| import torch |
|
|
|
|
| class Stat: |
| def __init__(self, keep_raw=False): |
| self.x = 0.0 |
| self.x2 = 0.0 |
| self.z = 0.0 |
| self.z2 = 0.0 |
| self.n = 0.0 |
| self.u = 0.0 |
| self.keep_raw = keep_raw |
| self.raw = [] |
|
|
| def update(self, new_x): |
| new_z = new_x.log() |
|
|
| self.x += new_x.sum() |
| self.x2 += (new_x**2).sum() |
| self.z += new_z.sum() |
| self.z2 += (new_z**2).sum() |
| self.n += len(new_x) |
| self.u += 1 |
|
|
| if self.keep_raw: |
| self.raw.append(new_x) |
|
|
| @property |
| def mean(self): |
| return self.x / self.n |
|
|
| @property |
| def std(self): |
| return (self.x2 / self.n - self.mean**2) ** 0.5 |
|
|
| @property |
| def mean_log(self): |
| return self.z / self.n |
|
|
| @property |
| def std_log(self): |
| return (self.z2 / self.n - self.mean_log**2) ** 0.5 |
|
|
| @property |
| def n_frms(self): |
| return self.n |
|
|
| @property |
| def n_utts(self): |
| return self.u |
|
|
| @property |
| def raw_data(self): |
| assert self.keep_raw, "does not support storing raw data!" |
| return torch.cat(self.raw) |
|
|
|
|
| class F0Stat(Stat): |
| def update(self, new_x): |
| |
| if new_x is not None: |
| super().update(new_x[new_x != 0]) |
|
|
|
|
| class Accuracy: |
| def __init__(self): |
| self.y, self.yhat = [], [] |
|
|
| def update(self, yhat, y): |
| self.yhat.append(yhat) |
| self.y.append(y) |
|
|
| def acc(self, tol): |
| yhat = torch.cat(self.yhat) |
| y = torch.cat(self.y) |
| acc = torch.abs(yhat - y) <= tol |
| acc = acc.float().mean().item() |
| return acc |
|
|
