简介

TensorBoard：Tensorflow中强大的可视化工具

TensorBoard测试代码

import numpy as np from torch.utils.tensorboard import SummaryWriter writer=SummaryWriter(comment='test_tensorboard') for x in range(100): writer.add_scalar('y=2x',x*2,x) writer.add_scalar('y=pow(2,x)',2**x,x) writer.add_scalars('data/scalar_group',{"sins":x*np.sin(x), "xcox":x*np.cos(x), "arctanx":np.arctan(x)},x) writer.close()

如何读取

在目标文件内命令行内，输入tensorboard --logdir=./runs，其中runs是event_file所在的文件夹

此后在终端内，会出现一个网址一般为:http://localhost:6006/

注意事项

安装时会提示报错，ModuleNotFoundError:No module named ‘past’

通过pip install future解决

SummaryWriter

功能：提供创建event file的高级接口 主要属性：

log_fir:event file输出文件夹如果不设置，会在当前文件夹下的runs文件夹comment:不指定log_dir时，文件夹后缀，如果log_dir为空会生效，给runs文件下的文件夹后缀filename_suffix:evebnt file 文件名后缀,给event file文件设置的后缀

方法

1.add_scalar()

功能：记录标量

tag:图像的标签名，图的唯一标识scalar_value:要记录的标量global_step:x轴

缺陷：只能记录一条曲线

2.add_scalars()

main_tag:该图的标签tag_scalar_dict:key是变量的tag,value是变量的值 max_epoch = 100 writer = SummaryWriter(comment='test_comment', filename_suffix="test_suffix") for x in range(max_epoch): writer.add_scalar('y=2x', x * 2, x) writer.add_scalar('y=pow_2_x', 2 ** x, x) writer.add_scalars('data/scalar_group', {"xsinx": x * np.sin(x), "xcosx": x * np.cos(x)}, x) writer.close()

3.add_histogram()

功能：统计直方图与多分位数折线图

tag:图像的标签名，图的唯一标识values:要统计的参数global_step:y轴bins:取直方图bins writer = SummaryWriter(comment='test_comment', filename_suffix="test_suffix") for x in range(2): np.random.seed(x) data_union = np.arange(100) data_normal = np.random.normal(size=1000) writer.add_histogram('distribution union', data_union, x) writer.add_histogram('distribution normal', data_normal, x) plt.subplot(121).hist(data_union, label="union") plt.subplot(122).hist(data_normal, label="normal") plt.legend() plt.show() writer.close()

4.add_image()

功能：记录图像

tag:图像的标签名，图的唯一标识img_tensor:图像数据，注意尺度，如果所有像素点都是(0,1)之间就默认乘以255，如果有像素点大于1就默认所有像素点都是0~255的尺度global_step:x轴dataformats:数据形式，CHW，HWC，HW writer = SummaryWriter(comment='test_your_comment', filename_suffix="_test_your_filename_suffix") # img 1 random fake_img = torch.randn(3, 512, 512) writer.add_image("fake_img", fake_img, 1) time.sleep(1) # img 2 ones fake_img = torch.ones(3, 512, 512) time.sleep(1) writer.add_image("fake_img", fake_img, 2) # img 3 1.1 fake_img = torch.ones(3, 512, 512) * 1.1 time.sleep(1) writer.add_image("fake_img", fake_img, 3) # img 4 HW fake_img = torch.rand(512, 512) writer.add_image("fake_img", fake_img, 4, dataformats="HW") # img 5 HWC fake_img = torch.rand(512, 512, 3) writer.add_image("fake_img", fake_img, 5, dataformats="HWC") writer.close()

torchvision.utils.make_grid

功能：制作网格图像 可视化MNIST数据集

transform = Compose([transforms.ToTensor()]) datasets=mnist.MNIST(root='./data',train=True,transform=transform,download=True) datalode=DataLoader(datasets,batch_size=16,num_workers=2) images,labels=next(iter(datalode)) imagesTogather=tutiles.make_grid(images,nrow=4) imagesTogather=imagesTogather.transpose(0,2) import matplotlib.pyplot as plt plt.imshow(imagesTogather) plt.show()

可视化卷积核

import torch.nn as nn from PIL import Image import torchvision.transforms as transforms from torch.utils.tensorboard import SummaryWriter import torchvision.utils as vutils from tools.common_tools import set_seed import torchvision.models as models set_seed(1) # 设置随机种子 writer = SummaryWriter(comment='test_your_comment', filename_suffix="_test_your_filename_suffix") alexnet = models.alexnet(pretrained=True) kernel_num = -1 vis_max = 1 for sub_module in alexnet.modules(): if isinstance(sub_module, nn.Conv2d): kernel_num += 1 if kernel_num > vis_max: break kernels = sub_module.weight c_out, c_int, k_w, k_h = tuple(kernels.shape) for o_idx in range(c_out): kernel_idx = kernels[o_idx, :, :, :].unsqueeze(1) # make_grid需要 BCHW，这里拓展C维度 kernel_grid = vutils.make_grid(kernel_idx, normalize=True, scale_each=True, nrow=c_int) writer.add_image('{}_Convlayer_split_in_channel'.format(kernel_num), kernel_grid, global_step=o_idx) kernel_all = kernels.view(-1, 3, k_h, k_w) # 3, h, w kernel_grid = vutils.make_grid(kernel_all, normalize=True, scale_each=True, nrow=8) # c, h, w writer.add_image('{}_all'.format(kernel_num), kernel_grid, global_step=322) print("{}_convlayer shape:{}".format(kernel_num, tuple(kernels.shape))) writer.close()

特征图可视化

writer = SummaryWriter(comment='test_your_comment', filename_suffix="_test_your_filename_suffix") # 数据 path_img = "./lena.png" # your path to image normMean = [0.49139968, 0.48215827, 0.44653124] normStd = [0.24703233, 0.24348505, 0.26158768] norm_transform = transforms.Normalize(normMean, normStd) img_transforms = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), norm_transform ]) img_pil = Image.open(path_img).convert('RGB') if img_transforms is not None: img_tensor = img_transforms(img_pil) img_tensor.unsqueeze_(0) # chw --> bchw # 模型 alexnet = models.alexnet(pretrained=True) # forward convlayer1 = alexnet.features[0] fmap_1 = convlayer1(img_tensor) # 预处理 fmap_1.transpose_(0, 1) # bchw=(1, 64, 55, 55) --> (64, 1, 55, 55) fmap_1_grid = vutils.make_grid(fmap_1, normalize=True, scale_each=True, nrow=8) writer.add_image('feature map in conv1', fmap_1_grid, global_step=322) writer.close()

5.add_graph()

Pytorch1.2版本存在bug需要求换到1.3版本

writer = SummaryWriter(comment='test_your_comment', filename_suffix="_test_your_filename_suffix") # 模型 fake_img = torch.randn(1, 3, 32, 32) lenet = LeNet(classes=2) writer.add_graph(lenet, fake_img) writer.close()

torchsummary

此工具包属于第三方工具包，需要pip install安装一下 pip install torchsummary

from torchvision.models import alexnet Alexnet=alexnet() from torchsummary import summary print(summary(Alexnet, (3, 256,256), device="cpu"))