Create vtoonify/train_vtoonify_t.py

vtoonify/train_vtoonify_t.py

@@ -0,0 +1,432 @@
+import os
+#os.environ['CUDA_VISIBLE_DEVICES'] = "0"
+import argparse
+import math
+import random
+
+import numpy as np
+import torch
+from torch import nn, optim
+from torch.nn import functional as F
+from torch.utils import data
+import torch.distributed as dist
+from torchvision import transforms, utils
+from tqdm import tqdm
+from PIL import Image
+from util import *
+from model.stylegan import lpips
+from model.stylegan.model import Generator, Downsample
+from model.vtoonify import VToonify, ConditionalDiscriminator
+from model.bisenet.model import BiSeNet
+from model.simple_augment import random_apply_affine
+from model.stylegan.distributed import (
+    get_rank,
+    synchronize,
+    reduce_loss_dict,
+    reduce_sum,
+    get_world_size,
+)
+
+# In the paper, --weight for each style is set as follows,
+# cartoon: default
+# caricature: default
+# pixar: 1 1 1 1 1 1 1 1 1 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
+# comic: 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1 1 1 1 1 1 1 1 1 1 1
+# arcane: 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1 1 1 1 1 1 1 1 1 1 1
+
+class TrainOptions():
+    def __init__(self):
+
+        self.parser = argparse.ArgumentParser(description="Train VToonify-T")
+        self.parser.add_argument("--iter", type=int, default=2000, help="total training iterations")
+        self.parser.add_argument("--batch", type=int, default=8, help="batch sizes for each gpus")
+        self.parser.add_argument("--lr", type=float, default=0.0001, help="learning rate")
+        self.parser.add_argument("--local_rank", type=int, default=0, help="local rank for distributed training")
+        self.parser.add_argument("--start_iter", type=int, default=0, help="start iteration")
+        self.parser.add_argument("--save_every", type=int, default=30000, help="interval of saving a checkpoint")
+        self.parser.add_argument("--save_begin", type=int, default=30000, help="when to start saving a checkpoint")
+        self.parser.add_argument("--log_every", type=int, default=200, help="interval of saving an intermediate image result")
+        
+        self.parser.add_argument("--adv_loss", type=float, default=0.01, help="the weight of adv loss")
+        self.parser.add_argument("--grec_loss", type=float, default=0.1, help="the weight of mse recontruction loss")
+        self.parser.add_argument("--perc_loss", type=float, default=0.01, help="the weight of perceptual loss")
+        self.parser.add_argument("--tmp_loss", type=float, default=1.0, help="the weight of temporal consistency loss")
+        
+        self.parser.add_argument("--encoder_path", type=str, default=None, help="path to the pretrained encoder model")    
+        self.parser.add_argument("--direction_path", type=str, default='./checkpoint/directions.npy', help="path to the editing direction latents")
+        self.parser.add_argument("--stylegan_path", type=str, default='./checkpoint/stylegan2-ffhq-config-f.pt', help="path to the stylegan model")
+        self.parser.add_argument("--finetunegan_path", type=str, default='./checkpoint/cartoon/finetune-000600.pt', help="path to the finetuned stylegan model")
+        self.parser.add_argument("--weight", type=float, nargs=18, default=[1]*9+[0]*9, help="the weight for blending two models")
+        self.parser.add_argument("--faceparsing_path", type=str, default='./checkpoint/faceparsing.pth', help="path of the face parsing model")
+        self.parser.add_argument("--style_encoder_path", type=str, default='./checkpoint/encoder.pt', help="path of the style encoder")
+        
+        self.parser.add_argument("--name", type=str, default='vtoonify_t_cartoon', help="saved model name")
+        self.parser.add_argument("--pretrain", action="store_true", help="if true, only pretrain the encoder")
+
+    def parse(self):
+        self.opt = self.parser.parse_args()
+        if self.opt.encoder_path is None:
+            self.opt.encoder_path = os.path.join('./checkpoint/', self.opt.name, 'pretrain.pt')
+        args = vars(self.opt)
+        if self.opt.local_rank == 0:
+            print('Load options')
+            for name, value in sorted(args.items()):
+                print('%s: %s' % (str(name), str(value)))
+        return self.opt
+    
+
+# pretrain E of vtoonify. 
+# We train E so that its the last-layer feature matches the original 8-th-layer input feature of G1
+# See Model initialization in Sec. 4.1.2 for the detail
+def pretrain(args, generator, g_optim, g_ema, parsingpredictor, down, directions, basemodel, device):
+    pbar = range(args.iter)
+
+    if get_rank() == 0:
+        pbar = tqdm(pbar, initial=args.start_iter, dynamic_ncols=True, smoothing=0.01)
+
+    recon_loss = torch.tensor(0.0, device=device)
+    loss_dict = {}
+
+    if args.distributed:
+        g_module = generator.module
+    else:
+        g_module = generator
+
+    accum = 0.5 ** (32 / (10 * 1000))
+    
+    requires_grad(g_module.encoder, True)
+    
+    for idx in pbar:
+        i = idx + args.start_iter
+        
+        if i > args.iter:
+            print("Done!")
+            break
+            
+        with torch.no_grad():
+            # during pretraining, no geometric transformations are applied.
+            noise_sample = torch.randn(args.batch, 512).cuda()
+            ws_ = basemodel.style(noise_sample).unsqueeze(1).repeat(1,18,1) # random w
+            ws_[:, 3:7] += directions[torch.randint(0, directions.shape[0], (args.batch,)), 3:7] # w''=w'=w+n
+            img_gen, _ = basemodel([ws_], input_is_latent=True, truncation=0.5, truncation_latent=0) # image part of x'
+            img_gen = torch.clamp(img_gen, -1, 1).detach() 
+            img_gen512 = down(img_gen.detach())
+            img_gen256 = down(img_gen512.detach()) # image part of x'_down
+            mask512 = parsingpredictor(2*torch.clamp(img_gen512, -1, 1))[0] 
+            real_input = torch.cat((img_gen256, down(mask512)/16.0), dim=1).detach() # x'_down
+            # f_G1^(8)(w'')
+            real_feat, real_skip = g_ema.generator([ws_], input_is_latent=True, return_feature_ind = 6, truncation=0.5, truncation_latent=0)
+            real_feat = real_feat.detach()
+            real_skip = real_skip.detach()
+            
+        # f_E^(last)(x'_down)
+        fake_feat, fake_skip = generator(real_input, style=None, return_feat=True)
+
+        # L_E in Eq.(1)
+        recon_loss = F.mse_loss(fake_feat, real_feat) + F.mse_loss(fake_skip, real_skip)
+
+        loss_dict["emse"] = recon_loss
+
+        generator.zero_grad()
+        recon_loss.backward()  
+        g_optim.step()    
+        
+        accumulate(g_ema.encoder, g_module.encoder, accum)     
+
+        loss_reduced = reduce_loss_dict(loss_dict)
+
+        emse_loss_val = loss_reduced["emse"].mean().item()
+
+        if get_rank() == 0:
+            pbar.set_description(
+                (
+                    f"iter: {i:d}; emse: {emse_loss_val:.3f}"
+                )
+            )
+
+            if ((i+1) >= args.save_begin and (i+1) % args.save_every == 0) or (i+1) == args.iter:
+                if (i+1) == args.iter:
+                    savename = f"checkpoint/%s/pretrain.pt"%(args.name)
+                else:
+                    savename = f"checkpoint/%s/pretrain-%05d.pt"%(args.name, i+1)
+                torch.save(
+                    {
+                        #"g": g_module.encoder.state_dict(),
+                        "g_ema": g_ema.encoder.state_dict(),
+                    },
+                    savename,
+                )
+                
+
+# generate paired data and train vtoonify, see Sec. 4.1.2 for the detail
+def train(args, generator, discriminator, g_optim, d_optim, g_ema, percept, parsingpredictor, down, pspencoder, directions, basemodel, device):
+    pbar = range(args.iter)
+
+    if get_rank() == 0:
+        pbar = tqdm(pbar, initial=args.start_iter, smoothing=0.01, ncols=120, dynamic_ncols=False)
+
+    d_loss = torch.tensor(0.0, device=device)
+    g_loss = torch.tensor(0.0, device=device)
+    grec_loss = torch.tensor(0.0, device=device)
+    gfeat_loss = torch.tensor(0.0, device=device)
+    temporal_loss = torch.tensor(0.0, device=device)
+    loss_dict = {}
+
+    if args.distributed:
+        g_module = generator.module
+        d_module = discriminator.module
+
+    else:
+        g_module = generator
+        d_module = discriminator
+
+    accum = 0.5 ** (32 / (10 * 1000))
+    
+    for idx in pbar:
+        i = idx + args.start_iter
+        
+        if i > args.iter:
+            print("Done!")
+            break
+        
+        ###### This part is for data generation. Generate pair (x, y, w'') as in Fig. 5 of the paper
+        with torch.no_grad():   
+            noise_sample = torch.randn(args.batch, 512).cuda()
+            wc = basemodel.style(noise_sample).unsqueeze(1).repeat(1,18,1) # random w
+            wc[:, 3:7] += directions[torch.randint(0, directions.shape[0], (args.batch,)), 3:7] # w'=w+n
+            wc = wc.detach()
+            xc, _ = basemodel([wc], input_is_latent=True, truncation=0.5, truncation_latent=0) 
+            xc = torch.clamp(xc, -1, 1).detach() # x'
+            xl = pspencoder(F.adaptive_avg_pool2d(xc, 256))
+            xl = basemodel.style(xl.reshape(xl.shape[0]*xl.shape[1], xl.shape[2])).reshape(xl.shape) # E_s(x'_down)
+            xl = torch.cat((wc[:,0:7]*0.5, xl[:,7:18]), dim=1).detach() # w'' = concatenate w' and E_s(x'_down)
+            xs, _ = g_ema.generator([xl], input_is_latent=True)
+            xs = torch.clamp(xs, -1, 1).detach() # y'
+            # during training, random geometric transformations are applied.
+            imgs, _ = random_apply_affine(torch.cat((xc.detach(),xs), dim=1), 0.2, None)
+            real_input1024 = imgs[:,0:3].detach() # image part of x
+            real_input512 = down(real_input1024).detach()
+            real_input256 = down(real_input512).detach()
+            mask512 = parsingpredictor(2*real_input512)[0]
+            mask256 = down(mask512).detach()
+            mask = F.adaptive_avg_pool2d(mask512, 1024).detach() # parsing part of x
+            real_output = imgs[:,3:].detach()  # y
+            real_input = torch.cat((real_input256, mask256/16.0), dim=1) # x_down
+            # for log, sample a fixed input-output pair (x_down, y, w'')
+            if idx == 0 or i == 0:
+                samplein = real_input.clone().detach()
+                sampleout = real_output.clone().detach()
+                samplexl = xl.clone().detach()
+        
+        ###### This part is for training discriminator
+        
+        requires_grad(g_module.encoder, False)
+        requires_grad(g_module.fusion_out, False)
+        requires_grad(g_module.fusion_skip, False)  
+        requires_grad(discriminator, True)
+        
+        fake_output = generator(real_input, xl)
+        fake_pred = discriminator(F.adaptive_avg_pool2d(fake_output, 256))
+        real_pred = discriminator(F.adaptive_avg_pool2d(real_output, 256))
+        
+        # L_adv in Eq.(3)
+        d_loss = d_logistic_loss(real_pred, fake_pred) * args.adv_loss 
+        loss_dict["d"] = d_loss
+        
+        discriminator.zero_grad()
+        d_loss.backward()
+        d_optim.step()    
+        
+        ###### This part is for training generator (encoder and fusion modules)
+        
+        requires_grad(g_module.encoder, True)
+        requires_grad(g_module.fusion_out, True)
+        requires_grad(g_module.fusion_skip, True)    
+        requires_grad(discriminator, False)        
+
+        fake_output = generator(real_input, xl)
+        fake_pred = discriminator(F.adaptive_avg_pool2d(fake_output, 256))
+        # L_adv in Eq.(3)
+        g_loss = g_nonsaturating_loss(fake_pred) * args.adv_loss
+        # L_rec in Eq.(2)
+        grec_loss = F.mse_loss(fake_output, real_output) * args.grec_loss
+        gfeat_loss = percept(F.adaptive_avg_pool2d(fake_output, 512), # 1024 will out of memory
+                             F.adaptive_avg_pool2d(real_output, 512)).sum() * args.perc_loss # 256 will get blurry output
+ 
+        loss_dict["g"] = g_loss
+        loss_dict["gr"] = grec_loss
+        loss_dict["gf"] = gfeat_loss
+
+        w = random.randint(0,1024-896)
+        h = random.randint(0,1024-896)
+        crop_input = torch.cat((real_input1024[:,:,w:w+896,h:h+896], mask[:,:,w:w+896,h:h+896]/16.0), dim=1).detach()
+        crop_input = down(down(crop_input))  
+        crop_fake_output = fake_output[:,:,w:w+896,h:h+896]
+        fake_crop_output = generator(crop_input, xl)
+        # L_tmp in Eq.(4), gradually increase the weight of L_tmp
+        temporal_loss = ((fake_crop_output-crop_fake_output)**2).mean() * max(idx/(args.iter/2.0)-1, 0) * args.tmp_loss
+        loss_dict["tp"] = temporal_loss
+
+        generator.zero_grad()
+        (g_loss + grec_loss + gfeat_loss + temporal_loss).backward() 
+        g_optim.step()        
+        
+        accumulate(g_ema.encoder, g_module.encoder, accum)
+        accumulate(g_ema.fusion_out, g_module.fusion_out, accum)
+        accumulate(g_ema.fusion_skip, g_module.fusion_skip, accum)        
+
+        loss_reduced = reduce_loss_dict(loss_dict)
+
+        d_loss_val = loss_reduced["d"].mean().item()
+        g_loss_val = loss_reduced["g"].mean().item()
+        gr_loss_val = loss_reduced["gr"].mean().item()
+        gf_loss_val = loss_reduced["gf"].mean().item()
+        tmp_loss_val = loss_reduced["tp"].mean().item()
+
+        if get_rank() == 0:
+            pbar.set_description(
+                (
+                    f"iter: {i:d}; advd: {d_loss_val:.3f}; advg: {g_loss_val:.3f}; mse: {gr_loss_val:.3f}; "
+                    f"perc: {gf_loss_val:.3f}; tmp: {tmp_loss_val:.3f}"
+                )
+            )
+
+            if i % args.log_every == 0 or (i+1) == args.iter:
+                with torch.no_grad():
+                    g_ema.eval()
+                    sample = g_ema(samplein, samplexl)
+                    sample = F.interpolate(torch.cat((sampleout, sample), dim=0), 256)
+                    utils.save_image(
+                        sample,
+                        f"log/%s/%05d.jpg"%(args.name, i),
+                        nrow=int(args.batch),
+                        normalize=True,
+                        range=(-1, 1),
+                    )
+
+            if ((i+1) >= args.save_begin and (i+1) % args.save_every == 0) or (i+1) == args.iter:
+                if (i+1) == args.iter:
+                    savename = f"checkpoint/%s/vtoonify.pt"%(args.name)
+                else:
+                    savename = f"checkpoint/%s/vtoonify_%05d.pt"%(args.name, i+1)                
+                torch.save(
+                    {
+                        #"g": g_module.state_dict(),
+                        #"d": d_module.state_dict(),
+                        "g_ema": g_ema.state_dict(),
+                    },
+                    savename,
+                )
+                
+                
+
+if __name__ == "__main__":
+    
+    device = "cuda"
+    parser = TrainOptions()  
+    args = parser.parse()
+    if args.local_rank == 0:
+        print('*'*98)
+        if not os.path.exists("log/%s/"%(args.name)):
+            os.makedirs("log/%s/"%(args.name))
+        if not os.path.exists("checkpoint/%s/"%(args.name)):
+            os.makedirs("checkpoint/%s/"%(args.name))
+        
+    n_gpu = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
+    args.distributed = n_gpu > 1
+
+    if args.distributed:
+        torch.cuda.set_device(args.local_rank)
+        torch.distributed.init_process_group(backend="nccl", init_method="env://")
+        synchronize()
+
+    generator = VToonify(backbone = 'toonify').to(device)
+    generator.apply(weights_init)
+    g_ema = VToonify(backbone = 'toonify').to(device)
+    g_ema.eval()
+
+    basemodel = Generator(1024, 512, 8, 2).to(device) # G0
+    finetunemodel = Generator(1024, 512, 8, 2).to(device) 
+    basemodel.load_state_dict(torch.load(args.stylegan_path, map_location=lambda storage, loc: storage)['g_ema'])
+    finetunemodel.load_state_dict(torch.load(args.finetunegan_path, map_location=lambda storage, loc: storage)['g_ema'])
+    fused_state_dict = blend_models(finetunemodel, basemodel, args.weight) # G1
+    generator.generator.load_state_dict(fused_state_dict) # load G1
+    g_ema.generator.load_state_dict(fused_state_dict)
+    requires_grad(basemodel, False)
+    requires_grad(generator.generator, False)
+    requires_grad(g_ema.generator, False)
+
+    if not args.pretrain:
+        generator.encoder.load_state_dict(torch.load(args.encoder_path, map_location=lambda storage, loc: storage)["g_ema"])
+        # we initialize the fusion modules to map f_G \otimes f_E to f_G.
+        for k in generator.fusion_out:
+            k.weight.data *= 0.01
+            k.weight[:,0:k.weight.shape[0],1,1].data += torch.eye(k.weight.shape[0]).cuda()
+        for k in generator.fusion_skip:
+            k.weight.data *= 0.01
+            k.weight[:,0:k.weight.shape[0],1,1].data += torch.eye(k.weight.shape[0]).cuda()
+
+    accumulate(g_ema.encoder, generator.encoder, 0)
+    accumulate(g_ema.fusion_out, generator.fusion_out, 0)
+    accumulate(g_ema.fusion_skip, generator.fusion_skip, 0) 
+
+    g_parameters = list(generator.encoder.parameters()) 
+    if not args.pretrain:
+        g_parameters = g_parameters + list(generator.fusion_out.parameters()) + list(generator.fusion_skip.parameters())
+
+    g_optim = optim.Adam(
+        g_parameters,
+        lr=args.lr,
+        betas=(0.9, 0.99),
+    )
+
+    if args.distributed:
+        generator = nn.parallel.DistributedDataParallel(
+            generator,
+            device_ids=[args.local_rank],
+            output_device=args.local_rank,
+            broadcast_buffers=False,
+            find_unused_parameters=True,
+        )
+
+    parsingpredictor = BiSeNet(n_classes=19)
+    parsingpredictor.load_state_dict(torch.load(args.faceparsing_path, map_location=lambda storage, loc: storage))
+    parsingpredictor.to(device).eval()
+    requires_grad(parsingpredictor, False)
+
+    # we apply gaussian blur to the images to avoid flickers caused during downsampling
+    down = Downsample(kernel=[1, 3, 3, 1], factor=2).to(device)
+    requires_grad(down, False)
+
+    directions = torch.tensor(np.load(args.direction_path)).to(device) 
+
+    if not args.pretrain:
+        discriminator = ConditionalDiscriminator(256).to(device)
+
+        d_optim = optim.Adam(
+            discriminator.parameters(),
+            lr=args.lr,
+            betas=(0.9, 0.99),
+        )    
+
+        if args.distributed:
+            discriminator = nn.parallel.DistributedDataParallel(
+                discriminator,
+                device_ids=[args.local_rank],
+                output_device=args.local_rank,
+                broadcast_buffers=False,
+                find_unused_parameters=True,
+            )
+
+        percept = lpips.PerceptualLoss(model="net-lin", net="vgg", use_gpu=device.startswith("cuda"), gpu_ids=[args.local_rank])
+        requires_grad(percept.model.net, False)
+
+        pspencoder = load_psp_standalone(args.style_encoder_path, device)  
+    
+    if args.local_rank == 0:
+        print('Load models and data successfully loaded!')
+
+    if args.pretrain:
+        pretrain(args, generator, g_optim, g_ema, parsingpredictor, down, directions, basemodel, device)
+    else:
+        train(args, generator, discriminator, g_optim, d_optim, g_ema, percept, parsingpredictor, down, pspencoder, directions, basemodel, device)