Python summary.scalar方法代码示例

# 需要导入模块: from tensorboard import summary [as 别名]
# 或者: from tensorboard.summary import scalar [as 别名]
def test_event_logging():
    logdir = './experiment/'
    summary_writer = FileWriter(logdir)
    scalar_value = 1.0
    s = scalar('test_scalar', scalar_value)
    summary_writer.add_summary(s, global_step=1)
    summary_writer.close()
    assert os.path.isdir(logdir)
    assert len(os.listdir(logdir)) == 1

    summary_writer = FileWriter(logdir)
    scalar_value = 1.0
    s = scalar('test_scalar', scalar_value)
    summary_writer.add_summary(s, global_step=1)
    summary_writer.close()
    assert os.path.isdir(logdir)
    assert len(os.listdir(logdir)) == 2

    # clean up.
    shutil.rmtree(logdir) 

# 需要导入模块: from tensorboard import summary [as 别名]
# 或者: from tensorboard.summary import scalar [as 别名]
def train_Dnet(self, idx, count):
        flag = count % 100
        batch_size = self.real_imgs[0].size(0)
        criterion = self.criterion

        netD, optD = self.netsD[idx], self.optimizersD[idx]
        real_imgs = self.real_imgs[idx]
        fake_imgs = self.fake_imgs[idx]
        real_labels = self.real_labels[:batch_size]
        fake_labels = self.fake_labels[:batch_size]
        #
        netD.zero_grad()
        #
        real_logits = netD(real_imgs)
        fake_logits = netD(fake_imgs.detach())
        #
        errD_real = criterion(real_logits[0], real_labels)
        errD_fake = criterion(fake_logits[0], fake_labels)
        #
        errD = errD_real + errD_fake
        errD.backward()
        # update parameters
        optD.step()
        # log
        if flag == 0:
            summary_D = summary.scalar('D_loss%d' % idx, errD.data[0])
            self.summary_writer.add_summary(summary_D, count)
        return errD 

# 需要导入模块: from tensorboard import summary [as 别名]
# 或者: from tensorboard.summary import scalar [as 别名]
def test_log_scalar_summary():
    logdir = './experiment/scalar'
    writer = FileWriter(logdir)
    for i in range(10):
        s = scalar('scalar', i)
        writer.add_summary(s, i+1)
    writer.flush()
    writer.close() 

# 需要导入模块: from tensorboard import summary [as 别名]
# 或者: from tensorboard.summary import scalar [as 别名]
def test_scalar_summary():
    scalar_value = 1.0
    s = scalar('test_scalar', scalar_value)
    values = s.value
    assert len(values) == 1
    assert values[0].tag == 'test_scalar'
    assert values[0].simple_value == 1.0

    byte_str = s.SerializeToString()
    s_recovered = summary_pb2.Summary()
    s_recovered.ParseFromString(byte_str)
    assert values[0].tag == s_recovered.value[0].tag
    assert values[0].simple_value == s_recovered.value[0].simple_value 

# 需要导入模块: from tensorboard import summary [as 别名]
# 或者: from tensorboard.summary import scalar [as 别名]
def test_log_scalar_summary():
    logdir = './experiment/scalar'
    writer = SummaryWriter(logdir)
    for i in range(10):
        writer.add_scalar('test_scalar', i+1)
    writer.close() 

# 需要导入模块: from tensorboard import summary [as 别名]
# 或者: from tensorboard.summary import scalar [as 别名]
def train_Gnet(self, count):
        self.netG.zero_grad()
        errG_total = 0
        flag = count % 100
        batch_size = self.real_imgs[0].size(0)
        criterion = self.criterion
        real_labels = self.real_labels[:batch_size]

        for i in range(self.num_Ds):
            netD = self.netsD[i]
            outputs = netD(self.fake_imgs[i])
            errG = criterion(outputs[0], real_labels)
            # errG = self.stage_coeff[i] * errG
            errG_total = errG_total + errG
            if flag == 0:
                summary_G = summary.scalar('G_loss%d' % i, errG.data[0])
                self.summary_writer.add_summary(summary_G, count)

        # Compute color preserve losses
        if cfg.TRAIN.COEFF.COLOR_LOSS > 0:
            if self.num_Ds > 1:
                mu1, covariance1 = compute_mean_covariance(self.fake_imgs[-1])
                mu2, covariance2 = \
                    compute_mean_covariance(self.fake_imgs[-2].detach())
                like_mu2 = cfg.TRAIN.COEFF.COLOR_LOSS * nn.MSELoss()(mu1, mu2)
                like_cov2 = cfg.TRAIN.COEFF.COLOR_LOSS * 5 * \
                    nn.MSELoss()(covariance1, covariance2)
                errG_total = errG_total + like_mu2 + like_cov2
            if self.num_Ds > 2:
                mu1, covariance1 = compute_mean_covariance(self.fake_imgs[-2])
                mu2, covariance2 = \
                    compute_mean_covariance(self.fake_imgs[-3].detach())
                like_mu1 = cfg.TRAIN.COEFF.COLOR_LOSS * nn.MSELoss()(mu1, mu2)
                like_cov1 = cfg.TRAIN.COEFF.COLOR_LOSS * 5 * \
                    nn.MSELoss()(covariance1, covariance2)
                errG_total = errG_total + like_mu1 + like_cov1

            if flag == 0:
                sum_mu = summary.scalar('G_like_mu2', like_mu2.data[0])
                self.summary_writer.add_summary(sum_mu, count)
                sum_cov = summary.scalar('G_like_cov2', like_cov2.data[0])
                self.summary_writer.add_summary(sum_cov, count)
                if self.num_Ds > 2:
                    sum_mu = summary.scalar('G_like_mu1', like_mu1.data[0])
                    self.summary_writer.add_summary(sum_mu, count)
                    sum_cov = summary.scalar('G_like_cov1', like_cov1.data[0])
                    self.summary_writer.add_summary(sum_cov, count)

        errG_total.backward()
        self.optimizerG.step()
        return errG_total 

# 需要导入模块: from tensorboard import summary [as 别名]
# 或者: from tensorboard.summary import scalar [as 别名]
def train_Gnet(self, count):
        self.netG.zero_grad()
        errG_total = 0
        flag = count % 100
        batch_size = self.real_imgs[0].size(0)
        criterion, mu, logvar = self.criterion, self.mu, self.logvar
        real_labels = self.real_labels[:batch_size]
        for i in range(self.num_Ds):
            outputs = self.netsD[i](self.fake_imgs[i], mu)
            errG = criterion(outputs[0], real_labels)
            if len(outputs) > 1 and cfg.TRAIN.COEFF.UNCOND_LOSS > 0:
                errG_patch = cfg.TRAIN.COEFF.UNCOND_LOSS *\
                    criterion(outputs[1], real_labels)
                errG = errG + errG_patch
            errG_total = errG_total + errG
            if flag == 0:
                summary_D = summary.scalar('G_loss%d' % i, errG.data[0])
                self.summary_writer.add_summary(summary_D, count)

        # Compute color consistency losses
        if cfg.TRAIN.COEFF.COLOR_LOSS > 0:
            if self.num_Ds > 1:
                mu1, covariance1 = compute_mean_covariance(self.fake_imgs[-1])
                mu2, covariance2 = \
                    compute_mean_covariance(self.fake_imgs[-2].detach())
                like_mu2 = cfg.TRAIN.COEFF.COLOR_LOSS * nn.MSELoss()(mu1, mu2)
                like_cov2 = cfg.TRAIN.COEFF.COLOR_LOSS * 5 * \
                    nn.MSELoss()(covariance1, covariance2)
                errG_total = errG_total + like_mu2 + like_cov2
                if flag == 0:
                    sum_mu = summary.scalar('G_like_mu2', like_mu2.data[0])
                    self.summary_writer.add_summary(sum_mu, count)
                    sum_cov = summary.scalar('G_like_cov2', like_cov2.data[0])
                    self.summary_writer.add_summary(sum_cov, count)
            if self.num_Ds > 2:
                mu1, covariance1 = compute_mean_covariance(self.fake_imgs[-2])
                mu2, covariance2 = \
                    compute_mean_covariance(self.fake_imgs[-3].detach())
                like_mu1 = cfg.TRAIN.COEFF.COLOR_LOSS * nn.MSELoss()(mu1, mu2)
                like_cov1 = cfg.TRAIN.COEFF.COLOR_LOSS * 5 * \
                    nn.MSELoss()(covariance1, covariance2)
                errG_total = errG_total + like_mu1 + like_cov1
                if flag == 0:
                    sum_mu = summary.scalar('G_like_mu1', like_mu1.data[0])
                    self.summary_writer.add_summary(sum_mu, count)
                    sum_cov = summary.scalar('G_like_cov1', like_cov1.data[0])
                    self.summary_writer.add_summary(sum_cov, count)

        kl_loss = KL_loss(mu, logvar) * cfg.TRAIN.COEFF.KL
        errG_total = errG_total + kl_loss
        errG_total.backward()
        self.optimizerG.step()
        return kl_loss, errG_total