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utility.py
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utility.py
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import math
from typing import Union, Tuple, Dict
import torch
import torch.nn.functional as F
from torch import Tensor
from nest import register, Context
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
@register
def multi_topk_meter(
ctx: Context,
train_ctx: Context,
k: int=1,
init_num: int=1,
end_num: int = 0) -> dict:
"""Multi topk meter.
"""
def accuracy(output, target, k=1):
batch_size = target.size(0)
_, pred = output.topk(k, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
return correct_k.mul_(100.0 / batch_size)
for i in range(init_num, len(train_ctx.output) - end_num):
if not "branch_"+str(i) in ctx:
setattr(ctx, "branch_"+str(i), AverageMeter())
if train_ctx.batch_idx == 0:
for i in range(init_num, len(train_ctx.output) - end_num):
getattr(ctx, "branch_"+str(i)).reset()
for i in range(init_num, len(train_ctx.output) - end_num):
acc = accuracy(train_ctx.output[i], train_ctx.target, k)
getattr(ctx, "branch_"+str(i)).update(acc.item())
acc_list = {}
for i in range(init_num, len(train_ctx.output) - end_num):
acc_list["branch_"+str(i)] = getattr(ctx, "branch_"+str(i)).avg
return acc_list
@register
def best_meter(ctx: Context, train_ctx: Context, best_branch: int = 1, k: int = 1) -> float:
"""Best meter.
"""
def accuracy(output, target, k=1):
batch_size = target.size(0)
_, pred = output.topk(k, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
return correct_k.mul_(100.0 / batch_size)
if not 'meter' in ctx:
ctx.meter = AverageMeter()
if train_ctx.batch_idx == 0:
ctx.meter.reset()
acc = accuracy(train_ctx.output[best_branch], train_ctx.target, k)
ctx.meter.update(acc.item())
return ctx.meter.avg