seed = int(sys.argv[1])
vis_dev = sys.argv[2]
# os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ["CUDA_VISIBLE_DEVICES"] = vis_dev
pred_folder = 'dpn92cls_cce_{}_tuned'.format(seed)
makedirs(pred_folder, exist_ok=True)
# cudnn.benchmark = True
models = []
snap_to_load = 'dpn92_cls_cce_{}_1_best'.format(seed)
model = Dpn92_Unet_Double().cuda()
model = nn.DataParallel(model).cuda()
print("=> loading checkpoint '{}'".format(snap_to_load))
checkpoint = torch.load(path.join(models_folder, snap_to_load),
map_location='cpu')
loaded_dict = checkpoint['state_dict']
sd = model.state_dict()
for k in model.state_dict():
if k in loaded_dict and sd[k].size() == loaded_dict[k].size():
sd[k] = loaded_dict[k]
loaded_dict = sd
model.load_state_dict(loaded_dict)
print("loaded checkpoint '{}' (epoch {}, best_score {})".format(
snap_to_load, checkpoint['epoch'], checkpoint['best_score']))
seed = int(sys.argv[1])
pre_file = sys.argv[2]
post_file = sys.argv[3]
loc_pred_file = sys.argv[4]
cls_pred_file = sys.argv[5]
pred_folder = 'dpn92cls_{}_tuned'.format(seed)
makedirs(pred_folder, exist_ok=True)
models = []
snap_to_load = 'dpn92_cls_cce_{}_tuned_best'.format(seed)
model = Dpn92_Unet_Double(pretrained=None)
model = nn.DataParallel(model)
print("=> loading checkpoint '{}'".format(snap_to_load))
checkpoint = torch.load(path.join(models_folder, snap_to_load),
map_location='cpu')
loaded_dict = checkpoint['state_dict']
sd = model.state_dict()
for k in model.state_dict():
if k in loaded_dict and sd[k].size() == loaded_dict[k].size():
sd[k] = loaded_dict[k]
loaded_dict = sd
model.load_state_dict(loaded_dict)
print("loaded checkpoint '{}' (epoch {}, best_score {})".format(
snap_to_load, checkpoint['epoch'], checkpoint['best_score']))