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train.py
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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: train.py
# Created Date: Monday December 27th 2021
# Author: Chen Xuanhong
# Email: [email protected]
# Last Modified: Friday, 22nd April 2022 10:49:26 am
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
#############################################################
import os
import time
import random
import argparse
import numpy as np
import torch
import torch.nn.functional as F
from torch.backends import cudnn
import torch.utils.tensorboard as tensorboard
from util import util
from util.plot import plot_batch
from models.projected_model import fsModel
from data.data_loader_Swapping import GetLoader
def str2bool(v):
return v.lower() in ('true')
class TrainOptions:
def __init__(self):
self.parser = argparse.ArgumentParser()
self.initialized = False
def initialize(self):
self.parser.add_argument('--name', type=str, default='simswap', help='name of the experiment. It decides where to store samples and models')
self.parser.add_argument('--gpu_ids', default='0')
self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
self.parser.add_argument('--isTrain', type=str2bool, default='True')
# input/output sizes
self.parser.add_argument('--batchSize', type=int, default=4, help='input batch size')
# for displays
self.parser.add_argument('--use_tensorboard', type=str2bool, default='False')
# for training
self.parser.add_argument('--dataset', type=str, default="/path/to/VGGFace2", help='path to the face swapping dataset')
self.parser.add_argument('--continue_train', type=str2bool, default='False', help='continue training: load the latest model')
self.parser.add_argument('--load_pretrain', type=str, default='./checkpoints/simswap224_test', help='load the pretrained model from the specified location')
self.parser.add_argument('--which_epoch', type=str, default='10000', help='which epoch to load? set to latest to use latest cached model')
self.parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')
self.parser.add_argument('--niter', type=int, default=10000, help='# of iter at starting learning rate')
self.parser.add_argument('--niter_decay', type=int, default=10000, help='# of iter to linearly decay learning rate to zero')
self.parser.add_argument('--beta1', type=float, default=0.0, help='momentum term of adam')
self.parser.add_argument('--lr', type=float, default=0.0004, help='initial learning rate for adam')
self.parser.add_argument('--Gdeep', type=str2bool, default='False')
# for discriminators
self.parser.add_argument('--lambda_feat', type=float, default=10.0, help='weight for feature matching loss')
self.parser.add_argument('--lambda_id', type=float, default=30.0, help='weight for id loss')
self.parser.add_argument('--lambda_rec', type=float, default=10.0, help='weight for reconstruction loss')
self.parser.add_argument("--Arc_path", type=str, default='arcface_model/arcface_checkpoint.tar', help="run ONNX model via TRT")
self.parser.add_argument("--total_step", type=int, default=1000000, help='total training step')
self.parser.add_argument("--log_frep", type=int, default=200, help='frequence for printing log information')
self.parser.add_argument("--sample_freq", type=int, default=1000, help='frequence for sampling')
self.parser.add_argument("--model_freq", type=int, default=10000, help='frequence for saving the model')
self.isTrain = True
def parse(self, save=True):
if not self.initialized:
self.initialize()
self.opt = self.parser.parse_args()
self.opt.isTrain = self.isTrain # train or test
args = vars(self.opt)
print('------------ Options -------------')
for k, v in sorted(args.items()):
print('%s: %s' % (str(k), str(v)))
print('-------------- End ----------------')
# save to the disk
if self.opt.isTrain:
expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name)
util.mkdirs(expr_dir)
if save and not self.opt.continue_train:
file_name = os.path.join(expr_dir, 'opt.txt')
with open(file_name, 'wt') as opt_file:
opt_file.write('------------ Options -------------\n')
for k, v in sorted(args.items()):
opt_file.write('%s: %s\n' % (str(k), str(v)))
opt_file.write('-------------- End ----------------\n')
return self.opt
if __name__ == '__main__':
opt = TrainOptions().parse()
iter_path = os.path.join(opt.checkpoints_dir, opt.name, 'iter.txt')
sample_path = os.path.join(opt.checkpoints_dir, opt.name, 'samples')
if not os.path.exists(sample_path):
os.makedirs(sample_path)
log_path = os.path.join(opt.checkpoints_dir, opt.name, 'summary')
if not os.path.exists(log_path):
os.makedirs(log_path)
if opt.continue_train:
try:
start_epoch, epoch_iter = np.loadtxt(iter_path , delimiter=',', dtype=int)
except:
start_epoch, epoch_iter = 1, 0
print('Resuming from epoch %d at iteration %d' % (start_epoch, epoch_iter))
else:
start_epoch, epoch_iter = 1, 0
os.environ['CUDA_VISIBLE_DEVICES'] = str(opt.gpu_ids)
print("GPU used : ", str(opt.gpu_ids))
cudnn.benchmark = True
model = fsModel()
model.initialize(opt)
#####################################################
if opt.use_tensorboard:
tensorboard_writer = tensorboard.SummaryWriter(log_path)
logger = tensorboard_writer
log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
with open(log_name, "a") as log_file:
now = time.strftime("%c")
log_file.write('================ Training Loss (%s) ================\n' % now)
optimizer_G, optimizer_D = model.optimizer_G, model.optimizer_D
loss_avg = 0
refresh_count = 0
imagenet_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1)
imagenet_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1)
train_loader = GetLoader(opt.dataset,opt.batchSize,8,1234)
randindex = [i for i in range(opt.batchSize)]
random.shuffle(randindex)
if not opt.continue_train:
start = 0
else:
start = int(opt.which_epoch)
total_step = opt.total_step
import datetime
print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
from util.logo_class import logo_class
logo_class.print_start_training()
model.netD.feature_network.requires_grad_(False)
# Training Cycle
for step in range(start, total_step):
model.netG.train()
for interval in range(2):
random.shuffle(randindex)
src_image1, src_image2 = train_loader.next()
if step%2 == 0:
img_id = src_image2
else:
img_id = src_image2[randindex]
img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic')
latent_id = model.netArc(img_id_112)
latent_id = F.normalize(latent_id, p=2, dim=1)
if interval:
img_fake = model.netG(src_image1, latent_id)
gen_logits,_ = model.netD(img_fake.detach(), None)
loss_Dgen = (F.relu(torch.ones_like(gen_logits) + gen_logits)).mean()
real_logits,_ = model.netD(src_image2,None)
loss_Dreal = (F.relu(torch.ones_like(real_logits) - real_logits)).mean()
loss_D = loss_Dgen + loss_Dreal
optimizer_D.zero_grad()
loss_D.backward()
optimizer_D.step()
else:
# model.netD.requires_grad_(True)
img_fake = model.netG(src_image1, latent_id)
# G loss
gen_logits,feat = model.netD(img_fake, None)
loss_Gmain = (-gen_logits).mean()
img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic')
latent_fake = model.netArc(img_fake_down)
latent_fake = F.normalize(latent_fake, p=2, dim=1)
loss_G_ID = (1 - model.cosin_metric(latent_fake, latent_id)).mean()
real_feat = model.netD.get_feature(src_image1)
feat_match_loss = model.criterionFeat(feat["3"],real_feat["3"])
loss_G = loss_Gmain + loss_G_ID * opt.lambda_id + feat_match_loss * opt.lambda_feat
if step%2 == 0:
#G_Rec
loss_G_Rec = model.criterionRec(img_fake, src_image1) * opt.lambda_rec
loss_G += loss_G_Rec
optimizer_G.zero_grad()
loss_G.backward()
optimizer_G.step()
############## Display results and errors ##########
### print out errors
# Print out log info
if (step + 1) % opt.log_frep == 0:
# errors = {k: v.data.item() if not isinstance(v, int) else v for k, v in loss_dict.items()}
errors = {
"G_Loss":loss_Gmain.item(),
"G_ID":loss_G_ID.item(),
"G_Rec":loss_G_Rec.item(),
"G_feat_match":feat_match_loss.item(),
"D_fake":loss_Dgen.item(),
"D_real":loss_Dreal.item(),
"D_loss":loss_D.item()
}
if opt.use_tensorboard:
for tag, value in errors.items():
logger.add_scalar(tag, value, step)
message = '( step: %d, ) ' % (step)
for k, v in errors.items():
message += '%s: %.3f ' % (k, v)
print(message)
with open(log_name, "a") as log_file:
log_file.write('%s\n' % message)
### display output images
if (step + 1) % opt.sample_freq == 0:
model.netG.eval()
with torch.no_grad():
imgs = list()
zero_img = (torch.zeros_like(src_image1[0,...]))
imgs.append(zero_img.cpu().numpy())
save_img = ((src_image1.cpu())* imagenet_std + imagenet_mean).numpy()
for r in range(opt.batchSize):
imgs.append(save_img[r,...])
arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic')
id_vector_src1 = model.netArc(arcface_112)
id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1)
for i in range(opt.batchSize):
imgs.append(save_img[i,...])
image_infer = src_image1[i, ...].repeat(opt.batchSize, 1, 1, 1)
img_fake = model.netG(image_infer, id_vector_src1).cpu()
img_fake = img_fake * imagenet_std
img_fake = img_fake + imagenet_mean
img_fake = img_fake.numpy()
for j in range(opt.batchSize):
imgs.append(img_fake[j,...])
print("Save test data")
imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1)
plot_batch(imgs, os.path.join(sample_path, 'step_'+str(step+1)+'.jpg'))
### save latest model
if (step+1) % opt.model_freq==0:
print('saving the latest model (steps %d)' % (step+1))
model.save(step+1)
np.savetxt(iter_path, (step+1, total_step), delimiter=',', fmt='%d')
wandb.finish()