File size: 2,100 Bytes
178d33b | 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 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | from typing import Any
import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm
from .base_postprocessor import BasePostprocessor
class ReactPostprocessor(BasePostprocessor):
def __init__(self, config):
super(ReactPostprocessor, self).__init__(config)
self.args = self.config.postprocessor.postprocessor_args
self.percentile = self.args.percentile
self.args_dict = self.config.postprocessor.postprocessor_sweep
self.setup_flag = False
def setup(self, net: nn.Module, id_loader_dict, ood_loader_dict):
if not self.setup_flag:
activation_log = []
net.eval()
with torch.no_grad():
for batch in tqdm(id_loader_dict['val'],
desc='Setup: ',
position=0,
leave=True):
data = batch['data'].cuda()
data = data.float()
_, feature = net(data, return_feature=True)
activation_log.append(feature.data.cpu().numpy())
self.activation_log = np.concatenate(activation_log, axis=0)
self.setup_flag = True
else:
pass
self.threshold = np.percentile(self.activation_log.flatten(),
self.percentile)
@torch.no_grad()
def postprocess(self, net: nn.Module, data: Any):
output = net.forward_threshold(data, self.threshold)
score = torch.softmax(output, dim=1)
_, pred = torch.max(score, dim=1)
energyconf = torch.logsumexp(output.data.cpu(), dim=1)
return pred, energyconf
def set_hyperparam(self, hyperparam: list):
self.percentile = hyperparam[0]
self.threshold = np.percentile(self.activation_log.flatten(),
self.percentile)
print('Threshold at percentile {:2d} over id data is: {}'.format(
self.percentile, self.threshold))
def get_hyperparam(self):
return self.percentile
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