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main.py
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main.py
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import torch
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
import argparse
from data import imagenet
from models import *
from utils import progress_bar
from mask import *
import utils
parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
parser.add_argument(
'--data_dir',
type=str,
default='./data',
help='dataset path')
parser.add_argument(
'--dataset',
type=str,
default='cifar10',
choices=('cifar10','imagenet'),
help='dataset')
parser.add_argument(
'--lr',
default=0.01,
type=float,
help='initial learning rate')
parser.add_argument(
'--lr_decay_step',
default='5,10',
type=str,
help='learning rate decay step')
parser.add_argument(
'--resume',
type=str,
default=None,
help='load the model from the specified checkpoint')
parser.add_argument(
'--resume_mask',
type=str,
default=None,
help='mask loading')
parser.add_argument(
'--gpu',
type=str,
default='0',
help='Select gpu to use')
parser.add_argument(
'--job_dir',
type=str,
default='./result/tmp/',
help='The directory where the summaries will be stored.')
parser.add_argument(
'--epochs',
type=int,
default=15,
help='The num of epochs to train.')
parser.add_argument(
'--train_batch_size',
type=int,
default=128,
help='Batch size for training.')
parser.add_argument(
'--eval_batch_size',
type=int,
default=100,
help='Batch size for validation.')
parser.add_argument(
'--start_cov',
type=int,
default=0,
help='The num of conv to start prune')
parser.add_argument(
'--compress_rate',
type=str,
default=None,
help='compress rate of each conv')
parser.add_argument(
'--arch',
type=str,
default='vgg_16_bn',
choices=('resnet_50','vgg_16_bn','resnet_56','resnet_110','densenet_40','googlenet'),
help='The architecture to prune')
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if len(args.gpu)==1:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
else:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
best_acc = 0 # best test accuracy
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
lr_decay_step = list(map(int, args.lr_decay_step.split(',')))
ckpt = utils.checkpoint(args)
print_logger = utils.get_logger(os.path.join(args.job_dir, "logger.log"))
utils.print_params(vars(args), print_logger.info)
# Data
print_logger.info('==> Preparing data..')
if args.dataset=='cifar10':
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
trainset = torchvision.datasets.CIFAR10(root=args.data_dir, train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.train_batch_size, shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root=args.data_dir, train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=args.eval_batch_size, shuffle=False, num_workers=2)
elif args.dataset=='imagenet':
data_tmp = imagenet.Data(args)
trainloader = data_tmp.loader_train
testloader = data_tmp.loader_test
else:
assert 1==0
if args.compress_rate:
import re
cprate_str=args.compress_rate
cprate_str_list=cprate_str.split('+')
pat_cprate = re.compile(r'\d+\.\d*')
pat_num = re.compile(r'\*\d+')
cprate=[]
for x in cprate_str_list:
num=1
find_num=re.findall(pat_num,x)
if find_num:
assert len(find_num) == 1
num=int(find_num[0].replace('*',''))
find_cprate = re.findall(pat_cprate, x)
assert len(find_cprate)==1
cprate+=[float(find_cprate[0])]*num
compress_rate=cprate
# Model
device_ids=list(map(int, args.gpu.split(',')))
print_logger.info('==> Building model..')
net = eval(args.arch)(compress_rate=compress_rate)
net = net.to(device)
if len(args.gpu)>1 and torch.cuda.is_available():
device_id = []
for i in range((len(args.gpu) + 1) // 2):
device_id.append(i)
net = torch.nn.DataParallel(net, device_ids=device_id)
cudnn.benchmark = True
print(net)
if len(args.gpu)>1:
m = eval('mask_'+args.arch)(model=net, compress_rate=net.module.compress_rate, job_dir=args.job_dir, device=device)
else:
m = eval('mask_' + args.arch)(model=net, compress_rate=net.compress_rate, job_dir=args.job_dir, device=device)
criterion = nn.CrossEntropyLoss()
# Training
def train(epoch, cov_id, optimizer, scheduler, pruning=True):
print_logger.info('\nEpoch: %d' % epoch)
net.train()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
with torch.cuda.device(device):
inputs = inputs.to(device)
targets = targets.to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
if pruning:
m.grad_mask(cov_id)
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
progress_bar(batch_idx,len(trainloader),
'Cov: %d | Loss: %.3f | Acc: %.3f%% (%d/%d)'
% (cov_id, train_loss / (batch_idx + 1), 100. * correct / total, correct, total))
def test(epoch, cov_id, optimizer, scheduler):
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
global best_acc
net.eval()
num_iterations = len(testloader)
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(device), targets.to(device)
outputs = net(inputs)
loss = criterion(outputs, targets)
prec1, prec5 = utils.accuracy(outputs, targets, topk=(1, 5))
top1.update(prec1[0], inputs.size(0))
top5.update(prec5[0], inputs.size(0))
print_logger.info(
'Epoch[{0}]({1}/{2}): '
'Prec@1(1,5) {top1.avg:.2f}, {top5.avg:.2f}'.format(
epoch, batch_idx, num_iterations, top1=top1, top5=top5))
if top1.avg > best_acc:
print_logger.info('Saving to '+args.arch+'_cov'+str(cov_id)+'.pt')
state = {
'state_dict': net.state_dict(),
'best_prec1': top1.avg,
'epoch': epoch,
'scheduler':scheduler.state_dict(),
'optimizer': optimizer.state_dict()
}
if not os.path.isdir(args.job_dir+'/pruned_checkpoint'):
os.mkdir(args.job_dir+'/pruned_checkpoint')
best_acc = top1.avg
torch.save(state, args.job_dir+'/pruned_checkpoint/'+args.arch+'_cov'+str(cov_id)+'.pt')
print_logger.info("=>Best accuracy {:.3f}".format(best_acc))
if len(args.gpu)>1:
convcfg = net.module.covcfg
else:
convcfg = net.covcfg
param_per_cov_dic={
'vgg_16_bn': 4,
'densenet_40': 3,
'googlenet': 28,
'resnet_50':3,
'resnet_56':3,
'resnet_110':3
}
if len(args.gpu)>1:
print_logger.info('compress rate: ' + str(net.module.compress_rate))
else:
print_logger.info('compress rate: ' + str(net.compress_rate))
for cov_id in range(args.start_cov, len(convcfg)):
# Load pruned_checkpoint
print_logger.info("cov-id: %d ====> Resuming from pruned_checkpoint..." % (cov_id))
m.layer_mask(cov_id + 1, resume=args.resume_mask, param_per_cov=param_per_cov_dic[args.arch], arch=args.arch)
optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=lr_decay_step, gamma=0.1)
if cov_id == 0:
pruned_checkpoint = torch.load(args.resume, map_location='cuda:0')
from collections import OrderedDict
new_state_dict = OrderedDict()
if args.arch == 'resnet_50':
tmp_ckpt = pruned_checkpoint
else:
tmp_ckpt = pruned_checkpoint['state_dict']
if len(args.gpu) > 1:
for k, v in tmp_ckpt.items():
new_state_dict['module.' + k.replace('module.', '')] = v
else:
for k, v in tmp_ckpt.items():
new_state_dict[k.replace('module.', '')] = v
net.load_state_dict(new_state_dict)#'''
else:
if args.arch=='resnet_50':
skip_list=[1,5,8,11,15,18,21,24,28,31,34,37,40,43,47,50,53]
if cov_id+1 not in skip_list:
continue
else:
pruned_checkpoint = torch.load(
args.job_dir + "/pruned_checkpoint/" + args.arch + "_cov" + str(skip_list[skip_list.index(cov_id+1)-1]) + '.pt')
net.load_state_dict(pruned_checkpoint['state_dict'])
else:
if len(args.gpu) == 1:
pruned_checkpoint = torch.load(args.job_dir + "/pruned_checkpoint/" + args.arch + "_cov" + str(cov_id) + '.pt', map_location='cuda:' + args.gpu)
else:
pruned_checkpoint = torch.load(args.job_dir + "/pruned_checkpoint/" + args.arch + "_cov" + str(cov_id) + '.pt')
net.load_state_dict(pruned_checkpoint['state_dict'])
best_acc=0.
for epoch in range(0, args.epochs):
train(epoch, cov_id + 1, optimizer, scheduler)
scheduler.step()
test(epoch, cov_id + 1, optimizer, scheduler)