File size: 1,671 Bytes
6257083 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 |
import math
from typing import Optional
import torch
class WarmupCosineLambda:
def __init__(self, warmup_steps: int, cycle_steps: int, decay_scale: float, exponential_warmup: bool = False):
self.warmup_steps = warmup_steps
self.cycle_steps = cycle_steps
self.decay_scale = decay_scale
self.exponential_warmup = exponential_warmup
def __call__(self, epoch: int):
if epoch < self.warmup_steps:
if self.exponential_warmup:
return self.decay_scale * pow(self.decay_scale, -epoch / self.warmup_steps)
ratio = epoch / self.warmup_steps
else:
ratio = (1 + math.cos(math.pi * (epoch - self.warmup_steps) / self.cycle_steps)) / 2
return self.decay_scale + (1 - self.decay_scale) * ratio
def topk_average_precision(output: torch.Tensor, y: torch.Tensor, k: int):
score_array = torch.tensor([1.0 / i for i in range(1, k + 1)], device=output.device)
topk = output.topk(k)[1]
match_mat = topk == y[:, None].expand(topk.shape)
return (match_mat * score_array).sum(dim=1)
def calc_map5(output: torch.Tensor, y: torch.Tensor, threshold: Optional[float]):
if threshold is not None:
output = torch.cat([output, torch.full((output.shape[0], 1), threshold, device=output.device)], dim=1)
return topk_average_precision(output, y, 5).mean().detach()
def map_dict(output: torch.Tensor, y: torch.Tensor, prefix: str):
d = {f"{prefix}/acc": topk_average_precision(output, y, 1).mean().detach()}
for threshold in [None, 0.3, 0.4, 0.5, 0.6, 0.7]:
d[f"{prefix}/map{threshold}"] = calc_map5(output, y, threshold)
return d
|