def main(opt, generator):
logger = logging.getLogger("logger")
logger.setLevel(logging.DEBUG)
handler1 = logging.StreamHandler()
handler1.setFormatter(
logging.Formatter("%(asctime)s %(levelname)8s %(message)s"))
logger.addHandler(handler1)
handler2 = logging.FileHandler(
filename=os.path.join(opt.loadDir, "is.log"))
handler2.setLevel(logging.INFO)
handler2.setFormatter(logging.Formatter("%(message)s"))
logger.addHandler(handler2)
logger.info(opt)
np.random.seed(seed=0)
#calcurate real dataset IS
if opt.calcRealIs:
dataset, _ = makeDataloader(opt)
IgnoreLabelDataset(dataset)
if opt.dataset == 'mnist':
calcIS = inception_score(IgnoreLabelDataset(dataset),
cuda=cuda,
batch_size=32,
resize=True,
expand=True)
else:
calcIS = inception_score(IgnoreLabelDataset(dataset),
cuda=cuda,
batch_size=32,
resize=True)
logger.info('real, ' + str(calcIS[0]))
#calcurate fake dataset init IS
calcIS = calcurateInceptionScore(opt, generator.cuda(), str(0))
logger.info('0, ' + str(calcIS[0]))
#calcurate fake dataset IS per iter
for model_path in sorted(
glob.glob(os.path.join(opt.loadDir, 'generator_*'))):
name = os.path.basename(model_path)
idx = name.replace('generator_model_', '')
calcG = generator.cuda()
calcG.load_state_dict(torch.load(model_path))
if int(idx) > 0:
calcIS = calcurateInceptionScore(opt, calcG, idx)
logger.info(str(int(idx)) + ', ' + str(calcIS[0]))
'latent_dim': 128,
'img_size': 32,
'n_critic': 1,
'clip_value': 0.01,
'sample_inNoneterval': 100,
'log_interval': 100,
'modelsave_interval': 1, #per epoch
'dataset': 'mnist',
'num_filters': 128,
'saveDir': None,
'resume': 0,
'logIS': True,
'loadDir': None
})
_, dataloader = dataset.makeDataloader(opt)
# Initialize generator and discriminator
#generator = models.NoiseGenerator2Dv6(opt)
#generator = models.MTNoiseGenerator2Dv6(opt)
generator = models.NoiseResGenerator2Dv1(opt,
noise_layers.NoiseBasicBlock2Dv2,
[2, 2, 2, 2, 1],
level=0.1)
discriminator = naiveresnet.NoiseResNet32(naiveresnet.NoiseBasicBlock,
[2, 2, 2, 2],
nchannels=1,
nfilters=opt.num_filters,
nclasses=1,
pool=2,
level=0.1)
