add some new python demos

examples/resnet/README.md

@@ -0,0 +1,165 @@
+# resnet
+
+## 1.Overview
+
+
+
+## 2.Model Download
+
+- **Open Source model**
+
+  - **Open Source projects:** 
+
+  - **Export Model Step:**	
+
+    - **Install ultralytics**
+
+        pip install torch==2.4.1
+
+        pip install torchvision==0.19.1
+
+        pip install ultralytics==8.3.0
+
+    - **Download weights**
+
+      
+
+    - **Export Model**
+
+      ```
+      
+      ```
+
+
+- **Exported Model**
+
+  ​	link to amlogic server( **onnx model   or quantized tflite**)
+
+  
+
+## 3. Model Conversion
+
+```
+cd model
+Usage:   ./adla_covnert.sh model_path adla_tookkit_path target_platform
+
+example
+
+```
+
+| Parameter         | Discription                                                  |
+| ----------------- | ------------------------------------------------------------ |
+| model_path        | onnx model path                                              |
+| adla_tookkit_path | path to adla_toolkit                                         |
+| target_platform   | Specify target platform. for A311D2 : PRODUCT_PID0XA003. for S905X5:  PRODUCT_PID0XA005 |
+
+
+
+## 4. Demo Run
+
+### CPP
+
+#### 1. Compile
+
+**Prerequisites:**
+- Android NDK (r25e recommended)
+- `ANDROID_NDK_PATH` environment variable set
+
+**Build:**
+```bash
+# Build for arm64-v8a
+cd examples/resnet/cpp
+./build-android.sh -a arm64-v8a
+```
+
+The executable will be generated at `build/android/resnet_demo` (Note: executable name may vary, verify in build folder).
+
+#### 2. Run
+
+```bash
+# Push executable to device
+adb push build/android/resnet_demo /data/local/tmp/
+adb push model/res2net50_int8_A311D2.adla /data/local/tmp/
+adb push imgs /data/local/tmp/
+adb push labels.txt /data/local/tmp/
+
+# Run on device
+adb shell
+cd /data/local/tmp
+chmod +x resnet_demo
+export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)
+
+# Usage: ./resnet_demo <model_path> <image_dir> <labels.txt>
+./resnet_demo res2net50_int8_A311D2.adla imgs/ labels.txt
+```
+
+**Note:** Replace `res2net50_int8_A311D2.adla` with your actual model file path.
+
+### Python
+
+**Prerequisites:**
+- Python 3.10
+- Required packages: `numpy`, `opencv-python`, `amlnnlite`
+
+**Install dependencies:**
+```bash
+pip install numpy opencv-python amlnnlite-1.0.0-cp310-cp310-linux_aarch64.whl
+```
+
+**Run on device:**
+```bash
+python resnet.py \
+    --model-path ./res2net50_int8_A311D2.adla \
+    --image-dir ./imgs \
+    --labels labels.txt \
+    --run-cycles 1 \
+    --loglevel INFO
+```
+Argument Descriptions:
+| Argument         | Description                                                  |
+| ----------------- | ------------------------------------------------------------ |
+| --board-work-path       | Work path on board, default is /data/local/tmp    |
+| --model-path | path to .adla model  |
+| --image-dir   | Directory containing test images |
+| --labels   | Path to synset_words.txt or labels.txt |
+| --run-cycles   | Number of inference cycles, default is 1 |
+| --loglevel   | Logging level: DEBUG / INFO / WARNING / ERROR, default is WARNING |
+
+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.
+
+## 5.Results
+**Performance Feedback**
+
+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:
+- Hardware Information: System and ADLA library versions.
+- Model Overview: Basic input/output configurations.
+- NPU Metrics: Total inference time (latency) and total DRAM bandwidth consumption.
+
+**Classification Output**
+
+For each image, the program prints the Top-5 classification results with their respective scores:
+```bash
+============================================================
+Processing image 1/1: dog.jpg
+============================================================                                                                                        Top-5 Results:                                                                                                          
+1: Pekinese                     score=9.851644                                                                                 
+2: West Highland white terrier  score=5.055449                                                                          
+3: Maltese dog                  score=4.796195                                                                                 
+4: basenji                      score=3.111045                                                                                 
+5: Scotch terrier               score=2.786978                                                                                 ============================================================ 
+```
+**Profiling Visualization**
+
+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:
+- `hard_op_chart.html` & `soft_op_chart.html`: Hardware/Software op execution details.
+- `dram_rd_chart.html` & `dram_wr_chart.html`: Bandwidth read/write distribution.
+- `pie_charts_distribution.html`: Overall resource allocation.
+
+You can pull the result folder back to view it:
+```bash
+adb pull /data/local/tmp/res2net50_int8_A311D2
+```
+
+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.
+
+![alt text](Visualization.png)

examples/resnet/py/resnet.py

@@ -52,8 +52,8 @@ def postprocess_topk(logits, labels, k=5):
 def main():
     parser = argparse.ArgumentParser(description="Classification AMLNNLite Demo")
 
-    parser.add_argument('--board-work-path', default='/data/nn', help='Work path on board')
-    parser.add_argument('--model-path', required=True, help='Path to .adla or .tflite model')
+    parser.add_argument('--board-work-path', default='/data/local/tmp', help='Work path on board')
+    parser.add_argument('--model-path', required=True, help='Path to .adla model')
     parser.add_argument('--image-dir', required=True, help='Directory containing test images')
     parser.add_argument('--labels', required=True, help='Path to synset_words.txt or labels.txt')
     parser.add_argument('--run-cycles', type=int, default=1, help='Number of inference cycles')