def test_other_rand(self):
a = jt.array([1.0, 2.0, 3.0])
b = jt.rand_like(a)
c = jt.randn_like(a)
assert b.shape == c.shape
assert b.shape == a.shape
print(b, c)
assert jt.randint(10, 20, (2000, )).min() == 10
assert jt.randint(10, 20, (2000, )).max() == 19
assert jt.randint(10, shape=(2000, )).max() == 9
assert jt.randint_like(a, 10).shape == a.shape
def check_gpu_with_cpu(T, C, N, S, S_min):
jt.set_global_seed(1)
# Initialize random batch of input vectors, for *size = (T,N,C)
input = jt.randn(T, N, C).log_softmax(2)
# input = -jt.ones((T, N, C))
# input[0,0,1] += 0.01
# Initialize random batch of targets (0 = blank, 1:C = classes)
target = jt.randint(low=1, high=C, shape=(N, S), dtype=jt.int)
_input_jt = input
input_lengths = jt.full((N, ), T, dtype=jt.int)
target_lengths = jt.randint(low=S_min,
high=S + 1,
shape=(N, ),
dtype=jt.int)
# ctc_loss = nn.CTCLoss()
loss = jt.ctc_loss(input,
target,
input_lengths,
target_lengths,
reduction='none')
_loss_jt = loss
loss_jt = loss.numpy()
dinput_jt = jt.grad(_loss_jt, _input_jt)
dinput_jt.sync()
with jt.flag_scope(use_cuda=1):
input = input.copy()
target = target.copy()
input_lengths = input_lengths.copy()
target_lengths = target_lengths.copy()
loss = jt.ctc_loss(input,
target,
input_lengths,
target_lengths,
reduction='none')
grad = jt.grad(loss, input)
np.testing.assert_allclose(_loss_jt.numpy(),
loss.numpy(),
atol=1e-5,
rtol=1e-5)
np.testing.assert_allclose(dinput_jt.numpy(),
grad.numpy(),
atol=1e-5,
rtol=1e-5)
def check(T, C, N, S, S_min):
jt.set_global_seed(0)
# Initialize random batch of input vectors, for *size = (T,N,C)
input = jt.randn(T, N, C).log_softmax(2)
# input = -jt.ones((T, N, C))
# input[0,0,1] += 0.01
# Initialize random batch of targets (0 = blank, 1:C = classes)
target = jt.randint(low=1, high=C, shape=(N, S), dtype=jt.int)
_input_jt = input
input_lengths = jt.full((N, ), T, dtype=jt.int)
target_lengths = jt.randint(low=S_min,
high=S + 1,
shape=(N, ),
dtype=jt.int)
# ctc_loss = nn.CTCLoss()
loss = jt.ctc_loss(input,
target,
input_lengths,
target_lengths,
reduction='none')
_loss_jt = loss
loss_jt = loss.numpy()
input = torch.Tensor(input.numpy()).detach().requires_grad_()
input_lengths = torch.full(size=(N, ),
fill_value=T,
dtype=torch.long)
target_lengths = torch.LongTensor(target_lengths.numpy())
input_lengths = torch.LongTensor(input_lengths.numpy())
target = torch.LongTensor(target.numpy())
loss = tnn.CTCLoss(reduction='none')(input, target, input_lengths,
target_lengths)
np.testing.assert_allclose(loss.detach().numpy(),
loss_jt,
rtol=1e-5,
atol=1e-5)
dinput_jt = jt.grad(_loss_jt, _input_jt)
dinput_jt.sync()
loss.sum().backward()