onnx 转rknn 不做量化处理，rknn推理精度下降 - Toybrick

if __name__ == '__main__':
# 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='rk3568', optimization_level=0)
print('done')
# Load ONNX model
print('--> Loading model')
ret = rknn.load_onnx(model=ONNX_MODEL)
if ret != 0:
print('Load model failed!')
exit(ret)
print('done')
# Build model
print('--> Building model')
# ret = rknn.build(do_quantization=QUANTIZE_ON, dataset=DATASET)
ret = rknn.build(do_quantization=False, dataset=DATASET)
if ret != 0:
print('Build model failed!')
exit(ret)
print('done')
# Accuracy test 连扳推理测试在板子上的查看每一层的推理精度是否下降
# ret = rknn.accuracy_**ysis(
# inputs=[IMG_PATH],
# output_dir="snapshat",
# target="rk3568")
if ret != 0:
print('Accuracy test failed!')
exit(ret)
print('done')
# Export RKNN model
print('--> Export rknn model')
ret = rknn.export_rknn(RKNN_MODEL)
if ret != 0:
print('Export rknn model failed!')
exit(ret)
print('done')
# Init runtime environment
print('--> Init runtime environment')
ret = rknn.init_runtime()
if ret != 0:
print('Init runtime environment failed!')
exit(ret)
print('done')
# Set inputs
img = cv2.imread(IMG_PATH)
# img, ratio, (dw, dh) = letterbox(img, new_shape=(IMG_SIZE, IMG_SIZE))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
# Inference
print('--> Running model')
img2 = np.expand_dims(img, 0)
print("++++++++++++++++")
print(img2.shape)
print("++++++++++++++++")
outputs = rknn.inference(inputs=[img2], data_format=['nhwc'])
# np.save('./onnx_yolov5_0.npy', outputs[0])
# np.save('./onnx_yolov5_1.npy', outputs[1])
# np.save('./onnx_yolov5_2.npy', outputs[2])
print('done')
# post process
input0_data = outputs[0]
input1_data = outputs[1]
input2_data = outputs[2]
input0_data = input0_data.reshape([3, -1]+list(input0_data.shape[-2:]))
input1_data = input1_data.reshape([3, -1]+list(input1_data.shape[-2:]))
input2_data = input2_data.reshape([3, -1]+list(input2_data.shape[-2:]))
input_data = list()
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 = yolov5_post_process(input_data)
img_1 = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
if boxes is not None:
draw(img_1, boxes, scores, classes)
cv2.imwrite('result.jpg', img_1)
print('Save results to result.jpg!')
rknn.release() 这是视频数据加载过程