models/pix2pix_model.py

import torch

import models.networks as networks
import util.util as util
import util.logging_wandb as wblogging
import os
import numpy as np
import torch.nn.functional as F
from tqdm import tqdm
from torchvision.utils import save_image
import torch.nn as nn
import torchvision.transforms as transforms
import torch.utils.data as data_torch
from torch.utils.data import Dataset
from .sampler import InfiniteSamplerWrapper,save_checkpoint
from .networks.generator_module.schedule import CosineAnnealingWarmUpLR
import torch.utils.data as data
from rich.progress import Progress, TimeRemainingColumn, BarColumn, TextColumn
import datetime

class Pix2PixModel(torch.nn.Module):
    @staticmethod
    def modify_commandline_options(parser, is_train):
        networks.modify_commandline_options(parser, is_train)
        parser.add_argument('--lrG_decay', type=float, default=1e-4, help='learning rate decay')
        parser.add_argument('--lrG', type=float, default=12e-4, help='initial learning rate for adam')
        parser.add_argument('--save_dir', default=r'/home/share/VAL_ImgTranslations/experiments',
                            help='Directory to save the model')
        parser.add_argument('--content_weight', type=float, default=2.0, help='weight for content reconstruction loss')
        parser.add_argument('--save_model_interval', type=int, default=300)
        parser.add_argument('--style_weight', type=float, default=3.0, help='weight for style reconstruction loss')
        parser.add_argument('--max_iter', type=int, default=700)
        parser.add_argument('--n_threads', type=int, default=1)
        parser.add_argument('--batch_size', type=int, default=1)
        parser.add_argument('--id1_weight', type=float, default=50)
        parser.add_argument('--id2_weight', type=float, default=1)
        parser.add_argument('--loss_count_interval', type=int, default=200)
        return parser

    def __init__(self, opt):
        super().__init__()
        self.opt = opt
        self.loss=0
        self.out=None
        self.content_weight=opt.content_weight
        self.style_weight=opt.style_weight
        self.lrG_decay=opt.lrG_decay
        self.lrG=opt.lrG
        self.loss_count_interval=opt.loss_count_interval
        self.save_model_interval=opt.save_model_interval
        self.n_threads=opt.n_threads
        self.save_dir=opt.save_dir
        self.id1_weight=opt.id1_weight
        self.id2_weight=opt.id2_weight
        self.max_iter=opt.max_iter
        self.epoch=0
        self.batch_size=opt.batch_size
        self.FloatTensor = torch.cuda.FloatTensor if self.use_gpu() \
            else torch.FloatTensor
        self.ByteTensor = torch.cuda.ByteTensor if self.use_gpu() \
            else torch.ByteTensor

        self.netG, self.netD,self.netD2, self.netE = self.initialize_networks(opt)
        self.device="cuda" if torch.cuda.is_available() else "cpu"

        # set loss functions
        if opt.isTrain:
            self.criterionGAN = networks.GANLoss(
                opt.gan_mode, tensor=self.FloatTensor, opt=self.opt)
            self.criterionFeat = torch.nn.L1Loss()
            if not opt.no_vgg_loss:
                self.criterionVGG = networks.VGGLoss(self.opt.gpu_ids)
            if opt.use_vae:
                self.KLDLoss = networks.KLDLoss()

    # Entry point for all calls involving forward pass
    # of deep networks. We used this approach since DataParallel module
    # can't parallelize custom functions, we branch to different
    # routines based on |mode|.
    def forward(self, data, mode,iters,progress,epochs,images_iters):
        input_semantics, real_image = self.preprocess_input(data)
        # print("input_semantics: ", input_semantics)
        # print("real_image: ", real_image.shape)
        if mode == 'generator':
            g_loss, generated = self.compute_generator_loss(input_semantics, real_image,iters=iters,progress=progress,epoch_model=epochs,images_iter=images_iters)
            return g_loss, generated
        elif mode == 'discriminator':
            d_loss, d2_loss = self.compute_discriminator_loss(input_semantics, real_image,iters=iters)
            return d_loss, d2_loss
        elif mode == 'encode_only':
            z, mu, logvar = self.encode_z(real_image)
            return mu, logvar
        elif mode == 'inference':
            contents = F.interpolate(real_image, size=(224, 224), mode='bilinear', align_corners=False)
            styles = F.interpolate(input_semantics, size=(224, 224), mode='bilinear', align_corners=False)
            model = self.generate_fake(contents, styles, iters, mode="gen",progress=None)
            with torch.no_grad():
                self.opt.alpha = self.opt.alpha.to(self.device)
                _, _, _, _, output = model(contents, styles)
                print("OUTPUT",output.shape)
            upsample_layer = nn.Sequential(nn.Upsample(scale_factor=8 / 7, mode='bilinear', align_corners=False))
            fake_image = upsample_layer(output)
            return fake_image
        else:
            raise ValueError("|mode| is invalid")

    def create_optimizers(self, opt):
        G_params = list(self.netG.parameters())
        if opt.use_vae:
            G_params += list(self.netE.parameters())
        if opt.isTrain:
            D_params = list(self.netD.parameters())
            D2_params = list(self.netD2.parameters())

        if opt.no_TTUR:
            beta1, beta2 = opt.beta1, opt.beta2
            G_lr, D_lr = opt.lr, opt.lr
        else:
            beta1, beta2 = 0, 0.9
            G_lr, D_lr = opt.lr / 2, opt.lr * 2

        optimizer_G = torch.optim.Adam(G_params, lr=G_lr, betas=(beta1, beta2))
        optimizer_D = torch.optim.Adam(D_params, lr=D_lr, betas=(beta1, beta2))
        optimizer_D2 = torch.optim.Adam(D2_params, lr=D_lr, betas=(beta1, beta2))

        return optimizer_G, optimizer_D, optimizer_D2

    def save(self, epoch):
        util.save_network(self.netG, 'G', epoch, self.opt)
        util.save_network(self.netD, 'D', epoch, self.opt)
        util.save_network(self.netD2, 'D2', epoch, self.opt)
        if self.opt.use_vae:
            util.save_network(self.netE, 'E', epoch, self.opt)

    ############################################################################
    # Private helper methods
    ############################################################################

    def initialize_networks(self, opt):
        netG = networks.define_G(opt)
        netD = networks.define_D(opt) if opt.isTrain else None
        netD2 = networks.define_D(opt) if opt.isTrain else None
        netE = networks.define_E(opt) if opt.use_vae else None

        if not opt.isTrain or opt.continue_train:
            netG = util.load_network(netG, 'G', opt.which_epoch, opt)
            if opt.isTrain:
                netD = util.load_network(netD, 'D', opt.which_epoch, opt)
                netD2 = util.load_network(netD2, 'D2', opt.which_epoch, opt)
            if opt.use_vae:
                netE = util.load_network(netE, 'E', opt.which_epoch, opt)

        return netG, netD, netD2, netE

    # preprocess the input, such as moving the tensors to GPUs
    # and transforming the label map to one-hot encoding (for SIS)
    # |data|: dictionary of the input data
    def preprocess_input(self, data):
        # move to GPU and change data types
        if self.opt.task == 'SIS':
            data['label'] = data['label'].long()
        if self.use_gpu():
            data['label'] = data['label'].cuda()
            if 'instance' in data:
                try:
                    data['instance'] = data['instance'].cuda()
                except:
                    data['instance'] = data['instance']
            else:
                data['instance'] = None  # or handle as appropriate for your model

            data['image'] = data['image'].cuda()

        # create one-hot label map for SIS
        if self.opt.task == 'SIS':
            label_map = data['label']
            bs, _, h, w = label_map.size()
            nc = self.opt.label_nc + 1 if self.opt.contain_dontcare_label \
                else self.opt.label_nc
            input_label = self.FloatTensor(bs, nc, h, w).zero_()
            input_semantics = input_label.scatter_(1, label_map, 1.0)

            # concatenate instance map if it exists
            if not self.opt.no_instance:
                inst_map = data['instance']
                instance_edge_map = self.get_edges(inst_map)
                input_semantics = torch.cat((input_semantics, instance_edge_map), dim=1)
        else:
            input_semantics = data['label']

        return input_semantics, data['image']

    def compute_generator_loss(self, content, style,iters,progress,epoch_model,images_iter):
        G_losses = {}

        fake_image, loss_StyTr2 = self.generate_fake(content, style,iters=iters,progress=progress,epochs=epoch_model,image_iters=images_iter)
        pred_fake, pred_real_c = self.discriminate(fake_image, content)

        # # computer loss using Discriminator 1
        G_losses['GAN'] = self.criterionGAN(pred_fake, True, for_discriminator=False) * (1 - self.opt.alpha)

        # computer loss using Discriminator 2
        pred_fake, pred_real = self.discriminate(fake_image, style, type='D2')
        G_losses['GAN'] = self.criterionGAN(pred_fake, True, for_discriminator=False) * self.opt.alpha

        if self.opt.task == 'SIS':
            pred_fake, pred_real = self.discriminate(fake_image, style, content)
        else:
            pred_fake, pred_real = self.discriminate(fake_image, style)

        G_losses['GAN'] = self.criterionGAN(pred_fake, True,for_discriminator=False)
        gan_loss_value = G_losses['GAN'].item()
        new_GAN_loss = gan_loss_value + loss_StyTr2
        G_losses['GAN'] = torch.tensor([new_GAN_loss], device=self.device)

        if not self.opt.no_ganFeat_loss:
            num_D = len(pred_fake)
            GAN_Feat_loss = torch.tensor(0.0, dtype=torch.float32, device=self.device)
            for i in range(num_D):  # for each discriminator
                # last output is the final prediction, so we exclude it
                num_intermediate_outputs = len(pred_fake[i]) - 1
                for j in range(num_intermediate_outputs):  # for each layer output
                    unweighted_loss = self.criterionFeat(
                        pred_fake[i][j], pred_real[i][j].detach())
                    GAN_Feat_loss += unweighted_loss * self.opt.lambda_feat / num_D
            G_losses['GAN_Feat'] = GAN_Feat_loss

        if not self.opt.no_vgg_loss:
            target = style if self.opt.task == 'SIS' else content
            G_losses['VGG'] = self.criterionVGG(fake_image, target) * self.opt.lambda_vgg

        return G_losses, fake_image

    def compute_discriminator_loss(self, content, style,iters):
        D_losses = {}
        D2_losses = {}
        contents = F.interpolate(content, size=(224, 224), mode='bilinear', align_corners=False)
        styles = F.interpolate(style, size=(224, 224), mode='bilinear', align_corners=False)
        model=self.generate_fake(contents,styles,iters,mode="gen",progress=None)
        with torch.no_grad():
            _, _, _, _, output=model(contents,styles)
        upsample_layer = nn.Sequential(nn.Upsample(scale_factor=8 / 7, mode='bilinear', align_corners=False))
        fake_image = upsample_layer(output)

        # For Discriminator 1 between fake and content
        pred_fake, pred_real = self.discriminate(fake_image, content)
        D_losses['D_Fake'] = self.criterionGAN(pred_fake, False, for_discriminator=True) * (1 - self.opt.alpha)
        D_losses['D_real'] = self.criterionGAN(pred_real, True, for_discriminator=True)

        pred_fake, pred_real = self.discriminate(fake_image, style, type='D2')
        D2_losses['D_Fake'] = self.criterionGAN(pred_fake, False, for_discriminator=True) * self.opt.alpha
        D2_losses['D_real'] = self.criterionGAN(pred_real, True, for_discriminator=True)

        return D_losses, D2_losses

    def encode_z(self, real_image):
        mu, logvar = self.netE(real_image)
        z = self.reparameterize(mu, logvar)
        return z, mu, logvar

    def custom_epoch(self, curr_index_image):
        if curr_index_image <= 5:
            return self.max_iter
        elif 5 < curr_index_image < 15:
            return int(self.max_iter * 0.6)
        elif 15 <= curr_index_image < 25:
            return int(self.max_iter * 0.55)
        elif 25 <= curr_index_image < 40:
            return int(self.max_iter * 0.5)
        elif 40 <= curr_index_image < 60:
            return int(self.max_iter * 0.375)
        elif 60 <= curr_index_image < 80:
            return int(self.max_iter * 0.25)
        elif 80 <= curr_index_image < 120:
            return int(self.max_iter * 0.1)
        else:
            return int(self.max_iter * 0.05)

    def save_generator_weights_by_iters(self,net, label, epoch,iters):
        util.save_generator_by_iter(net,label,epoch,iters,self.opt)
    def generate_fake(self, input_semantics, real_image, iters,mode="train",progress=None,epochs=None,image_iters=None):
        ### FOR GENERATE-FAKE IMAGES
        if mode == "train":
            models_Generator = self.netG()
            optimizer = torch.optim.Adam([
                {'params': models_Generator.SEencoder.parameters()},
                {'params': models_Generator.decoder.parameters()},
                {'params': models_Generator.transModule.parameters()},
            ], lr=self.lrG_decay)
            scheduler = CosineAnnealingWarmUpLR(optimizer, warmup_step=self.max_iter // 4, max_step=self.max_iter,
                                                min_lr=0)

            contents = real_image.to(self.device)
            style = input_semantics.to(self.device)

            content_images = F.interpolate(contents, size=(224, 224), mode='bilinear', align_corners=False)
            style_images = F.interpolate(style, size=(224, 224), mode='bilinear', align_corners=False)
            total_loss = 0


            # Mở file ghi log
            log_file = open("train_log.txt", "a")

            with progress:
                task = progress.add_task(
                    f"[green]Iters on one Images [{iters}|{self.custom_epoch(iters)}]- Total_Loss: {total_loss}",
                    total=self.custom_epoch(iters)
                )
                total_i = 0
                step_wb=0

                for i in range(self.custom_epoch(curr_index_image=iters)):
                    # Cộng dồn giá trị i vào tổng
                    if (i-self.custom_epoch(curr_index_image=iters)):
                        total_i=i
                        step_wb+=1
                    if (step_wb>=1):
                        total_i=total_i+1


                    loss_c, loss_s, loss_id_1, loss_id_2, out = models_Generator(content_images, style_images)
                    self.out = out
                    loss_all = (self.content_weight * loss_c + self.style_weight * loss_s +
                                self.id1_weight * loss_id_1 + self.id2_weight * loss_id_2)

                    # Tính tổng loss
                    total_loss = round(float(loss_all.sum().cpu().detach().numpy()), 3)
                    print("Loss_model", loss_all.sum().cpu().detach().numpy(), "==>Content_Loss",
                          loss_c.sum().cpu().detach().numpy(),
                          "==>Style_Loss", loss_s.sum().cpu().detach().numpy(), "==>ID_1_Loss",
                          loss_id_1.sum().cpu().detach().numpy(),
                          "==>ID_2_Loss", loss_id_2.sum().cpu().detach().numpy())

                    #### WANDB ONE IMAGES
                    # wblogging.upload_all_loss_on_one_images(loss_value=total_loss, iters_all_images=image_iters,
                    #                                         iters_one_imgs=total_i, epoch=epochs)
                    # wblogging.upload_l1_loss_on_one_images(
                    #     loss_value=round(float(loss_id_1.sum().cpu().detach().numpy()), 3),
                    #     iters_all_images=image_iters, iters_one_imgs=total_i, epoch=epochs)
                    # wblogging.upload_l2_loss_on_one_images(
                    #     loss_value=round(float(loss_id_2.sum().cpu().detach().numpy()), 3),
                    #     iters_all_images=image_iters, iters_one_imgs=total_i, epoch=epochs)
                    # wblogging.upload_content_loss_on_one_images(
                    #     loss_value=round(float(loss_c.sum().cpu().detach().numpy()), 3),
                    #     iters_all_images=image_iters, iters_one_imgs=total_i, epoch=epochs)
                    # wblogging.upload_style_loss_on_one_images(
                    #     loss_value=round(float(loss_s.sum().cpu().detach().numpy()), 3),
                    #     iters_all_images=image_iters, iters_one_imgs=total_i, epoch=epochs)

                    current_lr = optimizer.param_groups[0]['lr']
                    wblogging.upload_lr(current_lr, epoch=epochs)

                    ### END LOGGING ON ONE IMAGES

                    # Lấy thời gian hiện tại với format [ss/mm/hh/dd/mm/yyyy]
                    current_time = datetime.datetime.now().strftime("[%S-%M-%H-%d-%m-%Y]")

                    # Ghi log vào file
                    log_file.write(
                        f"{current_time} Iter: {i}, Total Loss: {total_loss}, Content Loss: {loss_c.sum().cpu().detach().numpy()}, "
                        f"Style Loss: {loss_s.sum().cpu().detach().numpy()}, ID1 Loss: {loss_id_1.sum().cpu().detach().numpy()}, "
                        f"ID2 Loss: {loss_id_2.sum().cpu().detach().numpy()}\n")


                    progress.update(task, advance=1,
                                    description=f"[green]Iters on one Images [{i + 1}|{self.custom_epoch(iters)}]- Total_Loss: {total_loss}")

                    # Update parameters
                    optimizer.zero_grad()
                    loss_all.backward()
                    optimizer.step()
                    scheduler.step()

                    # SAVE Weight by Iters
                    if ((i + 1) - (self.custom_epoch(curr_index_image=iters))) == 0:
                        self.save_generator_weights_by_iters(self.netG, 'G', epoch=self.epoch, iters=i + 1)

                    if i % 100 == 0:
                        output_name = f'{self.save_dir}/test/{str(i)}.jpg'
                        out = torch.cat((content_images, out), 0)
                        out = torch.cat((style_images, out), 0)
                        save_image(out, output_name)

            # Đóng file log sau khi kết thúc
            log_file.close()

            # #### WANDB ALL IMAGES
            # wblogging.upload_loss_all(loss_all=total_loss, iters=image_iters, epoch=epochs)
            # wblogging.upload_l1_loss_all(loss_values=round(float(loss_id_1.sum().cpu().detach().numpy()), 3),
            #                              iters_images=image_iters, epoch=epochs)
            # wblogging.upload_l2_loss(loss_values=round(float(loss_id_2.sum().cpu().detach().numpy()), 3),
            #                              iters=image_iters, epoch=epochs)
            # wblogging.upload_content_loss(loss_c=round(float(loss_c.sum().cpu().detach().numpy()), 3),
            #                          iters=image_iters, epoch=epochs)
            # wblogging.upload_style_loss(loss_s=round(float(loss_id_2.sum().cpu().detach().numpy()), 3),
            #                          iters=image_iters, epoch=epochs)
            ### END WANDB ON ALL IMAGES

            upsample_layer = nn.Sequential(nn.Upsample(scale_factor=8 / 7, mode='bilinear', align_corners=False))
            out = upsample_layer(self.out)

            return out, self.loss

        # FOR GENERATOR IMAGES - VAL
        if(mode=="gen"):
            models_Generator = self.netG()
            models_Generator.eval()
            models_Generator.to(self.device)
            return models_Generator


    # Given fake and real image, return the prediction of discriminator
    # for each fake and real image. The condition is used in SIS.
    def discriminate(self, fake_image, real_image, condition=None,type='D1'):
        if self.opt.task == 'SIS':
            assert condition is not None
            fake_concat = torch.cat([condition, fake_image], dim=1)
            real_concat = torch.cat([condition, real_image], dim=1)
        else:
            assert condition is None
            fake_concat = fake_image
            real_concat = real_image

        # In Batch Normalization, the fake and real images are
        # recommended to be in the same batch to avoid disparate
        # statistics in fake and real images.
        # So both fake and real images are fed to D all at once.
        fake_and_real = torch.cat([fake_concat, real_concat], dim=0)

        if type == 'D1':
            discriminator_out = self.netD(fake_and_real)
        else:
            discriminator_out = self.netD2(fake_and_real)

        pred_fake, pred_real = self.divide_pred(discriminator_out)

        return pred_fake, pred_real

    # Take the prediction of fake and real images from the combined batch
    def divide_pred(self, pred):
        # the prediction contains the intermediate outputs of multi-scale GAN,
        # so it's usually a list
        if type(pred) == list:
            fake = []
            real = []
            for p in pred:
                fake.append([tensor[:tensor.size(0) // 2] for tensor in p])
                real.append([tensor[tensor.size(0) // 2:] for tensor in p])
        else:
            fake = pred[:pred.size(0) // 2]
            real = pred[pred.size(0) // 2:]

        return fake, real

    def get_edges(self, t):
        edge = self.ByteTensor(t.size()).zero_()
        edge[:, :, :, 1:] = edge[:, :, :, 1:] | (t[:, :, :, 1:] != t[:, :, :, :-1])
        edge[:, :, :, :-1] = edge[:, :, :, :-1] | (t[:, :, :, 1:] != t[:, :, :, :-1])
        edge[:, :, 1:, :] = edge[:, :, 1:, :] | (t[:, :, 1:, :] != t[:, :, :-1, :])
        edge[:, :, :-1, :] = edge[:, :, :-1, :] | (t[:, :, 1:, :] != t[:, :, :-1, :])
        return edge.float()

    def reparameterize(self, mu, logvar):
        std = torch.exp(0.5 * logvar)
        eps = torch.randn_like(std)
        return eps.mul(std) + mu

    def use_gpu(self):
        return len(self.opt.gpu_ids) > 0