Write tensorboard events with simple function call.
Supports scalar, image, histogram, audio, text, graph, embedding and pr_curve.
see demo (result of demo.py and some images generated by BEGAN)
If the demo code onnx_graph.py is not working, you have to build pytorch and onnx from source.
#tested on anaconda2/anaconda3, pytorch 0.2, torchvision 0.1.9
pip install tensorboardX
pip install tensorflow (for tensorboard web server)
or build from source:
pip install git+https://github.com/lanpa/tensorboard-pytorch
http://tensorboard-pytorch.readthedocs.io/en/latest/tensorboard.html
import torch
import torchvision.utils as vutils
import numpy as np
import torchvision.models as models
from torchvision import datasets
from tensorboardX import SummaryWriter
resnet18 = models.resnet18(False)
writer = SummaryWriter()
sample_rate = 44100
freqs = [262, 294, 330, 349, 392, 440, 440, 440, 440, 440, 440]
for n_iter in range(100):
s1 = torch.rand(1) # value to keep
s2 = torch.rand(1)
writer.add_scalar('data/scalar1', s1[0], n_iter) #data grouping by `slash`
writer.add_scalar('data/scalar2', s2[0], n_iter)
writer.add_scalars('data/scalar_group', {"xsinx":n_iter*np.sin(n_iter),
"xcosx":n_iter*np.cos(n_iter),
"arctanx": np.arctan(n_iter)}, 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('myAudio', x, n_iter, sample_rate=sample_rate)
writer.add_text('Text', 'text logged at step:'+str(n_iter), n_iter)
for name, param in resnet18.named_parameters():
writer.add_histogram(name, param.clone().cpu().data.numpy(), n_iter)
writer.add_pr_curve('xoxo', np.random.randint(2, size=100), np.random.rand(100), n_iter) #needs tensorboard 0.4RC or later
dataset = datasets.MNIST('mnist', train=False, download=True)
images = dataset.test_data[:100].float()
label = dataset.test_labels[:100]
features = images.view(100, 784)
writer.add_embedding(features, metadata=label, label_img=images.unsqueeze(1))
# export scalar data to JSON for external processing
writer.export_scalars_to_json("./all_scalars.json")
writer.close()python demo.py
tensorboard --logdir runs
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.
For tensorflow-tensorboard>0.17 see lanpa#44