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train.py
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import os
import sys
import argparse
from process.data import FDDataset
from process.augmentation import get_augment
from metric import metric, do_valid_test
from model import get_model
from loss.cyclic_lr import CosineAnnealingLR_with_Restart
from utils import *
def run_train(config):
model_name = f'{config.model}_{config.image_mode}_{config.image_size}'
if 'FaceBagNet' not in config.model:
model_name += f'_{config.patch_size}'
config.save_dir = os.path.join(config.save_dir, model_name)
initial_checkpoint = config.pretrained_model
criterion = softmax_cross_entropy_criterion
## setup -----------------------------------------------------------------------------
if not os.path.exists(config.save_dir +'/checkpoint'):
os.makedirs(config.save_dir +'/checkpoint')
if not os.path.exists(config.save_dir +'/backup'):
os.makedirs(config.save_dir +'/backup')
if not os.path.exists(config.save_dir +'/backup'):
os.makedirs(config.save_dir +'/backup')
log = Logger()
log.open(os.path.join(config.save_dir,model_name+'.txt'),mode='a')
log.write('\tconfig.save_dir = %s\n' % config.save_dir)
log.write('\n')
log.write('\t<additional comments>\n')
log.write('\t ... xxx baseline ... \n')
log.write('\n')
## dataset ----------------------------------------
log.write('** dataset setting **\n')
augment = get_augment(config.image_mode)
train_dataset = FDDataset(mode = 'train', modality=config.image_mode,image_size=config.image_size,
fold_index=config.train_fold_index,augment=augment)
train_loader = DataLoader(train_dataset,
shuffle=True,
batch_size = config.batch_size,
drop_last = True,
num_workers = config.num_workers)
valid_dataset = FDDataset(mode = 'val', modality=config.image_mode,image_size=config.image_size,
fold_index=config.train_fold_index,augment=augment)
valid_loader = DataLoader( valid_dataset,
shuffle=False,
batch_size = config.batch_size // 36,
drop_last = False,
num_workers = config.num_workers)
assert(len(train_dataset)>=config.batch_size)
log.write('batch_size = %d\n'%(config.batch_size))
log.write('train_dataset : \n%s\n'%(train_dataset))
log.write('valid_dataset : \n%s\n'%(valid_dataset))
log.write('\n')
log.write('** net setting **\n')
net = get_model(model_name=config.model, image_size=config.image_size, patch_size=config.patch_size)
print(net)
net = torch.nn.DataParallel(net)
net = net.cuda()
if initial_checkpoint is not None:
initial_checkpoint = os.path.join(config.save_dir +'/checkpoint',initial_checkpoint)
print('\tinitial_checkpoint = %s\n' % initial_checkpoint)
net.load_state_dict(torch.load(initial_checkpoint, map_location=lambda storage, loc: storage))
log.write('%s\n'%(type(net)))
log.write('\n')
iter_smooth = 20
start_iter = 0
log.write('\n')
## start training here! ##############################################
log.write('** start training here! **\n')
log.write(' |------------ VALID -------------|-------- TRAIN/BATCH ----------| \n')
log.write('model_name lr iter epoch | loss acer acc | loss acc | time \n')
log.write('----------------------------------------------------------------------------------------------------\n')
iter = 0
i = 0
train_loss = np.zeros(6, np.float32)
valid_loss = np.zeros(6, np.float32)
batch_loss = np.zeros(6, np.float32)
start = timer()
#-----------------------------------------------
optimizer = optim.SGD(filter(lambda p: p.requires_grad, net.parameters()),
lr=0.1, momentum=0.9, weight_decay=0.0005)
sgdr = CosineAnnealingLR_with_Restart(optimizer,
T_max=config.cycle_inter,
T_mult=1,
model=net,
take_snapshot=False,
out_dir=None,
eta_min=1e-3)
global_min_acer = 1.0
for cycle_index in range(config.cycle_num):
print('cycle index: ' + str(cycle_index))
min_acer = 1.0
for epoch in range(0, config.cycle_inter):
sgdr.step()
lr = optimizer.param_groups[0]['lr']
print('lr : {:.4f}'.format(lr))
sum_train_loss = np.zeros(6,np.float32)
sum = 0
optimizer.zero_grad()
for input, truth in train_loader:
iter = i + start_iter
# one iteration update -------------
net.train()
input = input.cuda()
truth = truth.cuda()
logit = net.forward(input)
truth = truth.view(logit.shape[0])
loss = criterion(logit, truth)
precision,_ = metric(logit, truth)
loss.backward()
optimizer.step()
optimizer.zero_grad()
# print statistics ------------
batch_loss[:2] = np.array(( loss.item(), precision.item(),))
sum += 1
if iter%iter_smooth == 0:
train_loss = sum_train_loss/sum
sum = 0
i=i+1
if epoch >= config.cycle_inter // 2:
net.eval()
valid_loss,_ = do_valid_test(net, valid_loader, criterion)
net.train()
if valid_loss[1] < min_acer and epoch > 0:
min_acer = valid_loss[1]
ckpt_name = config.save_dir + '/checkpoint/Cycle_' + str(cycle_index) + '_min_acer_model.pth'
torch.save(net.state_dict(), ckpt_name)
log.write('save cycle ' + str(cycle_index) + ' min acer model: ' + str(min_acer) + '\n')
if valid_loss[1] < global_min_acer and epoch > 0:
global_min_acer = valid_loss[1]
ckpt_name = config.save_dir + '/checkpoint/global_min_acer_model.pth'
torch.save(net.state_dict(), ckpt_name)
log.write('save global min acer model: ' + str(min_acer) + '\n')
asterisk = ' '
log.write(model_name+' Cycle %d: %0.4f %5.1f %6.1f | %0.6f %0.6f %0.3f %s | %0.6f %0.6f |%s \n' % (
cycle_index, lr, iter, epoch,
valid_loss[0], valid_loss[1], valid_loss[2], asterisk,
batch_loss[0], batch_loss[1],
time_to_str((timer() - start), 'min')))
ckpt_name = config.save_dir + '/checkpoint/Cycle_' + str(cycle_index) + '_final_model.pth'
torch.save(net.state_dict(), ckpt_name)
log.write('save cycle ' + str(cycle_index) + ' final model \n')
def run_test(config, dir):
config.save_dir = './models'
model_name = config.model + '_' + config.image_mode + '_' + str(config.image_size)
config.save_dir = os.path.join(config.save_dir, model_name)
initial_checkpoint = config.pretrained_model
augment = get_augment(config.image_mode)
## net ---------------------------------------
net = get_model(model_name=config.model, num_class=2, is_first_bn=True)
net = torch.nn.DataParallel(net)
net = net.cuda()
if initial_checkpoint is not None:
save_dir = os.path.join(config.save_dir + '/checkpoint', dir, initial_checkpoint)
initial_checkpoint = os.path.join(config.save_dir +'/checkpoint',initial_checkpoint)
print('\tinitial_checkpoint = %s\n' % initial_checkpoint)
net.load_state_dict(torch.load(initial_checkpoint, map_location=lambda storage, loc: storage))
if not os.path.exists(os.path.join(config.save_dir + '/checkpoint', dir)):
os.makedirs(os.path.join(config.save_dir + '/checkpoint', dir))
valid_dataset = FDDataset(mode = 'val', modality=config.image_mode,image_size=config.image_size,
fold_index=config.train_fold_index,augment=augment)
valid_loader = DataLoader( valid_dataset,
shuffle=False,
batch_size = config.batch_size,
drop_last = False,
num_workers = config.num_workers)
test_dataset = FDDataset(mode = 'test', modality=config.image_mode,image_size=config.image_size,
fold_index=config.train_fold_index,augment=augment)
test_loader = DataLoader( test_dataset,
shuffle=False,
batch_size = config.batch_size,
drop_last = False,
num_workers = config.num_workers)
criterion = softmax_cross_entropy_criterion
net.eval()
valid_loss,out = do_valid_test(net, valid_loader, criterion)
print('%0.6f %0.6f %0.3f (%0.3f) \n' % (valid_loss[0], valid_loss[1], valid_loss[2], valid_loss[3]))
print('infer!!!!!!!!!')
out = infer_test(net, test_loader)
print('done')
submission(out,save_dir+'_noTTA.txt', mode='test')
def main(config):
if config.mode == 'train':
run_train(config)
if config.mode == 'infer_test':
config.pretrained_model = r'global_min_acer_model.pth'
run_test(config, dir='global_test_36_TTA')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--train_fold_index', type=int, default = -1)
parser.add_argument('--model', type=str, default='FaceBagNet')
parser.add_argument('--image_mode', type=str, default = 'ir')
parser.add_argument('--image_size', type=int, default = 96)
parser.add_argument('--patch_size', type=int, default = 16)
parser.add_argument('--batch_size', type=int, default=128)
parser.add_argument('--cycle_num', type=int, default=10)
parser.add_argument('--cycle_inter', type=int, default=50)
parser.add_argument('--num_workers', type=int, default=32)
parser.add_argument('--mode', type=str, default='train', choices=['train','infer_test'])
parser.add_argument('--pretrained_model', type=str, default=None)
parser.add_argument('--save_dir', type=str, default='./Models')
config = parser.parse_args()
print(config)
main(config)