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Earlier, the author mentioned PyTorch's exclusive visualization tool visdom, see PyTorch deep learning training visualization tool visdom. But before that, many TensorFlow users were more accustomed to using TensorBoard for visual display of training. In order to allow PyTorch users to use TensorBoard, some developers have provided a PyTorch version of TensorBoard, which is tensorboardX.
Install and start
Users who are familiar with TensorBoard can seamlessly connect to tensorboardX. The installation method is as follows:
pip install tensorboardXIn addition to installing PyTorch, you also need to install TensorFlow. Like TensorBoard, tensorboardX also supports the visualization of different types of objects such as scalar, image, figure, histogram, audio, text, graph, onnx_graph, embedding, pr_curve, video, etc.
TensorboardX starts in the same way as TensorBoard, and runs directly under the terminal:
tensorboard --logdir runsThen start another terminal to execute the Python file:
python demo.pyOpen localhost:6006 to see the tensorboardX visualization interface.
It is very easy to start tensorboardX locally, but in general, our training is done on the server, so some simple settings are required to start tensorboardX remotely. Take the virtual machine tool xshell as an example: set file->properties->ssh->tunnel->add, type local, fill in 127.0.0.1 (local machine) for the source host, set a port, such as 12345, and the target host is the server The address, the target port is generally 6006, if 6006 is occupied, it can be changed to other ports.
After executing the tensorboard and python scripts respectively, open 127.0.0.1:12345 locally to enter the remote TensorBoard interface.
Example of use
Take scalar as an example to see how tensorboardX is used:
import numpy as np
from tensorboardX import SummaryWriter
writer = SummaryWriter()
for i in range(100):
writer.add_scalar('data/scalar1', np.random.rand(), i)
writer.add_scalar('data/scalar2', {'xsinx': i*np.sin(i), 'xcosx': i*np.cos(i)}, i)
writer.close()The scalar visualization is shown below.
A complete tensorboardX demo is as follows:
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):
dummy_s1 = torch.rand(1)
dummy_s2 = torch.rand(1)
# data grouping by `slash`
writer.add_scalar('data/scalar1', dummy_s1[0], n_iter)
writer.add_scalar('data/scalar2', dummy_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)
dummy_img = torch.rand(32, 3, 64, 64) # output from network
if n_iter % 10 == 0:
x = vutils.make_grid(dummy_img, normalize=True, scale_each=True)
writer.add_image('Image', x, n_iter)
dummy_audio = torch.zeros(sample_rate * 2)
for i in range(x.size(0)):
# amplitude of sound should in [-1, 1]
dummy_audio[i] = np.cos(freqs[n_iter // 10] * np.pi * float(i) / float(sample_rate))
writer.add_audio('myAudio', dummy_audio, 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)
# needs tensorboard 0.4RC or later
writer.add_pr_curve('xoxo', np.random.randint(2, size=100), np.random.rand(100), 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()The visualization looks like this:
References:
https://github.com/lanpa/tensorboardX
https://www.tensorflow.org/tensorboard
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