| # tensorboardX | |
| [](https://travis-ci.org/lanpa/tensorboardX) | |
| [](https://badge.fury.io/py/tensorboardX) | |
| [](https://bigquery.cloud.google.com/savedquery/966219917372:edb59a0d70c54eb687ab2a9417a778ee) | |
| [](https://tensorboardx.readthedocs.io/en/latest/?badge=latest) | |
| [](https://codecov.io/gh/lanpa/tensorboardX/) | |
| Write TensorBoard events with simple function call. | |
| * Support `scalar`, `image`, `figure`, `histogram`, `audio`, `text`, `graph`, `onnx_graph`, `embedding`, `pr_curve`, `mesh`, `hyper-parameters` | |
| and `video` summaries. | |
| * requirement for `demo_graph.py` is tensorboardX>=1.6 and pytorch>=1.1 | |
| * [FAQ](https://github.com/lanpa/tensorboardX/wiki) | |
| ## Install | |
| Tested on anaconda2 / anaconda3, with PyTorch 1.1.0 / torchvision 0.3 / tensorboard 1.13.0 | |
| `pip install tensorboardX` | |
| or build from source: | |
| `git clone https://github.com/lanpa/tensorboardX && cd tensorboardX && python setup.py install` | |
| You can optionally install [`crc32c`](https://github.com/ICRAR/crc32c) to speed up saving a large amount of data. | |
| ## Example | |
| * Run the demo script: `python examples/demo.py` | |
| * Use TensorBoard with `tensorboard --logdir runs` (needs to install TensorFlow) | |
| ```python | |
| # demo.py | |
| 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() | |
| ``` | |
| ## Screenshots | |
| <img src="screenshots/Demo.gif"> | |
| ## Tweaks | |
| To add more ticks for the slider (show more image history), check https://github.com/lanpa/tensorboardX/issues/44 or | |
| https://github.com/tensorflow/tensorboard/pull/1138 | |
| ## Reference | |
| * [TeamHG-Memex/tensorboard_logger](https://github.com/TeamHG-Memex/tensorboard_logger) | |
| * [dmlc/tensorboard](https://github.com/dmlc/tensorboard) | |