init commit of samurai

lib/train/admin/stats.py

@@ -0,0 +1,71 @@
+
+
+class StatValue:
+    def __init__(self):
+        self.clear()
+
+    def reset(self):
+        self.val = 0
+
+    def clear(self):
+        self.reset()
+        self.history = []
+
+    def update(self, val):
+        self.val = val
+        self.history.append(self.val)
+
+
+class AverageMeter(object):
+    """Computes and stores the average and current value"""
+    def __init__(self):
+        self.clear()
+        self.has_new_data = False
+
+    def reset(self):
+        self.avg = 0
+        self.val = 0
+        self.sum = 0
+        self.count = 0
+
+    def clear(self):
+        self.reset()
+        self.history = []
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+    def new_epoch(self):
+        if self.count > 0:
+            self.history.append(self.avg)
+            self.reset()
+            self.has_new_data = True
+        else:
+            self.has_new_data = False
+
+
+def topk_accuracy(output, target, topk=(1,)):
+    """Computes the precision@k for the specified values of k"""
+    single_input = not isinstance(topk, (tuple, list))
+    if single_input:
+        topk = (topk,)
+
+    maxk = max(topk)
+    batch_size = target.size(0)
+
+    _, pred = output.topk(maxk, 1, True, True)
+    pred = pred.t()
+    correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+    res = []
+    for k in topk:
+        correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)[0]
+        res.append(correct_k * 100.0 / batch_size)
+
+    if single_input:
+        return res[0]
+
+    return res