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train_imagenet_fcf.py
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train_imagenet_fcf.py
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import argparse
from datetime import datetime
import os
os.environ["CUDA_VISIBLE_DEVICES"]= '0,1,2,3'
import torch
import torch.nn as nn
from functions import *
from models import *
parser = argparse.ArgumentParser(description='Training a cnn-fcf model on ImageNet')
parser.add_argument('--epochs', default=30, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--decay-epochs', default=10, type=int,
help='number of epochs to decay the learning rate')
parser.add_argument('--print-freq', '-p', default=100, type=int,
metavar='N', help='print frequency')
parser.add_argument('-b', '--batch-size', default=256, type=int,
metavar='N', help='mini-batch size')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
metavar='LR', help='initial learning rate')
parser.add_argument('--alpha', default=0.65, type=float,
help='the trade-off between the cnn and admm')
parser.add_argument('--sparse-rate', default=0.25, type=float,
help='the whole pruning weight ratio')
parser.add_argument('--model', default='resnet34', type=str,
help='choose the training mode')
parser.add_argument('--training-mode', default='sparse', type=str,
help='choose the training mode')
parser.add_argument('--sparse-mode', default='identical_ratio', type=str,
help='choose the mode to set sparse rate')
parser.add_argument('--pretrained-model', default='./checkpoints/pretrain/resnet34_full.pth', type=str,
help='the path to save the best result')
parser.add_argument('--checkpoint-name', default='./checkpoints/fcf/sparse_resnet34_same025', type=str,
help='the path to save the checkpoint')
best_prec1 = 0
training_models = {'resnet50':resnet50, 'resnet34':resnet34}
def main():
global args, best_prec1
args = parser.parse_args()
#dataset
trainloader=imagenet_traindata(args.batch_size)
testloader=imagenet_testdata(args.batch_size)
#model
model = training_models[args.model](mode = args.training_mode)
pretrained_dict = torch.load(args.pretrained_model)
model_dict = model.state_dict()
temp_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
model_dict.update(temp_dict)
model.load_state_dict(model_dict)
model = nn.DataParallel(model).cuda()
#loss
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), args.lr)
sparse_k(model, args)
#start training
train_loss=[]
train_accuracy=[]
test_accuracy=[]
test_loss=[]
for epoch in range(args.epochs):
adjust_learning_rate(optimizer, epoch)
prec1_tr,loss_tr = train(args, trainloader, model, criterion, optimizer, epoch)
train_accuracy.append(prec1_tr)
train_loss.append(loss_tr)
prec1,loss = validate(testloader, model, criterion)
test_accuracy.append(prec1)
test_loss.append(loss)
np_v_list = store_v(model)
pruning_prec1, _= validate(testloader, model, criterion)
reload_v(model, np_v_list)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
print('best_prec@1:{}'.format(best_prec1))
save_checkpoint(args,
{'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'optimizer' : optimizer.state_dict(),
'train_loss': train_loss,
'train_accuracy': train_accuracy,
'test_accuracy': test_accuracy,
'test_loss': test_loss}
, is_best)
def train(args, train_loader, model, criterion, optimizer, epoch):
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to train mode
model.train()
for i, (input, target) in enumerate(train_loader):
input_var = torch.autograd.Variable(input).cuda()
target_var = torch.autograd.Variable(target).cuda()
# compute output
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target_var.data, topk=(1, 5))
losses.update(loss.data[0], input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
#update z1,z2,v_admm
for m in model.modules():
if isinstance(m, SparseConv2d):
admm_update1(m, args.alpha)
#update gradient
optimizer.step()
#update y1,y2,rho
for m in model.modules():
if isinstance(m, SparseConv2d):
admm_update2(m,True)
if i % args.print_freq == 0:
for param_group in optimizer.param_groups:
lr_rate=param_group['lr']
break
time=datetime.now()
str_time = time.strftime("%Y-%m-%d %H:%M:%S")
print('Time:{0} LR:{1}\t Epoch: [{2}/{3}][{4}/{5}]\t''Loss {loss.val:.4f} ({loss.avg:.4f})\t''Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'.
format(str_time, lr_rate, epoch, args.epochs, i, len(train_loader), loss=losses, top1=top1))
return top1.avg,losses.avg
def validate(val_loader, model, criterion):
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
for i, (input, target) in enumerate(val_loader):
input_var = torch.autograd.Variable(input, volatile=True).cuda()
target_var = torch.autograd.Variable(target, volatile=True).cuda()
# compute output
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target_var.data, topk=(1, 5))
losses.update(loss.data[0], input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))
print('Test: *Loss {loss.avg:.4f} \tPrec@1 {top1.avg:.3f}'.format(loss=losses,top1=top1))
return top1.avg, losses.avg
def save_checkpoint(args, state, is_best):
torch.save(state, args.checkpoint_name+'.pth.tar')
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def adjust_learning_rate(optimizer, epoch):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
lr = args.lr * (0.1 ** (epoch // args.decay_epochs))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res
if __name__ == '__main__':
main()