rknn-toolkit2/examples/darknet/yolov3_416x416/test.py

import numpy as np
import cv2
from rknn.api import RKNN

from yolov3_utils import yolov3_post_process, draw, download_yolov3_weight

GRID0 = 13
GRID1 = 26
GRID2 = 52
LISTSIZE = 85
SPAN = 3

if __name__ == '__main__':

    # Model from https://pjreddie.com/darknet/yolo/
    # The yolov3.cfg changed the width & height from 608 to 416 on the basis of 
    # https://github.com/pjreddie/darknet/blob/master/cfg/yolov3.cfg
    MODEL_PATH = './yolov3.cfg'
    WEIGHT_PATH = './yolov3.weights'
    RKNN_MODEL_PATH = './yolov3_416.rknn'
    im_file = './dog_bike_car_416x416.jpg'
    DATASET = './dataset.txt'

    # Download yolov3.weight
    download_yolov3_weight(WEIGHT_PATH)

    # Create RKNN object
    rknn = RKNN(verbose=True)

    # Pre-process config
    print('--> Config model')
    rknn.config(mean_values=[0, 0, 0], std_values=[255, 255, 255], target_platform='rk3566')
    print('done')

    # Load model
    print('--> Loading model')
    ret = rknn.load_darknet(model=MODEL_PATH, weight=WEIGHT_PATH)
    if ret != 0:
        print('Load model failed!')
        exit(ret)
    print('done')

    # Build model
    print('--> Building model')
    ret = rknn.build(do_quantization=True, dataset=DATASET)
    if ret != 0:
        print('Build model failed!')
        exit(ret)
    print('done')

    # Export rknn model
    print('--> Export rknn model')
    ret = rknn.export_rknn(RKNN_MODEL_PATH)
    if ret != 0:
        print('Export rknn model failed!')
        exit(ret)
    print('done')

    # Set inputs
    img = cv2.imread(im_file)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = np.expand_dims(img, 0)

    # Init runtime environment
    print('--> Init runtime environment')
    ret = rknn.init_runtime()
    if ret != 0:
        print('Init runtime environment failed!')
        exit(ret)
    print('done')

    # Inference
    print('--> Running model')
    outputs = rknn.inference(inputs=[img], data_format=['nhwc'])
    print('done')

    input0_data = outputs[0]
    np.save('./darknet_yolov3_416x416_0.npy', input0_data)
    input1_data = outputs[1]
    np.save('./darknet_yolov3_416x416_1.npy', input1_data)
    input2_data = outputs[2]
    np.save('./darknet_yolov3_416x416_2.npy', input1_data)

    input0_data = input0_data.reshape(SPAN, LISTSIZE, GRID0, GRID0)
    input1_data = input1_data.reshape(SPAN, LISTSIZE, GRID1, GRID1)
    input2_data = input2_data.reshape(SPAN, LISTSIZE, GRID2, GRID2)

    input_data = []
    input_data.append(np.transpose(input0_data, (2, 3, 0, 1)))
    input_data.append(np.transpose(input1_data, (2, 3, 0, 1)))
    input_data.append(np.transpose(input2_data, (2, 3, 0, 1)))

    boxes, classes, scores = yolov3_post_process(input_data)

    image = cv2.imread(im_file)
    if boxes is not None:
        draw(image, boxes, scores, classes)

    print('Save results to result.jpg!')
    cv2.imwrite('result.jpg', image)

    rknn.release()