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858d4a6a04
Summary: This fixes ASAN test issues with https://github.com/pytorch/pytorch/pull/21786 seen at https://circleci.com/api/v1.1/project/github/pytorch/pytorch/2212325/output/105/0?file=true and lands it again. This cleans up the `torch.utils.tensorboard` API to remove all kwargs usage (which isn't clear to the user) and removes the "experimental" warning in prep for our 1.2 release. We also don't need the additional PyTorch version checks now that we are in the codebase itself. cc yf225, lanpa, natalialunova Pull Request resolved: https://github.com/pytorch/pytorch/pull/23000 Reviewed By: sanekmelnikov Differential Revision: D16349734 Pulled By: orionr fbshipit-source-id: 604a9cad56868a55e08b509a0c6f42b84f68de95
96 lines
3.1 KiB
ReStructuredText
96 lines
3.1 KiB
ReStructuredText
torch.utils.tensorboard
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===================================
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Before going further, more details on TensorBoard can be found at
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https://www.tensorflow.org/tensorboard/
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Once you've installed TensorBoard, these utilities let you log PyTorch models
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and metrics into a directory for visualization within the TensorBoard UI.
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Scalars, images, histograms, graphs, and embedding visualizations are all
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supported for PyTorch models and tensors as well as Caffe2 nets and blobs.
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The SummaryWriter class is your main entry to log data for consumption
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and visualization by TensorBoard. For example:
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.. code:: python
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import torch
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import torchvision
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from torch.utils.tensorboard import SummaryWriter
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from torchvision import datasets, transforms
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# Writer will output to ./runs/ directory by default
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writer = SummaryWriter()
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transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
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trainset = datasets.MNIST('mnist_train', train=True, download=True, transform=transform)
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trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
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model = torchvision.models.resnet50(False)
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# Have ResNet model take in grayscale rather than RGB
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model.conv1 = torch.nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
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images, labels = next(iter(trainloader))
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grid = torchvision.utils.make_grid(images)
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writer.add_image('images', grid, 0)
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writer.add_graph(model, images)
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writer.close()
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This can then be visualized with TensorBoard, which should be installable
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and runnable with::
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pip install tb-nightly # Until 1.14 moves to the release channel
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tensorboard --logdir=runs
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Lots of information can be logged for one experiment. To avoid cluttering
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the UI and have better result clustering, we can group plots by naming them
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hierarchically. For example, "Loss/train" and "Loss/test" will be grouped
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together, while "Accuracy/train" and "Accuracy/test" will be grouped separately
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in the TensorBoard interface.
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.. code:: python
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from torch.utils.tensorboard import SummaryWriter
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import numpy as np
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writer = SummaryWriter()
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for n_iter in range(100):
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writer.add_scalar('Loss/train', np.random.random(), n_iter)
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writer.add_scalar('Loss/test', np.random.random(), n_iter)
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writer.add_scalar('Accuracy/train', np.random.random(), n_iter)
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writer.add_scalar('Accuracy/test', np.random.random(), n_iter)
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Expected result:
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.. image:: _static/img/tensorboard/hier_tags.png
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:scale: 75 %
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.. currentmodule:: torch.utils.tensorboard.writer
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.. autoclass:: SummaryWriter
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.. automethod:: __init__
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.. automethod:: add_scalar
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.. automethod:: add_scalars
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.. automethod:: add_histogram
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.. automethod:: add_image
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.. automethod:: add_images
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.. automethod:: add_figure
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.. automethod:: add_video
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.. automethod:: add_audio
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.. automethod:: add_text
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.. automethod:: add_graph
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.. automethod:: add_embedding
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.. automethod:: add_pr_curve
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.. automethod:: add_custom_scalars
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.. automethod:: add_mesh
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.. automethod:: flush
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.. automethod:: close
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