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# Python
__pycache__/
*.py[cod]
*.pyo
*.pyd
.Python
*.egg-info/
dist/
build/
.eggs/
# 虛擬環境
venv/
env/
.env/
# 模型權重與二進位檔
*.pt
*.pth
*.onnx
*.bie
*.nef
*.engine
*.tflite
*.pb
# 訓練輸出（很大，不放 git）
runs/
work_dirs/
# 資料集圖片（不放 git，改用 DVC 或外部儲存）
data4/
data50/
test14data/
teachabledata/
numberocr/
# ONNX 輸出目錄
onnx/
# NumPy 暫存
npy/
# 暫放區
暫放區/
# 權重目錄（除了下載腳本）
weights/*.pt
weights/*.pth
# Claude Code 設定
.claude/
# Jupyter Notebook 輸出
.ipynb_checkpoints/
# 系統檔
.DS_Store
Thumbs.db
desktop.ini
# IDE
.vscode/
.idea/
*.swp
*.swo
# 日誌
*.log
wandb/
# 空的佔位檔
python

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# Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
FROM nvcr.io/nvidia/pytorch:20.10-py3
# Install dependencies
RUN pip install --upgrade pip
# COPY requirements.txt .
# RUN pip install -r requirements.txt
RUN pip install gsutil
# Create working directory
RUN mkdir -p /usr/src/app
WORKDIR /usr/src/app
# Copy contents
COPY . /usr/src/app
# Copy weights
#RUN python3 -c "from models import *; \
#attempt_download('weights/yolov5s.pt'); \
#attempt_download('weights/yolov5m.pt'); \
#attempt_download('weights/yolov5l.pt')"
# --------------------------------------------------- Extras Below ---------------------------------------------------
# Build and Push
# t=ultralytics/yolov5:latest && sudo docker build -t $t . && sudo docker push $t
# for v in {300..303}; do t=ultralytics/coco:v$v && sudo docker build -t $t . && sudo docker push $t; done
# Pull and Run
# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host $t
# Pull and Run with local directory access
# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/coco:/usr/src/coco $t
# Kill all
# sudo docker kill $(sudo docker ps -q)
# Kill all image-based
# sudo docker kill $(sudo docker ps -a -q --filter ancestor=ultralytics/yolov5:latest)
# Bash into running container
# sudo docker container exec -it ba65811811ab bash
# Bash into stopped container
# sudo docker commit 092b16b25c5b usr/resume && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/coco:/usr/src/coco --entrypoint=sh usr/resume
# Send weights to GCP
# python -c "from utils.general import *; strip_optimizer('runs/train/exp0_*/weights/best.pt', 'tmp.pt')" && gsutil cp tmp.pt gs://*.pt
# Clean up
# docker system prune -a --volumes

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Also add information on how to contact you by electronic and paper mail.
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<h1 align="center"> Object Detection </h1>
Object Detection task with YOLOv5 model.
This document contains the explanations of arguments of each script.
You can find the tutorial document for finetuning a pretrained model on COCO128 dataset under the `tutorial` folder, `tutorial/README.md`.
The ipython notebook tutorial is also prepared under the `tutorial` folder as `tutorial/tutorial.ipynb`. You may upload and run this ipython notebook on Google colab.
# Prerequisites
- Python 3.8 or above
# Installation
```bash
$ pip install -U pip
$ pip install -r requirements.txt
```
# Dataset & Preparation
The image data, annotations and dataset.yaml are required.
## MS COCO
Our traning script accepts MS COCO dataset. You may download the dataset using the following link:
- Download [2017 MS COCO Dataset](https://cocodataset.org/#download)
## Custom Datasets
You can also train the model on a custom dataset.
### Annotations Format
After using a tool like [CVAT](https://github.com/openvinotoolkit/cvat), [makesense.ai](https://www.makesense.ai) or [Labelbox](https://labelbox.com) to label your images, export your labels to YOLO format, with one `*.txt` file per image (if no objects in image, no `*.txt` file is required). The `*.txt` file specifications are:
- One row per object
- Each row is `class x_center y_center width height` format.
- Box coordinates must be in normalized xywh format (from 0 - 1). If your boxes are in pixels, divide `x_center` and `width` by image `width`, and `y_center` and `height` by image height.
- Class numbers are zero-indexed (start from 0).
<div align="center">
<img src="./tutorial/screenshots/readme_img.jpg" width="50%" />
</div>
The label file corresponding to the above image contains 2 persons (class 0) and a tie (class 27):
<div align="center">
<img src="./tutorial/screenshots/readme_img2.png" width="40%" />
</div>
### Directory Organization
Your own datasets are expected to have the following structure. We assume `/dataset` is next to the `/yolov5` directory. YOLOv5 locates labels automatically for each image by replacing the last instance of `/images/` in each image path with `/labels/`.
```bash
- Dataset name
-- images
-- train
--- img001.jpg
--- ...
-- val
--- img002.jpg
--- ...
-- labels
-- train
--- img001.txt
--- ...
-- val
--- img002.txt
--- ...
- yolov5
- generate_npy
- exporting
```
### dataset.yaml
The yaml file for COCO dataset has been prepared in `./data/coco.yaml`. For custom dataset, you need to prepare the yaml file and save it under `./data/`. The yaml file is expected to have the following format:
```bash
# train and val datasets (image directory or *.txt file with image paths)
train: ./datasets/images/train/
val: ./datasets/images/val/
# number of classes
nc: number of classes
# class names
names: list of class names
```
# Train
For training on MS COCO, execute commands in the folder `yolov5`:
```shell
CUDA_VISIBLE_DEVICES='0' python train.py --data coco.yaml --cfg yolov5s-noupsample.yaml --weights '' --batch-size 64
```
`CUDA_VISIBLE_DEVICES='0'` indicates the gpu ids.
`--data` the yaml file. (located under `./data/`)
`--cfg` the model configuration. (located under `./model/`) (`yolov5s-noupsample.yaml` for 520, `yolov5s.yaml` for 720)
`--hyp` the path to hyperparameters file. (located under `./data/`)
`--weights` the path to pretained model weights. ('' if train from scratch)
`--epochs` the number of epochs to train. (Default: 300)
`--batch-size` batch size. (Default: 16)
`--img-size` the input size of the model. (Default: (640, 640))
`--workers` the maximum number of dataloader workers. (Default: 8)
By default, the trained models are saved under `./runs/train/`.
## Generating .npy for different model input
We can generating `.npy` for different model input by using `yolov5_generate_npy.py`. Execute commands in the folder `generate_npy`:
```shell
python yolov5_generate_npy.py --input-h 640 --input-w 640
```
`--input-h` the input height. (Default: 640)
`--input-w` the input width. (Default: 640)
We could get `*.npy`
# Configure the paths yaml file
You are expected to create a yaml file which stores all the paths related to the trained models. This yaml file will be used in the following sections. You can check and modify the `pretrained_paths_520.yaml` and `pretrained_paths_720.yaml` under `/yolov5/data/`. The yaml file is expected to contain the following information:
```shell
grid_dir: path_to_npy_file_directory
grid20_path: path_to_grid20_npy_file
grid40_path: path_to_grid40_npy_file
grid80_path: path_to_grid80_npy_file
yolov5_dir: path_to_yolov5_directory
path: path_to_pretrained_yolov5_model_weights_pt_file
yaml_path: path_to_the_model_configuration_yaml_file
pt_path: path_to_export_yolov5_model_weights_kneron_supported_file
onnx_export_file: path_to_export_yolov5_onnx_model_file
input_w: model_input_weight
input_h: model_input_height
nc: number_of_classes
names: list_of_class_names
```
# Save and Convert to ONNX
This section will introduce how to save the trained model for pytorch1.4 supported format and convert to ONNX.
## Exporting ONNX model in the PyTorch 1.7 environment
We can convert the model to onnx by using `yolov5_export.py`. Execute commands in the folder `yolov5`:
```shell
python ../exporting/yolov5_export.py --data path_to_pretrained_path_yaml_file
```
`--data` the path to pretrained model paths yaml file (Default: ../yolov5/data/pretrained_paths_520.yaml)
We could get onnx model.
## Converting onnx by tool chain
Pull the latest [ONNX converter](https://github.com/kneron/ONNX_Convertor/tree/master/optimizer_scripts) from github. You may read the latest document from Github for converting ONNX model. Execute commands in the folder `ONNX_Convertor/optimizer_scripts`:
(reference: https://github.com/kneron/ONNX_Convertor/tree/master/optimizer_scripts)
```shell
python -m onnxsim input_onnx_model output_onnx_model
python pytorch2onnx.py input.pth output.onnx
```
We could get converted onnx model.
# Inference
Before model inference, we assume that the model has been converted to onnx model as in the previous section (even if only inference pth model). Create a yaml file containing the path information. For model inference on a single image, execute commands in the folder `yolov5`:
```shell
python inference.py --data path_to_pretrained_path_yaml_file --img-path path_to_image --save-path path_to_saved_image
```
`--img-path` the path to the image.
`--save-path` the path to draw and save the image with bbox.
`--data` the path to pretrained model paths yaml file. (Default: data/pretrained_paths_520.yaml)
`--conf_thres` the score threshold of bounding boxes. (Default: 0.3)
`--iou_thres` the iou threshold for NMS. (Default: 0.3)
`--onnx` whether is onnx model inference.
You could find preprocessing and postprocessing processes under the folder `exporting/yolov5/`.
# Evaluation
## Evaluation Metric
We will use mean Average Precision (mAP) for evaluation. You can find the script for computing mAP in `test.py`.
`mAP`: mAP is the average of Average Precision (AP). AP summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold, with the increase in recall from the previous threshold used as the weight:
<img src="https://latex.codecogs.com/svg.image?AP&space;=&space;\sum_n&space;(R_n-R_{n-1})P_n&space;" title="AP = \sum_n (R_n-R_{n-1})P_n " />
where <img src="https://latex.codecogs.com/svg.image?R_n" title="R_n" /> and <img src="https://latex.codecogs.com/svg.image?P_n" title="P_n" /> are the precision and recall at the nth threshold. The mAP compares the ground-truth bounding box to the detected box and returns a score. The higher the score, the more accurate the model is in its detections.
## Evaluation on a Dataset
For evaluating the trained model on dataset:
```shell
python test.py --weights path_to_pth_model_weight --data path_to_data_yaml_file
```
`--weights` The path to pretrained model weight. (Defalut: best.pt)
`--data` The path to data yaml file. (Default: data/coco128.yaml)
`--img-size` Input shape of the model (Default: (640, 640))
`--conf-thres` Object confidence threshold. (Default: 0.001)
`--device` Cuda device, i.e. 0 or 0,1,2,3 or cpu. (Default: cpu)
`--verbose` Whether report mAP by class.
## End-to-End Evaluation
If you would like to perform an end-to-end test with an image dataset, you can use `inference_e2e.py` under the directory `yolov5` to obtain the prediction results.
You have to prepare an initial parameter yaml file for the inference runner. You may check `utils/init_params.yaml` for the format.
```shell
python inference_e2e.py --img-path path_to_dataset_folder --params path_to_init_params_file --save-path path_to_save_json_file
```
`--img-path` Path to the dataset directory
`--params` Path to initial parameter yaml file for the inference runner
`--save-path` Path to save the prediction to a json file
`--gpu` GPU id (-1 if cpu) (Default: -1)
The predictions will be saved into a json file that has the following structure:
```bash
[
{'img_path':image_path_1
'bbox': [[l,t,w,h,score,class_id], [l,t,w,h,score,class_id]]
},
{'img_path':image_path_2
'bbox': [[l,t,w,h,score,class_id], [l,t,w,h,score,class_id]]
},
...
]
```
# Model
Backbone | Input Size | FPS on 520 | FPS on 720 | Model Size | mAP
--- | --- |:---:|:---:|:---:|:---:
[YOLOv5s (no upsample)](https://github.com/kneron/Model_Zoo/tree/main/detection/yolov5/yolov5s-noupsample) | 640x640 | 4.91429 | - | 13.1M | 40.4%
[YOLOv5s (with upsample)](https://github.com/kneron/Model_Zoo/tree/main/detection/yolov5/yolov5s) | 640x640 | - | 24.4114 | 14.6M | 50.9%
[YOLOv5s (no upsample)](https://github.com/kneron/Model_Zoo/tree/main/detection/yolov5/yolov5s-noupsample) is the yolov5s model backbone without upsampling operation, since 520 hardware does not support upsampling operation.

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grid_dir: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/
grid20_path: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/20_640x640.npy
grid40_path: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/40_640x640.npy
grid80_path: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/80_640x640.npy
yolov5_dir: C:/Users/rd_de/kneronyolov5/yolov5
path: C:/Users/rd_de/kneronyolov5/yolov5/runs/train/exp59/weights/best.pt
yaml_path: C:/Users/rd_de/kneronyolov5/yolov5/models/yolov5s-noupsample.yaml
pt_path: C:/Users/rd_de/kneronyolov5/yolov5/weights/for520best.pt
onnx_export_file: C:/Users/rd_de/kneronyolov5/yolov5/runs/train/exp59/weights/best.onnx
input_w: 640
input_h: 640
nc: 1
names: ['License Plate']

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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
path: ../datasets/coco # dataset root dir
train: /home/ziyan/Dataset/COCO/coco/images/train2017/ # 118287 images
val: /home/ziyan/Dataset/COCO/coco/images/val2017/ # 5000 images
# number of classes
nc: 80
# class names
names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush']
# Download script/URL (optional)
download: |
from utils.general import download, Path
# Download labels
segments = False # segment or box labels
dir = Path(yaml['path']) # dataset root dir
url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels
download(urls, dir=dir.parent)
# Download data
urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images
'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images
'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional)
download(urls, dir=dir / 'images', threads=3)

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# COCO 2017 dataset http://cocodataset.org - first 128 training images
# Train command: python train.py --data coco128.yaml
# Default dataset location is next to /yolov5:
# /parent_folder
# /coco128
# /yolov5
# download command/URL (optional)
download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: ../coco128/images/train2017/ # 128 images
val: ../coco128/images/train2017/ # 128 images
# number of classes
nc: 80
# class names
names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush']

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# train and val data as directory: path/labels/
train: ../image_data/images/train/
val: ../image_data/images/val/
# number of classes
nc: 3
# class names
names: ['air conditioner', 'dog', 'fence']

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# Hyperparameters for VOC finetuning
# python train.py --batch 64 --weights yolov5m.pt --data voc.yaml --img 512 --epochs 50
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
# Hyperparameter Evolution Results
# Generations: 306
# P R mAP.5 mAP.5:.95 box obj cls
# Metrics: 0.6 0.936 0.896 0.684 0.0115 0.00805 0.00146
lr0: 0.0032
lrf: 0.12
momentum: 0.843
weight_decay: 0.00036
warmup_epochs: 2.0
warmup_momentum: 0.5
warmup_bias_lr: 0.05
box: 0.0296
cls: 0.243
cls_pw: 0.631
obj: 0.301
obj_pw: 0.911
iou_t: 0.2
anchor_t: 2.91
# anchors: 3.63
fl_gamma: 0.0
hsv_h: 0.0138
hsv_s: 0.664
hsv_v: 0.464
degrees: 0.373
translate: 0.245
scale: 0.898
shear: 0.602
perspective: 0.0
flipud: 0.00856
fliplr: 0.5
mosaic: 1.0 #0.0
mixup: 0.243

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# Hyperparameters for COCO training from scratch
# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf)
momentum: 0.937 # SGD momentum/Adam beta1
weight_decay: 0.0005 # optimizer weight decay 5e-4
warmup_epochs: 3.0 # warmup epochs (fractions ok)
warmup_momentum: 0.8 # warmup initial momentum
warmup_bias_lr: 0.1 # warmup initial bias lr
box: 0.05 # box loss gain
cls: 0.5 # cls loss gain
cls_pw: 1.0 # cls BCELoss positive_weight
obj: 1.0 # obj loss gain (scale with pixels)
obj_pw: 1.0 # obj BCELoss positive_weight
iou_t: 0.20 # IoU training threshold
anchor_t: 4.0 # anchor-multiple threshold
# anchors: 3 # anchors per output layer (0 to ignore)
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
degrees: 0.0 # image rotation (+/- deg)
translate: 0.1 # image translation (+/- fraction)
scale: 0.5 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 1.0 # image mosaic (probability)
mixup: 0.0 # image mixup (probability)

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grid_dir: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/
grid20_path: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/20_640x640.npy
grid40_path: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/40_640x640.npy
grid80_path: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/80_640x640.npy
yolov5_dir: C:/Users/rd_de/kneronyolov5/yolov5
path: C:/Users/rd_de/golfaceyolov5/yolov5/runs/train/exp24/weights/best.pt
yaml_path: C:/Users/rd_de/golfaceyolov5/yolov5/models/yolov5s-noupsample.yaml
pt_path: C:/Users/rd_de/golfaceyolov5/yolov5/weights/for520best.pt
onnx_export_file: C:/Users/rd_de/golfaceyolov5/yolov5/runs/train/exp24/weights/best.onnx
input_w: 640
input_h: 640
nc: 4
names: ['100', '1000', '50', '500']
#nc: 6
#names: ['Break circuit', 'bulge', 'foreign object', 'scratch', 'short circuit', 'white spot']

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grid_dir: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/
grid20_path: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/20_640x640.npy
grid40_path: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/40_640x640.npy
grid80_path: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/80_640x640.npy
yolov5_dir: C:/Users/rd_de/kneronyolov5/yolov5
path: C:/Users/rd_de/golfaceyolov5/yolov5/runs/train/exp16/weights/best.pt
yaml_path: C:/Users/rd_de/golfaceyolov5/yolov5/models/yolov5s.yaml
pt_path: C:/Users/rd_de/golfaceyolov5/yolov5/weights/for720best.pt
onnx_export_file: C:/Users/rd_de/golfaceyolov5/yolov5/runs/train/exp16/weights/best.onnx
input_w: 640
input_h: 640
nc: 4
names: ['car', 'greenery', 'person', 'tree']

17
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grid_dir: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/
grid20_path: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/20_640x640.npy
grid40_path: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/40_640x640.npy
grid80_path: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/80_640x640.npy
yolov5_dir: C:/Users/rd_de/kneronyolov5/yolov5
path: C:/Users/rd_de/golfaceyolov5/yolov5/runs/train/exp13/weights/best.pt
yaml_path: C:/Users/rd_de/golfaceyolov5/yolov5/models/yolov5s.yaml
pt_path: C:/Users/rd_de/golfaceyolov5/yolov5/weights/for720best.pt
onnx_export_file: C:/Users/rd_de/golfaceyolov5/yolov5/runs/train/exp13/weights/best.onnx
input_w: 640
input_h: 640
nc: 2
names: ['bunker', 'pond']

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grid_dir: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/
grid20_path: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/20_640x640.npy
grid40_path: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/40_640x640.npy
grid80_path: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/80_640x640.npy
yolov5_dir: C:/Users/rd_de/kneronyolov5/yolov5
path: C:/Users/rd_de/golfaceyolov5/yolov5/runs/train/exp29/weights/best.pt
yaml_path: C:/Users/rd_de/golfaceyolov5/yolov5/models/yolov5s.yaml
pt_path: C:/Users/rd_de/golfaceyolov5/yolov5/weights/for720best.pt
onnx_export_file: C:/Users/rd_de/golfaceyolov5/yolov5/runs/train/exp29/weights/best.onnx
nc: 8
names: ['bunker', 'car', 'grass', 'greenery', 'person', 'pond', 'road', 'tree']
input_w: 640
input_h: 640

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grid_dir: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/
grid20_path: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/20_640x640.npy
grid40_path: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/40_640x640.npy
grid80_path: C:/Users/rd_de/kneronyolov5/ai_training/detection/yolov5/generate_npy/80_640x640.npy
yolov5_dir: C:/Users/rd_de/kneronyolov5/yolov5
path: C:/Users/rd_de/golfaceyolov5/yolov5/runs/train/exp19/weights/best.pt
yaml_path: C:/Users/rd_de/golfaceyolov5/yolov5/models/yolov5s.yaml
pt_path: C:/Users/rd_de/golfaceyolov5/yolov5/weights/for720best.pt
onnx_export_file: C:/Users/rd_de/golfaceyolov5/yolov5/runs/train/exp19/weights/best.onnx
input_w: 640
input_h: 640
nc: 4
names: ['car', 'greenery', 'person', 'tree']

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grid_dir: ../generate_npy/
grid20_path: ../generate_npy/20_640x640.npy
grid40_path: ../generate_npy/40_640x640.npy
grid80_path: ../generate_npy/80_640x640.npy
yolov5_dir: ./
path: ./runs/train/exp/weights/best.pt
yaml_path: ./models/yolov5s-noupsample.yaml
pt_path: ./yolov5s-noupsample-coco128.pt # pytorch 1.4
onnx_export_file: ./yolov5s-noupsample-coco128.onnx
input_w: 640
input_h: 640
# number of classes
nc: 80
# class names
names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush']

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grid_dir: ../generate_npy/
grid20_path: ../generate_npy/20_640x640.npy
grid40_path: ../generate_npy/40_640x640.npy
grid80_path: ../generate_npy/80_640x640.npy
yolov5_dir: ./
path: ./best.pt
yaml_path: ./models/yolov5s-noupsample.yaml
pt_path: ./yolov5s-noupsample.pt # pytorch 1.4
onnx_export_file: ./yolov5s-noupsample.onnx
input_w: 640
input_h: 640
# number of classes
nc: 80
# class names
names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush']

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grid_dir: ../generate_npy/
grid20_path: ../generate_npy/20_640x640.npy
grid40_path: ../generate_npy/40_640x640.npy
grid80_path: ../generate_npy/80_640x640.npy
yolov5_dir: ./
path: ./best.pt
yaml_path: ./models/yolov5s.yaml
pt_path: ./yolov5s.pt # pytorch 1.4
onnx_export_file: ./yolov5s.onnx
input_w: 640
input_h: 640
# number of classes
nc: 80
# class names
names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush']

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#!/bin/bash
# COCO 2017 dataset http://cocodataset.org
# Download command: bash data/scripts/get_coco.sh
# Train command: python train.py --data coco.yaml
# Default dataset location is next to /yolov5:
# /parent_folder
# /coco
# /yolov5
# Download/unzip labels
d='../' # unzip directory
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
f='coco2017labels.zip' # 68 MB
echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
# Download/unzip images
d='../coco/images' # unzip directory
url=http://images.cocodataset.org/zips/
f1='train2017.zip' # 19G, 118k images
f2='val2017.zip' # 1G, 5k images
f3='test2017.zip' # 7G, 41k images (optional)
for f in $f1 $f2; do
echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
done

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#!/bin/bash
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
# Download command: bash data/scripts/get_voc.sh
# Train command: python train.py --data voc.yaml
# Default dataset location is next to /yolov5:
# /parent_folder
# /VOC
# /yolov5
start=$(date +%s)
mkdir -p ../tmp
cd ../tmp/
# Download/unzip images and labels
d='.' # unzip directory
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
f1=VOCtrainval_06-Nov-2007.zip # 446MB, 5012 images
f2=VOCtest_06-Nov-2007.zip # 438MB, 4953 images
f3=VOCtrainval_11-May-2012.zip # 1.95GB, 17126 images
for f in $f1 $f2 $f3; do
echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
done
end=$(date +%s)
runtime=$((end - start))
echo "Completed in" $runtime "seconds"
echo "Splitting dataset..."
python3 - "$@" <<END
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
sets=[('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
def convert(size, box):
dw = 1./(size[0])
dh = 1./(size[1])
x = (box[0] + box[1])/2.0 - 1
y = (box[2] + box[3])/2.0 - 1
w = box[1] - box[0]
h = box[3] - box[2]
x = x*dw
w = w*dw
y = y*dh
h = h*dh
return (x,y,w,h)
def convert_annotation(year, image_id):
in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id))
out_file = open('VOCdevkit/VOC%s/labels/%s.txt'%(year, image_id), 'w')
tree=ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)
for obj in root.iter('object'):
difficult = obj.find('difficult').text
cls = obj.find('name').text
if cls not in classes or int(difficult)==1:
continue
cls_id = classes.index(cls)
xmlbox = obj.find('bndbox')
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
bb = convert((w,h), b)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
wd = getcwd()
for year, image_set in sets:
if not os.path.exists('VOCdevkit/VOC%s/labels/'%(year)):
os.makedirs('VOCdevkit/VOC%s/labels/'%(year))
image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split()
list_file = open('%s_%s.txt'%(year, image_set), 'w')
for image_id in image_ids:
list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n'%(wd, year, image_id))
convert_annotation(year, image_id)
list_file.close()
END
cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt >train.txt
cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt >train.all.txt
python3 - "$@" <<END
import shutil
import os
os.system('mkdir ../VOC/')
os.system('mkdir ../VOC/images')
os.system('mkdir ../VOC/images/train')
os.system('mkdir ../VOC/images/val')
os.system('mkdir ../VOC/labels')
os.system('mkdir ../VOC/labels/train')
os.system('mkdir ../VOC/labels/val')
import os
print(os.path.exists('../tmp/train.txt'))
f = open('../tmp/train.txt', 'r')
lines = f.readlines()
for line in lines:
line = "/".join(line.split('/')[-5:]).strip()
if (os.path.exists("../" + line)):
os.system("cp ../"+ line + " ../VOC/images/train")
line = line.replace('JPEGImages', 'labels')
line = line.replace('jpg', 'txt')
if (os.path.exists("../" + line)):
os.system("cp ../"+ line + " ../VOC/labels/train")
print(os.path.exists('../tmp/2007_test.txt'))
f = open('../tmp/2007_test.txt', 'r')
lines = f.readlines()
for line in lines:
line = "/".join(line.split('/')[-5:]).strip()
if (os.path.exists("../" + line)):
os.system("cp ../"+ line + " ../VOC/images/val")
line = line.replace('JPEGImages', 'labels')
line = line.replace('jpg', 'txt')
if (os.path.exists("../" + line)):
os.system("cp ../"+ line + " ../VOC/labels/val")
END
rm -rf ../tmp # remove temporary directory
echo "VOC download done."

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0 0.3703125 0.66875 0.1203125 0.0109375
1 0.6375 0.73046875 0.15625 0.14140625
1 0.41875 0.76875 0.28828125 0.0765625

2
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0 0.771875 0.50859375 0.03203125 0.0703125
0 0.71484375 0.46171875 0.05 0.0515625

3
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0 0.02265625 0.95078125 0.0453125 0.0953125
0 0.09375 0.7875 0.0328125 0.07421875
0 0.15703125 0.68046875 0.04921875 0.090625

2
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0 0.0203125 0.92109375 0.040625 0.1578125
0 0.1046875 0.73046875 0.0609375 0.07421875

2
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1 0.590625 0.2671875 0.0546875 0.03125
1 0.6546875 0.28359375 0.06875 0.03046875

2
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1 0.67265625 0.31640625 0.1453125 0.09765625
1 0.81484375 0.38359375 0.12421875 0.11015625

3
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1 0.8921875 0.42734375 0.0265625 0.03671875
1 0.94609375 0.47578125 0.02890625 0.05703125
1 0.98671875 0.53046875 0.025 0.05546875

3
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1 0.80078125 0.45234375 0.115625 0.07890625
1 0.93671875 0.53359375 0.1265625 0.1671875
1 0.678125 0.41171875 0.03125 0.0265625

1
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1 0.6609375 0.4421875 0.03046875 0.02265625

1
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1 0.5234375 0.390625 0.0328125 0.01640625

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import argparse
import time
from pathlib import Path
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, \
strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized
def detect(save_img=False):
source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
('rtsp://', 'rtmp://', 'http://'))
# Directories
save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Initialize
set_logging()
device = select_device(opt.device)
half = device.type != 'cpu' # half precision only supported on CUDA
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
if half:
model.half() # to FP16
# Second-stage classifier
classify = False
if classify:
modelc = load_classifier(name='resnet101', n=2) # initialize
modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
# Set Dataloader
vid_path, vid_writer = None, None
if webcam:
view_img = True
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz)
else:
save_img = True
dataset = LoadImages(source, img_size=imgsz)
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
# Run inference
t0 = time.time()
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
_ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
for path, img, im0s, vid_cap in dataset:
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = time_synchronized()
pred = model(img, augment=opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
t2 = time_synchronized()
# Apply Classifier
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
# Process detections
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, s, im0 = Path(path[i]), '%g: ' % i, im0s[i].copy()
else:
p, s, im0 = Path(path), '', im0s
save_path = str(save_dir / p.name)
txt_path = str(save_dir / 'labels' / p.stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += '%g %ss, ' % (n, names[int(c)]) # add to string
# Write results
for *xyxy, conf, cls in reversed(det):
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format
with open(txt_path + '.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or view_img: # Add bbox to image
label = '%s %.2f' % (names[int(cls)], conf)
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
# Print time (inference + NMS)
print('%sDone. (%.3fs)' % (s, t2 - t1))
# Stream results
if view_img:
cv2.imshow(p, im0)
if cv2.waitKey(1) == ord('q'): # q to quit
raise StopIteration
# Save results (image with detections)
if save_img:
if dataset.mode == 'images':
cv2.imwrite(save_path, im0)
else:
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release() # release previous video writer
fourcc = 'mp4v' # output video codec
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
vid_writer.write(im0)
if save_txt or save_img:
print('Results saved to %s' % save_dir)
print('Done. (%.3fs)' % (time.time() - t0))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='data/images', help='source') # file/folder, 0 for webcam
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', action='store_true', help='display results')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--update', action='store_true', help='update all models')
parser.add_argument('--project', default='runs/detect', help='save results to project/name')
parser.add_argument('--name', default='exp', help='save results to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
opt = parser.parse_args()
print(opt)
with torch.no_grad():
if opt.update: # update all models (to fix SourceChangeWarning)
for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
detect()
strip_optimizer(opt.weights)
else:
detect()

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"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
Usage:
$ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
"""
import argparse
import sys
import time
sys.path.append('./') # to run '$ python *.py' files in subdirectories
import torch
import torch.nn as nn
import models
from models.experimental import attempt_load
from utils.activations import Hardswish
from utils.general import set_logging, check_img_size
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path') # from yolov5/models/
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
opt = parser.parse_args()
opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand
print(opt)
set_logging()
t = time.time()
# Load PyTorch model
model = attempt_load(opt.weights, map_location=torch.device('cpu')) # load FP32 model
labels = model.names
# Checks
gs = int(max(model.stride)) # grid size (max stride)
opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples
# Input
img = torch.zeros(opt.batch_size, 3, *opt.img_size) # image size(1,3,320,192) iDetection
# Update model
for k, m in model.named_modules():
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
if isinstance(m, models.common.Conv) and isinstance(m.act, nn.Hardswish):
m.act = Hardswish() # assign activation
# if isinstance(m, models.yolo.Detect):
# m.forward = m.forward_export # assign forward (optional)
model.model[-1].export = True # set Detect() layer export=True
y = model(img) # dry run
# TorchScript export
try:
print('\nStarting TorchScript export with torch %s...' % torch.__version__)
f = opt.weights.replace('.pt', '.torchscript.pt') # filename
ts = torch.jit.trace(model, img)
ts.save(f)
print('TorchScript export success, saved as %s' % f)
except Exception as e:
print('TorchScript export failure: %s' % e)
# ONNX export
try:
import onnx
print('\nStarting ONNX export with onnx %s...' % onnx.__version__)
f = opt.weights.replace('.pt', '.onnx') # filename
torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],
output_names=['classes', 'boxes'] if y is None else ['output'])
# Checks
onnx_model = onnx.load(f) # load onnx model
onnx.checker.check_model(onnx_model) # check onnx model
# print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model
print('ONNX export success, saved as %s' % f)
except Exception as e:
print('ONNX export failure: %s' % e)
# CoreML export
try:
import coremltools as ct
print('\nStarting CoreML export with coremltools %s...' % ct.__version__)
# convert model from torchscript and apply pixel scaling as per detect.py
model = ct.convert(ts, inputs=[ct.ImageType(name='image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
f = opt.weights.replace('.pt', '.mlmodel') # filename
model.save(f)
print('CoreML export success, saved as %s' % f)
except Exception as e:
print('CoreML export failure: %s' % e)
# Finish
print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t))

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"""File for accessing YOLOv5 via PyTorch Hub https://pytorch.org/hub/
Usage:
import torch
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, channels=3, classes=80)
"""
from pathlib import Path
import torch
from models.yolo import Model
from utils.general import set_logging
from utils.google_utils import attempt_download
dependencies = ['torch', 'yaml']
set_logging()
def create(name, pretrained, channels, classes):
"""Creates a specified YOLOv5 model
Arguments:
name (str): name of model, i.e. 'yolov5s'
pretrained (bool): load pretrained weights into the model
channels (int): number of input channels
classes (int): number of model classes
Returns:
pytorch model
"""
config = Path(__file__).parent / 'models' / f'{name}.yaml' # model.yaml path
try:
model = Model(config, channels, classes)
if pretrained:
fname = f'{name}.pt' # checkpoint filename
attempt_download(fname) # download if not found locally
ckpt = torch.load(fname, map_location=torch.device('cpu')) # load
state_dict = ckpt['model'].float().state_dict() # to FP32
state_dict = {k: v for k, v in state_dict.items() if model.state_dict()[k].shape == v.shape} # filter
model.load_state_dict(state_dict, strict=False) # load
if len(ckpt['model'].names) == classes:
model.names = ckpt['model'].names # set class names attribute
# model = model.autoshape() # for PIL/cv2/np inputs and NMS
return model
except Exception as e:
help_url = 'https://github.com/ultralytics/yolov5/issues/36'
s = 'Cache maybe be out of date, try force_reload=True. See %s for help.' % help_url
raise Exception(s) from e
def yolov5s(pretrained=False, channels=3, classes=80):
"""YOLOv5-small model from https://github.com/ultralytics/yolov5
Arguments:
pretrained (bool): load pretrained weights into the model, default=False
channels (int): number of input channels, default=3
classes (int): number of model classes, default=80
Returns:
pytorch model
"""
return create('yolov5s', pretrained, channels, classes)
def yolov5m(pretrained=False, channels=3, classes=80):
"""YOLOv5-medium model from https://github.com/ultralytics/yolov5
Arguments:
pretrained (bool): load pretrained weights into the model, default=False
channels (int): number of input channels, default=3
classes (int): number of model classes, default=80
Returns:
pytorch model
"""
return create('yolov5m', pretrained, channels, classes)
def yolov5l(pretrained=False, channels=3, classes=80):
"""YOLOv5-large model from https://github.com/ultralytics/yolov5
Arguments:
pretrained (bool): load pretrained weights into the model, default=False
channels (int): number of input channels, default=3
classes (int): number of model classes, default=80
Returns:
pytorch model
"""
return create('yolov5l', pretrained, channels, classes)
def yolov5x(pretrained=False, channels=3, classes=80):
"""YOLOv5-xlarge model from https://github.com/ultralytics/yolov5
Arguments:
pretrained (bool): load pretrained weights into the model, default=False
channels (int): number of input channels, default=3
classes (int): number of model classes, default=80
Returns:
pytorch model
"""
return create('yolov5x', pretrained, channels, classes)
if __name__ == '__main__':
model = create(name='yolov5s', pretrained=True, channels=3, classes=80) # example
model = model.fuse().autoshape() # for PIL/cv2/np inputs and NMS
# Verify inference
from PIL import Image
imgs = [Image.open(x) for x in Path('data/images').glob('*.jpg')]
results = model(imgs)
results.show()
results.print()

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import ktc
import os
import shutil
import subprocess
# 設定 `.bie` 和 `.nef` 的存放目錄
onnx_dir = "runs/train/exp24/weights/"
bie_file = os.path.join(onnx_dir, "input.kdp720.scaled.bie") # 確保 `.bie` 路徑正確
# 確保 `.bie` 檔案存在
if not os.path.exists(bie_file):
raise FileNotFoundError(f"❌ Error: BIE file not found at {bie_file}")
print(f"✅ Found BIE file: {bie_file}")
# 初始化 ModelConfig
km = ktc.ModelConfig(20008, "0001", "720", bie_path=bie_file)
# 執行 `.nef` 轉換
nef_model_path = ktc.compile([km])
# 打印出 `.nef` 生成的路徑
print(f"🔍 Generated NEF file at: {nef_model_path}")
# 確保 `.nef` 轉換成功
if not nef_model_path or not os.path.exists(nef_model_path):
raise RuntimeError(f"❌ Error: NEF model was not generated at {nef_model_path}")
# 確保目標資料夾存在
os.makedirs(onnx_dir, exist_ok=True)
# 移動 `.nef` 到指定資料夾
nef_save_path = os.path.join(onnx_dir, os.path.basename(nef_model_path))
shutil.copy(nef_model_path, nef_save_path)
# **立即檢查 `.nef` 是否真的存在**
if os.path.exists(nef_save_path):
print(f"\n✅ NEF file successfully saved to: {nef_save_path}")
else:
raise RuntimeError(f"❌ Error: NEF file NOT found in {nef_save_path} after copying!")
# **執行 `ls` 指令來確認 Python 內部真的看到 `.nef`**
print("\n🔍 Listing files in target directory:")
subprocess.run(["ls", "-lh", onnx_dir])

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kneron/exporting/yolov5/__init__.py Normal file
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# This file contains modules common to various models
import torch.nn as nn
import torch
import torch.nn.functional as F
def autopad(k, p=None): # kernel, padding
# Pad to 'same'
if p is None:
p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
return p
def DWConv(c1, c2, k=1, s=1, act=True):
# Depthwise convolution
return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
class Conv(nn.Module):
# Standard convolution
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
super(Conv, self).__init__()
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
self.bn = nn.BatchNorm2d(c2)
self.act = nn.LeakyReLU(0.1, inplace=True) if act else nn.Identity()
def forward(self, x):
return self.act(self.bn(self.conv(x)))
def fuseforward(self, x):
return self.act(self.conv(x))
class Bottleneck(nn.Module):
# Standard bottleneck
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
super(Bottleneck, self).__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c_, c2, 3, 1, g=g)
self.add = shortcut and c1 == c2
def forward(self, x):
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
class BottleneckCSP(nn.Module):
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
super(BottleneckCSP, self).__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
self.cv4 = Conv(2 * c_, c2, 1, 1)
self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
self.act = nn.LeakyReLU(0.1, inplace=True)
self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
def forward(self, x):
y1 = self.cv3(self.m(self.cv1(x)))
y2 = self.cv2(x)
return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
class SPP(nn.Module):
# Spatial pyramid pooling layer used in YOLOv3-SPP
def __init__(self, c1, c2, k=(5, 9, 13)):
super(SPP, self).__init__()
c_ = c1 // 2 # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
def forward(self, x):
x = self.cv1(x)
return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
class Focus(nn.Module):#
# Focus wh information into c-space
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
super(Focus, self).__init__()
self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
w1_1 = torch.tensor([[[1., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]]])
w1_2 = torch.tensor([[[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[1., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]]])
w1_3 = torch.tensor([[[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[1., 0., 0.],[0., 0., 0.],[0., 0., 0.]]])
w3_1 = torch.tensor([[[0., 1., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]]])
w3_2 = torch.tensor([[[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 1., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]]])
w3_3 = torch.tensor([[[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 1., 0.],[0., 0., 0.],[0., 0., 0.]]])
w2_1 = torch.tensor([[[0., 0., 0.],[1., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]]])
w2_2 = torch.tensor([[[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[1., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]]])
w2_3 = torch.tensor([[[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[1., 0., 0.],[0., 0., 0.]]])
w4_1 = torch.tensor([[[0., 0., 0.],[0., 1., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]]])
w4_2 = torch.tensor([[[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 1., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]]])
w4_3 = torch.tensor([[[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 1., 0.],[0., 0., 0.]]])
w1_1 = w1_1.view(1, 3, 3, 3)
w1_2 = w1_2.view(1, 3, 3, 3)
w1_3 = w1_3.view(1, 3, 3, 3)
w2_1 = w2_1.view(1, 3, 3, 3)
w2_2 = w2_2.view(1, 3, 3, 3)
w2_3 = w2_3.view(1, 3, 3, 3)
w3_1 = w3_1.view(1, 3, 3, 3)
w3_2 = w3_2.view(1, 3, 3, 3)
w3_3 = w3_3.view(1, 3, 3, 3)
w4_1 = w4_1.view(1, 3, 3, 3)
w4_2 = w4_2.view(1, 3, 3, 3)
w4_3 = w4_3.view(1, 3, 3, 3)
self.w_cat = torch.cat([w1_1, w1_2,w1_3, w2_1,w2_2,w2_3, w3_1,w3_2,w3_3, w4_1,w4_2,w4_3], 0)
self.p2d = (0, 2, 0, 2)
def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
# x = x.type(torch.cuda.FloatTensor)
#x_gt = self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
x_pad = F.pad(x, self.p2d, 'constant', 0)
xx = F.conv2d(x_pad, self.w_cat.to(x.device),stride=2)
xx = self.conv(xx)
#print(torch.sum(x_gt - xx))
return xx
class Focus_ori(nn.Module):#
# Focus wh information into c-space
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
super(Focus, self).__init__()
self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
x = x.type(torch.cuda.FloatTensor)
return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
class Concat(nn.Module):
# Concatenate a list of tensors along dimension
def __init__(self, dimension=1):
super(Concat, self).__init__()
self.d = dimension
def forward(self, x):
return torch.cat(x, self.d)
class Flatten(nn.Module):
# Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions
@staticmethod
def forward(x):
return x.view(x.size(0), -1)
class Classify(nn.Module):
# Classification head, i.e. x(b,c1,20,20) to x(b,c2)
def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
super(Classify, self).__init__()
self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1)
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) # to x(b,c2,1,1)
self.flat = Flatten()
def forward(self, x):
z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list
return self.flat(self.conv(z)) # flatten to x(b,c2)
class MixConv2d(nn.Module):
# Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
super(MixConv2d, self).__init__()
groups = len(k)
if equal_ch: # equal c_ per group
i = torch.linspace(0, groups - 1E-6, c2).floor() # c2 indices
c_ = [(i == g).sum() for g in range(groups)] # intermediate channels
else: # equal weight.numel() per group
b = [c2] + [0] * groups
a = np.eye(groups + 1, groups, k=-1)
a -= np.roll(a, 1, axis=1)
a *= np.array(k) ** 2
a[0] = 1
c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b
self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
self.bn = nn.BatchNorm2d(c2)
self.act = nn.LeakyReLU(0.1, inplace=True)
def forward(self, x):
return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
class CrossConv(nn.Module):
# Cross Convolution Downsample
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
# ch_in, ch_out, kernel, stride, groups, expansion, shortcut
super(CrossConv, self).__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, (1, k), (1, s))
self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
self.add = shortcut and c1 == c2
def forward(self, x):
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
# class C3(nn.Module):
# # Cross Convolution CSP
# def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
# super(C3, self).__init__()
# c_ = int(c2 * e) # hidden channels
# self.cv1 = Conv(c1, c_, 1, 1)
# self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
# self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
# self.cv4 = Conv(2 * c_, c2, 1, 1)
# self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
# self.act = nn.LeakyReLU(0.1, inplace=True)
# self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
# def forward(self, x):
# y1 = self.cv3(self.m(self.cv1(x)))
# y2 = self.cv2(x)
# return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
class C3(nn.Module):
# CSP Bottleneck with 3 convolutions
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
super(C3, self).__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c1, c_, 1, 1)
self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2)
self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
def forward(self, x):
return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))

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# -*- coding: utf-8 -*-
import numpy as np
import os
from .funcs.utils import str2int, str2bool
from . import Flow
flow = Flow()
flow.set_numerical_type('floating')
flow_520 = Flow()
flow_520.set_numerical_type('520')
flow_720 = Flow()
flow_720.set_numerical_type('720')
DEFAULT = None
default = {
'crop':{
'align_w_to_4':False
},
'resize':{
'type':'bilinear',
'calculate_ratio_using_CSim':False
}
}
def set_default_as_520():
"""
Set some default parameter as 520 setting
crop.align_w_to_4 = True
crop.pad_square_to_4 = True
resize.type = 'fixed_520'
resize.calculate_ratio_using_CSim = True
"""
global default
default['crop']['align_w_to_4'] = True
default['resize']['type'] = 'fixed_520'
default['resize']['calculate_ratio_using_CSim'] = True
return
def set_default_as_floating():
"""
Set some default parameter as floating setting
crop.align_w_to_4 = False
crop.pad_square_to_4 = False
resize.type = 'bilinear'
resize.calculate_ratio_using_CSim = False
"""
global default
default['crop']['align_w_to_4'] = False
default['resize']['type'] = 'bilinear'
default['resize']['calculate_ratio_using_CSim'] = False
pass
def print_info_on():
"""
turn print infomation on.
"""
flow.set_print_info(True)
flow_520.set_print_info(True)
def print_info_off():
"""
turn print infomation off.
"""
flow.set_print_info(False)
flow_520.set_print_info(False)
def load_image(image):
"""
load_image function
load load_image and output as rgb888 format np.array
Args:
image: [np.array/str], can be np.array or image file path
Returns:
out: [np.array], rgb888 format
Examples:
"""
image = flow.load_image(image, is_raw = False)
return image
def load_bin(image, fmt=None, size=None):
"""
load_bin function
load bin file and output as rgb888 format np.array
Args:
image: [str], bin file path
fmt: [str], "rgb888" / "rgb565" / "nir"
size: [tuble], (image_w, image_h)
Returns:
out: [np.array], rgb888 format
Examples:
>>> image_data = kneron_preprocessing.API.load_bin(image,'rgb565',(raw_w,raw_h))
"""
assert isinstance(size, tuple)
assert isinstance(fmt, str)
# assert (fmt.lower() in ['rgb888', "rgb565" , "nir",'RGB888', "RGB565" , "NIR", 'NIR888', 'nir888'])
image = flow.load_image(image, is_raw = True, raw_img_type='bin', raw_img_fmt = fmt, img_in_width = size[0], img_in_height = size[1])
flow.set_color_conversion(source_format=fmt, out_format = 'rgb888')
image,_ = flow.funcs['color'](image)
return image
def load_hex(file, fmt=None, size=None):
"""
load_hex function
load hex file and output as rgb888 format np.array
Args:
image: [str], hex file path
fmt: [str], "rgb888" / "yuv444" / "ycbcr444" / "yuv422" / "ycbcr422" / "rgb565"
size: [tuble], (image_w, image_h)
Returns:
out: [np.array], rgb888 format
Examples:
>>> image_data = kneron_preprocessing.API.load_hex(image,'rgb565',(raw_w,raw_h))
"""
assert isinstance(size, tuple)
assert isinstance(fmt, str)
assert (fmt.lower() in ['rgb888',"yuv444" , "ycbcr444" , "yuv422" , "ycbcr422" , "rgb565"])
image = flow.load_image(file, is_raw = True, raw_img_type='hex', raw_img_fmt = fmt, img_in_width = size[0], img_in_height = size[1])
flow.set_color_conversion(source_format=fmt, out_format = 'rgb888')
image,_ = flow.funcs['color'](image)
return image
def dump_image(image, output=None, file_fmt='txt',image_fmt='rgb888',order=0):
"""
dump_image function
dump txt, bin or hex, default is txt
image format as following format: RGB888, RGBA8888, RGB565, NIR, YUV444, YCbCr444, YUV422, YCbCr422, default is RGB888
Args:
image: [np.array/str], can be np.array or image file path
output: [str], dump file path
file_fmt: [str], "bin" / "txt" / "hex", set dump file format, default is txt
image_fmt: [str], RGB888 / RGBA8888 / RGB565 / NIR / YUV444 / YCbCr444 / YUV422 / YCbCr422, default is RGB888
Examples:
>>> kneron_preprocessing.API.dump_image(image_data,out_path,fmt='bin')
"""
if isinstance(image, str):
image = load_image(image)
assert isinstance(image, np.ndarray)
if output is None:
return
flow.set_output_setting(is_dump=False, dump_format=file_fmt, image_format=image_fmt ,output_file=output)
flow.dump_image(image)
return
def convert(image, out_fmt = 'RGB888', source_fmt = 'RGB888'):
"""
color convert
Args:
image: [np.array], input
out_fmt: [str], "rgb888" / "rgba8888" / "rgb565" / "yuv" / "ycbcr" / "yuv422" / "ycbcr422"
source_fmt: [str], "rgb888" / "rgba8888" / "rgb565" / "yuv" / "ycbcr" / "yuv422" / "ycbcr422"
Returns:
out: [np.array]
Examples:
"""
flow.set_color_conversion(source_format = source_fmt, out_format=out_fmt, simulation=False)
image,_ = flow.funcs['color'](image)
return image
def get_crop_range(box,align_w_to_4=DEFAULT, pad_square_to_4=False,rounding_type=0):
"""
get exact crop box according different setting
Args:
box: [tuble], (x1, y1, x2, y2)
align_w_to_4: [bool], crop length in w direction align to 4 or not, default False
pad_square_to_4: [bool], pad to square(align 4) or not, default False
rounding_type: [int], 0-> x1,y1 take floor, x2,y2 take ceil; 1->all take rounding
Returns:
out: [tuble,4], (crop_x1, crop_y1, crop_x2, crop_y2)
Examples:
>>> image_data = kneron_preprocessing.API.get_crop_range((272,145,461,341), align_w_to_4=True, pad_square_to_4=True)
(272, 145, 460, 341)
"""
if box is None:
return (0,0,0,0)
if align_w_to_4 is None:
align_w_to_4 = default['crop']['align_w_to_4']
flow.set_crop(type='specific', start_x=box[0],start_y=box[1],end_x=box[2],end_y=box[3], align_w_to_4=align_w_to_4, pad_square_to_4=pad_square_to_4,rounding_type=rounding_type)
image = np.zeros((1,1,3)).astype('uint8')
_,info = flow.funcs['crop'](image)
return info['box']
def crop(image, box=None, align_w_to_4=DEFAULT, pad_square_to_4=False,rounding_type=0 ,info_out = {}):
"""
crop function
specific crop range by box
Args:
image: [np.array], input
box: [tuble], (x1, y1, x2, y2)
align_w_to_4: [bool], crop length in w direction align to 4 or not, default False
pad_square_to_4: [bool], pad to square(align 4) or not, default False
rounding_type: [int], 0-> x1,y1 take floor, x2,y2 take ceil; 1->all take rounding
info_out: [dic], save the final crop box into info_out['box']
Returns:
out: [np.array]
Examples:
>>> info = {}
>>> image_data = kneron_preprocessing.API.crop(image_data,(272,145,461,341), align_w_to_4=True, info_out=info)
>>> info['box']
(272, 145, 460, 341)
>>> info = {}
>>> image_data = kneron_preprocessing.API.crop(image_data,(272,145,461,341), pad_square_to_4=True, info_out=info)
>>> info['box']
(268, 145, 464, 341)
"""
assert isinstance(image, np.ndarray)
if box is None:
return image
if align_w_to_4 is None:
align_w_to_4 = default['crop']['align_w_to_4']
flow.set_crop(type='specific', start_x=box[0],start_y=box[1],end_x=box[2],end_y=box[3], align_w_to_4=align_w_to_4, pad_square_to_4=pad_square_to_4,rounding_type=rounding_type)
image,info = flow.funcs['crop'](image)
info_out['box'] = info['box']
return image
def crop_center(image, range=None, align_w_to_4=DEFAULT, pad_square_to_4=False,rounding_type=0 ,info_out = {}):
"""
crop function
center crop by range
Args:
image: [np.array], input
range: [tuble], (crop_w, crop_h)
align_w_to_4: [bool], crop length in w direction align to 4 or not, default False
pad_square_to_4: [bool], pad to square(align 4) or not, default False
rounding_type: [int], 0-> x1,y1 take floor, x2,y2 take ceil; 1->all take rounding
info_out: [dic], save the final crop box into info_out['box']
Returns:
out: [np.array]
Examples:
>>> info = {}
>>> image_data = kneron_preprocessing.API.crop_center(image_data,(102,40), align_w_to_4=True,info_out=info)
>>> info['box']
(268, 220, 372, 260)
>>> info = {}
>>> image_data = kneron_preprocessing.API.crop_center(image_data,(102,40), pad_square_to_4=True, info_out=info)
>>> info['box']
(269, 192, 371, 294)
"""
assert isinstance(image, np.ndarray)
if range is None:
return image
if align_w_to_4 is None:
align_w_to_4 = default['crop']['align_w_to_4']
flow.set_crop(type='center', crop_w=range[0],crop_h=range[1], align_w_to_4=align_w_to_4, pad_square_to_4=pad_square_to_4,rounding_type=rounding_type)
image,info = flow.funcs['crop'](image)
info_out['box'] = info['box']
return image
def crop_corner(image, range=None, align_w_to_4=DEFAULT,pad_square_to_4=False,rounding_type=0 ,info_out = {}):
"""
crop function
corner crop by range
Args:
image: [np.array], input
range: [tuble], (crop_w, crop_h)
align_w_to_4: [bool], crop length in w direction align to 4 or not, default False
pad_square_to_4: [bool], pad to square(align 4) or not, default False
rounding_type: [int], 0-> x1,y1 take floor, x2,y2 take ceil; 1->all take rounding
info_out: [dic], save the final crop box into info_out['box']
Returns:
out: [np.array]
Examples:
>>> info = {}
>>> image_data = kneron_preprocessing.API.crop_corner(image_data,(102,40), align_w_to_4=True,info_out=info)
>>> info['box']
(0, 0, 104, 40)
>>> info = {}
>>> image_data = kneron_preprocessing.API.crop_corner(image_data,(102,40), pad_square_to_4=True,info_out=info)
>>> info['box']
(0, -28, 102, 74)
"""
assert isinstance(image, np.ndarray)
if range is None:
return image
if align_w_to_4 is None:
align_w_to_4 = default['crop']['align_w_to_4']
flow.set_crop(type='corner', crop_w=range[0],crop_h=range[1], align_w_to_4=align_w_to_4, pad_square_to_4=pad_square_to_4)
image, info = flow.funcs['crop'](image)
info_out['box'] = info['box']
return image
def resize(image, size=None, keep_ratio = True, zoom = True, type=DEFAULT, calculate_ratio_using_CSim = DEFAULT, info_out = {}):
"""
resize function
resize type can be bilinear or bilicubic as floating type, fixed or fixed_520/fixed_720 as fixed type.
fixed_520/fixed_720 type has add some function to simulate 520/720 bug.
Args:
image: [np.array], input
size: [tuble], (input_w, input_h)
keep_ratio: [bool], keep_ratio or not, default True
zoom: [bool], enable resize can zoom image or not, default True
type: [str], "bilinear" / "bilicubic" / "cv2" / "fixed" / "fixed_520" / "fixed_720"
calculate_ratio_using_CSim: [bool], calculate the ratio and scale using Csim function and C float, default False
info_out: [dic], save the final scale size(w,h) into info_out['size']
Returns:
out: [np.array]
Examples:
>>> info = {}
>>> image_data = kneron_preprocessing.API.resize(image_data,size=(56,56),type='fixed',info_out=info)
>>> info_out['size']
(54,56)
"""
assert isinstance(image, np.ndarray)
if size is None:
return image
if type is None:
type = default['resize']['type']
if calculate_ratio_using_CSim is None:
calculate_ratio_using_CSim = default['resize']['calculate_ratio_using_CSim']
flow.set_resize(resize_w = size[0], resize_h = size[1], type=type, keep_ratio=keep_ratio,zoom=zoom, calculate_ratio_using_CSim=calculate_ratio_using_CSim)
image, info = flow.funcs['resize'](image)
info_out['size'] = info['size']
return image
def pad(image, pad_l=0, pad_r=0, pad_t=0, pad_b=0, pad_val=0):
"""
pad function
specific left, right, top and bottom pad size.
Args:
image[np.array]: input
pad_l: [int], pad size from left, default 0
pad_r: [int], pad size form right, default 0
pad_t: [int], pad size from top, default 0
pad_b: [int], pad size form bottom, default 0
pad_val: [float], the value of pad, , default 0
Returns:
out: [np.array]
Examples:
>>> image_data = kneron_preprocessing.API.pad(image_data,20,40,20,40,-0.5)
"""
assert isinstance(image, np.ndarray)
flow.set_padding(type='specific',pad_l=pad_l,pad_r=pad_r,pad_t=pad_t,pad_b=pad_b,pad_val=pad_val)
image, _ = flow.funcs['padding'](image)
return image
def pad_center(image,size=None, pad_val=0):
"""
pad function
center pad with pad size.
Args:
image[np.array]: input
size: [tuble], (padded_size_w, padded_size_h)
pad_val: [float], the value of pad, , default 0
Returns:
out: [np.array]
Examples:
>>> image_data = kneron_preprocessing.API.pad_center(image_data,size=(56,56),pad_val=-0.5)
"""
assert isinstance(image, np.ndarray)
if size is None:
return image
assert ( (image.shape[0] <= size[1]) & (image.shape[1] <= size[0]) )
flow.set_padding(type='center',padded_w=size[0],padded_h=size[1],pad_val=pad_val)
image, _ = flow.funcs['padding'](image)
return image
def pad_corner(image,size=None, pad_val=0):
"""
pad function
corner pad with pad size.
Args:
image[np.array]: input
size: [tuble], (padded_size_w, padded_size_h)
pad_val: [float], the value of pad, , default 0
Returns:
out: [np.array]
Examples:
>>> image_data = kneron_preprocessing.API.pad_corner(image_data,size=(56,56),pad_val=-0.5)
"""
assert isinstance(image, np.ndarray)
if size is None:
return image
assert ( (image.shape[0] <= size[1]) & (image.shape[1] <= size[0]) )
flow.set_padding(type='corner',padded_w=size[0],padded_h=size[1],pad_val=pad_val)
image, _ = flow.funcs['padding'](image)
return image
def norm(image,scale=256.,bias=-0.5, mean=None, std=None):
"""
norm function
x = (x/scale - bias)
x[0,1,2] = x - mean[0,1,2]
x[0,1,2] = x / std[0,1,2]
Args:
image: [np.array], input
scale: [float], default = 256
bias: [float], default = -0.5
mean: [tuble,3], default = None
std: [tuble,3], default = None
Returns:
out: [np.array]
Examples:
>>> image_data = kneron_preprocessing.API.norm(image_data)
>>> image_data = kneron_preprocessing.API.norm(image_data,mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
"""
assert isinstance(image, np.ndarray)
flow.set_normalize(type='specific',scale=scale, bias=bias, mean=mean, std =std)
image, _ = flow.funcs['normalize'](image)
return image
def inproc_520(image,raw_fmt='rgb565',raw_size=None,npu_size=None, crop_box=None, pad_mode=0, norm='kneron', gray=False, rotate=0, radix=8, bit_width=8, round_w_to_16=True, NUM_BANK_LINE=32,BANK_ENTRY_CNT=512,MAX_IMG_PREPROC_ROW_NUM=511,MAX_IMG_PREPROC_COL_NUM=256):
"""
inproc_520
Args:
image: [np.array], input
crop_box: [tuble], (x1, y1, x2, y2), if None will skip crop
pad_mode: [int], 0: pad 2 sides, 1: pad 1 side, 2: no pad. default = 0
norm: [str], default = 'kneron'
rotate: [int], 0 / 1 / 2 ,default = 0
radix: [int], default = 8
bit_width: [int], default = 8
round_w_to_16: [bool], default = True
gray: [bool], default = False
Returns:
out: [np.array]
Examples:
>>> image_data = kneron_preprocessing.API.inproc_520(image_data,npu_size=(56,56),crop_box=(272,145,460,341),pad_mode=1)
"""
# assert isinstance(image, np.ndarray)
if (not isinstance(image, np.ndarray)):
flow_520.set_raw_img(is_raw_img='yes',raw_img_type = 'bin',raw_img_fmt=raw_fmt, img_in_width=raw_size[0], img_in_height=raw_size[1])
else:
flow_520.set_raw_img(is_raw_img='no')
flow_520.set_color_conversion(source_format='rgb888')
if npu_size is None:
return image
flow_520.set_model_size(w=npu_size[0],h=npu_size[1])
## Crop
if crop_box != None:
flow_520.set_crop(start_x=crop_box[0],start_y=crop_box[1],end_x=crop_box[2],end_y=crop_box[3])
crop_fisrt = True
else:
crop_fisrt = False
## Color
if gray:
flow_520.set_color_conversion(out_format='l',simulation='no')
else:
flow_520.set_color_conversion(out_format='rgb888',simulation='no')
## Resize & Pad
pad_mode = str2int(pad_mode)
if (pad_mode == 0):
pad_type = 'center'
resize_keep_ratio = 'yes'
elif (pad_mode == 1):
pad_type = 'corner'
resize_keep_ratio = 'yes'
else:
pad_type = 'center'
resize_keep_ratio = 'no'
flow_520.set_resize(keep_ratio=resize_keep_ratio)
flow_520.set_padding(type=pad_type)
## Norm
flow_520.set_normalize(type=norm)
## 520 inproc
flow_520.set_520_setting(radix=radix,bit_width=bit_width,rotate=rotate,crop_fisrt=crop_fisrt,round_w_to_16=round_w_to_16,NUM_BANK_LINE=NUM_BANK_LINE,BANK_ENTRY_CNT=BANK_ENTRY_CNT,MAX_IMG_PREPROC_ROW_NUM=MAX_IMG_PREPROC_ROW_NUM,MAX_IMG_PREPROC_COL_NUM=MAX_IMG_PREPROC_COL_NUM)
image_data, _ = flow_520.run_whole_process(image)
return image_data
def inproc_720(image,raw_fmt='rgb565',raw_size=None,npu_size=None, crop_box=None, pad_mode=0, norm='kneron', gray=False):
"""
inproc_720
Args:
image: [np.array], input
crop_box: [tuble], (x1, y1, x2, y2), if None will skip crop
pad_mode: [int], 0: pad 2 sides, 1: pad 1 side, 2: no pad. default = 0
norm: [str], default = 'kneron'
rotate: [int], 0 / 1 / 2 ,default = 0
radix: [int], default = 8
bit_width: [int], default = 8
round_w_to_16: [bool], default = True
gray: [bool], default = False
Returns:
out: [np.array]
Examples:
>>> image_data = kneron_preprocessing.API.inproc_520(image_data,npu_size=(56,56),crop_box=(272,145,460,341),pad_mode=1)
"""
# assert isinstance(image, np.ndarray)
if (not isinstance(image, np.ndarray)):
flow_720.set_raw_img(is_raw_img='yes',raw_img_type = 'bin',raw_img_fmt=raw_fmt, img_in_width=raw_size[0], img_in_height=raw_size[1])
else:
flow_720.set_raw_img(is_raw_img='no')
flow_720.set_color_conversion(source_format='rgb888')
if npu_size is None:
return image
flow_720.set_model_size(w=npu_size[0],h=npu_size[1])
## Crop
if crop_box != None:
flow_720.set_crop(start_x=crop_box[0],start_y=crop_box[1],end_x=crop_box[2],end_y=crop_box[3])
crop_fisrt = True
else:
crop_fisrt = False
## Color
if gray:
flow_720.set_color_conversion(out_format='l',simulation='no')
else:
flow_720.set_color_conversion(out_format='rgb888',simulation='no')
## Resize & Pad
pad_mode = str2int(pad_mode)
if (pad_mode == 0):
pad_type = 'center'
resize_keep_ratio = 'yes'
elif (pad_mode == 1):
pad_type = 'corner'
resize_keep_ratio = 'yes'
else:
pad_type = 'center'
resize_keep_ratio = 'no'
flow_720.set_resize(keep_ratio=resize_keep_ratio)
flow_720.set_padding(type=pad_type)
## 720 inproc
# flow_720.set_720_setting(radix=radix,bit_width=bit_width,rotate=rotate,crop_fisrt=crop_fisrt,round_w_to_16=round_w_to_16,NUM_BANK_LINE=NUM_BANK_LINE,BANK_ENTRY_CNT=BANK_ENTRY_CNT,MAX_IMG_PREPROC_ROW_NUM=MAX_IMG_PREPROC_ROW_NUM,MAX_IMG_PREPROC_COL_NUM=MAX_IMG_PREPROC_COL_NUM)
image_data, _ = flow_720.run_whole_process(image)
return image_data
def bit_match(data1, data2):
"""
bit_match function
check data1 is equal to data2 or not.
Args:
data1: [np.array / str], can be array or txt/bin file
data2: [np.array / str], can be array or txt/bin file
Returns:
out1: [bool], is match or not
out2: [np.array], if not match, save the position for mismatched data
Examples:
>>> result, mismatched = kneron_preprocessing.API.bit_match(data1,data2)
"""
if isinstance(data1, str):
if os.path.splitext(data1)[1] == '.bin':
data1 = np.fromfile(data1, dtype='uint8')
elif os.path.splitext(data1)[1] == '.txt':
data1 = np.loadtxt(data1)
assert isinstance(data1, np.ndarray)
if isinstance(data2, str):
if os.path.splitext(data2)[1] == '.bin':
data2 = np.fromfile(data2, dtype='uint8')
elif os.path.splitext(data2)[1] == '.txt':
data2 = np.loadtxt(data2)
assert isinstance(data2, np.ndarray)
data1 = data1.reshape((-1,1))
data2 = data2.reshape((-1,1))
if not(len(data1) == len(data2)):
print('error len')
return False, np.zeros((1))
else:
ans = data2 - data1
if len(np.where(ans>0)[0]) > 0:
print('error',np.where(ans>0)[0])
return False, np.where(ans>0)[0]
else:
print('pass')
return True, np.zeros((1))
def cpr_to_crp(x_start, x_end, y_start, y_end, pad_l, pad_r, pad_t, pad_b, rx_start, rx_end, ry_start, ry_end):
"""
calculate the parameters of crop->pad->resize flow to HW crop->resize->padding flow
Args:
Returns:
Examples:
"""
pad_l = round(pad_l * (rx_end-rx_start) / (x_end - x_start + pad_l + pad_r))
pad_r = round(pad_r * (rx_end-rx_start) / (x_end - x_start + pad_l + pad_r))
pad_t = round(pad_t * (ry_end-ry_start) / (y_end - y_start + pad_t + pad_b))
pad_b = round(pad_b * (ry_end-ry_start) / (y_end - y_start + pad_t + pad_b))
rx_start +=pad_l
rx_end -=pad_r
ry_start +=pad_t
ry_end -=pad_b
return x_start, x_end, y_start, y_end, pad_l, pad_r, pad_t, pad_b, rx_start, rx_end, ry_start, ry_end

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import numpy as np
import argparse
import kneron_preprocessing
def main_(args):
image = args.input_file
filefmt = args.file_fmt
if filefmt == 'bin':
raw_format = args.raw_format
raw_w = args.input_width
raw_h = args.input_height
image_data = kneron_preprocessing.API.load_bin(image,raw_format,(raw_w,raw_h))
else:
image_data = kneron_preprocessing.API.load_image(image)
npu_w = args.width
npu_h = args.height
crop_first = True if args.crop_first == "True" else False
if crop_first:
x1 = args.x_pos
y1 = args.y_pos
x2 = args.crop_w + x1
y2 = args.crop_h + y1
crop_box = [x1,y1,x2,y2]
else:
crop_box = None
pad_mode = args.pad_mode
norm_mode = args.norm_mode
bitwidth = args.bitwidth
radix = args.radix
rotate = args.rotate_mode
##
image_data = kneron_preprocessing.API.inproc_520(image_data,npu_size=(npu_w,npu_h),crop_box=crop_box,pad_mode=pad_mode,norm=norm_mode,rotate=rotate,radix=radix,bit_width=bitwidth)
output_file = args.output_file
kneron_preprocessing.API.dump_image(image_data,output_file,'bin','rgba')
return
if __name__ == "__main__":
argparser = argparse.ArgumentParser(
description="preprocessing"
)
argparser.add_argument(
'-i',
'--input_file',
help="input file name"
)
argparser.add_argument(
'-ff',
'--file_fmt',
help="input file format, jpg or bin"
)
argparser.add_argument(
'-rf',
'--raw_format',
help="input file image format, rgb or rgb565 or nir"
)
argparser.add_argument(
'-i_w',
'--input_width',
type=int,
help="input image width"
)
argparser.add_argument(
'-i_h',
'--input_height',
type=int,
help="input image height"
)
argparser.add_argument(
'-o',
'--output_file',
help="output file name"
)
argparser.add_argument(
'-s_w',
'--width',
type=int,
help="output width for npu input",
)
argparser.add_argument(
'-s_h',
'--height',
type=int,
help="output height for npu input",
)
argparser.add_argument(
'-c_f',
'--crop_first',
help="crop first True or False",
)
argparser.add_argument(
'-x',
'--x_pos',
type=int,
help="left up coordinate x",
)
argparser.add_argument(
'-y',
'--y_pos',
type=int,
help="left up coordinate y",
)
argparser.add_argument(
'-c_w',
'--crop_w',
type=int,
help="crop width",
)
argparser.add_argument(
'-c_h',
'--crop_h',
type=int,
help="crop height",
)
argparser.add_argument(
'-p_m',
'--pad_mode',
type=int,
help=" 0: pad 2 sides, 1: pad 1 side, 2: no pad.",
)
argparser.add_argument(
'-n_m',
'--norm_mode',
help="normalizaton mode: yolo, kneron, tf."
)
argparser.add_argument(
'-r_m',
'--rotate_mode',
type=int,
help="rotate mode:0,1,2"
)
argparser.add_argument(
'-bw',
'--bitwidth',
type=int,
help="Int for bitwidth"
)
argparser.add_argument(
'-r',
'--radix',
type=int,
help="Int for radix"
)
args = argparser.parse_args()
main_(args)

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from .Flow import *
from .API import *

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import numpy as np
from PIL import Image
from .utils import signed_rounding, clip, str2bool
format_bit = 10
c00_yuv = 1
c02_yuv = 1436
c10_yuv = 1
c11_yuv = -354
c12_yuv = -732
c20_yuv = 1
c21_yuv = 1814
c00_ycbcr = 1192
c02_ycbcr = 1634
c10_ycbcr = 1192
c11_ycbcr = -401
c12_ycbcr = -833
c20_ycbcr = 1192
c21_ycbcr = 2065
Matrix_ycbcr_to_rgb888 = np.array(
[[1.16438356e+00, 1.16438356e+00, 1.16438356e+00],
[2.99747219e-07, - 3.91762529e-01, 2.01723263e+00],
[1.59602686e+00, - 8.12968294e-01, 3.04059479e-06]])
Matrix_rgb888_to_ycbcr = np.array(
[[0.25678824, - 0.14822353, 0.43921569],
[0.50412941, - 0.29099216, - 0.36778824],
[0.09790588, 0.43921569, - 0.07142745]])
Matrix_rgb888_to_yuv = np.array(
[[ 0.29899106, -0.16877996, 0.49988381],
[ 0.5865453, -0.33110385, -0.41826072],
[ 0.11446364, 0.49988381, -0.08162309]])
# Matrix_rgb888_to_yuv = np.array(
# [[0.299, - 0.147, 0.615],
# [0.587, - 0.289, - 0.515],
# [0.114, 0.436, - 0.100]])
# Matrix_yuv_to_rgb888 = np.array(
# [[1.000, 1.000, 1.000],
# [0.000, - 0.394, 2.032],
# [1.140, - 0.581, 0.000]])
class runner(object):
def __init__(self):
self.set = {
'print_info':'no',
'model_size':[0,0],
'numerical_type':'floating',
"source_format": "rgb888",
"out_format": "rgb888",
"options": {
"simulation": "no",
"simulation_format": "rgb888"
}
}
def update(self, **kwargs):
#
self.set.update(kwargs)
## simulation
self.funs = []
if str2bool(self.set['options']['simulation']) and self.set['source_format'].lower() in ['RGB888', 'rgb888', 'RGB', 'rgb']:
if self.set['options']['simulation_format'].lower() in ['YUV422', 'yuv422', 'YUV', 'yuv']:
self.funs.append(self._ColorConversion_RGB888_to_YUV422)
self.set['source_format'] = 'YUV422'
elif self.set['options']['simulation_format'].lower() in ['YCBCR422', 'YCbCr422', 'ycbcr422', 'YCBCR', 'YCbCr', 'ycbcr']:
self.funs.append(self._ColorConversion_RGB888_to_YCbCr422)
self.set['source_format'] = 'YCbCr422'
elif self.set['options']['simulation_format'].lower() in['RGB565', 'rgb565']:
self.funs.append(self._ColorConversion_RGB888_to_RGB565)
self.set['source_format'] = 'RGB565'
## to rgb888
if self.set['source_format'].lower() in ['YUV444', 'yuv444','YUV422', 'yuv422', 'YUV', 'yuv']:
self.funs.append(self._ColorConversion_YUV_to_RGB888)
elif self.set['source_format'].lower() in ['YCBCR444', 'YCbCr444', 'ycbcr444','YCBCR422', 'YCbCr422', 'ycbcr422', 'YCBCR', 'YCbCr', 'ycbcr']:
self.funs.append(self._ColorConversion_YCbCr_to_RGB888)
elif self.set['source_format'].lower() in ['RGB565', 'rgb565']:
self.funs.append(self._ColorConversion_RGB565_to_RGB888)
elif self.set['source_format'].lower() in ['l', 'L' , 'nir', 'NIR']:
self.funs.append(self._ColorConversion_L_to_RGB888)
elif self.set['source_format'].lower() in ['RGBA8888', 'rgba8888' , 'RGBA', 'rgba']:
self.funs.append(self._ColorConversion_RGBA8888_to_RGB888)
## output format
if self.set['out_format'].lower() in ['L', 'l']:
self.funs.append(self._ColorConversion_RGB888_to_L)
elif self.set['out_format'].lower() in['RGB565', 'rgb565']:
self.funs.append(self._ColorConversion_RGB888_to_RGB565)
elif self.set['out_format'].lower() in['RGBA', 'RGBA8888','rgba','rgba8888']:
self.funs.append(self._ColorConversion_RGB888_to_RGBA8888)
elif self.set['out_format'].lower() in['YUV', 'YUV444','yuv','yuv444']:
self.funs.append(self._ColorConversion_RGB888_to_YUV444)
elif self.set['out_format'].lower() in['YUV422','yuv422']:
self.funs.append(self._ColorConversion_RGB888_to_YUV422)
elif self.set['out_format'].lower() in['YCBCR', 'YCBCR444','YCbCr','YCbCr444','ycbcr','ycbcr444']:
self.funs.append(self._ColorConversion_RGB888_to_YCbCr444)
elif self.set['out_format'].lower() in['YCBCR422','YCbCr422','ycbcr422']:
self.funs.append(self._ColorConversion_RGB888_to_YCbCr422)
def print_info(self):
print("<colorConversion>",
"source_format:", self.set['source_format'],
', out_format:', self.set['out_format'],
', simulation:', self.set['options']['simulation'],
', simulation_format:', self.set['options']['simulation_format'])
def run(self, image_data):
assert isinstance(image_data, np.ndarray)
# print info
if str2bool(self.set['print_info']):
self.print_info()
# color
for _, f in enumerate(self.funs):
image_data = f(image_data)
# output
info = {}
return image_data, info
def _ColorConversion_RGB888_to_YUV444(self, image):
## floating
image = image.astype('float')
image = (image @ Matrix_rgb888_to_yuv + 0.5).astype('uint8')
return image
def _ColorConversion_RGB888_to_YUV422(self, image):
# rgb888 to yuv444
image = self._ColorConversion_RGB888_to_YUV444(image)
# yuv444 to yuv422
u2 = image[:, 0::2, 1]
u4 = np.repeat(u2, 2, axis=1)
v2 = image[:, 1::2, 2]
v4 = np.repeat(v2, 2, axis=1)
image[..., 1] = u4
image[..., 2] = v4
return image
def _ColorConversion_YUV_to_RGB888(self, image):
## fixed
h, w, c = image.shape
image_f = image.reshape((h * w, c))
image_rgb_f = np.zeros(image_f.shape, dtype=np.uint8)
for i in range(h * w):
image_y = image_f[i, 0] *1024
if image_f[i, 1] > 127:
image_u = -((~(image_f[i, 1] - 1)) & 0xFF)
else:
image_u = image_f[i, 1]
if image_f[i, 2] > 127:
image_v = -((~(image_f[i, 2] - 1)) & 0xFF)
else:
image_v = image_f[i, 2]
image_r = c00_yuv * image_y + c02_yuv * image_v
image_g = c10_yuv * image_y + c11_yuv * image_u + c12_yuv * image_v
image_b = c20_yuv * image_y + c21_yuv * image_u
image_r = signed_rounding(image_r, format_bit)
image_g = signed_rounding(image_g, format_bit)
image_b = signed_rounding(image_b, format_bit)
image_r = image_r >> format_bit
image_g = image_g >> format_bit
image_b = image_b >> format_bit
image_rgb_f[i, 0] = clip(image_r, 0, 255)
image_rgb_f[i, 1] = clip(image_g, 0, 255)
image_rgb_f[i, 2] = clip(image_b, 0, 255)
image_rgb = image_rgb_f.reshape((h, w, c))
return image_rgb
def _ColorConversion_RGB888_to_YCbCr444(self, image):
## floating
image = image.astype('float')
image = (image @ Matrix_rgb888_to_ycbcr + 0.5).astype('uint8')
image[:, :, 0] += 16
image[:, :, 1] += 128
image[:, :, 2] += 128
return image
def _ColorConversion_RGB888_to_YCbCr422(self, image):
# rgb888 to ycbcr444
image = self._ColorConversion_RGB888_to_YCbCr444(image)
# ycbcr444 to ycbcr422
cb2 = image[:, 0::2, 1]
cb4 = np.repeat(cb2, 2, axis=1)
cr2 = image[:, 1::2, 2]
cr4 = np.repeat(cr2, 2, axis=1)
image[..., 1] = cb4
image[..., 2] = cr4
return image
def _ColorConversion_YCbCr_to_RGB888(self, image):
## floating
if (self.set['numerical_type'] == 'floating'):
image = image.astype('float')
image[:, :, 0] -= 16
image[:, :, 1] -= 128
image[:, :, 2] -= 128
image = ((image @ Matrix_ycbcr_to_rgb888) + 0.5).astype('uint8')
return image
## fixed
h, w, c = image.shape
image_f = image.reshape((h * w, c))
image_rgb_f = np.zeros(image_f.shape, dtype=np.uint8)
for i in range(h * w):
image_y = (image_f[i, 0] - 16) * c00_ycbcr
image_cb = image_f[i, 1] - 128
image_cr = image_f[i, 2] - 128
image_r = image_y + c02_ycbcr * image_cr
image_g = image_y + c11_ycbcr * image_cb + c12_ycbcr * image_cr
image_b = image_y + c21_ycbcr * image_cb
image_r = signed_rounding(image_r, format_bit)
image_g = signed_rounding(image_g, format_bit)
image_b = signed_rounding(image_b, format_bit)
image_r = image_r >> format_bit
image_g = image_g >> format_bit
image_b = image_b >> format_bit
image_rgb_f[i, 0] = clip(image_r, 0, 255)
image_rgb_f[i, 1] = clip(image_g, 0, 255)
image_rgb_f[i, 2] = clip(image_b, 0, 255)
image_rgb = image_rgb_f.reshape((h, w, c))
return image_rgb
def _ColorConversion_RGB888_to_RGB565(self, image):
assert (len(image.shape)==3)
assert (image.shape[2]>=3)
image_rgb565 = np.zeros(image.shape, dtype=np.uint8)
image_rgb = image.astype('uint8')
image_rgb565[:, :, 0] = image_rgb[:, :, 0] >> 3
image_rgb565[:, :, 1] = image_rgb[:, :, 1] >> 2
image_rgb565[:, :, 2] = image_rgb[:, :, 2] >> 3
return image_rgb565
def _ColorConversion_RGB565_to_RGB888(self, image):
assert (len(image.shape)==3)
assert (image.shape[2]==3)
image_rgb = np.zeros(image.shape, dtype=np.uint8)
image_rgb[:, :, 0] = image[:, :, 0] << 3
image_rgb[:, :, 1] = image[:, :, 1] << 2
image_rgb[:, :, 2] = image[:, :, 2] << 3
return image_rgb
def _ColorConversion_L_to_RGB888(self, image):
image_L = image.astype('uint8')
img = Image.fromarray(image_L).convert('RGB')
image_data = np.array(img).astype('uint8')
return image_data
def _ColorConversion_RGB888_to_L(self, image):
image_rgb = image.astype('uint8')
img = Image.fromarray(image_rgb).convert('L')
image_data = np.array(img).astype('uint8')
return image_data
def _ColorConversion_RGBA8888_to_RGB888(self, image):
assert (len(image.shape)==3)
assert (image.shape[2]==4)
return image[:,:,:3]
def _ColorConversion_RGB888_to_RGBA8888(self, image):
assert (len(image.shape)==3)
assert (image.shape[2]==3)
imageA = np.concatenate((image, np.zeros((image.shape[0], image.shape[1], 1), dtype=np.uint8) ), axis=2)
return imageA

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import numpy as np
from PIL import Image
from .utils import str2int, str2float, str2bool, pad_square_to_4
from .utils_520 import round_up_n
from .Runner_base import Runner_base, Param_base
class General(Param_base):
type = 'center'
align_w_to_4 = False
pad_square_to_4 = False
rounding_type = 0
crop_w = 0
crop_h = 0
start_x = 0.
start_y = 0.
end_x = 0.
end_y = 0.
def update(self, **dic):
self.type = dic['type']
self.align_w_to_4 = str2bool(dic['align_w_to_4'])
self.rounding_type = str2int(dic['rounding_type'])
self.crop_w = str2int(dic['crop_w'])
self.crop_h = str2int(dic['crop_h'])
self.start_x = str2float(dic['start_x'])
self.start_y = str2float(dic['start_y'])
self.end_x = str2float(dic['end_x'])
self.end_y = str2float(dic['end_y'])
def __str__(self):
str_out = [
', type:',str(self.type),
', align_w_to_4:',str(self.align_w_to_4),
', pad_square_to_4:',str(self.pad_square_to_4),
', crop_w:',str(self.crop_w),
', crop_h:',str(self.crop_h),
', start_x:',str(self.start_x),
', start_y:',str(self.start_y),
', end_x:',str(self.end_x),
', end_y:',str(self.end_y)]
return(' '.join(str_out))
class runner(Runner_base):
## overwrite the class in Runner_base
general = General()
def __str__(self):
return('<Crop>')
def update(self, **kwargs):
##
super().update(**kwargs)
##
if (self.general.start_x != self.general.end_x) and (self.general.start_y != self.general.end_y):
self.general.type = 'specific'
elif(self.general.type != 'specific'):
if self.general.crop_w == 0 or self.general.crop_h == 0:
self.general.crop_w = self.common.model_size[0]
self.general.crop_h = self.common.model_size[1]
assert(self.general.crop_w > 0)
assert(self.general.crop_h > 0)
assert(self.general.type.lower() in ['CENTER', 'Center', 'center', 'CORNER', 'Corner', 'corner'])
else:
assert(self.general.type == 'specific')
def run(self, image_data):
## init
img = Image.fromarray(image_data)
w, h = img.size
## get range
if self.general.type.lower() in ['CENTER', 'Center', 'center']:
x1, y1, x2, y2 = self._calcuate_xy_center(w, h)
elif self.general.type.lower() in ['CORNER', 'Corner', 'corner']:
x1, y1, x2, y2 = self._calcuate_xy_corner(w, h)
else:
x1 = self.general.start_x
y1 = self.general.start_y
x2 = self.general.end_x
y2 = self.general.end_y
assert( ((x1 != x2) and (y1 != y2)) )
## rounding
if self.general.rounding_type == 0:
x1 = int(np.floor(x1))
y1 = int(np.floor(y1))
x2 = int(np.ceil(x2))
y2 = int(np.ceil(y2))
else:
x1 = int(round(x1))
y1 = int(round(y1))
x2 = int(round(x2))
y2 = int(round(y2))
if self.general.align_w_to_4:
# x1 = (x1+1) &(~3) #//+2
# x2 = (x2+2) &(~3) #//+1
x1 = (x1+3) &(~3) #//+2
left = w - x2
left = (left+3) &(~3)
x2 = w - left
## pad_square_to_4
if str2bool(self.general.pad_square_to_4):
x1,x2,y1,y2 = pad_square_to_4(x1,x2,y1,y2)
# do crop
box = (x1,y1,x2,y2)
img = img.crop(box)
# print info
if str2bool(self.common.print_info):
self.general.start_x = x1
self.general.start_y = y1
self.general.end_x = x2
self.general.end_y = y2
self.general.crop_w = x2 - x1
self.general.crop_h = y2 - y1
self.print_info()
# output
image_data = np.array(img)
info = {}
info['box'] = box
return image_data, info
## protect fun
def _calcuate_xy_center(self, w, h):
x1 = w/2 - self.general.crop_w / 2
y1 = h/2 - self.general.crop_h / 2
x2 = w/2 + self.general.crop_w / 2
y2 = h/2 + self.general.crop_h / 2
return x1, y1, x2, y2
def _calcuate_xy_corner(self, _1, _2):
x1 = 0
y1 = 0
x2 = self.general.crop_w
y2 = self.general.crop_h
return x1, y1, x2, y2
def do_crop(self, image_data, startW, startH, endW, endH):
return image_data[startH:endH, startW:endW, :]

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kneron/exporting/yolov5/kneron_preprocessing/funcs/Normalize.py Normal file
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import numpy as np
from .utils import str2bool, str2int, str2float, clip_ary
class runner(object):
def __init__(self):
self.set = {
'general': {
'print_info':'no',
'model_size':[0,0],
'numerical_type':'floating',
'type': 'kneron'
},
'floating':{
"scale": 1,
"bias": 0,
"mean": "",
"std": "",
},
'hw':{
"radix":8,
"shift":"",
"sub":""
}
}
return
def update(self, **kwargs):
#
self.set.update(kwargs)
#
if self.set['general']['numerical_type'] == '520':
if self.set['general']['type'].lower() in ['TF', 'Tf', 'tf']:
self.fun_normalize = self._chen_520
self.shift = 7 - self.set['hw']['radix']
self.sub = 128
elif self.set['general']['type'].lower() in ['YOLO', 'Yolo', 'yolo']:
self.fun_normalize = self._chen_520
self.shift = 8 - self.set['hw']['radix']
self.sub = 0
elif self.set['general']['type'].lower() in ['KNERON', 'Kneron', 'kneron']:
self.fun_normalize = self._chen_520
self.shift = 8 - self.set['hw']['radix']
self.sub = 128
else:
self.fun_normalize = self._chen_520
self.shift = 0
self.sub = 0
elif self.set['general']['numerical_type'] == '720':
self.fun_normalize = self._chen_720
self.shift = 0
self.sub = 0
else:
if self.set['general']['type'].lower() in ['TORCH', 'Torch', 'torch']:
self.fun_normalize = self._normalize_torch
self.set['floating']['scale'] = 255.
self.set['floating']['mean'] = [0.485, 0.456, 0.406]
self.set['floating']['std'] = [0.229, 0.224, 0.225]
elif self.set['general']['type'].lower() in ['TF', 'Tf', 'tf']:
self.fun_normalize = self._normalize_tf
self.set['floating']['scale'] = 127.5
self.set['floating']['bias'] = -1.
elif self.set['general']['type'].lower() in ['CAFFE', 'Caffe', 'caffe']:
self.fun_normalize = self._normalize_caffe
self.set['floating']['mean'] = [103.939, 116.779, 123.68]
elif self.set['general']['type'].lower() in ['YOLO', 'Yolo', 'yolo']:
self.fun_normalize = self._normalize_yolo
self.set['floating']['scale'] = 255.
elif self.set['general']['type'].lower() in ['KNERON', 'Kneron', 'kneron']:
self.fun_normalize = self._normalize_kneron
self.set['floating']['scale'] = 256.
self.set['floating']['bias'] = -0.5
else:
self.fun_normalize = self._normalize_customized
self.set['floating']['scale'] = str2float(self.set['floating']['scale'])
self.set['floating']['bias'] = str2float(self.set['floating']['bias'])
if self.set['floating']['mean'] != None:
if len(self.set['floating']['mean']) != 3:
self.set['floating']['mean'] = None
if self.set['floating']['std'] != None:
if len(self.set['floating']['std']) != 3:
self.set['floating']['std'] = None
def print_info(self):
if self.set['general']['numerical_type'] == '520':
print("<normalize>",
'numerical_type', self.set['general']['numerical_type'],
", type:", self.set['general']['type'],
', shift:',self.shift,
', sub:', self.sub)
else:
print("<normalize>",
'numerical_type', self.set['general']['numerical_type'],
", type:", self.set['general']['type'],
', scale:',self.set['floating']['scale'],
', bias:', self.set['floating']['bias'],
', mean:', self.set['floating']['mean'],
', std:',self.set['floating']['std'])
def run(self, image_data):
# print info
if str2bool(self.set['general']['print_info']):
self.print_info()
# norm
image_data = self.fun_normalize(image_data)
# output
info = {}
return image_data, info
def _normalize_torch(self, x):
if len(x.shape) != 3:
return x
x = x.astype('float')
x = x / self.set['floating']['scale']
x[..., 0] -= self.set['floating']['mean'][0]
x[..., 1] -= self.set['floating']['mean'][1]
x[..., 2] -= self.set['floating']['mean'][2]
x[..., 0] /= self.set['floating']['std'][0]
x[..., 1] /= self.set['floating']['std'][1]
x[..., 2] /= self.set['floating']['std'][2]
return x
def _normalize_tf(self, x):
# print('_normalize_tf')
x = x.astype('float')
x = x / self.set['floating']['scale']
x = x + self.set['floating']['bias']
return x
def _normalize_caffe(self, x):
if len(x.shape) != 3:
return x
x = x.astype('float')
x = x[..., ::-1]
x[..., 0] -= self.set['floating']['mean'][0]
x[..., 1] -= self.set['floating']['mean'][1]
x[..., 2] -= self.set['floating']['mean'][2]
return x
def _normalize_yolo(self, x):
# print('_normalize_yolo')
x = x.astype('float')
x = x / self.set['floating']['scale']
return x
def _normalize_kneron(self, x):
# print('_normalize_kneron')
x = x.astype('float')
x = x/self.set['floating']['scale']
x = x + self.set['floating']['bias']
return x
def _normalize_customized(self, x):
# print('_normalize_customized')
x = x.astype('float')
if self.set['floating']['scale'] != 0:
x = x/ self.set['floating']['scale']
x = x + self.set['floating']['bias']
if self.set['floating']['mean'] is not None:
x[..., 0] -= self.set['floating']['mean'][0]
x[..., 1] -= self.set['floating']['mean'][1]
x[..., 2] -= self.set['floating']['mean'][2]
if self.set['floating']['std'] is not None:
x[..., 0] /= self.set['floating']['std'][0]
x[..., 1] /= self.set['floating']['std'][1]
x[..., 2] /= self.set['floating']['std'][2]
return x
def _chen_520(self, x):
# print('_chen_520')
x = (x - self.sub).astype('uint8')
x = (np.right_shift(x,self.shift))
x=x.astype('uint8')
return x
def _chen_720(self, x):
# print('_chen_720')
if self.shift == 1:
x = x + np.array([[self.sub], [self.sub], [self.sub]])
else:
x = x + np.array([[self.sub], [self.sub], [self.sub]])
return x

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kneron/exporting/yolov5/kneron_preprocessing/funcs/Padding.py Normal file
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import numpy as np
from PIL import Image
from .utils import str2bool, str2int, str2float
from .Runner_base import Runner_base, Param_base
class General(Param_base):
type = ''
pad_val = ''
padded_w = ''
padded_h = ''
pad_l = ''
pad_r = ''
pad_t = ''
pad_b = ''
padding_ch = 3
padding_ch_type = 'RGB'
def update(self, **dic):
self.type = dic['type']
self.pad_val = dic['pad_val']
self.padded_w = str2int(dic['padded_w'])
self.padded_h = str2int(dic['padded_h'])
self.pad_l = str2int(dic['pad_l'])
self.pad_r = str2int(dic['pad_r'])
self.pad_t = str2int(dic['pad_t'])
self.pad_b = str2int(dic['pad_b'])
def __str__(self):
str_out = [
', type:',str(self.type),
', pad_val:',str(self.pad_val),
', pad_l:',str(self.pad_l),
', pad_r:',str(self.pad_r),
', pad_r:',str(self.pad_t),
', pad_b:',str(self.pad_b),
', padding_ch:',str(self.padding_ch)]
return(' '.join(str_out))
class Hw(Param_base):
radix = 8
normalize_type = 'floating'
def update(self, **dic):
self.radix = dic['radix']
self.normalize_type = dic['normalize_type']
def __str__(self):
str_out = [
', radix:', str(self.radix),
', normalize_type:',str(self.normalize_type)]
return(' '.join(str_out))
class runner(Runner_base):
## overwrite the class in Runner_base
general = General()
hw = Hw()
def __str__(self):
return('<Padding>')
def update(self, **kwargs):
super().update(**kwargs)
## update pad type & pad length
if (self.general.pad_l != 0) or (self.general.pad_r != 0) or (self.general.pad_t != 0) or (self.general.pad_b != 0):
self.general.type = 'specific'
assert(self.general.pad_l >= 0)
assert(self.general.pad_r >= 0)
assert(self.general.pad_t >= 0)
assert(self.general.pad_b >= 0)
elif(self.general.type != 'specific'):
if self.general.padded_w == 0 or self.general.padded_h == 0:
self.general.padded_w = self.common.model_size[0]
self.general.padded_h = self.common.model_size[1]
assert(self.general.padded_w > 0)
assert(self.general.padded_h > 0)
assert(self.general.type.lower() in ['CENTER', 'Center', 'center', 'CORNER', 'Corner', 'corner'])
else:
assert(self.general.type == 'specific')
## decide pad_val & padding ch
# if numerical_type is floating
if (self.common.numerical_type == 'floating'):
if self.general.pad_val != 'edge':
self.general.pad_val = str2float(self.general.pad_val)
self.general.padding_ch = 3
self.general.padding_ch_type = 'RGB'
# if numerical_type is 520 or 720
else:
if self.general.pad_val == '':
if self.hw.normalize_type.lower() in ['TF', 'Tf', 'tf']:
self.general.pad_val = np.uint8(-128 >> (7 - self.hw.radix))
elif self.hw.normalize_type.lower() in ['YOLO', 'Yolo', 'yolo']:
self.general.pad_val = np.uint8(0 >> (8 - self.hw.radix))
elif self.hw.normalize_type.lower() in ['KNERON', 'Kneron', 'kneron']:
self.general.pad_val = np.uint8(-128 >> (8 - self.hw.radix))
else:
self.general.pad_val = np.uint8(0 >> (8 - self.hw.radix))
else:
self.general.pad_val = str2int(self.general.pad_val)
self.general.padding_ch = 4
self.general.padding_ch_type = 'RGBA'
def run(self, image_data):
# init
shape = image_data.shape
w = shape[1]
h = shape[0]
if len(shape) < 3:
self.general.padding_ch = 1
self.general.padding_ch_type = 'L'
else:
if shape[2] == 3 and self.general.padding_ch == 4:
image_data = np.concatenate((image_data, np.zeros((h, w, 1), dtype=np.uint8) ), axis=2)
## padding
if self.general.type.lower() in ['CENTER', 'Center', 'center']:
img_pad = self._padding_center(image_data, w, h)
elif self.general.type.lower() in ['CORNER', 'Corner', 'corner']:
img_pad = self._padding_corner(image_data, w, h)
else:
img_pad = self._padding_sp(image_data, w, h)
# print info
if str2bool(self.common.print_info):
self.print_info()
# output
info = {}
return img_pad, info
## protect fun
def _padding_center(self, img, ori_w, ori_h):
# img_pad = Image.new(self.general.padding_ch_type, (self.general.padded_w, self.general.padded_h), int(self.general.pad_val[0]))
# img = Image.fromarray(img)
# img_pad.paste(img, ((self.general.padded_w-ori_w)//2, (self.general.padded_h-ori_h)//2))
# return img_pad
padH = self.general.padded_h - ori_h
padW = self.general.padded_w - ori_w
self.general.pad_t = padH // 2
self.general.pad_b = (padH // 2) + (padH % 2)
self.general.pad_l = padW // 2
self.general.pad_r = (padW // 2) + (padW % 2)
if self.general.pad_l < 0 or self.general.pad_r <0 or self.general.pad_t <0 or self.general.pad_b<0:
return img
img_pad = self._padding_sp(img,ori_w,ori_h)
return img_pad
def _padding_corner(self, img, ori_w, ori_h):
# img_pad = Image.new(self.general.padding_ch_type, (self.general.padded_w, self.general.padded_h), self.general.pad_val)
# img_pad.paste(img, (0, 0))
self.general.pad_l = 0
self.general.pad_r = self.general.padded_w - ori_w
self.general.pad_t = 0
self.general.pad_b = self.general.padded_h - ori_h
if self.general.pad_l < 0 or self.general.pad_r <0 or self.general.pad_t <0 or self.general.pad_b<0:
return img
img_pad = self._padding_sp(img,ori_w,ori_h)
return img_pad
def _padding_sp(self, img, ori_w, ori_h):
# block_t = np.zeros((self.general.pad_t, self.general.pad_l + self.general.pad_r + ori_w, self.general.padding_ch), dtype=np.float)
# block_l = np.zeros((ori_h, self.general.pad_l, self.general.padding_ch), dtype=np.float)
# block_r = np.zeros((ori_h, self.general.pad_r, self.general.padding_ch), dtype=np.float)
# block_b = np.zeros((self.general.pad_b, self.general.pad_l + self.general.pad_r + ori_w, self.general.padding_ch), dtype=np.float)
# for i in range(self.general.padding_ch):
# block_t[:, :, i] = np.ones(block_t[:, :, i].shape, dtype=np.float) * self.general.pad_val
# block_l[:, :, i] = np.ones(block_l[:, :, i].shape, dtype=np.float) * self.general.pad_val
# block_r[:, :, i] = np.ones(block_r[:, :, i].shape, dtype=np.float) * self.general.pad_val
# block_b[:, :, i] = np.ones(block_b[:, :, i].shape, dtype=np.float) * self.general.pad_val
# padded_image_hor = np.concatenate((block_l, img, block_r), axis=1)
# padded_image = np.concatenate((block_t, padded_image_hor, block_b), axis=0)
# return padded_image
if self.general.padding_ch == 1:
pad_range = ( (self.general.pad_t, self.general.pad_b),(self.general.pad_l, self.general.pad_r) )
else:
pad_range = ((self.general.pad_t, self.general.pad_b),(self.general.pad_l, self.general.pad_r),(0,0))
if isinstance(self.general.pad_val, str):
if self.general.pad_val == 'edge':
padded_image = np.pad(img, pad_range, mode="edge")
else:
padded_image = np.pad(img, pad_range, mode="constant",constant_values=0)
else:
padded_image = np.pad(img, pad_range, mode="constant",constant_values=self.general.pad_val)
return padded_image

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kneron/exporting/yolov5/kneron_preprocessing/funcs/Resize.py Normal file
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import numpy as np
import cv2
from PIL import Image
from .utils import str2bool, str2int
from ctypes import c_float
from .Runner_base import Runner_base, Param_base
class General(Param_base):
type = 'bilinear'
keep_ratio = True
zoom = True
calculate_ratio_using_CSim = True
resize_w = 0
resize_h = 0
resized_w = 0
resized_h = 0
def update(self, **dic):
self.type = dic['type']
self.keep_ratio = str2bool(dic['keep_ratio'])
self.zoom = str2bool(dic['zoom'])
self.calculate_ratio_using_CSim = str2bool(dic['calculate_ratio_using_CSim'])
self.resize_w = str2int(dic['resize_w'])
self.resize_h = str2int(dic['resize_h'])
def __str__(self):
str_out = [
', type:',str(self.type),
', keep_ratio:',str(self.keep_ratio),
', zoom:',str(self.zoom),
', calculate_ratio_using_CSim:',str(self.calculate_ratio_using_CSim),
', resize_w:',str(self.resize_w),
', resize_h:',str(self.resize_h),
', resized_w:',str(self.resized_w),
', resized_h:',str(self.resized_h)]
return(' '.join(str_out))
class Hw(Param_base):
resize_bit = 12
def update(self, **dic):
pass
def __str__(self):
str_out = [
', resize_bit:',str(self.resize_bit)]
return(' '.join(str_out))
class runner(Runner_base):
## overwrite the class in Runner_base
general = General()
hw = Hw()
def __str__(self):
return('<Resize>')
def update(self, **kwargs):
super().update(**kwargs)
## if resize size has not been assigned, then it will take model size as resize size
if self.general.resize_w == 0 or self.general.resize_h == 0:
self.general.resize_w = self.common.model_size[0]
self.general.resize_h = self.common.model_size[1]
assert(self.general.resize_w > 0)
assert(self.general.resize_h > 0)
##
if self.common.numerical_type == '520':
self.general.type = 'fixed_520'
elif self.common.numerical_type == '720':
self.general.type = 'fixed_720'
assert(self.general.type.lower() in ['BILINEAR', 'Bilinear', 'bilinear', 'BICUBIC', 'Bicubic', 'bicubic', 'FIXED', 'Fixed', 'fixed', 'FIXED_520', 'Fixed_520', 'fixed_520', 'FIXED_720', 'Fixed_720', 'fixed_720','CV', 'cv', 'opencv', 'OpenCV', 'CV2', 'cv2'])
def run(self, image_data):
## init
ori_w = image_data.shape[1]
ori_h = image_data.shape[0]
info = {}
##
if self.general.keep_ratio:
self.general.resized_w, self.general.resized_h = self.calcuate_scale_keep_ratio(self.general.resize_w,self.general.resize_h, ori_w, ori_h, self.general.calculate_ratio_using_CSim)
else:
self.general.resized_w = int(self.general.resize_w)
self.general.resized_h = int(self.general.resize_h)
assert(self.general.resized_w > 0)
assert(self.general.resized_h > 0)
##
if (self.general.resized_w > ori_w) or (self.general.resized_h > ori_h):
if not self.general.zoom:
info['size'] = (ori_w,ori_h)
if str2bool(self.common.print_info):
print('no resize')
self.print_info()
return image_data, info
## resize
if self.general.type.lower() in ['BILINEAR', 'Bilinear', 'bilinear']:
image_data = self.do_resize_bilinear(image_data, self.general.resized_w, self.general.resized_h)
elif self.general.type.lower() in ['BICUBIC', 'Bicubic', 'bicubic']:
image_data = self.do_resize_bicubic(image_data, self.general.resized_w, self.general.resized_h)
elif self.general.type.lower() in ['CV', 'cv', 'opencv', 'OpenCV', 'CV2', 'cv2']:
image_data = self.do_resize_cv2(image_data, self.general.resized_w, self.general.resized_h)
elif self.general.type.lower() in ['FIXED', 'Fixed', 'fixed', 'FIXED_520', 'Fixed_520', 'fixed_520', 'FIXED_720', 'Fixed_720', 'fixed_720']:
image_data = self.do_resize_fixed(image_data, self.general.resized_w, self.general.resized_h, self.hw.resize_bit, self.general.type)
# output
info['size'] = (self.general.resized_w, self.general.resized_h)
# print info
if str2bool(self.common.print_info):
self.print_info()
return image_data, info
def calcuate_scale_keep_ratio(self, tar_w, tar_h, ori_w, ori_h, calculate_ratio_using_CSim):
if not calculate_ratio_using_CSim:
scale_w = tar_w * 1.0 / ori_w*1.0
scale_h = tar_h * 1.0 / ori_h*1.0
scale = scale_w if scale_w < scale_h else scale_h
new_w = int(round(ori_w * scale))
new_h = int(round(ori_h * scale))
return new_w, new_h
## calculate_ratio_using_CSim
scale_w = c_float(tar_w * 1.0 / (ori_w * 1.0)).value
scale_h = c_float(tar_h * 1.0 / (ori_h * 1.0)).value
scale_ratio = 0.0
scale_target_w = 0
scale_target_h = 0
padH = 0
padW = 0
bScaleW = True if scale_w < scale_h else False
if bScaleW:
scale_ratio = scale_w
scale_target_w = int(c_float(scale_ratio * ori_w + 0.5).value)
scale_target_h = int(c_float(scale_ratio * ori_h + 0.5).value)
assert (abs(scale_target_w - tar_w) <= 1), "Error: scale down width cannot meet expectation\n"
padH = tar_h - scale_target_h
padW = 0
assert (padH >= 0), "Error: padH shouldn't be less than zero\n"
else:
scale_ratio = scale_h
scale_target_w = int(c_float(scale_ratio * ori_w + 0.5).value)
scale_target_h = int(c_float(scale_ratio * ori_h + 0.5).value)
assert (abs(scale_target_h - tar_h) <= 1), "Error: scale down height cannot meet expectation\n"
padW = tar_w - scale_target_w
padH = 0
assert (padW >= 0), "Error: padW shouldn't be less than zero\n"
new_w = tar_w - padW
new_h = tar_h - padH
return new_w, new_h
def do_resize_bilinear(self, image_data, resized_w, resized_h):
img = Image.fromarray(image_data)
img = img.resize((resized_w, resized_h), Image.BILINEAR)
image_data = np.array(img).astype('uint8')
return image_data
def do_resize_bicubic(self, image_data, resized_w, resized_h):
img = Image.fromarray(image_data)
img = img.resize((resized_w, resized_h), Image.BICUBIC)
image_data = np.array(img).astype('uint8')
return image_data
def do_resize_cv2(self, image_data, resized_w, resized_h):
image_data = cv2.resize(image_data, (resized_w, resized_h))
image_data = np.array(image_data)
# image_data = np.array(image_data).astype('uint8')
return image_data
def do_resize_fixed(self, image_data, resized_w, resized_h, resize_bit, type):
if len(image_data.shape) < 3:
m, n = image_data.shape
tmp = np.zeros((m,n,3), dtype=np.uint8)
tmp[:,:,0] = image_data
image_data = tmp
c = 3
gray = True
else:
m, n, c = image_data.shape
gray = False
resolution = 1 << resize_bit
# Width
ratio = int(((n - 1) << resize_bit) / (resized_w - 1))
ratio_cnt = 0
src_x = 0
resized_image_w = np.zeros((m, resized_w, c), dtype=np.uint8)
for dst_x in range(resized_w):
while ratio_cnt > resolution:
ratio_cnt = ratio_cnt - resolution
src_x = src_x + 1
mul1 = np.ones((m, c)) * (resolution - ratio_cnt)
mul2 = np.ones((m, c)) * ratio_cnt
resized_image_w[:, dst_x, :] = np.multiply(np.multiply(
image_data[:, src_x, :], mul1) + np.multiply(image_data[:, src_x + 1, :], mul2), 1/resolution)
ratio_cnt = ratio_cnt + ratio
# Height
ratio = int(((m - 1) << resize_bit) / (resized_h - 1))
## NPU HW special case 2 , only on 520
if type.lower() in ['FIXED_520', 'Fixed_520', 'fixed_520']:
if (((ratio * (resized_h - 1)) % 4096 == 0) and ratio != 4096):
ratio -= 1
ratio_cnt = 0
src_x = 0
resized_image = np.zeros(
(resized_h, resized_w, c), dtype=np.uint8)
for dst_x in range(resized_h):
while ratio_cnt > resolution:
ratio_cnt = ratio_cnt - resolution
src_x = src_x + 1
mul1 = np.ones((resized_w, c)) * (resolution - ratio_cnt)
mul2 = np.ones((resized_w, c)) * ratio_cnt
## NPU HW special case 1 , both on 520 / 720
if (((dst_x > 0) and ratio_cnt == resolution) and (ratio != resolution)):
if type.lower() in ['FIXED_520', 'Fixed_520', 'fixed_520','FIXED_720', 'Fixed_720', 'fixed_720' ]:
resized_image[dst_x, :, :] = np.multiply(np.multiply(
resized_image_w[src_x+1, :, :], mul1) + np.multiply(resized_image_w[src_x + 2, :, :], mul2), 1/resolution)
else:
resized_image[dst_x, :, :] = np.multiply(np.multiply(
resized_image_w[src_x, :, :], mul1) + np.multiply(resized_image_w[src_x + 1, :, :], mul2), 1/resolution)
ratio_cnt = ratio_cnt + ratio
if gray:
resized_image = resized_image[:,:,0]
return resized_image

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kneron/exporting/yolov5/kneron_preprocessing/funcs/Rotate.py Normal file
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import numpy as np
from .utils import str2bool, str2int
class runner(object):
def __init__(self, *args, **kwargs):
self.set = {
'operator': '',
"rotate_direction": 0,
}
self.update(*args, **kwargs)
def update(self, *args, **kwargs):
self.set.update(kwargs)
self.rotate_direction = str2int(self.set['rotate_direction'])
# print info
if str2bool(self.set['b_print']):
self.print_info()
def print_info(self):
print("<rotate>",
'rotate_direction', self.rotate_direction,)
def run(self, image_data):
image_data = self._rotate(image_data)
return image_data
def _rotate(self,img):
if self.rotate_direction == 1 or self.rotate_direction == 2:
col, row, unit = img.shape
pInBuf = img.reshape((-1,1))
pOutBufTemp = np.zeros((col* row* unit))
for r in range(row):
for c in range(col):
for u in range(unit):
if self.rotate_direction == 1:
pOutBufTemp[unit * (c * row + (row - r - 1))+u] = pInBuf[unit * (r * col + c)+u]
elif self.rotate_direction == 2:
pOutBufTemp[unit * (row * (col - c - 1) + r)+u] = pInBuf[unit * (r * col + c)+u]
img = pOutBufTemp.reshape((col,row,unit))
return img

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kneron/exporting/yolov5/kneron_preprocessing/funcs/Runner_base.py Normal file
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from abc import ABCMeta, abstractmethod
class Param_base(object):
@abstractmethod
def update(self,**dic):
raise NotImplementedError("Must override")
def load_dic(self, key, **dic):
if key in dic:
param = eval('self.'+key)
param = dic[key]
def __str__(self):
str_out = []
return(' '.join(str_out))
class Common(Param_base):
print_info = False
model_size = [0,0]
numerical_type = 'floating'
def update(self, **dic):
self.print_info = dic['print_info']
self.model_size = dic['model_size']
self.numerical_type = dic['numerical_type']
def __str__(self):
str_out = ['numerical_type:',str(self.numerical_type)]
return(' '.join(str_out))
class Runner_base(metaclass=ABCMeta):
common = Common()
general = Param_base()
floating = Param_base()
hw = Param_base()
def update(self, **kwargs):
## update param
self.common.update(**kwargs['common'])
self.general.update(**kwargs['general'])
assert(self.common.numerical_type.lower() in ['floating', '520', '720'])
if (self.common.numerical_type == 'floating'):
if (self.floating.__class__.__name__ != 'Param_base'):
self.floating.update(**kwargs['floating'])
else:
if (self.hw.__class__.__name__ != 'Param_base'):
self.hw.update(**kwargs['hw'])
def print_info(self):
if (self.common.numerical_type == 'floating'):
print(self, self.common, self.general, self.floating)
else:
print(self, self.common, self.general, self.hw)

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from . import ColorConversion, Padding, Resize, Crop, Normalize, Rotate

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import numpy as np
from PIL import Image
import struct
def pad_square_to_4(x_start, x_end, y_start, y_end):
w_int = x_end - x_start
h_int = y_end - y_start
pad = w_int - h_int
if pad > 0:
pad_s = (pad >> 1) &(~3)
pad_e = pad - pad_s
y_start -= pad_s
y_end += pad_e
else:#//pad <=0
pad_s = -(((pad) >> 1) &(~3))
pad_e = (-pad) - pad_s
x_start -= pad_s
x_end += pad_e
return x_start, x_end, y_start, y_end
def str_fill(value):
if len(value) == 1:
value = "0" + value
elif len(value) == 0:
value = "00"
return value
def clip_ary(value):
list_v = []
for i in range(len(value)):
v = value[i] % 256
list_v.append(v)
return list_v
def str2bool(v):
if isinstance(v,bool):
return v
return v.lower() in ('TRUE', 'True', 'true', '1', 'T', 't', 'Y', 'YES', 'y', 'yes')
def str2int(s):
if s == "":
s = 0
s = int(s)
return s
def str2float(s):
if s == "":
s = 0
s = float(s)
return s
def clip(value, mini, maxi):
if value < mini:
result = mini
elif value > maxi:
result = maxi
else:
result = value
return result
def clip_ary(value):
list_v = []
for i in range(len(value)):
v = value[i] % 256
list_v.append(v)
return list_v
def signed_rounding(value, bit):
if value < 0:
value = value - (1 << (bit - 1))
else:
value = value + (1 << (bit - 1))
return value
def hex_loader(data_folder,**kwargs):
format_mode = kwargs['raw_img_fmt']
src_h = kwargs['img_in_height']
src_w = kwargs['img_in_width']
if format_mode in ['YUV444', 'yuv444', 'YCBCR444', 'YCbCr444', 'ycbcr444']:
output = hex_yuv444(data_folder,src_h,src_w)
elif format_mode in ['RGB565', 'rgb565']:
output = hex_rgb565(data_folder,src_h,src_w)
elif format_mode in ['YUV422', 'yuv422', 'YCBCR422', 'YCbCr422', 'ycbcr422']:
output = hex_yuv422(data_folder,src_h,src_w)
return output
def hex_rgb565(hex_folder,src_h,src_w):
pix_per_line = 8
byte_per_line = 16
f = open(hex_folder)
pixel_r = []
pixel_g = []
pixel_b = []
# Ignore the first line
f.readline()
input_line = int((src_h * src_w)/pix_per_line)
for i in range(input_line):
readline = f.readline()
for j in range(int(byte_per_line/2)-1, -1, -1):
data1 = int(readline[(j * 4 + 0):(j * 4 + 2)], 16)
data0 = int(readline[(j * 4 + 2):(j * 4 + 4)], 16)
r = ((data1 & 0xf8) >> 3)
g = (((data0 & 0xe0) >> 5) + ((data1 & 0x7) << 3))
b = (data0 & 0x1f)
pixel_r.append(r)
pixel_g.append(g)
pixel_b.append(b)
ary_r = np.array(pixel_r, dtype=np.uint8)
ary_g = np.array(pixel_g, dtype=np.uint8)
ary_b = np.array(pixel_b, dtype=np.uint8)
output = np.concatenate((ary_r[:, None], ary_g[:, None], ary_b[:, None]), axis=1)
output = output.reshape((src_h, src_w, 3))
return output
def hex_yuv444(hex_folder,src_h,src_w):
pix_per_line = 4
byte_per_line = 16
f = open(hex_folder)
byte0 = []
byte1 = []
byte2 = []
byte3 = []
# Ignore the first line
f.readline()
input_line = int((src_h * src_w)/pix_per_line)
for i in range(input_line):
readline = f.readline()
for j in range(byte_per_line-1, -1, -1):
data = int(readline[(j*2):(j*2+2)], 16)
if (j+1) % 4 == 0:
byte0.append(data)
elif (j+2) % 4 == 0:
byte1.append(data)
elif (j+3) % 4 == 0:
byte2.append(data)
elif (j+4) % 4 == 0:
byte3.append(data)
# ary_a = np.array(byte0, dtype=np.uint8)
ary_v = np.array(byte1, dtype=np.uint8)
ary_u = np.array(byte2, dtype=np.uint8)
ary_y = np.array(byte3, dtype=np.uint8)
output = np.concatenate((ary_y[:, None], ary_u[:, None], ary_v[:, None]), axis=1)
output = output.reshape((src_h, src_w, 3))
return output
def hex_yuv422(hex_folder,src_h,src_w):
pix_per_line = 8
byte_per_line = 16
f = open(hex_folder)
pixel_y = []
pixel_u = []
pixel_v = []
# Ignore the first line
f.readline()
input_line = int((src_h * src_w)/pix_per_line)
for i in range(input_line):
readline = f.readline()
for j in range(int(byte_per_line/4)-1, -1, -1):
data3 = int(readline[(j * 8 + 0):(j * 8 + 2)], 16)
data2 = int(readline[(j * 8 + 2):(j * 8 + 4)], 16)
data1 = int(readline[(j * 8 + 4):(j * 8 + 6)], 16)
data0 = int(readline[(j * 8 + 6):(j * 8 + 8)], 16)
pixel_y.append(data3)
pixel_y.append(data1)
pixel_u.append(data2)
pixel_u.append(data2)
pixel_v.append(data0)
pixel_v.append(data0)
ary_y = np.array(pixel_y, dtype=np.uint8)
ary_u = np.array(pixel_u, dtype=np.uint8)
ary_v = np.array(pixel_v, dtype=np.uint8)
output = np.concatenate((ary_y[:, None], ary_u[:, None], ary_v[:, None]), axis=1)
output = output.reshape((src_h, src_w, 3))
return output
def bin_loader(data_folder,**kwargs):
format_mode = kwargs['raw_img_fmt']
src_h = kwargs['img_in_height']
src_w = kwargs['img_in_width']
if format_mode in ['YUV','yuv','YUV444', 'yuv444', 'YCBCR','YCbCr','ycbcr','YCBCR444', 'YCbCr444', 'ycbcr444']:
output = bin_yuv444(data_folder,src_h,src_w)
elif format_mode in ['RGB565', 'rgb565']:
output = bin_rgb565(data_folder,src_h,src_w)
elif format_mode in ['NIR', 'nir','NIR888', 'nir888']:
output = bin_nir(data_folder,src_h,src_w)
elif format_mode in ['YUV422', 'yuv422', 'YCBCR422', 'YCbCr422', 'ycbcr422']:
output = bin_yuv422(data_folder,src_h,src_w)
elif format_mode in ['RGB888','rgb888']:
output = np.fromfile(data_folder, dtype='uint8')
output = output.reshape(src_h,src_w,3)
elif format_mode in ['RGBA8888','rgba8888', 'RGBA' , 'rgba']:
output_temp = np.fromfile(data_folder, dtype='uint8')
output_temp = output_temp.reshape(src_h,src_w,4)
output = output_temp[:,:,0:3]
return output
def bin_yuv444(in_img_path,src_h,src_w):
# load bin
struct_fmt = '1B'
struct_len = struct.calcsize(struct_fmt)
struct_unpack = struct.Struct(struct_fmt).unpack_from
row = src_h
col = src_w
pixels = row*col
raw = []
with open(in_img_path, "rb") as f:
while True:
data = f.read(struct_len)
if not data: break
s = struct_unpack(data)
raw.append(s[0])
raw = raw[:pixels*4]
#
output = np.zeros((pixels * 3), dtype=np.uint8)
cnt = 0
for i in range(0, pixels*4, 4):
#Y
output[cnt] = raw[i+3]
#U
cnt += 1
output[cnt] = raw[i+2]
#V
cnt += 1
output[cnt] = raw[i+1]
cnt += 1
output = output.reshape((src_h,src_w,3))
return output
def bin_yuv422(in_img_path,src_h,src_w):
# load bin
struct_fmt = '1B'
struct_len = struct.calcsize(struct_fmt)
struct_unpack = struct.Struct(struct_fmt).unpack_from
row = src_h
col = src_w
pixels = row*col
raw = []
with open(in_img_path, "rb") as f:
while True:
data = f.read(struct_len)
if not data: break
s = struct_unpack(data)
raw.append(s[0])
raw = raw[:pixels*2]
#
output = np.zeros((pixels * 3), dtype=np.uint8)
cnt = 0
for i in range(0, pixels*2, 4):
#Y0
output[cnt] = raw[i+3]
#U0
cnt += 1
output[cnt] = raw[i+2]
#V0
cnt += 1
output[cnt] = raw[i]
#Y1
cnt += 1
output[cnt] = raw[i+1]
#U1
cnt += 1
output[cnt] = raw[i+2]
#V1
cnt += 1
output[cnt] = raw[i]
cnt += 1
output = output.reshape((src_h,src_w,3))
return output
def bin_rgb565(in_img_path,src_h,src_w):
# load bin
struct_fmt = '1B'
struct_len = struct.calcsize(struct_fmt)
struct_unpack = struct.Struct(struct_fmt).unpack_from
row = src_h
col = src_w
pixels = row*col
rgba565 = []
with open(in_img_path, "rb") as f:
while True:
data = f.read(struct_len)
if not data: break
s = struct_unpack(data)
rgba565.append(s[0])
rgba565 = rgba565[:pixels*2]
# rgb565_bin to numpy_array
output = np.zeros((pixels * 3), dtype=np.uint8)
cnt = 0
for i in range(0, pixels*2, 2):
temp = rgba565[i]
temp2 = rgba565[i+1]
#R-5
output[cnt] = (temp2 >>3)
#G-6
cnt += 1
output[cnt] = ((temp & 0xe0) >> 5) + ((temp2 & 0x07) << 3)
#B-5
cnt += 1
output[cnt] = (temp & 0x1f)
cnt += 1
output = output.reshape((src_h,src_w,3))
return output
def bin_nir(in_img_path,src_h,src_w):
# load bin
struct_fmt = '1B'
struct_len = struct.calcsize(struct_fmt)
struct_unpack = struct.Struct(struct_fmt).unpack_from
nir = []
with open(in_img_path, "rb") as f:
while True:
data = f.read(struct_len)
if not data: break
s = struct_unpack(data)
nir.append(s[0])
nir = nir[:src_h*src_w]
pixels = len(nir)
# nir_bin to numpy_array
output = np.zeros((len(nir) * 3), dtype=np.uint8)
for i in range(0, pixels):
output[i*3]=nir[i]
output[i*3+1]=nir[i]
output[i*3+2]=nir[i]
output = output.reshape((src_h,src_w,3))
return output

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kneron/exporting/yolov5/kneron_preprocessing/funcs/utils_520.py Normal file
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import math
def round_up_16(num):
return ((num + (16 - 1)) & ~(16 - 1))
def round_up_n(num, n):
if (num > 0):
temp = float(num) / n
return math.ceil(temp) * n
else:
return -math.ceil(float(-num) / n) * n
def cal_img_row_offset(crop_num, pad_num, start_row, out_row, orig_row):
scaled_img_row = int(out_row - (pad_num[1] + pad_num[3]))
if ((start_row - pad_num[1]) > 0):
img_str_row = int((start_row - pad_num[1]))
else:
img_str_row = 0
valid_row = int(orig_row - (crop_num[1] + crop_num[3]))
img_str_row = int(valid_row * img_str_row / scaled_img_row)
return int(img_str_row + crop_num[1])
def get_pad_num(pad_num_orig, left, up, right, bottom):
pad_num = [0]*4
for i in range(0,4):
pad_num[i] = pad_num_orig[i]
if not (left):
pad_num[0] = 0
if not (up):
pad_num[1] = 0
if not (right):
pad_num[2] = 0
if not (bottom):
pad_num[3] = 0
return pad_num
def get_byte_per_pixel(raw_fmt):
if raw_fmt.lower() in ['RGB888', 'rgb888', 'RGB', 'rgb888']:
return 4
elif raw_fmt.lower() in ['YUV', 'yuv', 'YUV422', 'yuv422']:
return 2
elif raw_fmt.lower() in ['RGB565', 'rgb565']:
return 2
elif raw_fmt.lower() in ['NIR888', 'nir888', 'NIR', 'nir']:
return 1
else:
return -1

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kneron/exporting/yolov5/kneron_preprocessing/funcs/utils_720.py Normal file
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import numpy as np
from PIL import Image
def twos_complement(value):
value = int(value)
# msb = (value & 0x8000) * (1/np.power(2, 15))
msb = (value & 0x8000) >> 15
if msb == 1:
if (((~value) & 0xFFFF) + 1) >= 0xFFFF:
result = ((~value) & 0xFFFF)
else:
result = (((~value) & 0xFFFF) + 1)
result = result * (-1)
else:
result = value
return result
def twos_complement_pix(value):
h, _ = value.shape
for i in range(h):
value[i, 0] = twos_complement(value[i, 0])
return value
def clip(value, mini, maxi):
if value < mini:
result = mini
elif value > maxi:
result = maxi
else:
result = value
return result
def clip_pix(value, mini, maxi):
h, _ = value.shape
for i in range(h):
value[i, 0] = clip(value[i, 0], mini, maxi)
return value

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kneron/exporting/yolov5/quantize_yolov5.py Normal file
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import os
import numpy as np
import torch
import ktc # Kneron Toolchain
from yolov5_preprocess import Yolov5_preprocess # 使用你的預處理
import kneron_preprocessing
# 設定裝置
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 設定圖片大小（與訓練時一致）
imgsz_h, imgsz_w = 640, 640
# 量化數據集目錄（請確保這個資料夾存在）
data_path = "/data50"
img_list = []
# 設定 ONNX 模型路徑（確保這個路徑在 Docker 內部是否正確）
onnx_model_path = "/workspace/yolov5/latest.opt.onnx"
# **初始化 Kneron ModelConfig 物件**
km = ktc.ModelConfig(20008, "0001", "720", onnx_model=onnx_model_path)
# 遍歷 data50 並進行預處理
for root, _, files in os.walk(data_path):
for f in files:
fullpath = os.path.join(root, f)
# 執行與訓練相同的預處理
img_data, _ = Yolov5_preprocess(fullpath, device, imgsz_h, imgsz_w)
# 確保格式為 NumPy 陣列
img_data = img_data.cpu().numpy()
print(f"Processed: {fullpath}")
img_list.append(img_data)
# 轉為 NumPy 格式
img_list = np.array(img_list)
# **執行 BIE 量化分析**
bie_model_path = km.analysis({"input": img_list})
# 輸出成功訊息
print("\n✅ Fixed-point analysis done! BIE model saved to:", bie_model_path)

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(1)yolov5_app.py for ploting moldel inference results
cd applications
python yolov5_app.py
(2)yolov5_evaluation.py for evaluating moldel mAP at hw_repo
cd applications
python yolov5_evaluation.py
#mAP @ yolov5s_v2_op9_sig_batch1_input05_640x640_nearest_convert.onnx with(upsampling rearest)
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.346
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.533
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.372
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.196
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.391
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.442
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.279
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.456
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.503
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.320
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.557
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.618
(3)yolov5_compare_pth_onnx.py for comparing the results of pytorch moldel and onnx model
cd applications
python yolov5_compare_pth_onnx.py
(4)v2 model is in the below link.
10.200.210.221:/mnt/models/Object_models/YOLOv5/yolov5s_v2_state_dict_input05.pt
10.200.210.221:/mnt/models/Object_models/YOLOv5/yolov5s_v2_op9_sig_batch1_input05_640x640_nearest_convert.onnx
(5)the parameters setting
(5.1)In order to get high mAP in coco val2017, please use
101620_yolov5_init_params.json
{
"model_path": "/mnt/models/Object_models/YOLOv5/yolov5s_v2_state_dict_input05.pt",
"grid20_path": "/mnt/models/Object_models/YOLOv5/20_640x640.npy",
"grid40_path": "/mnt/models/Object_models/YOLOv5/40_640x640.npy",
"grid80_path": "/mnt/models/Object_models/YOLOv5/80_640x640.npy",
"num_classes": 80,
"imgsz_h": 640,
"imgsz_w": 640,
"conf_thres": 0.001,
"iou_thres": 0.65,
"top_k_num": 3000
}
(5.2)For video usage scenarios, please use
102320_yolov5_init_params.json
{
"model_path": "/mnt/models/Object_models/YOLOv5/yolov5s_v2_state_dict_input05.pt",
"grid20_path": "/mnt/models/Object_models/YOLOv5/20_640x352.npy",
"grid40_path": "/mnt/models/Object_models/YOLOv5/40_640x352.npy",
"grid80_path": "/mnt/models/Object_models/YOLOv5/80_640x352.npy",
"num_classes": 80,
"imgsz_h": 352,
"imgsz_w": 640,
"conf_thres": 0.3,
"iou_thres": 0.5,
"top_k_num": 3000
}
(5.3)The differences of above setting are
(5.3.1) Video uses input (640w*352h) to run faster.
Coco has high or flat wide images, so it is better to use input (640w*640h)
(5.3.2) Using the yolov5 official website setting test coco val2017, the confidence setting is low "conf_thres": 0.001, and the iou setting of NMS is high "iou_thres": 0.65, which gets a better mAP.
But running video needs to be set to "conf_thres": 0.3, so that there are not too many false positives, and the iou setting of NMS "iou_thres": 0.5 is more friendly to close objects

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import argparse
from copy import deepcopy
import torch
#from experimental import *
from .common import *
#from .common_v3 import *
from pathlib import Path
import math
import yaml
class Detect(nn.Module):
def __init__(self, nc=80, anchors=(), ch=()): # detection layer
super(Detect, self).__init__()
self.stride = None # strides computed during build
self.nc = nc # number of classes
self.no = nc + 5 # number of outputs per anchor
self.nl = len(anchors) # number of detection layers
self.na = len(anchors[0]) // 2 # number of anchors
self.grid = [torch.zeros(1)] * self.nl # init grid
a = torch.tensor(anchors).float().view(self.nl, -1, 2)
self.register_buffer('anchors', a) # shape(nl,na,2)
self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2)
self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
self.export = False # onnx export
def forward(self, x):
# x = x.copy() # for profiling
z = [] # inference output
self.training |= self.export
for i in range(self.nl):
x[i] = self.m[i](x[i]) # conv
# bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
# x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
x[i] = x[i].sigmoid()
# return x if self.training else (torch.cat(z, 1), x)
return x
@staticmethod
def _make_grid(nx=20, ny=20):
yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
class Model(nn.Module):
def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None): # model, input channels, number of classes
super(Model, self).__init__()
with open(cfg) as f:
self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict
# Define model
if nc and nc != self.yaml['nc']:
print('Overriding %s nc=%g with nc=%g' % (cfg, self.yaml['nc'], nc))
self.yaml['nc'] = nc # override yaml value
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist, ch_out
# print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
# Build strides, anchors
m = self.model[-1] # Detect()
if isinstance(m, Detect):
s = 128 # 2x min stride
#m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward
# FocusNoSliceCat
m.stride = torch.tensor([8.0,16.0,32.0]) # forward
m.anchors /= m.stride.view(-1, 1, 1)
check_anchor_order(m)
self.stride = m.stride
self._initialize_biases() # only run once
# print('Strides: %s' % m.stride.tolist())
# Init weights, biases
initialize_weights(self)
def forward(self, x, augment=False, profile=False):
y, dt = [], [] # outputs
for m in self.model:
if m.f != -1: # if not from previous layer
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
x = m(x) # run
y.append(x if m.i in self.save else None) # save output
return x
def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency
# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
m = self.model[-1] # Detect() module
for mi, s in zip(m.m, m.stride): #  from
b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85)
b[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
b[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls
mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
def make_divisible(x, divisor):
# Returns x evenly divisble by divisor
return math.ceil(x / divisor) * divisor
def check_anchor_order(m):
# Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
a = m.anchor_grid.prod(-1).view(-1) # anchor area
da = a[-1] - a[0] # delta a
ds = m.stride[-1] - m.stride[0] # delta s
if da.sign() != ds.sign(): # same order
print('Reversing anchor order')
m.anchors[:] = m.anchors.flip(0)
m.anchor_grid[:] = m.anchor_grid.flip(0)
def initialize_weights(model):
for m in model.modules():
t = type(m)
if t is nn.Conv2d:
pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif t is nn.BatchNorm2d:
m.eps = 1e-3
m.momentum = 0.03
elif t in [nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
m.inplace = True
def parse_model(d, ch): # model_dict, input_channels(3) #original
#print('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args
m = eval(m) if isinstance(m, str) else m # eval strings
for j, a in enumerate(args):
try:
args[j] = eval(a) if isinstance(a, str) else a # eval strings
except:
pass
n = max(round(n * gd), 1) if n > 1 else n # depth gain
if m in [nn.Conv2d, Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP]:
#print('*m',m)
c1, c2 = ch[f], args[0]
c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
args = [c1, c2, *args[1:]]
if m in [BottleneckCSP]:
args.insert(2, n)
n = 1
elif m is nn.BatchNorm2d:
args = [ch[f]]
elif m is Concat:
c2 = sum([ch[-1 if x == -1 else x + 1] for x in f])
elif m is Detect:
args.append([ch[x + 1] for x in f])
if isinstance(args[1], int): # number of anchors
args[1] = [list(range(args[1] * 2))] * len(f)
else:
c2 = ch[f]
m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module
t = str(m)[8:-2].replace('__main__.', '') # module type
np = sum([x.numel() for x in m_.parameters()]) # number params
m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
#print('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
layers.append(m_)
ch.append(c2)
return nn.Sequential(*layers), sorted(save)

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# coding: utf-8
import torch
import torchvision
import time
import numpy as np
import sys
np.set_printoptions(threshold=sys.maxsize)
def box_iou(box1, box2):
# https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
"""
Return intersection-over-union (Jaccard index) of boxes.
Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
Arguments:
box1 (Tensor[N, 4])
box2 (Tensor[M, 4])
Returns:
iou (Tensor[N, M]): the NxM matrix containing the pairwise
IoU values for every element in boxes1 and boxes2
"""
def box_area(box):
# box = 4xn
return (box[2] - box[0]) * (box[3] - box[1])
area1 = box_area(box1.t())
area2 = box_area(box2.t())
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
return inter / (area1[:, None] + area2 - inter) # iou = inter / (area1 + area2 - inter)
def xywh2xyxy(x):
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x)
y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
return y
def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, top_k_num=3000, merge=False, classes=None, agnostic=False):
"""Performs Non-Maximum Suppression (NMS) on inference results
Returns:
detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
"""
# print('conf_thres',conf_thres)
if prediction.dtype is torch.float16:
prediction = prediction.float() # to FP32
nc = prediction[0].shape[1] - 5 # number of classes
xc = prediction[..., 4] > conf_thres # candidates
# Settings
min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height
max_det = 300 # maximum number of detections per image
time_limit = 10.0 # seconds to quit after
multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img)
t = time.time()
output = [None] * prediction.shape[0]
for xi, x in enumerate(prediction): # image index, image inference
# Apply constraints
# x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
x = x[xc[xi]] # confidence
# If none remain process next image
if not x.shape[0]:
continue
# Compute conf
x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf
# Box (center x, center y, width, height) to (x1, y1, x2, y2)
box = xywh2xyxy(x[:, :4])
# Detections matrix nx6 (xyxy, conf, cls)
if multi_label:
i, j = (x[:, 5:] > conf_thres).nonzero().t()
x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
else: # best class only
conf, j = x[:, 5:].max(1, keepdim=True)
x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
# If none remain process next image
n = x.shape[0] # number of boxes
if not n:
continue
# Sort by confidence
# x = x[x[:, 4].argsort(descending=True)]
# Batched NMS
c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
# # Sort by confidence
ind_Sort_by_confidence = x[:, 4].argsort(descending=True)
boxes = boxes[ind_Sort_by_confidence][:top_k_num] #
scores = scores[ind_Sort_by_confidence][:top_k_num] #
x = x[ind_Sort_by_confidence][:top_k_num] #
# cross classes nms
i = torchvision.ops.boxes.nms(boxes, scores, iou_thres)
if i.shape[0] > max_det: # limit detections
i = i[:max_det]
# if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
# update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
# iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
# weights = iou * scores[None] # box weights
# x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
# if redundant:
# i = i[iou.sum(1) > 1] # require redundancy
output[xi] = x[i]
if (time.time() - t) > time_limit:
break # time limit exceeded
return output
def non_max_suppression_kneron(prediction, conf_thres=0.1, iou_thres=0.6, top_k_num=3000, merge=False, classes=None, agnostic=False):
"""Performs Non-Maximum Suppression (NMS) on inference results
Returns:
detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
"""
if prediction.dtype is torch.float16:
prediction = prediction.float() # to FP32
nc = prediction[0].shape[1] - 5 # number of classes
xc = prediction[..., 4] > conf_thres # candidates
# Settings
min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height
max_det = 300 # maximum number of detections per image
time_limit = 10.0 # seconds to quit after
multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img)
t = time.time()
output = [None] * prediction.shape[0]
for xi, x in enumerate(prediction): # image index, image inference
# Apply constraints
# x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
x = x[xc[xi]] # confidence
# If none remain process next image
if not x.shape[0]:
continue
# Compute conf
x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf
# Box (center x, center y, width, height) to (x1, y1, x2, y2)
box = xywh2xyxy(x[:, :4])
# Detections matrix nx6 (xyxy, conf, cls)
if multi_label:
i, j = (x[:, 5:] > conf_thres).nonzero().t()
x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
else: # best class only
conf, j = x[:, 5:].max(1, keepdim=True)
x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
# If none remain process next image
n = x.shape[0] # number of boxes
if not n:
continue
# Sort by confidence
# x = x[x[:, 4].argsort(descending=True)]
# Batched NMS
c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
# Sort by confidence
ind_Sort_by_confidence = x[:, 4].argsort(descending=True)
boxes = boxes[ind_Sort_by_confidence][:top_k_num] #
scores = scores[ind_Sort_by_confidence][:top_k_num] #
x = x[ind_Sort_by_confidence][:top_k_num] #
# cross classes nms
i = torchvision.ops.boxes.nms(boxes, scores, iou_thres)
if i.shape[0] > max_det: # limit detections
i = i[:max_det]
output[xi] = x[i]
if (time.time() - t) > time_limit:
break # time limit exceeded
return output
def clip_coords(boxes, img_shape):
# Clip bounding xyxy bounding boxes to image shape (height, width)
boxes[:, 0].clamp_(0, img_shape[1]) # x1
boxes[:, 1].clamp_(0, img_shape[0]) # y1
boxes[:, 2].clamp_(0, img_shape[1]) # x2
boxes[:, 3].clamp_(0, img_shape[0]) # y2
def scale_coords_ori(img1_shape, coords, img0_shape, ratio_pad=None):
# Rescale coords (xyxy) from img1_shape to img0_shape
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
else:
gain = ratio_pad[0][0]
pad = ratio_pad[1]
coords[:, [0, 2]] -= pad[0] # x padding
coords[:, [1, 3]] -= pad[1] # y padding
coords[:, :4] /= gain
clip_coords(coords, img0_shape)
return coords
def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
# Rescale coords (xyxy) from img1_shape to img0_shape
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
#pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
else:
gain = ratio_pad[0][0]
#pad = ratio_pad[1]
# coords[:, [0, 2]] -= pad[0] # x padding
# coords[:, [1, 3]] -= pad[1] # y padding
coords[:, :4] /= gain
clip_coords(coords, img0_shape)
return coords
def scale_coords_test(img1_shape, coords, img0_shape, ratio_pad=None):
coords[:, 0] /= (img1_shape[1] / img0_shape[1])
coords[:, 2] /= (img1_shape[1] / img0_shape[1])
coords[:, 1] /= (img1_shape[0] / img0_shape[0])
coords[:, 3] /= (img1_shape[0] / img0_shape[0])
clip_coords(coords, img0_shape)
return coords
def classes_mapping(det, num_classes):
det[:, 5] = det[:, 5] + 1.0
def Yolov5_postprocess(pred, img_shape, im0_shape, conf_thres, iou_thres, top_k_num, num_classes, vanish_point, e2e_coco) :
classes, agnostic_nms = None, False#
img_h = im0_shape[0]
vanish_y2 = vanish_point * float(img_h)
# Apply NMS
pred = non_max_suppression(pred, conf_thres, iou_thres, top_k_num, classes=classes, agnostic=agnostic_nms)
#return pred
dets = []
for i, det in enumerate(pred): # detections per image
gn = torch.tensor(im0_shape)[[1, 0, 1, 0]] # normalization gain whwh
if det is not None and len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img_shape[2:], det[:, :4], im0_shape).round()
det = det[det[:,3]>=vanish_y2]
# (x1,y1,x2,y2) -> (x1,y1,w,h) for public_field.py
det[:, 2] = det[:, 2] - det[:, 0]
det[:, 3] = det[:, 3] - det[:, 1]
# classes(0~79) -> classes(1~80) for public_field.py
if e2e_coco:
classes_mapping(det, num_classes)
det = det.cpu().numpy()
dets.append(det)
if dets and len(dets) > 0:
dets = np.asarray(dets)
dets = np.squeeze(dets, axis=0) # remove outer []
dets = dets.tolist()
return dets
def make_grid(nx=20, ny=20):
yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
grids = torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
return grids
def Yolov5_postprocess_onnx_sig(out,img_shape, im0_shape, conf_thres, iou_thres, top_k_num, grids, num_classes, anchors,vanish_point, e2e_coco) :
nc = num_classes # number of classes
no = nc + 5 # number of outputs per anchor
nl = len(anchors) # number of detection layers
na = len(anchors[0]) // 2 # number of anchors
a = torch.tensor(anchors).float().view(3, -1, 2)
anchor_grid = a.clone().view(3, 1, -1, 1, 1, 2)
stride = torch.tensor([ 8., 16., 32.])
z = []
for i in range(nl):
x = torch.from_numpy(out[i])
# print('x.shape',x.shape)
bs, _, ny, nx = x.shape # x(bs,3,20,20,85)
x = x.view(bs, na, no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
# grid_r = make_grid(nx, ny) ##grid
# grid_r = grid_r.numpy() ##grid
# file_name = str(i)+'.npy' ##grid
# np.save(file_name,grid_r) ##grid
grid = grids[i]#
#y = x.sigmoid()
y = x
y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + grid) * stride[i] # xy
y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * anchor_grid[i] # wh
z.append(y.view(bs, -1, no))
pred = torch.cat(z, 1)
return Yolov5_postprocess(pred, img_shape, im0_shape, conf_thres, iou_thres, top_k_num, num_classes,vanish_point, e2e_coco)
def Yolov5_postprocess_sig(out,img_shape, im0_shape, conf_thres, iou_thres, top_k_num, grids, num_classes, anchors,vanish_point, e2e_coco) :
nc = num_classes # number of classes
no = nc + 5 # number of outputs per anchor
nl = len(anchors) # number of detection layers
na = len(anchors[0]) // 2 # number of anchors
a = torch.tensor(anchors).float().view(3, -1, 2)
anchor_grid = a.clone().view(3, 1, -1, 1, 1, 2).to(out[0].device)
stride = torch.tensor([ 8., 16., 32.]).to(out[0].device)
z = []
for i in range(nl):
x = out[i]
bs, _, ny, nx = x.shape # x(bs,255,20,20) to x(bs,3,20,20,85)
# print('x.shape',x.shape)
x = x.view(bs, na, no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
# grid_r = make_grid(nx, ny) ##grid
# grid_r = grid_r.numpy() ##grid
# file_name = str(i)+'.npy' ##grid
# np.save(file_name,grid_r) ##grid
grid = grids[i].to(out[0].device) #
#y = x.sigmoid()
y = x
y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + grid) * stride[i] # xy
y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * anchor_grid[i] # wh
z.append(y.view(bs, -1, no))
# exit(0)
pred = torch.cat(z, 1)
return Yolov5_postprocess(pred, img_shape, im0_shape, conf_thres, iou_thres, top_k_num, num_classes,vanish_point, e2e_coco)

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# coding: utf-8
import torch
import cv2
import numpy as np
import math
import time
from . import kneron_preprocessing
kneron_preprocessing.API.set_default_as_520()
torch.backends.cudnn.deterministic = True
img_formats = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.dng']
def make_divisible(x, divisor):
# Returns x evenly divisble by divisor
return math.ceil(x / divisor) * divisor
def check_img_size(img_size, s=32):
# Verify img_size is a multiple of stride s
new_size = make_divisible(img_size, int(s)) # ceil gs-multiple
if new_size != img_size:
print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size))
return new_size
def letterbox_ori(img, new_shape=(640, 640), color=(0, 0, 0), auto=True, scaleFill=False, scaleup=True):
# Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
shape = img.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # only scale down, do not scale up (for better test mAP)
r = min(r, 1.0)
# Compute padding
ratio = r, r # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) # width, height
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
#img = kneron_preprocessing.API.resize(img,size=new_unpad, keep_ratio = False)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
# top, bottom = int(0), int(round(dh + 0.1))
# left, right = int(0), int(round(dw + 0.1))
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
#img = kneron_preprocessing.API.pad(img, left, right, top, bottom, 0)
return img, ratio, (dw, dh)
def letterbox(img, new_shape=(640, 640), color=(0, 0, 0), auto=True, scaleFill=False, scaleup=True):
# Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
shape = img.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # only scale down, do not scale up (for better test mAP)
r = min(r, 1.0)
# Compute padding
ratio = r, r # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) # width, height
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
# dw /= 2 # divide padding into 2 sides
# dh /= 2
if shape[::-1] != new_unpad: # resize
#img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
img = kneron_preprocessing.API.resize(img,size=new_unpad, keep_ratio = False)
# top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
# left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
top, bottom = int(0), int(round(dh + 0.1))
left, right = int(0), int(round(dw + 0.1))
#img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
img = kneron_preprocessing.API.pad(img, left, right, top, bottom, 0)
return img, ratio, (dw, dh)
def letterbox_test(img, new_shape=(640, 640), color=(0, 0, 0), auto=True, scaleFill=False, scaleup=True):
ratio = 1.0, 1.0
dw, dh = 0, 0
img = kneron_preprocessing.API.resize(img, size=(480, 256), keep_ratio=False, type='bilinear')
return img, ratio, (dw, dh)
def LoadImages(path,img_size): #_rgb # for inference
if isinstance(path, str):
img0 = cv2.imread(path) # BGR
else:
img0 = path # BGR
# Padded resize
img = letterbox(img0, new_shape=img_size)[0]
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img)
return img, img0
def LoadImages_yyy(path,img_size): #_yyy # for inference
if isinstance(path, str):
img0 = cv2.imread(path) # BGR
else:
img0 = path # BGR
yvu = cv2.cvtColor(img0, cv2.COLOR_BGR2YCrCb)
y, v, u = cv2.split(yvu)
img0 = np.stack((y,)*3, axis=-1)
# Padded resize
img = letterbox(img0, new_shape=img_size)[0]
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img)
return img, img0
def LoadImages_yuv420(path,img_size): #_yuv420 # for inference
if isinstance(path, str):
img0 = cv2.imread(path) # BGR
else:
img0 = path # BGR
img_h, img_w = img0.shape[:2]
img_h = (img_h // 2) * 2
img_w = (img_w // 2) * 2
img = img0[:img_h,:img_w,:]
yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV_I420)
img0= cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR_I420) #yuv420
# Padded resize
img = letterbox(img0, new_shape=img_size)[0]
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img)
return img, img0
def Yolov5_preprocess(image_path, device, imgsz_h, imgsz_w) :
model_stride_max = 32
imgsz_h = check_img_size(imgsz_h, s=model_stride_max) # check img_size
imgsz_w = check_img_size(imgsz_w, s=model_stride_max) # check img_size
img, im0 = LoadImages(image_path, img_size=(imgsz_h,imgsz_w))
img = kneron_preprocessing.API.norm(img) #path1
#print('img',img.shape)
img = torch.from_numpy(img).to(device) #path1,path2
# img = img.float() # uint8 to fp16/32 #path2
# img /= 255.0#256.0 - 0.5 # 0 - 255 to -0.5 - 0.5 #path2
if img.ndimension() == 3:
img = img.unsqueeze(0)
return img, im0

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kneron/exporting/yolov5/yolov5_runner.py Normal file
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import torch
torch.set_printoptions(precision=10)
torch.set_printoptions(threshold=99999999999)
torch.backends.cudnn.deterministic = True
from .yolov5_preprocess import *
from .yolov5_postprocess import *
from .yolo_v2 import Model as Model_v2
import onnxruntime
import time
import os
from collections import Counter
import torch.nn.functional as F
import random
from pathlib import Path
class Yolov5Runner:
def __init__(self, model_path, yaml_path, grid20_path, grid40_path, grid80_path, num_classes, imgsz_h, imgsz_w, conf_thres, iou_thres, top_k_num, vanish_point, **kwargs):#is_onnx,
"""
inputs :
model_path : str ,path to model
"""
self.model_path = model_path
self.imgsz_h = imgsz_h
self.imgsz_w = imgsz_w
self.conf_thres = conf_thres
self.iou_thres = iou_thres
self.top_k_num = top_k_num
self.vanish_point = vanish_point
self.num_classes = num_classes
self.DEVICE = torch.device("cpu")#torch.device('cuda:0')#
self.grid20 = torch.from_numpy(np.load(grid20_path))
self.grid40 = torch.from_numpy(np.load(grid40_path))
self.grid80 = torch.from_numpy(np.load(grid80_path))
self.grids = [self.grid80, self.grid40, self.grid20]
if 'onnx' not in model_path:
self.yolov5_model = Model_v2(yaml_path, nc=num_classes)
self.yolov5_model.load_state_dict(torch.load(model_path, map_location=self.DEVICE))#,strict=False)
self.yolov5_model.float().eval()
self.yolov5_model.to(self.DEVICE)
self.yolov5_model.eval()
else:
#onnxruntime.set_default_logger_severity(0)
self.sess = onnxruntime.InferenceSession(model_path)
# self.sess.set_providers(['CUDAExecutionProvider'])
self.input_name = self.sess.get_inputs()[0].name
self.onnx_batch_size = self.sess.get_inputs()[0].shape[0]
self.onnx_img_size_h = self.sess.get_inputs()[0].shape[2]
self.onnx_img_size_w = self.sess.get_inputs()[0].shape[3]
self.anchors = [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]] #yolov5
print('self.vanish_point',self.vanish_point)
self.e2e_coco = kwargs.get('e2e_coco', False)
def run(self, img_path):
"""
inputs :
img_path : path of the image
outputs :
dets : list
"""
self.yolov5_model.eval()
with torch.no_grad():
img, im0 = Yolov5_preprocess(img_path, self.DEVICE, self.imgsz_h, self.imgsz_w)
if next(self.yolov5_model.parameters()).is_cuda:
img = img.type(torch.cuda.FloatTensor)
else:
img = img.type(torch.FloatTensor)
pred = self.yolov5_model(img, augment=False)
img_shape, im0_shape = img.shape, im0.shape
dets = Yolov5_postprocess_sig(pred,img_shape, im0_shape, self.conf_thres, self.iou_thres, self.top_k_num, self.grids, self.num_classes, self.anchors,self.vanish_point)
return dets
def run_onnx(self, img_path):
"""
inputs :
img_path : path of the image
outputs :
dets : list
"""
with torch.no_grad():
img, im0 = Yolov5_preprocess(img_path, self.DEVICE, self.imgsz_h, self.imgsz_w)
np_images = np.array(img.cpu())
np_images = np_images.astype(np.float32)
pred_onnx = self.sess.run(None, {self.input_name: np_images })
img_shape, im0_shape = img.shape, im0.shape
# print('img_shape',img_shape)
# print('im0_shape', im0_shape)
dets_onnx = Yolov5_postprocess_onnx_sig(pred_onnx,img_shape, im0_shape, self.conf_thres, self.iou_thres, self.top_k_num, self.grids, self.num_classes, self.anchors,self.vanish_point, self.e2e_coco)
return dets_onnx

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kneron/exporting/yolov5_export.py Normal file
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import os
import torch
import sys
import yaml
import argparse
from yolov5.yolov5_runner import Yolov5Runner
def save_weight(num_classes):
current_path=os.getcwd()
par_path = os.path.dirname(current_path)
sys.path.append(os.path.join(par_path, 'yolov5'))
from models.yolo import Model
num_classes = num_classes
device=torch.device('cpu')
ckpt = torch.load(path, map_location=device)
model = Model(yaml_path, nc=num_classes)
ckpt['model'] = {k: v for k, v in ckpt['model'].float().state_dict().items() if k in model.state_dict() and model.state_dict()[k].shape == v.shape}
model.load_state_dict(ckpt['model'])
torch.save(model.state_dict(),pt_path,_use_new_zipfile_serialization=False)
def export_onnx(input_h, input_w, num_classes):
onnx_batch_size, onnx_img_h, onnx_img_w = 1, input_h, input_w
yolov5_model = Yolov5Runner(model_path=pt_path, yaml_path=yaml_path, grid20_path=grid20_path, grid40_path=grid40_path, grid80_path=grid80_path, num_classes=num_classes, imgsz_h=onnx_img_h, imgsz_w=onnx_img_w, conf_thres=0.001, iou_thres=0.65, top_k_num=3000, vanish_point=0.0)
# Input
img = torch.zeros((onnx_batch_size, 3, onnx_img_h, onnx_img_w))
# img = img.type(torch.cuda.FloatTensor)
# Load PyTorch model
model = yolov5_model.yolov5_model
model.eval()
model.model[-1].export = True # set Detect() layer export=True
y = model(img) # dry run
# ONNX export
try:
import onnx
print('\nStarting ONNX export with onnx %s...' % onnx.__version__)
print('****onnx file****',onnx_export_file)
torch.onnx.export(model, img, onnx_export_file, verbose=False, opset_version=11, keep_initializers_as_inputs=True, input_names=['images'], output_names=['classes', 'boxes'] if y is None else ['output'])
# Checks
onnx_model = onnx.load(onnx_export_file) # load onnx model
onnx.checker.check_model(onnx_model) # check onnx model
print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model
print('ONNX export success, saved as %s' % onnx_export_file)
except Exception as e:
print('ONNX export failure: %s' % e)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, default='../yolov5/data/pretrained_paths_520.yaml', help='the path to pretrained model paths yaml file')
args = parser.parse_args()
with open(args.data) as f:
data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
num_classes = data_dict['nc']
input_w = data_dict['input_w']
input_h = data_dict['input_h']
grid_dir = data_dict['grid_dir']
grid20_path = data_dict['grid20_path']
grid40_path = data_dict['grid40_path']
grid80_path = data_dict['grid80_path']
path = data_dict['path']
pt_path=data_dict['pt_path']
yaml_path=data_dict['yaml_path']
onnx_export_file = data_dict['onnx_export_file']
save_weight(num_classes)
export_onnx(input_h, input_w, num_classes)

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import os
import sys
import argparse
import yaml
import cv2
import numpy as np
def draw(img_path, bboxes, save_path = None, names = None):
img = cv2.imread(img_path)
for bbox in bboxes:
l,t,w,h,score,class_id=bbox
if names is not None:
class_id = names[int(class_id)]
img = cv2.rectangle(img,(int(l),int(t)),(int(l+w),int(t+h)),(0, 255, 0),6)
text = "{}".format(class_id) + " {}".format(np.round(score, 3))
img = cv2.putText(img, text, (int(l), int(t)-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
if save_path is None:
save_path = img_path
output_file = os.path.join(save_path, "output.jpg") # 確保有圖片副檔名
cv2.imwrite(output_file, img)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--img-path', type=str, default=None, help='path to image')
parser.add_argument('--save-path', type=str, default=None, help='path to save image')
parser.add_argument('--data', type=str, default='data/pretrained_paths_520.yaml', help='the path to pretrained model paths yaml file')
parser.add_argument('--conf_thres', type=float, default=0.3, help='confidence threshold')
parser.add_argument('--iou_thres', type=float, default=0.5, help='iou threshold for NMS')
parser.add_argument('--onnx', help='inference onnx model',action='store_true')
args = parser.parse_args()
par_path = os.path.abspath(os.path.join(os.getcwd(), os.pardir))
sys.path.append(par_path)
sys.path.append(os.path.join(par_path, 'exporting') )
from yolov5.yolov5_runner import Yolov5Runner
with open(args.data) as f:
data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict
num_classes = data_dict['nc']
input_w = data_dict['input_w']
input_h = data_dict['input_h']
grid_dir = data_dict['grid_dir']
grid20_path = data_dict['grid20_path']
grid40_path = data_dict['grid40_path']
grid80_path = data_dict['grid80_path']
path = data_dict['path']
if args.onnx:
yolov5_model = Yolov5Runner(model_path=data_dict['onnx_export_file'], yaml_path=data_dict['yaml_path'], grid20_path=grid20_path, grid40_path=grid40_path, grid80_path=grid80_path, num_classes=num_classes, imgsz_h=input_h, imgsz_w=input_w, conf_thres=args.conf_thres, iou_thres=args.iou_thres, top_k_num=3000, vanish_point=0.0)
bboxes = yolov5_model.run_onnx(args.img_path)
else:
yolov5_model = Yolov5Runner(model_path=data_dict['pt_path'], yaml_path=data_dict['yaml_path'], grid20_path=grid20_path, grid40_path=grid40_path, grid80_path=grid80_path, num_classes=num_classes, imgsz_h=input_h, imgsz_w=input_w, conf_thres=args.conf_thres, iou_thres=args.iou_thres, top_k_num=3000, vanish_point=0.0)
bboxes = yolov5_model.run(args.img_path)
print(bboxes)
if args.save_path is not None:
draw(args.img_path, bboxes, save_path = args.save_path, names = data_dict['names'])

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kneron/inference_e2e.py Normal file
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import os
import sys
import argparse
import yaml
from tqdm import tqdm
import json
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--img-path', type=str, help='Path to the dataset directory.')
parser.add_argument('--params', type=str, help='Path to the init params file.')
parser.add_argument('--save-path', type=str, help='Path to save output in json.')
args = parser.parse_args()
par_path = os.path.abspath(os.path.join(os.getcwd(), os.pardir))
sys.path.append(par_path)
sys.path.append(os.path.join(par_path, 'exporting') )
from yolov5.yolov5_runner import Yolov5Runner
with open(args.params, "r", encoding="utf-8") as f:
params_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict
num_classes = params_dict['nc']
input_w = params_dict['input_w']
input_h = params_dict['input_h']
grid20_path = params_dict['grid20_path']
grid40_path = params_dict['grid40_path']
grid80_path = params_dict['grid80_path']
conf_thres = params_dict['conf_thres']
iou_thres = params_dict['iou_thres']
model_type = params_dict['model_type']
e2e_coco = params_dict['e2e_coco']
if model_type == 'onnx':
yolov5_model = Yolov5Runner(model_path=params_dict['onnx_path'], yaml_path=params_dict['model_yaml_path'], grid20_path=grid20_path, grid40_path=grid40_path, grid80_path=grid80_path, num_classes=num_classes, imgsz_h=input_h, imgsz_w=input_w, conf_thres=conf_thres, iou_thres=iou_thres, top_k_num=3000, vanish_point=0.0, e2e_coco=e2e_coco)
else:
yolov5_model = Yolov5Runner(model_path=params_dict['pt_path'], yaml_path=params_dict['model_yaml_path'], grid20_path=grid20_path, grid40_path=grid40_path, grid80_path=grid80_path, num_classes=num_classes, imgsz_h=input_h, imgsz_w=input_w, conf_thres=conf_thres, iou_thres=iou_thres, top_k_num=3000, vanish_point=0.0, e2e_coco=e2e_coco)
img_list = os.listdir(args.img_path)
results = []
for img_name in tqdm(img_list):
if img_name.split('.')[-1] not in ['png', 'jpg']:
continue
img_path = os.path.join(args.img_path, img_name)
if model_type == 'onnx':
bboxes = yolov5_model.run_onnx(img_path)
else:
bboxes = yolov5_model.run(img_path)
results.append({'img_path': img_path, 'bbox': bboxes } )
with open(args.save_path, 'w') as fp:
json.dump(results, fp)

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import ktc
import numpy as np
import os
import onnx
from PIL import Image
import torch
from yolov5_preprocess import Yolov5_preprocess
import kneron_preprocessing
onnx_path = 'runs/train/exp24/weights/best_simplified.onnx'
m = onnx.load(onnx_path)
m = ktc.onnx_optimizer.onnx2onnx_flow(m)
onnx.save(m,'latest.opt.onnx')
km = ktc.ModelConfig(20008, "0001", "720", onnx_model=m)
eval_result = km.evaluate()
print("\nNpu performance evaluation result:\n" + str(eval_result))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
imgsz_h, imgsz_w = 640, 640
data_path = "data50"
files_found = [f for _, _, files in os.walk(data_path) for f in files if f.lower().endswith((".jpg", ".jpeg", ".png", ".bmp"))]
if not files_found:
raise FileNotFoundError(f"❌ Error: No images found in {data_path}! Please check your dataset.")
print(f"✅ Found {len(files_found)} images in {data_path}")
# **獲取 ONNX 模型的輸入名稱**
input_name = m.graph.input[0].name # 確保 key 與 ONNX input name 一致
# 存儲預處理後的圖片數據
img_list = []
# 遍歷 data50 並進行預處理
for root, _, files in os.walk(data_path):
for f in files:
fullpath = os.path.join(root, f)
# **只處理圖片文件**
if not f.lower().endswith((".jpg", ".jpeg", ".png", ".bmp")):
print(f"⚠️ Skipping non-image file: {fullpath}")
continue
# **嘗試處理圖片**
try:
img_data, _ = Yolov5_preprocess(fullpath, device, imgsz_h, imgsz_w)
img_data = img_data.cpu().numpy()
print(f"✅ Processed: {fullpath}")
img_list.append(img_data)
except Exception as e:
print(f"❌ Failed to process {fullpath}: {e}")
# **確保 img_list 不是空的**
if not img_list:
raise ValueError("❌ Error: No valid images were processed! Please check the image paths and formats.")
# **執行 BIE 量化**
bie_model_path = km.analysis({input_name: img_list})
# **確認 BIE 模型是否生成**
if not os.path.exists(bie_model_path):
raise RuntimeError(f"❌ Error: BIE model was not generated! Please check your quantization process.")
# 顯示成功訊息
print("\n✅ Fixed-point analysis done! BIE model saved to:", bie_model_path)
# 確保 `km` 已經初始化，並且 `.bie` 模型已生成
nef_model_path = ktc.compile([km])
# 顯示成功訊息
print("\n✅ Compile done! NEF file saved to:", nef_model_path)

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kneron/oldquantize_yolov5.py Normal file
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import os
import numpy as np
import torch
from yolov5_preprocess import Yolov5_preprocess # 使用你的預處理
import kneron_preprocessing
# 設定裝置
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 設定圖片大小（與訓練時一致）
imgsz_h, imgsz_w = 640, 640
# 量化數據集目錄（請確保這個資料夾存在）
data_path = "/data50"
img_list = []
# 遍歷 voc_data50 並進行預處理
for root, _, files in os.walk(data_path):
for f in files:
fullpath = os.path.join(root, f)
# 執行與訓練相同的預處理
img_data, _ = Yolov5_preprocess(fullpath, device, imgsz_h, imgsz_w)
print(f"Processed: {fullpath}")
img_list.append(img_data)
# 轉為 NumPy 格式
img_list = np.array(img_list)
# 執行 BIE 量化分析
bie_model_path = km.analysis({"input": img_list})
print("\nFixed-point analysis done. Saved bie model to '" + str(bie_model_path) + "'")

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import ktc
import numpy as np
import os
import onnx
import shutil
from PIL import Image
import torch
from yolov5_preprocess import Yolov5_preprocess
import kneron_preprocessing
# 設定 ONNX 模型儲存路徑
onnx_dir = 'runs/train/exp24/weights/'
onnx_path = os.path.join(onnx_dir, 'best_no_sigmoid.onnx')
# 確保目標資料夾存在
os.makedirs(onnx_dir, exist_ok=True)
# 加載並優化 ONNX 模型
m = onnx.load(onnx_path)
m = ktc.onnx_optimizer.onnx2onnx_flow(m)
opt_onnx_path = os.path.join(onnx_dir, 'latest.opt.onnx')
onnx.save(m, opt_onnx_path)
km = ktc.ModelConfig(20008, "0001", "520", onnx_model=m)
eval_result = km.evaluate()
print("\nNpu performance evaluation result:\n" + str(eval_result))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
imgsz_h, imgsz_w = 640, 640
data_path = "datacoin"
files_found = [f for _, _, files in os.walk(data_path) for f in files if f.lower().endswith((".jpg", ".jpeg", ".png", ".bmp"))]
if not files_found:
raise FileNotFoundError(f"\u274c Error: No images found in {data_path}! Please check your dataset.")
print(f"\u2705 Found {len(files_found)} images in {data_path}")
# 獲取 ONNX 模型的輸入名稱
input_name = m.graph.input[0].name # 確保 key 與 ONNX input name 一致
# 存儲預處理後的圖片數據
img_list = []
# 遍歷 data50 並進行預處理
for root, _, files in os.walk(data_path):
for f in files:
fullpath = os.path.join(root, f)
# 只處理圖片文件
if not f.lower().endswith((".jpg", ".jpeg", ".png", ".bmp")):
print(f"\u26a0\ufe0f Skipping non-image file: {fullpath}")
continue
# 嘗試處理圖片
try:
img_data, _ = Yolov5_preprocess(fullpath, device, imgsz_h, imgsz_w)
img_data = img_data.cpu().numpy()
print(f"\u2705 Processed: {fullpath}")
img_list.append(img_data)
except Exception as e:
print(f"\u274c Failed to process {fullpath}: {e}")
# 確保 img_list 不是空的
if not img_list:
raise ValueError("\u274c Error: No valid images were processed! Please check the image paths and formats.")
# 執行 BIE 量化
bie_model_path = km.analysis({input_name: img_list})
# 確保 BIE 檔案儲存到指定目錄
bie_save_path = os.path.join(onnx_dir, os.path.basename(bie_model_path))
shutil.copy(bie_model_path, bie_save_path) # 使用 shutil.move 來處理跨磁碟移動
# 確認 BIE 模型是否生成
if not os.path.exists(bie_save_path):
raise RuntimeError(f"\u274c Error: BIE model was not generated! Please check your quantization process.")
print("\n\u2705 Fixed-point analysis done! BIE model saved to:", bie_save_path)
# 確保 `km` 已經初始化，並且 `.bie` 模型已生成
nef_model_path = ktc.compile([km])
# 確保 nef_model_path 不是 None 或空值
if not nef_model_path:
raise RuntimeError("❌ Error: ktc.compile() did not return a valid .nef file path!")
# 確保 NEF 目標資料夾存在
os.makedirs(onnx_dir, exist_ok=True)
# 確保 nef_model_path 不是 None 或空值
if not nef_model_path:
raise RuntimeError("❌ Error: ktc.compile() did not return a valid .nef file path!")
# 確保 .nef 檔案存在
if not os.path.exists(nef_model_path):
raise RuntimeError(f"❌ Error: NEF model was not generated at {nef_model_path}! Please check your compilation process.")
# 確保 NEF 檔案儲存到指定目錄
nef_save_path = os.path.join(onnx_dir, os.path.basename(nef_model_path))
if os.path.exists(nef_model_path):
shutil.copy(nef_model_path, nef_save_path)
else:
raise RuntimeError(f"❌ Error: NEF model was expected at {nef_model_path}, but it does not exist!") # 同樣使用 shutil.move
if not os.path.exists(nef_save_path):
raise RuntimeError(f"\u274c Error: NEF model was not generated! Please check your compilation process.")
print("\n\u2705 Compile done! NEF file saved to:", nef_save_path)

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kneron/onnx2nef630.py Normal file
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import ktc
import numpy as np
import os
import onnx
import shutil
from PIL import Image
import torch
from yolov5_preprocess import Yolov5_preprocess
import kneron_preprocessing
# 設定 ONNX 模型儲存路徑
onnx_dir = 'runs/train/exp29/weights/'
onnx_path = os.path.join(onnx_dir, 'best_simplified.onnx')
# 確保目標資料夾存在
os.makedirs(onnx_dir, exist_ok=True)
# 加載並優化 ONNX 模型
m = onnx.load(onnx_path)
m = ktc.onnx_optimizer.onnx2onnx_flow(m)
opt_onnx_path = os.path.join(onnx_dir, 'latest.opt.onnx')
onnx.save(m, opt_onnx_path)
km = ktc.ModelConfig(20008, "0001", "630", onnx_model=m)
eval_result = km.evaluate()
print("\nNpu performance evaluation result:\n" + str(eval_result))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
imgsz_h, imgsz_w = 640, 640
data_path = "data4"
files_found = [f for _, _, files in os.walk(data_path) for f in files if f.lower().endswith((".jpg", ".jpeg", ".png", ".bmp"))]
if not files_found:
raise FileNotFoundError(f"\u274c Error: No images found in {data_path}! Please check your dataset.")
print(f"\u2705 Found {len(files_found)} images in {data_path}")
# 獲取 ONNX 模型的輸入名稱
input_name = m.graph.input[0].name # 確保 key 與 ONNX input name 一致
# 存儲預處理後的圖片數據
img_list = []
# 遍歷 data50 並進行預處理
for root, _, files in os.walk(data_path):
for f in files:
fullpath = os.path.join(root, f)
# 只處理圖片文件
if not f.lower().endswith((".jpg", ".jpeg", ".png", ".bmp")):
print(f"\u26a0\ufe0f Skipping non-image file: {fullpath}")
continue
# 嘗試處理圖片
try:
img_data, _ = Yolov5_preprocess(fullpath, device, imgsz_h, imgsz_w)
img_data = img_data.cpu().numpy()
print(f"\u2705 Processed: {fullpath}")
img_list.append(img_data)
except Exception as e:
print(f"\u274c Failed to process {fullpath}: {e}")
# 確保 img_list 不是空的
if not img_list:
raise ValueError("\u274c Error: No valid images were processed! Please check the image paths and formats.")
# 執行 BIE 量化
bie_model_path = km.analysis({input_name: img_list})
# 確保 BIE 檔案儲存到指定目錄
bie_save_path = os.path.join(onnx_dir, os.path.basename(bie_model_path))
shutil.copy(bie_model_path, bie_save_path) # 使用 shutil.move 來處理跨磁碟移動
# 確認 BIE 模型是否生成
if not os.path.exists(bie_save_path):
raise RuntimeError(f"\u274c Error: BIE model was not generated! Please check your quantization process.")
print("\n\u2705 Fixed-point analysis done! BIE model saved to:", bie_save_path)
# 確保 `km` 已經初始化，並且 `.bie` 模型已生成
nef_model_path = ktc.compile([km])
# 確保 nef_model_path 不是 None 或空值
if not nef_model_path:
raise RuntimeError("❌ Error: ktc.compile() did not return a valid .nef file path!")
# 確保 NEF 目標資料夾存在
os.makedirs(onnx_dir, exist_ok=True)
# 確保 nef_model_path 不是 None 或空值
if not nef_model_path:
raise RuntimeError("❌ Error: ktc.compile() did not return a valid .nef file path!")
# 確保 .nef 檔案存在
if not os.path.exists(nef_model_path):
raise RuntimeError(f"❌ Error: NEF model was not generated at {nef_model_path}! Please check your compilation process.")
# 確保 NEF 檔案儲存到指定目錄
nef_save_path = os.path.join(onnx_dir, os.path.basename(nef_model_path))
if os.path.exists(nef_model_path):
shutil.copy(nef_model_path, nef_save_path)
else:
raise RuntimeError(f"❌ Error: NEF model was expected at {nef_model_path}, but it does not exist!") # 同樣使用 shutil.move
if not os.path.exists(nef_save_path):
raise RuntimeError(f"\u274c Error: NEF model was not generated! Please check your compilation process.")
print("\n\u2705 Compile done! NEF file saved to:", nef_save_path)

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kneron/onnx2nef720.py Normal file
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import ktc
import numpy as np
import os
import onnx
import shutil
from PIL import Image
import torch
from yolov5_preprocess import Yolov5_preprocess
import kneron_preprocessing
# 設定 ONNX 模型儲存路徑
onnx_dir = 'runs/train/exp73/weights/'
onnx_path = os.path.join(onnx_dir, 'best_simplified.onnx')
# 確保目標資料夾存在
os.makedirs(onnx_dir, exist_ok=True)
# 加載並優化 ONNX 模型
m = onnx.load(onnx_path)
m = ktc.onnx_optimizer.onnx2onnx_flow(m)
opt_onnx_path = os.path.join(onnx_dir, 'latest.opt.onnx')
onnx.save(m, opt_onnx_path)
km = ktc.ModelConfig(20008, "0001", "720", onnx_model=m)
eval_result = km.evaluate()
print("\nNpu performance evaluation result:\n" + str(eval_result))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
imgsz_h, imgsz_w = 640, 640
data_path = "data50"
files_found = [f for _, _, files in os.walk(data_path) for f in files if f.lower().endswith((".jpg", ".jpeg", ".png", ".bmp"))]
if not files_found:
raise FileNotFoundError(f"\u274c Error: No images found in {data_path}! Please check your dataset.")
print(f"\u2705 Found {len(files_found)} images in {data_path}")
# 獲取 ONNX 模型的輸入名稱
input_name = m.graph.input[0].name # 確保 key 與 ONNX input name 一致
# 存儲預處理後的圖片數據
img_list = []
# 遍歷 data50 並進行預處理
for root, _, files in os.walk(data_path):
for f in files:
fullpath = os.path.join(root, f)
# 只處理圖片文件
if not f.lower().endswith((".jpg", ".jpeg", ".png", ".bmp")):
print(f"\u26a0\ufe0f Skipping non-image file: {fullpath}")
continue
# 嘗試處理圖片
try:
img_data, _ = Yolov5_preprocess(fullpath, device, imgsz_h, imgsz_w)
img_data = img_data.cpu().numpy()
print(f"\u2705 Processed: {fullpath}")
img_list.append(img_data)
except Exception as e:
print(f"\u274c Failed to process {fullpath}: {e}")
# 確保 img_list 不是空的
if not img_list:
raise ValueError("\u274c Error: No valid images were processed! Please check the image paths and formats.")
# 執行 BIE 量化
bie_model_path = km.analysis({input_name: img_list})
# 確保 BIE 檔案儲存到指定目錄
bie_save_path = os.path.join(onnx_dir, os.path.basename(bie_model_path))
shutil.copy(bie_model_path, bie_save_path) # 使用 shutil.move 來處理跨磁碟移動
# 確認 BIE 模型是否生成
if not os.path.exists(bie_save_path):
raise RuntimeError(f"\u274c Error: BIE model was not generated! Please check your quantization process.")
print("\n\u2705 Fixed-point analysis done! BIE model saved to:", bie_save_path)
# 確保 `km` 已經初始化，並且 `.bie` 模型已生成
nef_model_path = ktc.compile([km])
# 確保 nef_model_path 不是 None 或空值
if not nef_model_path:
raise RuntimeError("❌ Error: ktc.compile() did not return a valid .nef file path!")
# 確保 NEF 目標資料夾存在
os.makedirs(onnx_dir, exist_ok=True)
# 確保 nef_model_path 不是 None 或空值
if not nef_model_path:
raise RuntimeError("❌ Error: ktc.compile() did not return a valid .nef file path!")
# 確保 .nef 檔案存在
if not os.path.exists(nef_model_path):
raise RuntimeError(f"❌ Error: NEF model was not generated at {nef_model_path}! Please check your compilation process.")
# 確保 NEF 檔案儲存到指定目錄
nef_save_path = os.path.join(onnx_dir, os.path.basename(nef_model_path))
if os.path.exists(nef_model_path):
shutil.copy(nef_model_path, nef_save_path)
else:
raise RuntimeError(f"❌ Error: NEF model was expected at {nef_model_path}, but it does not exist!") # 同樣使用 shutil.move
if not os.path.exists(nef_save_path):
raise RuntimeError(f"\u274c Error: NEF model was not generated! Please check your compilation process.")
print("\n\u2705 Compile done! NEF file saved to:", nef_save_path)

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kneron/onnx2nefSTDC630.py Normal file
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import ktc
import numpy as np
import os
import onnx
import shutil
from PIL import Image
import torch
from yolov5_preprocess import Yolov5_preprocess
import kneron_preprocessing
# 設定 ONNX 模型儲存路徑
onnx_dir = 'work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes/'
onnx_path = os.path.join(onnx_dir, 'latest.onnx')
# 確保目標資料夾存在
os.makedirs(onnx_dir, exist_ok=True)
# 加載並優化 ONNX 模型
m = onnx.load(onnx_path)
m = ktc.onnx_optimizer.onnx2onnx_flow(m)
opt_onnx_path = os.path.join(onnx_dir, 'latest.opt.onnx')
onnx.save(m, opt_onnx_path)
km = ktc.ModelConfig(20008, "0001", "630", onnx_model=m)
eval_result = km.evaluate()
print("\nNpu performance evaluation result:\n" + str(eval_result))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
imgsz_h, imgsz_w = 640, 640
data_path = "data50"
files_found = [f for _, _, files in os.walk(data_path) for f in files if f.lower().endswith((".jpg", ".jpeg", ".png", ".bmp"))]
if not files_found:
raise FileNotFoundError(f"\u274c Error: No images found in {data_path}! Please check your dataset.")
print(f"\u2705 Found {len(files_found)} images in {data_path}")
# 獲取 ONNX 模型的輸入名稱
input_name = m.graph.input[0].name # 確保 key 與 ONNX input name 一致
# 存儲預處理後的圖片數據
img_list = []
# 遍歷 data50 並進行預處理
for root, _, files in os.walk(data_path):
for f in files:
fullpath = os.path.join(root, f)
# 只處理圖片文件
if not f.lower().endswith((".jpg", ".jpeg", ".png", ".bmp")):
print(f"\u26a0\ufe0f Skipping non-image file: {fullpath}")
continue
# 嘗試處理圖片
try:
img_data, _ = Yolov5_preprocess(fullpath, device, imgsz_h, imgsz_w)
img_data = img_data.cpu().numpy()
print(f"\u2705 Processed: {fullpath}")
img_list.append(img_data)
except Exception as e:
print(f"\u274c Failed to process {fullpath}: {e}")
# 確保 img_list 不是空的
if not img_list:
raise ValueError("\u274c Error: No valid images were processed! Please check the image paths and formats.")
# 執行 BIE 量化
bie_model_path = km.analysis({input_name: img_list})
# 確保 BIE 檔案儲存到指定目錄
bie_save_path = os.path.join(onnx_dir, os.path.basename(bie_model_path))
shutil.copy(bie_model_path, bie_save_path) # 使用 shutil.move 來處理跨磁碟移動
# 確認 BIE 模型是否生成
if not os.path.exists(bie_save_path):
raise RuntimeError(f"\u274c Error: BIE model was not generated! Please check your quantization process.")
print("\n\u2705 Fixed-point analysis done! BIE model saved to:", bie_save_path)
# 確保 `km` 已經初始化，並且 `.bie` 模型已生成
nef_model_path = ktc.compile([km])
# 確保 nef_model_path 不是 None 或空值
if not nef_model_path:
raise RuntimeError("❌ Error: ktc.compile() did not return a valid .nef file path!")
# 確保 NEF 目標資料夾存在
os.makedirs(onnx_dir, exist_ok=True)
# 確保 nef_model_path 不是 None 或空值
if not nef_model_path:
raise RuntimeError("❌ Error: ktc.compile() did not return a valid .nef file path!")
# 確保 .nef 檔案存在
if not os.path.exists(nef_model_path):
raise RuntimeError(f"❌ Error: NEF model was not generated at {nef_model_path}! Please check your compilation process.")
# 確保 NEF 檔案儲存到指定目錄
nef_save_path = os.path.join(onnx_dir, os.path.basename(nef_model_path))
if os.path.exists(nef_model_path):
shutil.copy(nef_model_path, nef_save_path)
else:
raise RuntimeError(f"❌ Error: NEF model was expected at {nef_model_path}, but it does not exist!") # 同樣使用 shutil.move
if not os.path.exists(nef_save_path):
raise RuntimeError(f"\u274c Error: NEF model was not generated! Please check your compilation process.")
print("\n\u2705 Compile done! NEF file saved to:", nef_save_path)

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# -*- coding: utf-8 -*-
import numpy as np
import os
from .funcs.utils import str2int, str2bool
from . import Flow
flow = Flow()
flow.set_numerical_type('floating')
flow_520 = Flow()
flow_520.set_numerical_type('520')
flow_720 = Flow()
flow_720.set_numerical_type('720')
DEFAULT = None
default = {
'crop':{
'align_w_to_4':False
},
'resize':{
'type':'bilinear',
'calculate_ratio_using_CSim':False
}
}
def set_default_as_520():
"""
Set some default parameter as 520 setting
crop.align_w_to_4 = True
crop.pad_square_to_4 = True
resize.type = 'fixed_520'
resize.calculate_ratio_using_CSim = True
"""
global default
default['crop']['align_w_to_4'] = True
default['resize']['type'] = 'fixed_520'
default['resize']['calculate_ratio_using_CSim'] = True
return
def set_default_as_floating():
"""
Set some default parameter as floating setting
crop.align_w_to_4 = False
crop.pad_square_to_4 = False
resize.type = 'bilinear'
resize.calculate_ratio_using_CSim = False
"""
global default
default['crop']['align_w_to_4'] = False
default['resize']['type'] = 'bilinear'
default['resize']['calculate_ratio_using_CSim'] = False
pass
def print_info_on():
"""
turn print infomation on.
"""
flow.set_print_info(True)
flow_520.set_print_info(True)
def print_info_off():
"""
turn print infomation off.
"""
flow.set_print_info(False)
flow_520.set_print_info(False)
def load_image(image):
"""
load_image function
load load_image and output as rgb888 format np.array
Args:
image: [np.array/str], can be np.array or image file path
Returns:
out: [np.array], rgb888 format
Examples:
"""
image = flow.load_image(image, is_raw = False)
return image
def load_bin(image, fmt=None, size=None):
"""
load_bin function
load bin file and output as rgb888 format np.array
Args:
image: [str], bin file path
fmt: [str], "rgb888" / "rgb565" / "nir"
size: [tuble], (image_w, image_h)
Returns:
out: [np.array], rgb888 format
Examples:
>>> image_data = kneron_preprocessing.API.load_bin(image,'rgb565',(raw_w,raw_h))
"""
assert isinstance(size, tuple)
assert isinstance(fmt, str)
# assert (fmt.lower() in ['rgb888', "rgb565" , "nir",'RGB888', "RGB565" , "NIR", 'NIR888', 'nir888'])
image = flow.load_image(image, is_raw = True, raw_img_type='bin', raw_img_fmt = fmt, img_in_width = size[0], img_in_height = size[1])
flow.set_color_conversion(source_format=fmt, out_format = 'rgb888')
image,_ = flow.funcs['color'](image)
return image
def load_hex(file, fmt=None, size=None):
"""
load_hex function
load hex file and output as rgb888 format np.array
Args:
image: [str], hex file path
fmt: [str], "rgb888" / "yuv444" / "ycbcr444" / "yuv422" / "ycbcr422" / "rgb565"
size: [tuble], (image_w, image_h)
Returns:
out: [np.array], rgb888 format
Examples:
>>> image_data = kneron_preprocessing.API.load_hex(image,'rgb565',(raw_w,raw_h))
"""
assert isinstance(size, tuple)
assert isinstance(fmt, str)
assert (fmt.lower() in ['rgb888',"yuv444" , "ycbcr444" , "yuv422" , "ycbcr422" , "rgb565"])
image = flow.load_image(file, is_raw = True, raw_img_type='hex', raw_img_fmt = fmt, img_in_width = size[0], img_in_height = size[1])
flow.set_color_conversion(source_format=fmt, out_format = 'rgb888')
image,_ = flow.funcs['color'](image)
return image
def dump_image(image, output=None, file_fmt='txt',image_fmt='rgb888',order=0):
"""
dump_image function
dump txt, bin or hex, default is txt
image format as following format: RGB888, RGBA8888, RGB565, NIR, YUV444, YCbCr444, YUV422, YCbCr422, default is RGB888
Args:
image: [np.array/str], can be np.array or image file path
output: [str], dump file path
file_fmt: [str], "bin" / "txt" / "hex", set dump file format, default is txt
image_fmt: [str], RGB888 / RGBA8888 / RGB565 / NIR / YUV444 / YCbCr444 / YUV422 / YCbCr422, default is RGB888
Examples:
>>> kneron_preprocessing.API.dump_image(image_data,out_path,fmt='bin')
"""
if isinstance(image, str):
image = load_image(image)
assert isinstance(image, np.ndarray)
if output is None:
return
flow.set_output_setting(is_dump=False, dump_format=file_fmt, image_format=image_fmt ,output_file=output)
flow.dump_image(image)
return
def convert(image, out_fmt = 'RGB888', source_fmt = 'RGB888'):
"""
color convert
Args:
image: [np.array], input
out_fmt: [str], "rgb888" / "rgba8888" / "rgb565" / "yuv" / "ycbcr" / "yuv422" / "ycbcr422"
source_fmt: [str], "rgb888" / "rgba8888" / "rgb565" / "yuv" / "ycbcr" / "yuv422" / "ycbcr422"
Returns:
out: [np.array]
Examples:
"""
flow.set_color_conversion(source_format = source_fmt, out_format=out_fmt, simulation=False)
image,_ = flow.funcs['color'](image)
return image
def get_crop_range(box,align_w_to_4=DEFAULT, pad_square_to_4=False,rounding_type=0):
"""
get exact crop box according different setting
Args:
box: [tuble], (x1, y1, x2, y2)
align_w_to_4: [bool], crop length in w direction align to 4 or not, default False
pad_square_to_4: [bool], pad to square(align 4) or not, default False
rounding_type: [int], 0-> x1,y1 take floor, x2,y2 take ceil; 1->all take rounding
Returns:
out: [tuble,4], (crop_x1, crop_y1, crop_x2, crop_y2)
Examples:
>>> image_data = kneron_preprocessing.API.get_crop_range((272,145,461,341), align_w_to_4=True, pad_square_to_4=True)
(272, 145, 460, 341)
"""
if box is None:
return (0,0,0,0)
if align_w_to_4 is None:
align_w_to_4 = default['crop']['align_w_to_4']
flow.set_crop(type='specific', start_x=box[0],start_y=box[1],end_x=box[2],end_y=box[3], align_w_to_4=align_w_to_4, pad_square_to_4=pad_square_to_4,rounding_type=rounding_type)
image = np.zeros((1,1,3)).astype('uint8')
_,info = flow.funcs['crop'](image)
return info['box']
def crop(image, box=None, align_w_to_4=DEFAULT, pad_square_to_4=False,rounding_type=0 ,info_out = {}):
"""
crop function
specific crop range by box
Args:
image: [np.array], input
box: [tuble], (x1, y1, x2, y2)
align_w_to_4: [bool], crop length in w direction align to 4 or not, default False
pad_square_to_4: [bool], pad to square(align 4) or not, default False
rounding_type: [int], 0-> x1,y1 take floor, x2,y2 take ceil; 1->all take rounding
info_out: [dic], save the final crop box into info_out['box']
Returns:
out: [np.array]
Examples:
>>> info = {}
>>> image_data = kneron_preprocessing.API.crop(image_data,(272,145,461,341), align_w_to_4=True, info_out=info)
>>> info['box']
(272, 145, 460, 341)
>>> info = {}
>>> image_data = kneron_preprocessing.API.crop(image_data,(272,145,461,341), pad_square_to_4=True, info_out=info)
>>> info['box']
(268, 145, 464, 341)
"""
assert isinstance(image, np.ndarray)
if box is None:
return image
if align_w_to_4 is None:
align_w_to_4 = default['crop']['align_w_to_4']
flow.set_crop(type='specific', start_x=box[0],start_y=box[1],end_x=box[2],end_y=box[3], align_w_to_4=align_w_to_4, pad_square_to_4=pad_square_to_4,rounding_type=rounding_type)
image,info = flow.funcs['crop'](image)
info_out['box'] = info['box']
return image
def crop_center(image, range=None, align_w_to_4=DEFAULT, pad_square_to_4=False,rounding_type=0 ,info_out = {}):
"""
crop function
center crop by range
Args:
image: [np.array], input
range: [tuble], (crop_w, crop_h)
align_w_to_4: [bool], crop length in w direction align to 4 or not, default False
pad_square_to_4: [bool], pad to square(align 4) or not, default False
rounding_type: [int], 0-> x1,y1 take floor, x2,y2 take ceil; 1->all take rounding
info_out: [dic], save the final crop box into info_out['box']
Returns:
out: [np.array]
Examples:
>>> info = {}
>>> image_data = kneron_preprocessing.API.crop_center(image_data,(102,40), align_w_to_4=True,info_out=info)
>>> info['box']
(268, 220, 372, 260)
>>> info = {}
>>> image_data = kneron_preprocessing.API.crop_center(image_data,(102,40), pad_square_to_4=True, info_out=info)
>>> info['box']
(269, 192, 371, 294)
"""
assert isinstance(image, np.ndarray)
if range is None:
return image
if align_w_to_4 is None:
align_w_to_4 = default['crop']['align_w_to_4']
flow.set_crop(type='center', crop_w=range[0],crop_h=range[1], align_w_to_4=align_w_to_4, pad_square_to_4=pad_square_to_4,rounding_type=rounding_type)
image,info = flow.funcs['crop'](image)
info_out['box'] = info['box']
return image
def crop_corner(image, range=None, align_w_to_4=DEFAULT,pad_square_to_4=False,rounding_type=0 ,info_out = {}):
"""
crop function
corner crop by range
Args:
image: [np.array], input
range: [tuble], (crop_w, crop_h)
align_w_to_4: [bool], crop length in w direction align to 4 or not, default False
pad_square_to_4: [bool], pad to square(align 4) or not, default False
rounding_type: [int], 0-> x1,y1 take floor, x2,y2 take ceil; 1->all take rounding
info_out: [dic], save the final crop box into info_out['box']
Returns:
out: [np.array]
Examples:
>>> info = {}
>>> image_data = kneron_preprocessing.API.crop_corner(image_data,(102,40), align_w_to_4=True,info_out=info)
>>> info['box']
(0, 0, 104, 40)
>>> info = {}
>>> image_data = kneron_preprocessing.API.crop_corner(image_data,(102,40), pad_square_to_4=True,info_out=info)
>>> info['box']
(0, -28, 102, 74)
"""
assert isinstance(image, np.ndarray)
if range is None:
return image
if align_w_to_4 is None:
align_w_to_4 = default['crop']['align_w_to_4']
flow.set_crop(type='corner', crop_w=range[0],crop_h=range[1], align_w_to_4=align_w_to_4, pad_square_to_4=pad_square_to_4)
image, info = flow.funcs['crop'](image)
info_out['box'] = info['box']
return image
def resize(image, size=None, keep_ratio = True, zoom = True, type=DEFAULT, calculate_ratio_using_CSim = DEFAULT, info_out = {}):
"""
resize function
resize type can be bilinear or bilicubic as floating type, fixed or fixed_520/fixed_720 as fixed type.
fixed_520/fixed_720 type has add some function to simulate 520/720 bug.
Args:
image: [np.array], input
size: [tuble], (input_w, input_h)
keep_ratio: [bool], keep_ratio or not, default True
zoom: [bool], enable resize can zoom image or not, default True
type: [str], "bilinear" / "bilicubic" / "cv2" / "fixed" / "fixed_520" / "fixed_720"
calculate_ratio_using_CSim: [bool], calculate the ratio and scale using Csim function and C float, default False
info_out: [dic], save the final scale size(w,h) into info_out['size']
Returns:
out: [np.array]
Examples:
>>> info = {}
>>> image_data = kneron_preprocessing.API.resize(image_data,size=(56,56),type='fixed',info_out=info)
>>> info_out['size']
(54,56)
"""
assert isinstance(image, np.ndarray)
if size is None:
return image
if type is None:
type = default['resize']['type']
if calculate_ratio_using_CSim is None:
calculate_ratio_using_CSim = default['resize']['calculate_ratio_using_CSim']
flow.set_resize(resize_w = size[0], resize_h = size[1], type=type, keep_ratio=keep_ratio,zoom=zoom, calculate_ratio_using_CSim=calculate_ratio_using_CSim)
image, info = flow.funcs['resize'](image)
info_out['size'] = info['size']
return image
def pad(image, pad_l=0, pad_r=0, pad_t=0, pad_b=0, pad_val=0):
"""
pad function
specific left, right, top and bottom pad size.
Args:
image[np.array]: input
pad_l: [int], pad size from left, default 0
pad_r: [int], pad size form right, default 0
pad_t: [int], pad size from top, default 0
pad_b: [int], pad size form bottom, default 0
pad_val: [float], the value of pad, , default 0
Returns:
out: [np.array]
Examples:
>>> image_data = kneron_preprocessing.API.pad(image_data,20,40,20,40,-0.5)
"""
assert isinstance(image, np.ndarray)
flow.set_padding(type='specific',pad_l=pad_l,pad_r=pad_r,pad_t=pad_t,pad_b=pad_b,pad_val=pad_val)
image, _ = flow.funcs['padding'](image)
return image
def pad_center(image,size=None, pad_val=0):
"""
pad function
center pad with pad size.
Args:
image[np.array]: input
size: [tuble], (padded_size_w, padded_size_h)
pad_val: [float], the value of pad, , default 0
Returns:
out: [np.array]
Examples:
>>> image_data = kneron_preprocessing.API.pad_center(image_data,size=(56,56),pad_val=-0.5)
"""
assert isinstance(image, np.ndarray)
if size is None:
return image
assert ( (image.shape[0] <= size[1]) & (image.shape[1] <= size[0]) )
flow.set_padding(type='center',padded_w=size[0],padded_h=size[1],pad_val=pad_val)
image, _ = flow.funcs['padding'](image)
return image
def pad_corner(image,size=None, pad_val=0):
"""
pad function
corner pad with pad size.
Args:
image[np.array]: input
size: [tuble], (padded_size_w, padded_size_h)
pad_val: [float], the value of pad, , default 0
Returns:
out: [np.array]
Examples:
>>> image_data = kneron_preprocessing.API.pad_corner(image_data,size=(56,56),pad_val=-0.5)
"""
assert isinstance(image, np.ndarray)
if size is None:
return image
assert ( (image.shape[0] <= size[1]) & (image.shape[1] <= size[0]) )
flow.set_padding(type='corner',padded_w=size[0],padded_h=size[1],pad_val=pad_val)
image, _ = flow.funcs['padding'](image)
return image
def norm(image,scale=256.,bias=-0.5, mean=None, std=None):
"""
norm function
x = (x/scale - bias)
x[0,1,2] = x - mean[0,1,2]
x[0,1,2] = x / std[0,1,2]
Args:
image: [np.array], input
scale: [float], default = 256
bias: [float], default = -0.5
mean: [tuble,3], default = None
std: [tuble,3], default = None
Returns:
out: [np.array]
Examples:
>>> image_data = kneron_preprocessing.API.norm(image_data)
>>> image_data = kneron_preprocessing.API.norm(image_data,mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
"""
assert isinstance(image, np.ndarray)
flow.set_normalize(type='specific',scale=scale, bias=bias, mean=mean, std =std)
image, _ = flow.funcs['normalize'](image)
return image
def inproc_520(image,raw_fmt='rgb565',raw_size=None,npu_size=None, crop_box=None, pad_mode=0, norm='kneron', gray=False, rotate=0, radix=8, bit_width=8, round_w_to_16=True, NUM_BANK_LINE=32,BANK_ENTRY_CNT=512,MAX_IMG_PREPROC_ROW_NUM=511,MAX_IMG_PREPROC_COL_NUM=256):
"""
inproc_520
Args:
image: [np.array], input
crop_box: [tuble], (x1, y1, x2, y2), if None will skip crop
pad_mode: [int], 0: pad 2 sides, 1: pad 1 side, 2: no pad. default = 0
norm: [str], default = 'kneron'
rotate: [int], 0 / 1 / 2 ,default = 0
radix: [int], default = 8
bit_width: [int], default = 8
round_w_to_16: [bool], default = True
gray: [bool], default = False
Returns:
out: [np.array]
Examples:
>>> image_data = kneron_preprocessing.API.inproc_520(image_data,npu_size=(56,56),crop_box=(272,145,460,341),pad_mode=1)
"""
# assert isinstance(image, np.ndarray)
if (not isinstance(image, np.ndarray)):
flow_520.set_raw_img(is_raw_img='yes',raw_img_type = 'bin',raw_img_fmt=raw_fmt, img_in_width=raw_size[0], img_in_height=raw_size[1])
else:
flow_520.set_raw_img(is_raw_img='no')
flow_520.set_color_conversion(source_format='rgb888')
if npu_size is None:
return image
flow_520.set_model_size(w=npu_size[0],h=npu_size[1])
## Crop
if crop_box != None:
flow_520.set_crop(start_x=crop_box[0],start_y=crop_box[1],end_x=crop_box[2],end_y=crop_box[3])
crop_fisrt = True
else:
crop_fisrt = False
## Color
if gray:
flow_520.set_color_conversion(out_format='l',simulation='no')
else:
flow_520.set_color_conversion(out_format='rgb888',simulation='no')
## Resize & Pad
pad_mode = str2int(pad_mode)
if (pad_mode == 0):
pad_type = 'center'
resize_keep_ratio = 'yes'
elif (pad_mode == 1):
pad_type = 'corner'
resize_keep_ratio = 'yes'
else:
pad_type = 'center'
resize_keep_ratio = 'no'
flow_520.set_resize(keep_ratio=resize_keep_ratio)
flow_520.set_padding(type=pad_type)
## Norm
flow_520.set_normalize(type=norm)
## 520 inproc
flow_520.set_520_setting(radix=radix,bit_width=bit_width,rotate=rotate,crop_fisrt=crop_fisrt,round_w_to_16=round_w_to_16,NUM_BANK_LINE=NUM_BANK_LINE,BANK_ENTRY_CNT=BANK_ENTRY_CNT,MAX_IMG_PREPROC_ROW_NUM=MAX_IMG_PREPROC_ROW_NUM,MAX_IMG_PREPROC_COL_NUM=MAX_IMG_PREPROC_COL_NUM)
image_data, _ = flow_520.run_whole_process(image)
return image_data
def inproc_720(image,raw_fmt='rgb565',raw_size=None,npu_size=None, crop_box=None, pad_mode=0, norm='kneron', gray=False):
"""
inproc_720
Args:
image: [np.array], input
crop_box: [tuble], (x1, y1, x2, y2), if None will skip crop
pad_mode: [int], 0: pad 2 sides, 1: pad 1 side, 2: no pad. default = 0
norm: [str], default = 'kneron'
rotate: [int], 0 / 1 / 2 ,default = 0
radix: [int], default = 8
bit_width: [int], default = 8
round_w_to_16: [bool], default = True
gray: [bool], default = False
Returns:
out: [np.array]
Examples:
>>> image_data = kneron_preprocessing.API.inproc_520(image_data,npu_size=(56,56),crop_box=(272,145,460,341),pad_mode=1)
"""
# assert isinstance(image, np.ndarray)
if (not isinstance(image, np.ndarray)):
flow_720.set_raw_img(is_raw_img='yes',raw_img_type = 'bin',raw_img_fmt=raw_fmt, img_in_width=raw_size[0], img_in_height=raw_size[1])
else:
flow_720.set_raw_img(is_raw_img='no')
flow_720.set_color_conversion(source_format='rgb888')
if npu_size is None:
return image
flow_720.set_model_size(w=npu_size[0],h=npu_size[1])
## Crop
if crop_box != None:
flow_720.set_crop(start_x=crop_box[0],start_y=crop_box[1],end_x=crop_box[2],end_y=crop_box[3])
crop_fisrt = True
else:
crop_fisrt = False
## Color
if gray:
flow_720.set_color_conversion(out_format='l',simulation='no')
else:
flow_720.set_color_conversion(out_format='rgb888',simulation='no')
## Resize & Pad
pad_mode = str2int(pad_mode)
if (pad_mode == 0):
pad_type = 'center'
resize_keep_ratio = 'yes'
elif (pad_mode == 1):
pad_type = 'corner'
resize_keep_ratio = 'yes'
else:
pad_type = 'center'
resize_keep_ratio = 'no'
flow_720.set_resize(keep_ratio=resize_keep_ratio)
flow_720.set_padding(type=pad_type)
## 720 inproc
# flow_720.set_720_setting(radix=radix,bit_width=bit_width,rotate=rotate,crop_fisrt=crop_fisrt,round_w_to_16=round_w_to_16,NUM_BANK_LINE=NUM_BANK_LINE,BANK_ENTRY_CNT=BANK_ENTRY_CNT,MAX_IMG_PREPROC_ROW_NUM=MAX_IMG_PREPROC_ROW_NUM,MAX_IMG_PREPROC_COL_NUM=MAX_IMG_PREPROC_COL_NUM)
image_data, _ = flow_720.run_whole_process(image)
return image_data
def bit_match(data1, data2):
"""
bit_match function
check data1 is equal to data2 or not.
Args:
data1: [np.array / str], can be array or txt/bin file
data2: [np.array / str], can be array or txt/bin file
Returns:
out1: [bool], is match or not
out2: [np.array], if not match, save the position for mismatched data
Examples:
>>> result, mismatched = kneron_preprocessing.API.bit_match(data1,data2)
"""
if isinstance(data1, str):
if os.path.splitext(data1)[1] == '.bin':
data1 = np.fromfile(data1, dtype='uint8')
elif os.path.splitext(data1)[1] == '.txt':
data1 = np.loadtxt(data1)
assert isinstance(data1, np.ndarray)
if isinstance(data2, str):
if os.path.splitext(data2)[1] == '.bin':
data2 = np.fromfile(data2, dtype='uint8')
elif os.path.splitext(data2)[1] == '.txt':
data2 = np.loadtxt(data2)
assert isinstance(data2, np.ndarray)
data1 = data1.reshape((-1,1))
data2 = data2.reshape((-1,1))
if not(len(data1) == len(data2)):
print('error len')
return False, np.zeros((1))
else:
ans = data2 - data1
if len(np.where(ans>0)[0]) > 0:
print('error',np.where(ans>0)[0])
return False, np.where(ans>0)[0]
else:
print('pass')
return True, np.zeros((1))
def cpr_to_crp(x_start, x_end, y_start, y_end, pad_l, pad_r, pad_t, pad_b, rx_start, rx_end, ry_start, ry_end):
"""
calculate the parameters of crop->pad->resize flow to HW crop->resize->padding flow
Args:
Returns:
Examples:
"""
pad_l = round(pad_l * (rx_end-rx_start) / (x_end - x_start + pad_l + pad_r))
pad_r = round(pad_r * (rx_end-rx_start) / (x_end - x_start + pad_l + pad_r))
pad_t = round(pad_t * (ry_end-ry_start) / (y_end - y_start + pad_t + pad_b))
pad_b = round(pad_b * (ry_end-ry_start) / (y_end - y_start + pad_t + pad_b))
rx_start +=pad_l
rx_end -=pad_r
ry_start +=pad_t
ry_end -=pad_b
return x_start, x_end, y_start, y_end, pad_l, pad_r, pad_t, pad_b, rx_start, rx_end, ry_start, ry_end

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kneron/preprocessing/Cflow.py Normal file
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import numpy as np
import argparse
import kneron_preprocessing
def main_(args):
image = args.input_file
filefmt = args.file_fmt
if filefmt == 'bin':
raw_format = args.raw_format
raw_w = args.input_width
raw_h = args.input_height
image_data = kneron_preprocessing.API.load_bin(image,raw_format,(raw_w,raw_h))
else:
image_data = kneron_preprocessing.API.load_image(image)
npu_w = args.width
npu_h = args.height
crop_first = True if args.crop_first == "True" else False
if crop_first:
x1 = args.x_pos
y1 = args.y_pos
x2 = args.crop_w + x1
y2 = args.crop_h + y1
crop_box = [x1,y1,x2,y2]
else:
crop_box = None
pad_mode = args.pad_mode
norm_mode = args.norm_mode
bitwidth = args.bitwidth
radix = args.radix
rotate = args.rotate_mode
##
image_data = kneron_preprocessing.API.inproc_520(image_data,npu_size=(npu_w,npu_h),crop_box=crop_box,pad_mode=pad_mode,norm=norm_mode,rotate=rotate,radix=radix,bit_width=bitwidth)
output_file = args.output_file
kneron_preprocessing.API.dump_image(image_data,output_file,'bin','rgba')
return
if __name__ == "__main__":
argparser = argparse.ArgumentParser(
description="preprocessing"
)
argparser.add_argument(
'-i',
'--input_file',
help="input file name"
)
argparser.add_argument(
'-ff',
'--file_fmt',
help="input file format, jpg or bin"
)
argparser.add_argument(
'-rf',
'--raw_format',
help="input file image format, rgb or rgb565 or nir"
)
argparser.add_argument(
'-i_w',
'--input_width',
type=int,
help="input image width"
)
argparser.add_argument(
'-i_h',
'--input_height',
type=int,
help="input image height"
)
argparser.add_argument(
'-o',
'--output_file',
help="output file name"
)
argparser.add_argument(
'-s_w',
'--width',
type=int,
help="output width for npu input",
)
argparser.add_argument(
'-s_h',
'--height',
type=int,
help="output height for npu input",
)
argparser.add_argument(
'-c_f',
'--crop_first',
help="crop first True or False",
)
argparser.add_argument(
'-x',
'--x_pos',
type=int,
help="left up coordinate x",
)
argparser.add_argument(
'-y',
'--y_pos',
type=int,
help="left up coordinate y",
)
argparser.add_argument(
'-c_w',
'--crop_w',
type=int,
help="crop width",
)
argparser.add_argument(
'-c_h',
'--crop_h',
type=int,
help="crop height",
)
argparser.add_argument(
'-p_m',
'--pad_mode',
type=int,
help=" 0: pad 2 sides, 1: pad 1 side, 2: no pad.",
)
argparser.add_argument(
'-n_m',
'--norm_mode',
help="normalizaton mode: yolo, kneron, tf."
)
argparser.add_argument(
'-r_m',
'--rotate_mode',
type=int,
help="rotate mode:0,1,2"
)
argparser.add_argument(
'-bw',
'--bitwidth',
type=int,
help="Int for bitwidth"
)
argparser.add_argument(
'-r',
'--radix',
type=int,
help="Int for radix"
)
args = argparser.parse_args()
main_(args)

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kneron/preprocessing/Flow.py Normal file
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kneron/preprocessing/__init__.py Normal file
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from .Flow import *
from .API import *

285
kneron/preprocessing/funcs/ColorConversion.py Normal file
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import numpy as np
from PIL import Image
from .utils import signed_rounding, clip, str2bool
format_bit = 10
c00_yuv = 1
c02_yuv = 1436
c10_yuv = 1
c11_yuv = -354
c12_yuv = -732
c20_yuv = 1
c21_yuv = 1814
c00_ycbcr = 1192
c02_ycbcr = 1634
c10_ycbcr = 1192
c11_ycbcr = -401
c12_ycbcr = -833
c20_ycbcr = 1192
c21_ycbcr = 2065
Matrix_ycbcr_to_rgb888 = np.array(
[[1.16438356e+00, 1.16438356e+00, 1.16438356e+00],
[2.99747219e-07, - 3.91762529e-01, 2.01723263e+00],
[1.59602686e+00, - 8.12968294e-01, 3.04059479e-06]])
Matrix_rgb888_to_ycbcr = np.array(
[[0.25678824, - 0.14822353, 0.43921569],
[0.50412941, - 0.29099216, - 0.36778824],
[0.09790588, 0.43921569, - 0.07142745]])
Matrix_rgb888_to_yuv = np.array(
[[ 0.29899106, -0.16877996, 0.49988381],
[ 0.5865453, -0.33110385, -0.41826072],
[ 0.11446364, 0.49988381, -0.08162309]])
# Matrix_rgb888_to_yuv = np.array(
# [[0.299, - 0.147, 0.615],
# [0.587, - 0.289, - 0.515],
# [0.114, 0.436, - 0.100]])
# Matrix_yuv_to_rgb888 = np.array(
# [[1.000, 1.000, 1.000],
# [0.000, - 0.394, 2.032],
# [1.140, - 0.581, 0.000]])
class runner(object):
def __init__(self):
self.set = {
'print_info':'no',
'model_size':[0,0],
'numerical_type':'floating',
"source_format": "rgb888",
"out_format": "rgb888",
"options": {
"simulation": "no",
"simulation_format": "rgb888"
}
}
def update(self, **kwargs):
#
self.set.update(kwargs)
## simulation
self.funs = []
if str2bool(self.set['options']['simulation']) and self.set['source_format'].lower() in ['RGB888', 'rgb888', 'RGB', 'rgb']:
if self.set['options']['simulation_format'].lower() in ['YUV422', 'yuv422', 'YUV', 'yuv']:
self.funs.append(self._ColorConversion_RGB888_to_YUV422)
self.set['source_format'] = 'YUV422'
elif self.set['options']['simulation_format'].lower() in ['YCBCR422', 'YCbCr422', 'ycbcr422', 'YCBCR', 'YCbCr', 'ycbcr']:
self.funs.append(self._ColorConversion_RGB888_to_YCbCr422)
self.set['source_format'] = 'YCbCr422'
elif self.set['options']['simulation_format'].lower() in['RGB565', 'rgb565']:
self.funs.append(self._ColorConversion_RGB888_to_RGB565)
self.set['source_format'] = 'RGB565'
## to rgb888
if self.set['source_format'].lower() in ['YUV444', 'yuv444','YUV422', 'yuv422', 'YUV', 'yuv']:
self.funs.append(self._ColorConversion_YUV_to_RGB888)
elif self.set['source_format'].lower() in ['YCBCR444', 'YCbCr444', 'ycbcr444','YCBCR422', 'YCbCr422', 'ycbcr422', 'YCBCR', 'YCbCr', 'ycbcr']:
self.funs.append(self._ColorConversion_YCbCr_to_RGB888)
elif self.set['source_format'].lower() in ['RGB565', 'rgb565']:
self.funs.append(self._ColorConversion_RGB565_to_RGB888)
elif self.set['source_format'].lower() in ['l', 'L' , 'nir', 'NIR']:
self.funs.append(self._ColorConversion_L_to_RGB888)
elif self.set['source_format'].lower() in ['RGBA8888', 'rgba8888' , 'RGBA', 'rgba']:
self.funs.append(self._ColorConversion_RGBA8888_to_RGB888)
## output format
if self.set['out_format'].lower() in ['L', 'l']:
self.funs.append(self._ColorConversion_RGB888_to_L)
elif self.set['out_format'].lower() in['RGB565', 'rgb565']:
self.funs.append(self._ColorConversion_RGB888_to_RGB565)
elif self.set['out_format'].lower() in['RGBA', 'RGBA8888','rgba','rgba8888']:
self.funs.append(self._ColorConversion_RGB888_to_RGBA8888)
elif self.set['out_format'].lower() in['YUV', 'YUV444','yuv','yuv444']:
self.funs.append(self._ColorConversion_RGB888_to_YUV444)
elif self.set['out_format'].lower() in['YUV422','yuv422']:
self.funs.append(self._ColorConversion_RGB888_to_YUV422)
elif self.set['out_format'].lower() in['YCBCR', 'YCBCR444','YCbCr','YCbCr444','ycbcr','ycbcr444']:
self.funs.append(self._ColorConversion_RGB888_to_YCbCr444)
elif self.set['out_format'].lower() in['YCBCR422','YCbCr422','ycbcr422']:
self.funs.append(self._ColorConversion_RGB888_to_YCbCr422)
def print_info(self):
print("<colorConversion>",
"source_format:", self.set['source_format'],
', out_format:', self.set['out_format'],
', simulation:', self.set['options']['simulation'],
', simulation_format:', self.set['options']['simulation_format'])
def run(self, image_data):
assert isinstance(image_data, np.ndarray)
# print info
if str2bool(self.set['print_info']):
self.print_info()
# color
for _, f in enumerate(self.funs):
image_data = f(image_data)
# output
info = {}
return image_data, info
def _ColorConversion_RGB888_to_YUV444(self, image):
## floating
image = image.astype('float')
image = (image @ Matrix_rgb888_to_yuv + 0.5).astype('uint8')
return image
def _ColorConversion_RGB888_to_YUV422(self, image):
# rgb888 to yuv444
image = self._ColorConversion_RGB888_to_YUV444(image)
# yuv444 to yuv422
u2 = image[:, 0::2, 1]
u4 = np.repeat(u2, 2, axis=1)
v2 = image[:, 1::2, 2]
v4 = np.repeat(v2, 2, axis=1)
image[..., 1] = u4
image[..., 2] = v4
return image
def _ColorConversion_YUV_to_RGB888(self, image):
## fixed
h, w, c = image.shape
image_f = image.reshape((h * w, c))
image_rgb_f = np.zeros(image_f.shape, dtype=np.uint8)
for i in range(h * w):
image_y = image_f[i, 0] *1024
if image_f[i, 1] > 127:
image_u = -((~(image_f[i, 1] - 1)) & 0xFF)
else:
image_u = image_f[i, 1]
if image_f[i, 2] > 127:
image_v = -((~(image_f[i, 2] - 1)) & 0xFF)
else:
image_v = image_f[i, 2]
image_r = c00_yuv * image_y + c02_yuv * image_v
image_g = c10_yuv * image_y + c11_yuv * image_u + c12_yuv * image_v
image_b = c20_yuv * image_y + c21_yuv * image_u
image_r = signed_rounding(image_r, format_bit)
image_g = signed_rounding(image_g, format_bit)
image_b = signed_rounding(image_b, format_bit)
image_r = image_r >> format_bit
image_g = image_g >> format_bit
image_b = image_b >> format_bit
image_rgb_f[i, 0] = clip(image_r, 0, 255)
image_rgb_f[i, 1] = clip(image_g, 0, 255)
image_rgb_f[i, 2] = clip(image_b, 0, 255)
image_rgb = image_rgb_f.reshape((h, w, c))
return image_rgb
def _ColorConversion_RGB888_to_YCbCr444(self, image):
## floating
image = image.astype('float')
image = (image @ Matrix_rgb888_to_ycbcr + 0.5).astype('uint8')
image[:, :, 0] += 16
image[:, :, 1] += 128
image[:, :, 2] += 128
return image
def _ColorConversion_RGB888_to_YCbCr422(self, image):
# rgb888 to ycbcr444
image = self._ColorConversion_RGB888_to_YCbCr444(image)
# ycbcr444 to ycbcr422
cb2 = image[:, 0::2, 1]
cb4 = np.repeat(cb2, 2, axis=1)
cr2 = image[:, 1::2, 2]
cr4 = np.repeat(cr2, 2, axis=1)
image[..., 1] = cb4
image[..., 2] = cr4
return image
def _ColorConversion_YCbCr_to_RGB888(self, image):
## floating
if (self.set['numerical_type'] == 'floating'):
image = image.astype('float')
image[:, :, 0] -= 16
image[:, :, 1] -= 128
image[:, :, 2] -= 128
image = ((image @ Matrix_ycbcr_to_rgb888) + 0.5).astype('uint8')
return image
## fixed
h, w, c = image.shape
image_f = image.reshape((h * w, c))
image_rgb_f = np.zeros(image_f.shape, dtype=np.uint8)
for i in range(h * w):
image_y = (image_f[i, 0] - 16) * c00_ycbcr
image_cb = image_f[i, 1] - 128
image_cr = image_f[i, 2] - 128
image_r = image_y + c02_ycbcr * image_cr
image_g = image_y + c11_ycbcr * image_cb + c12_ycbcr * image_cr
image_b = image_y + c21_ycbcr * image_cb
image_r = signed_rounding(image_r, format_bit)
image_g = signed_rounding(image_g, format_bit)
image_b = signed_rounding(image_b, format_bit)
image_r = image_r >> format_bit
image_g = image_g >> format_bit
image_b = image_b >> format_bit
image_rgb_f[i, 0] = clip(image_r, 0, 255)
image_rgb_f[i, 1] = clip(image_g, 0, 255)
image_rgb_f[i, 2] = clip(image_b, 0, 255)
image_rgb = image_rgb_f.reshape((h, w, c))
return image_rgb
def _ColorConversion_RGB888_to_RGB565(self, image):
assert (len(image.shape)==3)
assert (image.shape[2]>=3)
image_rgb565 = np.zeros(image.shape, dtype=np.uint8)
image_rgb = image.astype('uint8')
image_rgb565[:, :, 0] = image_rgb[:, :, 0] >> 3
image_rgb565[:, :, 1] = image_rgb[:, :, 1] >> 2
image_rgb565[:, :, 2] = image_rgb[:, :, 2] >> 3
return image_rgb565
def _ColorConversion_RGB565_to_RGB888(self, image):
assert (len(image.shape)==3)
assert (image.shape[2]==3)
image_rgb = np.zeros(image.shape, dtype=np.uint8)
image_rgb[:, :, 0] = image[:, :, 0] << 3
image_rgb[:, :, 1] = image[:, :, 1] << 2
image_rgb[:, :, 2] = image[:, :, 2] << 3
return image_rgb
def _ColorConversion_L_to_RGB888(self, image):
image_L = image.astype('uint8')
img = Image.fromarray(image_L).convert('RGB')
image_data = np.array(img).astype('uint8')
return image_data
def _ColorConversion_RGB888_to_L(self, image):
image_rgb = image.astype('uint8')
img = Image.fromarray(image_rgb).convert('L')
image_data = np.array(img).astype('uint8')
return image_data
def _ColorConversion_RGBA8888_to_RGB888(self, image):
assert (len(image.shape)==3)
assert (image.shape[2]==4)
return image[:,:,:3]
def _ColorConversion_RGB888_to_RGBA8888(self, image):
assert (len(image.shape)==3)
assert (image.shape[2]==3)
imageA = np.concatenate((image, np.zeros((image.shape[0], image.shape[1], 1), dtype=np.uint8) ), axis=2)
return imageA

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kneron/preprocessing/funcs/Crop.py Normal file
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import numpy as np
from PIL import Image
from .utils import str2int, str2float, str2bool, pad_square_to_4
from .utils_520 import round_up_n
from .Runner_base import Runner_base, Param_base
class General(Param_base):
type = 'center'
align_w_to_4 = False
pad_square_to_4 = False
rounding_type = 0
crop_w = 0
crop_h = 0
start_x = 0.
start_y = 0.
end_x = 0.
end_y = 0.
def update(self, **dic):
self.type = dic['type']
self.align_w_to_4 = str2bool(dic['align_w_to_4'])
self.rounding_type = str2int(dic['rounding_type'])
self.crop_w = str2int(dic['crop_w'])
self.crop_h = str2int(dic['crop_h'])
self.start_x = str2float(dic['start_x'])
self.start_y = str2float(dic['start_y'])
self.end_x = str2float(dic['end_x'])
self.end_y = str2float(dic['end_y'])
def __str__(self):
str_out = [
', type:',str(self.type),
', align_w_to_4:',str(self.align_w_to_4),
', pad_square_to_4:',str(self.pad_square_to_4),
', crop_w:',str(self.crop_w),
', crop_h:',str(self.crop_h),
', start_x:',str(self.start_x),
', start_y:',str(self.start_y),
', end_x:',str(self.end_x),
', end_y:',str(self.end_y)]
return(' '.join(str_out))
class runner(Runner_base):
## overwrite the class in Runner_base
general = General()
def __str__(self):
return('<Crop>')
def update(self, **kwargs):
##
super().update(**kwargs)
##
if (self.general.start_x != self.general.end_x) and (self.general.start_y != self.general.end_y):
self.general.type = 'specific'
elif(self.general.type != 'specific'):
if self.general.crop_w == 0 or self.general.crop_h == 0:
self.general.crop_w = self.common.model_size[0]
self.general.crop_h = self.common.model_size[1]
assert(self.general.crop_w > 0)
assert(self.general.crop_h > 0)
assert(self.general.type.lower() in ['CENTER', 'Center', 'center', 'CORNER', 'Corner', 'corner'])
else:
assert(self.general.type == 'specific')
def run(self, image_data):
## init
img = Image.fromarray(image_data)
w, h = img.size
## get range
if self.general.type.lower() in ['CENTER', 'Center', 'center']:
x1, y1, x2, y2 = self._calcuate_xy_center(w, h)
elif self.general.type.lower() in ['CORNER', 'Corner', 'corner']:
x1, y1, x2, y2 = self._calcuate_xy_corner(w, h)
else:
x1 = self.general.start_x
y1 = self.general.start_y
x2 = self.general.end_x
y2 = self.general.end_y
assert( ((x1 != x2) and (y1 != y2)) )
## rounding
if self.general.rounding_type == 0:
x1 = int(np.floor(x1))
y1 = int(np.floor(y1))
x2 = int(np.ceil(x2))
y2 = int(np.ceil(y2))
else:
x1 = int(round(x1))
y1 = int(round(y1))
x2 = int(round(x2))
y2 = int(round(y2))
if self.general.align_w_to_4:
# x1 = (x1+1) &(~3) #//+2
# x2 = (x2+2) &(~3) #//+1
x1 = (x1+3) &(~3) #//+2
left = w - x2
left = (left+3) &(~3)
x2 = w - left
## pad_square_to_4
if str2bool(self.general.pad_square_to_4):
x1,x2,y1,y2 = pad_square_to_4(x1,x2,y1,y2)
# do crop
box = (x1,y1,x2,y2)
img = img.crop(box)
# print info
if str2bool(self.common.print_info):
self.general.start_x = x1
self.general.start_y = y1
self.general.end_x = x2
self.general.end_y = y2
self.general.crop_w = x2 - x1
self.general.crop_h = y2 - y1
self.print_info()
# output
image_data = np.array(img)
info = {}
info['box'] = box
return image_data, info
## protect fun
def _calcuate_xy_center(self, w, h):
x1 = w/2 - self.general.crop_w / 2
y1 = h/2 - self.general.crop_h / 2
x2 = w/2 + self.general.crop_w / 2
y2 = h/2 + self.general.crop_h / 2
return x1, y1, x2, y2
def _calcuate_xy_corner(self, _1, _2):
x1 = 0
y1 = 0
x2 = self.general.crop_w
y2 = self.general.crop_h
return x1, y1, x2, y2
def do_crop(self, image_data, startW, startH, endW, endH):
return image_data[startH:endH, startW:endW, :]

186
kneron/preprocessing/funcs/Normalize.py Normal file
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import numpy as np
from .utils import str2bool, str2int, str2float, clip_ary
class runner(object):
def __init__(self):
self.set = {
'general': {
'print_info':'no',
'model_size':[0,0],
'numerical_type':'floating',
'type': 'kneron'
},
'floating':{
"scale": 1,
"bias": 0,
"mean": "",
"std": "",
},
'hw':{
"radix":8,
"shift":"",
"sub":""
}
}
return
def update(self, **kwargs):
#
self.set.update(kwargs)
#
if self.set['general']['numerical_type'] == '520':
if self.set['general']['type'].lower() in ['TF', 'Tf', 'tf']:
self.fun_normalize = self._chen_520
self.shift = 7 - self.set['hw']['radix']
self.sub = 128
elif self.set['general']['type'].lower() in ['YOLO', 'Yolo', 'yolo']:
self.fun_normalize = self._chen_520
self.shift = 8 - self.set['hw']['radix']
self.sub = 0
elif self.set['general']['type'].lower() in ['KNERON', 'Kneron', 'kneron']:
self.fun_normalize = self._chen_520
self.shift = 8 - self.set['hw']['radix']
self.sub = 128
else:
self.fun_normalize = self._chen_520
self.shift = 0
self.sub = 0
elif self.set['general']['numerical_type'] == '720':
self.fun_normalize = self._chen_720
self.shift = 0
self.sub = 0
else:
if self.set['general']['type'].lower() in ['TORCH', 'Torch', 'torch']:
self.fun_normalize = self._normalize_torch
self.set['floating']['scale'] = 255.
self.set['floating']['mean'] = [0.485, 0.456, 0.406]
self.set['floating']['std'] = [0.229, 0.224, 0.225]
elif self.set['general']['type'].lower() in ['TF', 'Tf', 'tf']:
self.fun_normalize = self._normalize_tf
self.set['floating']['scale'] = 127.5
self.set['floating']['bias'] = -1.
elif self.set['general']['type'].lower() in ['CAFFE', 'Caffe', 'caffe']:
self.fun_normalize = self._normalize_caffe
self.set['floating']['mean'] = [103.939, 116.779, 123.68]
elif self.set['general']['type'].lower() in ['YOLO', 'Yolo', 'yolo']:
self.fun_normalize = self._normalize_yolo
self.set['floating']['scale'] = 255.
elif self.set['general']['type'].lower() in ['KNERON', 'Kneron', 'kneron']:
self.fun_normalize = self._normalize_kneron
self.set['floating']['scale'] = 256.
self.set['floating']['bias'] = -0.5
else:
self.fun_normalize = self._normalize_customized
self.set['floating']['scale'] = str2float(self.set['floating']['scale'])
self.set['floating']['bias'] = str2float(self.set['floating']['bias'])
if self.set['floating']['mean'] != None:
if len(self.set['floating']['mean']) != 3:
self.set['floating']['mean'] = None
if self.set['floating']['std'] != None:
if len(self.set['floating']['std']) != 3:
self.set['floating']['std'] = None
def print_info(self):
if self.set['general']['numerical_type'] == '520':
print("<normalize>",
'numerical_type', self.set['general']['numerical_type'],
", type:", self.set['general']['type'],
', shift:',self.shift,
', sub:', self.sub)
else:
print("<normalize>",
'numerical_type', self.set['general']['numerical_type'],
", type:", self.set['general']['type'],
', scale:',self.set['floating']['scale'],
', bias:', self.set['floating']['bias'],
', mean:', self.set['floating']['mean'],
', std:',self.set['floating']['std'])
def run(self, image_data):
# print info
if str2bool(self.set['general']['print_info']):
self.print_info()
# norm
image_data = self.fun_normalize(image_data)
# output
info = {}
return image_data, info
def _normalize_torch(self, x):
if len(x.shape) != 3:
return x
x = x.astype('float')
x = x / self.set['floating']['scale']
x[..., 0] -= self.set['floating']['mean'][0]
x[..., 1] -= self.set['floating']['mean'][1]
x[..., 2] -= self.set['floating']['mean'][2]
x[..., 0] /= self.set['floating']['std'][0]
x[..., 1] /= self.set['floating']['std'][1]
x[..., 2] /= self.set['floating']['std'][2]
return x
def _normalize_tf(self, x):
# print('_normalize_tf')
x = x.astype('float')
x = x / self.set['floating']['scale']
x = x + self.set['floating']['bias']
return x
def _normalize_caffe(self, x):
if len(x.shape) != 3:
return x
x = x.astype('float')
x = x[..., ::-1]
x[..., 0] -= self.set['floating']['mean'][0]
x[..., 1] -= self.set['floating']['mean'][1]
x[..., 2] -= self.set['floating']['mean'][2]
return x
def _normalize_yolo(self, x):
# print('_normalize_yolo')
x = x.astype('float')
x = x / self.set['floating']['scale']
return x
def _normalize_kneron(self, x):
# print('_normalize_kneron')
x = x.astype('float')
x = x/self.set['floating']['scale']
x = x + self.set['floating']['bias']
return x
def _normalize_customized(self, x):
# print('_normalize_customized')
x = x.astype('float')
if self.set['floating']['scale'] != 0:
x = x/ self.set['floating']['scale']
x = x + self.set['floating']['bias']
if self.set['floating']['mean'] is not None:
x[..., 0] -= self.set['floating']['mean'][0]
x[..., 1] -= self.set['floating']['mean'][1]
x[..., 2] -= self.set['floating']['mean'][2]
if self.set['floating']['std'] is not None:
x[..., 0] /= self.set['floating']['std'][0]
x[..., 1] /= self.set['floating']['std'][1]
x[..., 2] /= self.set['floating']['std'][2]
return x
def _chen_520(self, x):
# print('_chen_520')
x = (x - self.sub).astype('uint8')
x = (np.right_shift(x,self.shift))
x=x.astype('uint8')
return x
def _chen_720(self, x):
# print('_chen_720')
if self.shift == 1:
x = x + np.array([[self.sub], [self.sub], [self.sub]])
else:
x = x + np.array([[self.sub], [self.sub], [self.sub]])
return x

187
kneron/preprocessing/funcs/Padding.py Normal file
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import numpy as np
from PIL import Image
from .utils import str2bool, str2int, str2float
from .Runner_base import Runner_base, Param_base
class General(Param_base):
type = ''
pad_val = ''
padded_w = ''
padded_h = ''
pad_l = ''
pad_r = ''
pad_t = ''
pad_b = ''
padding_ch = 3
padding_ch_type = 'RGB'
def update(self, **dic):
self.type = dic['type']
self.pad_val = dic['pad_val']
self.padded_w = str2int(dic['padded_w'])
self.padded_h = str2int(dic['padded_h'])
self.pad_l = str2int(dic['pad_l'])
self.pad_r = str2int(dic['pad_r'])
self.pad_t = str2int(dic['pad_t'])
self.pad_b = str2int(dic['pad_b'])
def __str__(self):
str_out = [
', type:',str(self.type),
', pad_val:',str(self.pad_val),
', pad_l:',str(self.pad_l),
', pad_r:',str(self.pad_r),
', pad_r:',str(self.pad_t),
', pad_b:',str(self.pad_b),
', padding_ch:',str(self.padding_ch)]
return(' '.join(str_out))
class Hw(Param_base):
radix = 8
normalize_type = 'floating'
def update(self, **dic):
self.radix = dic['radix']
self.normalize_type = dic['normalize_type']
def __str__(self):
str_out = [
', radix:', str(self.radix),
', normalize_type:',str(self.normalize_type)]
return(' '.join(str_out))
class runner(Runner_base):
## overwrite the class in Runner_base
general = General()
hw = Hw()
def __str__(self):
return('<Padding>')
def update(self, **kwargs):
super().update(**kwargs)
## update pad type & pad length
if (self.general.pad_l != 0) or (self.general.pad_r != 0) or (self.general.pad_t != 0) or (self.general.pad_b != 0):
self.general.type = 'specific'
assert(self.general.pad_l >= 0)
assert(self.general.pad_r >= 0)
assert(self.general.pad_t >= 0)
assert(self.general.pad_b >= 0)
elif(self.general.type != 'specific'):
if self.general.padded_w == 0 or self.general.padded_h == 0:
self.general.padded_w = self.common.model_size[0]
self.general.padded_h = self.common.model_size[1]
assert(self.general.padded_w > 0)
assert(self.general.padded_h > 0)
assert(self.general.type.lower() in ['CENTER', 'Center', 'center', 'CORNER', 'Corner', 'corner'])
else:
assert(self.general.type == 'specific')
## decide pad_val & padding ch
# if numerical_type is floating
if (self.common.numerical_type == 'floating'):
if self.general.pad_val != 'edge':
self.general.pad_val = str2float(self.general.pad_val)
self.general.padding_ch = 3
self.general.padding_ch_type = 'RGB'
# if numerical_type is 520 or 720
else:
if self.general.pad_val == '':
if self.hw.normalize_type.lower() in ['TF', 'Tf', 'tf']:
self.general.pad_val = np.uint8(-128 >> (7 - self.hw.radix))
elif self.hw.normalize_type.lower() in ['YOLO', 'Yolo', 'yolo']:
self.general.pad_val = np.uint8(0 >> (8 - self.hw.radix))
elif self.hw.normalize_type.lower() in ['KNERON', 'Kneron', 'kneron']:
self.general.pad_val = np.uint8(-128 >> (8 - self.hw.radix))
else:
self.general.pad_val = np.uint8(0 >> (8 - self.hw.radix))
else:
self.general.pad_val = str2int(self.general.pad_val)
self.general.padding_ch = 4
self.general.padding_ch_type = 'RGBA'
def run(self, image_data):
# init
shape = image_data.shape
w = shape[1]
h = shape[0]
if len(shape) < 3:
self.general.padding_ch = 1
self.general.padding_ch_type = 'L'
else:
if shape[2] == 3 and self.general.padding_ch == 4:
image_data = np.concatenate((image_data, np.zeros((h, w, 1), dtype=np.uint8) ), axis=2)
## padding
if self.general.type.lower() in ['CENTER', 'Center', 'center']:
img_pad = self._padding_center(image_data, w, h)
elif self.general.type.lower() in ['CORNER', 'Corner', 'corner']:
img_pad = self._padding_corner(image_data, w, h)
else:
img_pad = self._padding_sp(image_data, w, h)
# print info
if str2bool(self.common.print_info):
self.print_info()
# output
info = {}
return img_pad, info
## protect fun
def _padding_center(self, img, ori_w, ori_h):
# img_pad = Image.new(self.general.padding_ch_type, (self.general.padded_w, self.general.padded_h), int(self.general.pad_val[0]))
# img = Image.fromarray(img)
# img_pad.paste(img, ((self.general.padded_w-ori_w)//2, (self.general.padded_h-ori_h)//2))
# return img_pad
padH = self.general.padded_h - ori_h
padW = self.general.padded_w - ori_w
self.general.pad_t = padH // 2
self.general.pad_b = (padH // 2) + (padH % 2)
self.general.pad_l = padW // 2
self.general.pad_r = (padW // 2) + (padW % 2)
if self.general.pad_l < 0 or self.general.pad_r <0 or self.general.pad_t <0 or self.general.pad_b<0:
return img
img_pad = self._padding_sp(img,ori_w,ori_h)
return img_pad
def _padding_corner(self, img, ori_w, ori_h):
# img_pad = Image.new(self.general.padding_ch_type, (self.general.padded_w, self.general.padded_h), self.general.pad_val)
# img_pad.paste(img, (0, 0))
self.general.pad_l = 0
self.general.pad_r = self.general.padded_w - ori_w
self.general.pad_t = 0
self.general.pad_b = self.general.padded_h - ori_h
if self.general.pad_l < 0 or self.general.pad_r <0 or self.general.pad_t <0 or self.general.pad_b<0:
return img
img_pad = self._padding_sp(img,ori_w,ori_h)
return img_pad
def _padding_sp(self, img, ori_w, ori_h):
# block_t = np.zeros((self.general.pad_t, self.general.pad_l + self.general.pad_r + ori_w, self.general.padding_ch), dtype=np.float)
# block_l = np.zeros((ori_h, self.general.pad_l, self.general.padding_ch), dtype=np.float)
# block_r = np.zeros((ori_h, self.general.pad_r, self.general.padding_ch), dtype=np.float)
# block_b = np.zeros((self.general.pad_b, self.general.pad_l + self.general.pad_r + ori_w, self.general.padding_ch), dtype=np.float)
# for i in range(self.general.padding_ch):
# block_t[:, :, i] = np.ones(block_t[:, :, i].shape, dtype=np.float) * self.general.pad_val
# block_l[:, :, i] = np.ones(block_l[:, :, i].shape, dtype=np.float) * self.general.pad_val
# block_r[:, :, i] = np.ones(block_r[:, :, i].shape, dtype=np.float) * self.general.pad_val
# block_b[:, :, i] = np.ones(block_b[:, :, i].shape, dtype=np.float) * self.general.pad_val
# padded_image_hor = np.concatenate((block_l, img, block_r), axis=1)
# padded_image = np.concatenate((block_t, padded_image_hor, block_b), axis=0)
# return padded_image
if self.general.padding_ch == 1:
pad_range = ( (self.general.pad_t, self.general.pad_b),(self.general.pad_l, self.general.pad_r) )
else:
pad_range = ((self.general.pad_t, self.general.pad_b),(self.general.pad_l, self.general.pad_r),(0,0))
if isinstance(self.general.pad_val, str):
if self.general.pad_val == 'edge':
padded_image = np.pad(img, pad_range, mode="edge")
else:
padded_image = np.pad(img, pad_range, mode="constant",constant_values=0)
else:
padded_image = np.pad(img, pad_range, mode="constant",constant_values=self.general.pad_val)
return padded_image

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kneron/preprocessing/funcs/Resize.py Normal file
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import numpy as np
import cv2
from PIL import Image
from .utils import str2bool, str2int
from ctypes import c_float
from .Runner_base import Runner_base, Param_base
class General(Param_base):
type = 'bilinear'
keep_ratio = True
zoom = True
calculate_ratio_using_CSim = True
resize_w = 0
resize_h = 0
resized_w = 0
resized_h = 0
def update(self, **dic):
self.type = dic['type']
self.keep_ratio = str2bool(dic['keep_ratio'])
self.zoom = str2bool(dic['zoom'])
self.calculate_ratio_using_CSim = str2bool(dic['calculate_ratio_using_CSim'])
self.resize_w = str2int(dic['resize_w'])
self.resize_h = str2int(dic['resize_h'])
def __str__(self):
str_out = [
', type:',str(self.type),
', keep_ratio:',str(self.keep_ratio),
', zoom:',str(self.zoom),
', calculate_ratio_using_CSim:',str(self.calculate_ratio_using_CSim),
', resize_w:',str(self.resize_w),
', resize_h:',str(self.resize_h),
', resized_w:',str(self.resized_w),
', resized_h:',str(self.resized_h)]
return(' '.join(str_out))
class Hw(Param_base):
resize_bit = 12
def update(self, **dic):
pass
def __str__(self):
str_out = [
', resize_bit:',str(self.resize_bit)]
return(' '.join(str_out))
class runner(Runner_base):
## overwrite the class in Runner_base
general = General()
hw = Hw()
def __str__(self):
return('<Resize>')
def update(self, **kwargs):
super().update(**kwargs)
## if resize size has not been assigned, then it will take model size as resize size
if self.general.resize_w == 0 or self.general.resize_h == 0:
self.general.resize_w = self.common.model_size[0]
self.general.resize_h = self.common.model_size[1]
assert(self.general.resize_w > 0)
assert(self.general.resize_h > 0)
##
if self.common.numerical_type == '520':
self.general.type = 'fixed_520'
elif self.common.numerical_type == '720':
self.general.type = 'fixed_720'
assert(self.general.type.lower() in ['BILINEAR', 'Bilinear', 'bilinear', 'BICUBIC', 'Bicubic', 'bicubic', 'FIXED', 'Fixed', 'fixed', 'FIXED_520', 'Fixed_520', 'fixed_520', 'FIXED_720', 'Fixed_720', 'fixed_720','CV', 'cv', 'opencv', 'OpenCV', 'CV2', 'cv2'])
def run(self, image_data):
## init
ori_w = image_data.shape[1]
ori_h = image_data.shape[0]
info = {}
##
if self.general.keep_ratio:
self.general.resized_w, self.general.resized_h = self.calcuate_scale_keep_ratio(self.general.resize_w,self.general.resize_h, ori_w, ori_h, self.general.calculate_ratio_using_CSim)
else:
self.general.resized_w = int(self.general.resize_w)
self.general.resized_h = int(self.general.resize_h)
assert(self.general.resized_w > 0)
assert(self.general.resized_h > 0)
##
if (self.general.resized_w > ori_w) or (self.general.resized_h > ori_h):
if not self.general.zoom:
info['size'] = (ori_w,ori_h)
if str2bool(self.common.print_info):
print('no resize')
self.print_info()
return image_data, info
## resize
if self.general.type.lower() in ['BILINEAR', 'Bilinear', 'bilinear']:
image_data = self.do_resize_bilinear(image_data, self.general.resized_w, self.general.resized_h)
elif self.general.type.lower() in ['BICUBIC', 'Bicubic', 'bicubic']:
image_data = self.do_resize_bicubic(image_data, self.general.resized_w, self.general.resized_h)
elif self.general.type.lower() in ['CV', 'cv', 'opencv', 'OpenCV', 'CV2', 'cv2']:
image_data = self.do_resize_cv2(image_data, self.general.resized_w, self.general.resized_h)
elif self.general.type.lower() in ['FIXED', 'Fixed', 'fixed', 'FIXED_520', 'Fixed_520', 'fixed_520', 'FIXED_720', 'Fixed_720', 'fixed_720']:
image_data = self.do_resize_fixed(image_data, self.general.resized_w, self.general.resized_h, self.hw.resize_bit, self.general.type)
# output
info['size'] = (self.general.resized_w, self.general.resized_h)
# print info
if str2bool(self.common.print_info):
self.print_info()
return image_data, info
def calcuate_scale_keep_ratio(self, tar_w, tar_h, ori_w, ori_h, calculate_ratio_using_CSim):
if not calculate_ratio_using_CSim:
scale_w = tar_w * 1.0 / ori_w*1.0
scale_h = tar_h * 1.0 / ori_h*1.0
scale = scale_w if scale_w < scale_h else scale_h
new_w = int(round(ori_w * scale))
new_h = int(round(ori_h * scale))
return new_w, new_h
## calculate_ratio_using_CSim
scale_w = c_float(tar_w * 1.0 / (ori_w * 1.0)).value
scale_h = c_float(tar_h * 1.0 / (ori_h * 1.0)).value
scale_ratio = 0.0
scale_target_w = 0
scale_target_h = 0
padH = 0
padW = 0
bScaleW = True if scale_w < scale_h else False
if bScaleW:
scale_ratio = scale_w
scale_target_w = int(c_float(scale_ratio * ori_w + 0.5).value)
scale_target_h = int(c_float(scale_ratio * ori_h + 0.5).value)
assert (abs(scale_target_w - tar_w) <= 1), "Error: scale down width cannot meet expectation\n"
padH = tar_h - scale_target_h
padW = 0
assert (padH >= 0), "Error: padH shouldn't be less than zero\n"
else:
scale_ratio = scale_h
scale_target_w = int(c_float(scale_ratio * ori_w + 0.5).value)
scale_target_h = int(c_float(scale_ratio * ori_h + 0.5).value)
assert (abs(scale_target_h - tar_h) <= 1), "Error: scale down height cannot meet expectation\n"
padW = tar_w - scale_target_w
padH = 0
assert (padW >= 0), "Error: padW shouldn't be less than zero\n"
new_w = tar_w - padW
new_h = tar_h - padH
return new_w, new_h
def do_resize_bilinear(self, image_data, resized_w, resized_h):
img = Image.fromarray(image_data)
img = img.resize((resized_w, resized_h), Image.BILINEAR)
image_data = np.array(img).astype('uint8')
return image_data
def do_resize_bicubic(self, image_data, resized_w, resized_h):
img = Image.fromarray(image_data)
img = img.resize((resized_w, resized_h), Image.BICUBIC)
image_data = np.array(img).astype('uint8')
return image_data
def do_resize_cv2(self, image_data, resized_w, resized_h):
image_data = cv2.resize(image_data, (resized_w, resized_h))
image_data = np.array(image_data)
# image_data = np.array(image_data).astype('uint8')
return image_data
def do_resize_fixed(self, image_data, resized_w, resized_h, resize_bit, type):
if len(image_data.shape) < 3:
m, n = image_data.shape
tmp = np.zeros((m,n,3), dtype=np.uint8)
tmp[:,:,0] = image_data
image_data = tmp
c = 3
gray = True
else:
m, n, c = image_data.shape
gray = False
resolution = 1 << resize_bit
# Width
ratio = int(((n - 1) << resize_bit) / (resized_w - 1))
ratio_cnt = 0
src_x = 0
resized_image_w = np.zeros((m, resized_w, c), dtype=np.uint8)
for dst_x in range(resized_w):
while ratio_cnt > resolution:
ratio_cnt = ratio_cnt - resolution
src_x = src_x + 1
mul1 = np.ones((m, c)) * (resolution - ratio_cnt)
mul2 = np.ones((m, c)) * ratio_cnt
resized_image_w[:, dst_x, :] = np.multiply(np.multiply(
image_data[:, src_x, :], mul1) + np.multiply(image_data[:, src_x + 1, :], mul2), 1/resolution)
ratio_cnt = ratio_cnt + ratio
# Height
ratio = int(((m - 1) << resize_bit) / (resized_h - 1))
## NPU HW special case 2 , only on 520
if type.lower() in ['FIXED_520', 'Fixed_520', 'fixed_520']:
if (((ratio * (resized_h - 1)) % 4096 == 0) and ratio != 4096):
ratio -= 1
ratio_cnt = 0
src_x = 0
resized_image = np.zeros(
(resized_h, resized_w, c), dtype=np.uint8)
for dst_x in range(resized_h):
while ratio_cnt > resolution:
ratio_cnt = ratio_cnt - resolution
src_x = src_x + 1
mul1 = np.ones((resized_w, c)) * (resolution - ratio_cnt)
mul2 = np.ones((resized_w, c)) * ratio_cnt
## NPU HW special case 1 , both on 520 / 720
if (((dst_x > 0) and ratio_cnt == resolution) and (ratio != resolution)):
if type.lower() in ['FIXED_520', 'Fixed_520', 'fixed_520','FIXED_720', 'Fixed_720', 'fixed_720' ]:
resized_image[dst_x, :, :] = np.multiply(np.multiply(
resized_image_w[src_x+1, :, :], mul1) + np.multiply(resized_image_w[src_x + 2, :, :], mul2), 1/resolution)
else:
resized_image[dst_x, :, :] = np.multiply(np.multiply(
resized_image_w[src_x, :, :], mul1) + np.multiply(resized_image_w[src_x + 1, :, :], mul2), 1/resolution)
ratio_cnt = ratio_cnt + ratio
if gray:
resized_image = resized_image[:,:,0]
return resized_image

45
kneron/preprocessing/funcs/Rotate.py Normal file
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import numpy as np
from .utils import str2bool, str2int
class runner(object):
def __init__(self, *args, **kwargs):
self.set = {
'operator': '',
"rotate_direction": 0,
}
self.update(*args, **kwargs)
def update(self, *args, **kwargs):
self.set.update(kwargs)
self.rotate_direction = str2int(self.set['rotate_direction'])
# print info
if str2bool(self.set['b_print']):
self.print_info()
def print_info(self):
print("<rotate>",
'rotate_direction', self.rotate_direction,)
def run(self, image_data):
image_data = self._rotate(image_data)
return image_data
def _rotate(self,img):
if self.rotate_direction == 1 or self.rotate_direction == 2:
col, row, unit = img.shape
pInBuf = img.reshape((-1,1))
pOutBufTemp = np.zeros((col* row* unit))
for r in range(row):
for c in range(col):
for u in range(unit):
if self.rotate_direction == 1:
pOutBufTemp[unit * (c * row + (row - r - 1))+u] = pInBuf[unit * (r * col + c)+u]
elif self.rotate_direction == 2:
pOutBufTemp[unit * (row * (col - c - 1) + r)+u] = pInBuf[unit * (r * col + c)+u]
img = pOutBufTemp.reshape((col,row,unit))
return img

59
kneron/preprocessing/funcs/Runner_base.py Normal file
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from abc import ABCMeta, abstractmethod
class Param_base(object):
@abstractmethod
def update(self,**dic):
raise NotImplementedError("Must override")
def load_dic(self, key, **dic):
if key in dic:
param = eval('self.'+key)
param = dic[key]
def __str__(self):
str_out = []
return(' '.join(str_out))
class Common(Param_base):
print_info = False
model_size = [0,0]
numerical_type = 'floating'
def update(self, **dic):
self.print_info = dic['print_info']
self.model_size = dic['model_size']
self.numerical_type = dic['numerical_type']
def __str__(self):
str_out = ['numerical_type:',str(self.numerical_type)]
return(' '.join(str_out))
class Runner_base(metaclass=ABCMeta):
common = Common()
general = Param_base()
floating = Param_base()
hw = Param_base()
def update(self, **kwargs):
## update param
self.common.update(**kwargs['common'])
self.general.update(**kwargs['general'])
assert(self.common.numerical_type.lower() in ['floating', '520', '720'])
if (self.common.numerical_type == 'floating'):
if (self.floating.__class__.__name__ != 'Param_base'):
self.floating.update(**kwargs['floating'])
else:
if (self.hw.__class__.__name__ != 'Param_base'):
self.hw.update(**kwargs['hw'])
def print_info(self):
if (self.common.numerical_type == 'floating'):
print(self, self.common, self.general, self.floating)
else:
print(self, self.common, self.general, self.hw)

2
kneron/preprocessing/funcs/__init__.py Normal file
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from . import ColorConversion, Padding, Resize, Crop, Normalize, Rotate

372
kneron/preprocessing/funcs/utils.py Normal file
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import numpy as np
from PIL import Image
import struct
def pad_square_to_4(x_start, x_end, y_start, y_end):
w_int = x_end - x_start
h_int = y_end - y_start
pad = w_int - h_int
if pad > 0:
pad_s = (pad >> 1) &(~3)
pad_e = pad - pad_s
y_start -= pad_s
y_end += pad_e
else:#//pad <=0
pad_s = -(((pad) >> 1) &(~3))
pad_e = (-pad) - pad_s
x_start -= pad_s
x_end += pad_e
return x_start, x_end, y_start, y_end
def str_fill(value):
if len(value) == 1:
value = "0" + value
elif len(value) == 0:
value = "00"
return value
def clip_ary(value):
list_v = []
for i in range(len(value)):
v = value[i] % 256
list_v.append(v)
return list_v
def str2bool(v):
if isinstance(v,bool):
return v
return v.lower() in ('TRUE', 'True', 'true', '1', 'T', 't', 'Y', 'YES', 'y', 'yes')
def str2int(s):
if s == "":
s = 0
s = int(s)
return s
def str2float(s):
if s == "":
s = 0
s = float(s)
return s
def clip(value, mini, maxi):
if value < mini:
result = mini
elif value > maxi:
result = maxi
else:
result = value
return result
def clip_ary(value):
list_v = []
for i in range(len(value)):
v = value[i] % 256
list_v.append(v)
return list_v
def signed_rounding(value, bit):
if value < 0:
value = value - (1 << (bit - 1))
else:
value = value + (1 << (bit - 1))
return value
def hex_loader(data_folder,**kwargs):
format_mode = kwargs['raw_img_fmt']
src_h = kwargs['img_in_height']
src_w = kwargs['img_in_width']
if format_mode in ['YUV444', 'yuv444', 'YCBCR444', 'YCbCr444', 'ycbcr444']:
output = hex_yuv444(data_folder,src_h,src_w)
elif format_mode in ['RGB565', 'rgb565']:
output = hex_rgb565(data_folder,src_h,src_w)
elif format_mode in ['YUV422', 'yuv422', 'YCBCR422', 'YCbCr422', 'ycbcr422']:
output = hex_yuv422(data_folder,src_h,src_w)
return output
def hex_rgb565(hex_folder,src_h,src_w):
pix_per_line = 8
byte_per_line = 16
f = open(hex_folder)
pixel_r = []
pixel_g = []
pixel_b = []
# Ignore the first line
f.readline()
input_line = int((src_h * src_w)/pix_per_line)
for i in range(input_line):
readline = f.readline()
for j in range(int(byte_per_line/2)-1, -1, -1):
data1 = int(readline[(j * 4 + 0):(j * 4 + 2)], 16)
data0 = int(readline[(j * 4 + 2):(j * 4 + 4)], 16)
r = ((data1 & 0xf8) >> 3)
g = (((data0 & 0xe0) >> 5) + ((data1 & 0x7) << 3))
b = (data0 & 0x1f)
pixel_r.append(r)
pixel_g.append(g)
pixel_b.append(b)
ary_r = np.array(pixel_r, dtype=np.uint8)
ary_g = np.array(pixel_g, dtype=np.uint8)
ary_b = np.array(pixel_b, dtype=np.uint8)
output = np.concatenate((ary_r[:, None], ary_g[:, None], ary_b[:, None]), axis=1)
output = output.reshape((src_h, src_w, 3))
return output
def hex_yuv444(hex_folder,src_h,src_w):
pix_per_line = 4
byte_per_line = 16
f = open(hex_folder)
byte0 = []
byte1 = []
byte2 = []
byte3 = []
# Ignore the first line
f.readline()
input_line = int((src_h * src_w)/pix_per_line)
for i in range(input_line):
readline = f.readline()
for j in range(byte_per_line-1, -1, -1):
data = int(readline[(j*2):(j*2+2)], 16)
if (j+1) % 4 == 0:
byte0.append(data)
elif (j+2) % 4 == 0:
byte1.append(data)
elif (j+3) % 4 == 0:
byte2.append(data)
elif (j+4) % 4 == 0:
byte3.append(data)
# ary_a = np.array(byte0, dtype=np.uint8)
ary_v = np.array(byte1, dtype=np.uint8)
ary_u = np.array(byte2, dtype=np.uint8)
ary_y = np.array(byte3, dtype=np.uint8)
output = np.concatenate((ary_y[:, None], ary_u[:, None], ary_v[:, None]), axis=1)
output = output.reshape((src_h, src_w, 3))
return output
def hex_yuv422(hex_folder,src_h,src_w):
pix_per_line = 8
byte_per_line = 16
f = open(hex_folder)
pixel_y = []
pixel_u = []
pixel_v = []
# Ignore the first line
f.readline()
input_line = int((src_h * src_w)/pix_per_line)
for i in range(input_line):
readline = f.readline()
for j in range(int(byte_per_line/4)-1, -1, -1):
data3 = int(readline[(j * 8 + 0):(j * 8 + 2)], 16)
data2 = int(readline[(j * 8 + 2):(j * 8 + 4)], 16)
data1 = int(readline[(j * 8 + 4):(j * 8 + 6)], 16)
data0 = int(readline[(j * 8 + 6):(j * 8 + 8)], 16)
pixel_y.append(data3)
pixel_y.append(data1)
pixel_u.append(data2)
pixel_u.append(data2)
pixel_v.append(data0)
pixel_v.append(data0)
ary_y = np.array(pixel_y, dtype=np.uint8)
ary_u = np.array(pixel_u, dtype=np.uint8)
ary_v = np.array(pixel_v, dtype=np.uint8)
output = np.concatenate((ary_y[:, None], ary_u[:, None], ary_v[:, None]), axis=1)
output = output.reshape((src_h, src_w, 3))
return output
def bin_loader(data_folder,**kwargs):
format_mode = kwargs['raw_img_fmt']
src_h = kwargs['img_in_height']
src_w = kwargs['img_in_width']
if format_mode in ['YUV','yuv','YUV444', 'yuv444', 'YCBCR','YCbCr','ycbcr','YCBCR444', 'YCbCr444', 'ycbcr444']:
output = bin_yuv444(data_folder,src_h,src_w)
elif format_mode in ['RGB565', 'rgb565']:
output = bin_rgb565(data_folder,src_h,src_w)
elif format_mode in ['NIR', 'nir','NIR888', 'nir888']:
output = bin_nir(data_folder,src_h,src_w)
elif format_mode in ['YUV422', 'yuv422', 'YCBCR422', 'YCbCr422', 'ycbcr422']:
output = bin_yuv422(data_folder,src_h,src_w)
elif format_mode in ['RGB888','rgb888']:
output = np.fromfile(data_folder, dtype='uint8')
output = output.reshape(src_h,src_w,3)
elif format_mode in ['RGBA8888','rgba8888', 'RGBA' , 'rgba']:
output_temp = np.fromfile(data_folder, dtype='uint8')
output_temp = output_temp.reshape(src_h,src_w,4)
output = output_temp[:,:,0:3]
return output
def bin_yuv444(in_img_path,src_h,src_w):
# load bin
struct_fmt = '1B'
struct_len = struct.calcsize(struct_fmt)
struct_unpack = struct.Struct(struct_fmt).unpack_from
row = src_h
col = src_w
pixels = row*col
raw = []
with open(in_img_path, "rb") as f:
while True:
data = f.read(struct_len)
if not data: break
s = struct_unpack(data)
raw.append(s[0])
raw = raw[:pixels*4]
#
output = np.zeros((pixels * 3), dtype=np.uint8)
cnt = 0
for i in range(0, pixels*4, 4):
#Y
output[cnt] = raw[i+3]
#U
cnt += 1
output[cnt] = raw[i+2]
#V
cnt += 1
output[cnt] = raw[i+1]
cnt += 1
output = output.reshape((src_h,src_w,3))
return output
def bin_yuv422(in_img_path,src_h,src_w):
# load bin
struct_fmt = '1B'
struct_len = struct.calcsize(struct_fmt)
struct_unpack = struct.Struct(struct_fmt).unpack_from
row = src_h
col = src_w
pixels = row*col
raw = []
with open(in_img_path, "rb") as f:
while True:
data = f.read(struct_len)
if not data: break
s = struct_unpack(data)
raw.append(s[0])
raw = raw[:pixels*2]
#
output = np.zeros((pixels * 3), dtype=np.uint8)
cnt = 0
for i in range(0, pixels*2, 4):
#Y0
output[cnt] = raw[i+3]
#U0
cnt += 1
output[cnt] = raw[i+2]
#V0
cnt += 1
output[cnt] = raw[i]
#Y1
cnt += 1
output[cnt] = raw[i+1]
#U1
cnt += 1
output[cnt] = raw[i+2]
#V1
cnt += 1
output[cnt] = raw[i]
cnt += 1
output = output.reshape((src_h,src_w,3))
return output
def bin_rgb565(in_img_path,src_h,src_w):
# load bin
struct_fmt = '1B'
struct_len = struct.calcsize(struct_fmt)
struct_unpack = struct.Struct(struct_fmt).unpack_from
row = src_h
col = src_w
pixels = row*col
rgba565 = []
with open(in_img_path, "rb") as f:
while True:
data = f.read(struct_len)
if not data: break
s = struct_unpack(data)
rgba565.append(s[0])
rgba565 = rgba565[:pixels*2]
# rgb565_bin to numpy_array
output = np.zeros((pixels * 3), dtype=np.uint8)
cnt = 0
for i in range(0, pixels*2, 2):
temp = rgba565[i]
temp2 = rgba565[i+1]
#R-5
output[cnt] = (temp2 >>3)
#G-6
cnt += 1
output[cnt] = ((temp & 0xe0) >> 5) + ((temp2 & 0x07) << 3)
#B-5
cnt += 1
output[cnt] = (temp & 0x1f)
cnt += 1
output = output.reshape((src_h,src_w,3))
return output
def bin_nir(in_img_path,src_h,src_w):
# load bin
struct_fmt = '1B'
struct_len = struct.calcsize(struct_fmt)
struct_unpack = struct.Struct(struct_fmt).unpack_from
nir = []
with open(in_img_path, "rb") as f:
while True:
data = f.read(struct_len)
if not data: break
s = struct_unpack(data)
nir.append(s[0])
nir = nir[:src_h*src_w]
pixels = len(nir)
# nir_bin to numpy_array
output = np.zeros((len(nir) * 3), dtype=np.uint8)
for i in range(0, pixels):
output[i*3]=nir[i]
output[i*3+1]=nir[i]
output[i*3+2]=nir[i]
output = output.reshape((src_h,src_w,3))
return output

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kneron/preprocessing/funcs/utils_520.py Normal file
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import math
def round_up_16(num):
return ((num + (16 - 1)) & ~(16 - 1))
def round_up_n(num, n):
if (num > 0):
temp = float(num) / n
return math.ceil(temp) * n
else:
return -math.ceil(float(-num) / n) * n
def cal_img_row_offset(crop_num, pad_num, start_row, out_row, orig_row):
scaled_img_row = int(out_row - (pad_num[1] + pad_num[3]))
if ((start_row - pad_num[1]) > 0):
img_str_row = int((start_row - pad_num[1]))
else:
img_str_row = 0
valid_row = int(orig_row - (crop_num[1] + crop_num[3]))
img_str_row = int(valid_row * img_str_row / scaled_img_row)
return int(img_str_row + crop_num[1])
def get_pad_num(pad_num_orig, left, up, right, bottom):
pad_num = [0]*4
for i in range(0,4):
pad_num[i] = pad_num_orig[i]
if not (left):
pad_num[0] = 0
if not (up):
pad_num[1] = 0
if not (right):
pad_num[2] = 0
if not (bottom):
pad_num[3] = 0
return pad_num
def get_byte_per_pixel(raw_fmt):
if raw_fmt.lower() in ['RGB888', 'rgb888', 'RGB', 'rgb888']:
return 4
elif raw_fmt.lower() in ['YUV', 'yuv', 'YUV422', 'yuv422']:
return 2
elif raw_fmt.lower() in ['RGB565', 'rgb565']:
return 2
elif raw_fmt.lower() in ['NIR888', 'nir888', 'NIR', 'nir']:
return 1
else:
return -1

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import numpy as np
from PIL import Image
def twos_complement(value):
value = int(value)
# msb = (value & 0x8000) * (1/np.power(2, 15))
msb = (value & 0x8000) >> 15
if msb == 1:
if (((~value) & 0xFFFF) + 1) >= 0xFFFF:
result = ((~value) & 0xFFFF)
else:
result = (((~value) & 0xFFFF) + 1)
result = result * (-1)
else:
result = value
return result
def twos_complement_pix(value):
h, _ = value.shape
for i in range(h):
value[i, 0] = twos_complement(value[i, 0])
return value
def clip(value, mini, maxi):
if value < mini:
result = mini
elif value > maxi:
result = maxi
else:
result = value
return result
def clip_pix(value, mini, maxi):
h, _ = value.shape
for i in range(h):
value[i, 0] = clip(value[i, 0], mini, maxi)
return value

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kneron/quantize_yolov5.py Normal file
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import os
import numpy as np
import torch
import ktc # Kneron Toolchain
import onnx
from yolov5_preprocess import Yolov5_preprocess
import kneron_preprocessing
# 設定裝置
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 設定圖片大小
imgsz_h, imgsz_w = 640, 640
# 設定正確的數據目錄
data_path = "/workspace/yolov5/data50"
# 確保 data50 內有圖片
files_found = [f for _, _, files in os.walk(data_path) for f in files if f.lower().endswith((".jpg", ".jpeg", ".png", ".bmp"))]
if not files_found:
raise FileNotFoundError(f"❌ Error: No images found in {data_path}! Please check your dataset.")
print(f"✅ Found {len(files_found)} images in {data_path}")
# **獲取 ONNX 模型的輸入名稱**
onnx_model_path = "/workspace/yolov5/runs/train/exp24/weights/latest.opt.onnx"
m = onnx.load(onnx_model_path)
input_name = m.graph.input[0].name # 確保 key 與 ONNX input name 一致
km = ktc.ModelConfig(20008, "0001", "720", onnx_model=onnx_model_path)
# 存儲預處理後的圖片數據
img_list = []
# 遍歷 data50 並進行預處理
for root, _, files in os.walk(data_path):
for f in files:
fullpath = os.path.join(root, f)
# **只處理圖片文件**
if not f.lower().endswith((".jpg", ".jpeg", ".png", ".bmp")):
print(f"⚠️ Skipping non-image file: {fullpath}")
continue
# **嘗試處理圖片**
try:
img_data, _ = Yolov5_preprocess(fullpath, device, imgsz_h, imgsz_w)
img_data = img_data.cpu().numpy()
print(f"✅ Processed: {fullpath}")
img_list.append(img_data)
except Exception as e:
print(f"❌ Failed to process {fullpath}: {e}")
# **確保 img_list 不是空的**
if not img_list:
raise ValueError("❌ Error: No valid images were processed! Please check the image paths and formats.")
# **執行 BIE 量化**
bie_model_path = km.analysis({input_name: img_list})
# **確認 BIE 模型是否生成**
if not os.path.exists(bie_model_path):
raise RuntimeError(f"❌ Error: BIE model was not generated! Please check your quantization process.")
# 顯示成功訊息
print("\n✅ Fixed-point analysis done! BIE model saved to:", bie_model_path)

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kneron/removenode.py Normal file
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import onnx
import ktc.onnx_optimizer as kneron_opt
from onnx import helper
def replace_sigmoid_with_identity(model):
"""
Replaces all Sigmoid nodes with Identity nodes to maintain model integrity.
"""
for node in model.graph.node:
if node.op_type == "Sigmoid":
print(f"Replacing {node.name} with Identity")
identity_node = helper.make_node(
"Identity",
inputs=node.input,
outputs=node.output,
name=node.name + "_identity"
)
model.graph.node.extend([identity_node])
model.graph.node.remove(node)
return model
def process_onnx(input_onnx_path, output_onnx_path):
""" Replaces Sigmoid with Identity and saves the new model """
model = onnx.load(input_onnx_path)
model = replace_sigmoid_with_identity(model)
onnx.save(model, output_onnx_path)
print(f"Modified ONNX model saved to: {output_onnx_path}")
# 使用 Docker 掛載的路徑
input_onnx = "/workspace/yolov5/runs/train/exp24/weights/best_simplified.onnx"
output_onnx = "/workspace/yolov5/runs/train/exp24/weights/best_no_sigmoid.onnx"
process_onnx(input_onnx, output_onnx)

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kneron/yolov5_export.py Normal file
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import os
import torch
import sys
import yaml
import argparse
from yolov5_runner import Yolov5Runner
def save_weight(num_classes):
current_path=os.getcwd()
par_path = os.path.dirname(current_path)
sys.path.append(os.path.join(par_path, 'yolov5'))
from models.yolo import Model
num_classes = num_classes
device=torch.device('cpu')
ckpt = torch.load(path, map_location=device)
model = Model(yaml_path, nc=num_classes)
ckpt['model'] = {k: v for k, v in ckpt['model'].float().state_dict().items() if k in model.state_dict() and model.state_dict()[k].shape == v.shape}
model.load_state_dict(ckpt['model'])
torch.save(model.state_dict(),pt_path,_use_new_zipfile_serialization=False)
def export_onnx(input_h, input_w, num_classes):
onnx_batch_size, onnx_img_h, onnx_img_w = 1, input_h, input_w
yolov5_model = Yolov5Runner(model_path=pt_path, yaml_path=yaml_path, grid20_path=grid20_path, grid40_path=grid40_path, grid80_path=grid80_path, num_classes=num_classes, imgsz_h=onnx_img_h, imgsz_w=onnx_img_w, conf_thres=0.001, iou_thres=0.65, top_k_num=3000, vanish_point=0.0)
# Input
img = torch.zeros((onnx_batch_size, 3, onnx_img_h, onnx_img_w))
# img = img.type(torch.cuda.FloatTensor)
# Load PyTorch model
model = yolov5_model.yolov5_model
model.eval()
model.model[-1].export = True # set Detect() layer export=True
y = model(img) # dry run
# ONNX export
try:
import onnx
print('\nStarting ONNX export with onnx %s...' % onnx.__version__)
print('****onnx file****',onnx_export_file)
torch.onnx.export(model, img, onnx_export_file, verbose=False, opset_version=11, keep_initializers_as_inputs=True, input_names=['images'], output_names=['classes', 'boxes'] if y is None else ['output'])
# Checks
onnx_model = onnx.load(onnx_export_file) # load onnx model
onnx.checker.check_model(onnx_model) # check onnx model
print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model
print('ONNX export success, saved as %s' % onnx_export_file)
except Exception as e:
print('ONNX export failure: %s' % e)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, default='../yolov5/data/pretrained_paths_520.yaml', help='the path to pretrained model paths yaml file')
args = parser.parse_args()
with open(args.data) as f:
data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
num_classes = data_dict['nc']
input_w = data_dict['input_w']
input_h = data_dict['input_h']
grid_dir = data_dict['grid_dir']
grid20_path = data_dict['grid20_path']
grid40_path = data_dict['grid40_path']
grid80_path = data_dict['grid80_path']
path = data_dict['path']
pt_path=data_dict['pt_path']
yaml_path=data_dict['yaml_path']
onnx_export_file = data_dict['onnx_export_file']
save_weight(num_classes)
export_onnx(input_h, input_w, num_classes)

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kneron/yolov5_preprocess.py Normal file
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# coding: utf-8
import torch
import cv2
import numpy as np
import math
import time
import kneron_preprocessing
kneron_preprocessing.API.set_default_as_520()
torch.backends.cudnn.deterministic = True
img_formats = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.dng']
def make_divisible(x, divisor):
# Returns x evenly divisble by divisor
return math.ceil(x / divisor) * divisor
def check_img_size(img_size, s=32):
# Verify img_size is a multiple of stride s
new_size = make_divisible(img_size, int(s)) # ceil gs-multiple
if new_size != img_size:
print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size))
return new_size
def letterbox_ori(img, new_shape=(640, 640), color=(0, 0, 0), auto=True, scaleFill=False, scaleup=True):
# Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
shape = img.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # only scale down, do not scale up (for better test mAP)
r = min(r, 1.0)
# Compute padding
ratio = r, r # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) # width, height
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
#img = kneron_preprocessing.API.resize(img,size=new_unpad, keep_ratio = False)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
# top, bottom = int(0), int(round(dh + 0.1))
# left, right = int(0), int(round(dw + 0.1))
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
#img = kneron_preprocessing.API.pad(img, left, right, top, bottom, 0)
return img, ratio, (dw, dh)
def letterbox(img, new_shape=(640, 640), color=(0, 0, 0), auto=True, scaleFill=False, scaleup=True):
# Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
shape = img.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # only scale down, do not scale up (for better test mAP)
r = min(r, 1.0)
# Compute padding
ratio = r, r # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) # width, height
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
# dw /= 2 # divide padding into 2 sides
# dh /= 2
if shape[::-1] != new_unpad: # resize
#img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
img = kneron_preprocessing.API.resize(img,size=new_unpad, keep_ratio = False)
# top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
# left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
top, bottom = int(0), int(round(dh + 0.1))
left, right = int(0), int(round(dw + 0.1))
#img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
img = kneron_preprocessing.API.pad(img, left, right, top, bottom, 0)
return img, ratio, (dw, dh)
def letterbox_test(img, new_shape=(640, 640), color=(0, 0, 0), auto=True, scaleFill=False, scaleup=True):
ratio = 1.0, 1.0
dw, dh = 0, 0
img = kneron_preprocessing.API.resize(img, size=(480, 256), keep_ratio=False, type='bilinear')
return img, ratio, (dw, dh)
def LoadImages(path,img_size): #_rgb # for inference
if isinstance(path, str):
img0 = cv2.imread(path) # BGR
else:
img0 = path # BGR
# Padded resize
img = letterbox(img0, new_shape=img_size)[0]
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img)
return img, img0
def LoadImages_yyy(path,img_size): #_yyy # for inference
if isinstance(path, str):
img0 = cv2.imread(path) # BGR
else:
img0 = path # BGR
yvu = cv2.cvtColor(img0, cv2.COLOR_BGR2YCrCb)
y, v, u = cv2.split(yvu)
img0 = np.stack((y,)*3, axis=-1)
# Padded resize
img = letterbox(img0, new_shape=img_size)[0]
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img)
return img, img0
def LoadImages_yuv420(path,img_size): #_yuv420 # for inference
if isinstance(path, str):
img0 = cv2.imread(path) # BGR
else:
img0 = path # BGR
img_h, img_w = img0.shape[:2]
img_h = (img_h // 2) * 2
img_w = (img_w // 2) * 2
img = img0[:img_h,:img_w,:]
yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV_I420)
img0= cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR_I420) #yuv420
# Padded resize
img = letterbox(img0, new_shape=img_size)[0]
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img)
return img, img0
def Yolov5_preprocess(image_path, device, imgsz_h, imgsz_w) :
model_stride_max = 32
imgsz_h = check_img_size(imgsz_h, s=model_stride_max) # check img_size
imgsz_w = check_img_size(imgsz_w, s=model_stride_max) # check img_size
img, im0 = LoadImages(image_path, img_size=(imgsz_h,imgsz_w))
img = kneron_preprocessing.API.norm(img) #path1
#print('img',img.shape)
img = torch.from_numpy(img).to(device) #path1,path2
# img = img.float() # uint8 to fp16/32 #path2
# img /= 255.0#256.0 - 0.5 # 0 - 255 to -0.5 - 0.5 #path2
if img.ndimension() == 3:
img = img.unsqueeze(0)
return img, im0

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import os
import torch
import sys
import argparse
import yaml
def save_weight(num_classes):
from models.yolo import Model
num_classes = num_classes
device=torch.device('cpu')
ckpt = torch.load(path, map_location=device)
model = Model(yaml_path, nc=num_classes)
ckpt['model'] = {k: v for k, v in ckpt['model'].float().state_dict().items() if k in model.state_dict() and model.state_dict()[k].shape == v.shape}
model.load_state_dict(ckpt['model'])
torch.save(model.state_dict(),pt_path,_use_new_zipfile_serialization=False)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, default='data/pretrained_paths_520.yaml', help='the path to pretrained model paths yaml file')
args = parser.parse_args()
with open(args.data) as f:
data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
input_w = data_dict['input_w']
input_h = data_dict['input_h']
grid_dir = data_dict['grid_dir']
grid20_path = data_dict['grid20_path']
grid40_path = data_dict['grid40_path']
grid80_path = data_dict['grid80_path']
path = data_dict['path']
pt_path=data_dict['pt_path']
yaml_path=data_dict['yaml_path']
save_weight(data_dict['nc'])

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# This file contains modules common to various models
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image, ImageDraw
from utils.datasets import letterbox
from utils.general import non_max_suppression, make_divisible, scale_coords, xyxy2xywh
from utils.plots import color_list
def autopad(k, p=None): # kernel, padding
# Pad to 'same'
if p is None:
p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
return p
def DWConv(c1, c2, k=1, s=1, act=True):
# Depthwise convolution
return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
class Conv(nn.Module):
# Standard convolution
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
super(Conv, self).__init__()
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
self.bn = nn.BatchNorm2d(c2)
#self.act = nn.Hardswish() if act else nn.Identity()
self.act = nn.LeakyReLU(0.1, inplace=True) if act else nn.Identity()
def forward(self, x):
# print('x',x.size())
y = self.act(self.bn(self.conv(x)))
# print('y',y.size())
return y
def fuseforward(self, x):
return self.act(self.conv(x))
class Bottleneck(nn.Module):
# Standard bottleneck
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
super(Bottleneck, self).__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c_, c2, 3, 1, g=g)
self.add = shortcut and c1 == c2
def forward(self, x):
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
class BottleneckCSP(nn.Module):
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
super(BottleneckCSP, self).__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
self.cv4 = Conv(2 * c_, c2, 1, 1)
self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
self.act = nn.LeakyReLU(0.1, inplace=True)
self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
def forward(self, x):
y1 = self.cv3(self.m(self.cv1(x)))
y2 = self.cv2(x)
return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
class Focus(nn.Module):
# Focus wh information into c-space
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
super(Focus, self).__init__()
self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
class Concat(nn.Module):
# Concatenate a list of tensors along dimension
def __init__(self, dimension=1):
super(Concat, self).__init__()
self.d = dimension
def forward(self, x):
# print('Concat x.size()',x.size())
y=torch.cat(x, self.d)
# print('Concat y.size()',y.size())
return y
class NMS(nn.Module):
# Non-Maximum Suppression (NMS) module
conf = 0.25 # confidence threshold
iou = 0.45 # IoU threshold
classes = None # (optional list) filter by class
def __init__(self):
super(NMS, self).__init__()
def forward(self, x):
return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
class autoShape(nn.Module):
# input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
img_size = 640 # inference size (pixels)
conf = 0.25 # NMS confidence threshold
iou = 0.45 # NMS IoU threshold
classes = None # (optional list) filter by class
def __init__(self, model):
super(autoShape, self).__init__()
self.model = model.eval()
def forward(self, imgs, size=640, augment=False, profile=False):
# supports inference from various sources. For height=720, width=1280, RGB images example inputs are:
# opencv: imgs = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(720,1280,3)
# PIL: imgs = Image.open('image.jpg') # HWC x(720,1280,3)
# numpy: imgs = np.zeros((720,1280,3)) # HWC
# torch: imgs = torch.zeros(16,3,720,1280) # BCHW
# multiple: imgs = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
p = next(self.model.parameters()) # for device and type
if isinstance(imgs, torch.Tensor): # torch
return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
# Pre-process
if not isinstance(imgs, list):
imgs = [imgs]
shape0, shape1 = [], [] # image and inference shapes
batch = range(len(imgs)) # batch size
for i in batch:
imgs[i] = np.array(imgs[i]) # to numpy
imgs[i] = imgs[i][:, :, :3] if imgs[i].ndim == 3 else np.tile(imgs[i][:, :, None], 3) # enforce 3ch input
s = imgs[i].shape[:2] # HWC
shape0.append(s) # image shape
g = (size / max(s)) # gain
shape1.append([y * g for y in s])
shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape
x = [letterbox(imgs[i], new_shape=shape1, auto=False)[0] for i in batch] # pad
x = np.stack(x, 0) if batch[-1] else x[0][None] # stack
x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW
x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32
# Inference
with torch.no_grad():
y = self.model(x, augment, profile)[0] # forward
y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS
# Post-process
for i in batch:
if y[i] is not None:
y[i][:, :4] = scale_coords(shape1, y[i][:, :4], shape0[i])
return Detections(imgs, y, self.names)
class Detections:
# detections class for YOLOv5 inference results
def __init__(self, imgs, pred, names=None):
super(Detections, self).__init__()
self.imgs = imgs # list of images as numpy arrays
self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
self.names = names # class names
self.xyxy = pred # xyxy pixels
self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
gn = [torch.Tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.]) for im in imgs] # normalization gains
self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
def display(self, pprint=False, show=False, save=False):
colors = color_list()
for i, (img, pred) in enumerate(zip(self.imgs, self.pred)):
str = f'Image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} '
if pred is not None:
for c in pred[:, -1].unique():
n = (pred[:, -1] == c).sum() # detections per class
str += f'{n} {self.names[int(c)]}s, ' # add to string
if show or save:
img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img # from np
for *box, conf, cls in pred: # xyxy, confidence, class
# str += '%s %.2f, ' % (names[int(cls)], conf) # label
ImageDraw.Draw(img).rectangle(box, width=4, outline=colors[int(cls) % 10]) # plot
if save:
f = f'results{i}.jpg'
str += f"saved to '{f}'"
img.save(f) # save
if show:
img.show(f'Image {i}') # show
if pprint:
print(str)
def print(self):
self.display(pprint=True) # print results
def show(self):
self.display(show=True) # show results
def save(self):
self.display(save=True) # save results
class Flatten(nn.Module):
# Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions
@staticmethod
def forward(x):
return x.view(x.size(0), -1)
class Classify(nn.Module):
# Classification head, i.e. x(b,c1,20,20) to x(b,c2)
def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
super(Classify, self).__init__()
self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1)
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) # to x(b,c2,1,1)
self.flat = Flatten()
def forward(self, x):
z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list
return self.flat(self.conv(z)) # flatten to x(b,c2)
class SPP(nn.Module):
# Spatial pyramid pooling layer used in YOLOv3-SPP
def __init__(self, c1, c2, k=(5, 9, 13)):
super(SPP, self).__init__()
c_ = c1 // 2 # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
def forward(self, x):
x = self.cv1(x)
return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))

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# This file contains experimental modules
import numpy as np
import torch
import torch.nn as nn
from models.common import Conv, DWConv
from utils.google_utils import attempt_download
class CrossConv(nn.Module):
# Cross Convolution Downsample
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
# ch_in, ch_out, kernel, stride, groups, expansion, shortcut
super(CrossConv, self).__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, (1, k), (1, s))
self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
self.add = shortcut and c1 == c2
def forward(self, x):
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
class C3(nn.Module):
# Cross Convolution CSP
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
super(C3, self).__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
self.cv4 = Conv(2 * c_, c2, 1, 1)
self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
self.act = nn.LeakyReLU(0.1, inplace=True)
self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
def forward(self, x):
y1 = self.cv3(self.m(self.cv1(x)))
y2 = self.cv2(x)
return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
class Sum(nn.Module):
# Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
def __init__(self, n, weight=False): # n: number of inputs
super(Sum, self).__init__()
self.weight = weight # apply weights boolean
self.iter = range(n - 1) # iter object
if weight:
self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True) # layer weights
def forward(self, x):
y = x[0] # no weight
if self.weight:
w = torch.sigmoid(self.w) * 2
for i in self.iter:
y = y + x[i + 1] * w[i]
else:
for i in self.iter:
y = y + x[i + 1]
return y
class GhostConv(nn.Module):
# Ghost Convolution https://github.com/huawei-noah/ghostnet
def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups
super(GhostConv, self).__init__()
c_ = c2 // 2 # hidden channels
self.cv1 = Conv(c1, c_, k, s, None, g, act)
self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
def forward(self, x):
y = self.cv1(x)
return torch.cat([y, self.cv2(y)], 1)
class GhostBottleneck(nn.Module):
# Ghost Bottleneck https://github.com/huawei-noah/ghostnet
def __init__(self, c1, c2, k, s):
super(GhostBottleneck, self).__init__()
c_ = c2 // 2
self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw
DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
def forward(self, x):
return self.conv(x) + self.shortcut(x)
class MixConv2d(nn.Module):
# Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
super(MixConv2d, self).__init__()
groups = len(k)
if equal_ch: # equal c_ per group
i = torch.linspace(0, groups - 1E-6, c2).floor() # c2 indices
c_ = [(i == g).sum() for g in range(groups)] # intermediate channels
else: # equal weight.numel() per group
b = [c2] + [0] * groups
a = np.eye(groups + 1, groups, k=-1)
a -= np.roll(a, 1, axis=1)
a *= np.array(k) ** 2
a[0] = 1
c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b
self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
self.bn = nn.BatchNorm2d(c2)
self.act = nn.LeakyReLU(0.1, inplace=True)
def forward(self, x):
return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
class Ensemble(nn.ModuleList):
# Ensemble of models
def __init__(self):
super(Ensemble, self).__init__()
def forward(self, x, augment=False):
y = []
for module in self:
y.append(module(x, augment)[0])
# y = torch.stack(y).max(0)[0] # max ensemble
# y = torch.cat(y, 1) # nms ensemble
y = torch.stack(y).mean(0) # mean ensemble
return y, None # inference, train output
def attempt_load(weights, map_location=None):
# Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
model = Ensemble()
for w in weights if isinstance(weights, list) else [weights]:
ckpt = torch.load(w, map_location=map_location)
model.append( ckpt['model'].float().fuse().eval() ) # load FP32 model
# Compatibility updates
for m in model.modules():
if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
m.inplace = True # pytorch 1.7.0 compatibility
elif type(m) is Conv:
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
if len(model) == 1:
return model[-1] # return model
else:
print('Ensemble created with %s\n' % weights)
for k in ['names', 'stride']:
setattr(model, k, getattr(model[-1], k))
return model # return ensemble

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"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
Usage:
$ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
"""
import argparse
import sys
import time
sys.path.append('./') # to run '$ python *.py' files in subdirectories
import torch
import torch.nn as nn
import models
from models.experimental import attempt_load
from utils.activations import Hardswish
from utils.general import set_logging, check_img_size
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path') # from yolov5/models/
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
opt = parser.parse_args()
opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand
print(opt)
set_logging()
t = time.time()
# Load PyTorch model
model = attempt_load(opt.weights, map_location=torch.device('cpu')) # load FP32 model
labels = model.names
# Checks
gs = int(max(model.stride)) # grid size (max stride)
opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples
# Input
img = torch.zeros(opt.batch_size, 3, *opt.img_size) # image size(1,3,320,192) iDetection
# Update model
for k, m in model.named_modules():
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
if isinstance(m, models.common.Conv) and isinstance(m.act, nn.Hardswish):
m.act = Hardswish() # assign activation
# if isinstance(m, models.yolo.Detect):
# m.forward = m.forward_export # assign forward (optional)
model.model[-1].export = True # set Detect() layer export=True
y = model(img) # dry run
# TorchScript export
try:
print('\nStarting TorchScript export with torch %s...' % torch.__version__)
f = opt.weights.replace('.pt', '.torchscript.pt') # filename
ts = torch.jit.trace(model, img)
ts.save(f)
print('TorchScript export success, saved as %s' % f)
except Exception as e:
print('TorchScript export failure: %s' % e)
# ONNX export
try:
import onnx
print('\nStarting ONNX export with onnx %s...' % onnx.__version__)
f = opt.weights.replace('.pt', '.onnx') # filename
torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],
output_names=['classes', 'boxes'] if y is None else ['output'])
# Checks
onnx_model = onnx.load(f) # load onnx model
onnx.checker.check_model(onnx_model) # check onnx model
# print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model
print('ONNX export success, saved as %s' % f)
except Exception as e:
print('ONNX export failure: %s' % e)
# CoreML export
try:
import coremltools as ct
print('\nStarting CoreML export with coremltools %s...' % ct.__version__)
# convert model from torchscript and apply pixel scaling as per detect.py
model = ct.convert(ts, inputs=[ct.ImageType(name='image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
f = opt.weights.replace('.pt', '.mlmodel') # filename
model.save(f)
print('CoreML export success, saved as %s' % f)
except Exception as e:
print('CoreML export failure: %s' % e)
# Finish
print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t))

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