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.
This commit is contained in:
dian.yuan
parent
f960c5030d
commit
bd891a96dd
136 changed files with 14413 additions and 9399 deletions
|
|
@ -1,118 +1,118 @@
|
|||
## Model Description
|
||||
|
||||
This model is converted from MobileNetV2 pretrained weights
|
||||
originally released by Google under the Apache License 2.0.
|
||||
|
||||
Original model:
|
||||
- Architecture: MobileNetV2
|
||||
- Source: TensorFlow / Keras official implementation
|
||||
|
||||
The model has been converted and optimized into ADLA format
|
||||
for deployment on Amlogic NPU platforms.
|
||||
|
||||
## 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
|
||||
AMLNN_HOME=/path/to/amlnn-toolkit ./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.
|
||||
|
||||
|
||||
## Model Description
|
||||
|
||||
This model is converted from MobileNetV2 pretrained weights
|
||||
originally released by Google under the Apache License 2.0.
|
||||
|
||||
Original model:
|
||||
- Architecture: MobileNetV2
|
||||
- Source: TensorFlow / Keras official implementation
|
||||
|
||||
The model has been converted and optimized into ADLA format
|
||||
for deployment on Amlogic NPU platforms.
|
||||
|
||||
## 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
|
||||
AMLNN_HOME=/path/to/amlnn-toolkit ./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.
|
||||
|
||||
|
||||
|
|
|
|||
0
examples/mobilenet/cpp/.gitkeep
Normal file → Executable file
0
examples/mobilenet/cpp/.gitkeep
Normal file → Executable file
|
|
@ -1,4 +1,20 @@
|
|||
#!/bin/bash
|
||||
|
||||
#
|
||||
# Copyright (C) 2026 Amlogic, Inc. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
set -e
|
||||
|
||||
usage() {
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@
|
|||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
|
||||
#include <iostream>
|
||||
#include <fstream>
|
||||
#include <string>
|
||||
|
|
@ -107,7 +107,7 @@ int main(int argc, char** argv) {
|
|||
memset(&outconfig, 0, sizeof(aml_output_config_t));
|
||||
outconfig.typeSize = sizeof(aml_output_config_t);
|
||||
outconfig.format = AML_OUTDATA_FLOAT32;
|
||||
|
||||
|
||||
nn_output* outdata = (nn_output*)aml_module_output_get(context, outconfig);
|
||||
if (!outdata) {
|
||||
std::cerr << "Failed to run network (get output)." << std::endl;
|
||||
|
|
|
|||
0
examples/mobilenet/model/.gitkeep
Normal file → Executable file
0
examples/mobilenet/model/.gitkeep
Normal file → Executable file
|
|
@ -240,7 +240,7 @@ miniature pinscher
|
|||
Greater Swiss Mountain dog
|
||||
Bernese mountain dog
|
||||
Appenzeller
|
||||
EntleBucher
|
||||
Entlebucher
|
||||
boxer
|
||||
bull mastiff
|
||||
Tibetan mastiff
|
||||
|
|
@ -420,7 +420,7 @@ balloon
|
|||
ballpoint
|
||||
Band Aid
|
||||
banjo
|
||||
bannister
|
||||
banister
|
||||
barbell
|
||||
barber chair
|
||||
barbershop
|
||||
|
|
@ -468,7 +468,7 @@ bulletproof vest
|
|||
bullet train
|
||||
butcher shop
|
||||
cab
|
||||
caldron
|
||||
cauldron
|
||||
candle
|
||||
cannon
|
||||
canoe
|
||||
|
|
|
|||
0
examples/mobilenet/py/.gitkeep
Normal file → Executable file
0
examples/mobilenet/py/.gitkeep
Normal file → Executable file
|
|
@ -1,3 +1,19 @@
|
|||
#
|
||||
# Copyright (C) 2026 Amlogic, Inc. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
import numpy as np
|
||||
import os
|
||||
import glob
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue