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retrain_best_choice.py
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import argparse
import logging
import os
import sys
import time
import torch
import torch.nn as nn
import torchvision
from thop import profile
from torchvision import datasets
import utils
from models.model import SinglePath_Network
from utils import data_transforms
parser = argparse.ArgumentParser("Single_Path_One_Shot")
parser.add_argument('--exp_name', type=str, default='spos_c10_train_choice_model', help='experiment name')
# Supernet Settings
parser.add_argument('--layers', type=int, default=20, help='batch size')
parser.add_argument('--num_choices', type=int, default=4, help='number choices per layer')
# Training Settings
parser.add_argument('--batch_size', type=int, default=96, help='batch size')
parser.add_argument('--epochs', type=int, default=600, help='batch size')
parser.add_argument('--learning_rate', type=float, default=0.025, help='initial learning rate')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
parser.add_argument('--weight-decay', type=float, default=3e-4, help='weight decay')
parser.add_argument('--print_freq', type=int, default=100, help='print frequency of training')
parser.add_argument('--val_interval', type=int, default=5, help='validate and save frequency')
parser.add_argument('--ckpt_dir', type=str, default='./checkpoints/', help='checkpoints direction')
parser.add_argument('--seed', type=int, default=0, help='training seed')
# Dataset Settings
parser.add_argument('--data_root', type=str, default='./dataset/', help='dataset dir')
parser.add_argument('--classes', type=int, default=10, help='dataset classes')
parser.add_argument('--dataset', type=str, default='cifar10', help='path to the dataset')
parser.add_argument('--cutout', action='store_true', help='use cutout')
parser.add_argument('--cutout_length', type=int, default=16, help='cutout length')
parser.add_argument('--auto_aug', action='store_true', default=False, help='use auto augmentation')
parser.add_argument('--resize', action='store_true', default=False, help='use resize')
args = parser.parse_args()
args.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout, level=logging.INFO,
format=log_format, datefmt='%m/%d %I:%M:%S %p')
logging.info(args)
utils.set_seed(args.seed)
def train(args, epoch, train_loader, model, criterion, optimizer):
model.train()
lr = optimizer.param_groups[0]["lr"]
train_acc = utils.AverageMeter()
train_loss = utils.AverageMeter()
steps_per_epoch = len(train_loader)
for step, (inputs, targets) in enumerate(train_loader):
inputs, targets = inputs.to(args.device), targets.to(args.device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
prec1, prec5 = utils.accuracy(outputs, targets, topk=(1, 5))
n = inputs.size(0)
train_loss.update(loss.item(), n)
train_acc.update(prec1.item(), n)
if step % args.print_freq == 0 or step == len(train_loader) - 1:
logging.info(
'[Model Training] lr: %.5f epoch: %03d/%03d, step: %03d/%03d, '
'train_loss: %.3f(%.3f), train_acc: %.3f(%.3f)'
% (lr, epoch+1, args.epochs, step+1, steps_per_epoch,
loss.item(), train_loss.avg, prec1, train_acc.avg)
)
return train_loss.avg, train_acc.avg
def validate(args, val_loader, model, criterion):
model.eval()
val_loss = utils.AverageMeter()
val_acc = utils.AverageMeter()
with torch.no_grad():
for step, (inputs, targets) in enumerate(val_loader):
inputs, targets = inputs.to(args.device), targets.to(args.device)
outputs = model(inputs)
loss = criterion(outputs, targets)
prec1, prec5 = utils.accuracy(outputs, targets, topk=(1, 5))
n = inputs.size(0)
val_loss.update(loss.item(), n)
val_acc.update(prec1.item(), n)
return val_loss.avg, val_acc.avg
def main():
# Define Dataset
assert args.dataset in ['cifar10', 'imagenet']
train_transform, valid_transform = data_transforms(args)
if args.dataset == 'cifar10':
trainset = torchvision.datasets.CIFAR10(root=os.path.join(args.data_root, args.dataset),
train=True, download=True, transform=train_transform)
train_loader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size,
shuffle=True, pin_memory=True, num_workers=8)
valset = torchvision.datasets.CIFAR10(root=os.path.join(args.data_root, args.dataset),
train=False, download=True, transform=valid_transform)
val_loader = torch.utils.data.DataLoader(valset, batch_size=args.batch_size,
shuffle=False, pin_memory=True, num_workers=8)
elif args.dataset == 'imagenet':
train_data_set = datasets.ImageNet(os.path.join(args.data_root, args.dataset, 'train'), train_transform)
val_data_set = datasets.ImageNet(os.path.join(args.data_root, args.dataset, 'valid'), valid_transform)
train_loader = torch.utils.data.DataLoader(train_data_set, batch_size=args.batch_size, shuffle=True,
num_workers=8, pin_memory=True, sampler=None)
val_loader = torch.utils.data.DataLoader(val_data_set, batch_size=args.batch_size, shuffle=False,
num_workers=8, pin_memory=True)
else:
raise ValueError('Undefined dataset !!!')
# Define Choice Model
choice = [1, 0, 3, 1, 3, 0, 3, 0, 0, 3, 3, 0, 1, 0, 1, 2, 2, 1, 1, 3]
model = SinglePath_Network(args.dataset, args.resize, args.classes, args.layers, choice)
criterion = nn.CrossEntropyLoss().to(args.device)
optimizer = torch.optim.SGD(model.parameters(), args.learning_rate, args.momentum, args.weight_decay)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lambda epoch: 1 - (epoch / args.epochs))
# Print Model Information
flops, params = profile(model, inputs=(torch.randn(1, 3, 32, 32),) if args.dataset == 'cifar10'
else (torch.randn(1, 3, 224, 224),), verbose=False)
model = model.to(args.device)
logging.info(model)
logging.info('Choice Model Information: params: %.2fM, flops:%.2fM' % ((params / 1e6), (flops / 1e6)))
print('\n')
# Running
start = time.time()
best_val_acc = 0.0
for epoch in range(args.epochs):
# Choice Model Training
train_loss, train_acc = train(args, epoch, train_loader, model, criterion, optimizer)
scheduler.step()
logging.info(
'[Model Training] epoch: %03d, train_loss: %.3f, train_acc: %.3f' %
(epoch + 1, train_loss, train_acc)
)
# Choice Model Validation
val_loss, val_acc = validate(args, val_loader, model, criterion)
# Save Best Supernet Weights
if best_val_acc < val_acc:
best_val_acc = val_acc
best_ckpt = os.path.join(args.ckpt_dir, '%s_%s' % (args.exp_name, 'best.pth'))
torch.save(model.state_dict(), best_ckpt)
logging.info('Save best checkpoints to %s' % best_ckpt)
logging.info(
'[Model Validation] epoch: %03d, val_loss: %.3f, val_acc: %.3f, best_acc: %.3f'
% (epoch + 1, val_loss, val_acc, best_val_acc)
)
print('\n')
# Record Time
utils.time_record(start)
if __name__ == '__main__':
main()