rknn-toolkit2/examples/tensorflow/inception_v3_qat/test.py

import numpy as np
import cv2
import os
import urllib
import tarfile
import shutil
import traceback
import time
import sys
from rknn.api import RKNN

PB_FILE = './inception_v3_quant_frozen.pb'
RKNN_MODEL_PATH = './inception_v3_quant_frozen.rknn'
INPUTS = ['input']
OUTPUTS = ['InceptionV3/Logits/SpatialSqueeze']
IMG_PATH = './goldfish_299x299.jpg'
INPUT_SIZE = 299


def show_outputs(outputs):
    output = outputs[0][0]
    index = sorted(range(len(output)), key=lambda k : output[k], reverse=True)
    fp = open('./labels.txt', 'r')
    labels = fp.readlines()
    top5_str = 'inception_v3\n-----TOP 5-----\n'
    for i in range(5):
        value = output[index[i]]
        if value > 0:
            topi = '[{:>4d}] 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())


def readable_speed(speed):
    speed_bytes = float(speed)
    speed_kbytes = speed_bytes / 1024
    if speed_kbytes > 1024:
        speed_mbytes = speed_kbytes / 1024
        if speed_mbytes > 1024:
            speed_gbytes = speed_mbytes / 1024
            return "{:.2f} GB/s".format(speed_gbytes)
        else:
            return "{:.2f} MB/s".format(speed_mbytes)
    else:
        return "{:.2f} KB/s".format(speed_kbytes)


def show_progress(blocknum, blocksize, totalsize):
    speed = (blocknum * blocksize) / (time.time() - start_time)
    speed_str = " Speed: {}".format(readable_speed(speed))
    recv_size = blocknum * blocksize

    f = sys.stdout
    progress = (recv_size / totalsize)
    progress_str = "{:.2f}%".format(progress * 100)
    n = round(progress * 50)
    s = ('#' * n).ljust(50, '-')
    f.write(progress_str.ljust(8, ' ') + '[' + s + ']' + speed_str)
    f.flush()
    f.write('\r\n')


if __name__ == '__main__':

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

    # If inception_v3_quant_frozen.pb does not exist, download it.
    # Download address:
    # https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz
    # https://ftzr.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/asset/inception_v3_qat/inception_v3_quant_frozen.pb
    if not os.path.exists(PB_FILE):
        print('--> Download {}'.format(PB_FILE))
        url = 'https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz'
        download_file = 'inception_v3_quant.tgz'
        try:
            start_time = time.time()
            urllib.request.urlretrieve(url, download_file, show_progress)
        except:
            print('Download {} failed.'.format(download_file))
            print(traceback.format_exc())
            exit(-1)
        try:
            tar = tarfile.open(download_file)
            target_dir = os.path.splitext(download_file)[0]
            if os.path.isdir(target_dir):
                pass
            else:
                os.mkdir(target_dir)
            tar.extractall(target_dir)
            tar.close()
        except:
            print('Extract {} failed.'.format(download_file))
            exit(-1)
        pb_file = os.path.join(target_dir, PB_FILE)
        if os.path.exists(pb_file):
            shutil.copyfile(pb_file, './inception_v3_quant_frozen.pb')
            shutil.rmtree(target_dir)
            os.remove(download_file)
        print('done')

    # Pre-process config
    print('--> Config model')
    rknn.config(mean_values=[104, 117, 123], std_values=[128, 128, 128], target_platform='rk3566')
    print('done')

    # Load model
    print('--> Loading model')
    ret = rknn.load_tensorflow(tf_pb=PB_FILE,
                               inputs=INPUTS,
                               outputs=OUTPUTS,
                               input_size_list=[[1, INPUT_SIZE, INPUT_SIZE, 3]])
    if ret != 0:
        print('Load model failed!')
        exit(ret)
    print('done')

    # Build model
    print('--> Building model')
    ret = rknn.build(do_quantization=False)
    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(IMG_PATH)
    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'])
    np.save('./tensorflow_inception_v3_qat_0.npy', outputs[0])
    x = outputs[0]
    output = np.exp(x)/np.sum(np.exp(x))
    outputs = [output]
    show_outputs(outputs)
    print('done')

    rknn.release()