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. Iflog_diris 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dictadd_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Pillowadd_figure(tag, figure, global_step=None, close=True, walltime=None)：贴一个matplotlibfigure上去，依赖matplotlibadd_video(tag, vid_tensor, global_step=None, fps=4, walltime=None)：贴一段视频上去，依赖moviepyadd_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_hparamsflush()close()