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cifar_train_eval.py
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import os
import time
import argparse
from datetime import datetime
import torch
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torch.utils.tensorboard import SummaryWriter
cudnn.benchmark = True
import torchvision
from models.resnet_cifar import *
from utils.preprocess import *
from utils.bar_show import progress_bar
# Training settings
parser = argparse.ArgumentParser(description='dorefa-net implementation')
parser.add_argument('--root_dir', type=str, default='./')
parser.add_argument('--data_dir', type=str, default='./data')
parser.add_argument('--log_name', type=str, default='resnet_8w8f_cifar')
parser.add_argument('--pretrain', action='store_true', default=False)
parser.add_argument('--pretrain_dir', type=str, default='resnet_8w8f_cifar')
parser.add_argument('--cifar', type=int, default=10)
parser.add_argument('--lr', type=float, default=0.1)
parser.add_argument('--wd', type=float, default=1e-4)
parser.add_argument('--train_batch_size', type=int, default=256)
parser.add_argument('--eval_batch_size', type=int, default=100)
parser.add_argument('--max_epochs', type=int, default=250)
parser.add_argument('--log_interval', type=int, default=40)
parser.add_argument('--num_workers', type=int, default=2)
parser.add_argument('--Wbits', type=int, default=8)
parser.add_argument('--Abits', type=int, default=8)
cfg = parser.parse_args()
best_acc = 0 # best test accuracy
start_epoch = 0
cfg.log_dir = os.path.join(cfg.root_dir, 'logs', cfg.log_name)
cfg.ckpt_dir = os.path.join(cfg.root_dir, 'ckpt', cfg.pretrain_dir)
os.makedirs(cfg.log_dir, exist_ok=True)
os.makedirs(cfg.ckpt_dir, exist_ok=True)
def main():
if cfg.cifar == 10:
print('training CIFAR-10 !')
dataset = torchvision.datasets.CIFAR10
elif cfg.cifar == 100:
print('training CIFAR-100 !')
dataset = torchvision.datasets.CIFAR100
else:
assert False, 'dataset unknown !'
print('===> Preparing data ..')
train_dataset = dataset(root=cfg.data_dir, train=True, download=True,
transform=cifar_transform(is_training=True))
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=cfg.train_batch_size, shuffle=True,
num_workers=cfg.num_workers)
eval_dataset = dataset(root=cfg.data_dir, train=False, download=True,
transform=cifar_transform(is_training=False))
eval_loader = torch.utils.data.DataLoader(eval_dataset, batch_size=cfg.eval_batch_size, shuffle=False,
num_workers=cfg.num_workers)
print('===> Building ResNet..')
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = ResNet18(wbit=cfg.Wbits,abit=cfg.Abits).to(device)
if device == 'cuda':
model = torch.nn.DataParallel(model)
cudnn.benchmark = True
optimizer = torch.optim.SGD(model.parameters(), lr=cfg.lr, momentum=0.9, weight_decay=cfg.wd)
# optimizer = torch.optim.Adam(model.parameters(),lr=cfg.lr,weight_decay=cfg.wd)
lr_schedu = optim.lr_scheduler.MultiStepLR(optimizer, [90, 150, 200], gamma=0.1)
criterion = torch.nn.CrossEntropyLoss().cuda()
summary_writer = SummaryWriter(cfg.log_dir)
if cfg.pretrain:
ckpt = torch.load(os.path.join(cfg.ckpt_dir, f'checkpoint.t7'))
model.load_state_dict(ckpt['model_state_dict'])
optimizer.load_state_dict(ckpt['optimizer_state_dict'])
start_epoch = ckpt['epoch']
print('===> Load last checkpoint data')
else:
start_epoch = 0
print('===> Start from scratch')
def train(epoch):
print('\nEpoch: %d' % epoch)
model.train()
train_loss, correct, total = 0, 0 ,0
for batch_idx, (inputs, targets) in enumerate(train_loader):
inputs, targets = inputs.to('cuda'), targets.to('cuda')
optimizer.zero_grad()
outputs = model(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()
progress_bar(batch_idx, len(train_loader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
% (train_loss / (batch_idx + 1), 100. * correct / total, correct, total))
if batch_idx % cfg.log_interval == 0: #every log_interval mini_batches...
summary_writer.add_scalar('Loss/train', train_loss / (batch_idx + 1), epoch * len(train_loader) + batch_idx)
summary_writer.add_scalar('Accuracy/train', 100. * correct / total, epoch * len(train_loader) + batch_idx)
summary_writer.add_scalar('learning rate', optimizer.param_groups[0]['lr'], epoch * len(train_loader) + batch_idx)
# for tag, value in model.named_parameters():
# tag = tag.replace('.', '/')
# summary_writer.add_histogram(tag, value.detach(), global_step=epoch * len(train_loader) + batch_idx)
# summary_writer.add_histogram(tag + '/grad', value.grad.detach(), global_step=epoch * len(train_loader) + batch_idx)
def test(epoch):
# pass
global best_acc
model.eval()
test_loss, correct, total = 0, 0, 0
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(eval_loader):
inputs, targets = inputs.to('cuda'), targets.to('cuda')
outputs = model(inputs)
loss = criterion(outputs, targets)
test_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
progress_bar(batch_idx, len(eval_loader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
% (test_loss/(batch_idx+1), 100.*correct/total, correct, total))
if batch_idx % cfg.log_interval == 0: # every log_interval mini_batches...
summary_writer.add_scalar('Loss/test', test_loss / (batch_idx + 1), epoch * len(train_loader) + batch_idx)
summary_writer.add_scalar('Accuracy/test', 100. * correct / total, epoch * len(train_loader) + batch_idx)
acc = 100. * correct / total
if acc > best_acc:
print('Saving..')
state = {
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'acc': acc,
'epoch': epoch,
}
torch.save(state, os.path.join(cfg.ckpt_dir, f'checkpoint.t7'))
best_acc = acc
for epoch in range(start_epoch, cfg.max_epochs):
train(epoch)
test(epoch)
lr_schedu.step(epoch)
summary_writer.close()
if __name__ == '__main__':
main()