--- a
+++ b/CaraNet/utils/utils.py
@@ -0,0 +1,62 @@
+import torch
+import numpy as np
+from thop import profile
+from thop import clever_format
+
+
+def clip_gradient(optimizer, grad_clip):
+    """
+    For calibrating misalignment gradient via cliping gradient technique
+    :param optimizer:
+    :param grad_clip:
+    :return:
+    """
+    for group in optimizer.param_groups:
+        for param in group['params']:
+            if param.grad is not None:
+                param.grad.data.clamp_(-grad_clip, grad_clip)
+
+
+def adjust_lr(optimizer, init_lr, epoch, decay_rate=0.1, decay_epoch=30):
+    decay = decay_rate ** (epoch // decay_epoch)
+    for param_group in optimizer.param_groups:
+        param_group['lr'] *= decay
+
+
+class AvgMeter(object):
+    def __init__(self, num=40):
+        self.num = num
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+        self.losses = []
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+        self.losses.append(val)
+
+    def show(self):
+        return torch.mean(torch.stack(self.losses[np.maximum(len(self.losses)-self.num, 0):]))
+
+
+def CalParams(model, input_tensor):
+    """
+    Usage:
+        Calculate Params and FLOPs via [THOP](https://github.com/Lyken17/pytorch-OpCounter)
+    Necessarity:
+        from thop import profile
+        from thop import clever_format
+    :param model:
+    :param input_tensor:
+    :return:
+    """
+    flops, params = profile(model, inputs=(input_tensor,))
+    flops, params = clever_format([flops, params], "%.3f")
+    print('[Statistics Information]\nFLOPs: {}\nParams: {}'.format(flops, params))
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