|
|
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: |
|
|
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 ) |
|
|
|
|
|
|
|
|
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() |
|
|
|
|
|
|
