train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
else:
train_iters = [
MultithreadIterator(i, int(args.batchsize / len(devices)),
n_threads=4)
for i in split_dataset_n_random(train, len(devices))]
test_iter = MultithreadIterator(test, args.batchsize, repeat=False,
shuffle=False, n_threads=4)
# Set up a trainer
if len(args.gpus) < 2:
updater = training.StandardUpdater(train_iter, optimizer,
device=args.gpus[0])
else:
updater = MultiprocessParallelUpdater(train_iters, optimizer,
devices=devices)
trainer = training.Trainer(updater, (args.epoch, 'epoch'),
out=output_dir)
if args.cosine:
trainer.extend(
CosineAnnealing('lr', int(args.epoch),
len(train) / args.batchsize,
eta_min=args.eta_min,
init=args.lr))
else:
trainer.extend(
extensions.ExponentialShift('lr', 0.1, init=args.lr),
trigger=triggers.ManualScheduleTrigger(
[int(args.epoch * 0.50), int(args.epoch * 0.75)], 'epoch'))
localization_net.disable_update()
# if we are using more than one GPU, we need to evenly split the datasets
if len(args.gpus) > 1:
gpu_datasets = split_dataset_n_random(train_dataset, len(args.gpus))
if not len(gpu_datasets[0]) == len(gpu_datasets[-1]):
adapted_second_split = split_dataset(gpu_datasets[-1], len(gpu_datasets[0]))[0]
gpu_datasets[-1] = adapted_second_split
else:
gpu_datasets = [train_dataset]
train_iterators = [chainer.iterators.MultiprocessIterator(dataset, args.batch_size) for dataset in gpu_datasets]
validation_iterator = chainer.iterators.MultiprocessIterator(validation_dataset, args.batch_size, repeat=False)
# use the MultiProcessParallelUpdater in order to harness the full power of data parallel computation
updater = MultiprocessParallelUpdater(train_iterators, optimizer, devices=args.gpus)
log_dir = os.path.join(args.log_dir, "{}_{}".format(datetime.datetime.now().isoformat(), args.log_name))
args.log_dir = log_dir
# backup current file
if not os.path.exists(log_dir):
os.makedirs(log_dir, exist_ok=True)
shutil.copy(__file__, log_dir)
# backup all necessary configuration params
report = {
'log_dir': log_dir,
'image_size': image_size,
'target_size': target_shape,
'localization_net': [localization_net.__class__.__name__, get_definition_filename(localization_net)],
train_iterators = [
MultiprocessIterator(dataset,
args.batch_size,
n_processes=args.num_processes)
for dataset in gpu_datasets
]
validation_iterator = MultiprocessIterator(
validation_dataset,
args.batch_size,
n_processes=args.num_processes,
repeat=False)
updater = MultiprocessParallelUpdater(
train_iterators,
optimizer,
devices=args.gpus,
converter=get_concat_and_pad_examples(args.blank_label))
updater.setup_workers()
log_dir = os.path.join(
args.log_dir, "{}_{}".format(datetime.datetime.now().isoformat(),
args.log_name))
args.log_dir = log_dir
# backup current file
if not os.path.exists(log_dir):
os.makedirs(log_dir, exist_ok=True)
shutil.copy(__file__, log_dir)
# log all necessary configuration params
