rknn-toolkit2/examples/pytorch/resnet18_qat/test.py

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


def export_pytorch_model():
    import torch
    import torchvision.models as models
    net = models.quantization.resnet18(pretrained=True, quantize=True)
    net.eval()
    trace_model = torch.jit.trace(net, torch.Tensor(1, 3, 224, 224))
    trace_model.save('./resnet18_i8.pt')

def show_outputs(output):
    index = sorted(range(len(output)), key=lambda k : output[k], reverse=True)
    fp = open('./labels.txt', 'r')
    labels = fp.readlines()
    top5_str = 'resnet18\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())


def show_perfs(perfs):
    perfs = 'perfs: {}\n'.format(perfs)
    print(perfs)


def softmax(x):
    return np.exp(x)/sum(np.exp(x))

def torch_version():
    import torch
    torch_ver = torch.__version__.split('.')
    torch_ver[2] = torch_ver[2].split('+')[0]
    return [int(v) for v in torch_ver]

if __name__ == '__main__':

    if torch_version() < [1, 9, 0]:
        import torch
        print("Your torch version is '{}', in order to better support the Quantization Aware Training (QAT) model,\n"
              "Please update the torch version to '1.9.0' or higher!".format(torch.__version__))
        exit(0)

    model = './resnet18_i8.pt'
    if not os.path.exists(model):
        export_pytorch_model()

    input_size_list = [[1, 3, 224, 224]]

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

    # Pre-process config
    print('--> Config model')
    rknn.config(mean_values=[123.675, 116.28, 103.53], std_values=[58.395, 58.395, 58.395], target_platform='rk3566')
    print('done')

    # Load model
    print('--> Loading model')
    ret = rknn.load_pytorch(model=model, input_size_list=input_size_list)
    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('./resnet_18.rknn')
    if ret != 0:
        print('Export rknn model failed!')
        exit(ret)
    print('done')

    # Set inputs
    img = cv2.imread('./space_shuttle_224.jpg')
    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('./pytorch_resnet18_qat_0.npy', outputs[0])
    show_outputs(softmax(np.array(outputs[0][0])))
    print('done')

    rknn.release()