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train_fbb_gal_64x64.py
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train_fbb_gal_64x64.py
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"""
Copyright StrangeAI Authors @2019
original forked from deepfakes repo
edit and promoted by StrangeAI authors
"""
from __future__ import print_function
import argparse
import os
import cv2
import numpy as np
import torch
import torch.utils.data
from torch import nn, optim
from torch.autograd import Variable
from torch.nn import functional as F
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from models.swapnet import SwapNet, toTensor, var_to_np
from utils.util import get_image_paths, load_images, stack_images
from dataset.training_data import get_training_data
from alfred.dl.torch.common import device
from shutil import copyfile
from loguru import logger
from dataset.face_pair_dataset import FacePairDataset, FacePairDataset64x64
from torchvision import transforms
import sys
logger.remove() # Remove the pre-configured handler
logger.start(sys.stderr, format="<lvl>{level}</lvl> {time:MM-DD HH:mm:ss} {file}:{line} - {message}")
batch_size = 64
epochs = 100000
save_per_epoch = 300
a_dir = './data/galgadot_fbb/fanbingbing_faces'
b_dir = './data/galgadot_fbb/galgadot_faces'
# we start to train on bigger size
target_size = 64
dataset_name = 'galgadot_fbb'
log_img_dir = './checkpoint/results_{}_{}x{}'.format(dataset_name, target_size, target_size)
log_model_dir = './checkpoint/{}_{}x{}'.format(dataset_name,
target_size, target_size)
check_point_save_path = os.path.join(
log_model_dir, 'faceswap_{}_{}x{}.pth'.format(dataset_name, target_size, target_size))
def main():
os.makedirs(log_img_dir, exist_ok=True)
os.makedirs(log_model_dir, exist_ok=True)
transform = transforms.Compose([
# transforms.Resize((target_size, target_size)),
transforms.RandomHorizontalFlip(),
# transforms.RandomVerticalFlip(),
# transforms.ToTensor(),
])
ds = FacePairDataset64x64(a_dir=a_dir, b_dir=b_dir,
target_size=target_size, transform=transform)
dataloader = DataLoader(ds, batch_size, shuffle=True)
model = SwapNet()
model.to(device)
start_epoch = 0
logger.info('try resume from checkpoint')
if os.path.isdir('checkpoint'):
try:
if torch.cuda.is_available():
checkpoint = torch.load(check_point_save_path)
else:
checkpoint = torch.load(
check_point_save_path, map_location={'cuda:0': 'cpu'})
model.load_state_dict(checkpoint['state'])
start_epoch = checkpoint['epoch']
logger.info('checkpoint loaded.')
except FileNotFoundError:
print('Can\'t found faceswap_trump_cage.pth')
criterion = nn.L1Loss()
optimizer_1 = optim.Adam([{'params': model.encoder.parameters()},
{'params': model.decoder_A.parameters()}], lr=5e-5, betas=(0.5, 0.999))
optimizer_2 = optim.Adam([{'params': model.encoder.parameters()},
{'params': model.decoder_B.parameters()}], lr=5e-5, betas=(0.5, 0.999))
logger.info('Start training, from epoch {} '.format(start_epoch))
try:
for epoch in range(start_epoch, epochs):
iter = 0
for data in dataloader:
iter += 1
img_a_target, img_a_input, img_b_target, img_b_input = data
img_a_target = img_a_target.to(device)
img_a_input = img_a_input.to(device)
img_b_target = img_b_target.to(device)
img_b_input = img_b_input.to(device)
# print(img_a.size())
# print(img_b.size())
optimizer_1.zero_grad()
optimizer_2.zero_grad()
predict_a = model(img_a_input, select='A')
predict_b = model(img_b_input, select='B')
loss1 = criterion(predict_a, img_a_target)
loss2 = criterion(predict_b, img_b_target)
loss1.backward()
loss2.backward()
optimizer_1.step()
optimizer_2.step()
logger.info('Epoch: {}, iter: {}, lossA: {}, lossB: {}'.format(
epoch, iter, loss1.item(), loss2.item()))
if epoch % save_per_epoch == 0 and epoch != 0:
logger.info('Saving models...')
state = {
'state': model.state_dict(),
'epoch': epoch
}
torch.save(state, os.path.join(os.path.dirname(
check_point_save_path), 'faceswap_trump_cage_128x128_{}.pth'.format(epoch)))
copyfile(os.path.join(os.path.dirname(check_point_save_path), 'faceswap_trump_cage_128x128_{}.pth'.format(epoch)),
check_point_save_path)
if epoch % 10 == 0 and epoch != 0 and iter == 1:
img_a_original = np.array(img_a_target.detach().cpu().numpy()[0].transpose(2, 1, 0)*255, dtype=np.uint8)
img_b_original = np.array(img_b_target.detach().cpu().numpy()[0].transpose(2, 1, 0)*255, dtype=np.uint8)
a_predict_a = np.array(predict_a.detach().cpu().numpy()[0].transpose(2, 1, 0)*255, dtype=np.uint8)
b_predict_b = np.array(predict_b.detach().cpu().numpy()[0].transpose(2, 1, 0)*255, dtype=np.uint8)
a_predict_b = model(img_a_input, select='B')
b_predict_a = model(img_b_input, select='A')
a_predict_b = np.array(a_predict_b.detach().cpu().numpy()[0].transpose(2, 1, 0)*255, dtype=np.uint8)
b_predict_a = np.array(b_predict_a.detach().cpu().numpy()[0].transpose(2, 1, 0)*255, dtype=np.uint8)
cv2.imwrite(os.path.join(log_img_dir, '{}_0.png'.format(epoch)), cv2.cvtColor(img_a_original, cv2.COLOR_BGR2RGB))
cv2.imwrite(os.path.join(log_img_dir, '{}_3.png'.format(epoch)), cv2.cvtColor(img_b_original, cv2.COLOR_BGR2RGB))
cv2.imwrite(os.path.join(log_img_dir, '{}_1.png'.format(epoch)), cv2.cvtColor(a_predict_a, cv2.COLOR_BGR2RGB))
cv2.imwrite(os.path.join(log_img_dir, '{}_4.png'.format(epoch)), cv2.cvtColor(b_predict_b, cv2.COLOR_BGR2RGB))
cv2.imwrite(os.path.join(log_img_dir, '{}_2.png'.format(epoch)), cv2.cvtColor(a_predict_b, cv2.COLOR_BGR2RGB))
cv2.imwrite(os.path.join(log_img_dir, '{}_5.png'.format(epoch)), cv2.cvtColor(b_predict_a, cv2.COLOR_BGR2RGB))
logger.info('Record a result')
except KeyboardInterrupt:
logger.warning('try saving models...do not interrupt')
state = {
'state': model.state_dict(),
'epoch': epoch
}
torch.save(state, os.path.join(os.path.dirname(
check_point_save_path), 'faceswap_trump_cage_256x256_{}.pth'.format(epoch)))
copyfile(os.path.join(os.path.dirname(check_point_save_path), 'faceswap_trump_cage_256x256_{}.pth'.format(epoch)),
check_point_save_path)
if __name__ == "__main__":
main()