feat: Update NNSDK path and library linking in CMake configurations and Android build script, and add a new script to build all Android examples.
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dian.yuan
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35 changed files with 1438 additions and 1222 deletions
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## Model Description
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This model is converted from MobileNetV2 pretrained weights
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originally released by Google under the Apache License 2.0.
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Original model:
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- Architecture: MobileNetV2
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- Source: TensorFlow / Keras official implementation
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The model has been converted and optimized into ADLA format
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for deployment on Amlogic NPU platforms.
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## Demo Run
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### CPP
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#### 1. Compile
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**Prerequisites:**
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- Android NDK (r25e recommended)
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- `ANDROID_NDK_PATH` environment variable set
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**Build:**
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```bash
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# Build for arm64-v8a
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cd examples/mobilenet/cpp
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./build-android.sh -a arm64-v8a
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```
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The executable will be generated at `build/android/mobilenet_v2_demo` (Note: executable name may vary, verify in build folder).
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#### 2. Run
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```bash
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# Push executable to device
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adb push build/android/mobilenet_v2_demo /data/local/tmp/
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adb push model/mobilenet_v2_1.0_224_quant_A311D2.adla /data/local/tmp/
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adb push model/cat_224x224.jpg /data/local/tmp/
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adb push model/labels.txt /data/local/tmp/
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# Run on device
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adb shell
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cd /data/local/tmp
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chmod +x mobilenet_v2_demo
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export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)
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# Usage: ./mobilenet_v2_demo <model_path> <image_path> <labels_path>
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./mobilenet_v2_demo mobilenet_v2_1.0_224_quant_A311D2.adla cat_224x224.jpg labels.txt
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```
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**Note:** Replace `mobilenet_v2_1.0_224_quant_A311D2.adla` with your actual model file path.
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### Python
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**Prerequisites:**
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- Python 3.10
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- Required packages: `numpy`, `Pillow`, `amlnnlite`
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**Install dependencies:**
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```bash
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pip install numpy Pillow amlnnlite-1.0.0-cp310-cp310-linux_aarch64.whl
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```
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**Run on device:**
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```bash
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# Basic usage
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python mobilenetv2.py --model-path ./mobilenet_v2_1.0_224_quant_A311D2.adla
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# Run with performance testing (100 cycles)
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python mobilenetv2.py --model-path ./mobilenet_v2_1.0_224_quant_A311D2.adla --run-cycles 100
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```
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The script will automatically process all image files (`.jpg`, `.jpeg`, `.png`, `.bmp`) in the current directory and display top-5 classification results for each image.
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## Results
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The program will print the top-5 classification results with probabilities for each processed image.
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**Example output:**
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```
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# python demo result
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============================================================
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Processing image 1/3: dog_224x224.jpg
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============================================================
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Top-5 Classification Results:
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1. Shih-Tzu (probability: 0.9239)
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2. Pekinese (probability: 0.0476)
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3. Lhasa (probability: 0.0263)
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4. Brabancon griffon (probability: 0.0004)
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5. Dandie Dinmont (probability: 0.0003)
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============================================================
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Processing image 2/3: cat_224x224.jpg
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============================================================
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Top-5 Classification Results:
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1. tiger cat (probability: 0.4774)
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2. tabby (probability: 0.4324)
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3. Egyptian cat (probability: 0.0542)
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4. lynx (probability: 0.0150)
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5. Persian cat (probability: 0.0025)
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============================================================
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Processing image 3/3: fish_224x224.jpeg
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============================================================
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Top-5 Classification Results:
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1. goldfish (probability: 0.9998)
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2. conch (probability: 0.0001)
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3. trifle (probability: 0.0000)
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4. axolotl (probability: 0.0000)
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5. American lobster (probability: 0.0000)
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```
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The classification results show the model's confidence scores (probabilities) for each detected class, with the highest probability indicating the most likely classification.
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## Model Description
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This model is converted from MobileNetV2 pretrained weights
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originally released by Google under the Apache License 2.0.
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Original model:
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- Architecture: MobileNetV2
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- Source: TensorFlow / Keras official implementation
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The model has been converted and optimized into ADLA format
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for deployment on Amlogic NPU platforms.
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## Demo Run
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### CPP
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#### 1. Compile
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#### AMLNN SDK Setup
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Resolve the AMLNN nnsdk dependency using one of the following methods:
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- **Priority 1 – Environment variable (recommended)**
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```bash
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export AMLNN_HOME=/path/to/amlnn-toolkit
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```
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- **Priority 3 – Sibling directory fallback** *(automatic)*
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Place `amlnn-toolkit` as a sibling to `amlnn-model-playground`:
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```bash
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git clone git@github.com:Amlogic-NN/amlnn-toolkit.git ../amlnn-toolkit
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```
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**Prerequisites:**
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- Android NDK (r25e recommended)
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- `ANDROID_NDK_PATH` environment variable set
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**Build:**
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```bash
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# Build for arm64-v8a
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cd examples/mobilenet/cpp
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./build-android.sh -a arm64-v8a
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```
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The executable will be generated at `build/android/mobilenet_v2_demo` (Note: executable name may vary, verify in build folder).
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#### 2. Run
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```bash
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# Push executable to device
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adb push build/android/mobilenet_v2_demo /data/local/tmp/
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adb push model/mobilenet_v2_1.0_224_quant_A311D2.adla /data/local/tmp/
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adb push model/cat_224x224.jpg /data/local/tmp/
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adb push model/labels.txt /data/local/tmp/
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# Run on device
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adb shell
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cd /data/local/tmp
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chmod +x mobilenet_v2_demo
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export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)
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# Usage: ./mobilenet_v2_demo <model_path> <image_path> <labels_path>
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./mobilenet_v2_demo mobilenet_v2_1.0_224_quant_A311D2.adla cat_224x224.jpg labels.txt
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```
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**Note:** Replace `mobilenet_v2_1.0_224_quant_A311D2.adla` with your actual model file path.
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### Python
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**Prerequisites:**
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- Python 3.10
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- Required packages: `numpy`, `Pillow`, `amlnnlite`
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**Install dependencies:**
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```bash
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pip install numpy Pillow amlnnlite-1.0.0-cp310-cp310-linux_aarch64.whl
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```
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**Run on device:**
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```bash
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# Basic usage
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python mobilenetv2.py --model-path ./mobilenet_v2_1.0_224_quant_A311D2.adla
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# Run with performance testing (100 cycles)
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python mobilenetv2.py --model-path ./mobilenet_v2_1.0_224_quant_A311D2.adla --run-cycles 100
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```
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The script will automatically process all image files (`.jpg`, `.jpeg`, `.png`, `.bmp`) in the current directory and display top-5 classification results for each image.
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## Results
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The program will print the top-5 classification results with probabilities for each processed image.
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**Example output:**
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```
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# python demo result
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============================================================
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Processing image 1/3: dog_224x224.jpg
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============================================================
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Top-5 Classification Results:
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1. Shih-Tzu (probability: 0.9239)
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2. Pekinese (probability: 0.0476)
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3. Lhasa (probability: 0.0263)
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4. Brabancon griffon (probability: 0.0004)
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5. Dandie Dinmont (probability: 0.0003)
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============================================================
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Processing image 2/3: cat_224x224.jpg
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============================================================
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Top-5 Classification Results:
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1. tiger cat (probability: 0.4774)
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2. tabby (probability: 0.4324)
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3. Egyptian cat (probability: 0.0542)
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4. lynx (probability: 0.0150)
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5. Persian cat (probability: 0.0025)
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============================================================
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Processing image 3/3: fish_224x224.jpeg
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============================================================
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Top-5 Classification Results:
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1. goldfish (probability: 0.9998)
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2. conch (probability: 0.0001)
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3. trifle (probability: 0.0000)
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4. axolotl (probability: 0.0000)
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5. American lobster (probability: 0.0000)
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```
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The classification results show the model's confidence scores (probabilities) for each detected class, with the highest probability indicating the most likely classification.
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