def _test_add_forward(test_case, shape, device):
x = flow.tensor(np.random.randn(*shape), device=flow.device(device))
y = flow.tensor(np.random.randn(*shape), device=flow.device(device))
of_out = flow.add(x, y)
np_out = np.add(x.numpy(), y.numpy())
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 0.0001, 0.0001))
x = 5
y = flow.tensor(np.random.randn(*shape), device=flow.device(device))
of_out = flow.add(x, y)
np_out = np.add(x, y.numpy())
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 0.0001, 0.0001))
x = flow.tensor(np.random.randn(*shape), device=flow.device(device))
y = 5
of_out = flow.add(x, y)
np_out = np.add(x.numpy(), y)
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 0.0001, 0.0001))
x = flow.tensor(np.random.randn(*shape), device=flow.device(device))
y = flow.tensor(np.array([5.0]), device=flow.device(device))
of_out = flow.add(x, y)
np_out = np.add(x.numpy(), y.numpy())
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 0.0001, 0.0001))
x = flow.tensor(np.random.randn(1, 1), device=flow.device(device))
y = flow.tensor(np.random.randn(*shape), device=flow.device(device))
of_out = flow.add(x, y)
np_out = np.add(x.numpy(), y.numpy())
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 0.0001, 0.0001))
def test_add(test_case):
x = flow.Tensor(np.random.randn(2, 3))
y = flow.Tensor(np.random.randn(2, 3))
of_out = flow.add(x, y)
np_out = np.add(x.numpy(), y.numpy())
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-4, 1e-4))
x = 5
y = flow.Tensor(np.random.randn(2, 3))
of_out = flow.add(x, y)
np_out = np.add(x, y.numpy())
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-4, 1e-4))
x = flow.Tensor(np.random.randn(2, 3))
y = 5
of_out = flow.add(x, y)
np_out = np.add(x.numpy(), y)
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-4, 1e-4))
x = flow.Tensor(np.random.randn(2, 3))
y = flow.Tensor(np.array([5]))
of_out = flow.add(x, y)
np_out = np.add(x.numpy(), y.numpy())
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-4, 1e-4))
x = flow.Tensor(np.random.randn(1, 1))
y = flow.Tensor(np.random.randn(2, 3))
of_out = flow.add(x, y)
np_out = np.add(x.numpy(), y.numpy())
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-4, 1e-4))
def forward(self, inputs, targets):
"""
Args:
inputs (torch.Tensor): feature matrix with shape (batch_size, feat_dim).
targets (torch.LongTensor): ground truth labels with shape (num_classes).
"""
n = inputs.size(0)
# Compute pairwise distance, replace by the official when merged
dist = flow.pow(inputs, 2).sum(dim=1).expand(n, n)
dist = dist + flow.transpose(dist, dim0=1, dim1=0)
temp1 = -2 * flow.matmul(inputs, flow.transpose(inputs, dim0=1,
dim1=0))
dist = flow.add(dist, temp1)
dist = flow.sqrt(flow.clamp(dist, min=1e-12))
# For each anchor, find the hardest positive and negative
mask = targets.expand(n, n).eq(
flow.transpose(targets.expand(n, n), dim0=1, dim1=0))
dist_ap, dist_an = [], []
y1 = flow.zeros((1, n), dtype=flow.float32).to("cuda")
y2 = flow.Tensor(np.exp(100 * np.ones((1, n)))).to("cuda")
for i in range(n):
temp_dist = flow.slice(dist, [(i, i + 1, 1)])
temp_mask = flow.slice(mask, [(i, i + 1, 1)])
temp_mask_rev = flow.slice(1 - mask, [(i, i + 1, 1)])
dist_ap.append(temp_mask.where(temp_dist, y1).max().unsqueeze(0))
dist_an.append(
temp_mask_rev.where(temp_dist, y2).min().unsqueeze(0))
dist_ap = flow.cat(dist_ap)
dist_an = flow.cat(dist_an)
# Compute ranking hinge loss
y = flow.ones_like(dist_an)
return self.ranking_loss(dist_an, dist_ap, y)
def _test_add_backward(test_case, shape, device):
x = 5
y = flow.tensor(np.random.randn(*shape),
requires_grad=True,
device=flow.device(device))
of_out = flow.add(x, y).sum()
of_out.backward()
test_case.assertTrue(
np.allclose(y.grad.numpy(), np.ones(shape=shape), 0.0001, 0.0001))
def test_multi_input_with_diff_placement(test_case):
x = flow.tensor([1, 2, 3, 4],
placement=flow.placement("cuda", [0]),
sbp=flow.sbp.broadcast)
y = flow.tensor([2, 4, 6, 8],
placement=flow.placement("cuda", [1]),
sbp=flow.sbp.broadcast)
with test_case.assertRaises(RuntimeError) as ctx:
z = flow.add(x, y)
test_case.assertTrue(
"Expected all tensors to be on the same placement, but found at least two placements"
in str(ctx.exception))
def test_multi_input_with_diff_device(test_case):
# torch exception and messge:
#
# RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
#
x = flow.tensor([1, 2, 3, 4])
y = flow.tensor([2, 4, 6, 8], device="cuda")
with test_case.assertRaises(RuntimeError) as ctx:
z = flow.add(x, y)
test_case.assertTrue(
"Expected all tensors to be on the same device, but found at least two devices"
in str(ctx.exception))
def _radd(self, other):
return flow.add(self, other)
def forward(self, x):
out = flow.add(1.0, x)
return out