def run():
# arguments
parser = argparse.ArgumentParser()
parser.add_argument('-eid', '--experiment_id', type=str)
parser.add_argument('-d',
'--model_directory',
type=str,
default=MODEL_DIRECTORY)
parser.add_argument('-dd',
'--dataset_directory',
type=str,
default=DATASET_DIRECTORY)
parser.add_argument('-cls', '--class_ids', type=str, default=CLASS_IDS_ALL)
parser.add_argument('-s', '--seed', type=int, default=RANDOM_SEED)
parser.add_argument('-g', '--gpu', type=int, default=GPU)
args = parser.parse_args()
directory_output = os.path.join(args.model_directory, args.experiment_id)
# set random seed, gpu
random.seed(args.seed)
np.random.seed(args.seed)
cp.random.seed(args.seed)
chainer.cuda.get_device(args.gpu).use()
# load dataset
dataset_test = datasets.ShapeNet(args.dataset_directory,
args.class_ids.split(','), 'test')
# setup model & optimizer
model = models.Model()
model.to_gpu()
chainer.serializers.load_npz(os.path.join(directory_output, 'model.npz'),
model)
# evaluate
reporter = chainer.Reporter()
observation = {}
with reporter.scope(observation):
training.validation(None, model, dataset_test)
for key in sorted(observation.keys()):
key_display = key
for class_id in CLASS_NAMES.keys():
key_display = key_display.replace(class_id, CLASS_NAMES[class_id])
print '%s: %.4f' % (key_display, observation[key])
model = models_vpl2.Model('data/obj/sphere/sphere_642.obj', args=args)
print("Made model")
model = model.cuda()
optimizer = torch.optim.Adam(model.model_param(), args.learning_rate)
print("Made optimizer")
start_iter = START_ITERATION
if args.resume_path:
state_dicts = torch.load(args.resume_path)
model.load_state_dict(state_dicts['model'])
optimizer.load_state_dict(state_dicts['optimizer'])
start_iter = int(os.path.split(args.resume_path)[1][11:].split('.')[0]) + 1
print('Resuming from %s iteration' % start_iter)
dataset_train = datasets.ShapeNet(args.dataset_directory,
args.class_ids.split(','), 'train')
print("Loaded dataset_train")
dataset_val = datasets.ShapeNet(args.dataset_directory,
args.class_ids.split(','), 'val')
print("Loaded dataset_val")
def train():
writer = SummaryWriter()
print("Made summary writer")
end = time.time()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
def run():
# arguments
parser = argparse.ArgumentParser()
parser.add_argument('-eid', '--experiment_id', type=str)
parser.add_argument('-d',
'--model_directory',
type=str,
default=MODEL_DIRECTORY)
parser.add_argument('-dd',
'--dataset_directory',
type=str,
default=DATASET_DIRECTORY)
parser.add_argument('-cls', '--class_ids', type=str, default=CLASS_IDS_ALL)
parser.add_argument('-bs', '--batch_size', type=int, default=BATCH_SIZE)
parser.add_argument('-ls',
'--lambda_smoothness',
type=float,
default=LAMBDA_SMOOTHNESS)
parser.add_argument('-lr',
'--learning_rate',
type=float,
default=LEARNING_RATE)
parser.add_argument('-lrp',
'--lr_reduce_point',
type=float,
default=LR_REDUCE_POINT)
parser.add_argument('-ni',
'--num_iterations',
type=int,
default=NUM_ITERATIONS)
parser.add_argument('-li',
'--log_interval',
type=int,
default=LOG_INTERVAL)
parser.add_argument('-s', '--seed', type=int, default=RANDOM_SEED)
parser.add_argument('-g', '--gpu', type=int, default=GPU)
args = parser.parse_args()
directory_output = os.path.join(args.model_directory, args.experiment_id)
# set random seed, gpu
random.seed(args.seed)
np.random.seed(args.seed)
cp.random.seed(args.seed)
chainer.cuda.get_device(args.gpu).use()
# load dataset
dataset_train = datasets.ShapeNet(args.dataset_directory,
args.class_ids.split(','), 'train')
dataset_val = datasets.ShapeNet(args.dataset_directory,
args.class_ids.split(','), 'val')
train_iter = training.MyIterator(dataset_train, args.batch_size)
# setup model & optimizer
model = models.Model(lambda_smoothness=args.lambda_smoothness)
model.to_gpu()
optimizer = neural_renderer.Adam(args.learning_rate)
optimizer.setup(model)
# setup trainer
updater = chainer.training.StandardUpdater(train_iter,
optimizer,
converter=training.my_convertor)
trainer = chainer.training.Trainer(updater,
stop_trigger=(args.num_iterations,
'iteration'),
out=directory_output)
trainer.extend(
chainer.training.extensions.LogReport(trigger=(args.log_interval,
'iteration')))
trainer.extend(
chainer.training.extensions.PrintReport([
'iteration', 'main/loss_silhouettes', 'main/loss_smoothness',
'val/iou', 'elapsed_time'
]))
trainer.extend(chainer.training.extensions.ProgressBar(update_interval=10))
trainer.extend(functools.partial(training.validation,
model=model,
dataset=dataset_val),
name='validation',
priority=chainer.training.PRIORITY_WRITER,
trigger=(args.log_interval, 'iteration'))
trainer.extend(functools.partial(
training.lr_shift,
optimizer=optimizer,
iterations=[args.num_iterations * args.lr_reduce_point]),
name='lr_shift',
trigger=(1, 'iteration'))
# main loop
trainer.run()
# save model
chainer.serializers.save_npz(os.path.join(directory_output, 'model.npz'),
model)
