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import os
import time
from tensorboardX import SummaryWriter
from validate import validate
from data import create_dataloader
from earlystop import EarlyStopping
from networks.trainer import Trainer
from options.train_options import TrainOptions
"""Currently assumes jpg_prob, blur_prob 0 or 1"""
def get_val_opt():
val_opt = TrainOptions().parse(print_options=False)
val_opt.isTrain = False
val_opt.no_resize = False
val_opt.no_crop = False
val_opt.serial_batches = True
val_opt.data_label = 'val'
val_opt.jpg_method = ['pil']
if len(val_opt.blur_sig) == 2:
b_sig = val_opt.blur_sig
val_opt.blur_sig = [(b_sig[0] + b_sig[1]) / 2]
if len(val_opt.jpg_qual) != 1:
j_qual = val_opt.jpg_qual
val_opt.jpg_qual = [int((j_qual[0] + j_qual[-1]) / 2)]
return val_opt
if __name__ == '__main__':
opt = TrainOptions().parse()
val_opt = get_val_opt()
model = Trainer(opt)
data_loader = create_dataloader(opt)
val_loader = create_dataloader(val_opt)
train_writer = SummaryWriter(os.path.join(opt.checkpoints_dir, opt.name, "train"))
val_writer = SummaryWriter(os.path.join(opt.checkpoints_dir, opt.name, "val"))
early_stopping = EarlyStopping(patience=opt.earlystop_epoch, delta=-0.001, verbose=True)
start_time = time.time()
print ("Length of data loader: %d" %(len(data_loader)))
for epoch in range(opt.niter):
for i, data in enumerate(data_loader):
model.total_steps += 1
model.set_input(data)
model.optimize_parameters()
if model.total_steps % opt.loss_freq == 0:
print("Train loss: {} at step: {}".format(model.loss, model.total_steps))
train_writer.add_scalar('loss', model.loss, model.total_steps)
print("Iter time: ", ((time.time()-start_time)/model.total_steps) )
if model.total_steps in [10,30,50,100,1000,5000,10000] and False: # save models at these iters
model.save_networks('model_iters_%s.pth' % model.total_steps)
if epoch % opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d' % (epoch))
model.save_networks( 'model_epoch_best.pth' )
model.save_networks( 'model_epoch_%s.pth' % epoch )
# Validation
model.eval()
ap, r_acc, f_acc, acc = validate(model.model, val_loader)
val_writer.add_scalar('accuracy', acc, model.total_steps)
val_writer.add_scalar('ap', ap, model.total_steps)
print("(Val @ epoch {}) acc: {}; ap: {}".format(epoch, acc, ap))
early_stopping(acc, model)
if early_stopping.early_stop:
cont_train = model.adjust_learning_rate()
if cont_train:
print("Learning rate dropped by 10, continue training...")
early_stopping = EarlyStopping(patience=opt.earlystop_epoch, delta=-0.002, verbose=True)
else:
print("Early stopping.")
break
model.train()
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