def test_ctc_loss():
A = np.ones((2, INT_OVERFLOW, 4))
A.attach_grad()
with mx.autograd.record():
B = npx.ctc_loss(A, np.ones((INT_OVERFLOW, 2)))
assert B.shape == (INT_OVERFLOW, )
assert type(B).__name__ == 'ndarray'
B.backward()
assert A.grad.shape == (2, INT_OVERFLOW, 4)
assert A.grad[0][0][0] == 0
def test_ctc_loss():
def test_ctc_loss_size_check(A, label):
assertRaises(ValueError, npx.ctc_loss, A, label)
L_SEQ, L_ALP, L_LAB, BAT = 2**10, 2**20, 2**6, 2
A = np.zeros((L_SEQ, BAT, L_ALP))
label = np.random.randint(0, L_ALP, (BAT, L_LAB))
# test for expected exception
test_ctc_loss_size_check(A, label)
# now we shrink the size a little bit and test for an allowed case
L_ALP = 2**20 - 1
A = np.zeros((L_SEQ, BAT, L_ALP))
label = np.random.randint(0, L_ALP, (BAT, L_LAB))
A.attach_grad()
with mx.autograd.record():
B = npx.ctc_loss(A, label)
assert B.shape == (BAT, )
assert type(B[0]).__name__ == 'ndarray'
B.backward()
assert A.grad.shape == (L_SEQ, BAT, L_ALP)
assert type(A[0]).__name__ == 'ndarray'
