TorchVista
Demos
Basic Examples
Basic examples demonstrating core TorchVista functionality
Simple linear model
Artificially made complex model
CNN with Branching
MLP with Skip Connections
Simple Resnet
Self Attention Classifier
Positional Transformer
Deep CNN
Visual control
Initial visual expansion depth of modules to keep the initial graph small and ready for selective expansion
Show/hide non-gradient (constant) nodes
MobileVit (All modules fully collapsed)
MobileVit (Collapse modules at depth = 1)
MobileVit (Collapse modules at depth = 2)
MobileVit (Collapse depth = 2) + hide non-gradient (constant) nodes
MobileVit (Collapse modules at depth = 3)
Forced module tracing up to a depth (Unavailable — In testing)
By default Pytorch standard or inbuilt modules' internals aren't traced. Use this flag to force trace all modules (including standard Pytorch modules) up to a desired depth
Force trace to see the contents of the TransformerEncoder module
Force trace deeper (depth = 2)
Force trace deeper (depth = 2) with increased visual collapse depth
Force trace deeper (depth = 3) with increased visual collapse depth
Force trace (depth = 4) on a deep CNN model
Error Handling
Demonstrations of how TorchVista handles models that encounter runtime errors
Tensor size mismatch error
Index out of bounds error
Complex Input & Output
Models with atypical input/output objects and unusual structures
Complex input and output
Same output tensor repeated and returned
Model nodes that don't connect to the output
Real world Examples
Real-world models
XLNetBaseCased (Large model)
Unet PP