input2.numpy()), axis=0)
test_case.assertTrue(np.array_equal(of_out.numpy(), np_out))