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train_trump_cage_64x64.py
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train_trump_cage_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 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
batch_size = 64
epochs = 100000
save_per_epoch = 300
a_dir = './data/trump_cage/trump'
b_dir = './data/trump_cage/cage'
# we start to train on bigger size
target_size = 64
dataset_name = 'trump_cage'
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)
logger.info("loading datasets")
images_A = get_image_paths(a_dir)
images_B = get_image_paths(b_dir)
images_A = load_images(images_A) / 255.0
images_B = load_images(images_B) / 255.0
print('mean value to remember: ', images_B.mean(
axis=(0, 1, 2)) - images_A.mean(axis=(0, 1, 2)))
images_A += images_B.mean(axis=(0, 1, 2)) - images_A.mean(axis=(0, 1, 2))
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('./checkpoint/faceswap_trump_cage_64x64.pth')
else:
checkpoint = torch.load(
'./checkpoint/faceswap_trump_cage_64x64.pth', 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))
for epoch in range(start_epoch, epochs):
warped_A, target_A = get_training_data(images_A, batch_size)
# print(warped_A.shape)
# t_a = np.array(warped_A[0] * 255, dtype=np.uint8)
# print(t_a)
# print(t_a.shape)
# cv2.imshow('rr', t_a)
# cv2.waitKey(0)
# warped a and target a are not rotated, where did rotate?
warped_B, target_B = get_training_data(images_B, batch_size)
warped_A, target_A = toTensor(warped_A), toTensor(target_A)
warped_B, target_B = toTensor(warped_B), toTensor(target_B)
# warp_a = np.array(warped_A[0].detach().cpu().numpy().transpose(2, 1, 0)*255, dtype=np.uint8)
# cv2.imshow('rr', warp_a)
# cv2.waitKey(0)
warped_A, target_A, warped_B, target_B = Variable(warped_A.float()), Variable(target_A.float()), \
Variable(warped_B.float()), Variable(target_B.float())
optimizer_1.zero_grad()
optimizer_2.zero_grad()
warped_A_out = model(warped_A, 'A')
warped_B_out = model(warped_B, 'B')
loss1 = criterion(warped_A_out, target_A)
loss2 = criterion(warped_B_out, target_B)
loss1.backward()
loss2.backward()
optimizer_1.step()
optimizer_2.step()
logger.info('epoch: {}, lossA: {}, lossB: {}'.format(epoch, loss1.item(), loss2.item()))
if epoch % save_per_epoch == 0 and iter == 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_{}_64x64_{}.pth'.format(dataset_name, epoch)))
copyfile(os.path.join(os.path.dirname(check_point_save_path), 'faceswap_{}_64x64_{}.pth'.format(dataset_name, epoch)),
check_point_save_path)
if epoch % 100 == 0:
test_A_ = warped_A[0:2]
a_predict_a = var_to_np(model(test_A_, 'A'))[0]*255
# warped a out
# print(test_A_[0].detach().cpu().numpy().shape)
a_predict_b = var_to_np(model(test_A_, 'B'))[0]*255
warp_a = test_A_[0].detach().cpu().numpy()*255
target_a = target_A[0].detach().cpu().numpy()*255
cv2.imwrite(os.path.join(log_img_dir, "{}_res_a_to_a.png".format(epoch)), np.array(a_predict_a.transpose(2, 1, 0)).astype('uint8'))
cv2.imwrite(os.path.join(log_img_dir, "{}_res_a_to_b.png".format(epoch)), np.array(a_predict_b.transpose(2, 1, 0)).astype('uint8'))
cv2.imwrite(os.path.join(log_img_dir, "{}_test_A_warped.png".format(epoch)), np.array(warp_a.transpose(2, 1, 0)).astype('uint8'))
cv2.imwrite(os.path.join(log_img_dir, "{}_test_A_target.png".format(epoch)), np.array(target_a.transpose(2, 1, 0)).astype('uint8'))
logger.info('Record a result')
if __name__ == "__main__":
main()