z = (z[0, 1, :, :] > z[0, 0, :, :]).float()
for a, b in [(0, 0), (0, 1), (1, 0), (1, 1)]:
cm[k][a][b] = torch.sum(
(z == a).float() * (y == b).float() * D)
if True:
nextI = len(os.listdir("build"))
print(nextI)
debug = digitanie.torchTOpil(globalresize(x))
debug = PIL.Image.fromarray(numpy.uint8(debug))
debug.save("build/" + str(nextI) + "_x.png")
debug = (2.0 * y - 1) * D * 127 + 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=", digitanie.perf(cm[k]))
print(cm[k])
numpy.savetxt("build/tmp.txt", digitanie.perf(cm).cpu().numpy())
print("-------- summary ----------")
for k, city in enumerate(miniworld.cities):
print(city, digitanie.perf(cm[k]))
cm = torch.sum(cm, dim=0)
print("digitanie", digitanie.perf(cm))
z = largeforward(net, x.unsqueeze(0))
z = globalresize(z)
z = (z[0, 1, :, :] > z[0, 0, :, :]).float()
cm[k] += digitanie.confusion(y, z, size=size)
if False:
debug = digitanie.torchTOpil(globalresize(x))
debug = PIL.Image.fromarray(numpy.uint8(debug))
debug.save("build/" + city + str(i) + "_x.png")
debug = y.float()
debug = debug * 2 * (1 - digitanie.isborder(y, size=size))
debug = debug + digitanie.isborder(y, size=size)
debug *= 127
debug = debug.cpu().numpy()
debug = PIL.Image.fromarray(numpy.uint8(debug))
debug.save("build/" + city + str(i) + "_y.png")
debug = z.cpu().numpy() * 255
debug = PIL.Image.fromarray(numpy.uint8(debug))
debug.save("build/" + city + str(i) + "_z.png")
print("perf=", digitanie.perf(cm[k]))
perfs = digitanie.perf(cm)
print("digitanie", perfs[-1])
print(perfs)
numpy.savetxt(name,
numpy.int16(perfs.cpu().numpy() * 10),
fmt="%i",
delimiter="\t")