rknn-toolkit2/examples/functions/model_pruning/test.py

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


def show_outputs(outputs):
    np.save('./functions_model_pruning_0.npy', outputs[0])
    output = outputs[0].reshape(-1)
    print(output.shape)
    index = sorted(range(len(output)), key=lambda k : output[k], reverse=True)
    fp = open('./labels.txt', 'r')
    labels = fp.readlines()
    top5_str = 'mobilenet\n-----TOP 5-----\n'
    for i in range(5):
        value = output[index[i]]
        if value > 0:
            topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1])
        else:
            topi = '[ -1]: 0.0\n'
        top5_str += topi
    print(top5_str.strip())


if __name__ == '__main__':

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

    # Pre-process config
    print('--> Config model')
    rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[58.82, 58.82, 58.82], quant_img_RGB2BGR=True, target_platform='rk3566', model_pruning=True)
    print('done')

    # Load model (from https://github.com/shicai/MobileNet-Caffe)
    print('--> Loading model')
    ret = rknn.load_caffe(model='./mobilenet_deploy.prototxt',
                          blobs='./mobilenet.caffemodel')
    if ret != 0:
        print('Load model failed!')
        exit(ret)
    print('done')

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

    # Tips
    print('')
    print('======================================== Tips =================================================================')
    print('When verbose = True, the following similar prompts will appear during the build process, indicating that ')
    print('model pruning has been effective for this model:')
    print('')
    print('  I model_pruning ...')
    print('  I model_pruning results:')
    print('  I     Weight: -1.12145 MB (-6.9%)')
    print('  I     GFLOPs: -0.15563    (-13.4%)')
    print('  I model_pruning done.')
    print('')
    print('The meaning of this prompts is that 6.9% of the weight was removed, and approximately 13.4% of computility were saved.')
    print('Please note that not all models can be pruned, only models with sparse weights can be pruned.')
    print('===============================================================================================================')
    print('')

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

    # Set inputs
    img = cv2.imread('./dog_224x224.jpg')
    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'])
    show_outputs(outputs)
    print('done')

    rknn.release()