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main.py
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main.py
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import argparse
import os
import random
import shutil
import time
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
from torch.utils.data import DataLoader
from torchvision import transforms
from data import TrashDataset
import resnet
from utils import indexes_to_one_hot
def get_arguments():
parser = argparse.ArgumentParser(description='RecycleNet')
parser.add_argument('--b', '--batch', type=int, default=16)
parser.add_argument('--gpu', type=str, help='0; 0,1; 0,3; etc', required=True)
parser.add_argument('--root_dir', type=str, default='data/')
parser.add_argument('--save_dir', type=str, default='save/')
parser.add_argument('--resume', type=str, default=None)
parser.add_argument('--lr', type=float, default=2e-4)
parser.add_argument('--momentum', default=0.9, type=float, metavar='M', help='momentum')
parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float, metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('--arch', type=str, default='resnet18_base', help='resnet18, 34, 50, 101, 152')
# parser.add_argument('--lr_finetune', type=float, default=5e-5)
# parser.add_argument('--save_model_interval', type=int, default=5000)
# parser.add_argument('--save_training_img_interval', type=int, default=5000)
# parser.add_argument('--vis_interval', type=int, default=5)
# parser.add_argument('--max_iter', type=int, default=1000000)
# parser.add_argument('--display_id', type=int, default=10)
parser.add_argument('--att_mode', type=str, default='ours', help='attention module mode: ours, cbam, se')
parser.add_argument('--use_att', action='store_true', help='use attention module')
parser.add_argument('--no_pretrain', action='store_false', help='training from scratch')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true', help='evaluate model on validation set')
parser.add_argument('--epochs', default=100, type=int, metavar='N', help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N', help='manual epoch number (useful on restarts)')
parser.add_argument('--adjust-freq', type=int, default=40, help='learning rate adjustment frequency (default: 40)')
parser.add_argument('--print-freq', '-p', default=10, type=int, metavar='N', help='print frequency (default: 10)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N', help='number of data loading workers (default: 4)')
parser.add_argument('--seed', default=1234, type=int, help='seed for initializing training. ')
return parser.parse_args()
def main():
args = get_arguments()
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
BATCH_SIZE = args.b
GPU = args.gpu
ROOT_DIR = args.root_dir
MEAN = [0.485, 0.456, 0.406]
STD = [0.229, 0.224, 0.225]
os.environ['CUDA_VISIBLE_DEVICES'] = GPU
if torch.cuda.is_available():
print('using Cuda devices, num:', torch.cuda.device_count())
if not args.evaluate:
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
def ToCudaVariable(xs, volatile=False, requires_grad=True):
if torch.cuda.is_available():
return [Variable(x.cuda(), volatile=volatile, requires_grad=requires_grad) for x in xs]
else:
return [Variable(x, volatile=volatile, requires_grad=requires_grad) for x in xs]
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
if args.arch == 'resnet18_base':
model = nn.DataParallel( resnet.resnet18(pretrained=True if not args.resume else False, num_classes=6, use_att=args.use_att, att_mode=args.att_mode).to(device) )
elif args.arch == 'resnet34_base':
model = nn.DataParallel( resnet.resnet34(pretrained=not args.no_pretrain if not args.resume else False, num_classes=6, use_att=args.use_att,att_mode=args.att_mode).to(device) )
elif args.arch == 'resnet50_base':
model = nn.DataParallel( resnet.resnet50(pretrained=not args.no_pretrain if not args.resume else False, num_classes=6, use_att=args.use_att, att_mode=args.att_mode).to(device) )
elif args.arch == 'resnet101_base':
model = nn.DataParallel( resnet.resnet101(pretrained=not args.no_pretrain if not args.resume else False, num_classes=6, use_att=args.use_att, att_mode=args.att_mode).to(device) )
elif args.arch == 'resnet152_base':
model = nn.DataParallel( resnet.resnet152(pretrained=not args.no_pretrain if not args.resume else False, num_classes=6, use_att=args.use_att, att_mode=args.att_mode).to(device) )
print(model)
print('Number of model parameters: {}'.format(
sum([p.data.nelement() for p in model.parameters()])))
criterion = nn.CrossEntropyLoss().to(device)
# att_params = [p for n,p in model.named_parameters() if n.startswith('module.att') and p.requires_grad]
# non_att_params = [p for n,p in model.named_parameters() if not n.startswith('module.att') and p.requires_grad]
# params = [{'params': non_att_params, 'lr': args.lr / 10.0}, {'params': att_params}]
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
print('=> best accuracy {}'.format(best_acc1))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
train_img_transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=MEAN, std=STD)])
train_dataset = TrashDataset(ROOT_DIR, train_img_transform, 'train')
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=args.workers, pin_memory=True)
val_img_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=MEAN, std=STD)])
val_dataset = TrashDataset(ROOT_DIR, val_img_transform, 'val')
val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=args.workers, pin_memory=True)
test_img_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=MEAN, std=STD)])
test_dataset = TrashDataset(ROOT_DIR, test_img_transform, 'test')
test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=args.workers, pin_memory=True)
if args.evaluate:
# validate(args, val_loader, model, criterion, device)
test(args, test_loader, model, criterion, device)
return
best_acc1 = 0
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(args, optimizer, epoch, args.adjust_freq)
train(args, train_loader, model, criterion, optimizer, epoch, device)
acc1 = validate(args, val_loader, model, criterion, device)
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
save_checkpoint({
'epoch' : epoch + 1,
'arch' : args.arch,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer': optimizer.state_dict(),
}, is_best, args.save_dir)
def train(args, train_loader, model, criterion, optimizer, epoch, device):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
model.train()
end = time.time()
for i, (input, target) in enumerate(train_loader):
data_time.update(time.time() - end)
if args.gpu is not None:
input = input.to(device)
target = torch.from_numpy(np.asarray(target))
target = target.to(device)
output = model(input)
loss = criterion(output[0], target)
acc1 = accuracy(output[0], target)
losses.update(loss.item(), input.size(0))
top1.update(acc1[0].item(), input.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
batch_time.update(time.time() - end)
end = time.time()
#import pdb
#pdb.set_trace()
if i % args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
epoch, i, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses, top1=top1))
def validate(args, val_loader, model, criterion, device):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
model.eval()
with torch.no_grad():
end = time.time()
for i, (input, target) in enumerate(val_loader):
if args.gpu is not None:
input = input.to(device)
target = torch.from_numpy(np.asarray(target))
target = target.to(device)
output = model(input)
loss = criterion(output[0], target)
acc1 = accuracy(output[0], target)
losses.update(loss.item(), input.size(0))
top1.update(acc1[0].item(), input.size(0))
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
i, len(val_loader), batch_time=batch_time, loss=losses,
top1=top1))
print(' * Acc@1 {top1.avg:.3f}'.format(top1=top1))
return top1.avg
def test(args, val_loader, model, criterion, device):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
model.eval()
with torch.no_grad():
end = time.time()
for i, (input, target, input_path) in enumerate(val_loader):
if args.gpu is not None:
input = input.to(device)
target = torch.from_numpy(np.asarray(target))
target = target.to(device)
output = model(input)
# import pdb
# npdb.set_trace()
loss = criterion(output[0], target)
acc1 = accuracy(output[0], target)
losses.update(loss.item(), input.size(0))
top1.update(acc1[0].item(), input.size(0))
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
i, len(val_loader), batch_time=batch_time, loss=losses,
top1=top1))
print(' * Acc@1 {top1.avg:.3f}'.format(top1=top1))
return top1.avg
def save_checkpoint(state, is_best, save_dir, filename='checkpoint.pth.tar'):
torch.save(state, os.path.join(save_dir, filename))
if is_best:
shutil.copyfile(os.path.join(save_dir, filename), os.path.join(save_dir, 'model_best.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(args, optimizer, epoch, N):
"""Sets the learning rate to the initial LR decayed by 10 every N epochs"""
lr = args.lr * (0.1 ** (epoch // N))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
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, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
if __name__ == '__main__':
main()