docs: Update README and compilation guides for clarity and consistency, including path corrections and improved formatting. Add copyright notices to source files and adjust file permissions for several scripts and directories.
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# resnet
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## 1.Overview
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## 2.Model Download
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- **Open Source model**
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- **Open Source projects:**
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- **Export Model Step:**
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- **Install ultralytics**
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pip install torch==2.4.1
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pip install torchvision==0.19.1
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pip install ultralytics==8.3.0
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- **Download weights**
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- **Export Model**
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```
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```
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- **Exported Model**
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link to amlogic server( **onnx model or quantized tflite**)
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## 3. Model Conversion
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```
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cd model
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Usage: ./adla_covnert.sh model_path adla_tookkit_path target_platform
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example
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```
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| Parameter | Discription |
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| ----------------- | ------------------------------------------------------------ |
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| model_path | onnx model path |
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| adla_tookkit_path | path to adla_toolkit |
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| target_platform | Specify target platform. for A311D2 : PRODUCT_PID0XA003. for S905X5: PRODUCT_PID0XA005 |
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## 4. 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/resnet/cpp
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AMLNN_HOME=/path/to/amlnn-toolkit ./build-android.sh -a arm64-v8a
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```
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The executable will be generated at `build/android/resnet_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/resnet_demo /data/local/tmp/
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adb push model/res2net50_int8_A311D2.adla /data/local/tmp/
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adb push imgs /data/local/tmp/
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adb push 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 resnet_demo
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export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)
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# Usage: ./resnet_demo <model_path> <image_dir> <labels.txt>
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./resnet_demo res2net50_int8_A311D2.adla imgs/ labels.txt
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```
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**Note:** Replace `res2net50_int8_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`, `opencv-python`, `amlnnlite`
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**Install dependencies:**
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```bash
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pip install numpy opencv-python 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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python resnet.py \
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--model-path ./res2net50_int8_A311D2.adla \
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--image-dir ./imgs \
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--labels labels.txt \
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--run-cycles 1 \
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--loglevel INFO
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```
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Argument Descriptions:
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| Argument | Description |
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| ----------------- | ------------------------------------------------------------ |
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| --board-work-path | Work path on board, default is /data/local/tmp |
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| --model-path | path to .adla model |
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| --image-dir | Directory containing test images |
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| --labels | Path to synset_words.txt or labels.txt |
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| --run-cycles | Number of inference cycles, default is 1 |
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| --loglevel | Logging level: DEBUG / INFO / WARNING / ERROR, default is WARNING |
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The script will automatically process all image files (`.jpg`, `.jpeg`, `.png`, `.bmp`) in the current directory and save results to a `{model_name}_result` folder.
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## 5.Results
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**Performance Feedback**
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By setting the loglevel to INFO, the program provides real-time performance metrics upon completion. The console log will display essential hardware and execution details, including:
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- Hardware Information: System and ADLA library versions.
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- Model Overview: Basic input/output configurations.
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- NPU Metrics: Total inference time (latency) and total DRAM bandwidth consumption.
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**Classification Output**
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For each image, the program prints the Top-5 classification results with their respective scores:
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```bash
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============================================================
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Processing image 1/1: dog.jpg
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============================================================ Top-5 Results:
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1: Pekinese score=9.851644
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2: West Highland white terrier score=5.055449
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3: Maltese dog score=4.796195
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4: basenji score=3.111045
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5: Scotch terrier score=2.786978 ============================================================
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```
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**Profiling Visualization**
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After a successful run of the Python demo, a folder named after the model (e.g., `{model_name}`) will be generated in the script directory. This folder contains 5 HTML files that provide a visual and detailed breakdown of per-layer performance:
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- `hard_op_chart.html` & `soft_op_chart.html`: Hardware/Software op execution details.
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- `dram_rd_chart.html` & `dram_wr_chart.html`: Bandwidth read/write distribution.
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- `pie_charts_distribution.html`: Overall resource allocation.
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You can pull the result folder back to view it:
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```bash
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adb pull /data/local/tmp/res2net50_int8_A311D2
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```
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Taking hard_op_chart.html as an example (shown below), each layer's ADLA operator name includes parentheses containing the index of the corresponding quantized .tflite layer(s); by default, these indices are suppressed, and operators are labeled generically as "hardware" or "software" without numerical suffixes.
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# resnet
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## 1.Overview
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## 2.Model Download
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- **Open Source model**
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- **Open Source projects:**
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- **Export Model Step:**
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- **Install ultralytics**
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pip install torch==2.4.1
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pip install torchvision==0.19.1
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pip install ultralytics==8.3.0
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- **Download weights**
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- **Export Model**
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```
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```
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- **Exported Model**
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link to amlogic server( **onnx model or quantized tflite**)
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## 3. Model Conversion
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```
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cd model
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Usage: ./adla_convert.sh model_path adla_toolkit_path target_platform
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example
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```
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| Parameter | Description |
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| ----------------- | ------------------------------------------------------------ |
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| model_path | onnx model path |
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| adla_toolkit_path | path to adla_toolkit |
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| target_platform | Specify target platform. for A311D2 : PRODUCT_PID0XA003. for S905X5: PRODUCT_PID0XA005 |
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## 4. 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/resnet/cpp
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AMLNN_HOME=/path/to/amlnn-toolkit ./build-android.sh -a arm64-v8a
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```
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The executable will be generated at `build/android/resnet_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/resnet_demo /data/local/tmp/
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adb push model/res2net50_int8_A311D2.adla /data/local/tmp/
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adb push imgs /data/local/tmp/
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adb push 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 resnet_demo
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export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)
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# Usage: ./resnet_demo <model_path> <image_dir> <labels.txt>
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./resnet_demo res2net50_int8_A311D2.adla imgs/ labels.txt
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```
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**Note:** Replace `res2net50_int8_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`, `opencv-python`, `amlnnlite`
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**Install dependencies:**
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```bash
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pip install numpy opencv-python 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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python resnet.py \
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--model-path ./res2net50_int8_A311D2.adla \
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--image-dir ./imgs \
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--labels labels.txt \
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--run-cycles 1 \
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--loglevel INFO
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```
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Argument Descriptions:
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| Argument | Description |
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| ----------------- | ------------------------------------------------------------ |
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| --board-work-path | Work path on board, default is /data/local/tmp |
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| --model-path | path to .adla model |
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| --image-dir | Directory containing test images |
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| --labels | Path to synset_words.txt or labels.txt |
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| --run-cycles | Number of inference cycles, default is 1 |
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| --loglevel | Logging level: DEBUG / INFO / WARNING / ERROR, default is WARNING |
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The script will automatically process all image files (`.jpg`, `.jpeg`, `.png`, `.bmp`) in the current directory and save results to a `{model_name}_result` folder.
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## 5.Results
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**Performance Feedback**
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By setting the loglevel to INFO, the program provides real-time performance metrics upon completion. The console log will display essential hardware and execution details, including:
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- Hardware Information: System and ADLA library versions.
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- Model Overview: Basic input/output configurations.
|
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- NPU Metrics: Total inference time (latency) and total DRAM bandwidth consumption.
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**Classification Output**
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For each image, the program prints the Top-5 classification results with their respective scores:
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```bash
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============================================================
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Processing image 1/1: dog.jpg
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============================================================ Top-5 Results:
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1: Pekinese score=9.851644
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2: West Highland white terrier score=5.055449
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3: Maltese dog score=4.796195
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4: basenji score=3.111045
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5: Scotch terrier score=2.786978 ============================================================
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```
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**Profiling Visualization**
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After a successful run of the Python demo, a folder named after the model (e.g., `{model_name}`) will be generated in the script directory. This folder contains 5 HTML files that provide a visual and detailed breakdown of per-layer performance:
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- `hard_op_chart.html` & `soft_op_chart.html`: Hardware/Software op execution details.
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- `dram_rd_chart.html` & `dram_wr_chart.html`: Bandwidth read/write distribution.
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- `pie_charts_distribution.html`: Overall resource allocation.
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You can pull the result folder back to view it:
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```bash
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adb pull /data/local/tmp/res2net50_int8_A311D2
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```
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Taking hard_op_chart.html as an example (shown below), each layer's ADLA operator name includes parentheses containing the index of the corresponding quantized .tflite layer(s); by default, these indices are suppressed, and operators are labeled generically as "hardware" or "software" without numerical suffixes.
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