Write tensorboard events with simple function call.
including scalar, image, histogram, audio, text, graph and embedding.
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)
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.