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"""
Created on Dec 13, 2018
@author: Yuedong Chen
"""
from data import create_dataloader
from model import create_model
from visualizer import Visualizer
import copy
import time
import os
import torch
import numpy as np
from PIL import Image
def create_solver(opt):
instance = Solver()
instance.initialize(opt)
return instance
class Solver(object):
"""docstring for Solver"""
def __init__(self):
super(Solver, self).__init__()
def initialize(self, opt):
self.opt = opt
self.visual = Visualizer()
self.visual.initialize(self.opt)
def run_solver(self):
if self.opt.mode == "train":
self.train_networks()
else:
self.test_networks(self.opt)
def train_networks(self):
# init train setting
self.init_train_setting()
# for every epoch
for epoch in range(self.opt.epoch_count, self.epoch_len + 1):
# train network
self.train_epoch(epoch)
# update learning rate
self.cur_lr = self.train_model.update_learning_rate()
# save checkpoint if needed
if epoch % self.opt.save_epoch_freq == 0:
self.train_model.save_ckpt(epoch)
# save the last epoch
self.train_model.save_ckpt(self.epoch_len)
def init_train_setting(self):
self.train_dataset = create_dataloader(self.opt)
self.train_model = create_model(self.opt)
self.train_total_steps = 0
self.epoch_len = self.opt.niter + self.opt.niter_decay
self.cur_lr = self.opt.lr
def train_epoch(self, epoch):
epoch_start_time = time.time()
epoch_steps = 0
last_print_step_t = time.time()
for idx, batch in enumerate(self.train_dataset):
self.train_total_steps += self.opt.batch_size
epoch_steps += self.opt.batch_size
# train network
self.train_model.feed_batch(batch)
self.train_model.optimize_paras(train_gen=(idx % self.opt.train_gen_iter == 0))
# print losses
if self.train_total_steps % self.opt.print_losses_freq == 0:
cur_losses = self.train_model.get_latest_losses()
avg_step_t = (time.time() - last_print_step_t) / self.opt.print_losses_freq
last_print_step_t = time.time()
# print loss info to command line
info_dict = {'epoch': epoch, 'epoch_len': self.epoch_len,
'epoch_steps': idx * self.opt.batch_size, 'epoch_steps_len': len(self.train_dataset),
'step_time': avg_step_t, 'cur_lr': self.cur_lr,
'log_path': os.path.join(self.opt.ckpt_dir, self.opt.log_file),
'losses': cur_losses
}
self.visual.print_losses_info(info_dict)
# plot loss map to visdom
if self.train_total_steps % self.opt.plot_losses_freq == 0 and self.visual.display_id > 0:
cur_losses = self.train_model.get_latest_losses()
epoch_steps = idx * self.opt.batch_size
self.visual.display_current_losses(epoch - 1, epoch_steps / len(self.train_dataset), cur_losses)
# display image on visdom
if self.train_total_steps % self.opt.sample_img_freq == 0 and self.visual.display_id > 0:
cur_vis = self.train_model.get_latest_visuals()
self.visual.display_online_results(cur_vis, epoch)
# latest_aus = model.get_latest_aus()
# visual.log_aus(epoch, epoch_steps, latest_aus, opt.ckpt_dir)
def test_networks(self, opt):
self.init_test_setting(opt)
self.test_ops()
def init_test_setting(self, opt):
self.test_dataset = create_dataloader(opt)
self.test_model = create_model(opt)
def test_ops(self):
for batch_idx, batch in enumerate(self.test_dataset):
with torch.no_grad():
# interpolate several times
faces_list = [batch['src_img'].float().numpy()]
paths_list = [batch['src_path'], batch['tar_path']]
for idx in range(self.opt.interpolate_len):
cur_alpha = (idx + 1.) / float(self.opt.interpolate_len)
cur_tar_aus = cur_alpha * batch['tar_aus'] + (1 - cur_alpha) * batch['src_aus']
# print(batch['src_aus'])
# print(cur_tar_aus)
test_batch = {'src_img': batch['src_img'], 'tar_aus': cur_tar_aus, 'src_aus':batch['src_aus'], 'tar_img':batch['tar_img']}
self.test_model.feed_batch(test_batch)
self.test_model.forward()
cur_gen_faces = self.test_model.fake_img.cpu().float().numpy()
faces_list.append(cur_gen_faces)
faces_list.append(batch['tar_img'].float().numpy())
self.test_save_imgs(faces_list, paths_list)
def test_save_imgs(self, faces_list, paths_list):
for idx in range(len(paths_list[0])):
src_name = os.path.splitext(os.path.basename(paths_list[0][idx]))[0]
tar_name = os.path.splitext(os.path.basename(paths_list[1][idx]))[0]
if self.opt.save_test_gif:
import imageio
imgs_numpy_list = []
for face_idx in range(len(faces_list) - 1): # remove target image
cur_numpy = np.array(self.visual.numpy2im(faces_list[face_idx][idx]))
imgs_numpy_list.extend([cur_numpy for _ in range(3)])
saved_path = os.path.join(self.opt.results, "%s_%s.gif" % (src_name, tar_name))
imageio.mimsave(saved_path, imgs_numpy_list)
else:
# concate src, inters, tar faces
concate_img = np.array(self.visual.numpy2im(faces_list[0][idx]))
for face_idx in range(1, len(faces_list)):
concate_img = np.concatenate((concate_img, np.array(self.visual.numpy2im(faces_list[face_idx][idx]))), axis=1)
concate_img = Image.fromarray(concate_img)
# save image
saved_path = os.path.join(self.opt.results, "%s_%s.jpg" % (src_name, tar_name))
concate_img.save(saved_path)
print("[Success] Saved images to %s" % saved_path)
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