cm[a][b] = torch.sum(
(z[j] == a).float() * (y[j] == b).float() * D[j])
stats[batchchoise[j]] += cm
if i < 10:
print(i, "/", nbbatchs, printloss)
if i < 1000 and i % 100 == 99:
print(i, "/", nbbatchs, printloss / 100)
printloss = torch.zeros(1).cuda()
if i >= 1000 and i % 300 == 299:
print(i, "/", nbbatchs, printloss / 300)
printloss = torch.zeros(1).cuda()
if i % 1000 == 999:
torch.save(net, "build/model.pth")
perf = miniworld.perf(torch.sum(stats, dim=0))
print(i, "perf", perf)
if perf[0] > 92:
print("training stops after reaching high training accuracy")
os._exit(0)
else:
stats = torch.zeros(
(len(miniworlddataset.cities), 2, 2)).cuda()
if i > nbbatchs * 0.1:
loss = loss * 0.5
if i > nbbatchs * 0.2:
loss = loss * 0.5
if i > nbbatchs * 0.5:
loss = loss * 0.5
if i > nbbatchs * 0.8:
z = largeforward(net, x.unsqueeze(0))
z = globalresize(z)
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 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 = (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=", miniworld.perf(cm[k]))
numpy.savetxt("build/tmp.txt", miniworld.perf(cm).cpu().numpy())
print("-------- results ----------")
for k, city in enumerate(miniworlddataset.cities):
print(city, miniworld.perf(cm[k]))
cm = torch.sum(cm, dim=0)
print("miniworld", miniworld.perf(cm))
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,
numpy.int16(perfs.cpu().numpy() * 10),
fmt="%i",
delimiter="\t")
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)
printloss = torch.zeros(1).cuda()
if i >= 1000 and i % 300 == 299:
print(i, "/", nbbatchs, printloss / 300)
printloss = torch.zeros(1).cuda()
if i % 1000 == 999:
torch.save(net, "build/model.pth")
perf = miniworld.perf(stats)
print(i, "perf", perf)
if perf[-1][0] > 95:
print("training stops after reaching high training accuracy")
os._exit(0)
else:
stats = torch.zeros((len(dataset.cities), 2, 2)).cuda()
if i > nbbatchs * 0.1:
loss = loss * 0.5
if i > nbbatchs * 0.2:
loss = loss * 0.5
if i > nbbatchs * 0.5:
loss = loss * 0.5
if i > nbbatchs * 0.8:
loss = loss * 0.5