best_model_wts = copy.deepcopy(model.state_dict())
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
# 가장 나은 모델 가중치를 불러옴
model.load_state_dict(best_model_wts)
return model
if __name__ == "__main__":
freeze_support()
model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = torch.nn.Linear(num_ftrs, 2)
learning_rate = 0.001
criterion = torch.nn.CrossEntropyLoss()
optimizer_ft = torch.optim.SGD(model_ft.parameters(),
lr=0.001,
momentum=0.9)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft,
step_size=7,
gamma=0.1)
if is_cuda:
model_ft = model_ft.cuda()
# przypisanie kolorów wierzchołką zbioru
# @timing
def main(n):
time1 = time.time()
for node in n:
colors_of_nodes[node] = get_color_for_node(node)
time2 = time.time()
myfile = open("test.txt", "a")
myfile.write('%s: \t %0.4f s\n' % ("main", time2 - time1))
myfile.close()
return colors_of_nodes
if __name__ == "__main__":
#wykonanie symulacji
for cores in range(1, 5):
n = G.nodes()
n = [n[i::cores] for i in range(cores)]
with Pool(cores) as p:
freeze_support()
time1 = time.time()
print(p.map(main, n))
time2 = time.time()
myfile = open("test.txt", "a")
myfile.write('%s: \t %0.4f s\n' % (cores, time2 - time1))
myfile.close()
