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inheritable_random_trainnum200_wEWC.py
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import argparse
import time
import datetime
import sys
import copy
import numpy as np
import os
import shutil
import random
import warnings
import xlwt
import dill as pickle
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader, ConcatDataset
from torch.autograd import Variable
from utils.network_wider_imagenet import Netwider
from utils.train import train, test, train_ewc, test_ewc, train_ewc_vgg, test_ewc_vgg
from utils.model_zoo_imagenet import vgg_compression_ONE
from utils.imagenetDataloader import getDataloader_imagenet_inheritable
torch.cuda.set_device(0)
parser = argparse.ArgumentParser(description='i_inheritable_random_trainnum200_wEWC')
parser.add_argument('--batch_size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)')
parser.add_argument('--epochs', type=int, default=100, metavar='N', help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=0.005, metavar='LR', help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M', help='SGD momentum (default: 0.9)')
parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)')
parser.add_argument('--num_works', type=int, default=21, help='number of tasks')
parser.add_argument('--num_works_tt', type=int, default=10, help='number of comp_tasks')
parser.add_argument('--lr_drop', type=float, default=0.4)
parser.add_argument('--epochs_drop', type=int, default=110)
parser.add_argument('--print_freq', type=int, default=50)
parser.add_argument('--num_imgs_per_cat_train', type=int, default=20)
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
lr_ =args.lr
CIFAR100_TRAIN_MEAN = (0.5070751592371323, 0.48654887331495095, 0.4409178433670343)
CIFAR100_TRAIN_STD = (0.2673342858792401, 0.2564384629170883, 0.27615047132568404)
record_time = str((datetime.datetime.now() + datetime.timedelta(hours=8)).strftime('%Y-%m-%d %H:%M:%S'))
RESULT_PATH_VAL = ''
def save_checkpoint(states, is_best, output_dir, filename='checkpoint.pth'):
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
print('making dir: %s'%output_dir)
torch.save(states, os.path.join(output_dir, filename))
if is_best and 'state_dict' in states:
torch.save(states['state_dict'], os.path.join(output_dir, 'model_best.pth'))
def main():
print("Data loading...")
trainloader_inheritable, testloader_inheritable = getDataloader_imagenet_inheritable(args.num_works_tt, args.batch_size, subtask_classes_num=5, num_imgs_per_cate=args.num_imgs_per_cat_train, path=inherit_path)
book = xlwt.Workbook(encoding='utf-8',style_compression=0)
print("Model constructing...")
model = Netwider(13)
model.printf()
for task in range(args.num_works):
print("LL_Task {} begins !".format(task))
start = time.time()
if task != 0:
model_ = copy.deepcopy(model)
del model
model = model_
model.wider(task-1)
model.printf()
if args.cuda:
model = model.cuda()
args.lr = lr_
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=5e-4)
criterion = nn.CrossEntropyLoss()
best_acc = 0.0
best_epoch = 0
snapshot = './imagenet_exp/outputs/i_inheritable_random_trainnum200_wEWC/record_{0}/Task_{1}'.format(record_time, task)
snapshot_model = './imagenet_exp/val_outputs//Val_lifelong_scratch_cifar_imagenet/task_{0}'.format(task)
if not os.path.isdir(snapshot):
print("Building snapshot file: {0}".format(snapshot))
os.makedirs(snapshot)
checkpoint_path = os.path.join(snapshot_model, 'checkpoint.pth')
if os.path.isfile(checkpoint_path):
checkpoint = torch.load(checkpoint_path)
best_epoch = checkpoint['epoch']
best_acc = checkpoint['best_acc']
optimizer.load_state_dict(checkpoint['optimizer'])
model.load_state_dict(checkpoint['state_dict'])
train_name = 'LL_Train_'+str(task)
test_name = 'LL_Test_'+str(task)
print("LL_Task {0} finished ! ".format(task))
if task == 20:
layers = model.get_layers_19_20()
model_vgg = vgg_compression_ONE(layers, 2048, num_class = 5)
model_v = copy.deepcopy(model_vgg)
del model_vgg
model_vgg = model_v
# model_vgg.printf()
model_vgg = model_vgg.cuda()
for task_tt in range(args.num_works_tt):
print("TT_Task {} begins !".format(task_tt))
train_tt_name = 'TT_Train_200_' + str(task_tt)
test_tt_name = 'TT_Test_200_' + str(task_tt)
sheet_task = book.add_sheet('TT_Task_200_{0}'.format(task_tt), cell_overwrite_ok=True)
cnt_epoch = 0
for epoch in range(args.epochs):
optimizer = optim.SGD(model_vgg.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=5e-4)
criterion = nn.CrossEntropyLoss()
train_loss, train_acc = train_ewc(trainloader_inheritable[task_tt](epoch), model_vgg, criterion, optimizer, epoch, args, \
snapshot=snapshot, name=train_tt_name)
test_loss, test_acc = test_ewc(testloader_inheritable[task_tt](epoch), model_vgg, criterion, optimizer, epoch, args, \
snapshot=snapshot, name=test_tt_name)
sheet_task.write(cnt_epoch, 0, 'Epoch_{0}'.format(cnt_epoch))
sheet_task.write(cnt_epoch, 1, train_loss)
sheet_task.write(cnt_epoch, 2, train_acc.item())
sheet_task.write(cnt_epoch, 3, test_loss)
sheet_task.write(cnt_epoch, 4, test_acc.item())
cnt_epoch = cnt_epoch + 1
del model_vgg
model_vgg = model_v
# model_vgg.printf()
model_vgg = model_vgg.cuda()
print("TT_Task {0} finished !".format(task_tt))
book.save(r'./imagenet_exp/outputs/i_inheritable_random_trainnum200_wEWC/i_inheritable_random_trainnum200_wEWC.xls')
if __name__ == "__main__":
main()