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test.py
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# coding=utf-8
from __future__ import absolute_import, print_function
import argparse
import torch
from torch.backends import cudnn
from evaluations import extract_features, pairwise_distance, extract_features_classification
from evaluations import Recall_at_ks, NMI, Recall_at_ks_products
# import DataSet
import os
import numpy as np
from utils import to_numpy
# import pdb
from torch.nn import functional as F
import torchvision.transforms as transforms
# from ImageFolder import *
from utils import *
from sklearn.metrics.pairwise import euclidean_distances
from scipy.special import softmax
# from CIFAR100 import CIFAR100
from MiniImageNet import *
# from tensorboardX import SummaryWriter
# writer = SummaryWriter('logs')
cudnn.benchmark = True
parser = argparse.ArgumentParser(description='Testing')
parser.add_argument('-img_sz', type=int, help='img_sz', default=84)
# n_way, k_shot setting
parser.add_argument('-n_way', type=int, help='n way', default=5)
parser.add_argument('-k_spt', type=int, help='k shot for support set', default=1)
parser.add_argument('-k_qry', type=int, help='k shot for query set', default=15)
parser.add_argument('-data', type=str, default='miniimagenet')
parser.add_argument('-r', type=str, default='checkpoints/logs', metavar='PATH')
parser.add_argument('-name', type=str, default='tmp', metavar='PATH')
parser.add_argument("-gpu", type=str, default='0', help='which gpu to choose')
parser.add_argument('-seed', default=2001, type=int, metavar='N',
help='seeds for training process')
parser.add_argument('-epochs', default=150, type=int, metavar='N', help='epochs for training process')
parser.add_argument('-num_task', type=int, default=1, help="learning rate of new parameters")
parser.add_argument('-nb_cl_fg', type=int, default=50, help="learning rate of new parameters")
parser.add_argument('-num_class', type=int, default=100, help="learning rate of new parameters")
parser.add_argument('-dir', default='/home/abhilash/trial',
help='data dir')
parser.add_argument('-top5', action = 'store_true', help='output top5 accuracy')
parser.add_argument('-BatchSize', '-b', default=4, type=int, metavar='N', help='meta-batch size')
parser.add_argument('-update_lr', type=float, help='meta task-level inner update learning rate', default=0.01)
parser.add_argument('-update_step_test', type=int, help='task-level inner update steps', default=5)
args = parser.parse_args()
cudnn.benchmark = True
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
models = []
for i in os.listdir(args.r):
if i.endswith("%d_model.pkl" % (args.epochs - 1)): # 500_model.pkl
models.append(os.path.join(args.r, i))
models.sort()
#print(models)
# if 'cifar' in args.data:
# transform_test = transforms.Compose([
# transforms.ToTensor(),
# transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
# ])
# testdir = args.dir + '/cifar'
# if args.data == 'imagenet_sub' or args.data == 'imagenet_full':
# mean_values = [0.485, 0.456, 0.406]
# std_values = [0.229, 0.224, 0.225]
# transform_test = transforms.Compose([
# #transforms.Resize(256),
# transforms.CenterCrop(224),
# #transforms.RandomHorizontalFlip(),
# transforms.ToTensor(),
# transforms.Normalize(mean=mean_values,
# std=std_values)
# ])
# testdir = os.path.join(args.dir, 'ILSVRC12_256', 'val')
num_classes = args.num_class
num_task = args.num_task
#num_class_per_task = (num_classes - args.nb_cl_fg) // num_task
num_class_per_task = 5
np.random.seed(args.seed)
#random_perm = np.random.permutation(num_classes)
random_perm = list(range(num_classes))
print('Test starting -->\t')
acc_all = np.zeros((num_task+3, num_task+1), dtype = 'float') # Save for csv
for task_id in range(num_task+1):
if task_id ==0:
index = random_perm[:args.nb_cl_fg]
else:
index = random_perm[:args.nb_cl_fg + (task_id) * num_class_per_task]
if 'imagenet' in args.data:
testdir = os.path.join(args.dir, 'miniimagenet')
testfolder = MiniImagenet(testdir, mode='test', n_way=args.n_way, k_shot=args.k_spt,
k_query=args.k_qry,
batchsz=300, resize=args.img_sz, cls_index=index)
test_loader = torch.utils.data.DataLoader(
testfolder, batch_size=args.BatchSize,
shuffle=True,
drop_last=True, num_workers=2)
elif args.data =='cifar100':
np.random.seed(args.seed)
testdir = os.path.join(args.dir, 'cifar100')
testfolder = MiniImagenet(testdir, mode='test', n_way=args.n_way, k_shot=args.k_spt,
k_query=args.k_qry,
batchsz=300, resize=args.img_sz, cls_index=index)
test_loader = torch.utils.data.DataLoader(
testfolder, batch_size=args.BatchSize,
shuffle=True,
drop_last=True, num_workers=2)
print('Test %d\t' % task_id)
#model_loop = list(range(task_id, task_id + 1))
#model_id = model_loop[0]
model_id = task_id
model = torch.load(models[model_id])
val_embeddings_cl, val_labels_cl = extract_features_classification(model, test_loader,
update_step_test = args.update_step_test,
update_lr = args.update_lr,print_freq=32, metric=None)
# Unknown task ID
num_class = 0
ave = 0.0
weighted_ave = 0.0
for k in range(task_id + 1):
if k==0:
tmp = random_perm[:args.nb_cl_fg]
else:
tmp = random_perm[args.nb_cl_fg + (k-1) * num_class_per_task:args.nb_cl_fg + (k) * num_class_per_task]
gt = np.isin(val_labels_cl, tmp)
if args.top5:
estimate = np.argsort(val_embeddings_cl, axis=1)[:,-5:]
estimate_label = estimate
estimate_tmp = np.asarray(estimate_label)[gt]
labels_tmp = np.tile(val_labels_cl[gt].reshape([len(val_labels_cl[gt]),1]),[1,5])
acc = np.sum(estimate_tmp == labels_tmp) / float(len(estimate_tmp))
else:
estimate = np.argmax(val_embeddings_cl, axis=1)
estimate_label = estimate
estimate_tmp = np.asarray(estimate_label)[gt]
acc = np.sum(estimate_tmp == val_labels_cl[gt]) / float(len(estimate_tmp))
ave += acc
weighted_ave += acc * len(tmp)
num_class += len(tmp)
print("Accuracy of Model %d on Task %d with unknown task boundary is %.3f" % (model_id, k, acc))
acc_all[k, task_id] = acc
print('Average: %.3f Weighted Average: %.3f' %(ave / (task_id + 1), weighted_ave / num_class))
acc_all[num_task + 1, task_id] = ave / (task_id + 1)
acc_all[num_task + 2, task_id] = weighted_ave / num_class
if not os.path.exists('results/csv/'):
os.makedirs('results/csv/')
np.savetxt('results/csv/' + args.name + '.csv', acc_all*100, delimiter=",")