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import argparse
from engine import *
from models import *
from coco import *
from util import *
from tensorboardX import SummaryWriter
parser = argparse.ArgumentParser(description='WILDCAT Training')
parser.add_argument('data', metavar='DIR',
help='path to dataset (e.g. data/')
parser.add_argument('--image-size', '-i', default=448, type=int,
metavar='N', help='image size (default: 224)')
parser.add_argument('-j', '--workers', default=16, type=int, metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=150, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--epoch_step', default=30, type=int,
help='number of epochs to change learning rate')
parser.add_argument('--device_ids', default=[2,3], type=int, nargs='+',
help='number of epochs to change learning rate')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=16, type=int,
metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
metavar='LR', help='initial learning rate')
parser.add_argument('--lrp', '--learning-rate-pretrained', default=0.1, type=float,
metavar='LR', help='learning rate for pre-trained layers')
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('--print-freq', '-p', default=0, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
help='evaluate model on validation set')
parser.add_argument('--patches', '-pt', default=1, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--t', '-t', default=0.4, type=float,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--mix-layers', '-mls', default=4, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--pretrained', '-pre', default=0, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--ppir', '-pp', default=0, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--warm-up', '-wu', default=0, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--freeze', '-f', default=0, type=int,
metavar='N', help='print frequency (default: 10)')
def main_coco():
global args, best_prec1, use_gpu
args = parser.parse_args()
args.model = 'mix'
key = "runs/coco/" + args.model
writer = SummaryWriter(key)
use_gpu = torch.cuda.is_available()
train_dataset = COCO2014(args.data, phase='train', inp_name='data/coco/coco_glove_word2vec.pkl')
val_dataset = COCO2014(args.data, phase='val', inp_name='data/coco/coco_glove_word2vec.pkl')
num_classes = 80
t = args.t
# load model
model = mix_resnet101(num_classes=num_classes, base_patches=args.patches,freeze=args.freeze, pretrained=args.pretrained, mix_layers=args.mix_layers, t=t, adj_file='data/coco/coco_adj.pkl')
# define loss function (criterion)
criterion = nn.MultiLabelSoftMarginLoss()
# define optimizer
optimizer = torch.optim.SGD(model.get_config_optim(args.lr, args.lrp, args.freeze),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
state = {'batch_size': args.batch_size, 'image_size': args.image_size, 'max_epochs': args.epochs,
'evaluate': args.evaluate, 'resume': args.resume, 'num_classes':num_classes}
state['difficult_examples'] = True
state['save_model_path'] = 'checkpoint/coco/'
state['workers'] = args.workers
state['epoch_step'] = args.epoch_step
state['lr'] = args.lr
state['model_name'] = args.model
state['use_PPIR'] = args.ppir
state['writer'] = writer
state['warm_up'] = args.warm_up
state['dataset'] = 'coco'
if args.evaluate:
state['evaluate'] = True
engine = GCNMultiLabelMAPEngine(state)
engine.learning(model, criterion, train_dataset, val_dataset, optimizer)
if __name__ == '__main__':
main_coco()
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