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eval_hypo.py
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import argparse
import math
import os
import time
from datetime import datetime
import logging
# import tensorboard_logger as tb_logger
import pprint
import torch
import torch.nn.parallel
import torch.nn.functional as F
import torch.optim
import torch.utils.data
import numpy as np
from make_datasets_cifar import *
from sklearn.metrics import accuracy_score
from utils import (CompLoss, DisLoss, DisLPLoss, set_loader_small, set_loader_ImageNet, set_model)
parser = argparse.ArgumentParser(description='Eval HYPO')
parser.add_argument('--gpu', default=7, type=int, help='which GPU to use')
parser.add_argument('--seed', default=4, type=int, help='random seed') # original 4
parser.add_argument('--w', default=2, type=float,
help='loss scale')
parser.add_argument('--proto_m', default= 0.99, type=float,
help='weight of prototype update')
parser.add_argument('--feat_dim', default = 128, type=int,
help='feature dim')
parser.add_argument('--in-dataset', default="CIFAR-10", type=str, help='ID dataset name', choices=['PACS', 'CIFAR-10', 'ImageNet-100'])
parser.add_argument('--id_loc', default="datasets", type=str, help='location of ID dataset')
parser.add_argument('--ood_loc', default="datasets", type=str, help='location of OOD dataset')
parser.add_argument('--model', default='resnet18', type=str, help='model architecture: [resnet18, wrt40, wrt28, densenet100, resnet50, resnet34]')
parser.add_argument('--head', default='mlp', type=str, help='either mlp or linear head')
parser.add_argument('--loss', default = 'hypo', type=str, choices = ['hypo'],
help='name of experiment')
parser.add_argument('--ckpt_name', type=str, default='ckpt_hypo_resnet18_cifar10',
help='name of the model checkpoint')
parser.add_argument('--ckpt_loc', type=str, default='checkpoints/CIFAR-10',
help='loc of the model checkpoint')
parser.add_argument('-b', '--batch_size', default= 128, type=int,
help='mini-batch size (default: 64)')
parser.add_argument('--print-freq', '-p', default=10, type=int,
help='print frequency (default: 10)')
parser.add_argument('--temp', type=float, default=0.1,
help='temperature for loss function')
parser.add_argument('--normalize', action='store_true',
help='normalize feat embeddings')
parser.add_argument('--prefetch', type=int, default=4, help='Pre-fetching threads.')
parser.add_argument('--target_domain', type=str, default='sketch', choices=['sketch', 'photo', 'art_painting', 'cartoon'])
parser.add_argument('--cortype', type=str, default='gaussian_noise', help='data type of corrupted datasets')
parser.set_defaults(bottleneck=True)
parser.set_defaults(augment=True)
args = parser.parse_args()
state = {k: v for k, v in args._get_kwargs()}
date_time = datetime.now().strftime("%d_%m_%H:%M")
args.log_directory = "logs/eval/{in_dataset}/{name}/".format(in_dataset=args.in_dataset, name= args.ckpt_name)
if not os.path.exists(args.log_directory):
os.makedirs(args.log_directory)
#init log
log = logging.getLogger(__name__)
formatter = logging.Formatter('%(asctime)s : %(message)s')
fileHandler = logging.FileHandler(os.path.join(args.log_directory, "eval_info.log"), mode='w')
fileHandler.setFormatter(formatter)
streamHandler = logging.StreamHandler()
streamHandler.setFormatter(formatter)
log.setLevel(logging.DEBUG)
log.addHandler(fileHandler)
log.addHandler(streamHandler)
log.debug(state)
if args.in_dataset == "CIFAR-10":
args.n_cls = 10
elif args.in_dataset == "PACS":
args.n_cls = 7
elif args.in_dataset == "VLCS":
args.n_cls = 5
elif args.in_dataset == "OfficeHome":
args.n_cls = 65
elif args.in_dataset == 'terra_incognita':
args.n_cls = 10
elif args.in_dataset in ["CIFAR-100", "ImageNet-100"]:
args.n_cls = 100
#set seeds
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
np.random.seed(args.seed)
log.debug(f"Evaluating {args.ckpt_name}")
def to_np(x): return x.data.cpu().numpy()
if args.in_dataset == 'CIFAR-10':
val_loader, test_loader_ood = make_datasets(args.id_loc, args.ood_loc, args.in_dataset, state, args.cortype)
else:
train_loader, val_loader, test_loader_ood = set_loader_small(args)
print("\n len(loader_in.dataset) {}, " \
"len(test_loader_ood.dataset) {}".format(
len(val_loader.dataset),
len(test_loader_ood.dataset)))
def main():
model = set_model(args)
model_name=f'{args.ckpt_loc}/{args.ckpt_name}.pth.tar'
model.load_state_dict(torch.load(model_name)['state_dict'])
criterion_dis = DisLoss(args, model, val_loader, temperature=args.temp).cuda() # V2: prototypes with EMA style update
criterion_dis.load_state_dict(torch.load(model_name)['dis_state_dict'])
model.eval()
print("computing over distribution ID dataset. \n")
with torch.no_grad():
accuracies_in = []
for data, target in val_loader:
data, target = data.cuda(), target.cuda()
penultimate = model.encoder(data).squeeze()
penultimate = F.normalize(penultimate, dim=1)
features = model.forward(data)
feat_dot_prototype = torch.div(torch.matmul(features, criterion_dis.prototypes.T), args.temp)
# for numerical stability
logits_max, _ = torch.max(feat_dot_prototype, dim=1, keepdim=True)
logits = feat_dot_prototype - logits_max.detach()
pred = logits.data.max(1)[1]
accuracies_in.append(accuracy_score(list(to_np(pred)), list(to_np(target))))
acc = sum(accuracies_in) / len(accuracies_in)
print("ID accuracy: {}".format(acc))
print("computing over test distribution cor dataset. \n")
with torch.no_grad():
accuracies_cor = []
for data, target in test_loader_ood:
data, target = data.cuda(), target.cuda()
penultimate = model.encoder(data).squeeze()
penultimate = F.normalize(penultimate, dim=1)
features = model.forward(data)
feat_dot_prototype = torch.div(torch.matmul(features, criterion_dis.prototypes.T), args.temp)
# for numerical stability
logits_max, _ = torch.max(feat_dot_prototype, dim=1, keepdim=True)
logits = feat_dot_prototype - logits_max.detach()
pred = logits.data.max(1)[1]
accuracies_cor.append(accuracy_score(list(to_np(pred)), list(to_np(target))))
acc_cor = sum(accuracies_cor) / len(accuracies_cor)
if args.in_dataset == 'CIFAR-10':
print("OOD accuracy for generalization: {}, corrupted types is: {}".format(acc_cor, args.cortype))
else:
print("OOD accuracy for generalization: {}, target domain is: {}".format(acc_cor, args.target_domain))
if __name__ == '__main__':
main()