Esempio n. 1
0
                ((h // 64) * 64, (w // 64) * 64))
            x = power2resize(x)

            z = largeforward(net, x.unsqueeze(0))
            z = globalresize(z)
            z = (z[0, 1, :, :] > z[0, 0, :, :]).float()

            cm[k] += miniworld.confusion(y, z, size=size)

            if False:
                nextI = len(os.listdir("build"))
                debug = miniworld.torchTOpil(globalresize(x))
                debug = PIL.Image.fromarray(numpy.uint8(debug))
                debug.save("build/" + str(nextI) + "_x.png")
                debug = y.float()
                debug = debug * 2 * (1 - miniworld.isborder(y, size=size))
                debug = debug + miniworld.isborder(y, size=size)
                debug *= 127
                debug = debug.cpu().numpy()
                debug = PIL.Image.fromarray(numpy.uint8(debug))
                debug.save("build/" + str(nextI) + "_y.png")
                debug = z.cpu().numpy() * 255
                debug = PIL.Image.fromarray(numpy.uint8(debug))
                debug.save("build/" + str(nextI) + "_z.png")

        print("perf=", miniworld.perf(cm[k]))

perfs = miniworld.perf(cm)
print("miniworld", perfs[-1])
print(perfs)
numpy.savetxt(name,
Esempio n. 2
0
    inter0, inter1 = (y0 * z0 * D).sum(), (y1 * z1 * D).sum()
    union0, union1 = ((y0 + z1 * y0) * D).sum(), ((y1 + z0 * y1) * D).sum()
    iou0, iou1 = (inter0 + eps) / (union0 + eps), (inter1 + eps) / (union1 + eps)
    iou = 0.5 * (iou0 + iou1)

    return 1 - iou


for i in range(nbbatchs):
    x, y, batchchoise, _ = dataset.getBatch(batchsize)
    x, y, batchchoise = x.cuda(), y.cuda(), batchchoise.cuda()
    z = net(x)

    if flagborder:
        D = 1 - miniworld.isborder(y, size=size)
    else:
        if size == 0:
            D = 1 + 9 * miniworld.isborder(y, size=1)
        else:
            border = miniworld.isborder(y, size=1)
            borderbis = miniworld.isborder(y, size=2)
            borderbis = (borderbis == 1).float() * (border == 0).float()
            D = 1 - border + 9 * borderbis

    CE = criterion(z, y)
    CE = torch.mean(CE * D)
    dice = diceloss(y, z, D)
    loss = CE + dice

    with torch.no_grad():
Esempio n. 3
0
    inter0, inter1 = (y0 * z0 * D).sum(), (y1 * z1 * D).sum()
    union0, union1 = ((y0 + z1 * y0) * D).sum(), ((y1 + z0 * y1) * D).sum()
    iou0, iou1 = (inter0 + eps) / (union0 + eps), (inter1 + eps) / (union1 +
                                                                    eps)
    iou = 0.5 * (iou0 + iou1)

    return 1 - iou


for i in range(nbbatchs):
    x, y, batchchoise, _ = dataset.getBatch(batchsize)
    x, y, batchchoise = x.cuda(), y.cuda(), batchchoise.cuda()
    z = net(x)

    D = 1 - miniworld.isborder(y)
    CE = criterion(z, y)
    CE = torch.mean(CE * D)
    dice = diceloss(y, z, D)
    loss = CE + dice

    with torch.no_grad():
        printloss += loss.clone().detach()
        z = (z[:, 1, :, :] > z[:, 0, :, :]).clone().detach().float()
        for j in range(batchsize):
            stats[batchchoise[j]] += miniworld.confusion(y[j], z[j])

        if i < 10:
            print(i, "/", nbbatchs, printloss)
        if i < 1000 and i % 100 == 99:
            print(i, "/", nbbatchs, printloss / 100)
Esempio n. 4
0
                ((h // 64) * 64, (w // 64) * 64))
            x = power2resize(x)

            z = largeforward(net, x.unsqueeze(0))
            z = globalresize(z)
            z = (z[0, 1, :, :] > z[0, 0, :, :]).float()

            cm[k] += miniworld.confusion(y, z)

            if False:
                nextI = len(os.listdir("build"))
                debug = miniworld.torchTOpil(globalresize(x))
                debug = PIL.Image.fromarray(numpy.uint8(debug))
                debug.save("build/" + str(nextI) + "_x.png")
                debug = y.float()
                debug = debug * 2 * (1 - miniworld.isborder(y))
                debug = debug + miniworld.isborder(y)
                debug *= 127
                debug = debug.cpu().numpy()
                debug = PIL.Image.fromarray(numpy.uint8(debug))
                debug.save("build/" + str(nextI) + "_y.png")
                debug = z.cpu().numpy() * 255
                debug = PIL.Image.fromarray(numpy.uint8(debug))
                debug.save("build/" + str(nextI) + "_z.png")

        print("perf=", miniworld.perf(cm[k]))
        numpy.savetxt("build/tmp.txt", miniworld.perf(cm).cpu().numpy())

perfs = miniworld.perf(cm)
print(perfs)