问题描述
我有训练有素的模型。在培训期间，字符串字符存在一些问题。所以我将标签转换成数字：
red : 0
blue: 1
green: 2
现在可以将我的标签重命名为实际的标签名称了。 培训时间。如果有人有主意，将会很有帮助。
训练并验证模型
for epoch in range(1,epoch_num + 1):
loss_train,acc_train = train(train_loader,model,criterion,optimizer,epoch)
loss_val,acc_val = validate(val_loader,epoch)
total_loss_val.append(loss_val)
total_acc_val.append(acc_val)
测试单个图像：
def eval_image(file_path):
model = torch.load(file_path)
model.eval()
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
X = Image.open('red.jpeg')
test_transforms = transforms.Compose([transforms.ToTensor()])
image_tensor = test_transforms(X).float()
image_tensor = image_tensor.unsqueeze_(0)
input = Variable(image_tensor)
input = input.to(device)
output = model(input)
index = output.data.cpu().numpy().argmax()
print(index)
一旦训练完成，就不能使评估脚本通用。我将必须通过以下代码
idx_to_class = {
0: "red",1: "blue",2: "green",}
class_name = idx_to_class[index]
但是我不想传递上面的代码。由于我的评估脚本需要通用。
解决方法
为此使用字典！
labels = [0,1,2,1]
dct = {0: "red",1: "blue",2: "green"}
renamed_labels = [dct[x] for x in labels]
renamed_labels ## ["red","blue","green","red","blue"]
labels = ["red","green"]
dataset = ["red","blue"]
创建标签字典：
l_dict = {v:k for k,v in enumerate(labels)}
print(l_dict)
输出：
{'red': 0,'blue': 1,'green': 2}
l_dict_reverse = {k:v for k,v in enumerate(labels)}
print(l_dict_reverse)
输出：
{0: 'red',1: 'blue',2: 'green'}
char_to_num = [l_dict[x] for x in dataset]
print(char_to_num)
输出：
[0,1]
num_to_char = [l_dict_reverse[x] for x in char_to_num]
print(num_to_char)
输出：
['red','blue','green','red','blue']
完整的代码段：
labels = ["red","blue"]
l_dict = {v:k for k,v in enumerate(labels)}
# Output: {'red': 0,'green': 2}
l_dict_reverse = {k:v for k,v in enumerate(labels)}
# Output: {0: 'red',2: 'green'}
char_to_num = [l_dict[x] for x in dataset]
# Output: `[0,1]
num_to_char = [l_dict_reverse[x] for x in char_to_num]
# Output: `['red','blue']