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734281c3d6
Summary: Working towards https://docs.google.com/document/d/10yx2-4gs0gTMOimVS403MnoAWkqitS8TUHX73PN8EjE/edit?pli=1# This PR: - Ensure that all the submodules are listed in a rst file (that ensure they are considered by the coverage tool) - Remove some long deprecated code that just error out on import - Remove the allow list altogether to ensure nothing gets added back there Pull Request resolved: https://github.com/pytorch/pytorch/pull/73983 Reviewed By: anjali411 Differential Revision: D34787908 Pulled By: albanD fbshipit-source-id: 163ce61e133b12b2f2e1cbe374f979e3d6858db7 (cherry picked from commit c9edfead7a01dc45bfc24eaf7220d2a84ab1f62e)
98 lines
3.1 KiB
ReStructuredText
98 lines
3.1 KiB
ReStructuredText
torch.utils.tensorboard
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===================================
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.. automodule:: torch.utils.tensorboard
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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 tensorboard
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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:: add_hparams
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.. automethod:: flush
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.. automethod:: close
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