tensorboard-pytorch

Write tensorboard events with simple command.

see demo (result of demo.py and some images generated by BEGAN)

install

#tested on anaconda3, tensorflow 1.2.1, pytorch 0.1.12, torchvision 0.1.8

pip install tensorboard-pytorch

pip install tensorflow or pip install tensorflow-gpu

usage

import torch
import torchvision.utils as vutils
import numpy as np
import torchvision.models as models
from datetime import datetime
from tensorboard import SummaryWriter
resnet18 = models.resnet18(True)
writer = SummaryWriter('runs/'+datetime.now().strftime('%B%d  %H:%M:%S'))
sample_rate = 44100
freqs = [262, 294, 330, 349, 392, 440, 440, 440, 440, 440, 440]
for n_iter in range(100):
    M_global = torch.rand(1) # value to keep
    writer.add_scalar('M_global', M_global[0], n_iter)
    x = torch.rand(32, 3, 64, 64) # output from network
    if n_iter%10==0:
        x = vutils.make_grid(x, normalize=True, scale_each=True)   
        writer.add_image('Image', x, n_iter)
        x = torch.zeros(sample_rate*2)
        for i in range(x.size(0)):
            x[i] = np.cos(freqs[n_iter//10]*np.pi*float(i)/float(sample_rate)) # sound amplitude should in [-1, 1]
        writer.add_audio('Audio', x, n_iter)
        writer.add_text('Text', 'testtext', n_iter)
        for name, param in resnet18.named_parameters():
            writer.add_histogram(name, param.clone().cpu().data.numpy(), n_iter)
writer.close()

python demo.py

tensorboard --logdir runs

scalar

image

audio

distribution

histogram

text

tweaks

To show more images in tensorboard's image tab, just modify the hardcoded event_accumulator in ~/anaconda3/lib/python3.6/site-packages/tensorflow/tensorboard/backend/application.py as you wish.

API

class SummaryWriter(str log_dir)

parameter:

• log_dir (str) - the location of the log folder.

add_scalar(str tag, float value, int global_step=None)

parameter:

• value (float) - the value to keep.

add_image(str tag, torch.Tensor t, int global_step=None)

parameter:

• t (torch.Tensor) - torch tensor of size (3,H,W). I suggest use torchvision.utils.make_grid() to prepare it.

add_audio(str tag, torch.Tensor t, int global_step=None)

parameter:

• t (torch.Tensor) - one dimensional torch tensor. The value should between [-1, 1]. The sample rate is currently fixed at 44100 KHz.

add_histogram(str tag, numpy.ndarray values, int global_step=None, bins='tensorflow')

parameter:

• values (numpy.ndarray) - one dimensional numpy array.
• bins (str) - one of {'tensorflow', numpy_arguments}, determines how the bins are made.

add_text(str tag, str text_to_save, int global_step=None)

parameter:

• text_to_save (str) - the string to keep.

Common parameters:

• tag (str) - values with same tag group together.
• global_step (int) - logs the training step on which the value is saved.

TODO

audio sample rate histogram binning method embedding

reference:

https://github.com/TeamHG-Memex/tensorboard_logger

https://github.com/dmlc/tensorboard