if __name__ == '__main__':
parser = Flags()
parser.set_arguments()
FG = parser.parse_args()
c_code, axis, z_dim = FG.c_code, FG.axis, FG.z_dim
device = torch.device(FG.devices[0])
torch.cuda.set_device(FG.devices[0])
nets = []
for i in range(FG.fold):
parser.configure('cur_fold', i)
parser.configure('ckpt_dir')
FG = parser.load()
net = Baseline(FG.ckpt_dir, len(FG.labels))
net.to(device)
net.load(epoch=None, optimizer=None, is_best=True)
net.eval()
nets += [net]
#G = Generator(FG)
G = torch.nn.DataParallel(Generator(z_dim, c_code, axis))
# state_dict = torch.load(os.path.join('BiGAN-info-c4-f', 'G.pth'), 'cpu')
state_dict = torch.load(os.path.join('157-G8', 'G.pth'), 'cpu')
G.load_state_dict(state_dict)
G.to(device)
G.eval()
if axis == 1:
ns = ((160, 160), (112, 112))
elif axis == 0:
def run_fold(parser, vis):
devices = parser.args.devices
parser.args.ckpt_dir = os.path.join('checkpoint', parser.args.model,
'f' + str(parser.args.cur_fold))
FG = parser.load()
FG.devices = devices
print(FG)
torch.cuda.set_device(FG.devices[0])
device = torch.device(FG.devices[0])
net = Baseline(FG.ckpt_dir, len(FG.labels))
performances = net.load(epoch=None, is_best=True)
net = net.to(device)
trainloader, testloader = get_dataloader(k=FG.fold,
cur_fold=FG.cur_fold,
modality=FG.modality,
axis=FG.axis,
labels=FG.labels,
batch_size=FG.batch_size)
evaluator = create_supervised_evaluator(
net,
device=device,
non_blocking=True,
prepare_batch=process_ninecrop_batch,
metrics={
'sensitivity': Recall(False, mean_over_ninecrop),
'precision': Precision(False, mean_over_ninecrop),
'specificity': Specificity(False, mean_over_ninecrop)
})
class Tracker(object):
def __init__(self):
self.data = []
outputs = Tracker()
targets = Tracker()
@evaluator.on(Events.ITERATION_COMPLETED)
def transform_ninecrop_output(engine):
output, target = engine.state.output
if output.size(0) != target.size(0):
n = target.size(0)
npatches = output.size(0) // n
output = output.view(n, npatches, *output.shape[1:])
output = torch.mean(output, dim=1)
outputs.data += [output]
targets.data += [target]
evaluator.run(testloader)
string = 'Fold {}'.format(FG.cur_fold) + '<br>'
string += 'Epoch {}'.format(performances.pop('epoch')) + '<br>'
for k in sorted(performances.keys()):
string += k + ': ' + '{:.4f}'.format(performances[k])
string += '<br>'
string += 'pre : ' + str(evaluator.state.metrics['precision']) + '<br>'
string += 'sen : ' + str(evaluator.state.metrics['sensitivity']) + '<br>'
string += 'spe : ' + str(evaluator.state.metrics['specificity']) + '<br>'
vis.text(string, win=FG.model + '_result_fold{}'.format(FG.cur_fold))
del net
return outputs.data, targets.data