| 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 |
|
|
