start_epoch = 0
# Restore model
if args.load != '':
for i in range(300 - 1, -1, -1):
if 'baseline' in args.method_name:
subdir = 'baseline'
elif 'oe_tune' in args.method_name:
subdir = 'oe_tune'
else:
subdir = 'oe_scratch'
model_name = os.path.join(os.path.join(args.load, subdir), args.method_name + '_epoch_' + str(i) + '.pt')
if os.path.isfile(model_name):
net.load_state_dict(torch.load(model_name))
print('Model restored! Epoch:', i)
start_epoch = i + 1
break
if start_epoch == 0:
assert False, "could not resume"
net.eval()
if args.ngpu > 1:
net = torch.nn.DataParallel(net, device_ids=list(range(args.ngpu)))
if args.ngpu > 0:
net.cuda()
# torch.cuda.manual_seed(1)
return file_list
if __name__ == "__main__":
opt = eval_parse()
device = torch.device("cuda" if cuda.is_available() else "cpu")
train_iter, val_iter = dataset.setup(opt)
# Get list of model paths
checkpoints = get_model_paths(opt.checkpoint)
for path in checkpoints:
model = ConvNet(opt.nClasses)
model.load_state_dict(torch.load(path, map_location=device))
model = model.to(device)
line = "Loading/evaluating model {}".format(path)
sys.stderr.write('\r\033[K' + line)
sys.stderr.flush()
trainer = Trainer(model, None, train_iter, val_iter, opt)
error_rate = trainer.val()
line ="Error rate: {:.2f} | Model: {}\n".format(error_rate, path)
sys.stderr.write('\r\033[K' + line)
sys.stderr.flush()