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print("started imports")
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import sys
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import argparse
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import time
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import cv2
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import wandb
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from PIL import Image
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import os
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from torch.utils.data import DataLoader
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import torch.optim as optim
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import torch.nn.functional as F
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import torch
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import torchvision.transforms as transforms
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import torch.optim.lr_scheduler as scheduler
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# custom imports
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sys.path.append('./apex/')
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from apex import amp
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from network.AEI_Net import *
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from network.MultiscaleDiscriminator import *
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from utils.training.Dataset import FaceEmbedVGG2, FaceEmbed
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from utils.training.image_processing import make_image_list, get_faceswap
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from utils.training.losses import hinge_loss, compute_discriminator_loss, compute_generator_losses
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from utils.training.detector import detect_landmarks, paint_eyes
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from AdaptiveWingLoss.core import models
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from arcface_model.iresnet import iresnet100
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print("finished imports")
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def train_one_epoch(G: 'generator model', 
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                    D: 'discriminator model', 
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                    opt_G: "generator opt", 
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                    opt_D: "discriminator opt",
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                    scheduler_G: "scheduler G opt",
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                    scheduler_D: "scheduler D opt",
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                    netArc: 'ArcFace model',
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                    model_ft: 'Landmark Detector',
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                    args: 'Args Namespace',
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                    dataloader: torch.utils.data.DataLoader,
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                    device: 'torch device',
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                    epoch:int,
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                    loss_adv_accumulated:int):
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    for iteration, data in enumerate(dataloader):
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        start_time = time.time()
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        Xs_orig, Xs, Xt, same_person = data
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        Xs_orig = Xs_orig.to(device)
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        Xs = Xs.to(device)
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        Xt = Xt.to(device)
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        same_person = same_person.to(device)
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        # get the identity embeddings of Xs
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        with torch.no_grad():
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            embed = netArc(F.interpolate(Xs_orig, [112, 112], mode='bilinear', align_corners=False))
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        diff_person = torch.ones_like(same_person)
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        if args.diff_eq_same:
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            same_person = diff_person
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        # generator training
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        opt_G.zero_grad()
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        Y, Xt_attr = G(Xt, embed)
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        Di = D(Y)
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        ZY = netArc(F.interpolate(Y, [112, 112], mode='bilinear', align_corners=False))
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        if args.eye_detector_loss:
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            Xt_eyes, Xt_heatmap_left, Xt_heatmap_right = detect_landmarks(Xt, model_ft)
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            Y_eyes, Y_heatmap_left, Y_heatmap_right = detect_landmarks(Y, model_ft)
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            eye_heatmaps = [Xt_heatmap_left, Xt_heatmap_right, Y_heatmap_left, Y_heatmap_right]
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        else:
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            eye_heatmaps = None
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        lossG, loss_adv_accumulated, L_adv, L_attr, L_id, L_rec, L_l2_eyes = compute_generator_losses(G, Y, Xt, Xt_attr, Di,
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                                                                             embed, ZY, eye_heatmaps,loss_adv_accumulated, 
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                                                                             diff_person, same_person, args)
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        with amp.scale_loss(lossG, opt_G) as scaled_loss:
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            scaled_loss.backward()
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        opt_G.step()
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        if args.scheduler:
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            scheduler_G.step()
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        # discriminator training
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        opt_D.zero_grad()
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        lossD = compute_discriminator_loss(D, Y, Xs, diff_person)
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        with amp.scale_loss(lossD, opt_D) as scaled_loss:
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            scaled_loss.backward()
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        if (not args.discr_force) or (loss_adv_accumulated < 4.):
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            opt_D.step()
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        if args.scheduler:
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            scheduler_D.step()
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        batch_time = time.time() - start_time
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        if iteration % args.show_step == 0:
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            images = [Xs, Xt, Y]
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            if args.eye_detector_loss:
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                Xt_eyes_img = paint_eyes(Xt, Xt_eyes)
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                Yt_eyes_img = paint_eyes(Y, Y_eyes)
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                images.extend([Xt_eyes_img, Yt_eyes_img])
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            image = make_image_list(images)
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            if args.use_wandb:
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                wandb.log({"gen_images":wandb.Image(image, caption=f"{epoch:03}" + '_' + f"{iteration:06}")})
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            else:
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                cv2.imwrite('./images/generated_image.jpg', image[:,:,::-1])
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        if iteration % 10 == 0:
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            print(f'epoch: {epoch}    {iteration} / {len(dataloader)}')
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            print(f'lossD: {lossD.item()}    lossG: {lossG.item()} batch_time: {batch_time}s')
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            print(f'L_adv: {L_adv.item()} L_id: {L_id.item()} L_attr: {L_attr.item()} L_rec: {L_rec.item()}')
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            if args.eye_detector_loss:
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                print(f'L_l2_eyes: {L_l2_eyes.item()}')
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            print(f'loss_adv_accumulated: {loss_adv_accumulated}')
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            if args.scheduler:
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                print(f'scheduler_G lr: {scheduler_G.get_last_lr()} scheduler_D lr: {scheduler_D.get_last_lr()}')
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        if args.use_wandb:
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            if args.eye_detector_loss:
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                wandb.log({"loss_eyes": L_l2_eyes.item()}, commit=False)
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            wandb.log({"loss_id": L_id.item(),
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                       "lossD": lossD.item(),
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                       "lossG": lossG.item(),
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                       "loss_adv": L_adv.item(),
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                       "loss_attr": L_attr.item(),
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                       "loss_rec": L_rec.item()})
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        if iteration % 5000 == 0:
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            torch.save(G.state_dict(), f'./saved_models_{args.run_name}/G_latest.pth')
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            torch.save(D.state_dict(), f'./saved_models_{args.run_name}/D_latest.pth')
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            torch.save(G.state_dict(), f'./current_models_{args.run_name}/G_' + str(epoch)+ '_' + f"{iteration:06}" + '.pth')
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            torch.save(D.state_dict(), f'./current_models_{args.run_name}/D_' + str(epoch)+ '_' + f"{iteration:06}" + '.pth')
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        if (iteration % 250 == 0) and (args.use_wandb):
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            ### Посмотрим как выглядит свап на трех конкретных фотках, чтобы проследить динамику
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            G.eval()
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            res1 = get_faceswap('examples/images/training//source1.png', 'examples/images/training//target1.png', G, netArc, device)
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            res2 = get_faceswap('examples/images/training//source2.png', 'examples/images/training//target2.png', G, netArc, device)  
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            res3 = get_faceswap('examples/images/training//source3.png', 'examples/images/training//target3.png', G, netArc, device)
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            res4 = get_faceswap('examples/images/training//source4.png', 'examples/images/training//target4.png', G, netArc, device)
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            res5 = get_faceswap('examples/images/training//source5.png', 'examples/images/training//target5.png', G, netArc, device)  
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            res6 = get_faceswap('examples/images/training//source6.png', 'examples/images/training//target6.png', G, netArc, device)
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            output1 = np.concatenate((res1, res2, res3), axis=0)
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            output2 = np.concatenate((res4, res5, res6), axis=0)
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            output = np.concatenate((output1, output2), axis=1)
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            wandb.log({"our_images":wandb.Image(output, caption=f"{epoch:03}" + '_' + f"{iteration:06}")})
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            G.train()
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def train(args, device):
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    # training params
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    batch_size = args.batch_size
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    max_epoch = args.max_epoch
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    # initializing main models
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    G = AEI_Net(args.backbone, num_blocks=args.num_blocks, c_id=512).to(device)
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    D = MultiscaleDiscriminator(input_nc=3, n_layers=5, norm_layer=torch.nn.InstanceNorm2d).to(device)
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    G.train()
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    D.train()
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    # initializing model for identity extraction
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    netArc = iresnet100(fp16=False)
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    netArc.load_state_dict(torch.load('arcface_model/backbone.pth'))
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    netArc=netArc.cuda()
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    netArc.eval()
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    if args.eye_detector_loss:
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        model_ft = models.FAN(4, "False", "False", 98)
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        checkpoint = torch.load('./AdaptiveWingLoss/AWL_detector/WFLW_4HG.pth')
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        if 'state_dict' not in checkpoint:
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            model_ft.load_state_dict(checkpoint)
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        else:
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            pretrained_weights = checkpoint['state_dict']
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            model_weights = model_ft.state_dict()
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            pretrained_weights = {k: v for k, v in pretrained_weights.items() \
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                                  if k in model_weights}
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            model_weights.update(pretrained_weights)
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            model_ft.load_state_dict(model_weights)
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        model_ft = model_ft.to(device)
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        model_ft.eval()
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    else:
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        model_ft=None
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    opt_G = optim.Adam(G.parameters(), lr=args.lr_G, betas=(0, 0.999), weight_decay=1e-4)
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    opt_D = optim.Adam(D.parameters(), lr=args.lr_D, betas=(0, 0.999), weight_decay=1e-4)
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    G, opt_G = amp.initialize(G, opt_G, opt_level=args.optim_level)
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    D, opt_D = amp.initialize(D, opt_D, opt_level=args.optim_level)
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    if args.scheduler:
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        scheduler_G = scheduler.StepLR(opt_G, step_size=args.scheduler_step, gamma=args.scheduler_gamma)
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        scheduler_D = scheduler.StepLR(opt_D, step_size=args.scheduler_step, gamma=args.scheduler_gamma)
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    else:
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        scheduler_G = None
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        scheduler_D = None
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    if args.pretrained:
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        try:
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            G.load_state_dict(torch.load(args.G_path, map_location=torch.device('cpu')), strict=False)
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            D.load_state_dict(torch.load(args.D_path, map_location=torch.device('cpu')), strict=False)
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            print("Loaded pretrained weights for G and D")
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        except FileNotFoundError as e:
219
            print("Not found pretrained weights. Continue without any pretrained weights.")
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    if args.vgg:
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        dataset = FaceEmbedVGG2(args.dataset_path, same_prob=args.same_person, same_identity=args.same_identity)
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    else:
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        dataset = FaceEmbed([args.dataset_path], same_prob=args.same_person)
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    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=8, drop_last=True)
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    # Будем считать аккумулированный adv loss, чтобы обучать дискриминатор только когда он ниже порога, если discr_force=True
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    loss_adv_accumulated = 20.
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    for epoch in range(0, max_epoch):
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        train_one_epoch(G,
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                        D,
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                        opt_G,
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                        opt_D,
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                        scheduler_G,
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                        scheduler_D,
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                        netArc,
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                        model_ft,
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                        args,
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                        dataloader,
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                        device,
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                        epoch,
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                        loss_adv_accumulated)
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def main(args):
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    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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    if not torch.cuda.is_available():
249
        print('cuda is not available. using cpu. check if it\'s ok')
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    print("Starting traing")
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    train(args, device=device)
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if __name__ == "__main__":
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    parser = argparse.ArgumentParser()
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    # dataset params
259
    parser.add_argument('--dataset_path', default='/VggFace2-crop/', help='Path to the dataset. If not VGG2 dataset is used, param --vgg should be set False')
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    parser.add_argument('--G_path', default='./saved_models/G.pth', help='Path to pretrained weights for G. Only used if pretrained=True')
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    parser.add_argument('--D_path', default='./saved_models/D.pth', help='Path to pretrained weights for D. Only used if pretrained=True')
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    parser.add_argument('--vgg', default=True, type=bool, help='When using VGG2 dataset (or any other dataset with several photos for one identity)')
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    # weights for loss
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    parser.add_argument('--weight_adv', default=1, type=float, help='Adversarial Loss weight')
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    parser.add_argument('--weight_attr', default=10, type=float, help='Attributes weight')
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    parser.add_argument('--weight_id', default=20, type=float, help='Identity Loss weight')
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    parser.add_argument('--weight_rec', default=10, type=float, help='Reconstruction Loss weight')
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    parser.add_argument('--weight_eyes', default=0., type=float, help='Eyes Loss weight')
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    # training params you may want to change
270
    
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    parser.add_argument('--backbone', default='unet', const='unet', nargs='?', choices=['unet', 'linknet', 'resnet'], help='Backbone for attribute encoder')
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    parser.add_argument('--num_blocks', default=2, type=int, help='Numbers of AddBlocks at AddResblock')
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    parser.add_argument('--same_person', default=0.2, type=float, help='Probability of using same person identity during training')
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    parser.add_argument('--same_identity', default=True, type=bool, help='Using simswap approach, when source_id = target_id. Only possible with vgg=True')
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    parser.add_argument('--diff_eq_same', default=False, type=bool, help='Don\'t use info about where is defferent identities')
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    parser.add_argument('--pretrained', default=True, type=bool, help='If using the pretrained weights for training or not')
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    parser.add_argument('--discr_force', default=False, type=bool, help='If True Discriminator would not train when adversarial loss is high')
278
    parser.add_argument('--scheduler', default=False, type=bool, help='If True decreasing LR is used for learning of generator and discriminator')
279
    parser.add_argument('--scheduler_step', default=5000, type=int)
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    parser.add_argument('--scheduler_gamma', default=0.2, type=float, help='It is value, which shows how many times to decrease LR')
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    parser.add_argument('--eye_detector_loss', default=False, type=bool, help='If True eye loss with using AdaptiveWingLoss detector is applied to generator')
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    # info about this run
283
    parser.add_argument('--use_wandb', default=False, type=bool, help='Use wandb to track your experiments or not')
284
    parser.add_argument('--run_name', required=True, type=str, help='Name of this run. Used to create folders where to save the weights.')
285
    parser.add_argument('--wandb_project', default='your-project-name', type=str)
286
    parser.add_argument('--wandb_entity', default='your-login', type=str)
287
    # training params you probably don't want to change
288
    parser.add_argument('--batch_size', default=16, type=int)
289
    parser.add_argument('--lr_G', default=4e-4, type=float)
290
    parser.add_argument('--lr_D', default=4e-4, type=float)
291
    parser.add_argument('--max_epoch', default=2000, type=int)
292
    parser.add_argument('--show_step', default=500, type=int)
293
    parser.add_argument('--save_epoch', default=1, type=int)
294
    parser.add_argument('--optim_level', default='O2', type=str)
295

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    args = parser.parse_args()
297
    
298
    if args.vgg==False and args.same_identity==True:
299
        raise ValueError("Sorry, you can't use some other dataset than VGG2 Faces with param same_identity=True")
300
    
301
    if args.use_wandb==True:
302
        wandb.init(project=args.wandb_project, entity=args.wandb_entity, settings=wandb.Settings(start_method='fork'))
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        config = wandb.config
305
        config.dataset_path = args.dataset_path
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        config.weight_adv = args.weight_adv
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        config.weight_attr = args.weight_attr
308
        config.weight_id = args.weight_id
309
        config.weight_rec = args.weight_rec
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        config.weight_eyes = args.weight_eyes
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        config.same_person = args.same_person
312
        config.Vgg2Face = args.vgg
313
        config.same_identity = args.same_identity
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        config.diff_eq_same = args.diff_eq_same
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        config.discr_force = args.discr_force
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        config.scheduler = args.scheduler
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        config.scheduler_step = args.scheduler_step
318
        config.scheduler_gamma = args.scheduler_gamma
319
        config.eye_detector_loss = args.eye_detector_loss
320
        config.pretrained = args.pretrained
321
        config.run_name = args.run_name
322
        config.G_path = args.G_path
323
        config.D_path = args.D_path
324
        config.batch_size = args.batch_size
325
        config.lr_G = args.lr_G
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        config.lr_D = args.lr_D
327
    elif not os.path.exists('./images'):
328
        os.mkdir('./images')
329
    
330
    # Создаем папки, чтобы было куда сохранять последние веса моделей, а также веса с каждой эпохи
331
    if not os.path.exists(f'./saved_models_{args.run_name}'):
332
        os.mkdir(f'./saved_models_{args.run_name}')
333
        os.mkdir(f'./current_models_{args.run_name}')
334
    
335
    main(args)
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