def main():
parser = argparse.ArgumentParser()
parser.add_argument('--batchsize', type=int, default=1)
parser.add_argument('--lr', type=float, default=1e-3)
parser.add_argument('--out', default='result')
parser.add_argument('--resume')
args = parser.parse_args()
comm = chainermn.create_communicator()
device = comm.intra_rank
faster_rcnn = FasterRCNNVGG16(
n_fg_class=len(epic_kitchens_bbox_label_names),
pretrained_model='imagenet')
faster_rcnn.use_preset('evaluate')
model = FasterRCNNTrainChain(faster_rcnn)
chainer.cuda.get_device_from_id(device).use()
model.to_gpu()
train = EpicKitchensBboxDataset(year='2018', split='train')
if comm.rank == 0:
indices = np.arange(len(train))
else:
indices = None
train = TransformDataset(train, ('img', 'bbox', 'label', 'scale'),
Transform(faster_rcnn))
indices = chainermn.scatter_dataset(indices, comm, shuffle=True)
train = train.slice[indices]
train_iter = chainer.iterators.SerialIterator(train,
batch_size=args.batchsize)
optimizer = chainermn.create_multi_node_optimizer(
chainer.optimizers.MomentumSGD(), comm)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer_hooks.WeightDecay(rate=0.0005))
updater = training.updaters.StandardUpdater(train_iter,
optimizer,
device=device)
trainer = training.Trainer(updater, (18, 'epoch'), args.out)
trainer.extend(extensions.ExponentialShift('lr', 0.1, init=args.lr),
trigger=triggers.ManualScheduleTrigger([12, 15], 'epoch'))
if comm.rank == 0:
log_interval = 10, 'iteration'
trainer.extend(
extensions.LogReport(log_name='log.json', trigger=log_interval))
trainer.extend(extensions.observe_lr(), trigger=log_interval)
trainer.extend(extensions.PrintReport([
'iteration', 'epoch', 'elapsed_time', 'lr', 'main/loss',
'main/roi_loc_loss', 'main/roi_cls_loss', 'main/rpn_loc_loss',
'main/rpn_cls_loss'
]),
trigger=log_interval)
trainer.extend(extensions.ProgressBar(update_interval=1))
trainer.extend(extensions.snapshot_object(
model.faster_rcnn, 'model_iter_{.updater.iteration}.npz'),
trigger=(1, 'epoch'))
if args.resume:
serializers.load_npz(args.resume, trainer)
trainer.run()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model',
choices=('ssd300', 'ssd512'),
default='ssd300')
parser.add_argument('--batchsize', type=int, default=32)
parser.add_argument('--lr', type=float, default=1e-3)
parser.add_argument('--out', default='result')
parser.add_argument('--resume')
args = parser.parse_args()
comm = chainermn.create_communicator()
device = comm.intra_rank
if args.model == 'ssd300':
model = SSD300(n_fg_class=len(epic_kitchens_bbox_category_names),
pretrained_model='imagenet')
elif args.model == 'ssd512':
model = SSD512(n_fg_class=len(epic_kitchens_bbox_category_names),
pretrained_model='imagenet')
model.use_preset('evaluate')
train_chain = MultiboxTrainChain(model)
chainer.cuda.get_device_from_id(device).use()
model.to_gpu()
train = EpicKitchensBboxDataset(year='2018', split='train')
if comm.rank == 0:
indices = np.arange(len(train))
else:
indices = None
train = TransformDataset(train, ('img', 'mb_loc', 'mb_label'),
Transform(model.coder, model.insize, model.mean))
indices = chainermn.scatter_dataset(indices, comm, shuffle=True)
train = train.slice[indices]
# http://chainermn.readthedocs.io/en/latest/tutorial/tips_faqs.html#using-multiprocessiterator
if hasattr(multiprocessing, 'set_start_method'):
multiprocessing.set_start_method('forkserver')
train_iter = chainer.iterators.MultiprocessIterator(train,
args.batchsize,
n_processes=2)
optimizer = chainermn.create_multi_node_optimizer(
chainer.optimizers.MomentumSGD(), comm)
optimizer.setup(train_chain)
for param in train_chain.params():
if param.name == 'b':
param.update_rule.add_hook(GradientScaling(2))
else:
param.update_rule.add_hook(WeightDecay(0.0005))
updater = training.updaters.StandardUpdater(train_iter,
optimizer,
device=device)
trainer = training.Trainer(updater, (18, 'epoch'), args.out)
trainer.extend(extensions.ExponentialShift('lr', 0.1, init=args.lr),
trigger=triggers.ManualScheduleTrigger([12, 15], 'epoch'))
if comm.rank == 0:
log_interval = 10, 'iteration'
trainer.extend(
extensions.LogReport(log_name='log.json', trigger=log_interval))
trainer.extend(extensions.observe_lr(), trigger=log_interval)
trainer.extend(extensions.PrintReport([
'epoch', 'iteration', 'lr', 'main/loss', 'main/loss/loc',
'main/loss/conf'
]),
trigger=log_interval)
trainer.extend(extensions.ProgressBar(update_interval=1))
trainer.extend(extensions.snapshot_object(
model, 'model_iter_{.updater.iteration}.npz'),
trigger=(1, 'epoch'))
if args.resume:
serializers.load_npz(args.resume, trainer)
trainer.run()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--pretrained-model', default='imagenet')
parser.add_argument('--batch-size', type=int, default=1)
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--no-display', action='store_true')
parser.add_argument('--shuffle', action='store_true')
parser.add_argument('--save-path', type=str, default=None)
parser.add_argument('--split', choices=('train', 'val'), default='val')
parser.add_argument('--skip', action='store_true')
parser.add_argument('--score-thresh', type=float, default=0.6)
args = parser.parse_args()
model = FasterRCNNVGG16(n_fg_class=len(epic_kitchens_bbox_label_names),
pretrained_model=args.pretrained_model)
model.score_thresh = args.score_thresh
if args.gpu >= 0:
cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
dataset = EpicKitchensBboxDataset(split=args.split)
dataset_iter = chainer.iterators.SerialIterator(dataset,
args.batch_size,
shuffle=args.shuffle,
repeat=False)
dataset_iter.reset()
if dataset_iter._order is None:
indices = dataset.ids
else:
indices = dataset_iter._order
for batch_data in dataset_iter:
imgs = []
for data in batch_data:
img, _, _ = data
imgs.append(img)
bboxes, labels, scores = model.predict(imgs)
base_index = dataset_iter.current_position - args.batch_size
for b_i in range(args.batch_size):
img = imgs[b_i]
bbox, label, score = bboxes[b_i], labels[b_i], scores[b_i]
if args.skip:
if len(bbox) == 0:
print('skip {}.jpg'.format(indices[base_index + b_i]))
continue
vis_bbox(img,
bbox,
label,
score,
label_names=epic_kitchens_bbox_label_names)
if args.save_path is not None:
save_path = os.path.join(thisdir, args.save_path)
if not os.path.exists(save_path):
os.makedirs(save_path)
plt.savefig(
os.path.join(
save_path,
'{}.png'.format(dataset.ids[base_index + b_i].replace(
'/', '_'))))
if not args.no_display:
plt.show()