tutorials/application/source_zh_cn/generative/pix2pix.ipynb · MindSpore/docs - Gitee.com
Pix2Pix概述
Pix2Pix是基于条件生成对抗网络（cGAN, Condition Generative Adversarial Networks ）实现的一种深度学习图像转换模型，该模型是由Phillip Isola等作者在2017年CVPR上提出的，可以实现语义/标签到真实图片、灰度图到彩色图、航空图到地图、白天到黑夜、线稿图到实物图的转换。Pix2Pix是将cGAN应用于有监督的图像到图像翻译的经典之作，其包括两个模型：生成器和判别器。
传统上，尽管此类任务的目标都是相同的从像素预测像素，但每项都是用单独的专用机器来处理的。而Pix2Pix使用的网络作为一个通用框架，使用相同的架构和目标，只在不同的数据上进行训练，即可得到令人满意的结果，鉴于此许多人已经使用此网络发布了他们自己的艺术作品。
基础原理
cGAN的生成器与传统GAN的生成器在原理上有一些区别，cGAN的生成器是将输入图片作为指导信息，由输入图像不断尝试生成用于迷惑判别器的“假”图像，由输入图像转换输出为相应“假”图像的本质是从像素到另一个像素的映射，而传统GAN的生成器是基于一个给定的随机噪声生成图像，输出图像通过其他约束条件控制生成，这是cGAN和GAN的在图像翻译任务中的差异。Pix2Pix中判别器的任务是判断从生成器输出的图像是真实的训练图像还是生成的“假”图像。在生成器与判别器的不断博弈过程中，模型会达到一个平衡点，生成器输出的图像与真实训练数据使得判别器刚好具有50%的概率判断正确。
在教程开始前，首先定义一些在整个过程中需要用到的符号：
- 𝑥：代表观测图像的数据。
- 𝑧：代表随机噪声的数据。
- 𝑦=𝐺(𝑥,𝑧)：生成器网络，给出由观测图像𝑥与随机噪声𝑧生成的“假”图片，其中𝑥来自于训练数据而非生成器。
- 𝐷(𝑥,𝐺(𝑥,𝑧))：判别器网络，给出图像判定为真实图像的概率，其中𝑥来自于训练数据，𝐺(𝑥,𝑧)来自于生成器。
cGAN的目标可以表示为：
𝐿𝑐𝐺𝐴𝑁(𝐺,𝐷)=𝐸(𝑥,𝑦)[𝑙𝑜𝑔(𝐷(𝑥,𝑦))]+𝐸(𝑥,𝑧)[𝑙𝑜𝑔(1−𝐷(𝑥,𝐺(𝑥,𝑧)))]
该公式是cGAN的损失函数，D想要尽最大努力去正确分类真实图像与“假”图像，也就是使参数𝑙𝑜𝑔𝐷(𝑥,𝑦)最大化；而G则尽最大努力用生成的“假”图像𝑦欺骗D，避免被识破，也就是使参数𝑙𝑜𝑔(1−𝐷(𝐺(𝑥,𝑧)))最小化。cGAN的目标可简化为：
为了对比cGAN和GAN的不同，我们将GAN的目标也进行了说明：
𝐿𝐺𝐴𝑁(𝐺,𝐷)=𝐸𝑦[𝑙𝑜𝑔(𝐷(𝑦))]+𝐸(𝑥,𝑧)[𝑙𝑜𝑔(1−𝐷(𝑥,𝑧))]
从公式可以看出，GAN直接由随机噪声𝑧z生成“假”图像，不借助观测图像𝑥x的任何信息。过去的经验告诉我们，GAN与传统损失混合使用是有好处的，判别器的任务不变，依旧是区分真实图像与“假”图像，但是生成器的任务不仅要欺骗判别器，还要在传统损失的基础上接近训练数据。假设cGAN与L1正则化混合使用，那么有:
𝐿𝐿1(𝐺)=𝐸(𝑥,𝑦,𝑧)[||𝑦−𝐺(𝑥,𝑧)||1]
进而得到最终目标：
𝑎𝑟𝑔min𝐺max𝐷𝐿𝑐𝐺𝐴𝑁(𝐺,𝐷)+𝜆𝐿𝐿1(𝐺)
图像转换问题本质上其实就是像素到像素的映射问题，Pix2Pix使用完全一样的网络结构和目标函数，仅更换不同的训练数据集就能分别实现以上的任务。本任务将借助MindSpore框架来实现Pix2Pix的应用。
准备环节
配置环境文件
本案例在GPU，CPU和Ascend平台的动静态模式都支持。
准备数据
在本教程中，我们将使用指定数据集，该数据集是已经经过处理的外墙（facades）数据，可以直接使用mindspore.dataset的方法读取。
%%capture captured_output
# 实验环境已经预装了mindspore==2.3.0，如需更换mindspore版本，可更改下面 MINDSPORE_VERSION 变量
!pip uninstall mindspore -y
%env MINDSPORE_VERSION=2.3.0
!pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/${MINDSPORE_VERSION}/MindSpore/unified/aarch64/mindspore-${MINDSPORE_VERSION}-cp39-cp39-linux_aarch64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.mirrors.ustc.edu.cn/simple# 查看当前 mindspore 版本
!pip show mindsporeName: mindspore Version: 2.3.0 Summary: MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios. Home-page: https://www.mindspore.cn Author: The MindSpore Authors Author-email: contact@mindspore.cn License: Apache 2.0 Location: /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages Requires: asttokens, astunparse, numpy, packaging, pillow, protobuf, psutil, scipy Required-by:
from download import downloadurl = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/models/application/dataset_pix2pix.tar"download(url, "./dataset", kind="tar", replace=True)Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/models/application/dataset_pix2pix.tar (840.0 MB)file_sizes: 100%|█████████████████████████████| 881M/881M [00:05<00:00, 175MB/s] Extracting tar file... Successfully downloaded / unzipped to ./dataset[3]:
'./dataset'
数据展示
调用Pix2PixDataset和create_train_dataset读取训练集，这里我们直接下载已经处理好的数据集。
from mindspore import dataset as ds
import matplotlib.pyplot as pltdataset = ds.MindDataset("./dataset/dataset_pix2pix/train.mindrecord", columns_list=["input_images", "target_images"], shuffle=True)
data_iter = next(dataset.create_dict_iterator(output_numpy=True))
# 可视化部分训练数据
plt.figure(figsize=(10, 3), dpi=140)
for i, image in enumerate(data_iter['input_images'][:10], 1):plt.subplot(3, 10, i)plt.axis("off")plt.imshow((image.transpose(1, 2, 0) + 1) / 2)
plt.show()
创建网络
当处理完数据后，就可以来进行网络的搭建了。网络搭建将逐一详细讨论生成器、判别器和损失函数。生成器G用到的是U-Net结构，输入的轮廓图𝑥x编码再解码成真是图片，判别器D用到的是作者自己提出来的条件判别器PatchGAN，判别器D的作用是在轮廓图 𝑥x的条件下，对于生成的图片𝐺(𝑥)G(x)判断为假，对于真实判断为真。
生成器G结构
U-Net是德国Freiburg大学模式识别和图像处理组提出的一种全卷积结构。它分为两个部分，其中左侧是由卷积和降采样操作组成的压缩路径，右侧是由卷积和上采样组成的扩张路径，扩张的每个网络块的输入由上一层上采样的特征和压缩路径部分的特征拼接而成。网络模型整体是一个U形的结构，因此被叫做U-Net。和常见的先降采样到低维度，再升采样到原始分辨率的编解码结构的网络相比，U-Net的区别是加入skip-connection，对应的feature maps和decode之后的同样大小的feature maps按通道拼一起，用来保留不同分辨率下像素级的细节信息。
定义UNet Skip Connection Block
import mindspore
import mindspore.nn as nn
import mindspore.ops as opsclass UNetSkipConnectionBlock(nn.Cell):def __init__(self, outer_nc, inner_nc, in_planes=None, dropout=False,submodule=None, outermost=False, innermost=False, alpha=0.2, norm_mode='batch'):super(UNetSkipConnectionBlock, self).__init__()down_norm = nn.BatchNorm2d(inner_nc)up_norm = nn.BatchNorm2d(outer_nc)use_bias = Falseif norm_mode == 'instance':down_norm = nn.BatchNorm2d(inner_nc, affine=False)up_norm = nn.BatchNorm2d(outer_nc, affine=False)use_bias = Trueif in_planes is None:in_planes = outer_ncdown_conv = nn.Conv2d(in_planes, inner_nc, kernel_size=4,stride=2, padding=1, has_bias=use_bias, pad_mode='pad')down_relu = nn.LeakyReLU(alpha)up_relu = nn.ReLU()if outermost:up_conv = nn.Conv2dTranspose(inner_nc * 2, outer_nc,kernel_size=4, stride=2,padding=1, pad_mode='pad')down = [down_conv]up = [up_relu, up_conv, nn.Tanh()]model = down + [submodule] + upelif innermost:up_conv = nn.Conv2dTranspose(inner_nc, outer_nc,kernel_size=4, stride=2,padding=1, has_bias=use_bias, pad_mode='pad')down = [down_relu, down_conv]up = [up_relu, up_conv, up_norm]model = down + upelse:up_conv = nn.Conv2dTranspose(inner_nc * 2, outer_nc,kernel_size=4, stride=2,padding=1, has_bias=use_bias, pad_mode='pad')down = [down_relu, down_conv, down_norm]up = [up_relu, up_conv, up_norm]model = down + [submodule] + upif dropout:model.append(nn.Dropout(p=0.5))self.model = nn.SequentialCell(model)self.skip_connections = not outermostdef construct(self, x):out = self.model(x)if self.skip_connections:out = ops.concat((out, x), axis=1)return out基于UNet的生成器
class UNetGenerator(nn.Cell):def __init__(self, in_planes, out_planes, ngf=64, n_layers=8, norm_mode='bn', dropout=False):super(UNetGenerator, self).__init__()unet_block = UNetSkipConnectionBlock(ngf * 8, ngf * 8, in_planes=None, submodule=None,norm_mode=norm_mode, innermost=True)for _ in range(n_layers - 5):unet_block = UNetSkipConnectionBlock(ngf * 8, ngf * 8, in_planes=None, submodule=unet_block,norm_mode=norm_mode, dropout=dropout)unet_block = UNetSkipConnectionBlock(ngf * 4, ngf * 8, in_planes=None, submodule=unet_block,norm_mode=norm_mode)unet_block = UNetSkipConnectionBlock(ngf * 2, ngf * 4, in_planes=None, submodule=unet_block,norm_mode=norm_mode)unet_block = UNetSkipConnectionBlock(ngf, ngf * 2, in_planes=None, submodule=unet_block,norm_mode=norm_mode)self.model = UNetSkipConnectionBlock(out_planes, ngf, in_planes=in_planes, submodule=unet_block,outermost=True, norm_mode=norm_mode)def construct(self, x):return self.model(x)原始cGAN的输入是条件x和噪声z两种信息，这里的生成器只使用了条件信息，因此不能生成多样性的结果。因此Pix2Pix在训练和测试时都使用了dropout，这样可以生成多样性的结果。
基于PatchGAN的判别器
判别器使用的PatchGAN结构，可看做卷积。生成的矩阵中的每个点代表原图的一小块区域（patch）。通过矩阵中的各个值来判断原图中对应每个Patch的真假。
import mindspore.nn as nnclass ConvNormRelu(nn.Cell):def __init__(self,in_planes,out_planes,kernel_size=4,stride=2,alpha=0.2,norm_mode='batch',pad_mode='CONSTANT',use_relu=True,padding=None):super(ConvNormRelu, self).__init__()norm = nn.BatchNorm2d(out_planes)if norm_mode == 'instance':norm = nn.BatchNorm2d(out_planes, affine=False)has_bias = (norm_mode == 'instance')if not padding:padding = (kernel_size - 1) // 2if pad_mode == 'CONSTANT':conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, pad_mode='pad',has_bias=has_bias, padding=padding)layers = [conv, norm]else:paddings = ((0, 0), (0, 0), (padding, padding), (padding, padding))pad = nn.Pad(paddings=paddings, mode=pad_mode)conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, pad_mode='pad', has_bias=has_bias)layers = [pad, conv, norm]if use_relu:relu = nn.ReLU()if alpha > 0:relu = nn.LeakyReLU(alpha)layers.append(relu)self.features = nn.SequentialCell(layers)def construct(self, x):output = self.features(x)return outputclass Discriminator(nn.Cell):def __init__(self, in_planes=3, ndf=64, n_layers=3, alpha=0.2, norm_mode='batch'):super(Discriminator, self).__init__()kernel_size = 4layers = [nn.Conv2d(in_planes, ndf, kernel_size, 2, pad_mode='pad', padding=1),nn.LeakyReLU(alpha)]nf_mult = ndffor i in range(1, n_layers):nf_mult_prev = nf_multnf_mult = min(2 ** i, 8) * ndflayers.append(ConvNormRelu(nf_mult_prev, nf_mult, kernel_size, 2, alpha, norm_mode, padding=1))nf_mult_prev = nf_multnf_mult = min(2 ** n_layers, 8) * ndflayers.append(ConvNormRelu(nf_mult_prev, nf_mult, kernel_size, 1, alpha, norm_mode, padding=1))layers.append(nn.Conv2d(nf_mult, 1, kernel_size, 1, pad_mode='pad', padding=1))self.features = nn.SequentialCell(layers)def construct(self, x, y):x_y = ops.concat((x, y), axis=1)output = self.features(x_y)return outputPix2Pix的生成器和判别器初始化
实例化Pix2Pix生成器和判别器。
import mindspore.nn as nn
from mindspore.common import initializer as initg_in_planes = 3
g_out_planes = 3
g_ngf = 64
g_layers = 8
d_in_planes = 6
d_ndf = 64
d_layers = 3
alpha = 0.2
init_gain = 0.02
init_type = 'normal'net_generator = UNetGenerator(in_planes=g_in_planes, out_planes=g_out_planes,ngf=g_ngf, n_layers=g_layers)
for _, cell in net_generator.cells_and_names():if isinstance(cell, (nn.Conv2d, nn.Conv2dTranspose)):if init_type == 'normal':cell.weight.set_data(init.initializer(init.Normal(init_gain), cell.weight.shape))elif init_type == 'xavier':cell.weight.set_data(init.initializer(init.XavierUniform(init_gain), cell.weight.shape))elif init_type == 'constant':cell.weight.set_data(init.initializer(0.001, cell.weight.shape))else:raise NotImplementedError('initialization method [%s] is not implemented' % init_type)elif isinstance(cell, nn.BatchNorm2d):cell.gamma.set_data(init.initializer('ones', cell.gamma.shape))cell.beta.set_data(init.initializer('zeros', cell.beta.shape))net_discriminator = Discriminator(in_planes=d_in_planes, ndf=d_ndf,alpha=alpha, n_layers=d_layers)
for _, cell in net_discriminator.cells_and_names():if isinstance(cell, (nn.Conv2d, nn.Conv2dTranspose)):if init_type == 'normal':cell.weight.set_data(init.initializer(init.Normal(init_gain), cell.weight.shape))elif init_type == 'xavier':cell.weight.set_data(init.initializer(init.XavierUniform(init_gain), cell.weight.shape))elif init_type == 'constant':cell.weight.set_data(init.initializer(0.001, cell.weight.shape))else:raise NotImplementedError('initialization method [%s] is not implemented' % init_type)elif isinstance(cell, nn.BatchNorm2d):cell.gamma.set_data(init.initializer('ones', cell.gamma.shape))cell.beta.set_data(init.initializer('zeros', cell.beta.shape))class Pix2Pix(nn.Cell):"""Pix2Pix模型网络"""def __init__(self, discriminator, generator):super(Pix2Pix, self).__init__(auto_prefix=True)self.net_discriminator = discriminatorself.net_generator = generatordef construct(self, reala):fakeb = self.net_generator(reala)return fakeb训练
训练分为两个主要部分：训练判别器和训练生成器。训练判别器的目的是最大程度地提高判别图像真伪的概率。训练生成器是希望能产生更好的虚假图像。在这两个部分中，分别获取训练过程中的损失，并在每个周期结束时进行统计。
下面进行训练：
%%time
import numpy as np
import os
import datetime
from mindspore import value_and_grad, Tensorepoch_num = 100
ckpt_dir = "results/ckpt"
dataset_size = 400
val_pic_size = 256
lr = 0.0002
n_epochs = 100
n_epochs_decay = 100def get_lr():lrs = [lr] * dataset_size * n_epochslr_epoch = 0for epoch in range(n_epochs_decay):lr_epoch = lr * (n_epochs_decay - epoch) / n_epochs_decaylrs += [lr_epoch] * dataset_sizelrs += [lr_epoch] * dataset_size * (epoch_num - n_epochs_decay - n_epochs)return Tensor(np.array(lrs).astype(np.float32))dataset = ds.MindDataset("./dataset/dataset_pix2pix/train.mindrecord", columns_list=["input_images", "target_images"], shuffle=True, num_parallel_workers=1)
steps_per_epoch = dataset.get_dataset_size()
loss_f = nn.BCEWithLogitsLoss()
l1_loss = nn.L1Loss()def forword_dis(reala, realb):lambda_dis = 0.5fakeb = net_generator(reala)pred0 = net_discriminator(reala, fakeb)pred1 = net_discriminator(reala, realb)loss_d = loss_f(pred1, ops.ones_like(pred1)) + loss_f(pred0, ops.zeros_like(pred0))loss_dis = loss_d * lambda_disreturn loss_disdef forword_gan(reala, realb):lambda_gan = 0.5lambda_l1 = 100fakeb = net_generator(reala)pred0 = net_discriminator(reala, fakeb)loss_1 = loss_f(pred0, ops.ones_like(pred0))loss_2 = l1_loss(fakeb, realb)loss_gan = loss_1 * lambda_gan + loss_2 * lambda_l1return loss_gand_opt = nn.Adam(net_discriminator.trainable_params(), learning_rate=get_lr(),beta1=0.5, beta2=0.999, loss_scale=1)
g_opt = nn.Adam(net_generator.trainable_params(), learning_rate=get_lr(),beta1=0.5, beta2=0.999, loss_scale=1)grad_d = value_and_grad(forword_dis, None, net_discriminator.trainable_params())
grad_g = value_and_grad(forword_gan, None, net_generator.trainable_params())def train_step(reala, realb):loss_dis, d_grads = grad_d(reala, realb)loss_gan, g_grads = grad_g(reala, realb)d_opt(d_grads)g_opt(g_grads)return loss_dis, loss_ganif not os.path.isdir(ckpt_dir):os.makedirs(ckpt_dir)g_losses = []
d_losses = []
data_loader = dataset.create_dict_iterator(output_numpy=True, num_epochs=epoch_num)for epoch in range(epoch_num):for i, data in enumerate(data_loader):start_time = datetime.datetime.now()input_image = Tensor(data["input_images"])target_image = Tensor(data["target_images"])dis_loss, gen_loss = train_step(input_image, target_image)end_time = datetime.datetime.now()delta = (end_time - start_time).microsecondsif i % 2 == 0:print("ms per step:{:.2f} epoch:{}/{} step:{}/{} Dloss:{:.4f} Gloss:{:.4f} ".format((delta / 1000), (epoch + 1), (epoch_num), i, steps_per_epoch, float(dis_loss), float(gen_loss)))d_losses.append(dis_loss.asnumpy())g_losses.append(gen_loss.asnumpy())if (epoch + 1) == epoch_num:mindspore.save_checkpoint(net_generator, ckpt_dir + "Generator.ckpt")ms per step:204.75 epoch:1/100 step:0/25 Dloss:0.6897 Gloss:37.1031 ms per step:110.92 epoch:1/100 step:2/25 Dloss:0.6353 Gloss:32.0685 ms per step:109.04 epoch:1/100 step:4/25 Dloss:0.4980 Gloss:38.4479 ms per step:109.70 epoch:1/100 step:6/25 Dloss:0.4706 Gloss:40.6548 ms per step:111.17 epoch:1/100 step:8/25 Dloss:0.3736 Gloss:37.2207 ms per step:116.04 epoch:1/100 step:10/25 Dloss:0.3313 Gloss:39.9487 ms per step:114.72 epoch:1/100 step:12/25 Dloss:0.5754 Gloss:37.0347 ms per step:115.50 epoch:1/100 step:14/25 Dloss:0.2367 Gloss:32.5301 ms per step:115.18 epoch:1/100 step:16/25 Dloss:0.2420 Gloss:36.9372 ms per step:116.61 epoch:1/100 step:18/25 Dloss:0.3123 Gloss:39.1208 ms per step:116.03 epoch:1/100 step:20/25 Dloss:0.3024 Gloss:32.9262 ms per step:114.12 epoch:1/100 step:22/25 Dloss:0.2165 Gloss:38.9377 ms per step:115.46 epoch:1/100 step:24/25 Dloss:0.1766 Gloss:36.7368 ms per step:110.67 epoch:2/100 step:0/25 Dloss:0.3093 Gloss:36.5221 ms per step:106.14 epoch:2/100 step:2/25 Dloss:0.1656 Gloss:35.4346 ms per step:111.77 epoch:2/100 step:4/25 Dloss:0.1827 Gloss:35.6615 ms per step:112.56 epoch:2/100 step:6/25 Dloss:0.8171 Gloss:35.8147 ms per step:109.08 epoch:2/100 step:8/25 Dloss:0.3058 Gloss:33.9187 ms per step:112.14 epoch:2/100 step:10/25 Dloss:0.8390 Gloss:42.6125 ms per step:156.64 epoch:2/100 step:12/25 Dloss:0.3088 Gloss:31.6007 ms per step:138.70 epoch:2/100 step:14/25 Dloss:0.2673 Gloss:37.3562 ms per step:165.71 epoch:2/100 step:16/25 Dloss:0.2179 Gloss:35.6225 ms per step:161.63 epoch:2/100 step:18/25 Dloss:0.3507 Gloss:37.7050 ms per step:124.58 epoch:2/100 step:20/25 Dloss:0.2014 Gloss:37.8277 ms per step:109.08 epoch:2/100 step:22/25 Dloss:4.5342 Gloss:39.6272 ms per step:106.03 epoch:2/100 step:24/25 Dloss:0.2985 Gloss:39.3276 ms per step:112.50 epoch:3/100 step:0/25 Dloss:0.3666 Gloss:39.4434 ms per step:127.56 epoch:3/100 step:2/25 Dloss:0.3360 Gloss:37.8373 ms per step:117.78 epoch:3/100 step:4/25 Dloss:0.3265 Gloss:35.4772 ms per step:116.28 epoch:3/100 step:6/25 Dloss:0.2673 Gloss:38.2814 ms per step:117.82 epoch:3/100 step:8/25 Dloss:0.2192 Gloss:36.0040 ms per step:127.89 epoch:3/100 step:10/25 Dloss:0.5457 Gloss:35.2886 ms per step:115.94 epoch:3/100 step:12/25 Dloss:0.3134 Gloss:33.9577 ms per step:113.05 epoch:3/100 step:14/25 Dloss:0.2128 Gloss:36.5794 ms per step:113.79 epoch:3/100 step:16/25 Dloss:0.1982 Gloss:38.8131 ms per step:145.94 epoch:3/100 step:18/25 Dloss:0.3664 Gloss:33.1946 ms per step:123.02 epoch:3/100 step:20/25 Dloss:0.3942 Gloss:35.1858 ms per step:111.32 epoch:3/100 step:22/25 Dloss:0.2949 Gloss:33.3575 ms per step:107.27 epoch:3/100 step:24/25 Dloss:0.3199 Gloss:35.6960 ms per step:114.69 epoch:4/100 step:0/25 Dloss:0.3226 Gloss:30.9485 ms per step:106.25 epoch:4/100 step:2/25 Dloss:0.6456 Gloss:34.4001 ms per step:104.81 epoch:4/100 step:4/25 Dloss:0.3166 Gloss:33.7080 ms per step:99.45 epoch:4/100 step:6/25 Dloss:0.2429 Gloss:36.3314 ms per step:101.94 epoch:4/100 step:8/25 Dloss:0.3957 Gloss:36.2481 ms per step:106.84 epoch:4/100 step:10/25 Dloss:0.3102 Gloss:34.8957 ms per step:117.80 epoch:4/100 step:12/25 Dloss:0.2448 Gloss:34.8868 ms per step:118.18 epoch:4/100 step:14/25 Dloss:0.4172 Gloss:35.7913 ms per step:114.14 epoch:4/100 step:16/25 Dloss:0.7911 Gloss:32.5833 ms per step:113.04 epoch:4/100 step:18/25 Dloss:0.2795 Gloss:30.4272 ms per step:108.48 epoch:4/100 step:20/25 Dloss:0.2095 Gloss:34.1284 ms per step:112.32 epoch:4/100 step:22/25 Dloss:0.2670 Gloss:34.6068 ms per step:101.79 epoch:4/100 step:24/25 Dloss:0.5286 Gloss:34.2540 ms per step:102.98 epoch:5/100 step:0/25 Dloss:0.4048 Gloss:39.6592 ms per step:107.02 epoch:5/100 step:2/25 Dloss:0.3032 Gloss:33.6503 ms per step:112.57 epoch:5/100 step:4/25 Dloss:0.1963 Gloss:36.0851 ms per step:101.16 epoch:5/100 step:6/25 Dloss:0.2779 Gloss:35.6111 ms per step:109.16 epoch:5/100 step:8/25 Dloss:0.1857 Gloss:34.5232 ms per step:110.14 epoch:5/100 step:10/25 Dloss:0.5421 Gloss:30.3469 ms per step:110.88 epoch:5/100 step:12/25 Dloss:0.3997 Gloss:30.9148 ms per step:108.31 epoch:5/100 step:14/25 Dloss:0.2931 Gloss:33.7271 ms per step:112.18 epoch:5/100 step:16/25 Dloss:0.2698 Gloss:30.7518 ms per step:112.77 epoch:5/100 step:18/25 Dloss:0.2204 Gloss:35.0685 ms per step:107.06 epoch:5/100 step:20/25 Dloss:0.2335 Gloss:35.6151 ms per step:100.25 epoch:5/100 step:22/25 Dloss:0.2479 Gloss:35.1270 ms per step:105.97 epoch:5/100 step:24/25 Dloss:0.2322 Gloss:33.7123 ms per step:99.31 epoch:6/100 step:0/25 Dloss:0.2245 Gloss:30.2303 ms per step:107.41 epoch:6/100 step:2/25 Dloss:0.1197 Gloss:34.8541 ms per step:102.88 epoch:6/100 step:4/25 Dloss:0.8449 Gloss:37.6315 ms per step:111.83 epoch:6/100 step:6/25 Dloss:0.2133 Gloss:33.7175 ms per step:103.20 epoch:6/100 step:8/25 Dloss:0.2666 Gloss:30.4968 ms per step:108.35 epoch:6/100 step:10/25 Dloss:0.1703 Gloss:34.2822 ms per step:117.95 epoch:6/100 step:12/25 Dloss:0.1685 Gloss:33.4703 ms per step:111.74 epoch:6/100 step:14/25 Dloss:0.1708 Gloss:35.2430 ms per step:118.85 epoch:6/100 step:16/25 Dloss:0.1398 Gloss:35.2619 ms per step:108.81 epoch:6/100 step:18/25 Dloss:0.1568 Gloss:33.8083 ms per step:115.50 epoch:6/100 step:20/25 Dloss:0.3280 Gloss:36.0523 ms per step:108.38 epoch:6/100 step:22/25 Dloss:0.2311 Gloss:36.7951 ms per step:100.83 epoch:6/100 step:24/25 Dloss:0.1700 Gloss:33.5755 ms per step:107.63 epoch:7/100 step:0/25 Dloss:0.2986 Gloss:34.4059 ms per step:102.86 epoch:7/100 step:2/25 Dloss:0.1548 Gloss:37.1741 ms per step:105.17 epoch:7/100 step:4/25 Dloss:0.2311 Gloss:35.0723 ms per step:102.77 epoch:7/100 step:6/25 Dloss:0.4507 Gloss:36.0862 ms per step:105.28 epoch:7/100 step:8/25 Dloss:0.4507 Gloss:30.9377 ms per step:118.02 epoch:7/100 step:10/25 Dloss:0.1870 Gloss:34.9944 ms per step:123.09 epoch:7/100 step:12/25 Dloss:0.1602 Gloss:30.1066 ms per step:116.36 epoch:7/100 step:14/25 Dloss:0.1795 Gloss:36.0846 ms per step:107.29 epoch:7/100 step:16/25 Dloss:0.1186 Gloss:35.2313 ms per step:114.19 epoch:7/100 step:18/25 Dloss:0.1104 Gloss:33.9729 ms per step:111.15 epoch:7/100 step:20/25 Dloss:0.3128 Gloss:37.1534 ms per step:100.68 epoch:7/100 step:22/25 Dloss:0.2224 Gloss:34.6298 ms per step:101.16 epoch:7/100 step:24/25 Dloss:0.0838 Gloss:35.4592 ms per step:101.17 epoch:8/100 step:0/25 Dloss:0.2538 Gloss:35.1292 ms per step:105.37 epoch:8/100 step:2/25 Dloss:0.1362 Gloss:36.2886 ms per step:105.59 epoch:8/100 step:4/25 Dloss:0.1124 Gloss:36.5091 ms per step:104.59 epoch:8/100 step:6/25 Dloss:0.1557 Gloss:32.0394 ms per step:100.29 epoch:8/100 step:8/25 Dloss:0.1529 Gloss:36.9445 ms per step:107.35 epoch:8/100 step:10/25 Dloss:0.1066 Gloss:36.0111 ms per step:116.61 epoch:8/100 step:12/25 Dloss:0.1983 Gloss:31.5163 ms per step:119.83 epoch:8/100 step:14/25 Dloss:0.1146 Gloss:35.5416 ms per step:116.71 epoch:8/100 step:16/25 Dloss:0.2489 Gloss:33.4765 ms per step:107.50 epoch:8/100 step:18/25 Dloss:1.1940 Gloss:37.1177 ms per step:99.72 epoch:8/100 step:20/25 Dloss:0.2300 Gloss:34.8493 ms per step:99.67 epoch:8/100 step:22/25 Dloss:0.1331 Gloss:34.4320 ms per step:103.94 epoch:8/100 step:24/25 Dloss:0.1599 Gloss:33.5074 ms per step:105.40 epoch:9/100 step:0/25 Dloss:0.2450 Gloss:34.0505 ms per step:108.44 epoch:9/100 step:2/25 Dloss:0.1436 Gloss:35.5095 ms per step:105.19 epoch:9/100 step:4/25 Dloss:0.1030 Gloss:34.1643 ms per step:99.91 epoch:9/100 step:6/25 Dloss:0.1102 Gloss:29.2541 ms per step:108.14 epoch:9/100 step:8/25 Dloss:0.1679 Gloss:34.8311 ms per step:108.99 epoch:9/100 step:10/25 Dloss:0.1670 Gloss:33.6990 ms per step:113.90 epoch:9/100 step:12/25 Dloss:0.0852 Gloss:35.4889 ms per step:113.92 epoch:9/100 step:14/25 Dloss:0.1765 Gloss:38.7903 ms per step:108.45 epoch:9/100 step:16/25 Dloss:0.1475 Gloss:32.4022 ms per step:106.38 epoch:9/100 step:18/25 Dloss:0.1035 Gloss:35.0014 ms per step:106.81 epoch:9/100 step:20/25 Dloss:0.2029 Gloss:31.7662 ms per step:97.29 epoch:9/100 step:22/25 Dloss:0.2387 Gloss:36.9439 ms per step:99.65 epoch:9/100 step:24/25 Dloss:0.6291 Gloss:33.8078 ms per step:96.38 epoch:10/100 step:0/25 Dloss:0.7573 Gloss:37.2553 ms per step:135.43 epoch:10/100 step:2/25 Dloss:0.1455 Gloss:37.1506 ms per step:97.36 epoch:10/100 step:4/25 Dloss:0.2290 Gloss:32.2825 ms per step:97.25 epoch:10/100 step:6/25 Dloss:0.2324 Gloss:36.1145 ms per step:96.77 epoch:10/100 step:8/25 Dloss:0.1912 Gloss:34.6552 ms per step:103.31 epoch:10/100 step:10/25 Dloss:0.1369 Gloss:35.1498 ms per step:137.80 epoch:10/100 step:12/25 Dloss:0.1725 Gloss:33.3661 ms per step:111.68 epoch:10/100 step:14/25 Dloss:0.2208 Gloss:34.4513 ms per step:141.62 epoch:10/100 step:16/25 Dloss:0.2574 Gloss:33.2548 ms per step:149.45 epoch:10/100 step:18/25 Dloss:0.2365 Gloss:28.9793 ms per step:128.19 epoch:10/100 step:20/25 Dloss:0.2035 Gloss:32.4743 ms per step:125.08 epoch:10/100 step:22/25 Dloss:0.7574 Gloss:36.6609 ms per step:99.11 epoch:10/100 step:24/25 Dloss:0.1860 Gloss:34.6567 ms per step:123.55 epoch:11/100 step:0/25 Dloss:0.2223 Gloss:28.5527 ms per step:95.78 epoch:11/100 step:2/25 Dloss:0.1632 Gloss:33.4401 ms per step:149.95 epoch:11/100 step:4/25 Dloss:0.2714 Gloss:33.2374 ms per step:149.31 epoch:11/100 step:6/25 Dloss:0.1149 Gloss:32.7205 ms per step:122.46 epoch:11/100 step:8/25 Dloss:0.1357 Gloss:32.8428 ms per step:101.08 epoch:11/100 step:10/25 Dloss:0.1149 Gloss:30.7533 ms per step:101.09 epoch:11/100 step:12/25 Dloss:0.1705 Gloss:32.3486 ms per step:129.94 epoch:11/100 step:14/25 Dloss:0.1711 Gloss:30.2202 ms per step:103.29 epoch:11/100 step:16/25 Dloss:0.2044 Gloss:33.3232 ms per step:95.99 epoch:11/100 step:18/25 Dloss:0.5314 Gloss:36.5913 ms per step:95.58 epoch:11/100 step:20/25 Dloss:0.3198 Gloss:33.0979 ms per step:95.93 epoch:11/100 step:22/25 Dloss:0.2615 Gloss:34.2509 ms per step:95.08 epoch:11/100 step:24/25 Dloss:0.1590 Gloss:36.7118 ms per step:95.77 epoch:12/100 step:0/25 Dloss:0.1776 Gloss:32.5703 ms per step:95.53 epoch:12/100 step:2/25 Dloss:0.2224 Gloss:31.7528 ms per step:95.47 epoch:12/100 step:4/25 Dloss:0.1915 Gloss:32.9494 ms per step:95.50 epoch:12/100 step:6/25 Dloss:0.1451 Gloss:35.0644 ms per step:95.43 epoch:12/100 step:8/25 Dloss:0.1969 Gloss:35.8737 ms per step:105.98 epoch:12/100 step:10/25 Dloss:0.3251 Gloss:31.5041 ms per step:106.46 epoch:12/100 step:12/25 Dloss:0.2659 Gloss:31.5948 ms per step:105.40 epoch:12/100 step:14/25 Dloss:0.1684 Gloss:30.4842 ms per step:102.45 epoch:12/100 step:16/25 Dloss:0.2178 Gloss:32.0689 ms per step:95.56 epoch:12/100 step:18/25 Dloss:0.1996 Gloss:36.5052 ms per step:95.78 epoch:12/100 step:20/25 Dloss:0.1974 Gloss:34.0614 ms per step:106.77 epoch:12/100 step:22/25 Dloss:0.1365 Gloss:28.7357 ms per step:95.95 epoch:12/100 step:24/25 Dloss:0.1066 Gloss:32.5434 ms per step:96.25 epoch:13/100 step:0/25 Dloss:0.1326 Gloss:34.3436 ms per step:95.90 epoch:13/100 step:2/25 Dloss:0.2720 Gloss:35.8570 ms per step:95.71 epoch:13/100 step:4/25 Dloss:0.4157 Gloss:32.6792 ms per step:96.05 epoch:13/100 step:6/25 Dloss:0.4953 Gloss:35.1763 ms per step:96.15 epoch:13/100 step:8/25 Dloss:0.1667 Gloss:28.0880 ms per step:100.28 epoch:13/100 step:10/25 Dloss:0.1568 Gloss:32.0826 ms per step:100.30 epoch:13/100 step:12/25 Dloss:0.1554 Gloss:31.7710 ms per step:101.40 epoch:13/100 step:14/25 Dloss:0.1292 Gloss:31.0753 ms per step:100.00 epoch:13/100 step:16/25 Dloss:0.1329 Gloss:32.0049 ms per step:96.21 epoch:13/100 step:18/25 Dloss:0.0770 Gloss:30.7780 ms per step:95.55 epoch:13/100 step:20/25 Dloss:0.1226 Gloss:33.3695 ms per step:95.54 epoch:13/100 step:22/25 Dloss:0.3987 Gloss:32.6538 ms per step:95.69 epoch:13/100 step:24/25 Dloss:0.2782 Gloss:31.2371 ms per step:97.27 epoch:14/100 step:0/25 Dloss:0.3108 Gloss:31.3417 ms per step:96.36 epoch:14/100 step:2/25 Dloss:0.2313 Gloss:30.3941 ms per step:95.96 epoch:14/100 step:4/25 Dloss:0.1593 Gloss:30.4305 ms per step:96.17 epoch:14/100 step:6/25 Dloss:0.1321 Gloss:29.9868 ms per step:95.92 epoch:14/100 step:8/25 Dloss:0.1414 Gloss:29.1535 ms per step:99.88 epoch:14/100 step:10/25 Dloss:0.1748 Gloss:27.6295 ms per step:102.80 epoch:14/100 step:12/25 Dloss:0.0851 Gloss:30.4776 ms per step:104.72 epoch:14/100 step:14/25 Dloss:0.1030 Gloss:33.5051 ms per step:105.77 epoch:14/100 step:16/25 Dloss:0.2157 Gloss:28.4780 ms per step:99.08 epoch:14/100 step:18/25 Dloss:0.3448 Gloss:31.6692 ms per step:96.08 epoch:14/100 step:20/25 Dloss:0.6413 Gloss:33.1093 ms per step:94.78 epoch:14/100 step:22/25 Dloss:0.3616 Gloss:31.4116 ms per step:96.94 epoch:14/100 step:24/25 Dloss:0.2936 Gloss:32.5213 ms per step:96.04 epoch:15/100 step:0/25 Dloss:0.2943 Gloss:29.0930 ms per step:101.20 epoch:15/100 step:2/25 Dloss:0.2802 Gloss:27.3098 ms per step:97.18 epoch:15/100 step:4/25 Dloss:0.2515 Gloss:29.4319 ms per step:97.17 epoch:15/100 step:6/25 Dloss:0.1181 Gloss:32.0332 ms per step:98.63 epoch:15/100 step:8/25 Dloss:0.1632 Gloss:29.9418 ms per step:103.15 epoch:15/100 step:10/25 Dloss:0.2113 Gloss:32.2050 ms per step:101.50 epoch:15/100 step:12/25 Dloss:0.4008 Gloss:27.3341 ms per step:106.53 epoch:15/100 step:14/25 Dloss:0.3969 Gloss:29.2508 ms per step:137.32 epoch:15/100 step:16/25 Dloss:0.2252 Gloss:31.2349 ms per step:109.99 epoch:15/100 step:18/25 Dloss:0.2692 Gloss:26.9864 ms per step:97.49 epoch:15/100 step:20/25 Dloss:0.1570 Gloss:28.7567 ms per step:98.15 epoch:15/100 step:22/25 Dloss:0.2171 Gloss:27.2928 ms per step:102.67 epoch:15/100 step:24/25 Dloss:0.1300 Gloss:32.1528 ms per step:99.73 epoch:16/100 step:0/25 Dloss:0.1215 Gloss:28.0065 ms per step:98.55 epoch:16/100 step:2/25 Dloss:0.3855 Gloss:31.0583 ms per step:97.59 epoch:16/100 step:4/25 Dloss:0.3456 Gloss:29.2974 ms per step:96.91 epoch:16/100 step:6/25 Dloss:0.6300 Gloss:29.3516 ms per step:98.30 epoch:16/100 step:8/25 Dloss:0.2407 Gloss:26.1584 ms per step:131.25 epoch:16/100 step:10/25 Dloss:0.1509 Gloss:30.9893 ms per step:106.43 epoch:16/100 step:12/25 Dloss:0.1725 Gloss:31.1133 ms per step:107.43 epoch:16/100 step:14/25 Dloss:0.1371 Gloss:28.4015 ms per step:107.44 epoch:16/100 step:16/25 Dloss:0.5244 Gloss:27.1161 ms per step:99.51 epoch:16/100 step:18/25 Dloss:0.6425 Gloss:29.8100 ms per step:100.53 epoch:16/100 step:20/25 Dloss:0.3396 Gloss:30.0259 ms per step:105.17 epoch:16/100 step:22/25 Dloss:0.5974 Gloss:30.8600 ms per step:100.24 epoch:16/100 step:24/25 Dloss:0.2338 Gloss:23.1849 ms per step:99.72 epoch:17/100 step:0/25 Dloss:0.1453 Gloss:28.1197 ms per step:96.99 epoch:17/100 step:2/25 Dloss:0.1593 Gloss:27.4453 ms per step:98.94 epoch:17/100 step:4/25 Dloss:0.0717 Gloss:30.1956 ms per step:99.35 epoch:17/100 step:6/25 Dloss:0.1333 Gloss:29.3688 ms per step:97.92 epoch:17/100 step:8/25 Dloss:0.2118 Gloss:29.1178 ms per step:109.60 epoch:17/100 step:10/25 Dloss:0.1913 Gloss:24.0773 ms per step:107.75 epoch:17/100 step:12/25 Dloss:0.2053 Gloss:26.8610 ms per step:110.58 epoch:17/100 step:14/25 Dloss:0.4358 Gloss:25.2621 ms per step:105.61 epoch:17/100 step:16/25 Dloss:0.2057 Gloss:26.8026 ms per step:97.85 epoch:17/100 step:18/25 Dloss:0.1685 Gloss:28.7992 ms per step:97.79 epoch:17/100 step:20/25 Dloss:0.2067 Gloss:28.2258 ms per step:97.57 epoch:17/100 step:22/25 Dloss:0.3917 Gloss:32.2305 ms per step:98.86 epoch:17/100 step:24/25 Dloss:0.4569 Gloss:27.4586 ms per step:103.15 epoch:18/100 step:0/25 Dloss:0.3312 Gloss:31.0248 ms per step:98.32 epoch:18/100 step:2/25 Dloss:0.2100 Gloss:27.5618 ms per step:101.75 epoch:18/100 step:4/25 Dloss:0.1853 Gloss:27.0157 ms per step:102.89 epoch:18/100 step:6/25 Dloss:0.1962 Gloss:24.5970 ms per step:102.29 epoch:18/100 step:8/25 Dloss:0.1631 Gloss:30.0126 ms per step:106.48 epoch:18/100 step:10/25 Dloss:0.2848 Gloss:28.3787 ms per step:117.61 epoch:18/100 step:12/25 Dloss:0.4688 Gloss:24.4731 ms per step:112.87 epoch:18/100 step:14/25 Dloss:0.3016 Gloss:25.3336 ms per step:107.60 epoch:18/100 step:16/25 Dloss:0.1850 Gloss:27.0883 ms per step:101.59 epoch:18/100 step:18/25 Dloss:0.1652 Gloss:26.5043 ms per step:98.58 epoch:18/100 step:20/25 Dloss:0.1916 Gloss:29.1534 ms per step:101.63 epoch:18/100 step:22/25 Dloss:0.1935 Gloss:28.6713 ms per step:97.22 epoch:18/100 step:24/25 Dloss:0.2222 Gloss:29.5466 ms per step:98.96 epoch:19/100 step:0/25 Dloss:0.4617 Gloss:26.9988 ms per step:100.26 epoch:19/100 step:2/25 Dloss:0.2117 Gloss:25.0168 ms per step:102.04 epoch:19/100 step:4/25 Dloss:0.2702 Gloss:23.6539 ms per step:102.21 epoch:19/100 step:6/25 Dloss:0.2227 Gloss:27.0165 ms per step:99.51 epoch:19/100 step:8/25 Dloss:0.2021 Gloss:27.7990 ms per step:133.01 epoch:19/100 step:10/25 Dloss:0.4087 Gloss:25.5694 ms per step:136.31 epoch:19/100 step:12/25 Dloss:0.3771 Gloss:26.0101 ms per step:119.35 epoch:19/100 step:14/25 Dloss:0.3873 Gloss:26.1754 ms per step:107.84 epoch:19/100 step:16/25 Dloss:0.1979 Gloss:28.9217 ms per step:104.08 epoch:19/100 step:18/25 Dloss:0.1825 Gloss:27.0119 ms per step:110.75 epoch:19/100 step:20/25 Dloss:0.1285 Gloss:27.8270 ms per step:100.82 epoch:19/100 step:22/25 Dloss:0.1690 Gloss:26.4484 ms per step:101.63 epoch:19/100 step:24/25 Dloss:0.1743 Gloss:25.0522 ms per step:101.40 epoch:20/100 step:0/25 Dloss:0.3100 Gloss:25.8043 ms per step:104.71 epoch:20/100 step:2/25 Dloss:0.1535 Gloss:24.0780 ms per step:106.20 epoch:20/100 step:4/25 Dloss:0.2306 Gloss:25.1991 ms per step:106.81 epoch:20/100 step:6/25 Dloss:0.3823 Gloss:24.3071 ms per step:100.66 epoch:20/100 step:8/25 Dloss:0.3268 Gloss:25.5527 ms per step:110.43 epoch:20/100 step:10/25 Dloss:0.2606 Gloss:26.6634 ms per step:110.04 epoch:20/100 step:12/25 Dloss:0.3721 Gloss:24.7309 ms per step:114.23 epoch:20/100 step:14/25 Dloss:0.2918 Gloss:25.2503 ms per step:107.06 epoch:20/100 step:16/25 Dloss:0.5828 Gloss:26.4883 ms per step:100.38 epoch:20/100 step:18/25 Dloss:0.3215 Gloss:26.1511 ms per step:107.65 epoch:20/100 step:20/25 Dloss:0.3962 Gloss:26.0596 ms per step:105.98 epoch:20/100 step:22/25 Dloss:0.2283 Gloss:24.3636 ms per step:112.09 epoch:20/100 step:24/25 Dloss:0.1482 Gloss:23.4873 ms per step:107.53 epoch:21/100 step:0/25 Dloss:0.1888 Gloss:23.9143 ms per step:106.06 epoch:21/100 step:2/25 Dloss:0.2235 Gloss:23.5166 ms per step:106.08 epoch:21/100 step:4/25 Dloss:0.3671 Gloss:23.3758 ms per step:107.00 epoch:21/100 step:6/25 Dloss:0.2277 Gloss:23.7860 ms per step:111.38 epoch:21/100 step:8/25 Dloss:0.2986 Gloss:24.1286 ms per step:145.41 epoch:21/100 step:10/25 Dloss:0.2126 Gloss:24.9463 ms per step:155.08 epoch:21/100 step:12/25 Dloss:0.4170 Gloss:20.5937 ms per step:145.67 epoch:21/100 step:14/25 Dloss:0.1705 Gloss:22.0651 ms per step:110.08 epoch:21/100 step:16/25 Dloss:0.1604 Gloss:23.1511 ms per step:105.61 epoch:21/100 step:18/25 Dloss:0.1992 Gloss:22.6401 ms per step:107.22 epoch:21/100 step:20/25 Dloss:0.2130 Gloss:24.3708 ms per step:106.88 epoch:21/100 step:22/25 Dloss:0.1574 Gloss:26.2599 ms per step:107.16 epoch:21/100 step:24/25 Dloss:0.2495 Gloss:23.6248 ms per step:106.06 epoch:22/100 step:0/25 Dloss:0.2879 Gloss:23.0740 ms per step:107.99 epoch:22/100 step:2/25 Dloss:0.2362 Gloss:25.2399 ms per step:109.37 epoch:22/100 step:4/25 Dloss:0.3611 Gloss:22.4944 ms per step:109.48 epoch:22/100 step:6/25 Dloss:0.1763 Gloss:24.2273 ms per step:107.45 epoch:22/100 step:8/25 Dloss:0.1354 Gloss:25.5558 ms per step:150.88 epoch:22/100 step:10/25 Dloss:0.4557 Gloss:25.0474 ms per step:124.16 epoch:22/100 step:12/25 Dloss:0.1416 Gloss:24.7038 ms per step:141.37 epoch:22/100 step:14/25 Dloss:0.2144 Gloss:24.5439 ms per step:112.18 epoch:22/100 step:16/25 Dloss:0.2571 Gloss:21.0980 ms per step:107.39 epoch:22/100 step:18/25 Dloss:0.3910 Gloss:22.7223 ms per step:109.88 epoch:22/100 step:20/25 Dloss:0.4821 Gloss:23.1425 ms per step:107.32 epoch:22/100 step:22/25 Dloss:0.3530 Gloss:23.3428 ms per step:131.98 epoch:22/100 step:24/25 Dloss:0.1457 Gloss:24.1709 ms per step:107.23 epoch:23/100 step:0/25 Dloss:0.2751 Gloss:21.7048 ms per step:105.82 epoch:23/100 step:2/25 Dloss:0.2921 Gloss:20.3530 ms per step:135.46 epoch:23/100 step:4/25 Dloss:0.2665 Gloss:20.7926 ms per step:117.90 epoch:23/100 step:6/25 Dloss:0.2724 Gloss:21.5450 ms per step:129.55 epoch:23/100 step:8/25 Dloss:0.1904 Gloss:21.7360 ms per step:148.38 epoch:23/100 step:10/25 Dloss:0.3040 Gloss:25.2340 ms per step:157.44 epoch:23/100 step:12/25 Dloss:0.3371 Gloss:21.3238 ms per step:148.18 epoch:23/100 step:14/25 Dloss:0.3259 Gloss:23.6548 ms per step:112.67 epoch:23/100 step:16/25 Dloss:0.4190 Gloss:22.5656 ms per step:109.69 epoch:23/100 step:18/25 Dloss:0.3652 Gloss:30.2355 ms per step:119.71 epoch:23/100 step:20/25 Dloss:0.2824 Gloss:24.4302 ms per step:106.29 epoch:23/100 step:22/25 Dloss:0.3743 Gloss:20.1809 ms per step:107.00 epoch:23/100 step:24/25 Dloss:0.2729 Gloss:21.7424 ms per step:105.27 epoch:24/100 step:0/25 Dloss:0.2781 Gloss:20.8999 ms per step:106.17 epoch:24/100 step:2/25 Dloss:0.3746 Gloss:20.4720 ms per step:113.64 epoch:24/100 step:4/25 Dloss:0.3610 Gloss:20.9705 ms per step:106.86 epoch:24/100 step:6/25 Dloss:0.1568 Gloss:23.6787 ms per step:106.80 epoch:24/100 step:8/25 Dloss:0.1742 Gloss:20.8628 ms per step:121.54 epoch:24/100 step:10/25 Dloss:0.2986 Gloss:20.7585 ms per step:124.39 epoch:24/100 step:12/25 Dloss:0.3298 Gloss:19.5449 ms per step:144.50 epoch:24/100 step:14/25 Dloss:0.7190 Gloss:20.5618 ms per step:112.32 epoch:24/100 step:16/25 Dloss:0.3819 Gloss:20.5090 ms per step:109.80 epoch:24/100 step:18/25 Dloss:0.2862 Gloss:25.5357 ms per step:108.76 epoch:24/100 step:20/25 Dloss:0.1987 Gloss:20.4038 ms per step:116.22 epoch:24/100 step:22/25 Dloss:0.1325 Gloss:22.4773 ms per step:125.85 epoch:24/100 step:24/25 Dloss:0.2102 Gloss:21.4302 ms per step:135.29 epoch:25/100 step:0/25 Dloss:0.2338 Gloss:20.3356 ms per step:108.27 epoch:25/100 step:2/25 Dloss:0.2542 Gloss:18.7456 ms per step:109.94 epoch:25/100 step:4/25 Dloss:0.2538 Gloss:23.3988 ms per step:105.46 epoch:25/100 step:6/25 Dloss:0.2688 Gloss:20.0536 ms per step:107.33 epoch:25/100 step:8/25 Dloss:0.4805 Gloss:23.2786 ms per step:139.00 epoch:25/100 step:10/25 Dloss:0.4271 Gloss:19.5007 ms per step:146.20 epoch:25/100 step:12/25 Dloss:0.4810 Gloss:21.0232 ms per step:158.40 epoch:25/100 step:14/25 Dloss:0.2606 Gloss:21.1494 ms per step:113.16 epoch:25/100 step:16/25 Dloss:0.1751 Gloss:21.9246 ms per step:106.23 epoch:25/100 step:18/25 Dloss:0.1847 Gloss:21.1607 ms per step:107.99 epoch:25/100 step:20/25 Dloss:0.1819 Gloss:20.2439 ms per step:109.67 epoch:25/100 step:22/25 Dloss:0.2381 Gloss:22.7654 ms per step:109.93 epoch:25/100 step:24/25 Dloss:0.4142 Gloss:18.6555 ms per step:109.81 epoch:26/100 step:0/25 Dloss:0.4442 Gloss:18.2933 ms per step:115.37 epoch:26/100 step:2/25 Dloss:0.3686 Gloss:18.8838 ms per step:112.83 epoch:26/100 step:4/25 Dloss:0.1920 Gloss:21.8669 ms per step:115.69 epoch:26/100 step:6/25 Dloss:0.2021 Gloss:20.8979 ms per step:109.81 epoch:26/100 step:8/25 Dloss:0.1659 Gloss:22.1169 ms per step:156.42 epoch:26/100 step:10/25 Dloss:0.4690 Gloss:19.3535 ms per step:144.08 epoch:26/100 step:12/25 Dloss:0.3853 Gloss:21.8180 ms per step:146.60 epoch:26/100 step:14/25 Dloss:0.8582 Gloss:23.1792 ms per step:145.29 epoch:26/100 step:16/25 Dloss:0.3972 Gloss:18.5702 ms per step:105.71 epoch:26/100 step:18/25 Dloss:0.3009 Gloss:18.0110 ms per step:113.97 epoch:26/100 step:20/25 Dloss:0.1982 Gloss:22.1415 ms per step:104.94 epoch:26/100 step:22/25 Dloss:0.2368 Gloss:19.1651 ms per step:103.54 epoch:26/100 step:24/25 Dloss:0.1345 Gloss:21.4350 ms per step:104.73 epoch:27/100 step:0/25 Dloss:0.1930 Gloss:18.7527 ms per step:115.34 epoch:27/100 step:2/25 Dloss:0.3141 Gloss:19.4527 ms per step:103.81 epoch:27/100 step:4/25 Dloss:0.2596 Gloss:18.8677 ms per step:137.30 epoch:27/100 step:6/25 Dloss:0.4001 Gloss:20.6983 ms per step:106.33 epoch:27/100 step:8/25 Dloss:0.2148 Gloss:20.9220 ms per step:144.01 epoch:27/100 step:10/25 Dloss:0.2687 Gloss:17.7448 ms per step:160.82 epoch:27/100 step:12/25 Dloss:0.3151 Gloss:17.5041 ms per step:115.42 epoch:27/100 step:14/25 Dloss:0.1501 Gloss:20.9493 ms per step:132.04 epoch:27/100 step:16/25 Dloss:0.1722 Gloss:18.4998 ms per step:105.80 epoch:27/100 step:18/25 Dloss:0.2183 Gloss:19.3421 ms per step:105.88 epoch:27/100 step:20/25 Dloss:0.2117 Gloss:18.6341 ms per step:110.59 epoch:27/100 step:22/25 Dloss:0.3359 Gloss:18.6644 ms per step:104.71 epoch:27/100 step:24/25 Dloss:0.7957 Gloss:21.9831 ms per step:104.67 epoch:28/100 step:0/25 Dloss:1.0866 Gloss:20.1007 ms per step:104.95 epoch:28/100 step:2/25 Dloss:0.2559 Gloss:20.0169 ms per step:108.22 epoch:28/100 step:4/25 Dloss:0.3900 Gloss:18.2745 ms per step:105.57 epoch:28/100 step:6/25 Dloss:0.4240 Gloss:20.6456 ms per step:126.93 epoch:28/100 step:8/25 Dloss:0.3940 Gloss:19.8655 ms per step:151.86 epoch:28/100 step:10/25 Dloss:0.3842 Gloss:17.5028 ms per step:120.17 epoch:28/100 step:12/25 Dloss:0.2199 Gloss:19.2721 ms per step:130.35 epoch:28/100 step:14/25 Dloss:0.2378 Gloss:19.7780 ms per step:100.73 epoch:28/100 step:16/25 Dloss:0.2630 Gloss:18.0365 ms per step:97.35 epoch:28/100 step:18/25 Dloss:0.4417 Gloss:17.6489 ms per step:97.63 epoch:28/100 step:20/25 Dloss:0.3447 Gloss:17.7328 ms per step:97.47 epoch:28/100 step:22/25 Dloss:0.2281 Gloss:19.0124 ms per step:96.63 epoch:28/100 step:24/25 Dloss:0.3365 Gloss:21.2767 ms per step:96.69 epoch:29/100 step:0/25 Dloss:0.3501 Gloss:18.8439 ms per step:97.70 epoch:29/100 step:2/25 Dloss:0.1753 Gloss:17.5204 ms per step:96.50 epoch:29/100 step:4/25 Dloss:0.1180 Gloss:21.5239 ms per step:101.27 epoch:29/100 step:6/25 Dloss:0.1844 Gloss:17.3276 ms per step:100.82 epoch:29/100 step:8/25 Dloss:0.2104 Gloss:19.1877 ms per step:109.41 epoch:29/100 step:10/25 Dloss:0.3102 Gloss:19.8071 ms per step:111.29 epoch:29/100 step:12/25 Dloss:0.3283 Gloss:18.7604 ms per step:105.91 epoch:29/100 step:14/25 Dloss:0.3761 Gloss:19.6189 ms per step:128.20 epoch:29/100 step:16/25 Dloss:0.4078 Gloss:17.3511 ms per step:99.12 epoch:29/100 step:18/25 Dloss:0.2064 Gloss:17.7340 ms per step:97.59 epoch:29/100 step:20/25 Dloss:0.1378 Gloss:17.9424 ms per step:96.99 epoch:29/100 step:22/25 Dloss:0.1534 Gloss:17.1386 ms per step:100.01 epoch:29/100 step:24/25 Dloss:0.1198 Gloss:18.1769 ms per step:96.71 epoch:30/100 step:0/25 Dloss:0.1499 Gloss:17.5272 ms per step:96.42 epoch:30/100 step:2/25 Dloss:0.2267 Gloss:18.4305 ms per step:97.52 epoch:30/100 step:4/25 Dloss:0.7221 Gloss:17.5034 ms per step:97.38 epoch:30/100 step:6/25 Dloss:0.3298 Gloss:16.9349 ms per step:96.48 epoch:30/100 step:8/25 Dloss:0.3057 Gloss:15.6239 ms per step:105.22 epoch:30/100 step:10/25 Dloss:0.2766 Gloss:16.5417 ms per step:109.19 epoch:30/100 step:12/25 Dloss:0.2776 Gloss:16.9973 ms per step:106.38 epoch:30/100 step:14/25 Dloss:0.3216 Gloss:18.8120 ms per step:118.66 epoch:30/100 step:16/25 Dloss:0.2904 Gloss:17.1248 ms per step:96.77 epoch:30/100 step:18/25 Dloss:0.3097 Gloss:18.0417 ms per step:97.33 epoch:30/100 step:20/25 Dloss:0.1950 Gloss:17.6192 ms per step:96.68 epoch:30/100 step:22/25 Dloss:0.4548 Gloss:16.7320 ms per step:96.68 epoch:30/100 step:24/25 Dloss:0.2114 Gloss:19.2640 ms per step:97.28 epoch:31/100 step:0/25 Dloss:0.3883 Gloss:16.7202 ms per step:96.58 epoch:31/100 step:2/25 Dloss:0.3661 Gloss:20.4554 ms per step:96.39 epoch:31/100 step:4/25 Dloss:0.1620 Gloss:19.9417 ms per step:96.81 epoch:31/100 step:6/25 Dloss:0.3559 Gloss:18.4848 ms per step:98.02 epoch:31/100 step:8/25 Dloss:0.2191 Gloss:17.3360 ms per step:130.18 epoch:31/100 step:10/25 Dloss:0.2818 Gloss:16.3180 ms per step:114.04 epoch:31/100 step:12/25 Dloss:0.3184 Gloss:16.2521 ms per step:104.49 epoch:31/100 step:14/25 Dloss:0.4552 Gloss:18.1078 ms per step:106.10 epoch:31/100 step:16/25 Dloss:0.4582 Gloss:15.3059 ms per step:98.56 epoch:31/100 step:18/25 Dloss:0.4000 Gloss:16.1711 ms per step:97.65 epoch:31/100 step:20/25 Dloss:0.2066 Gloss:16.5775 ms per step:102.48 epoch:31/100 step:22/25 Dloss:0.2979 Gloss:17.3286 ms per step:97.75 epoch:31/100 step:24/25 Dloss:0.2257 Gloss:16.9297 ms per step:97.03 epoch:32/100 step:0/25 Dloss:0.2910 Gloss:16.3272 ms per step:96.90 epoch:32/100 step:2/25 Dloss:0.4548 Gloss:21.5088 ms per step:95.97 epoch:32/100 step:4/25 Dloss:0.2637 Gloss:17.1083 ms per step:95.88 epoch:32/100 step:6/25 Dloss:0.3128 Gloss:16.7091 ms per step:96.58 epoch:32/100 step:8/25 Dloss:0.2581 Gloss:16.3464 ms per step:103.91 epoch:32/100 step:10/25 Dloss:0.5005 Gloss:15.9861 ms per step:106.04 epoch:32/100 step:12/25 Dloss:0.4440 Gloss:17.2590 ms per step:106.62 epoch:32/100 step:14/25 Dloss:0.2370 Gloss:16.6737 ms per step:111.68 epoch:32/100 step:16/25 Dloss:0.1941 Gloss:15.1668 ms per step:98.03 epoch:32/100 step:18/25 Dloss:0.1818 Gloss:17.0351 ms per step:96.82 epoch:32/100 step:20/25 Dloss:0.2495 Gloss:15.4847 ms per step:96.42 epoch:32/100 step:22/25 Dloss:0.2207 Gloss:16.4981 ms per step:97.41 epoch:32/100 step:24/25 Dloss:0.2997 Gloss:15.8290 ms per step:95.90 epoch:33/100 step:0/25 Dloss:0.4188 Gloss:14.7267 ms per step:97.80 epoch:33/100 step:2/25 Dloss:0.3564 Gloss:16.7468 ms per step:97.43 epoch:33/100 step:4/25 Dloss:0.2642 Gloss:17.0630 ms per step:99.74 epoch:33/100 step:6/25 Dloss:0.3512 Gloss:17.0884 ms per step:96.72 epoch:33/100 step:8/25 Dloss:0.2911 Gloss:16.4367 ms per step:111.66 epoch:33/100 step:10/25 Dloss:0.3139 Gloss:16.3974 ms per step:108.81 epoch:33/100 step:12/25 Dloss:0.1941 Gloss:16.2110 ms per step:105.40 epoch:33/100 step:14/25 Dloss:0.2710 Gloss:18.0199 ms per step:104.96 epoch:33/100 step:16/25 Dloss:0.5882 Gloss:16.4506 ms per step:99.89 epoch:33/100 step:18/25 Dloss:0.5652 Gloss:14.9657 ms per step:97.34 epoch:33/100 step:20/25 Dloss:0.3347 Gloss:16.5341 ms per step:98.17 epoch:33/100 step:22/25 Dloss:0.2739 Gloss:16.6867 ms per step:96.66 epoch:33/100 step:24/25 Dloss:0.2046 Gloss:17.0413 ms per step:96.54 epoch:34/100 step:0/25 Dloss:0.1508 Gloss:16.1241 ms per step:96.50 epoch:34/100 step:2/25 Dloss:0.2339 Gloss:13.5640 ms per step:96.04 epoch:34/100 step:4/25 Dloss:0.2181 Gloss:17.3504 ms per step:97.40 epoch:34/100 step:6/25 Dloss:0.5518 Gloss:15.0297 ms per step:96.96 epoch:34/100 step:8/25 Dloss:0.4233 Gloss:16.3343 ms per step:103.60 epoch:34/100 step:10/25 Dloss:0.3041 Gloss:15.7255 ms per step:128.23 epoch:34/100 step:12/25 Dloss:0.2994 Gloss:15.9517 ms per step:103.36 epoch:34/100 step:14/25 Dloss:0.3098 Gloss:14.9478 ms per step:126.48 epoch:34/100 step:16/25 Dloss:0.2625 Gloss:16.0261 ms per step:98.90 epoch:34/100 step:18/25 Dloss:0.5825 Gloss:16.1667 ms per step:97.76 epoch:34/100 step:20/25 Dloss:0.5257 Gloss:15.5318 ms per step:97.73 epoch:34/100 step:22/25 Dloss:0.7086 Gloss:17.2141 ms per step:101.68 epoch:34/100 step:24/25 Dloss:0.3330 Gloss:16.7725 ms per step:96.30 epoch:35/100 step:0/25 Dloss:0.3603 Gloss:15.8449 ms per step:101.27 epoch:35/100 step:2/25 Dloss:0.2219 Gloss:16.0918 ms per step:95.76 epoch:35/100 step:4/25 Dloss:0.4089 Gloss:16.0361 ms per step:96.60 epoch:35/100 step:6/25 Dloss:0.3502 Gloss:16.9639 ms per step:98.02 epoch:35/100 step:8/25 Dloss:0.3991 Gloss:15.7897 ms per step:104.94 epoch:35/100 step:10/25 Dloss:0.2450 Gloss:15.1704 ms per step:109.84 epoch:35/100 step:12/25 Dloss:0.3833 Gloss:14.8413 ms per step:106.89 epoch:35/100 step:14/25 Dloss:0.1805 Gloss:14.5697 ms per step:109.18 epoch:35/100 step:16/25 Dloss:0.2484 Gloss:14.7032 ms per step:96.79 epoch:35/100 step:18/25 Dloss:0.2053 Gloss:16.2399 ms per step:97.28 epoch:35/100 step:20/25 Dloss:0.3633 Gloss:14.9062 ms per step:97.22 epoch:35/100 step:22/25 Dloss:0.4150 Gloss:15.4078 ms per step:99.72 epoch:35/100 step:24/25 Dloss:0.2037 Gloss:16.7491 ms per step:96.56 epoch:36/100 step:0/25 Dloss:0.3851 Gloss:14.2723 ms per step:97.28 epoch:36/100 step:2/25 Dloss:0.2257 Gloss:16.8603 ms per step:96.26 epoch:36/100 step:4/25 Dloss:0.3138 Gloss:15.3638 ms per step:96.58 epoch:36/100 step:6/25 Dloss:0.2694 Gloss:15.1657 ms per step:100.73 epoch:36/100 step:8/25 Dloss:0.5680 Gloss:15.9754 ms per step:143.54 epoch:36/100 step:10/25 Dloss:0.3387 Gloss:17.9910 ms per step:103.72 epoch:36/100 step:12/25 Dloss:0.7280 Gloss:14.3611 ms per step:106.62 epoch:36/100 step:14/25 Dloss:0.3644 Gloss:15.6079 ms per step:106.44 epoch:36/100 step:16/25 Dloss:0.4030 Gloss:13.8805 ms per step:98.01 epoch:36/100 step:18/25 Dloss:0.4007 Gloss:14.3715 ms per step:98.03 epoch:36/100 step:20/25 Dloss:0.3061 Gloss:17.1476 ms per step:95.91 epoch:36/100 step:22/25 Dloss:0.3463 Gloss:17.9034 ms per step:118.11 epoch:36/100 step:24/25 Dloss:0.2396 Gloss:15.2886 ms per step:98.30 epoch:37/100 step:0/25 Dloss:0.5822 Gloss:15.3606 ms per step:97.77 epoch:37/100 step:2/25 Dloss:0.2864 Gloss:14.9943 ms per step:101.28 epoch:37/100 step:4/25 Dloss:0.4345 Gloss:15.4389 ms per step:95.34 epoch:37/100 step:6/25 Dloss:0.4290 Gloss:14.6737 ms per step:99.81 epoch:37/100 step:8/25 Dloss:0.4440 Gloss:13.8994 ms per step:107.71 epoch:37/100 step:10/25 Dloss:0.3487 Gloss:13.1993 ms per step:110.99 epoch:37/100 step:12/25 Dloss:0.3297 Gloss:15.1093 ms per step:130.89 epoch:37/100 step:14/25 Dloss:0.1877 Gloss:15.6246 ms per step:107.19 epoch:37/100 step:16/25 Dloss:0.1978 Gloss:14.8823 ms per step:100.42 epoch:37/100 step:18/25 Dloss:0.3243 Gloss:14.6991 ms per step:96.94 epoch:37/100 step:20/25 Dloss:0.3623 Gloss:13.9333 ms per step:97.56 epoch:37/100 step:22/25 Dloss:0.8251 Gloss:15.5981 ms per step:98.40 epoch:37/100 step:24/25 Dloss:0.3141 Gloss:14.6158 ms per step:112.60 epoch:38/100 step:0/25 Dloss:0.2940 Gloss:14.3617 ms per step:107.98 epoch:38/100 step:2/25 Dloss:0.2311 Gloss:15.4742 ms per step:101.60 epoch:38/100 step:4/25 Dloss:0.2054 Gloss:14.2449 ms per step:97.68 epoch:38/100 step:6/25 Dloss:0.1813 Gloss:13.1655 ms per step:99.64 epoch:38/100 step:8/25 Dloss:0.2529 Gloss:13.8493 ms per step:139.75 epoch:38/100 step:10/25 Dloss:0.5853 Gloss:15.7463 ms per step:140.51 epoch:38/100 step:12/25 Dloss:0.6124 Gloss:15.2568 ms per step:129.07 epoch:38/100 step:14/25 Dloss:0.4089 Gloss:14.1301 ms per step:102.14 epoch:38/100 step:16/25 Dloss:0.2702 Gloss:13.6872 ms per step:101.78 epoch:38/100 step:18/25 Dloss:0.2379 Gloss:14.7659 ms per step:102.41 epoch:38/100 step:20/25 Dloss:0.2659 Gloss:15.7267 ms per step:101.50 epoch:38/100 step:22/25 Dloss:0.1935 Gloss:16.8655 ms per step:100.05 epoch:38/100 step:24/25 Dloss:0.2827 Gloss:15.6609 ms per step:99.65 epoch:39/100 step:0/25 Dloss:0.4028 Gloss:12.8613 ms per step:98.20 epoch:39/100 step:2/25 Dloss:0.3947 Gloss:15.7700 ms per step:101.94 epoch:39/100 step:4/25 Dloss:0.3869 Gloss:14.1484 ms per step:98.92 epoch:39/100 step:6/25 Dloss:0.2303 Gloss:14.7239 ms per step:106.24 epoch:39/100 step:8/25 Dloss:0.2705 Gloss:14.9515 ms per step:106.33 epoch:39/100 step:10/25 Dloss:0.2508 Gloss:14.1135 ms per step:106.70 epoch:39/100 step:12/25 Dloss:0.4355 Gloss:14.8306 ms per step:110.41 epoch:39/100 step:14/25 Dloss:0.4513 Gloss:14.5726 ms per step:135.72 epoch:39/100 step:16/25 Dloss:0.6247 Gloss:15.3168 ms per step:98.45 epoch:39/100 step:18/25 Dloss:0.3027 Gloss:14.8332 ms per step:99.15 epoch:39/100 step:20/25 Dloss:0.2331 Gloss:15.1761 ms per step:98.64 epoch:39/100 step:22/25 Dloss:0.2657 Gloss:14.6750 ms per step:98.76 epoch:39/100 step:24/25 Dloss:0.4145 Gloss:14.2217 ms per step:98.15 epoch:40/100 step:0/25 Dloss:0.7223 Gloss:13.7114 ms per step:97.60 epoch:40/100 step:2/25 Dloss:0.8602 Gloss:13.8505 ms per step:98.88 epoch:40/100 step:4/25 Dloss:0.4073 Gloss:15.1188 ms per step:97.34 epoch:40/100 step:6/25 Dloss:0.3739 Gloss:15.1431 ms per step:98.03 epoch:40/100 step:8/25 Dloss:0.2511 Gloss:14.2960 ms per step:127.32 epoch:40/100 step:10/25 Dloss:0.2506 Gloss:14.3230 ms per step:106.12 epoch:40/100 step:12/25 Dloss:0.3550 Gloss:17.5432 ms per step:105.25 epoch:40/100 step:14/25 Dloss:0.1936 Gloss:16.7018 ms per step:105.45 epoch:40/100 step:16/25 Dloss:0.2809 Gloss:13.4992 ms per step:98.29 epoch:40/100 step:18/25 Dloss:0.3368 Gloss:12.2888 ms per step:99.62 epoch:40/100 step:20/25 Dloss:0.3178 Gloss:14.7571 ms per step:97.46 epoch:40/100 step:22/25 Dloss:0.2947 Gloss:13.9504 ms per step:97.73 epoch:40/100 step:24/25 Dloss:0.3001 Gloss:13.5222 ms per step:98.75 epoch:41/100 step:0/25 Dloss:0.2473 Gloss:14.4535 ms per step:97.16 epoch:41/100 step:2/25 Dloss:0.2101 Gloss:15.0425 ms per step:98.30 epoch:41/100 step:4/25 Dloss:0.2087 Gloss:15.7633 ms per step:97.57 epoch:41/100 step:6/25 Dloss:0.4985 Gloss:13.6644 ms per step:98.06 epoch:41/100 step:8/25 Dloss:0.5168 Gloss:14.9434 ms per step:105.78 epoch:41/100 step:10/25 Dloss:0.5632 Gloss:14.8322 ms per step:105.74 epoch:41/100 step:12/25 Dloss:0.3882 Gloss:14.5167 ms per step:125.86 epoch:41/100 step:14/25 Dloss:0.3156 Gloss:13.4330 ms per step:108.53 epoch:41/100 step:16/25 Dloss:0.2077 Gloss:15.2035 ms per step:98.00 epoch:41/100 step:18/25 Dloss:0.2644 Gloss:13.5223 ms per step:98.52 epoch:41/100 step:20/25 Dloss:0.2473 Gloss:13.8799 ms per step:99.64 epoch:41/100 step:22/25 Dloss:0.3658 Gloss:11.9632 ms per step:97.83 epoch:41/100 step:24/25 Dloss:0.5015 Gloss:13.5292 ms per step:99.21 epoch:42/100 step:0/25 Dloss:0.5054 Gloss:13.9852 ms per step:97.32 epoch:42/100 step:2/25 Dloss:0.3696 Gloss:13.3376 ms per step:100.23 epoch:42/100 step:4/25 Dloss:0.2582 Gloss:13.5459 ms per step:97.76 epoch:42/100 step:6/25 Dloss:0.2201 Gloss:12.5249 ms per step:98.61 epoch:42/100 step:8/25 Dloss:0.2892 Gloss:12.2936 ms per step:105.34 epoch:42/100 step:10/25 Dloss:0.2926 Gloss:13.2115 ms per step:106.55 epoch:42/100 step:12/25 Dloss:0.8180 Gloss:15.4382 ms per step:108.12 epoch:42/100 step:14/25 Dloss:0.5467 Gloss:14.1481 ms per step:105.28 epoch:42/100 step:16/25 Dloss:0.3230 Gloss:13.8746 ms per step:99.18 epoch:42/100 step:18/25 Dloss:0.3606 Gloss:14.1825 ms per step:97.81 epoch:42/100 step:20/25 Dloss:0.2653 Gloss:13.1178 ms per step:99.04 epoch:42/100 step:22/25 Dloss:0.3527 Gloss:16.1646 ms per step:98.61 epoch:42/100 step:24/25 Dloss:0.3772 Gloss:15.8270 ms per step:99.15 epoch:43/100 step:0/25 Dloss:0.3760 Gloss:13.8284 ms per step:100.23 epoch:43/100 step:2/25 Dloss:0.5371 Gloss:12.5458 ms per step:105.79 epoch:43/100 step:4/25 Dloss:0.3896 Gloss:14.1019 ms per step:100.85 epoch:43/100 step:6/25 Dloss:0.3092 Gloss:14.4113 ms per step:104.89 epoch:43/100 step:8/25 Dloss:0.3022 Gloss:12.4935 ms per step:135.75 epoch:43/100 step:10/25 Dloss:0.2667 Gloss:13.8905 ms per step:106.41 epoch:43/100 step:12/25 Dloss:0.6177 Gloss:12.8958 ms per step:103.78 epoch:43/100 step:14/25 Dloss:0.5342 Gloss:13.3103 ms per step:105.20 epoch:43/100 step:16/25 Dloss:0.4589 Gloss:13.0687 ms per step:101.37 epoch:43/100 step:18/25 Dloss:0.3227 Gloss:16.5795 ms per step:96.69 epoch:43/100 step:20/25 Dloss:0.4688 Gloss:13.0339 ms per step:99.33 epoch:43/100 step:22/25 Dloss:0.4984 Gloss:12.2296 ms per step:98.08 epoch:43/100 step:24/25 Dloss:0.4598 Gloss:14.5670 ms per step:96.34 epoch:44/100 step:0/25 Dloss:0.8077 Gloss:14.2064 ms per step:97.27 epoch:44/100 step:2/25 Dloss:0.5531 Gloss:12.7416 ms per step:96.36 epoch:44/100 step:4/25 Dloss:0.4489 Gloss:13.7550 ms per step:98.28 epoch:44/100 step:6/25 Dloss:0.5393 Gloss:12.6501 ms per step:98.07 epoch:44/100 step:8/25 Dloss:0.3647 Gloss:13.5786 ms per step:107.02 epoch:44/100 step:10/25 Dloss:0.3748 Gloss:13.9286 ms per step:104.73 epoch:44/100 step:12/25 Dloss:0.3769 Gloss:14.9645 ms per step:104.83 epoch:44/100 step:14/25 Dloss:0.3898 Gloss:13.8285 ms per step:119.18 epoch:44/100 step:16/25 Dloss:0.4265 Gloss:13.5696 ms per step:97.16 epoch:44/100 step:18/25 Dloss:0.5924 Gloss:12.6545 ms per step:97.23 epoch:44/100 step:20/25 Dloss:0.4313 Gloss:12.8075 ms per step:96.39 epoch:44/100 step:22/25 Dloss:0.3763 Gloss:12.5626 ms per step:96.83 epoch:44/100 step:24/25 Dloss:0.4616 Gloss:11.5262 ms per step:97.44 epoch:45/100 step:0/25 Dloss:0.4459 Gloss:14.1252 ms per step:96.61 epoch:45/100 step:2/25 Dloss:0.2761 Gloss:13.4958 ms per step:96.92 epoch:45/100 step:4/25 Dloss:0.5254 Gloss:12.6856 ms per step:96.94 epoch:45/100 step:6/25 Dloss:0.4519 Gloss:12.6776 ms per step:97.35 epoch:45/100 step:8/25 Dloss:0.7917 Gloss:13.1139 ms per step:126.68 epoch:45/100 step:10/25 Dloss:0.5488 Gloss:12.7250 ms per step:105.11 epoch:45/100 step:12/25 Dloss:0.4481 Gloss:12.8375 ms per step:106.17 epoch:45/100 step:14/25 Dloss:0.4875 Gloss:12.3398 ms per step:132.50 epoch:45/100 step:16/25 Dloss:0.3656 Gloss:12.2982 ms per step:97.33 epoch:45/100 step:18/25 Dloss:0.3069 Gloss:12.6054 ms per step:95.55 epoch:45/100 step:20/25 Dloss:0.3213 Gloss:12.9775 ms per step:97.07 epoch:45/100 step:22/25 Dloss:0.4016 Gloss:11.3416 ms per step:96.62 epoch:45/100 step:24/25 Dloss:0.3987 Gloss:12.4437 ms per step:108.90 epoch:46/100 step:0/25 Dloss:0.3723 Gloss:12.7536 ms per step:97.98 epoch:46/100 step:2/25 Dloss:0.5461 Gloss:14.9380 ms per step:95.82 epoch:46/100 step:4/25 Dloss:0.2856 Gloss:13.2420 ms per step:96.09 epoch:46/100 step:6/25 Dloss:0.2898 Gloss:12.3241 ms per step:96.97 epoch:46/100 step:8/25 Dloss:0.5355 Gloss:12.8218 ms per step:105.72 epoch:46/100 step:10/25 Dloss:0.7188 Gloss:13.1113 ms per step:106.38 epoch:46/100 step:12/25 Dloss:0.5568 Gloss:13.1963 ms per step:107.95 epoch:46/100 step:14/25 Dloss:0.4144 Gloss:12.8916 ms per step:104.67 epoch:46/100 step:16/25 Dloss:0.3715 Gloss:11.9469 ms per step:96.00 epoch:46/100 step:18/25 Dloss:0.3513 Gloss:12.4033 ms per step:96.96 epoch:46/100 step:20/25 Dloss:0.3673 Gloss:13.0759 ms per step:106.88 epoch:46/100 step:22/25 Dloss:0.5264 Gloss:12.4882 ms per step:101.91 epoch:46/100 step:24/25 Dloss:0.6276 Gloss:11.7067 ms per step:97.86 epoch:47/100 step:0/25 Dloss:0.8157 Gloss:14.1273 ms per step:97.61 epoch:47/100 step:2/25 Dloss:0.3069 Gloss:13.6492 ms per step:98.55 epoch:47/100 step:4/25 Dloss:0.3247 Gloss:12.2684 ms per step:96.82 epoch:47/100 step:6/25 Dloss:0.2620 Gloss:13.1911 ms per step:97.44 epoch:47/100 step:8/25 Dloss:0.3609 Gloss:13.2669 ms per step:103.90 epoch:47/100 step:10/25 Dloss:0.7484 Gloss:13.2896 ms per step:105.86 epoch:47/100 step:12/25 Dloss:0.5265 Gloss:13.4602 ms per step:104.97 epoch:47/100 step:14/25 Dloss:0.5921 Gloss:12.4433 ms per step:121.51 epoch:47/100 step:16/25 Dloss:0.4571 Gloss:12.3026 ms per step:96.06 epoch:47/100 step:18/25 Dloss:0.3974 Gloss:11.7008 ms per step:97.47 epoch:47/100 step:20/25 Dloss:0.3674 Gloss:12.4367 ms per step:102.13 epoch:47/100 step:22/25 Dloss:0.2900 Gloss:12.4297 ms per step:101.64 epoch:47/100 step:24/25 Dloss:0.4196 Gloss:10.8767 ms per step:97.30 epoch:48/100 step:0/25 Dloss:0.4383 Gloss:10.7088 ms per step:100.98 epoch:48/100 step:2/25 Dloss:0.4166 Gloss:13.5078 ms per step:100.49 epoch:48/100 step:4/25 Dloss:0.5590 Gloss:11.7251 ms per step:98.61 epoch:48/100 step:6/25 Dloss:0.6213 Gloss:11.8545 ms per step:98.95 epoch:48/100 step:8/25 Dloss:0.3619 Gloss:11.7317 ms per step:105.81 epoch:48/100 step:10/25 Dloss:0.4362 Gloss:12.9007 ms per step:107.01 epoch:48/100 step:12/25 Dloss:0.2797 Gloss:12.7246 ms per step:104.52 epoch:48/100 step:14/25 Dloss:0.4781 Gloss:11.5047 ms per step:115.96 epoch:48/100 step:16/25 Dloss:0.6003 Gloss:11.5007 ms per step:100.01 epoch:48/100 step:18/25 Dloss:0.4442 Gloss:12.2953 ms per step:99.22 epoch:48/100 step:20/25 Dloss:0.5811 Gloss:12.2399 ms per step:97.80 epoch:48/100 step:22/25 Dloss:0.4685 Gloss:12.3062 ms per step:101.98 epoch:48/100 step:24/25 Dloss:0.5672 Gloss:12.7262 ms per step:97.31 epoch:49/100 step:0/25 Dloss:0.5458 Gloss:14.0043 ms per step:97.26 epoch:49/100 step:2/25 Dloss:0.2912 Gloss:12.3867 ms per step:96.60 epoch:49/100 step:4/25 Dloss:0.3920 Gloss:13.0228 ms per step:101.64 epoch:49/100 step:6/25 Dloss:0.6817 Gloss:13.0263 ms per step:96.77 epoch:49/100 step:8/25 Dloss:0.6422 Gloss:11.8432 ms per step:104.06 epoch:49/100 step:10/25 Dloss:0.5683 Gloss:12.0917 ms per step:117.51 epoch:49/100 step:12/25 Dloss:0.3371 Gloss:12.7969 ms per step:110.67 epoch:49/100 step:14/25 Dloss:0.4230 Gloss:11.2478 ms per step:111.12 epoch:49/100 step:16/25 Dloss:0.3103 Gloss:11.3678 ms per step:101.08 epoch:49/100 step:18/25 Dloss:0.4252 Gloss:12.0863 ms per step:96.92 epoch:49/100 step:20/25 Dloss:0.4168 Gloss:12.8535 ms per step:98.02 epoch:49/100 step:22/25 Dloss:0.7757 Gloss:11.7664 ms per step:97.42 epoch:49/100 step:24/25 Dloss:0.5229 Gloss:13.2126 ms per step:97.67 epoch:50/100 step:0/25 Dloss:0.5795 Gloss:12.2427 ms per step:96.35 epoch:50/100 step:2/25 Dloss:0.3731 Gloss:12.0884 ms per step:99.31 epoch:50/100 step:4/25 Dloss:0.3703 Gloss:11.1971 ms per step:103.30 epoch:50/100 step:6/25 Dloss:0.3482 Gloss:12.9322 ms per step:100.52 epoch:50/100 step:8/25 Dloss:0.4353 Gloss:12.5127 ms per step:106.95 epoch:50/100 step:10/25 Dloss:0.5801 Gloss:11.3761 ms per step:119.65 epoch:50/100 step:12/25 Dloss:0.3625 Gloss:11.7094 ms per step:134.82 epoch:50/100 step:14/25 Dloss:0.5007 Gloss:12.0538 ms per step:108.04 epoch:50/100 step:16/25 Dloss:0.5092 Gloss:12.1419 ms per step:99.83 epoch:50/100 step:18/25 Dloss:0.6654 Gloss:11.6835 ms per step:100.93 epoch:50/100 step:20/25 Dloss:0.3819 Gloss:12.3631 ms per step:104.23 epoch:50/100 step:22/25 Dloss:0.3556 Gloss:11.9356 ms per step:99.91 epoch:50/100 step:24/25 Dloss:0.3005 Gloss:11.9859 ms per step:100.95 epoch:51/100 step:0/25 Dloss:0.2768 Gloss:11.7612 ms per step:99.77 epoch:51/100 step:2/25 Dloss:0.5767 Gloss:11.7958 ms per step:99.35 epoch:51/100 step:4/25 Dloss:0.6627 Gloss:11.8194 ms per step:101.07 epoch:51/100 step:6/25 Dloss:0.7372 Gloss:11.3262 ms per step:99.29 epoch:51/100 step:8/25 Dloss:0.4244 Gloss:12.5333 ms per step:108.02 epoch:51/100 step:10/25 Dloss:0.3954 Gloss:11.1332 ms per step:106.32 epoch:51/100 step:12/25 Dloss:0.3181 Gloss:11.9812 ms per step:107.33 epoch:51/100 step:14/25 Dloss:0.2828 Gloss:11.4513 ms per step:104.24 epoch:51/100 step:16/25 Dloss:0.4032 Gloss:12.0914 ms per step:97.96 epoch:51/100 step:18/25 Dloss:0.4253 Gloss:12.0994 ms per step:98.07 epoch:51/100 step:20/25 Dloss:0.4757 Gloss:13.8628 ms per step:96.75 epoch:51/100 step:22/25 Dloss:0.4933 Gloss:11.4924 ms per step:96.71 epoch:51/100 step:24/25 Dloss:0.3689 Gloss:12.6341 ms per step:97.12 epoch:52/100 step:0/25 Dloss:0.3674 Gloss:12.0886 ms per step:100.29 epoch:52/100 step:2/25 Dloss:0.3505 Gloss:10.7783 ms per step:96.45 epoch:52/100 step:4/25 Dloss:0.4006 Gloss:12.5076 ms per step:97.44 epoch:52/100 step:6/25 Dloss:0.3684 Gloss:12.9985 ms per step:100.46 epoch:52/100 step:8/25 Dloss:0.4971 Gloss:11.6595 ms per step:106.68 epoch:52/100 step:10/25 Dloss:0.8337 Gloss:13.3431 ms per step:105.35 epoch:52/100 step:12/25 Dloss:0.4074 Gloss:11.3735 ms per step:104.86 epoch:52/100 step:14/25 Dloss:0.4305 Gloss:11.6835 ms per step:105.58 epoch:52/100 step:16/25 Dloss:0.5156 Gloss:11.4562 ms per step:98.04 epoch:52/100 step:18/25 Dloss:0.6212 Gloss:11.4215 ms per step:100.43 epoch:52/100 step:20/25 Dloss:0.3508 Gloss:13.1385 ms per step:97.51 epoch:52/100 step:22/25 Dloss:0.3570 Gloss:11.8557 ms per step:97.45 epoch:52/100 step:24/25 Dloss:0.4029 Gloss:11.9170 ms per step:97.04 epoch:53/100 step:0/25 Dloss:0.4193 Gloss:10.9841 ms per step:98.54 epoch:53/100 step:2/25 Dloss:0.3667 Gloss:12.0504 ms per step:97.63 epoch:53/100 step:4/25 Dloss:0.5321 Gloss:13.2151 ms per step:98.98 epoch:53/100 step:6/25 Dloss:0.5907 Gloss:11.9479 ms per step:97.90 epoch:53/100 step:8/25 Dloss:0.4285 Gloss:11.9451 ms per step:105.47 epoch:53/100 step:10/25 Dloss:0.2677 Gloss:12.7194 ms per step:105.37 epoch:53/100 step:12/25 Dloss:0.2697 Gloss:11.3483 ms per step:105.60 epoch:53/100 step:14/25 Dloss:0.3215 Gloss:11.4588 ms per step:107.64 epoch:53/100 step:16/25 Dloss:0.4777 Gloss:12.1538 ms per step:97.42 epoch:53/100 step:18/25 Dloss:0.8412 Gloss:13.3639 ms per step:98.07 epoch:53/100 step:20/25 Dloss:0.7679 Gloss:11.9476 ms per step:95.86 epoch:53/100 step:22/25 Dloss:0.4972 Gloss:12.5294 ms per step:96.95 epoch:53/100 step:24/25 Dloss:0.3818 Gloss:11.2317 ms per step:96.02 epoch:54/100 step:0/25 Dloss:0.3488 Gloss:12.7709 ms per step:97.36 epoch:54/100 step:2/25 Dloss:0.2984 Gloss:12.1337 ms per step:97.03 epoch:54/100 step:4/25 Dloss:0.5031 Gloss:10.2937 ms per step:96.95 epoch:54/100 step:6/25 Dloss:0.5138 Gloss:10.4693 ms per step:96.68 epoch:54/100 step:8/25 Dloss:0.4432 Gloss:11.4550 ms per step:105.67 epoch:54/100 step:10/25 Dloss:0.4123 Gloss:12.3023 ms per step:105.79 epoch:54/100 step:12/25 Dloss:0.4407 Gloss:11.1429 ms per step:105.08 epoch:54/100 step:14/25 Dloss:0.3392 Gloss:11.7460 ms per step:107.09 epoch:54/100 step:16/25 Dloss:0.3291 Gloss:11.0476 ms per step:100.47 epoch:54/100 step:18/25 Dloss:0.4755 Gloss:11.1438 ms per step:98.54 epoch:54/100 step:20/25 Dloss:0.3497 Gloss:11.1534 ms per step:100.46 epoch:54/100 step:22/25 Dloss:0.3545 Gloss:11.6293 ms per step:98.02 epoch:54/100 step:24/25 Dloss:0.4226 Gloss:13.0790 ms per step:98.10 epoch:55/100 step:0/25 Dloss:0.4310 Gloss:11.3384 ms per step:97.43 epoch:55/100 step:2/25 Dloss:0.4633 Gloss:10.7768 ms per step:97.95 epoch:55/100 step:4/25 Dloss:0.3709 Gloss:11.9606 ms per step:99.28 epoch:55/100 step:6/25 Dloss:0.4031 Gloss:11.2025 ms per step:97.27 epoch:55/100 step:8/25 Dloss:0.5278 Gloss:11.8508 ms per step:104.06 epoch:55/100 step:10/25 Dloss:0.5248 Gloss:11.8630 ms per step:104.53 epoch:55/100 step:12/25 Dloss:0.6093 Gloss:12.1857 ms per step:103.06 epoch:55/100 step:14/25 Dloss:0.3471 Gloss:12.1212 ms per step:103.18 epoch:55/100 step:16/25 Dloss:0.4096 Gloss:12.1174 ms per step:97.54 epoch:55/100 step:18/25 Dloss:0.5687 Gloss:11.3148 ms per step:97.44 epoch:55/100 step:20/25 Dloss:0.5130 Gloss:11.4408 ms per step:105.66 epoch:55/100 step:22/25 Dloss:0.5215 Gloss:11.8262 ms per step:100.71 epoch:55/100 step:24/25 Dloss:0.5147 Gloss:11.5154 ms per step:97.40 epoch:56/100 step:0/25 Dloss:0.4542 Gloss:10.5607 ms per step:98.20 epoch:56/100 step:2/25 Dloss:0.3198 Gloss:12.7340 ms per step:97.62 epoch:56/100 step:4/25 Dloss:0.4362 Gloss:12.6521 ms per step:97.11 epoch:56/100 step:6/25 Dloss:0.2941 Gloss:11.1877 ms per step:97.08 epoch:56/100 step:8/25 Dloss:0.3534 Gloss:10.9625 ms per step:103.18 epoch:56/100 step:10/25 Dloss:0.3315 Gloss:11.5538 ms per step:102.90 epoch:56/100 step:12/25 Dloss:0.4579 Gloss:12.8862 ms per step:103.25 epoch:56/100 step:14/25 Dloss:0.2711 Gloss:12.2871 ms per step:105.80 epoch:56/100 step:16/25 Dloss:0.4124 Gloss:11.0175 ms per step:100.82 epoch:56/100 step:18/25 Dloss:0.3928 Gloss:11.3780 ms per step:97.52 epoch:56/100 step:20/25 Dloss:0.3673 Gloss:11.2428 ms per step:100.53 epoch:56/100 step:22/25 Dloss:0.4246 Gloss:11.2912 ms per step:100.54 epoch:56/100 step:24/25 Dloss:0.3636 Gloss:11.5546 ms per step:97.86 epoch:57/100 step:0/25 Dloss:0.3135 Gloss:12.6024 ms per step:97.02 epoch:57/100 step:2/25 Dloss:0.2846 Gloss:12.7947 ms per step:98.53 epoch:57/100 step:4/25 Dloss:0.1950 Gloss:13.1210 ms per step:97.19 epoch:57/100 step:6/25 Dloss:0.4789 Gloss:12.1808 ms per step:98.77 epoch:57/100 step:8/25 Dloss:1.0249 Gloss:11.2005 ms per step:105.06 epoch:57/100 step:10/25 Dloss:0.5618 Gloss:12.2865 ms per step:105.16 epoch:57/100 step:12/25 Dloss:0.4195 Gloss:11.5979 ms per step:107.21 epoch:57/100 step:14/25 Dloss:0.2279 Gloss:11.9391 ms per step:105.54 epoch:57/100 step:16/25 Dloss:0.2636 Gloss:12.2098 ms per step:95.31 epoch:57/100 step:18/25 Dloss:0.4779 Gloss:12.2958 ms per step:95.60 epoch:57/100 step:20/25 Dloss:0.6658 Gloss:12.0602 ms per step:97.24 epoch:57/100 step:22/25 Dloss:0.7232 Gloss:12.4160 ms per step:94.89 epoch:57/100 step:24/25 Dloss:0.3936 Gloss:11.1692 ms per step:96.56 epoch:58/100 step:0/25 Dloss:0.4153 Gloss:10.0797 ms per step:96.52 epoch:58/100 step:2/25 Dloss:0.4698 Gloss:10.8337 ms per step:95.51 epoch:58/100 step:4/25 Dloss:0.7222 Gloss:11.4187 ms per step:95.96 epoch:58/100 step:6/25 Dloss:0.5471 Gloss:12.5028 ms per step:98.09 epoch:58/100 step:8/25 Dloss:0.5436 Gloss:10.6078 ms per step:123.89 epoch:58/100 step:10/25 Dloss:0.3751 Gloss:10.7369 ms per step:104.70 epoch:58/100 step:12/25 Dloss:0.3950 Gloss:10.9655 ms per step:104.99 epoch:58/100 step:14/25 Dloss:0.3142 Gloss:11.3345 ms per step:105.56 epoch:58/100 step:16/25 Dloss:0.2803 Gloss:11.4462 ms per step:97.42 epoch:58/100 step:18/25 Dloss:0.3864 Gloss:10.9695 ms per step:97.40 epoch:58/100 step:20/25 Dloss:0.5120 Gloss:11.7873 ms per step:97.50 epoch:58/100 step:22/25 Dloss:0.4303 Gloss:10.8452 ms per step:96.77 epoch:58/100 step:24/25 Dloss:0.4576 Gloss:10.7594 ms per step:96.20 epoch:59/100 step:0/25 Dloss:0.2809 Gloss:12.4908 ms per step:97.94 epoch:59/100 step:2/25 Dloss:0.4187 Gloss:11.1622 ms per step:96.26 epoch:59/100 step:4/25 Dloss:0.3375 Gloss:11.2609 ms per step:96.20 epoch:59/100 step:6/25 Dloss:0.3192 Gloss:10.9699 ms per step:97.05 epoch:59/100 step:8/25 Dloss:0.2934 Gloss:11.1831 ms per step:105.21 epoch:59/100 step:10/25 Dloss:0.3769 Gloss:13.4488 ms per step:103.70 epoch:59/100 step:12/25 Dloss:0.3773 Gloss:11.7507 ms per step:139.62 epoch:59/100 step:14/25 Dloss:0.2886 Gloss:11.8666 ms per step:111.05 epoch:59/100 step:16/25 Dloss:0.2437 Gloss:12.5939 ms per step:96.58 epoch:59/100 step:18/25 Dloss:0.4620 Gloss:11.3511 ms per step:98.33 epoch:59/100 step:20/25 Dloss:0.7487 Gloss:10.5775 ms per step:101.42 epoch:59/100 step:22/25 Dloss:0.4970 Gloss:14.6681 ms per step:97.38 epoch:59/100 step:24/25 Dloss:0.3267 Gloss:11.2459 ms per step:104.91 epoch:60/100 step:0/25 Dloss:0.3448 Gloss:11.3164 ms per step:96.43 epoch:60/100 step:2/25 Dloss:0.3652 Gloss:10.4771 ms per step:96.51 epoch:60/100 step:4/25 Dloss:0.5229 Gloss:10.8432 ms per step:97.19 epoch:60/100 step:6/25 Dloss:0.4796 Gloss:13.1254 ms per step:95.40 epoch:60/100 step:8/25 Dloss:0.6358 Gloss:12.9104 ms per step:117.81 epoch:60/100 step:10/25 Dloss:0.4124 Gloss:11.6393 ms per step:112.62 epoch:60/100 step:12/25 Dloss:0.4005 Gloss:10.8148 ms per step:106.44 epoch:60/100 step:14/25 Dloss:0.4766 Gloss:10.9539 ms per step:116.96 epoch:60/100 step:16/25 Dloss:0.4589 Gloss:9.6456 ms per step:98.36 epoch:60/100 step:18/25 Dloss:0.5077 Gloss:10.9015 ms per step:99.44 epoch:60/100 step:20/25 Dloss:0.3087 Gloss:11.1825 ms per step:96.82 epoch:60/100 step:22/25 Dloss:0.3830 Gloss:11.9578 ms per step:97.30 epoch:60/100 step:24/25 Dloss:0.2887 Gloss:12.6586 ms per step:100.26 epoch:61/100 step:0/25 Dloss:0.3832 Gloss:11.3697 ms per step:100.00 epoch:61/100 step:2/25 Dloss:0.3762 Gloss:11.2534 ms per step:102.41 epoch:61/100 step:4/25 Dloss:0.3569 Gloss:11.0815 ms per step:100.03 epoch:61/100 step:6/25 Dloss:0.3109 Gloss:10.9940 ms per step:97.14 epoch:61/100 step:8/25 Dloss:0.2491 Gloss:11.1991 ms per step:135.14 epoch:61/100 step:10/25 Dloss:0.2806 Gloss:10.0623 ms per step:106.83 epoch:61/100 step:12/25 Dloss:0.2456 Gloss:11.3681 ms per step:134.62 epoch:61/100 step:14/25 Dloss:0.2156 Gloss:10.7684 ms per step:103.08 epoch:61/100 step:16/25 Dloss:0.3906 Gloss:11.2433 ms per step:100.48 epoch:61/100 step:18/25 Dloss:0.9109 Gloss:10.9749 ms per step:95.47 epoch:61/100 step:20/25 Dloss:0.5805 Gloss:12.0455 ms per step:96.95 epoch:61/100 step:22/25 Dloss:0.3869 Gloss:9.9103 ms per step:95.59 epoch:61/100 step:24/25 Dloss:0.3435 Gloss:10.6366 ms per step:97.84 epoch:62/100 step:0/25 Dloss:0.3136 Gloss:10.3077 ms per step:95.54 epoch:62/100 step:2/25 Dloss:0.3806 Gloss:9.5601 ms per step:98.72 epoch:62/100 step:4/25 Dloss:0.4390 Gloss:9.9382 ms per step:96.04 epoch:62/100 step:6/25 Dloss:0.4904 Gloss:10.8363 ms per step:98.72 epoch:62/100 step:8/25 Dloss:0.5896 Gloss:10.2382 ms per step:103.72 epoch:62/100 step:10/25 Dloss:0.5475 Gloss:10.2236 ms per step:104.88 epoch:62/100 step:12/25 Dloss:0.4197 Gloss:9.6912 ms per step:126.90 epoch:62/100 step:14/25 Dloss:0.2477 Gloss:10.6298 ms per step:104.92 epoch:62/100 step:16/25 Dloss:0.2212 Gloss:10.2353 ms per step:96.66 epoch:62/100 step:18/25 Dloss:0.2580 Gloss:10.5120 ms per step:96.77 epoch:62/100 step:20/25 Dloss:0.2491 Gloss:10.9265 ms per step:97.32 epoch:62/100 step:22/25 Dloss:0.2607 Gloss:10.7973 ms per step:95.70 epoch:62/100 step:24/25 Dloss:0.3067 Gloss:12.3965 ms per step:112.41 epoch:63/100 step:0/25 Dloss:0.2871 Gloss:11.0337 ms per step:95.48 epoch:63/100 step:2/25 Dloss:0.5242 Gloss:10.8298 ms per step:96.20 epoch:63/100 step:4/25 Dloss:0.6841 Gloss:10.2130 ms per step:96.77 epoch:63/100 step:6/25 Dloss:0.3946 Gloss:11.1338 ms per step:98.42 epoch:63/100 step:8/25 Dloss:0.2881 Gloss:11.2007 ms per step:130.04 epoch:63/100 step:10/25 Dloss:0.2628 Gloss:10.8245 ms per step:131.72 epoch:63/100 step:12/25 Dloss:0.2894 Gloss:10.2425 ms per step:106.34 epoch:63/100 step:14/25 Dloss:0.4948 Gloss:11.4727 ms per step:126.67 epoch:63/100 step:16/25 Dloss:0.5281 Gloss:10.6207 ms per step:96.15 epoch:63/100 step:18/25 Dloss:0.3243 Gloss:10.6962 ms per step:95.76 epoch:63/100 step:20/25 Dloss:0.3467 Gloss:9.6237 ms per step:101.15 epoch:63/100 step:22/25 Dloss:0.3269 Gloss:10.4317 ms per step:97.84 epoch:63/100 step:24/25 Dloss:0.5747 Gloss:10.8460 ms per step:96.83 epoch:64/100 step:0/25 Dloss:0.5359 Gloss:9.5159 ms per step:96.60 epoch:64/100 step:2/25 Dloss:0.3563 Gloss:10.8629 ms per step:95.59 epoch:64/100 step:4/25 Dloss:0.2906 Gloss:10.3898 ms per step:97.83 epoch:64/100 step:6/25 Dloss:0.2906 Gloss:10.1712 ms per step:95.21 epoch:64/100 step:8/25 Dloss:0.3272 Gloss:10.4990 ms per step:120.87 epoch:64/100 step:10/25 Dloss:0.4307 Gloss:12.1158 ms per step:102.03 epoch:64/100 step:12/25 Dloss:0.8277 Gloss:10.1580 ms per step:98.41 epoch:64/100 step:14/25 Dloss:0.4002 Gloss:12.2934 ms per step:99.78 epoch:64/100 step:16/25 Dloss:0.5348 Gloss:9.7776 ms per step:93.82 epoch:64/100 step:18/25 Dloss:0.4108 Gloss:10.9043 ms per step:94.38 epoch:64/100 step:20/25 Dloss:0.4693 Gloss:9.6580 ms per step:93.85 epoch:64/100 step:22/25 Dloss:0.4357 Gloss:10.8924 ms per step:94.60 epoch:64/100 step:24/25 Dloss:0.4319 Gloss:10.8629 ms per step:93.27 epoch:65/100 step:0/25 Dloss:0.3080 Gloss:10.6347 ms per step:94.37 epoch:65/100 step:2/25 Dloss:0.3609 Gloss:10.1289 ms per step:94.04 epoch:65/100 step:4/25 Dloss:0.4060 Gloss:9.7318 ms per step:94.33 epoch:65/100 step:6/25 Dloss:0.4503 Gloss:10.0633 ms per step:94.54 epoch:65/100 step:8/25 Dloss:0.4129 Gloss:10.1854 ms per step:103.14 epoch:65/100 step:10/25 Dloss:0.4164 Gloss:9.8382 ms per step:103.32 epoch:65/100 step:12/25 Dloss:0.2852 Gloss:11.2488 ms per step:126.55 epoch:65/100 step:14/25 Dloss:0.2692 Gloss:11.3541 ms per step:103.07 epoch:65/100 step:16/25 Dloss:0.2951 Gloss:11.4776 ms per step:98.30 epoch:65/100 step:18/25 Dloss:0.3812 Gloss:10.0599 ms per step:98.18 epoch:65/100 step:20/25 Dloss:0.3825 Gloss:10.1102 ms per step:98.26 epoch:65/100 step:22/25 Dloss:0.2820 Gloss:12.0682 ms per step:98.59 epoch:65/100 step:24/25 Dloss:0.3236 Gloss:10.8278 ms per step:98.15 epoch:66/100 step:0/25 Dloss:0.4436 Gloss:9.5112 ms per step:97.53 epoch:66/100 step:2/25 Dloss:0.6358 Gloss:10.2870 ms per step:98.02 epoch:66/100 step:4/25 Dloss:0.5921 Gloss:10.0417 ms per step:98.59 epoch:66/100 step:6/25 Dloss:0.3965 Gloss:10.0804 ms per step:97.91 epoch:66/100 step:8/25 Dloss:0.3912 Gloss:9.9749 ms per step:105.38 epoch:66/100 step:10/25 Dloss:0.3262 Gloss:10.9261 ms per step:104.97 epoch:66/100 step:12/25 Dloss:0.3048 Gloss:11.8741 ms per step:105.59 epoch:66/100 step:14/25 Dloss:0.3400 Gloss:10.1248 ms per step:105.43 epoch:66/100 step:16/25 Dloss:0.4324 Gloss:10.6249 ms per step:99.85 epoch:66/100 step:18/25 Dloss:0.5531 Gloss:10.2458 ms per step:99.83 epoch:66/100 step:20/25 Dloss:0.5239 Gloss:11.1489 ms per step:99.70 epoch:66/100 step:22/25 Dloss:0.4444 Gloss:10.8603 ms per step:104.29 epoch:66/100 step:24/25 Dloss:0.3388 Gloss:10.2709 ms per step:98.11 epoch:67/100 step:0/25 Dloss:0.5258 Gloss:10.1396 ms per step:97.73 epoch:67/100 step:2/25 Dloss:0.4375 Gloss:11.0381 ms per step:99.01 epoch:67/100 step:4/25 Dloss:0.4873 Gloss:10.1493 ms per step:104.88 epoch:67/100 step:6/25 Dloss:0.5326 Gloss:9.9929 ms per step:103.57 epoch:67/100 step:8/25 Dloss:0.5997 Gloss:9.1982 ms per step:121.43 epoch:67/100 step:10/25 Dloss:0.3866 Gloss:12.0144 ms per step:107.92 epoch:67/100 step:12/25 Dloss:0.3841 Gloss:9.5720 ms per step:104.81 epoch:67/100 step:14/25 Dloss:0.4080 Gloss:10.1461 ms per step:103.20 epoch:67/100 step:16/25 Dloss:0.2889 Gloss:9.8612 ms per step:96.74 epoch:67/100 step:18/25 Dloss:0.2261 Gloss:12.4217 ms per step:97.94 epoch:67/100 step:20/25 Dloss:0.3116 Gloss:11.0826 ms per step:97.79 epoch:67/100 step:22/25 Dloss:0.3531 Gloss:11.3827 ms per step:102.96 epoch:67/100 step:24/25 Dloss:0.3600 Gloss:9.9141 ms per step:99.96 epoch:68/100 step:0/25 Dloss:0.3486 Gloss:10.8720 ms per step:97.52 epoch:68/100 step:2/25 Dloss:0.4211 Gloss:10.1502 ms per step:96.71 epoch:68/100 step:4/25 Dloss:0.4289 Gloss:9.7176 ms per step:96.65 epoch:68/100 step:6/25 Dloss:0.2981 Gloss:10.2061 ms per step:97.47 epoch:68/100 step:8/25 Dloss:0.2923 Gloss:10.2384 ms per step:121.56 epoch:68/100 step:10/25 Dloss:0.4898 Gloss:10.8271 ms per step:103.25 epoch:68/100 step:12/25 Dloss:0.4413 Gloss:11.7350 ms per step:112.28 epoch:68/100 step:14/25 Dloss:0.3606 Gloss:10.3653 ms per step:112.31 epoch:68/100 step:16/25 Dloss:0.3274 Gloss:12.0149 ms per step:111.80 epoch:68/100 step:18/25 Dloss:0.2556 Gloss:10.6520 ms per step:111.65 epoch:68/100 step:20/25 Dloss:0.3072 Gloss:10.7027 ms per step:112.14 epoch:68/100 step:22/25 Dloss:0.3712 Gloss:9.9511 ms per step:111.61 epoch:68/100 step:24/25 Dloss:0.3914 Gloss:10.1926 ms per step:110.93 epoch:69/100 step:0/25 Dloss:0.3120 Gloss:10.7071 ms per step:111.46 epoch:69/100 step:2/25 Dloss:0.2765 Gloss:11.9192 ms per step:111.76 epoch:69/100 step:4/25 Dloss:0.6389 Gloss:10.4836 ms per step:113.68 epoch:69/100 step:6/25 Dloss:0.6278 Gloss:9.6649 ms per step:113.53 epoch:69/100 step:8/25 Dloss:0.5052 Gloss:10.2801 ms per step:113.64 epoch:69/100 step:10/25 Dloss:0.3726 Gloss:10.4907 ms per step:114.46 epoch:69/100 step:12/25 Dloss:0.3663 Gloss:11.2954 ms per step:111.95 epoch:69/100 step:14/25 Dloss:0.4155 Gloss:10.5536 ms per step:112.16 epoch:69/100 step:16/25 Dloss:0.4200 Gloss:10.1128 ms per step:112.88 epoch:69/100 step:18/25 Dloss:0.3785 Gloss:10.2815 ms per step:111.22 epoch:69/100 step:20/25 Dloss:0.3550 Gloss:10.1851 ms per step:112.35 epoch:69/100 step:22/25 Dloss:0.2829 Gloss:10.3639 ms per step:111.65 epoch:69/100 step:24/25 Dloss:0.2831 Gloss:10.9097 ms per step:111.14 epoch:70/100 step:0/25 Dloss:0.3712 Gloss:9.6637 ms per step:111.43 epoch:70/100 step:2/25 Dloss:0.3807 Gloss:11.0406 ms per step:111.42 epoch:70/100 step:4/25 Dloss:0.2961 Gloss:9.7218 ms per step:112.72 epoch:70/100 step:6/25 Dloss:0.3864 Gloss:10.8362 ms per step:110.67 epoch:70/100 step:8/25 Dloss:0.6103 Gloss:10.2481 ms per step:112.17 epoch:70/100 step:10/25 Dloss:0.4058 Gloss:9.8044 ms per step:111.24 epoch:70/100 step:12/25 Dloss:0.3210 Gloss:11.3604 ms per step:112.28 epoch:70/100 step:14/25 Dloss:0.3232 Gloss:9.9605 ms per step:111.45 epoch:70/100 step:16/25 Dloss:0.4388 Gloss:9.5607 ms per step:111.71 epoch:70/100 step:18/25 Dloss:0.3801 Gloss:10.5030 ms per step:111.56 epoch:70/100 step:20/25 Dloss:0.2966 Gloss:10.2318 ms per step:112.76 epoch:70/100 step:22/25 Dloss:0.3002 Gloss:11.1848 ms per step:112.84 epoch:70/100 step:24/25 Dloss:0.3088 Gloss:9.8187 ms per step:112.43 epoch:71/100 step:0/25 Dloss:0.3538 Gloss:11.0118 ms per step:112.82 epoch:71/100 step:2/25 Dloss:0.3106 Gloss:10.8807 ms per step:114.76 epoch:71/100 step:4/25 Dloss:0.8549 Gloss:10.5364 ms per step:115.19 epoch:71/100 step:6/25 Dloss:0.3765 Gloss:10.2776 ms per step:114.84 epoch:71/100 step:8/25 Dloss:0.3905 Gloss:9.7115 ms per step:115.85 epoch:71/100 step:10/25 Dloss:0.2988 Gloss:11.0878 ms per step:111.42 epoch:71/100 step:12/25 Dloss:0.4946 Gloss:10.1506 ms per step:112.42 epoch:71/100 step:14/25 Dloss:0.4960 Gloss:9.9633 ms per step:111.59 epoch:71/100 step:16/25 Dloss:0.5449 Gloss:10.2751 ms per step:112.21 epoch:71/100 step:18/25 Dloss:0.4223 Gloss:9.7398 ms per step:112.36 epoch:71/100 step:20/25 Dloss:0.3043 Gloss:10.6444 ms per step:111.82 epoch:71/100 step:22/25 Dloss:0.3263 Gloss:10.0748 ms per step:111.41 epoch:71/100 step:24/25 Dloss:0.3390 Gloss:9.7063 ms per step:111.92 epoch:72/100 step:0/25 Dloss:0.4619 Gloss:9.6069 ms per step:112.23 epoch:72/100 step:2/25 Dloss:0.4932 Gloss:10.3540 ms per step:112.62 epoch:72/100 step:4/25 Dloss:0.3847 Gloss:10.5311 ms per step:113.04 epoch:72/100 step:6/25 Dloss:0.3851 Gloss:10.2513 ms per step:111.44 epoch:72/100 step:8/25 Dloss:0.5441 Gloss:11.1488 ms per step:112.60 epoch:72/100 step:10/25 Dloss:0.5738 Gloss:9.7600 ms per step:111.59 epoch:72/100 step:12/25 Dloss:0.4670 Gloss:10.6421 ms per step:112.97 epoch:72/100 step:14/25 Dloss:0.5086 Gloss:9.4348 ms per step:112.40 epoch:72/100 step:16/25 Dloss:0.3026 Gloss:10.3118 ms per step:112.79 epoch:72/100 step:18/25 Dloss:0.5338 Gloss:9.6670 ms per step:111.19 epoch:72/100 step:20/25 Dloss:0.7645 Gloss:9.2889 ms per step:111.90 epoch:72/100 step:22/25 Dloss:0.6808 Gloss:9.9566 ms per step:111.58 epoch:72/100 step:24/25 Dloss:0.5532 Gloss:10.4015 ms per step:112.16 epoch:73/100 step:0/25 Dloss:0.4996 Gloss:9.9770 ms per step:111.42 epoch:73/100 step:2/25 Dloss:0.4413 Gloss:9.9476 ms per step:112.00 epoch:73/100 step:4/25 Dloss:0.5027 Gloss:9.6874 ms per step:112.40 epoch:73/100 step:6/25 Dloss:0.5505 Gloss:10.0737 ms per step:111.56 epoch:73/100 step:8/25 Dloss:0.5534 Gloss:9.5679 ms per step:113.33 epoch:73/100 step:10/25 Dloss:0.3968 Gloss:10.0447 ms per step:111.56 epoch:73/100 step:12/25 Dloss:0.2536 Gloss:9.9949 ms per step:110.88 epoch:73/100 step:14/25 Dloss:0.2582 Gloss:10.2137 ms per step:111.64 epoch:73/100 step:16/25 Dloss:0.2401 Gloss:10.7389 ms per step:111.17 epoch:73/100 step:18/25 Dloss:0.3058 Gloss:10.3998 ms per step:124.51 epoch:73/100 step:20/25 Dloss:0.3300 Gloss:9.2162 ms per step:110.21 epoch:73/100 step:22/25 Dloss:0.3060 Gloss:10.1422 ms per step:111.54 epoch:73/100 step:24/25 Dloss:0.4356 Gloss:9.8226 ms per step:111.31 epoch:74/100 step:0/25 Dloss:0.4134 Gloss:11.4354 ms per step:111.58 epoch:74/100 step:2/25 Dloss:0.5282 Gloss:11.6185 ms per step:111.64 epoch:74/100 step:4/25 Dloss:0.5384 Gloss:10.6329 ms per step:111.57 epoch:74/100 step:6/25 Dloss:0.3445 Gloss:10.6945 ms per step:110.93 epoch:74/100 step:8/25 Dloss:0.3406 Gloss:9.4997 ms per step:111.73 epoch:74/100 step:10/25 Dloss:0.3698 Gloss:9.7121 ms per step:110.27 epoch:74/100 step:12/25 Dloss:0.4388 Gloss:10.2939 ms per step:110.73 epoch:74/100 step:14/25 Dloss:0.3199 Gloss:10.0521 ms per step:110.61 epoch:74/100 step:16/25 Dloss:0.3250 Gloss:10.9757 ms per step:110.03 epoch:74/100 step:18/25 Dloss:0.2886 Gloss:10.3067 ms per step:111.62 epoch:74/100 step:20/25 Dloss:0.3874 Gloss:9.9333 ms per step:113.19 epoch:74/100 step:22/25 Dloss:0.5159 Gloss:10.2885 ms per step:113.12 epoch:74/100 step:24/25 Dloss:0.8759 Gloss:10.2262 ms per step:113.26 epoch:75/100 step:0/25 Dloss:0.6922 Gloss:11.1287 ms per step:111.76 epoch:75/100 step:2/25 Dloss:0.4278 Gloss:10.1106 ms per step:111.76 epoch:75/100 step:4/25 Dloss:0.3800 Gloss:9.7830 ms per step:111.43 epoch:75/100 step:6/25 Dloss:0.3365 Gloss:10.3153 ms per step:112.49 epoch:75/100 step:8/25 Dloss:0.4983 Gloss:12.7500 ms per step:111.86 epoch:75/100 step:10/25 Dloss:0.5590 Gloss:9.9113 ms per step:112.02 epoch:75/100 step:12/25 Dloss:0.5224 Gloss:9.8311 ms per step:112.36 epoch:75/100 step:14/25 Dloss:0.3127 Gloss:11.4523 ms per step:113.19 epoch:75/100 step:16/25 Dloss:0.2899 Gloss:10.0867 ms per step:113.29 epoch:75/100 step:18/25 Dloss:0.2013 Gloss:10.2714 ms per step:112.32 epoch:75/100 step:20/25 Dloss:0.3988 Gloss:10.7119 ms per step:112.18 epoch:75/100 step:22/25 Dloss:0.5196 Gloss:8.9916 ms per step:111.33 epoch:75/100 step:24/25 Dloss:0.4304 Gloss:9.6492 ms per step:112.17 epoch:76/100 step:0/25 Dloss:0.2842 Gloss:9.7472 ms per step:111.65 epoch:76/100 step:2/25 Dloss:0.2529 Gloss:10.6721 ms per step:112.44 epoch:76/100 step:4/25 Dloss:0.3277 Gloss:11.1472 ms per step:112.20 epoch:76/100 step:6/25 Dloss:0.3904 Gloss:9.3650 ms per step:112.96 epoch:76/100 step:8/25 Dloss:0.3469 Gloss:10.3913 ms per step:111.85 epoch:76/100 step:10/25 Dloss:0.2060 Gloss:10.7349 ms per step:112.29 epoch:76/100 step:12/25 Dloss:0.2695 Gloss:10.6654 ms per step:112.07 epoch:76/100 step:14/25 Dloss:0.4983 Gloss:9.9978 ms per step:112.23 epoch:76/100 step:16/25 Dloss:0.5894 Gloss:10.4508 ms per step:115.19 epoch:76/100 step:18/25 Dloss:0.3223 Gloss:10.7518 ms per step:115.01 epoch:76/100 step:20/25 Dloss:0.2871 Gloss:9.8419 ms per step:115.21 epoch:76/100 step:22/25 Dloss:0.4450 Gloss:9.5872 ms per step:113.28 epoch:76/100 step:24/25 Dloss:0.4459 Gloss:10.4134 ms per step:112.89 epoch:77/100 step:0/25 Dloss:0.6214 Gloss:11.0200 ms per step:111.46 epoch:77/100 step:2/25 Dloss:0.3538 Gloss:11.6674 ms per step:111.79 epoch:77/100 step:4/25 Dloss:0.4930 Gloss:11.1073 ms per step:111.67 epoch:77/100 step:6/25 Dloss:0.4974 Gloss:10.0012 ms per step:111.43 epoch:77/100 step:8/25 Dloss:0.2700 Gloss:10.0305 ms per step:111.14 epoch:77/100 step:10/25 Dloss:0.2416 Gloss:11.2681 ms per step:111.37 epoch:77/100 step:12/25 Dloss:0.2444 Gloss:9.7211 ms per step:112.92 epoch:77/100 step:14/25 Dloss:0.4125 Gloss:9.3450 ms per step:112.30 epoch:77/100 step:16/25 Dloss:0.4492 Gloss:8.8163 ms per step:111.68 epoch:77/100 step:18/25 Dloss:0.4431 Gloss:8.9012 ms per step:111.80 epoch:77/100 step:20/25 Dloss:0.3819 Gloss:9.4937 ms per step:111.55 epoch:77/100 step:22/25 Dloss:0.3244 Gloss:10.6494 ms per step:111.16 epoch:77/100 step:24/25 Dloss:0.7647 Gloss:10.6731 ms per step:111.70 epoch:78/100 step:0/25 Dloss:0.5483 Gloss:9.0661 ms per step:112.23 epoch:78/100 step:2/25 Dloss:0.5922 Gloss:9.6511 ms per step:111.20 epoch:78/100 step:4/25 Dloss:0.4281 Gloss:11.5016 ms per step:111.92 epoch:78/100 step:6/25 Dloss:0.3651 Gloss:10.4079 ms per step:112.22 epoch:78/100 step:8/25 Dloss:0.3027 Gloss:9.9539 ms per step:110.91 epoch:78/100 step:10/25 Dloss:0.2464 Gloss:9.6554 ms per step:111.01 epoch:78/100 step:12/25 Dloss:0.2238 Gloss:9.5541 ms per step:111.18 epoch:78/100 step:14/25 Dloss:0.1853 Gloss:9.2444 ms per step:111.54 epoch:78/100 step:16/25 Dloss:0.2081 Gloss:9.9307 ms per step:111.87 epoch:78/100 step:18/25 Dloss:0.3129 Gloss:10.4714 ms per step:110.80 epoch:78/100 step:20/25 Dloss:0.3006 Gloss:10.7240 ms per step:112.41 epoch:78/100 step:22/25 Dloss:0.2183 Gloss:10.0669 ms per step:112.15 epoch:78/100 step:24/25 Dloss:0.2740 Gloss:9.9920 ms per step:111.84 epoch:79/100 step:0/25 Dloss:0.2426 Gloss:10.7850 ms per step:111.26 epoch:79/100 step:2/25 Dloss:0.4743 Gloss:10.5548 ms per step:111.05 epoch:79/100 step:4/25 Dloss:0.8711 Gloss:12.3339 ms per step:110.82 epoch:79/100 step:6/25 Dloss:0.4723 Gloss:10.3027 ms per step:111.03 epoch:79/100 step:8/25 Dloss:0.3755 Gloss:9.7425 ms per step:112.22 epoch:79/100 step:10/25 Dloss:0.3536 Gloss:9.2374 ms per step:122.59 epoch:79/100 step:12/25 Dloss:0.4414 Gloss:9.9007 ms per step:110.90 epoch:79/100 step:14/25 Dloss:0.4690 Gloss:9.4154 ms per step:112.08 epoch:79/100 step:16/25 Dloss:0.4539 Gloss:10.3059 ms per step:114.24 epoch:79/100 step:18/25 Dloss:0.3346 Gloss:10.2512 ms per step:122.56 epoch:79/100 step:20/25 Dloss:0.3908 Gloss:9.8570 ms per step:117.47 epoch:79/100 step:22/25 Dloss:0.2460 Gloss:9.6681 ms per step:117.53 epoch:79/100 step:24/25 Dloss:0.2672 Gloss:10.6654 ms per step:116.22 epoch:80/100 step:0/25 Dloss:0.3916 Gloss:10.1822 ms per step:117.14 epoch:80/100 step:2/25 Dloss:0.4663 Gloss:9.3014 ms per step:119.41 epoch:80/100 step:4/25 Dloss:0.3364 Gloss:11.1980 ms per step:98.69 epoch:80/100 step:6/25 Dloss:0.2556 Gloss:10.9685 ms per step:99.76 epoch:80/100 step:8/25 Dloss:0.3316 Gloss:9.9553 ms per step:106.58 epoch:80/100 step:10/25 Dloss:0.3088 Gloss:11.1560 ms per step:104.32 epoch:80/100 step:12/25 Dloss:0.4410 Gloss:10.9212 ms per step:106.49 epoch:80/100 step:14/25 Dloss:0.2633 Gloss:10.4481 ms per step:105.40 epoch:80/100 step:16/25 Dloss:0.3358 Gloss:9.8952 ms per step:101.08 epoch:80/100 step:18/25 Dloss:0.4061 Gloss:9.2761 ms per step:96.76 epoch:80/100 step:20/25 Dloss:0.4931 Gloss:9.5494 ms per step:96.94 epoch:80/100 step:22/25 Dloss:0.4216 Gloss:9.3181 ms per step:98.28 epoch:80/100 step:24/25 Dloss:0.2979 Gloss:9.3917 ms per step:96.64 epoch:81/100 step:0/25 Dloss:0.3254 Gloss:8.5578 ms per step:112.01 epoch:81/100 step:2/25 Dloss:0.2435 Gloss:10.0758 ms per step:110.70 epoch:81/100 step:4/25 Dloss:0.2373 Gloss:11.5544 ms per step:111.66 epoch:81/100 step:6/25 Dloss:0.2365 Gloss:10.5612 ms per step:112.11 epoch:81/100 step:8/25 Dloss:0.4334 Gloss:9.8110 ms per step:103.20 epoch:81/100 step:10/25 Dloss:0.4046 Gloss:9.2686 ms per step:103.37 epoch:81/100 step:12/25 Dloss:0.3842 Gloss:9.8059 ms per step:103.44 epoch:81/100 step:14/25 Dloss:0.2537 Gloss:10.0492 ms per step:103.40 epoch:81/100 step:16/25 Dloss:0.3426 Gloss:10.3981 ms per step:97.66 epoch:81/100 step:18/25 Dloss:0.5250 Gloss:10.3311 ms per step:96.33 epoch:81/100 step:20/25 Dloss:0.3806 Gloss:9.7836 ms per step:96.84 epoch:81/100 step:22/25 Dloss:0.2444 Gloss:10.3146 ms per step:97.20 epoch:81/100 step:24/25 Dloss:0.2971 Gloss:10.1281 ms per step:97.98 epoch:82/100 step:0/25 Dloss:0.4627 Gloss:8.6214 ms per step:96.33 epoch:82/100 step:2/25 Dloss:0.3178 Gloss:10.0734 ms per step:96.86 epoch:82/100 step:4/25 Dloss:0.6950 Gloss:10.5191 ms per step:96.69 epoch:82/100 step:6/25 Dloss:0.4393 Gloss:9.4402 ms per step:103.70 epoch:82/100 step:8/25 Dloss:0.3814 Gloss:9.0396 ms per step:108.10 epoch:82/100 step:10/25 Dloss:0.2999 Gloss:9.3065 ms per step:109.13 epoch:82/100 step:12/25 Dloss:0.3494 Gloss:10.0600 ms per step:108.16 epoch:82/100 step:14/25 Dloss:0.5465 Gloss:11.4129 ms per step:111.67 epoch:82/100 step:16/25 Dloss:0.6140 Gloss:9.5745 ms per step:111.10 epoch:82/100 step:18/25 Dloss:0.4674 Gloss:9.5563 ms per step:110.31 epoch:82/100 step:20/25 Dloss:0.3460 Gloss:9.4380 ms per step:111.88 epoch:82/100 step:22/25 Dloss:0.3767 Gloss:9.5148 ms per step:111.83 epoch:82/100 step:24/25 Dloss:0.4124 Gloss:10.1881 ms per step:111.58 epoch:83/100 step:0/25 Dloss:0.4318 Gloss:10.3649 ms per step:111.19 epoch:83/100 step:2/25 Dloss:0.4005 Gloss:9.4482 ms per step:111.20 epoch:83/100 step:4/25 Dloss:0.4203 Gloss:10.4656 ms per step:112.31 epoch:83/100 step:6/25 Dloss:0.2092 Gloss:11.4033 ms per step:112.11 epoch:83/100 step:8/25 Dloss:0.4368 Gloss:11.4867 ms per step:113.54 epoch:83/100 step:10/25 Dloss:0.4125 Gloss:10.2429 ms per step:112.94 epoch:83/100 step:12/25 Dloss:0.4689 Gloss:10.0852 ms per step:112.96 epoch:83/100 step:14/25 Dloss:0.4591 Gloss:9.8234 ms per step:112.47 epoch:83/100 step:16/25 Dloss:0.4539 Gloss:10.0137 ms per step:112.92 epoch:83/100 step:18/25 Dloss:0.3868 Gloss:10.1371 ms per step:113.74 epoch:83/100 step:20/25 Dloss:0.3291 Gloss:10.2661 ms per step:111.80 epoch:83/100 step:22/25 Dloss:0.2847 Gloss:10.7631 ms per step:116.49 epoch:83/100 step:24/25 Dloss:0.4388 Gloss:9.0304 ms per step:98.09 epoch:84/100 step:0/25 Dloss:0.3811 Gloss:10.2457 ms per step:98.95 epoch:84/100 step:2/25 Dloss:0.3117 Gloss:9.6367 ms per step:97.07 epoch:84/100 step:4/25 Dloss:0.1935 Gloss:9.6929 ms per step:102.96 epoch:84/100 step:6/25 Dloss:0.3382 Gloss:9.5341 ms per step:104.37 epoch:84/100 step:8/25 Dloss:0.5542 Gloss:9.2897 ms per step:110.86 epoch:84/100 step:10/25 Dloss:0.6423 Gloss:9.5739 ms per step:103.04 epoch:84/100 step:12/25 Dloss:0.5439 Gloss:10.2187 ms per step:103.47 epoch:84/100 step:14/25 Dloss:0.4691 Gloss:10.5195 ms per step:102.74 epoch:84/100 step:16/25 Dloss:0.3856 Gloss:10.5953 ms per step:97.92 epoch:84/100 step:18/25 Dloss:0.5081 Gloss:9.8506 ms per step:96.77 epoch:84/100 step:20/25 Dloss:0.4532 Gloss:9.6790 ms per step:97.48 epoch:84/100 step:22/25 Dloss:0.3681 Gloss:9.0480 ms per step:98.72 epoch:84/100 step:24/25 Dloss:0.2787 Gloss:10.1615 ms per step:98.09 epoch:85/100 step:0/25 Dloss:0.2226 Gloss:10.8064 ms per step:98.46 epoch:85/100 step:2/25 Dloss:0.2190 Gloss:10.2913 ms per step:96.77 epoch:85/100 step:4/25 Dloss:0.3360 Gloss:8.1038 ms per step:97.44 epoch:85/100 step:6/25 Dloss:0.5133 Gloss:9.9374 ms per step:97.29 epoch:85/100 step:8/25 Dloss:0.6348 Gloss:8.9125 ms per step:102.85 epoch:85/100 step:10/25 Dloss:0.4057 Gloss:10.2970 ms per step:102.54 epoch:85/100 step:12/25 Dloss:0.4176 Gloss:9.1480 ms per step:102.58 epoch:85/100 step:14/25 Dloss:0.3760 Gloss:9.2915 ms per step:103.10 epoch:85/100 step:16/25 Dloss:0.6374 Gloss:9.9738 ms per step:97.23 epoch:85/100 step:18/25 Dloss:0.5142 Gloss:9.3254 ms per step:97.31 epoch:85/100 step:20/25 Dloss:0.4496 Gloss:9.2737 ms per step:96.60 epoch:85/100 step:22/25 Dloss:0.4027 Gloss:9.8223 ms per step:97.64 epoch:85/100 step:24/25 Dloss:0.4317 Gloss:10.5317 ms per step:96.93 epoch:86/100 step:0/25 Dloss:0.5546 Gloss:10.6139 ms per step:101.15 epoch:86/100 step:2/25 Dloss:0.7265 Gloss:10.3918 ms per step:97.68 epoch:86/100 step:4/25 Dloss:0.6294 Gloss:10.8395 ms per step:113.81 epoch:86/100 step:6/25 Dloss:0.5798 Gloss:10.2184 ms per step:97.24 epoch:86/100 step:8/25 Dloss:0.4173 Gloss:8.7923 ms per step:126.92 epoch:86/100 step:10/25 Dloss:0.3636 Gloss:9.7361 ms per step:102.60 epoch:86/100 step:12/25 Dloss:0.2965 Gloss:10.0618 ms per step:102.48 epoch:86/100 step:14/25 Dloss:0.3613 Gloss:9.8821 ms per step:102.11 epoch:86/100 step:16/25 Dloss:0.5689 Gloss:8.5053 ms per step:97.25 epoch:86/100 step:18/25 Dloss:0.4054 Gloss:9.7452 ms per step:95.95 epoch:86/100 step:20/25 Dloss:0.4335 Gloss:9.1996 ms per step:96.91 epoch:86/100 step:22/25 Dloss:0.3853 Gloss:10.1427 ms per step:96.66 epoch:86/100 step:24/25 Dloss:0.3381 Gloss:8.8921 ms per step:97.49 epoch:87/100 step:0/25 Dloss:0.2935 Gloss:10.6559 ms per step:97.17 epoch:87/100 step:2/25 Dloss:0.3589 Gloss:10.4310 ms per step:96.56 epoch:87/100 step:4/25 Dloss:0.5330 Gloss:8.5873 ms per step:97.11 epoch:87/100 step:6/25 Dloss:0.4251 Gloss:9.0821 ms per step:97.10 epoch:87/100 step:8/25 Dloss:0.2924 Gloss:9.5639 ms per step:102.99 epoch:87/100 step:10/25 Dloss:0.4142 Gloss:10.6429 ms per step:98.28 epoch:87/100 step:12/25 Dloss:0.4027 Gloss:9.6695 ms per step:111.54 epoch:87/100 step:14/25 Dloss:0.5504 Gloss:9.1726 ms per step:111.38 epoch:87/100 step:16/25 Dloss:0.6566 Gloss:8.5626 ms per step:111.60 epoch:87/100 step:18/25 Dloss:0.7008 Gloss:8.8900 ms per step:111.31 epoch:87/100 step:20/25 Dloss:0.4265 Gloss:9.2961 ms per step:110.92 epoch:87/100 step:22/25 Dloss:0.3628 Gloss:9.4010 ms per step:108.55 epoch:87/100 step:24/25 Dloss:0.3666 Gloss:9.3868 ms per step:108.61 epoch:88/100 step:0/25 Dloss:0.3866 Gloss:8.3818 ms per step:109.78 epoch:88/100 step:2/25 Dloss:0.4337 Gloss:8.5507 ms per step:109.50 epoch:88/100 step:4/25 Dloss:0.6346 Gloss:10.9413 ms per step:109.84 epoch:88/100 step:6/25 Dloss:0.4474 Gloss:9.9396 ms per step:110.41 epoch:88/100 step:8/25 Dloss:0.3321 Gloss:9.2524 ms per step:116.23 epoch:88/100 step:10/25 Dloss:0.2697 Gloss:9.8194 ms per step:115.25 epoch:88/100 step:12/25 Dloss:0.3174 Gloss:9.2472 ms per step:113.67 epoch:88/100 step:14/25 Dloss:0.3723 Gloss:8.9942 ms per step:112.36 epoch:88/100 step:16/25 Dloss:0.4940 Gloss:9.2127 ms per step:112.88 epoch:88/100 step:18/25 Dloss:0.3799 Gloss:9.2897 ms per step:112.39 epoch:88/100 step:20/25 Dloss:0.3532 Gloss:9.7581 ms per step:113.19 epoch:88/100 step:22/25 Dloss:0.3137 Gloss:9.8068 ms per step:112.45 epoch:88/100 step:24/25 Dloss:0.3797 Gloss:9.6468 ms per step:112.32 epoch:89/100 step:0/25 Dloss:0.4515 Gloss:8.9195 ms per step:112.55 epoch:89/100 step:2/25 Dloss:0.3397 Gloss:9.8530 ms per step:120.04 epoch:89/100 step:4/25 Dloss:0.2947 Gloss:9.5219 ms per step:117.49 epoch:89/100 step:6/25 Dloss:0.8607 Gloss:9.6555 ms per step:117.80 epoch:89/100 step:8/25 Dloss:0.5896 Gloss:9.2845 ms per step:116.03 epoch:89/100 step:10/25 Dloss:0.4360 Gloss:9.1805 ms per step:116.45 epoch:89/100 step:12/25 Dloss:0.2512 Gloss:10.1967 ms per step:118.49 epoch:89/100 step:14/25 Dloss:0.2471 Gloss:10.0135 ms per step:112.87 epoch:89/100 step:16/25 Dloss:0.2895 Gloss:9.3571 ms per step:107.90 epoch:89/100 step:18/25 Dloss:0.3593 Gloss:8.2896 ms per step:109.69 epoch:89/100 step:20/25 Dloss:0.3980 Gloss:8.8242 ms per step:109.80 epoch:89/100 step:22/25 Dloss:0.3117 Gloss:8.8560 ms per step:108.83 epoch:89/100 step:24/25 Dloss:0.3242 Gloss:9.6835 ms per step:108.99 epoch:90/100 step:0/25 Dloss:0.3349 Gloss:10.0468 ms per step:109.92 epoch:90/100 step:2/25 Dloss:0.3614 Gloss:10.2525 ms per step:112.68 epoch:90/100 step:4/25 Dloss:0.5712 Gloss:10.6646 ms per step:111.89 epoch:90/100 step:6/25 Dloss:0.4600 Gloss:9.0134 ms per step:112.62 epoch:90/100 step:8/25 Dloss:0.2493 Gloss:9.9602 ms per step:117.01 epoch:90/100 step:10/25 Dloss:0.3217 Gloss:8.6008 ms per step:114.07 epoch:90/100 step:12/25 Dloss:0.4104 Gloss:9.5935 ms per step:115.49 epoch:90/100 step:14/25 Dloss:0.5306 Gloss:9.5365 ms per step:114.34 epoch:90/100 step:16/25 Dloss:0.3813 Gloss:9.3473 ms per step:110.82 epoch:90/100 step:18/25 Dloss:0.4325 Gloss:8.3572 ms per step:108.99 epoch:90/100 step:20/25 Dloss:0.4072 Gloss:9.2610 ms per step:110.05 epoch:90/100 step:22/25 Dloss:0.3754 Gloss:9.0940 ms per step:109.43 epoch:90/100 step:24/25 Dloss:0.5651 Gloss:8.9833 ms per step:109.68 epoch:91/100 step:0/25 Dloss:0.4573 Gloss:9.2881 ms per step:110.47 epoch:91/100 step:2/25 Dloss:0.4180 Gloss:8.7367 ms per step:109.96 epoch:91/100 step:4/25 Dloss:0.2625 Gloss:9.1983 ms per step:109.52 epoch:91/100 step:6/25 Dloss:0.3542 Gloss:9.0335 ms per step:108.10 epoch:91/100 step:8/25 Dloss:0.9353 Gloss:9.6542 ms per step:115.75 epoch:91/100 step:10/25 Dloss:0.5399 Gloss:9.5484 ms per step:121.84 epoch:91/100 step:12/25 Dloss:0.3733 Gloss:9.5674 ms per step:114.77 epoch:91/100 step:14/25 Dloss:0.3088 Gloss:9.4505 ms per step:111.03 epoch:91/100 step:16/25 Dloss:0.2578 Gloss:10.2645 ms per step:108.86 epoch:91/100 step:18/25 Dloss:0.4453 Gloss:9.1961 ms per step:108.47 epoch:91/100 step:20/25 Dloss:0.6168 Gloss:9.7312 ms per step:109.57 epoch:91/100 step:22/25 Dloss:0.5744 Gloss:9.5293 ms per step:108.97 epoch:91/100 step:24/25 Dloss:0.3736 Gloss:9.7246 ms per step:109.59 epoch:92/100 step:0/25 Dloss:0.3723 Gloss:8.5488 ms per step:111.46 epoch:92/100 step:2/25 Dloss:0.2898 Gloss:8.7300 ms per step:109.57 epoch:92/100 step:4/25 Dloss:0.3565 Gloss:9.6917 ms per step:112.70 epoch:92/100 step:6/25 Dloss:0.3713 Gloss:9.4799 ms per step:109.44 epoch:92/100 step:8/25 Dloss:0.5026 Gloss:9.7731 ms per step:130.81 epoch:92/100 step:10/25 Dloss:0.3742 Gloss:11.1001 ms per step:113.76 epoch:92/100 step:12/25 Dloss:0.3412 Gloss:9.7544 ms per step:113.85 epoch:92/100 step:14/25 Dloss:0.4109 Gloss:8.3546 ms per step:110.97 epoch:92/100 step:16/25 Dloss:0.4218 Gloss:8.6867 ms per step:109.30 epoch:92/100 step:18/25 Dloss:0.5266 Gloss:9.3781 ms per step:107.89 epoch:92/100 step:20/25 Dloss:0.4983 Gloss:9.4584 ms per step:107.67 epoch:92/100 step:22/25 Dloss:0.4492 Gloss:10.1151 ms per step:108.39 epoch:92/100 step:24/25 Dloss:0.3090 Gloss:8.9276 ms per step:109.53 epoch:93/100 step:0/25 Dloss:0.4238 Gloss:8.6132 ms per step:108.22 epoch:93/100 step:2/25 Dloss:0.5983 Gloss:9.5171 ms per step:109.87 epoch:93/100 step:4/25 Dloss:0.4518 Gloss:9.2334 ms per step:108.43 epoch:93/100 step:6/25 Dloss:0.5205 Gloss:8.6449 ms per step:108.50 epoch:93/100 step:8/25 Dloss:0.4218 Gloss:9.0550 ms per step:113.74 epoch:93/100 step:10/25 Dloss:0.3159 Gloss:9.4041 ms per step:112.95 epoch:93/100 step:12/25 Dloss:0.3870 Gloss:10.0409 ms per step:114.25 epoch:93/100 step:14/25 Dloss:0.5505 Gloss:10.1617 ms per step:113.36 epoch:93/100 step:16/25 Dloss:0.5675 Gloss:9.0700 ms per step:109.72 epoch:93/100 step:18/25 Dloss:0.4776 Gloss:9.0256 ms per step:107.72 epoch:93/100 step:20/25 Dloss:0.4682 Gloss:9.4622 ms per step:109.42 epoch:93/100 step:22/25 Dloss:0.3775 Gloss:9.3905 ms per step:111.35 epoch:93/100 step:24/25 Dloss:0.4195 Gloss:8.7059 ms per step:110.19 epoch:94/100 step:0/25 Dloss:0.3528 Gloss:10.4851 ms per step:110.85 epoch:94/100 step:2/25 Dloss:0.3705 Gloss:9.7367 ms per step:112.05 epoch:94/100 step:4/25 Dloss:0.3281 Gloss:9.7149 ms per step:108.98 epoch:94/100 step:6/25 Dloss:0.4161 Gloss:9.2867 ms per step:108.63 epoch:94/100 step:8/25 Dloss:0.4961 Gloss:9.0830 ms per step:113.79 epoch:94/100 step:10/25 Dloss:0.3964 Gloss:9.4060 ms per step:113.84 epoch:94/100 step:12/25 Dloss:0.5112 Gloss:9.2747 ms per step:114.72 epoch:94/100 step:14/25 Dloss:0.4263 Gloss:9.8867 ms per step:113.31 epoch:94/100 step:16/25 Dloss:0.4281 Gloss:9.9811 ms per step:109.11 epoch:94/100 step:18/25 Dloss:0.4899 Gloss:8.9997 ms per step:108.89 epoch:94/100 step:20/25 Dloss:0.4239 Gloss:8.1569 ms per step:109.12 epoch:94/100 step:22/25 Dloss:0.6578 Gloss:9.2922 ms per step:108.82 epoch:94/100 step:24/25 Dloss:0.4397 Gloss:8.8495 ms per step:108.65 epoch:95/100 step:0/25 Dloss:0.3590 Gloss:9.7673 ms per step:108.55 epoch:95/100 step:2/25 Dloss:0.3656 Gloss:9.7763 ms per step:108.13 epoch:95/100 step:4/25 Dloss:0.3840 Gloss:9.6572 ms per step:108.67 epoch:95/100 step:6/25 Dloss:0.3910 Gloss:10.0574 ms per step:114.06 epoch:95/100 step:8/25 Dloss:0.5389 Gloss:9.5577 ms per step:122.28 epoch:95/100 step:10/25 Dloss:0.4720 Gloss:8.2654 ms per step:121.15 epoch:95/100 step:12/25 Dloss:0.4213 Gloss:9.6620 ms per step:121.15 epoch:95/100 step:14/25 Dloss:0.4435 Gloss:8.9988 ms per step:119.30 epoch:95/100 step:16/25 Dloss:0.4864 Gloss:9.3086 ms per step:115.39 epoch:95/100 step:18/25 Dloss:0.5011 Gloss:8.6628 ms per step:115.83 epoch:95/100 step:20/25 Dloss:0.5114 Gloss:9.4756 ms per step:118.84 epoch:95/100 step:22/25 Dloss:0.4530 Gloss:8.8176 ms per step:118.08 epoch:95/100 step:24/25 Dloss:0.4862 Gloss:9.1391 ms per step:118.40 epoch:96/100 step:0/25 Dloss:0.4929 Gloss:9.3366 ms per step:117.15 epoch:96/100 step:2/25 Dloss:0.5273 Gloss:9.7053 ms per step:117.04 epoch:96/100 step:4/25 Dloss:0.3610 Gloss:10.0717 ms per step:116.71 epoch:96/100 step:6/25 Dloss:0.3710 Gloss:9.0325 ms per step:115.58 epoch:96/100 step:8/25 Dloss:0.4207 Gloss:9.5342 ms per step:119.91 epoch:96/100 step:10/25 Dloss:0.7068 Gloss:8.9157 ms per step:120.43 epoch:96/100 step:12/25 Dloss:0.7362 Gloss:9.0200 ms per step:120.88 epoch:96/100 step:14/25 Dloss:0.6148 Gloss:9.1065 ms per step:120.18 epoch:96/100 step:16/25 Dloss:0.4713 Gloss:9.4374 ms per step:116.16 epoch:96/100 step:18/25 Dloss:0.4350 Gloss:9.1553 ms per step:115.53 epoch:96/100 step:20/25 Dloss:0.3483 Gloss:9.1159 ms per step:117.04 epoch:96/100 step:22/25 Dloss:0.4225 Gloss:9.3998 ms per step:116.63 epoch:96/100 step:24/25 Dloss:0.3894 Gloss:8.8221 ms per step:115.70 epoch:97/100 step:0/25 Dloss:0.2969 Gloss:11.4602 ms per step:116.13 epoch:97/100 step:2/25 Dloss:0.3374 Gloss:8.8577 ms per step:116.38 epoch:97/100 step:4/25 Dloss:0.4653 Gloss:10.1228 ms per step:113.70 epoch:97/100 step:6/25 Dloss:0.5599 Gloss:8.7278 ms per step:115.82 epoch:97/100 step:8/25 Dloss:0.4568 Gloss:9.1296 ms per step:121.05 epoch:97/100 step:10/25 Dloss:0.3903 Gloss:9.8830 ms per step:120.40 epoch:97/100 step:12/25 Dloss:0.4433 Gloss:10.3950 ms per step:114.96 epoch:97/100 step:14/25 Dloss:0.3300 Gloss:9.2907 ms per step:114.69 epoch:97/100 step:16/25 Dloss:0.4651 Gloss:8.2343 ms per step:118.24 epoch:97/100 step:18/25 Dloss:0.5483 Gloss:9.1674 ms per step:118.30 epoch:97/100 step:20/25 Dloss:0.5074 Gloss:8.7715 ms per step:116.01 epoch:97/100 step:22/25 Dloss:0.4239 Gloss:8.6857 ms per step:114.81 epoch:97/100 step:24/25 Dloss:0.3656 Gloss:9.4334 ms per step:113.52 epoch:98/100 step:0/25 Dloss:0.5340 Gloss:8.7831 ms per step:114.07 epoch:98/100 step:2/25 Dloss:0.5204 Gloss:9.3759 ms per step:115.33 epoch:98/100 step:4/25 Dloss:0.4783 Gloss:10.1390 ms per step:115.68 epoch:98/100 step:6/25 Dloss:0.5141 Gloss:9.6782 ms per step:115.75 epoch:98/100 step:8/25 Dloss:0.4573 Gloss:10.2644 ms per step:122.51 epoch:98/100 step:10/25 Dloss:0.3828 Gloss:9.3207 ms per step:118.74 epoch:98/100 step:12/25 Dloss:0.3671 Gloss:9.4600 ms per step:122.06 epoch:98/100 step:14/25 Dloss:0.4081 Gloss:8.8109 ms per step:112.10 epoch:98/100 step:16/25 Dloss:0.3973 Gloss:9.4212 ms per step:110.98 epoch:98/100 step:18/25 Dloss:0.4818 Gloss:9.2266 ms per step:108.19 epoch:98/100 step:20/25 Dloss:0.5731 Gloss:8.4570 ms per step:106.78 epoch:98/100 step:22/25 Dloss:0.5965 Gloss:9.1554 ms per step:107.09 epoch:98/100 step:24/25 Dloss:0.6044 Gloss:9.3033 ms per step:107.77 epoch:99/100 step:0/25 Dloss:0.6031 Gloss:9.0418 ms per step:107.35 epoch:99/100 step:2/25 Dloss:0.4760 Gloss:8.5322 ms per step:107.44 epoch:99/100 step:4/25 Dloss:0.3417 Gloss:8.8869 ms per step:108.21 epoch:99/100 step:6/25 Dloss:0.4138 Gloss:8.9011 ms per step:107.55 epoch:99/100 step:8/25 Dloss:0.3341 Gloss:9.8052 ms per step:112.92 epoch:99/100 step:10/25 Dloss:0.4165 Gloss:9.5205 ms per step:113.04 epoch:99/100 step:12/25 Dloss:0.4862 Gloss:8.9653 ms per step:115.19 epoch:99/100 step:14/25 Dloss:0.5382 Gloss:9.0894 ms per step:113.17 epoch:99/100 step:16/25 Dloss:0.4197 Gloss:9.1876 ms per step:110.12 epoch:99/100 step:18/25 Dloss:0.5220 Gloss:9.8974 ms per step:109.67 epoch:99/100 step:20/25 Dloss:0.3995 Gloss:9.1445 ms per step:115.75 epoch:99/100 step:22/25 Dloss:0.4541 Gloss:8.3840 ms per step:115.30 epoch:99/100 step:24/25 Dloss:0.4710 Gloss:9.3341 ms per step:115.22 epoch:100/100 step:0/25 Dloss:0.4944 Gloss:9.2594 ms per step:115.19 epoch:100/100 step:2/25 Dloss:0.5674 Gloss:9.4491 ms per step:116.61 epoch:100/100 step:4/25 Dloss:0.6164 Gloss:9.5221 ms per step:116.62 epoch:100/100 step:6/25 Dloss:0.4499 Gloss:9.0963 ms per step:114.83 epoch:100/100 step:8/25 Dloss:0.3913 Gloss:9.3877 ms per step:115.59 epoch:100/100 step:10/25 Dloss:0.4446 Gloss:9.7656 ms per step:115.14 epoch:100/100 step:12/25 Dloss:0.5224 Gloss:8.4429 ms per step:116.18 epoch:100/100 step:14/25 Dloss:0.5398 Gloss:8.8629 ms per step:115.41 epoch:100/100 step:16/25 Dloss:0.6848 Gloss:8.9019 ms per step:116.34 epoch:100/100 step:18/25 Dloss:0.5459 Gloss:9.1481 ms per step:114.94 epoch:100/100 step:20/25 Dloss:0.5987 Gloss:9.4928 ms per step:116.42 epoch:100/100 step:22/25 Dloss:0.5061 Gloss:8.3426 ms per step:114.89 epoch:100/100 step:24/25 Dloss:0.4972 Gloss:8.8630 CPU times: user 20min 45s, sys: 5min 35s, total: 26min 20s Wall time: 4min 58s
推理
获取上述训练过程完成后的ckpt文件，通过load_checkpoint和load_param_into_net将ckpt中的权重参数导入到模型中，获取数据进行推理并对推理的效果图进行演示（由于时间问题，训练过程只进行了3个epoch，可根据需求调整epoch）。
from mindspore import load_checkpoint, load_param_into_netparam_g = load_checkpoint(ckpt_dir + "Generator.ckpt")
load_param_into_net(net_generator, param_g)
dataset = ds.MindDataset("./dataset/dataset_pix2pix/train.mindrecord", columns_list=["input_images", "target_images"], shuffle=True)
data_iter = next(dataset.create_dict_iterator())
predict_show = net_generator(data_iter["input_images"])
plt.figure(figsize=(10, 3), dpi=140)
for i in range(10):plt.subplot(2, 10, i + 1)plt.imshow((data_iter["input_images"][i].asnumpy().transpose(1, 2, 0) + 1) / 2)plt.axis("off")plt.subplots_adjust(wspace=0.05, hspace=0.02)plt.subplot(2, 10, i + 11)plt.imshow((predict_show[i].asnumpy().transpose(1, 2, 0) + 1) / 2)plt.axis("off")plt.subplots_adjust(wspace=0.05, hspace=0.02)
plt.show()
各数据集分别推理的效果如下
引用
[1] Phillip Isola,Jun-Yan Zhu,Tinghui Zhou,Alexei A. Efros. Image-to-Image Translation with Conditional Adversarial Networks.[J]. CoRR,2016,abs/1611.07004.
![[C++]使用纯opencv部署yolov11目标检测onnx模型](https://csdnimg.cn/release/blog_editor_html/release2.3.7/ckeditor/plugins/CsdnLink/icons/icon-default.png?t=O83A){kind=icon}
