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LEIP Evaluate

The LEIP Evaluate tool is provided to evaluate the accuracy of a model in a consistent way across several stages of the LEIP tool chain. It allows inference and evaluation in TensorFlow, TFLite, PyTorch and LRE runtimes.

The leip evaluate command takes as input a path to a model folder or file, a path to a testset file and a path to a classnames file. The model is loaded into the specified runtime and performs inference of the input files specified in the testset. The command outputs the accuracy metric along with information about the number of inferences per second.

If you are evaluating a model on a device with an Nvidia GPU, the system will build and cache the compute engine the first time you run it. This will result in significant initial startup times, and will skew your inference timing results. On a device with an Nvidia GPU, run leip evaluate once as a dry run, and then a second time to generate accurate timing. You will need to do this each time you recompile the model.

CLI Usage

The basic command is:

leip evaluate --input_path optimizedModel/ \
              --test_path workspace/datasets/open-images-10-classes/eval/index.txt \
              --class_names workspace/datasets/open-images-10-classes/eval/class_names.txt

The output of the command will be:

accuracy: top1: XXXXXX%, top5: XXXXXX%, top1: rate YY.YY inferences/s (ZZ.ZZ)

# please note
The first number (Y, usually higher) represents the inference speed without including the overhead of preprocessing.
The second number inside the parentheses (Z) represents the inference speed that includes the preprocessing overhead.

For a detailed explanation of each option see the CLI Reference for LEIP Evaluate.

Batch Size

Batch sizes are supported in LEIP Evaluate. The batch size is controlled by two parameters:

  1. The first dimension of the input shapes of the model, and

  2. The --batch_size option in the leip evaluate command.

When --batch_size is NOT explicitly passed, the batch dimension of the first input’s shape is checked. If it is None, the batch is set to 1. If N is present in the shape, the batch size is set to N.

When --batch_size is explicitly passed, if the batch dimension of the first input’s shape is None, the --batch_size option is used. If the the batch dimension of the first input is not None and does not match the --batch_size option, an error is raised.

Here are some examples:

--input_shapes None,224,224,3                # Batch size 1 is used
--input_shapes None,224,224,3 --batch_size 8 # Batch size 8 is used, without padding
--input_shapes 8,224,224,3                   # Batch size 8 is used, with padding
--input_shapes 8,224,224,3 --batch_size 8    # Batch size 8 is used with padding
--input_shapes 2,224,224,3 --batch_size 8    # ValueError since the batch size and batch dim in input shape are explicitly defined and do not match

Padding is needed for some execution frameworks whenever they do not support None as an N dimension in the input shapes and there is a remainder of test items in an incomplete batch given the --test_size.

When compiling a model, the batch size must be set as the N part of the input shapes. This shape must then be used while running leip evaluate. Compiled models do not accept None as a shape dimension, and will not allow overriding the --batch_size from the CLI unless the input shape matches the batch size.

Testset Files

A testset file is a text file with test file information separated by newlines. Each line has a path for the test image/sound/etc, followed by a space, followed by an integer that represents the number of the correct output class.

The testset files are specified as follows:

  • A text file, typically ending in .txt but not required to do so, containing a list of input examples to be evaluated.

  • Each example is separated by a newline.

  • Each example is a line with 2 columns separated by a space.

  • The first column is a path to the input file of that example

  • The second column is the ground truth output for that example. It is usually a number specifying the class offset into the classnames file.

  • In the case of detection models, the output column is a comma-separated list of numbers which specify the bounding box coordinates in addition to the class offset.

  • The classnames file is documented below.

Here is an example row of a testset file:

$ head -1 testset.preprocessed.1001.txt
resources/images/imagenet_images/preprocessed/ILSVRC2012_val_00000001.JPEG 66

There is also an analogous JSON (or more specifically JSONL) file format supported. This is identical to the text format but instead of a space-separated row of newline-separated examples, each row is a single line JSON document. Each row has keys path and output. The value for the output key is an array so it can handle the multiple-output case. Here is an example row:

$ head -1 testset.preprocessed.1001.json
{"path": "resources/images/imagenet_images/preprocessed/ILSVRC2012_val_00000001.JPEG", "output": [66]}

Classname Files

The classnames file is the list of strings describing the names of the output classes. The first one in the file is 0, the next is 1 etc.

The classnames files are specified as follows:

  • A text file, typically ending in .txt but not required to do so, containing a list of human readable class names.

  • The name corresponds to the line number it appears on the file, zero-indexed.

Hardware Accelerator Optimized Model

When running inference based on optimized models for hardware accelerators the --inference_context target needs to be set.

For example, if a model was compiled with:

$ leip compile --target cuda:2080ti ...

Then the evaluate/run command must be called like:

$ leip evaluate --inference_context cuda ...

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