/
main_pretrain.py
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/
main_pretrain.py
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from __future__ import print_function
import os
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from tqdm import tqdm
from utils.network_utils import get_network
from utils.data_utils import get_dataloader
from utils.common_utils import PresetLRScheduler
# fetch args
parser = argparse.ArgumentParser()
# model parameter
parser.add_argument('--dataset', default='cifar10', type=str)
parser.add_argument('--network', default='vgg', type=str)
parser.add_argument('--depth', default=16, type=int)
parser.add_argument('--epoch', default=200, type=int)
parser.add_argument('--device', default='cuda:0', type=str)
# learning parameter
parser.add_argument('--learning_rate', default=0.1, type=float)
parser.add_argument('--weight_decay', default=0.0002, type=float)
parser.add_argument('--batch_size', default=128, type=float)
args = parser.parse_args()
# init model
net = get_network(network=args.network,
depth=args.depth,
dataset=args.dataset,
device=args.device)
net = net.to(args.device)
# init dataloader
trainloader, testloader = get_dataloader(dataset=args.dataset,
train_batch_size=args.batch_size,
test_batch_size=256)
# init optimizer and lr scheduler
optimizer = optim.SGD(net.parameters(), lr=args.learning_rate, momentum=0.9, weight_decay=args.weight_decay)
lr_schedule = {0: args.learning_rate,
int(args.epoch*0.5): args.learning_rate*0.1,
int(args.epoch*0.75): args.learning_rate*0.01}
lr_scheduler = PresetLRScheduler(lr_schedule)
# init criterion
criterion = nn.CrossEntropyLoss()
start_epoch = 0
best_acc = 0
def train(epoch):
print('\nEpoch: %d' % epoch)
net.train()
train_loss = 0
correct = 0
total = 0
lr_scheduler(optimizer, epoch)
desc = ('[Train/LR=%s] Loss: %.3f | Acc: %.3f%% (%d/%d)' %
(lr_scheduler.get_lr(optimizer), 0, 0, correct, total))
prog_bar = tqdm(enumerate(trainloader), total=len(trainloader), desc=desc, leave=True)
for batch_idx, (inputs, targets) in prog_bar:
inputs, targets = inputs.to(args.device), targets.to(args.device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
desc = ('[Train/LR=%s] Loss: %.3f | Acc: %.3f%% (%d/%d)' %
(lr_scheduler.get_lr(optimizer), train_loss / (batch_idx + 1), 100. * correct / total, correct, total))
prog_bar.set_description(desc, refresh=True)
def test(epoch):
global best_acc
net.eval()
test_loss = 0
correct = 0
total = 0
desc = ('[Test/LR=%s] Loss: %.3f | Acc: %.3f%% (%d/%d)'
% (lr_scheduler.get_lr(optimizer), test_loss/(0+1), 0, correct, total))
prog_bar = tqdm(enumerate(testloader), total=len(testloader), desc=desc, leave=True)
with torch.no_grad():
for batch_idx, (inputs, targets) in prog_bar:
inputs, targets = inputs.to(args.device), targets.to(args.device)
outputs = net(inputs)
loss = criterion(outputs, targets)
test_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
desc = ('[Test/LR=%s] Loss: %.3f | Acc: %.3f%% (%d/%d)'
% (lr_scheduler.get_lr(optimizer), test_loss / (batch_idx + 1), 100. * correct / total, correct, total))
prog_bar.set_description(desc, refresh=True)
# Save checkpoint.
acc = 100.*correct/total
if acc > best_acc:
print('Saving..')
state = {
'net': net.state_dict(),
'acc': acc,
'epoch': epoch,
'loss': loss,
'args': args
}
if not os.path.isdir('checkpoint'):
os.mkdir('checkpoint')
if not os.path.isdir('checkpoint/pretrain'):
os.mkdir('checkpoint/pretrain')
torch.save(state, './checkpoint/pretrain/%s/%s_%s%s_best.t7' % (args.dataset, args.dataset,
args.network,
args.depth))
print('./checkpoint/pretrain/%s/%s_%s%s_best.t7' % (args.dataset, args.dataset,
args.network,
args.depth))
best_acc = acc
for epoch in range(start_epoch, args.epoch):
train(epoch)
test(epoch)