def test_empty_label():
probs = torch.FloatTensor([
[[0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.6, 0.1, 0.1]],
[[0.6, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.5, 0.2, 0.1]]
]).contiguous()
grads = torch.zeros(probs.size())
labels = torch.IntTensor([1, 2])
label_sizes = torch.IntTensor([2, 0])
sizes = torch.IntTensor([2, 2])
minibatch_size = probs.size(1)
costs = torch.zeros(minibatch_size)
warp_ctc.cpu_ctc(probs,
grads,
labels,
label_sizes,
sizes,
minibatch_size,
costs,
0)
print('CPU_cost: %f' % costs.sum())
probs = probs.clone().cuda()
grads = torch.zeros(probs.size()).cuda()
costs = torch.zeros(minibatch_size)
warp_ctc.gpu_ctc(probs,
grads,
labels,
label_sizes,
sizes,
minibatch_size,
costs,
0)
print('GPU_cost: %f' % costs.sum())
print(grads.view(grads.size(0) * grads.size(1), grads.size(2)))
def test_simple():
probs = torch.FloatTensor([[[0.1, 0.6, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.6, 0.1,
0.1]]]).transpose(0, 1).contiguous()
grads = torch.zeros(probs.size())
labels = torch.IntTensor([1, 2])
label_sizes = torch.IntTensor([2])
sizes = torch.IntTensor(probs.size(1)).fill_(probs.size(0))
minibatch_size = probs.size(1)
costs = torch.zeros(minibatch_size)
warp_ctc.cpu_ctc(probs, grads, labels, label_sizes, sizes, minibatch_size,
costs)
print('CPU_cost: %f' % costs.sum())
def test_medium(multiplier):
probs = torch.FloatTensor([
[[0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.6, 0.1, 0.1]],
[[0.6, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.5, 0.2, 0.1]]
]).contiguous() * multiplier
grads = torch.zeros(probs.size())
labels = torch.IntTensor([1, 2, 1, 2])
label_sizes = torch.IntTensor([2, 2])
sizes = torch.IntTensor([2, 2])
minibatch_size = probs.size(1)
costs = torch.zeros(minibatch_size)
warp_ctc.cpu_ctc(probs, grads, labels, label_sizes, sizes, minibatch_size,
costs)
print('CPU_cost: %f' % costs.sum())