Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer
Paper • 2206.04584 • Published
GKT: Optimized for Qualcomm Devices
Geometry-guided Kernel Transformer is a machine learning model for generating a birds eye view represenation from the sensors(cameras) mounted on a vehicle.
This is based on the implementation of GKT found here. This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the Qualcomm® AI Hub Models library to export with custom configurations. More details on model performance across various devices, can be found here.
Qualcomm AI Hub Models uses Qualcomm AI Hub Workbench to compile, profile, and evaluate this model. Sign up to run these models on a hosted Qualcomm® device.
Getting Started
There are two ways to deploy this model on your device:
Option 1: Download Pre-Exported Models
Below are pre-exported model assets ready for deployment.
| Runtime | Precision | Chipset | SDK Versions | Download |
|---|---|---|---|---|
| PRECOMPILED_QNN_ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | QAIRT 2.42, ONNX Runtime 1.25.0 | Download |
| PRECOMPILED_QNN_ONNX | float | Snapdragon® X2 Elite | QAIRT 2.42, ONNX Runtime 1.25.0 | Download |
| PRECOMPILED_QNN_ONNX | float | Snapdragon® X Elite | QAIRT 2.42, ONNX Runtime 1.25.0 | Download |
| PRECOMPILED_QNN_ONNX | float | Snapdragon® 8 Gen 3 Mobile | QAIRT 2.42, ONNX Runtime 1.25.0 | Download |
| PRECOMPILED_QNN_ONNX | float | Qualcomm® QCS8550 (Proxy) | QAIRT 2.42, ONNX Runtime 1.25.0 | Download |
| PRECOMPILED_QNN_ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | QAIRT 2.42, ONNX Runtime 1.25.0 | Download |
| PRECOMPILED_QNN_ONNX | float | Qualcomm® QCS9075 | QAIRT 2.42, ONNX Runtime 1.25.0 | Download |
| PRECOMPILED_QNN_ONNX | w8a16_mixed_fp16 | Snapdragon® 8 Elite Gen 5 Mobile | QAIRT 2.42, ONNX Runtime 1.25.0 | Download |
| PRECOMPILED_QNN_ONNX | w8a16_mixed_fp16 | Snapdragon® X2 Elite | QAIRT 2.42, ONNX Runtime 1.25.0 | Download |
| PRECOMPILED_QNN_ONNX | w8a16_mixed_fp16 | Snapdragon® X Elite | QAIRT 2.42, ONNX Runtime 1.25.0 | Download |
| PRECOMPILED_QNN_ONNX | w8a16_mixed_fp16 | Snapdragon® 8 Gen 3 Mobile | QAIRT 2.42, ONNX Runtime 1.25.0 | Download |
| PRECOMPILED_QNN_ONNX | w8a16_mixed_fp16 | Qualcomm® QCS8550 (Proxy) | QAIRT 2.42, ONNX Runtime 1.25.0 | Download |
| PRECOMPILED_QNN_ONNX | w8a16_mixed_fp16 | Snapdragon® 8 Elite For Galaxy Mobile | QAIRT 2.42, ONNX Runtime 1.25.0 | Download |
| PRECOMPILED_QNN_ONNX | w8a16_mixed_fp16 | Qualcomm® QCS9075 | QAIRT 2.42, ONNX Runtime 1.25.0 | Download |
| QNN_CONTEXT_BINARY | float | Snapdragon® 8 Elite Gen 5 Mobile | QAIRT 2.45 | Download |
| QNN_CONTEXT_BINARY | float | Snapdragon® X2 Elite | QAIRT 2.45 | Download |
| QNN_CONTEXT_BINARY | float | Snapdragon® X Elite | QAIRT 2.45 | Download |
| QNN_CONTEXT_BINARY | float | Snapdragon® 8 Gen 3 Mobile | QAIRT 2.45 | Download |
| QNN_CONTEXT_BINARY | float | Qualcomm® QCS8550 (Proxy) | QAIRT 2.45 | Download |
| QNN_CONTEXT_BINARY | float | Qualcomm® SA8775P | QAIRT 2.45 | Download |
| QNN_CONTEXT_BINARY | float | Snapdragon® 8 Elite For Galaxy Mobile | QAIRT 2.45 | Download |
| QNN_CONTEXT_BINARY | float | Qualcomm® SA7255P | QAIRT 2.45 | Download |
| QNN_CONTEXT_BINARY | float | Qualcomm® SA8295P | QAIRT 2.45 | Download |
| QNN_CONTEXT_BINARY | float | Qualcomm® QCS9075 | QAIRT 2.45 | Download |
| QNN_CONTEXT_BINARY | float | Qualcomm® QCS8450 (Proxy) | QAIRT 2.45 | Download |
For more device-specific assets and performance metrics, visit GKT on Qualcomm® AI Hub.
Option 2: Export with Custom Configurations
Use the Qualcomm® AI Hub Models Python library to compile and export the model with your own:
- Custom weights (e.g., fine-tuned checkpoints)
- Custom input shapes
- Target device and runtime configurations
This option is ideal if you need to customize the model beyond the default configuration provided here.
See our repository for GKT on GitHub for usage instructions.
Model Details
Model Type: Model_use_case.driver_assistance
Model Stats:
- Model checkpoint: map_segmentation_gkt_7x1_conv_setting2.ckpt
- Input resolution: 1 x 6 x 3 x 224 x 480
- Number of parameters: 1.18M
- Model size: 4.66 MB
Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |
|---|---|---|---|---|---|---|
| GKT | PRECOMPILED_QNN_ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 56.903 ms | 8 - 18 MB | NPU |
| GKT | PRECOMPILED_QNN_ONNX | float | Snapdragon® X2 Elite | 62.042 ms | 9 - 9 MB | NPU |
| GKT | PRECOMPILED_QNN_ONNX | float | Snapdragon® X Elite | 108.068 ms | 7 - 7 MB | NPU |
| GKT | PRECOMPILED_QNN_ONNX | float | Snapdragon® 8 Gen 3 Mobile | 79.759 ms | 8 - 15 MB | NPU |
| GKT | PRECOMPILED_QNN_ONNX | float | Qualcomm® QCS8550 (Proxy) | 111.333 ms | 8 - 12 MB | NPU |
| GKT | PRECOMPILED_QNN_ONNX | float | Qualcomm® QCS9075 | 110.854 ms | 7 - 10 MB | NPU |
| GKT | PRECOMPILED_QNN_ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 71.062 ms | 1 - 9 MB | NPU |
| GKT | PRECOMPILED_QNN_ONNX | float | Qualcomm® QCS8750 | 71.062 ms | 1 - 9 MB | NPU |
| GKT | PRECOMPILED_QNN_ONNX | float | Qualcomm® QCS7181 | 108.068 ms | 7 - 7 MB | NPU |
| GKT | PRECOMPILED_QNN_ONNX | w8a16_mixed_fp16 | Snapdragon® 8 Elite Gen 5 Mobile | 37.164 ms | 4 - 14 MB | NPU |
| GKT | PRECOMPILED_QNN_ONNX | w8a16_mixed_fp16 | Snapdragon® X2 Elite | 39.965 ms | 4 - 4 MB | NPU |
| GKT | PRECOMPILED_QNN_ONNX | w8a16_mixed_fp16 | Snapdragon® X Elite | 87.059 ms | 6 - 6 MB | NPU |
| GKT | PRECOMPILED_QNN_ONNX | w8a16_mixed_fp16 | Snapdragon® 8 Gen 3 Mobile | 59.337 ms | 4 - 11 MB | NPU |
| GKT | PRECOMPILED_QNN_ONNX | w8a16_mixed_fp16 | Qualcomm® QCS8550 (Proxy) | 87.897 ms | 0 - 8 MB | NPU |
| GKT | PRECOMPILED_QNN_ONNX | w8a16_mixed_fp16 | Snapdragon® 8 Elite For Galaxy Mobile | 45.356 ms | 0 - 8 MB | NPU |
| GKT | PRECOMPILED_QNN_ONNX | w8a16_mixed_fp16 | Qualcomm® QCS9075 | 88.293 ms | 4 - 6 MB | NPU |
| GKT | PRECOMPILED_QNN_ONNX | w8a16_mixed_fp16 | Qualcomm® QCS8750 | 45.356 ms | 0 - 8 MB | NPU |
| GKT | PRECOMPILED_QNN_ONNX | w8a16_mixed_fp16 | Qualcomm® QCS7181 | 87.059 ms | 6 - 6 MB | NPU |
| GKT | QNN_CONTEXT_BINARY | float | Snapdragon® 8 Elite Gen 5 Mobile | 58.928 ms | 8 - 17 MB | NPU |
| GKT | QNN_CONTEXT_BINARY | float | Snapdragon® X2 Elite | 57.465 ms | 7 - 7 MB | NPU |
| GKT | QNN_CONTEXT_BINARY | float | Snapdragon® X Elite | 106.08 ms | 7 - 7 MB | NPU |
| GKT | QNN_CONTEXT_BINARY | float | Snapdragon® 8 Gen 3 Mobile | 81.106 ms | 8 - 16 MB | NPU |
| GKT | QNN_CONTEXT_BINARY | float | Qualcomm® QCS8275 | 190.598 ms | 1 - 11 MB | NPU |
| GKT | QNN_CONTEXT_BINARY | float | Qualcomm® QCS8550 (Proxy) | 110.968 ms | 9 - 10 MB | NPU |
| GKT | QNN_CONTEXT_BINARY | float | Qualcomm® SA8775P | 111.467 ms | 0 - 10 MB | NPU |
| GKT | QNN_CONTEXT_BINARY | float | Qualcomm® SA8650P | 111.467 ms | 0 - 10 MB | NPU |
| GKT | QNN_CONTEXT_BINARY | float | Qualcomm® SA8255P | 111.467 ms | 0 - 10 MB | NPU |
| GKT | QNN_CONTEXT_BINARY | float | Qualcomm® QCS9075 | 110.124 ms | 7 - 17 MB | NPU |
| GKT | QNN_CONTEXT_BINARY | float | Qualcomm® SA7255P | 190.598 ms | 1 - 11 MB | NPU |
| GKT | QNN_CONTEXT_BINARY | float | Qualcomm® SA8295P | 140.633 ms | 0 - 6 MB | NPU |
| GKT | QNN_CONTEXT_BINARY | float | Snapdragon® 8 Elite For Galaxy Mobile | 70.743 ms | 7 - 20 MB | NPU |
| GKT | QNN_CONTEXT_BINARY | float | Qualcomm® QCS8450 (Proxy) | 205.997 ms | 8 - 17 MB | NPU |
| GKT | QNN_CONTEXT_BINARY | float | Qualcomm® QCS8750 | 70.743 ms | 7 - 20 MB | NPU |
| GKT | QNN_CONTEXT_BINARY | float | Qualcomm® QCS7181 | 106.08 ms | 7 - 7 MB | NPU |
License
- The license for the original implementation of GKT can be found here.
References
- Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer
- Source Model Implementation
Community
- Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
- For questions or feedback please reach out to us.
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