test_gpu.py

import torch
import warpctc_pytorch as warp_ctc
from warpctc_pytorch import CTCLoss


def test_empty_label(test_cpu=True, test_gpu=True):
    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)

    if test_cpu:
        costs = torch.zeros(minibatch_size)
        warp_ctc.cpu_ctc(probs, grads, labels, label_sizes, sizes, minibatch_size, costs, 0)
        print('CPU cost sum = %f' % costs.sum())
        print('CPU probs={}\ngrads={}\ncosts={}\n\n'.format(probs, grads, costs))

    if test_gpu:
        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 sum = %f' % costs.sum())
        print(grads.view(grads.size(0) * grads.size(1), grads.size(2)))
        print('GPU probs={}\ngrads={}\ncosts={}\n\n'.format(probs, grads, costs))


def test_ctcloss(test_cpu=True, test_gpu=True):
    criterion = CTCLoss(blank=0, size_average=False, length_average=False)
    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()
    labels = torch.IntTensor([1, 2])
    probs_sizes = torch.IntTensor([2])
    label_sizes = torch.IntTensor([2])

    print('probs shape ', probs.shape)
    print('labels shape ', labels.shape)
    print('label_sizes ', sum(label_sizes))
    if test_cpu:
        probs_cpu = probs.clone().cpu().requires_grad_(True)  # tells autograd to compute gradients for probs
        cost = criterion(probs_cpu, labels, probs_sizes, label_sizes)
        cost.backward()
        print('CPU probs={}\ngrads={}\ncosts={}\n\n'.format(probs_cpu, probs_cpu.grad, cost))

    if test_gpu:
        probs_gpu = probs.clone().cuda().requires_grad_(True)  # tells autograd to compute gradients for probs
        cost = criterion(probs_gpu, labels, probs_sizes, label_sizes)
        cost.backward()
        print('GPU probs={}\ngrads={}\ncosts={}\n\n'.format(probs_gpu, probs_gpu.grad, cost))


def main():
    print('torch.cuda.is_available() ', torch.cuda.is_available())
    test_gpu = False
    if torch.cuda.is_available():
        test_gpu = True

    # test_empty_label(test_cpu=True, test_gpu=test_gpu)
    test_ctcloss(test_cpu=True, test_gpu=test_gpu)


if __name__ == '__main__':
    main()