l3ickey / mprg-spring-seminar-competition-2021 Goto Github PK

# 評価
print("評価を始めるっぴ！")
class_correct = list(0. for i in range(len(classes)))
class_total = list(0. for i in range(len(classes)))
with torch.no_grad():
    for data in testloader:
        images, labels = data[0].to(device), data[1].to(device)
        outputs = net(images)
        _, predicted = torch.max(outputs, 1)
        c = (predicted == labels).squeeze()

        for i in range(args.batch_size):
            label = labels[i]
            class_correct[label] += c[i].item()
            class_total[label] += 1