标签：acc self labels top1 测试 print total
def eval(self, phase='val', openset=False):
print("Enter eval")
print_str = ['Phase: %s' % (phase)]
print_write(print_str, self.log_file) # Phase: test
time.sleep(0.25) # 暂停程序执行0.25秒，以便给打印输出留出时间。
if openset: # 检查是否处于openset测试模式
print('Under openset test mode. Open threshold is %.1f'
% self.training_opt['open_threshold'])
torch.cuda.empty_cache() # 清空GPU缓存，以释放内存。
# In validation or testing mode, set model to eval() and initialize running loss/correct
for model in self.networks.values():
model.eval()
# 创建一个空的张量，用于存储所有样本的预测logits,labels,paths。
self.total_logits = torch.empty((0, self.training_opt['num_classes'])).to(self.device)
self.total_labels = torch.empty(0, dtype=torch.long).to(self.device)
self.total_paths = np.empty(0)
# Iterate over dataset
# 对数据集中的每个样本执行以下操作，并使用tqdm库显示进度条：
for inputs, labels, paths in tqdm(self.data[phase]):
inputs, labels = inputs.to(self.device), labels.to(self.device)
# print(labels)
# If on training phase, enable gradients
with torch.set_grad_enabled(False): # 不计算梯度
# In validation or testing
self.batch_forward(inputs,
labels,
centroids=self.memory['centroids'],
phase=phase)
# 将当前样本的x连接到张量x中
self.total_logits = torch.cat((self.total_logits, self.logits))
self.total_labels = torch.cat((self.total_labels, labels))
self.total_paths = np.concatenate((self.total_paths, paths))
# 对self.total_logits张量进行softmax操作，并获取每个样本的概率和预测结果。
probs, preds = F.softmax(self.total_logits.detach(), dim=1).max(dim=1)
if openset: # 处于openset测试模式
# 将概率低于开放阈值的样本的预测结果设置为 - 1。
preds[probs < self.training_opt['open_threshold']] = -1
self.openset_acc = mic_acc_cal(preds[self.total_labels == -1],
self.total_labels[self.total_labels == -1])
print('\n\nOpenset Accuracy: %.3f' % self.openset_acc)
# Calculate the overall accuracy and F measurement
# 计算总体准确率。
self.eval_acc_mic_top1 = mic_acc_cal(preds[self.total_labels != -1],
self.total_labels[self.total_labels != -1])
# 计算F度量值。
self.eval_f_measure = F_measure(preds, self.total_labels, openset=openset,
theta=self.training_opt['open_threshold'])
# 计算多样本准确率、中等样本准确率和少样本准确率。
self.many_acc_top1, \
self.median_acc_top1, \
self.low_acc_top1 = shot_acc(preds[self.total_labels != -1],
self.total_labels[self.total_labels != -1],
self.data['train'])
# Top-1 accuracy and additional string
# format:
# Phase: val
# 总体准确率Evaluation_accuracy_micro_top1: 0.011
# 计算F度量值Averaged F-measure: 0.002
# 多中少样本准确率Many_shot_accuracy_top1: 0.029 Median_shot_accuracy_top1: 0.000 Low_shot_accuracy_top1: 0.000
print_str = ['\n\n',
'Phase: %s'
% (phase),
'\n\n',
'总体准确率(Evaluation_accuracy_micro_top1): %.3f'
% (self.eval_acc_mic_top1),
'\n',
'计算F度量值(Averaged F-measure): %.3f'
% (self.eval_f_measure),
'\n',
'多样本准确率(Many_shot_accuracy_top1): %.3f'
% (self.many_acc_top1),
'中等样本准确率(Median_shot_accuracy_top1): %.3f'
% (self.median_acc_top1),
'少样本准确率(Low_shot_accuracy_top1): %.3f'
% (self.low_acc_top1),
'\n']
if phase == 'val':
print_write(print_str, self.log_file)
else:
print(*print_str)
标签：acc,
self,
labels,
top1,
测试,
print,
total
From： https://www.cnblogs.com/ZarkY/p/17893363.html