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train_gan.py
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train_gan.py
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import os
import time
import numpy as np
import torch.utils.data as torch_data
from PIL import Image
from torchvision import transforms
from data.my_data_loader import FaceImageLoader
from models.base_models import ModelFactory
from utils.tb_visualizer import TBVisualizer
from options.train_options_model_combine_pconv_fsrnet import TrainOptions
import utils.misc as misc
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
class Train:
def __init__(self):
self._opt = TrainOptions().parse()
self.face_train_list = self._opt.face_train_list
self.face_test_list = self._opt.face_test_list
self.face_img_root = self._opt.face_img_root
self.face_parsing_root = self._opt.face_parsing_root
self.face_landmark_train = self._opt.face_landmark_train
self.face_landmark_test = self._opt.face_landmark_test
self.img_size = self._opt.img_size
self.scale_factor = self._opt.scale_factor
self.img_size = self._opt.img_size
self.heatmap_size = self._opt.heatmap_size
self.scale_factor = self._opt.scale_factor
self.train_transform = transforms.Compose([
transforms.ToTensor(),
# transforms.Normalize(mean=[0.5, 0.5, 0.5],
# std=[0.5, 0.5, 0.5])
])
self.test_transform = transforms.Compose([
transforms.ToTensor(),
# transforms.Normalize(mean=[0.5, 0.5, 0.5],
# std=[0.5, 0.5, 0.5])
])
self._dataset_train = torch_data.DataLoader(dataset=FaceImageLoader(
self._opt.face_img_root,
self._opt.face_parsing_root,
self._opt.face_landmark_train,
self.face_train_list,
transform=self.train_transform,
scale_factor=self.scale_factor,
img_size=self.img_size,
heatmap_size=self.heatmap_size,
mode='train',
upsample=self._opt.upsample),
batch_size=self._opt.batch_size,
num_workers=self._opt.n_threads_train,
shuffle=True, drop_last=True)
self._dataset_test = torch_data.DataLoader(dataset=FaceImageLoader(
self._opt.face_img_root,
self._opt.face_parsing_root,
self._opt.face_landmark_test,
self.face_test_list,
transform=self.test_transform,
scale_factor=self.scale_factor,
img_size=self.img_size,
heatmap_size=self.heatmap_size,
mode='val',
upsample=self._opt.upsample),
batch_size=10,
num_workers=self._opt.n_threads_test,
shuffle=False, drop_last=True)
self._dataset_train_size = len(self._dataset_train)
self._dataset_test_size = len(self._dataset_test)
print('#train images = %d' % self._dataset_train_size)
print('#test images = %d' % self._dataset_test_size)
self._model = ModelFactory.get_by_name(self._opt.model, self._opt)
self._tb_visualizer = TBVisualizer(self._opt)
self._save_path = os.path.join(self._opt.checkpoints_dir, self._opt.name)
self._val_path = os.path.join(self._save_path, 'val_log.txt')
self._train()
def _train(self):
self._total_steps = self._opt.load_epoch * self._dataset_train_size
self._iters_per_epoch = self._dataset_train_size
self._last_display_time = None
self._last_save_latest_time = None
self._last_print_time = time.time()
for i_epoch in range(self._opt.load_epoch + 1, self._opt.nepochs_no_decay + self._opt.nepochs_decay + 1):
if i_epoch > 1:
epoch_val_start_time = time.time()
self._val_epoch(i_epoch)
epoch_val_end_time = time.time()
time_epoch = epoch_val_end_time - epoch_val_start_time
print('End of epoch %d / %d \t Val Time Taken: %d sec (%d min or %d h)' %
(i_epoch, self._opt.nepochs_no_decay + self._opt.nepochs_decay, time_epoch,
time_epoch / 60.0, time_epoch / 3600.0))
epoch_start_time = time.time()
self._train_epoch(i_epoch)
print('saving the model at the end of epoch %d, iters %d' % (i_epoch, self._total_steps))
self._model.save(i_epoch)
time_epoch = time.time() - epoch_start_time
print('End of epoch %d / %d \t Time Taken: %d sec (%d min or %d h)' %
(i_epoch, self._opt.nepochs_no_decay + self._opt.nepochs_decay, time_epoch,
time_epoch / 60.0, time_epoch / 3600.0))
if i_epoch > self._opt.nepochs_no_decay:
self._model.update_learning_rate()
def _val_epoch(self, i_epoch):
self._model.set_eval()
for i_val_batch, val_batch in enumerate(self._dataset_test):
point = val_batch['point'].numpy()
self._model.set_input(val_batch)
self._model.forward(keep_data_for_visuals=True)
visuals = self._model.get_current_visuals()
img_sr = visuals['batch_img_fine'].transpose((1, 2, 0))
img_gt = visuals['batch_img_SR'].transpose((1, 2, 0))
self._tb_visualizer.display_current_results(visuals,
i_epoch,
i_val_batch,
is_train=False,
save_visuals=True)
self._model.set_train()
def _train_epoch(self, i_epoch):
epoch_iter = 0
self._model.set_train()
for i_train_batch, train_batch in enumerate(self._dataset_train):
iter_start_time = time.time()
do_visuals = (self._last_display_time is None) or \
(time.time() - self._last_display_time > self._opt.display_freq_s)
do_print_terminal = time.time() - self._last_print_time > self._opt.print_freq_s or do_visuals
self._model.set_input(train_batch)
train_generator = ((i_train_batch + 1) % self._opt.train_G_every_n_iterations == 0) or do_visuals
self._model.optimize_parameters(train_generator, keep_data_for_visuals=do_visuals)
self._total_steps += self._opt.batch_size
epoch_iter += self._opt.batch_size
if do_print_terminal:
self._display_terminal(iter_start_time, i_epoch, i_train_batch, do_visuals)
self._last_print_time = time.time()
if do_visuals:
self._display_visualizer_train(i_epoch, self._total_steps)
self._last_display_time = time.time()
if self._last_save_latest_time is None or \
time.time() - self._last_save_latest_time > self._opt.save_latest_freq_s:
print('saving the latest model (epoch %d, total_steps %d)' % (i_epoch, self._total_steps))
self._model.save(i_epoch)
self._last_save_latest_time = time.time()
def _display_terminal(self, iter_start_time, i_epoch, i_train_batch, visuals_flag):
errors = self._model.get_current_errors()
t = (time.time() - iter_start_time) / self._opt.batch_size
self._tb_visualizer.print_current_train_errors(i_epoch, i_train_batch,
self._iters_per_epoch, errors, t, visuals_flag)
def _display_visualizer_train(self, i_epoch, total_steps):
self._tb_visualizer.display_current_results(self._model.get_current_visuals(),
i_epoch,
total_steps,
is_train=True,
save_visuals=True)
self._tb_visualizer.plot_scalars(self._model.get_current_errors(), total_steps, is_train=True)
self._tb_visualizer.plot_scalars(self._model.get_current_scalars(), total_steps, is_train=True)
if __name__ == "__main__":
Train()