add some new python demos

2026-01-14 16:59:57 +08:00 · 2026-01-14 16:59:57 +08:00 · c598b3aef4
commit c598b3aef4
parent d631c4d009
23 changed files with 2174 additions and 11 deletions
--- a/examples/mobilenet/README.md
+++ b/examples/mobilenet/README.md
@ -0,0 +1,106 @@
+
+##  Demo Run
+
+### CPP
+
+#### 1. Compile
+
+**Prerequisites:**
+- Android NDK (r25e recommended)
+- `ANDROID_NDK_PATH` environment variable set
+
+**Build:**
+```bash
+# Build for arm64-v8a
+cd examples/mobilenet/cpp
+./build-android.sh -a arm64-v8a
+```
+
+The executable will be generated at `build/android/mobilenet_v2_demo` (Note: executable name may vary, verify in build folder).
+
+#### 2. Run
+
+```bash
+# Push executable to device
+adb push build/android/mobilenet_v2_demo /data/local/tmp/
+adb push model/mobilenet_v2_1.0_224_quant_A311D2.adla /data/local/tmp/
+adb push model/cat_224x224.jpg /data/local/tmp/
+adb push model/labels.txt /data/local/tmp/
+
+# Run on device
+adb shell
+cd /data/local/tmp
+chmod +x mobilenet_v2_demo
+export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)
+
+# Usage: ./mobilenet_v2_demo <model_path> <image_path> <labels_path>
+./mobilenet_v2_demo mobilenet_v2_1.0_224_quant_A311D2.adla cat_224x224.jpg labels.txt
+```
+
+**Note:** Replace `mobilenet_v2_1.0_224_quant_A311D2.adla` with your actual model file path.
+
+### Python
+
+**Prerequisites:**
+- Python 3.10
+- Required packages: `numpy`, `Pillow`, `amlnnlite`
+
+**Install dependencies:**
+```bash
+pip install numpy Pillow amlnnlite-1.0.0-cp310-cp310-linux_aarch64.whl
+```
+
+**Run on device:**
+```bash
+# Basic usage
+python mobilenetv2.py --model-path ./mobilenet_v2_1.0_224_quant_A311D2.adla
+
+# Run with performance testing (100 cycles)
+python mobilenetv2.py --model-path ./mobilenet_v2_1.0_224_quant_A311D2.adla --run-cycles 100
+```
+
+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.
+
+## Results
+
+The program will print the top-5 classification results with probabilities for each processed image. 
+
+**Example output:**
+```
+# python demo result
+============================================================
+Processing image 1/3: dog_224x224.jpg
+============================================================
+
+Top-5 Classification Results:
+  1. Shih-Tzu                  (probability: 0.9239)
+  2. Pekinese                  (probability: 0.0476)
+  3. Lhasa                     (probability: 0.0263)
+  4. Brabancon griffon         (probability: 0.0004)
+  5. Dandie Dinmont            (probability: 0.0003)
+
+============================================================
+Processing image 2/3: cat_224x224.jpg
+============================================================
+
+Top-5 Classification Results:
+  1. tiger cat                 (probability: 0.4774)
+  2. tabby                     (probability: 0.4324)
+  3. Egyptian cat              (probability: 0.0542)
+  4. lynx                      (probability: 0.0150)
+  5. Persian cat               (probability: 0.0025)
+
+============================================================
+Processing image 3/3: fish_224x224.jpeg
+============================================================
+
+Top-5 Classification Results:
+  1. goldfish                  (probability: 0.9998)
+  2. conch                     (probability: 0.0001)
+  3. trifle                    (probability: 0.0000)
+  4. axolotl                   (probability: 0.0000)
+  5. American lobster          (probability: 0.0000)
+```
+
+The classification results show the model's confidence scores (probabilities) for each detected class, with the highest probability indicating the most likely classification.
+