View Inference Results

Inference Results

Once an initial inference has been run with a model, sample dataset, and target, you can view performance results on the Configurations Page.

The components specified below provide visual representation of a model performance on a selected dataset and help find potential bottlenecks and areas for improvement:

  • The Execution Time by Layer donut chart represents information on execution time of each layer group. A tooltip with information on execution time in milliseconds appears when you hover your mouse over the chart.
  • The Execution Attributes donut chart represents information on execution time of each layer group. A tooltip with information on execution time in milliseconds appears when you hover your mouse over the chart.
  • The Layers Table data table shows each layer of a model. Expand rows representing fused layers to see layers parameters. For details, go to Visualize Model.
  • The Runtime Graph and Original IR are visualization tools. Go to Visualize Model for details.

Model Analyzer

The Model Analyzer is used for generating estimated performance information on neural networks. The tool analyzes of the following characteristics:

Parameter Explanation Unit of Measurement
Flop Total number of floating-point operations required to infer a model. Summed up over known layers only. Number of operations
Iop Total number of integer operations required to infer a model. Summed up over known layers only. Number of operations
Total number of weights Total number of trainable network parameters excluding custom constants. Summed up over known layers only. Number of weights
Minimum Memory Consumption Theoretical minimum of memory used by a network for inference given that the memory is reused as much as possible. Minimum Memory Consumption does not depend on weights. Number of activations
Maximum Memory Consumption Theoretical maximum of memory used by a network for inference given that the memory is not reused, which means all internal feature maps are stored in the memory simultaneously. Maximum Memory Consumption does not depend on weights. Number of activations
Sparsity Percentage of zero weights Percentage

Model analysis data is collected when the model is imported. All parameters depend on the size of a batch. Currently, information is gathered on the default model batch.

To view analysis data, click Details next to the name of a model in the table:

The details appear on the right:

See Also