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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 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | from typing import Any
import numpy as np
import torch
import torch.nn as nn
from tqdm import tqdm
from .base_postprocessor import BasePostprocessor
from math import ceil
""" Code borrowed from https://github.com/snu-mllab/Neural-Relation-Graph
"""
def normalize(feat, nc=50000):
with torch.no_grad():
split = ceil(len(feat) / nc)
for i in range(split):
feat_ = feat[i * nc:(i + 1) * nc]
feat[i * nc:(i + 1) *
nc] = feat_ / torch.sqrt((feat_**2).sum(-1) + 1e-10).reshape(-1, 1)
return feat
def kernel(feat, feat_t, prob, prob_t, split=2):
"""Kernel function (assume feature is normalized)
"""
size = ceil(len(feat_t) / split)
rel_full = []
for i in range(split):
feat_t_ = feat_t[i * size:(i + 1) * size]
prob_t_ = prob_t[i * size:(i + 1) * size]
with torch.no_grad():
dot = torch.matmul(feat, feat_t_.transpose(1, 0))
dot = torch.clamp(dot, min=0.)
sim = torch.matmul(prob, prob_t_.transpose(1, 0))
rel = dot * sim
rel_full.append(rel)
rel_full = torch.cat(rel_full, dim=-1)
return rel_full
def get_relation(feat, feat_t, prob, prob_t, pow=1, chunk=50, thres=0.03):
"""Get relation values (top-k and summation)
Args:
feat (torch.Tensor [N,D]): features of the source data
feat_t (torch.Tensor [N',D]): features of the target data
prob (torch.Tensor [N,C]): probabilty vectors of the source data
prob_t (torch.Tensor [N',C]): probabilty vectors of the target data
pow (int): Temperature of kernel function
chunk (int): batch size of kernel calculation (trade off between memory and speed)
thres (float): cut off value for small relation graph edges. Defaults to 0.03.
Returns:
graph: statistics of relation graph
"""
n = feat.shape[0]
n_chunk = ceil(n / chunk)
score = []
for i in range(n_chunk):
feat_ = feat[i * chunk:(i + 1) * chunk]
prob_ = prob[i * chunk:(i + 1) * chunk]
rel = kernel(feat_, feat_t, prob_, prob_t)
mask = (rel.abs() > thres)
rel_mask = mask * rel
edge_sum = (rel_mask.sign() * (rel_mask.abs()**pow)).sum(-1)
score.append(edge_sum.cpu())
score = torch.cat(score, dim=0)
return score
class RelationPostprocessor(BasePostprocessor):
def __init__(self, config):
super(RelationPostprocessor, self).__init__(config)
self.args = self.config.postprocessor.postprocessor_args
self.pow = self.args.pow
self.feature_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:
feature_log = []
prob_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()
data = data.float()
logit, feature = net(data, return_feature=True)
prob = torch.softmax(logit, dim=1)
feature_log.append(normalize(feature))
prob_log.append(prob)
self.feat_train = torch.cat(feature_log, axis=0)
self.prob_train = torch.cat(prob_log, axis=0)
self.setup_flag = True
else:
pass
@torch.no_grad()
def postprocess(self, net: nn.Module, data: Any):
output, feature = net(data, return_feature=True)
feature = normalize(feature)
prob = torch.softmax(output, dim=1)
score = get_relation(feature, self.feat_train, prob, self.prob_train, pow=self.pow)
_, pred = torch.max(prob, dim=1)
return pred, score
def set_hyperparam(self, hyperparam: list):
self.pow = hyperparam[0]
def get_hyperparam(self):
return self.pow
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