v1.0

utils/misc.py

+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+# @Time : 2022/2/10 12:43
+# @Author : ''
+# @FileName: misc.py
+import os
+import torch
+
+
+def make_dir(folder):
+    if not os.path.isdir(folder):
+        os.makedirs(folder)
+
+
+class AverageMeter(object):
+    """Computes and stores the average and current value"""
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+
+class Logger():
+    def __init__(self, args, filename='log.txt'):
+
+        self.filename = filename
+        self.file = open(filename, 'a')
+        # Write model configuration at top of file
+        for arg in vars(args):
+            self.file.write(arg+': '+str(getattr(args, arg))+'\n')
+        self.file.flush()
+
+    def writerow(self, row):
+        for k in row:
+            self.file.write(k+': '+row[k]+'  ')
+        self.file.write('\n')
+        self.file.flush()
+
+    def close(self):
+        self.file.close()
+
+
+def format_time(seconds):
+    days = int(seconds / 3600/24)
+    seconds = seconds - days*3600*24
+    hours = int(seconds / 3600)
+    seconds = seconds - hours*3600
+    minutes = int(seconds / 60)
+    seconds = seconds - minutes*60
+    secondsf = int(seconds)
+    seconds = seconds - secondsf
+    millis = int(seconds*1000)
+
+    f = ''
+    i = 1
+    if days > 0:
+        f += str(days) + 'D'
+        i += 1
+    if hours > 0 and i <= 2:
+        f += str(hours) + 'h'
+        i += 1
+    if minutes > 0 and i <= 2:
+        f += str(minutes) + 'm'
+        i += 1
+    if secondsf > 0 and i <= 2:
+        f += str(secondsf) + 's'
+        i += 1
+    if millis > 0 and i <= 2:
+        f += str(millis) + 'ms'
+        i += 1
+    if f == '':
+        f = '0ms'
+    return f
+
+
+def accuracy(output, target, topk=(1,)):
+    """Compute the accuracy over the k top predictions for the specified values of k"""
+    with torch.no_grad():
+        maxk = max(topk)
+        batch_size = target.size(0)
+        # print(output)
+
+        _, pred = output.topk(maxk, dim=1, largest=True, sorted=True)
+        pred = pred.t()
+        # print(pred)
+        correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+        res = []
+        for k in topk:
+            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
+            res.append(correct_k.mul_(100./batch_size))
+        return res