v0.46.1
Browse filesSee https://github.com/quic/ai-hub-models/releases/v0.46.1 for changelog.
- MobileNet-v2_float.dlc +0 -3
- MobileNet-v2_float.onnx.zip +0 -3
- MobileNet-v2_float.tflite +0 -3
- MobileNet-v2_w8a16.dlc +0 -3
- MobileNet-v2_w8a16_mixed_int16.dlc +0 -3
- MobileNet-v2_w8a8.dlc +0 -3
- MobileNet-v2_w8a8.tflite +0 -3
- README.md +130 -279
- tool-versions.yaml +0 -3
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README.md
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## Imagenet classifier and general purpose backbone
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MobileNetV2 is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.
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weights and runs this model on a sample input.
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```bash
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python -m qai_hub_models.models.mobilenet_v2.demo
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```
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The above demo runs a reference implementation of pre-processing, model
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inference, and post processing.
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**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
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environment, please add the following to your cell (instead of the above).
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```
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%run -m qai_hub_models.models.mobilenet_v2.demo
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```
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### Run model on a cloud-hosted device
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In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
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device. This script does the following:
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* Performance check on-device on a cloud-hosted device
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* Downloads compiled assets that can be deployed on-device for Android.
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* Accuracy check between PyTorch and on-device outputs.
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```bash
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python -m qai_hub_models.models.mobilenet_v2.export
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```
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## How does this work?
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This [export script](https://aihub.qualcomm.com/models/mobilenet_v2/qai_hub_models/models/MobileNet-v2/export.py)
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leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
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on-device. Lets go through each step below in detail:
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Step 1: **Compile model for on-device deployment**
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To compile a PyTorch model for on-device deployment, we first trace the model
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in memory using the `jit.trace` and then call the `submit_compile_job` API.
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```python
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import torch
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import qai_hub as hub
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from qai_hub_models.models.mobilenet_v2 import Model
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# Load the model
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torch_model = Model.from_pretrained()
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# Device
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device = hub.Device("Samsung Galaxy S25")
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# Trace model
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input_shape = torch_model.get_input_spec()
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sample_inputs = torch_model.sample_inputs()
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pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
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# Compile model on a specific device
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compile_job = hub.submit_compile_job(
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input_specs=torch_model.get_input_spec(),
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)
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# Get target model to run on-device
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target_model = compile_job.get_target_model()
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```
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Step 2: **Performance profiling on cloud-hosted device**
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After compiling models from step 1. Models can be profiled model on-device using the
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`target_model`. Note that this scripts runs the model on a device automatically
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provisioned in the cloud. Once the job is submitted, you can navigate to a
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provided job URL to view a variety of on-device performance metrics.
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```python
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)
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```
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Step 3: **Verify on-device accuracy**
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on sample input data on the same cloud hosted device.
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input_data = torch_model.sample_inputs()
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on_device_output = inference_job.download_output_data()
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```
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With the output of the model, you can compute like PSNR, relative errors or
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spot check the output with expected output.
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**Note**: This on-device profiling and inference requires access to Qualcomm®
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AI Hub Workbench. [Sign up for access](https://myaccount.qualcomm.com/signup).
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## Run demo on a cloud-hosted device
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You can also run the demo on-device.
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**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
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environment, please add the following to your cell (instead of the above).
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%run -m qai_hub_models.models.mobilenet_v2.demo -- --eval-mode on-device
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```
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## Deploying compiled model to Android
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The models can be deployed using multiple runtimes:
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- TensorFlow Lite (`.tflite` export): [This
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tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
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guide to deploy the .tflite model in an Android application.
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- QNN (`.so` export ): This [sample
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app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
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provides instructions on how to use the `.so` shared library in an Android application.
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## View on Qualcomm® AI Hub
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Get more details on MobileNet-v2's performance across various devices [here](https://aihub.qualcomm.com/models/mobilenet_v2).
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Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
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## License
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* The license for the original implementation of MobileNet-v2 can be found
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[here](https://github.com/tonylins/pytorch-mobilenet-v2/blob/master/LICENSE).
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## References
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* [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381)
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* [Source Model Implementation](https://github.com/tonylins/pytorch-mobilenet-v2/tree/master)
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## Community
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* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
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* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
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# MobileNet-v2: Optimized for Qualcomm Devices
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This is based on the implementation of MobileNet-v2 found [here](https://github.com/tonylins/pytorch-mobilenet-v2/tree/master).
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This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/mobilenet_v2) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
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Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
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## Getting Started
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There are two ways to deploy this model on your device:
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### Option 1: Download Pre-Exported Models
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Below are pre-exported model assets ready for deployment.
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| Runtime | Precision | Chipset | SDK Versions | Download |
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|---|---|---|---|---|
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| ONNX | float | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mobilenet_v2/releases/v0.46.1/mobilenet_v2-onnx-float.zip)
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| QNN_DLC | float | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mobilenet_v2/releases/v0.46.1/mobilenet_v2-qnn_dlc-float.zip)
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| QNN_DLC | w8a16 | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mobilenet_v2/releases/v0.46.1/mobilenet_v2-qnn_dlc-w8a16.zip)
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| QNN_DLC | w8a16_mixed_int16 | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mobilenet_v2/releases/v0.46.1/mobilenet_v2-qnn_dlc-w8a16_mixed_int16.zip)
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| QNN_DLC | w8a8 | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mobilenet_v2/releases/v0.46.1/mobilenet_v2-qnn_dlc-w8a8.zip)
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| TFLITE | float | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mobilenet_v2/releases/v0.46.1/mobilenet_v2-tflite-float.zip)
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| TFLITE | w8a8 | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mobilenet_v2/releases/v0.46.1/mobilenet_v2-tflite-w8a8.zip)
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For more device-specific assets and performance metrics, visit **[MobileNet-v2 on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/mobilenet_v2)**.
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### Option 2: Export with Custom Configurations
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|
| 45 |
+
Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/mobilenet_v2) Python library to compile and export the model with your own:
|
| 46 |
+
- Custom weights (e.g., fine-tuned checkpoints)
|
| 47 |
+
- Custom input shapes
|
| 48 |
+
- Target device and runtime configurations
|
| 49 |
+
|
| 50 |
+
This option is ideal if you need to customize the model beyond the default configuration provided here.
|
| 51 |
+
|
| 52 |
+
See our repository for [MobileNet-v2 on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/mobilenet_v2) for usage instructions.
|
| 53 |
+
|
| 54 |
+
## Model Details
|
| 55 |
+
|
| 56 |
+
**Model Type:** Model_use_case.image_classification
|
| 57 |
+
|
| 58 |
+
**Model Stats:**
|
| 59 |
+
- Model checkpoint: Imagenet
|
| 60 |
+
- Input resolution: 224x224
|
| 61 |
+
- Number of parameters: 3.49M
|
| 62 |
+
- Model size (float): 13.3 MB
|
| 63 |
+
- Model size (w8a16): 4.39 MB
|
| 64 |
+
|
| 65 |
+
## Performance Summary
|
| 66 |
+
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|
| 67 |
+
|---|---|---|---|---|---|---
|
| 68 |
+
| MobileNet-v2 | ONNX | float | Snapdragon® X Elite | 0.77 ms | 7 - 7 MB | NPU
|
| 69 |
+
| MobileNet-v2 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 0.523 ms | 0 - 120 MB | NPU
|
| 70 |
+
| MobileNet-v2 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 0.801 ms | 0 - 48 MB | NPU
|
| 71 |
+
| MobileNet-v2 | ONNX | float | Qualcomm® QCS9075 | 1.078 ms | 1 - 3 MB | NPU
|
| 72 |
+
| MobileNet-v2 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.465 ms | 0 - 100 MB | NPU
|
| 73 |
+
| MobileNet-v2 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.377 ms | 0 - 100 MB | NPU
|
| 74 |
+
| MobileNet-v2 | QNN_DLC | float | Snapdragon® X Elite | 1.048 ms | 1 - 1 MB | NPU
|
| 75 |
+
| MobileNet-v2 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 0.603 ms | 0 - 51 MB | NPU
|
| 76 |
+
| MobileNet-v2 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 2.666 ms | 1 - 32 MB | NPU
|
| 77 |
+
| MobileNet-v2 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 0.897 ms | 1 - 2 MB | NPU
|
| 78 |
+
| MobileNet-v2 | QNN_DLC | float | Qualcomm® SA8775P | 1.209 ms | 1 - 33 MB | NPU
|
| 79 |
+
| MobileNet-v2 | QNN_DLC | float | Qualcomm® QCS9075 | 1.127 ms | 1 - 3 MB | NPU
|
| 80 |
+
| MobileNet-v2 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 1.715 ms | 0 - 54 MB | NPU
|
| 81 |
+
| MobileNet-v2 | QNN_DLC | float | Qualcomm® SA7255P | 2.666 ms | 1 - 32 MB | NPU
|
| 82 |
+
| MobileNet-v2 | QNN_DLC | float | Qualcomm® SA8295P | 1.468 ms | 0 - 30 MB | NPU
|
| 83 |
+
| MobileNet-v2 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.444 ms | 1 - 31 MB | NPU
|
| 84 |
+
| MobileNet-v2 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.333 ms | 1 - 35 MB | NPU
|
| 85 |
+
| MobileNet-v2 | QNN_DLC | w8a16 | Snapdragon® X Elite | 0.959 ms | 0 - 0 MB | NPU
|
| 86 |
+
| MobileNet-v2 | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 0.572 ms | 0 - 41 MB | NPU
|
| 87 |
+
| MobileNet-v2 | QNN_DLC | w8a16 | Qualcomm® QCS6490 | 2.276 ms | 0 - 2 MB | NPU
|
| 88 |
+
| MobileNet-v2 | QNN_DLC | w8a16 | Qualcomm® QCS8275 (Proxy) | 1.775 ms | 0 - 29 MB | NPU
|
| 89 |
+
| MobileNet-v2 | QNN_DLC | w8a16 | Qualcomm® QCS8550 (Proxy) | 0.799 ms | 0 - 2 MB | NPU
|
| 90 |
+
| MobileNet-v2 | QNN_DLC | w8a16 | Qualcomm® SA8775P | 4.295 ms | 0 - 30 MB | NPU
|
| 91 |
+
| MobileNet-v2 | QNN_DLC | w8a16 | Qualcomm® QCS9075 | 0.982 ms | 2 - 4 MB | NPU
|
| 92 |
+
| MobileNet-v2 | QNN_DLC | w8a16 | Qualcomm® QCM6690 | 3.38 ms | 0 - 141 MB | NPU
|
| 93 |
+
| MobileNet-v2 | QNN_DLC | w8a16 | Qualcomm® QCS8450 (Proxy) | 0.989 ms | 0 - 44 MB | NPU
|
| 94 |
+
| MobileNet-v2 | QNN_DLC | w8a16 | Qualcomm® SA7255P | 1.775 ms | 0 - 29 MB | NPU
|
| 95 |
+
| MobileNet-v2 | QNN_DLC | w8a16 | Qualcomm® SA8295P | 1.267 ms | 0 - 27 MB | NPU
|
| 96 |
+
| MobileNet-v2 | QNN_DLC | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 0.375 ms | 0 - 28 MB | NPU
|
| 97 |
+
| MobileNet-v2 | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 0.878 ms | 0 - 145 MB | NPU
|
| 98 |
+
| MobileNet-v2 | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 0.301 ms | 0 - 32 MB | NPU
|
| 99 |
+
| MobileNet-v2 | QNN_DLC | w8a16_mixed_int16 | Snapdragon® X Elite | 1.01 ms | 0 - 0 MB | NPU
|
| 100 |
+
| MobileNet-v2 | QNN_DLC | w8a16_mixed_int16 | Snapdragon® 8 Gen 3 Mobile | 0.618 ms | 0 - 41 MB | NPU
|
| 101 |
+
| MobileNet-v2 | QNN_DLC | w8a16_mixed_int16 | Qualcomm® QCS8275 (Proxy) | 2.007 ms | 0 - 29 MB | NPU
|
| 102 |
+
| MobileNet-v2 | QNN_DLC | w8a16_mixed_int16 | Qualcomm® QCS8550 (Proxy) | 0.863 ms | 0 - 12 MB | NPU
|
| 103 |
+
| MobileNet-v2 | QNN_DLC | w8a16_mixed_int16 | Qualcomm® SA8775P | 1.051 ms | 0 - 31 MB | NPU
|
| 104 |
+
| MobileNet-v2 | QNN_DLC | w8a16_mixed_int16 | Qualcomm® QCS9075 | 1.048 ms | 0 - 2 MB | NPU
|
| 105 |
+
| MobileNet-v2 | QNN_DLC | w8a16_mixed_int16 | Qualcomm® QCM6690 | 4.756 ms | 0 - 142 MB | NPU
|
| 106 |
+
| MobileNet-v2 | QNN_DLC | w8a16_mixed_int16 | Qualcomm® SA7255P | 2.007 ms | 0 - 29 MB | NPU
|
| 107 |
+
| MobileNet-v2 | QNN_DLC | w8a16_mixed_int16 | Snapdragon® 8 Elite For Galaxy Mobile | 0.41 ms | 0 - 29 MB | NPU
|
| 108 |
+
| MobileNet-v2 | QNN_DLC | w8a16_mixed_int16 | Snapdragon® 7 Gen 4 Mobile | 1.004 ms | 0 - 142 MB | NPU
|
| 109 |
+
| MobileNet-v2 | QNN_DLC | w8a16_mixed_int16 | Snapdragon® 8 Elite Gen 5 Mobile | 0.324 ms | 0 - 33 MB | NPU
|
| 110 |
+
| MobileNet-v2 | QNN_DLC | w8a8 | Snapdragon® X Elite | 0.579 ms | 0 - 0 MB | NPU
|
| 111 |
+
| MobileNet-v2 | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.326 ms | 0 - 40 MB | NPU
|
| 112 |
+
| MobileNet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 1.373 ms | 0 - 2 MB | NPU
|
| 113 |
+
| MobileNet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 1.062 ms | 0 - 29 MB | NPU
|
| 114 |
+
| MobileNet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.46 ms | 0 - 1 MB | NPU
|
| 115 |
+
| MobileNet-v2 | QNN_DLC | w8a8 | Qualcomm® SA8775P | 0.631 ms | 0 - 31 MB | NPU
|
| 116 |
+
| MobileNet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 0.525 ms | 0 - 2 MB | NPU
|
| 117 |
+
| MobileNet-v2 | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 1.76 ms | 0 - 28 MB | NPU
|
| 118 |
+
| MobileNet-v2 | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 0.609 ms | 0 - 41 MB | NPU
|
| 119 |
+
| MobileNet-v2 | QNN_DLC | w8a8 | Qualcomm® SA7255P | 1.062 ms | 0 - 29 MB | NPU
|
| 120 |
+
| MobileNet-v2 | QNN_DLC | w8a8 | Qualcomm® SA8295P | 0.832 ms | 0 - 27 MB | NPU
|
| 121 |
+
| MobileNet-v2 | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.223 ms | 0 - 32 MB | NPU
|
| 122 |
+
| MobileNet-v2 | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.469 ms | 0 - 28 MB | NPU
|
| 123 |
+
| MobileNet-v2 | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.181 ms | 0 - 31 MB | NPU
|
| 124 |
+
| MobileNet-v2 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 0.593 ms | 0 - 55 MB | NPU
|
| 125 |
+
| MobileNet-v2 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 2.729 ms | 0 - 35 MB | NPU
|
| 126 |
+
| MobileNet-v2 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 0.898 ms | 0 - 2 MB | NPU
|
| 127 |
+
| MobileNet-v2 | TFLITE | float | Qualcomm® SA8775P | 5.179 ms | 0 - 35 MB | NPU
|
| 128 |
+
| MobileNet-v2 | TFLITE | float | Qualcomm® QCS9075 | 1.13 ms | 0 - 10 MB | NPU
|
| 129 |
+
| MobileNet-v2 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 1.708 ms | 0 - 56 MB | NPU
|
| 130 |
+
| MobileNet-v2 | TFLITE | float | Qualcomm® SA7255P | 2.729 ms | 0 - 35 MB | NPU
|
| 131 |
+
| MobileNet-v2 | TFLITE | float | Qualcomm® SA8295P | 1.519 ms | 0 - 32 MB | NPU
|
| 132 |
+
| MobileNet-v2 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 0.451 ms | 0 - 40 MB | NPU
|
| 133 |
+
| MobileNet-v2 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.341 ms | 0 - 39 MB | NPU
|
| 134 |
+
| MobileNet-v2 | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 0.289 ms | 0 - 41 MB | NPU
|
| 135 |
+
| MobileNet-v2 | TFLITE | w8a8 | Qualcomm® QCS6490 | 1.136 ms | 0 - 7 MB | NPU
|
| 136 |
+
| MobileNet-v2 | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 0.987 ms | 0 - 31 MB | NPU
|
| 137 |
+
| MobileNet-v2 | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 0.408 ms | 0 - 1 MB | NPU
|
| 138 |
+
| MobileNet-v2 | TFLITE | w8a8 | Qualcomm® SA8775P | 0.605 ms | 0 - 33 MB | NPU
|
| 139 |
+
| MobileNet-v2 | TFLITE | w8a8 | Qualcomm® QCS9075 | 0.539 ms | 0 - 6 MB | NPU
|
| 140 |
+
| MobileNet-v2 | TFLITE | w8a8 | Qualcomm® QCM6690 | 1.649 ms | 0 - 27 MB | NPU
|
| 141 |
+
| MobileNet-v2 | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 0.546 ms | 0 - 45 MB | NPU
|
| 142 |
+
| MobileNet-v2 | TFLITE | w8a8 | Qualcomm® SA7255P | 0.987 ms | 0 - 31 MB | NPU
|
| 143 |
+
| MobileNet-v2 | TFLITE | w8a8 | Qualcomm® SA8295P | 0.808 ms | 0 - 28 MB | NPU
|
| 144 |
+
| MobileNet-v2 | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 0.214 ms | 0 - 28 MB | NPU
|
| 145 |
+
| MobileNet-v2 | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 0.421 ms | 0 - 28 MB | NPU
|
| 146 |
+
| MobileNet-v2 | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 0.184 ms | 0 - 34 MB | NPU
|
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|
| 147 |
|
| 148 |
## License
|
| 149 |
* The license for the original implementation of MobileNet-v2 can be found
|
| 150 |
[here](https://github.com/tonylins/pytorch-mobilenet-v2/blob/master/LICENSE).
|
| 151 |
|
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|
| 152 |
## References
|
| 153 |
* [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381)
|
| 154 |
* [Source Model Implementation](https://github.com/tonylins/pytorch-mobilenet-v2/tree/master)
|
| 155 |
|
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|
| 156 |
## Community
|
| 157 |
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
|
| 158 |
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
|
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|
|
tool-versions.yaml
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
tool_versions:
|
| 2 |
-
qnn_dlc:
|
| 3 |
-
qairt: 2.41.0.251128145156_191518
|
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