def main():
args = parser.parse_args()
if 'dpn' not in args.model:
print('Error: Can only convert DPN models.')
exit(1)
if not has_mxnet:
print('Error: Cannot import MXNet module. Please install.')
exit(1)
model = dpn_model_factory.create_model(args.model, num_classes=1000, pretrained=False)
model_prefix = args.model
if model_prefix in ['dpn107', 'dpn68b', 'dpn92']:
model_prefix += '-extra'
checkpoint_base = os.path.join(args.checkpoint_path, model_prefix)
convert_from_mxnet(model, checkpoint_base)
output_checkpoint = os.path.join(args.checkpoint_path, model_prefix + '.pth')
torch.save(model.state_dict(), output_checkpoint)
from models.dpn_model_factory import create_model
from iceberg import *
from torch.optim import SGD
lr = 0.0005
mom = 0.9
wd = 1e-4
bsize = 172
mname = 'dpn92'
model = create_model(mname, 1000, True)
model = torch.nn.DataParallel(model).cuda()
optim = SGD(model.parameters(), lr, momentum=mom, weight_decay=wd)
iceberg = Iceberg('./data/train.json',
'./data/{}_lr5e-4_mom9e-1_wd1e-4_bs{}_pretrained_model.pth'.format(mname, bsize), model, optim, 10000, bsize)
iceberg.run(transforms.Compose([
transforms.Resize(256),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()]))