| 50 | |
| 51 | |
| 52 | class AvgMeter(object): |
| 53 | def __init__(self, num=40): |
| 54 | self.num = num |
| 55 | self.reset() |
| 56 | |
| 57 | def reset(self): |
| 58 | self.val = 0 |
| 59 | self.avg = 0 |
| 60 | self.sum = 0 |
| 61 | self.count = 0 |
| 62 | self.losses = [] |
| 63 | |
| 64 | def update(self, val, n=1): |
| 65 | self.val = val |
| 66 | self.sum += val * n |
| 67 | self.count += n |
| 68 | self.avg = self.sum / self.count |
| 69 | self.losses.append(val) |
| 70 | |
| 71 | def show(self): |
| 72 | return torch.mean(torch.stack(self.losses[np.maximum(len(self.losses)-self.num, 0):])) |
| 73 | |
| 74 | |
| 75 | def CalParams(model, input_tensor): |
nothing calls this directly
no outgoing calls
no test coverage detected