autovc/solver_encoder.py at master · nicolalandro/autovc · GitHub

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from model_vc import Generator
import torch
import torch.nn.functional as F
import time
import datetime


class Solver(object):

    def __init__(self, vcc_loader, config):
        """Initialize configurations."""

        # Data loader.
        self.vcc_loader = vcc_loader

        # Model configurations.
        self.lambda_cd = config.lambda_cd
        self.dim_neck = config.dim_neck
        self.dim_emb = config.dim_emb
        self.dim_pre = config.dim_pre
        self.freq = config.freq

        # load and save path configurations.
        self.pretrained = config.pretrained
        self.outfile_path = config.outfile_path

        # Training configurations.
        self.batch_size = config.batch_size
        self.num_iters = config.num_iters

        # Miscellaneous.
        self.use_cuda = torch.cuda.is_available()
        self.device = torch.device('cuda:0' if self.use_cuda else 'cpu')
        self.log_step = config.log_step

        # Build the model and tensorboard.
        self.build_model()


    def build_model(self):
        self.G = Generator(self.dim_neck, self.dim_emb, self.dim_pre, self.freq)
        if self.pretrained:
            print('load pretrained...')
            g_checkpoint = torch.load(self.pretrained, map_location='cpu')
            self.G.load_state_dict(g_checkpoint['model'])
        self.g_optimizer = torch.optim.Adam(self.G.parameters(), 0.0001)

        self.G.to(self.device)


    def reset_grad(self):
        """Reset the gradient buffers."""
        self.g_optimizer.zero_grad()


    #=====================================================================================================================================#


    def train(self):
        # Set data loader.
        data_loader = self.vcc_loader

        # Print logs in specified order
        keys = ['G/loss_id','G/loss_id_psnt','G/loss_cd']

        # Start training.
        print('Start training...')
        start_time = time.time()
        for i in range(self.num_iters):

            # =================================================================================== #
            #                             1. Preprocess input data                                #
            # =================================================================================== #

            # Fetch data.
            try:
                x_real, emb_org = next(data_iter)
            except:
                data_iter = iter(data_loader)
                x_real, emb_org = next(data_iter)


            x_real = x_real.to(self.device)
            emb_org = emb_org.to(self.device)


            # =================================================================================== #
            #                               2. Train the generator                                #
            # =================================================================================== #

            self.G = self.G.train()

            # Identity mapping loss
            x_identic, x_identic_psnt, code_real = self.G(x_real, emb_org, emb_org)
            g_loss_id = F.mse_loss(x_real, x_identic.squeeze(1))
            g_loss_id_psnt = F.mse_loss(x_real, x_identic_psnt.squeeze(1))

            # Code semantic loss.
            code_reconst = self.G(x_identic_psnt, emb_org, None)
            g_loss_cd = F.l1_loss(code_real, code_reconst)


            # Backward and optimize.
            g_loss = g_loss_id + g_loss_id_psnt + self.lambda_cd * g_loss_cd
            self.reset_grad()
            g_loss.backward()
            self.g_optimizer.step()

            # Logging.
            loss = {}
            loss['G/loss_id'] = g_loss_id.item()
            loss['G/loss_id_psnt'] = g_loss_id_psnt.item()
            loss['G/loss_cd'] = g_loss_cd.item()

            # =================================================================================== #
            #                                 4. Miscellaneous                                    #
            # =================================================================================== #

            # Print out training information.
            if (i+1) % self.log_step == 0:
                et = time.time() - start_time
                et = str(datetime.timedelta(seconds=et))[:-7]
                log = "Elapsed [{}], Iteration [{}/{}]".format(et, i+1, self.num_iters)
                for tag in keys:
                    log += ", {}: {:.4f}".format(tag, loss[tag])
                print(log)
                print('\tsaving model...')
                torch.save(self.G.state_dict(), self.outfile_path + f'.tmp')
        print('saving model...')
        torch.save(self.G.state_dict(), self.outfile_path)