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main.py
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main.py
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import torch
import torch.utils.data
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import StepLR
from torch.autograd import Variable
import os
import math
import argparse
import resource
import pandas as pd
import numpy as np
import sklearn.metrics
from scipy import stats
from PIL import Image
from calibrateCIL import calibrateCIL
from ResNet import resnet18_cbam
from myNetwork import network
from iCIFAR100 import iCIFAR100
parser = argparse.ArgumentParser(description='Calibration for Non-exemplar based Class-incremental Learning')
parser.add_argument('--epochs', default=101, type=int, help='Total number of epochs to run')
parser.add_argument('--batch_size', default=64, type=int, help='Batch size for training')
parser.add_argument('--print_freq', default=10, type=int, help='print frequency (default: 10)')
parser.add_argument('--data_name', default='cifar100', type=str, help='Dataset name to use')
parser.add_argument('--total_nc', default=100, type=int, help='class number for the dataset')
parser.add_argument('--fg_nc', default=10, type=int, help='the number of classes in first task')
parser.add_argument('--task_num', default=9, type=int, help='the number of incremental steps')
parser.add_argument('--learning_rate', default=0.001, type=float, help='initial learning rate')
parser.add_argument('--temp', default=0.1, type=float, help='trianing time temperature')
parser.add_argument('--gpu', default='0', type=str, help='GPU id to use')
parser.add_argument('--save_path', default='model_saved_check/', type=str, help='save files directory')
args = parser.parse_args()
print(args)
def main():
cuda_index = 'cuda:' + args.gpu
device = torch.device(cuda_index if torch.cuda.is_available() else "cpu")
task_size = int((args.total_nc - args.fg_nc) / args.task_num) # number of classes in each incremental step
file_name = args.data_name + '_' + str(args.fg_nc) + '_' + str(args.task_num) + '*' + str(task_size)
feature_extractor = resnet18_cbam()
model = calibrateCIL(args, file_name, feature_extractor, task_size, device)
for i in range(args.task_num+1):
model.beforeTrain(i)
model.train()
model.afterTrain()
####### Test ######
test_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761))])
print("############# Test for each Task #############")
test_dataset = iCIFAR100('./dataset', test_transform=test_transform, train=False, download=True)
acc_all = []
for current_task in range(args.task_num+1):
class_index = args.fg_nc + current_task*task_size
filename = args.save_path + file_name + '/' + '%d_model.pkl' % (class_index)
model = torch.load(filename)
model.eval()
acc_up2now = []
for i in range(current_task+1):
if i == 0:
classes = [0, args.fg_nc]
else:
classes = [args.fg_nc + (i - 1) * task_size, args.fg_nc + i * task_size]
test_dataset.getTestData_up2now(classes)
test_loader = DataLoader(dataset=test_dataset,
shuffle=True,
batch_size=args.batch_size)
correct, total = 0.0, 0.0
for setp, (indexs, imgs, labels) in enumerate(test_loader):
imgs, labels = imgs.to(device), labels.to(device)
with torch.no_grad():
outputs = model(imgs)
predicts = torch.max(outputs, dim=1)[1]
correct += (predicts.cpu() == labels.cpu()).sum()
total += len(labels)
accuracy = correct.item() / total
acc_up2now.append(accuracy)
if current_task < args.task_num:
acc_up2now.extend((args.task_num-current_task)*[0])
acc_all.append(acc_up2now)
print(acc_up2now)
print(acc_all)
print("############# Test for up2now Task #############")
test_dataset = iCIFAR100('./dataset', test_transform=test_transform, train=False, download=True)
for current_task in range(args.task_num+1):
class_index = args.fg_nc + current_task*task_size
filename = args.save_path + file_name + '/' + '%d_model.pkl' % (class_index)
model = torch.load(filename)
model.to(device)
model.eval()
classes = [0, args.fg_nc + current_task * task_size]
test_dataset.getTestData_up2now(classes)
test_loader = DataLoader(dataset=test_dataset,
shuffle=True,
batch_size=args.batch_size)
correct, total = 0.0, 0.0
for setp, (indexs, imgs, labels) in enumerate(test_loader):
imgs, labels = imgs.to(device), labels.to(device)
with torch.no_grad():
outputs = model(imgs)
predicts = torch.max(outputs, dim=1)[1]
correct += (predicts.cpu() == labels.cpu()).sum()
total += len(labels)
accuracy = correct.item() / total
print(accuracy)
if __name__ == "__main__":
main()