跳转至

TensorBoard

import torch
import torchvision
from torch.utils.tensorboard import SummaryWriter
from torchvision import datasets, transforms

# Writer will output to ./runs/ directory by default
writer = SummaryWriter()

transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
trainset = datasets.MNIST('mnist_train', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
model = torchvision.models.resnet50(False)
# Have ResNet model take in grayscale rather than RGB
model.conv1 = torch.nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
images, labels = next(iter(trainloader))

grid = torchvision.utils.make_grid(images)
writer.add_image('images', grid, 0)
writer.add_graph(model, images)
writer.close()
pip install tensorboard
tensorboard --logdir=runs

具有相同 prefix/ 的绘图会被归到同一个标签下

from torch.utils.tensorboard import SummaryWriter
import numpy as np

writer = SummaryWriter()

for n_iter in range(100):
    writer.add_scalar('Loss/train', np.random.random(), n_iter)
    writer.add_scalar('Loss/test', np.random.random(), n_iter)
    writer.add_scalar('Accuracy/train', np.random.random(), n_iter)
    writer.add_scalar('Accuracy/test', np.random.random(), n_iter)

构造器

class torch.utils.tensorboard.writer.SummaryWriter(log_dir=None, comment='', purge_step=None, max_queue=10, flush_secs=120, filename_suffix='')
  • log_dir (string) – Save directory location. Default is runs/CURRENT_DATETIME_HOSTNAME, which changes after each run. Use hierarchical folder structure to compare between runs easily. e.g. pass in ‘runs/exp1’, ‘runs/exp2’, etc. for each new experiment to compare across them.
  • comment (string) – Comment log_dir suffix appended to the default log_dir. If log_dir is assigned, this argument has no effect.
  • purge_step (int) – When logging crashes at step T+XT+X and restarts at step TT, any events whose global_step larger or equal to TT will be purged and hidden from TensorBoard. Note that crashed and resumed experiments should have the same log_dir.
  • max_queue (int) – Size of the queue for pending events and summaries before one of the ‘add’ calls forces a flush to disk. Default is ten items.
  • flush_secs (int) – How often, in seconds, to flush the pending events and summaries to disk. Default is every two minutes.
  • filename_suffix (string) – Suffix added to all event filenames in the log_dir directory. More details on filename construction in tensorboard.summary.writer.event_file_writer.EventFileWriter.
from torch.utils.tensorboard import SummaryWriter

# create a summary writer with automatically generated folder name.
writer = SummaryWriter()
# folder location: runs/May04_22-14-54_s-MacBook-Pro.local/

# create a summary writer using the specified folder name.
writer = SummaryWriter("my_experiment")
# folder location: my_experiment

# create a summary writer with comment appended.
writer = SummaryWriter(comment="LR_0.1_BATCH_16")
# folder location: runs/May04_22-14-54_s-MacBook-Pro.localLR_0.1_BATCH_16/

方法

  • add_scalar(tag, scalar_value, global_step=None, walltime=None, new_style=False, double_precision=False)
  • add_scalars(main_tag, tag_scalar_dict, global_step=None, walltime=None)：与上一个不同就是传进去个 dict
  • add_histogram(tag, values, global_step=None, bins='tensorflow', walltime=None, max_bins=None)
  • add_image(tag, img_tensor, global_step=None, walltime=None, dataformats='CHW')：至少不同一次弹出个窗口来看图片效果
  • add_images(tag, img_tensor, global_step=None, walltime=None, dataformats='NCHW')：比上边多了一维 Batch，依赖 Pillow
  • add_figure(tag, figure, global_step=None, close=True, walltime=None)：贴一个 matplotlib figure 上去，依赖 matplotlib
  • add_video(tag, vid_tensor, global_step=None, fps=4, walltime=None)：贴一段视频上去，依赖 moviepy
  • add_audio(tag, snd_tensor, global_step=None, sample_rate=44100, walltime=None)：贴一段音频上去
  • add_text(tag, text_string, global_step=None, walltime=None)：文字
  • add_graph(model, input_to_model=None, verbose=False, use_strict_trace=True)：计算图，通过 trace 方式得到 Module 的计算图并显示在 TensorBoard 中。input_to_model 是模型的伪输入，可以是任意内容，只要形状对就可以
  • add_embedding(mat, metadata=None, label_img=None, global_step=None, tag='default', metadata_header=None)
  • add_pr_curve(tag, labels, predictions, global_step=None, num_thresholds=127, weights=None, walltime=None)：这就更高级了……直接把 Precision-Recall 曲线贴上去
  • add_custom_scalars(layout)
  • add_mesh(tag, vertices, colors=None, faces=None, config_dict=None, global_step=None, walltime=None)：贴一块三维点云上去，使用 Three.js 进行可视化
  • add_hparams(hparam_dict, metric_dict, hparam_domain_discrete=None, run_name=None)：把一堆超参数贴上去进行比较。调参必备https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_hparams
  • flush()
  • close()
最后更新: 2023-01-31