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Anchor Based Plain Net (ABPN)

This document describes how to run AIMET quantization on the following model(s) and verify the performance of the quantized models.

Environment Setup

1. Setup AI Model Efficiency Toolkit

Please install and setup AIMET before proceeding further. This model was tested with the torch_gpu variant of AIMET version 1.24.0.

2. Install AIMET-Model-Zoo

Clone the AIMET Model Zoo repo into your workspace:
git clone https://github.com/quic/aimet-model-zoo.git
export PYTHONPATH=$PYTHONPATH:<path to parent of aimet_model_zoo>

3. Download dataset

Download the Set14 Super Resolution Dataset from here: https://deepai.org/dataset/set14-super-resolution to any location in your workspace.

Extract the set5.zip file, then the SR_testing_datasets.zip found within.

The images of interest are located in the following path:
<root-path>/set5/SR_testing_datasets/Set14

Running Evaluation

To run evaluation with QuantSim in AIMET, use the following

 python3  abpn_quanteval.py \
                --model-config <configuration to be tested> \
                --dataset-path <path to directory containing High Resolution (ground truth) images>

Available model configurations are:

  • abpn_28_2x_w8a8
  • abpn_28_3x_w8a8
  • abpn_28_4x_w8a8
  • abpn_32_2x_w8a8
  • abpn_32_3x_w8a8
  • abpn_32_4x_w8a8

Models

Model checkpoints are available in the Releases page.

Please note the following regarding the available checkpoints:

  • All model architectures were reimplemented from scratch and trained on the DIV2k dataset (available here).
  • The float32 model is the full-precision model with the highest validation accuracy on the DIV2k dataset.
  • The int8 model is the quantized model with the highest validation accuracy obtained using AIMET's Quantization-aware Training.
  • The above quantized model along with the Encodings were exported using AIMET's export tool.

Quantization Configuration

In the evaluation notebook included, we have used the default config file, which configures the quantizer ops with the following assumptions:

  • Weight quantization: 8 bits, per tensor symmetric quantization
  • Bias parameters are not quantized
  • Activation quantization: 8 bits, asymmetric quantization
  • Model inputs are quantized
  • TF_enhanced was used as the quantization scheme

Results

NOTE: All results below used a Scaling factor (LR-to-HR upscaling) of 2x and the Set14 dataset.

Model Config[1] Channels PSNR
FP32 INT8
ABPN N/A 28 32.71 32.64
N/A 32 32.75 32.69

[1] Config: This parameter denotes a model configuration corresponding to a certain number of residual blocks used. The Mx models have 16 feature channels, whereas the XL model has 32 feature channels.