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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() | |