| from typing import Any |
|
|
| import faiss |
| import numpy as np |
| import torch |
| import torch.nn as nn |
| from tqdm import tqdm |
|
|
| from .base_postprocessor import BasePostprocessor |
|
|
|
|
| class CIDERPostprocessor(BasePostprocessor): |
| def __init__(self, config): |
| super(CIDERPostprocessor, self).__init__(config) |
| self.args = self.config.postprocessor.postprocessor_args |
| self.K = self.args.K |
| self.activation_log = None |
| 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['train'], |
| desc='Setup: ', |
| position=0, |
| leave=True): |
| data = batch['data'].cuda() |
|
|
| feature = net.intermediate_forward(data) |
| activation_log.append(feature.data.cpu().numpy()) |
|
|
| self.activation_log = np.concatenate(activation_log, axis=0) |
| self.index = faiss.IndexFlatL2(feature.shape[1]) |
| self.index.add(self.activation_log) |
| self.setup_flag = True |
| else: |
| pass |
|
|
| @torch.no_grad() |
| def postprocess(self, net: nn.Module, data: Any): |
| feature = net.intermediate_forward(data) |
| D, _ = self.index.search( |
| feature.cpu().numpy(), |
| self.K, |
| ) |
| kth_dist = -D[:, -1] |
| |
| |
| pred = torch.zeros(len(kth_dist)) |
| return pred, torch.from_numpy(kth_dist) |
|
|
| def set_hyperparam(self, hyperparam: list): |
| self.K = hyperparam[0] |
|
|
| def get_hyperparam(self): |
| return self.K |
|
|
