MobilenetV2.md

Pytorch MobileNetV2

Environment Setup

Setup AI Model Efficiency Toolkit (AIMET)

Please install and setup AIMET before proceeding further. This model was tested with the torch_gpu variant of AIMET 1.23.

Append the repo location to your PYTHONPATH with the following:

export PYTHONPATH=$PYTHONPATH:<path to parent of aimet_model_zoo>

Dataset

This evaluation was designed for the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC2012), which can be obtained from: http://www.image-net.org/
The dataset directory is expected to have 3 subdirectories: train, valid, and test (only the valid test is used, hence if the other subdirectories are missing that is ok). Each of the {train, valid, test} directories is then expected to have 1000 subdirectories, each containing the images from the 1000 classes present in the ILSVRC2012 dataset, such as in the example below:

  train/
  ├── n01440764
  │   ├── n01440764_10026.JPEG
  │   ├── n01440764_10027.JPEG
  │   ├── ......
  ├── ......
  val/
  ├── n01440764
  │   ├── ILSVRC2012_val_00000293.JPEG
  │   ├── ILSVRC2012_val_00002138.JPEG
  │   ├── ......
  ├── ......

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Usage

To run evaluation with QuantSim in AIMET, use the following

python3  aimet_zoo_torch/mobilenetv2/evaluators/mobilenetv2_quanteval.py \
                --model-config <configuration to be tested> \
                --dataset-path <path to ImageNet validation images> \
                --batch-size  <batch size as an integer value, defaults to 16> \

Available model configurations are:

• mobilenetv2_w8a8

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Quantization Configuration

• Weight quantization: 8 bits, per tensor asymmetric quantization
• Bias parameters are not quantized
• Activation quantization: 8 bits, asymmetric quantization
• Model inputs are not quantized
• TF_enhanced was used as quantization scheme
• Data Free Quantization and Quantization aware Training has been performed on the optimized checkpoint