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