v0.44.0

See https://github.com/quic/ai-hub-models/releases/v0.44.0 for changelog.

LICENSE

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+The license of the original trained model can be found at https://github.com/hustvl/GKT/blob/main/LICENSE.

README.md

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+---
+library_name: pytorch
+license: other
+tags:
+- android
+pipeline_tag: other
+
+---
+
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/gkt/web-assets/model_demo.png)
+
+# GKT: Optimized for Mobile Deployment
+## Construct a bird’s eye view from sensors mounted on a vehicle
+
+
+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 model is an implementation of GKT found [here](https://github.com/hustvl/GKT/ https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf).
+
+
+This repository provides scripts to run GKT on Qualcomm® devices.
+More details on model performance across various devices, can be found
+[here](https://aihub.qualcomm.com/models/gkt).
+
+
+
+### 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
+
+| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
+|---|---|---|---|---|---|---|---|---|
+| GKT | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_CONTEXT_BINARY | 178.364 ms | 4 - 13 MB | NPU | Use Export Script |
+| GKT | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_CONTEXT_BINARY | 204.304 ms | 7 - 25 MB | NPU | Use Export Script |
+| GKT | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_CONTEXT_BINARY | 105.774 ms | 8 - 10 MB | NPU | Use Export Script |
+| GKT | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | PRECOMPILED_QNN_ONNX | 80.205 ms | 7 - 12 MB | NPU | Use Export Script |
+| GKT | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_CONTEXT_BINARY | 107.699 ms | 0 - 9 MB | NPU | Use Export Script |
+| GKT | float | SA7255P ADP | Qualcomm® SA7255P | QNN_CONTEXT_BINARY | 178.364 ms | 4 - 13 MB | NPU | Use Export Script |
+| GKT | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_CONTEXT_BINARY | 106.111 ms | 8 - 10 MB | NPU | Use Export Script |
+| GKT | float | SA8295P ADP | Qualcomm® SA8295P | QNN_CONTEXT_BINARY | 139.075 ms | 0 - 17 MB | NPU | Use Export Script |
+| GKT | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_CONTEXT_BINARY | 105.282 ms | 8 - 10 MB | NPU | Use Export Script |
+| GKT | float | SA8775P ADP | Qualcomm® SA8775P | QNN_CONTEXT_BINARY | 107.699 ms | 0 - 9 MB | NPU | Use Export Script |
+| GKT | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_CONTEXT_BINARY | 76.659 ms | 8 - 28 MB | NPU | Use Export Script |
+| GKT | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | PRECOMPILED_QNN_ONNX | 55.17 ms | 8 - 26 MB | NPU | Use Export Script |
+| GKT | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_CONTEXT_BINARY | 67.44 ms | 7 - 20 MB | NPU | Use Export Script |
+| GKT | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | PRECOMPILED_QNN_ONNX | 50.897 ms | 1 - 11 MB | NPU | Use Export Script |
+| GKT | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | QNN_CONTEXT_BINARY | 56.469 ms | 7 - 18 MB | NPU | Use Export Script |
+| GKT | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | PRECOMPILED_QNN_ONNX | 40.318 ms | 8 - 18 MB | NPU | Use Export Script |
+| GKT | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_CONTEXT_BINARY | 99.942 ms | 7 - 7 MB | NPU | Use Export Script |
+| GKT | float | Snapdragon X Elite CRD | Snapdragon® X Elite | PRECOMPILED_QNN_ONNX | 77.464 ms | 7 - 7 MB | NPU | Use Export Script |
+| GKT | w8a16_mixed_fp16 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_CONTEXT_BINARY | 192.276 ms | 4 - 13 MB | NPU | Use Export Script |
+| GKT | w8a16_mixed_fp16 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_CONTEXT_BINARY | 145.984 ms | 4 - 24 MB | NPU | Use Export Script |
+| GKT | w8a16_mixed_fp16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_CONTEXT_BINARY | 130.987 ms | 4 - 6 MB | NPU | Use Export Script |
+| GKT | w8a16_mixed_fp16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | PRECOMPILED_QNN_ONNX | 128.848 ms | 4 - 6 MB | NPU | Use Export Script |
+| GKT | w8a16_mixed_fp16 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_CONTEXT_BINARY | 130.062 ms | 1 - 11 MB | NPU | Use Export Script |
+| GKT | w8a16_mixed_fp16 | SA7255P ADP | Qualcomm® SA7255P | QNN_CONTEXT_BINARY | 192.276 ms | 4 - 13 MB | NPU | Use Export Script |
+| GKT | w8a16_mixed_fp16 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_CONTEXT_BINARY | 129.938 ms | 4 - 6 MB | NPU | Use Export Script |
+| GKT | w8a16_mixed_fp16 | SA8295P ADP | Qualcomm® SA8295P | QNN_CONTEXT_BINARY | 153.875 ms | 0 - 17 MB | NPU | Use Export Script |
+| GKT | w8a16_mixed_fp16 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_CONTEXT_BINARY | 130.476 ms | 4 - 7 MB | NPU | Use Export Script |
+| GKT | w8a16_mixed_fp16 | SA8775P ADP | Qualcomm® SA8775P | QNN_CONTEXT_BINARY | 130.062 ms | 1 - 11 MB | NPU | Use Export Script |
+| GKT | w8a16_mixed_fp16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_CONTEXT_BINARY | 93.949 ms | 4 - 22 MB | NPU | Use Export Script |
+| GKT | w8a16_mixed_fp16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | PRECOMPILED_QNN_ONNX | 97.679 ms | 4 - 23 MB | NPU | Use Export Script |
+| GKT | w8a16_mixed_fp16 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_CONTEXT_BINARY | 82.23 ms | 4 - 20 MB | NPU | Use Export Script |
+| GKT | w8a16_mixed_fp16 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | PRECOMPILED_QNN_ONNX | 83.95 ms | 0 - 14 MB | NPU | Use Export Script |
+| GKT | w8a16_mixed_fp16 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | QNN_CONTEXT_BINARY | 74.446 ms | 4 - 15 MB | NPU | Use Export Script |
+| GKT | w8a16_mixed_fp16 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | PRECOMPILED_QNN_ONNX | 70.609 ms | 4 - 14 MB | NPU | Use Export Script |
+| GKT | w8a16_mixed_fp16 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_CONTEXT_BINARY | 130.182 ms | 4 - 4 MB | NPU | Use Export Script |
+| GKT | w8a16_mixed_fp16 | Snapdragon X Elite CRD | Snapdragon® X Elite | PRECOMPILED_QNN_ONNX | 129.483 ms | 7 - 7 MB | NPU | Use Export Script |
+
+
+
+
+## Installation
+
+
+Install the package via pip:
+```bash
+# NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported.
+pip install nuscenes-devkit==1.2.0 --no-deps
+pip install "qai-hub-models[gkt]"
+```
+
+
+## Configure Qualcomm® AI Hub Workbench to run this model on a cloud-hosted device
+
+Sign-in to [Qualcomm® AI Hub Workbench](https://workbench.aihub.qualcomm.com/) with your
+Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.
+
+With this API token, you can configure your client to run models on the cloud
+hosted devices.
+```bash
+qai-hub configure --api_token API_TOKEN
+```
+Navigate to [docs](https://workbench.aihub.qualcomm.com/docs/) for more information.
+
+
+
+## Demo off target
+
+The package contains a simple end-to-end demo that downloads pre-trained
+weights and runs this model on a sample input.
+
+```bash
+python -m qai_hub_models.models.gkt.demo
+```
+
+The above demo runs a reference implementation of pre-processing, model
+inference, and post processing.
+
+**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
+environment, please add the following to your cell (instead of the above).
+```
+%run -m qai_hub_models.models.gkt.demo
+```
+
+
+### Run model on a cloud-hosted device
+
+In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
+device. This script does the following:
+* Performance check on-device on a cloud-hosted device
+* Downloads compiled assets that can be deployed on-device for Android.
+* Accuracy check between PyTorch and on-device outputs.
+
+```bash
+python -m qai_hub_models.models.gkt.export
+```
+
+
+
+## How does this work?
+
+This [export script](https://aihub.qualcomm.com/models/gkt/qai_hub_models/models/GKT/export.py)
+leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
+on-device. Lets go through each step below in detail:
+
+Step 1: **Compile model for on-device deployment**
+
+To compile a PyTorch model for on-device deployment, we first trace the model
+in memory using the `jit.trace` and then call the `submit_compile_job` API.
+
+```python
+import torch
+
+import qai_hub as hub
+from qai_hub_models.models.gkt import Model
+
+# Load the model
+torch_model = Model.from_pretrained()
+
+# Device
+device = hub.Device("Samsung Galaxy S25")
+
+# Trace model
+input_shape = torch_model.get_input_spec()
+sample_inputs = torch_model.sample_inputs()
+
+pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
+
+# Compile model on a specific device
+compile_job = hub.submit_compile_job(
+    model=pt_model,
+    device=device,
+    input_specs=torch_model.get_input_spec(),
+)
+
+# Get target model to run on-device
+target_model = compile_job.get_target_model()
+
+```
+
+
+Step 2: **Performance profiling on cloud-hosted device**
+
+After compiling models from step 1. Models can be profiled model on-device using the
+`target_model`. Note that this scripts runs the model on a device automatically
+provisioned in the cloud.  Once the job is submitted, you can navigate to a
+provided job URL to view a variety of on-device performance metrics.
+```python
+profile_job = hub.submit_profile_job(
+    model=target_model,
+    device=device,
+)
+        
+```
+
+Step 3: **Verify on-device accuracy**
+
+To verify the accuracy of the model on-device, you can run on-device inference
+on sample input data on the same cloud hosted device.
+```python
+input_data = torch_model.sample_inputs()
+inference_job = hub.submit_inference_job(
+    model=target_model,
+    device=device,
+    inputs=input_data,
+)
+    on_device_output = inference_job.download_output_data()
+
+```
+With the output of the model, you can compute like PSNR, relative errors or
+spot check the output with expected output.
+
+**Note**: This on-device profiling and inference requires access to Qualcomm®
+AI Hub Workbench. [Sign up for access](https://myaccount.qualcomm.com/signup).
+
+
+
+## Run demo on a cloud-hosted device
+
+You can also run the demo on-device.
+
+```bash
+python -m qai_hub_models.models.gkt.demo --eval-mode on-device
+```
+
+**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
+environment, please add the following to your cell (instead of the above).
+```
+%run -m qai_hub_models.models.gkt.demo -- --eval-mode on-device
+```
+
+
+## Deploying compiled model to Android
+
+
+The models can be deployed using multiple runtimes:
+- TensorFlow Lite (`.tflite` export): [This
+  tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
+  guide to deploy the .tflite model in an Android application.
+
+
+- QNN (`.so` export ): This [sample
+  app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
+provides instructions on how to use the `.so` shared library  in an Android application.
+
+
+## View on Qualcomm® AI Hub
+Get more details on GKT's performance across various devices [here](https://aihub.qualcomm.com/models/gkt).
+Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
+
+
+## License
+* The license for the original implementation of GKT can be found
+  [here](https://github.com/hustvl/GKT/blob/main/LICENSE).
+
+
+
+## References
+* [Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer](https://arxiv.org/abs/2206.04584)
+* [Source Model Implementation](https://github.com/hustvl/GKT/ https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
+
+
+
+## Community
+* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
+* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
+
+

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