waynelwz / torchstudio

IDE for PyTorch and its ecosystem

TorchStudio Package

The torchstudio package contains 2 levels of scripts:

• Modules (files in the subfolders of the torchstudio package folder)
• Root (files in the torchstudio package folder)

Modules

Additional modules scripts can be added to the following torchstudio subfolders:

• analyzers: analyze a dataset, fill a weights list if relevant, and generate a PIL image report (classes inheriting from torchstudio.modules.Analyzer)
• datasets: define a tensors dataset (classes inheriting from torch.utils.data.Dataset)
• loss: calculate loss between inference and target (classes inheriting from torch.nn.Modules._Loss)
• metrics: calculate evaluation metric between inference and target (classes inheriting from torchstudio.modules.Metric or torchmetrics.metric.Metric or - catalyst.metrics._metric.IMetric or ignite.metrics.metric.Metric (with some adaptation to update()))
• models: define a neural network model (classes inheriting from torch.nn.Module)
• optim: optimize model's weights (classes inheriting from torch.optim.Optimizer)
• renderers: render a numpy tensor into a PIL image (classes inheriting from torchstudio.modules.Renderer)
• schedulers: adjust optimizer learning rate (classes inheriting from torch.optim._LRScheduler)

These modules are exposed in the application as follow:

• The Dataset tab expose the modules from the following folders: datasets, analyzers, renderers
• The Model tabs expose the modules from the following folders: models, loss, metrics, optim, schedulers

Root Scripts

The root scripts are management routines interfacing the modules and other processing tasks with the main application.

• datasetload.py: handles dataset tensors transfer (started locally or remotely when clicking the Load button in the Dataset tab)
• datasetanalyze.py: handles dataset tensors analysis (started locally or remotely when clicking the Analyze button in the Dataset tab)
• graphdraw.py: draw model graph into svg (started locally by the graph display in the Model tabs)
• metricsplot.py: plot training metrics into an image (started locally by the metrics display in the Model and Dashboard tabs)
• modelbuild.py: build, package and graph a model (started locally when clicking the Build button in the Model tabs)
• modeltrain.py: handles the model training and inference (started locally or remotely when clicking the Train button in the Model tabs)
• modules.py: definition for base classes used by modules in sub folders
• parametersplot.py: plot parameters into an image (started locally by the parameters display in the Dashboard tab)
• pythoncheck.py: check python satisfies the requirements (started locally when launching the application)
• pythoninstall.py: install the necessary python conda packages (started locally when setting up the application)
• pythonparse.py: parse modules and code chunks (started locally when launching the application)
• sshtunnel.py: ssh tunnel to execute scripts remotely as if they were local (started locally to launch remote scripts)
• tcpcodec.py: tcp socket communication functions (used locally and remotely by the other root scripts)
• tensorrender.py: handles the rendering of tensors into images (started locally by the tensor displays in the Dataset and Model tabs)