243 lines
8.6 KiB
Python
243 lines
8.6 KiB
Python
import os
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from copy import deepcopy
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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import models.transform_layers as TL
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from utils.utils import set_random_seed, normalize
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from evals.evals import get_auroc
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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hflip = TL.HorizontalFlipLayer().to(device)
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def eval_ood_detection(P, model, id_loader, ood_loaders, ood_scores, train_loader=None, simclr_aug=None):
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auroc_dict = dict()
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for ood in ood_loaders.keys():
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auroc_dict[ood] = dict()
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assert len(ood_scores) == 1 # assume single ood_score for simplicity
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ood_score = ood_scores[0]
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base_path = os.path.split(P.load_path)[0] # checkpoint directory
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prefix = f'{P.ood_samples}'
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if P.resize_fix:
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prefix += f'_resize_fix_{P.resize_factor}'
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else:
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prefix += f'_resize_range_{P.resize_factor}'
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prefix = os.path.join(base_path, f'feats_{prefix}')
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kwargs = {
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'simclr_aug': simclr_aug,
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'sample_num': P.ood_samples,
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'layers': P.ood_layer,
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}
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print('Pre-compute global statistics...')
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feats_train = get_features(P, f'{P.dataset}_train', model, train_loader, prefix=prefix, **kwargs) # (M, T, d)
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P.axis = []
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for f in feats_train['simclr'].chunk(P.K_shift, dim=1):
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axis = f.mean(dim=1) # (M, d)
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P.axis.append(normalize(axis, dim=1).to(device))
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print('axis size: ' + ' '.join(map(lambda x: str(len(x)), P.axis)))
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f_sim = [f.mean(dim=1) for f in feats_train['simclr'].chunk(P.K_shift, dim=1)] # list of (M, d)
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f_shi = [f.mean(dim=1) for f in feats_train['shift'].chunk(P.K_shift, dim=1)] # list of (M, 4)
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weight_sim = []
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weight_shi = []
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for shi in range(P.K_shift):
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sim_norm = f_sim[shi].norm(dim=1) # (M)
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shi_mean = f_shi[shi][:, shi] # (M)
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weight_sim.append(1 / sim_norm.mean().item())
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weight_shi.append(1 / shi_mean.mean().item())
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if ood_score == 'simclr':
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P.weight_sim = [1]
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P.weight_shi = [0]
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elif ood_score == 'CSI':
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P.weight_sim = weight_sim
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P.weight_shi = weight_shi
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else:
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raise ValueError()
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print(f'weight_sim:\t' + '\t'.join(map('{:.4f}'.format, P.weight_sim)))
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print(f'weight_shi:\t' + '\t'.join(map('{:.4f}'.format, P.weight_shi)))
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print('Pre-compute features...')
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feats_id = get_features(P, P.dataset, model, id_loader, prefix=prefix, **kwargs) # (N, T, d)
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feats_ood = dict()
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for ood, ood_loader in ood_loaders.items():
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if ood == 'interp':
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feats_ood[ood] = get_features(P, ood, model, id_loader, interp=True, prefix=prefix, **kwargs)
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else:
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feats_ood[ood] = get_features(P, ood, model, ood_loader, prefix=prefix, **kwargs)
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print(f'Compute OOD scores... (score: {ood_score})')
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scores_id = get_scores(P, feats_id, ood_score).numpy()
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scores_ood = dict()
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if P.one_class_idx is not None:
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one_class_score = []
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for ood, feats in feats_ood.items():
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scores_ood[ood] = get_scores(P, feats, ood_score).numpy()
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auroc_dict[ood][ood_score] = get_auroc(scores_id, scores_ood[ood])
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if P.one_class_idx is not None:
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one_class_score.append(scores_ood[ood])
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if P.one_class_idx is not None:
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one_class_score = np.concatenate(one_class_score)
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one_class_total = get_auroc(scores_id, one_class_score)
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print(f'One_class_real_mean: {one_class_total}')
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if P.print_score:
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print_score(P.dataset, scores_id)
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for ood, scores in scores_ood.items():
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print_score(ood, scores)
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return auroc_dict
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def get_scores(P, feats_dict, ood_score):
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# convert to gpu tensor
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feats_sim = feats_dict['simclr'].to(device)
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feats_shi = feats_dict['shift'].to(device)
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N = feats_sim.size(0)
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# compute scores
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scores = []
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for f_sim, f_shi in zip(feats_sim, feats_shi):
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f_sim = [f.mean(dim=0, keepdim=True) for f in f_sim.chunk(P.K_shift)] # list of (1, d)
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f_shi = [f.mean(dim=0, keepdim=True) for f in f_shi.chunk(P.K_shift)] # list of (1, 4)
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score = 0
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for shi in range(P.K_shift):
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score += (f_sim[shi] * P.axis[shi]).sum(dim=1).max().item() * P.weight_sim[shi]
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score += f_shi[shi][:, shi].item() * P.weight_shi[shi]
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score = score / P.K_shift
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scores.append(score)
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scores = torch.tensor(scores)
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assert scores.dim() == 1 and scores.size(0) == N # (N)
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return scores.cpu()
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def get_features(P, data_name, model, loader, interp=False, prefix='',
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simclr_aug=None, sample_num=1, layers=('simclr', 'shift')):
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if not isinstance(layers, (list, tuple)):
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layers = [layers]
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# load pre-computed features if exists
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feats_dict = dict()
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# for layer in layers:
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# path = prefix + f'_{data_name}_{layer}.pth'
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# if os.path.exists(path):
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# feats_dict[layer] = torch.load(path)
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# pre-compute features and save to the path
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left = [layer for layer in layers if layer not in feats_dict.keys()]
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if len(left) > 0:
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_feats_dict = _get_features(P, model, loader, interp, (P.dataset == 'imagenet' or
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P.dataset == 'CNMC' or
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P.dataset == 'CNMC_grayscale'),
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simclr_aug, sample_num, layers=left)
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for layer, feats in _feats_dict.items():
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path = prefix + f'_{data_name}_{layer}.pth'
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torch.save(_feats_dict[layer], path)
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feats_dict[layer] = feats # update value
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return feats_dict
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def _get_features(P, model, loader, interp=False, imagenet=False, simclr_aug=None,
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sample_num=1, layers=('simclr', 'shift')):
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if not isinstance(layers, (list, tuple)):
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layers = [layers]
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# check if arguments are valid
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assert simclr_aug is not None
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if imagenet is True: # assume batch_size = 1 for ImageNet
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sample_num = 1
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# compute features in full dataset
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model.eval()
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feats_all = {layer: [] for layer in layers} # initialize: empty list
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for i, (x, _) in enumerate(loader):
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if interp:
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x_interp = (x + last) / 2 if i > 0 else x # omit the first batch, assume batch sizes are equal
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last = x # save the last batch
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x = x_interp # use interp as current batch
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if imagenet is True:
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x = torch.cat(x[0], dim=0) # augmented list of x
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x = x.to(device) # gpu tensor
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# compute features in one batch
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feats_batch = {layer: [] for layer in layers} # initialize: empty list
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for seed in range(sample_num):
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set_random_seed(seed)
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if P.K_shift > 1:
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x_t = torch.cat([P.shift_trans(hflip(x), k) for k in range(P.K_shift)])
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else:
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x_t = x # No shifting: SimCLR
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x_t = simclr_aug(x_t)
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# compute augmented features
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with torch.no_grad():
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kwargs = {layer: True for layer in layers} # only forward selected layers
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_, output_aux = model(x_t, **kwargs)
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# add features in one batch
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for layer in layers:
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feats = output_aux[layer].cpu()
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if imagenet is False:
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feats_batch[layer] += feats.chunk(P.K_shift)
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else:
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feats_batch[layer] += [feats] # (B, d) cpu tensor
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# concatenate features in one batch
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for key, val in feats_batch.items():
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if imagenet:
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feats_batch[key] = torch.stack(val, dim=0) # (B, T, d)
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else:
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feats_batch[key] = torch.stack(val, dim=1) # (B, T, d)
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# add features in full dataset
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for layer in layers:
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feats_all[layer] += [feats_batch[layer]]
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# concatenate features in full dataset
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for key, val in feats_all.items():
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feats_all[key] = torch.cat(val, dim=0) # (N, T, d)
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# reshape order
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if imagenet is False:
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# Convert [1,2,3,4, 1,2,3,4] -> [1,1, 2,2, 3,3, 4,4]
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for key, val in feats_all.items():
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N, T, d = val.size() # T = K * T'
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val = val.view(N, -1, P.K_shift, d) # (N, T', K, d)
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val = val.transpose(2, 1) # (N, 4, T', d)
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val = val.reshape(N, T, d) # (N, T, d)
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feats_all[key] = val
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return feats_all
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def print_score(data_name, scores):
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quantile = np.quantile(scores, np.arange(0, 1.1, 0.1))
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print('{:18s} '.format(data_name) +
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'{:.4f} +- {:.4f} '.format(np.mean(scores), np.std(scores)) +
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' '.join(['q{:d}: {:.4f}'.format(i * 10, quantile[i]) for i in range(11)]))
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