def test_reproduce_same_permutation(self):
dataset = [1, 2, 3, 4, 5, 6]
order_sampler1 = iterators.ShuffleOrderSampler(
numpy.random.RandomState(self._seed))
it1 = iterators.MultiprocessIterator(
dataset, 6, order_sampler=order_sampler1)
order_sampler2 = iterators.ShuffleOrderSampler(
numpy.random.RandomState(self._seed))
it2 = iterators.MultiprocessIterator(
dataset, 6, order_sampler=order_sampler2)
for _ in range(5):
self.assertEqual(it1.next(), it2.next())
def main():
import multiprocessing
multiprocessing.set_start_method('forkserver')
parser = argparse.ArgumentParser(description='Cats training.')
parser.add_argument('--gpu',
'-g',
type=int,
default=0,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--out',
'-o',
default='result',
help='Directory to output the result')
parser.add_argument('--normalization',
type=str,
choices=NORMALIZATIONS,
required=True,
help='Normalization method')
args = parser.parse_args()
gpu = args.gpu
out_dir = args.out
image_dir = 'images'
batch_size = 32
short_edge = 256
crop_edge = 224
seed = 3141592653
n_processes = len(os.sched_getaffinity(0))
normalization = get_normalization(args.normalization)
initial_lr = 0.1
epochs = 300
lr_reduce_interval = (100, 'epoch')
lr_reduce_rate = 0.1
weight_decay = 5e-4
numpy_random = numpy.random.RandomState(seed)
random = Random.from_numpy_random(numpy_random)
train_dataset, valid_dataset, _ = CatsDataset.train_valid(
image_dir, short_edge, crop_edge, random)
order_sampler = iterators.ShuffleOrderSampler(numpy_random)
train_iter = iterators.MultiprocessIterator(train_dataset,
batch_size,
repeat=True,
shuffle=None,
n_processes=n_processes,
n_prefetch=4,
order_sampler=order_sampler)
valid_iter = iterators.MultiprocessIterator(valid_dataset,
batch_size,
repeat=False,
shuffle=False,
n_processes=n_processes,
n_prefetch=4)
numpy.random.seed(seed)
model = ResNet50(len(CatsDataset.classes), normalization)
model = chainer.links.Classifier(model)
if gpu >= 0:
chainer.cuda.get_device_from_id(gpu).use()
model.to_gpu()
optimizer = optimizers.MomentumSGD(lr=initial_lr)
optimizer.setup(model)
optimizer.add_hook(optimizer_hooks.WeightDecay(weight_decay))
updater = training.updaters.StandardUpdater(train_iter,
optimizer,
device=gpu)
trainer = training.Trainer(updater, (epochs, 'epoch'), out=out_dir)
trainer.extend(extensions.ExponentialShift('lr', lr_reduce_rate),
trigger=lr_reduce_interval)
trainer.extend(extensions.Evaluator(valid_iter, model, device=gpu),
trigger=(1, 'epoch'))
trainer.extend(extensions.LogReport())
trainer.extend(
extensions.PrintReport([
'epoch', 'main/loss', 'validation/main/loss', 'main/accuracy',
'validation/main/accuracy', 'elapsed_time'
]))
trainer.run()
chainer.serializers.save_npz(os.path.join(out_dir, 'model.npz'), model)