1import numpy as np 2import cv2 3from rknn.api import RKNN 4 5 6def show_outputs(outputs): 7 output_ = outputs[0].reshape((-1, 1000)) 8 for output in output_: 9 output_sorted = sorted(output, reverse=True) 10 top5_str = 'mobilenet_v1\n-----TOP 5-----\n' 11 for i in range(5): 12 value = output_sorted[i] 13 index = np.where(output == value) 14 for j in range(len(index)): 15 if (i + j) >= 5: 16 break 17 if value > 0: 18 topi = '{}: {}\n'.format(index[j], value) 19 else: 20 topi = '-1: 0.0\n' 21 top5_str += topi 22 print(top5_str) 23 24 25def show_perfs(perfs): 26 perfs = 'perfs: {}\n'.format(outputs) 27 print(perfs) 28 29 30if __name__ == '__main__': 31 32 # Create RKNN object 33 rknn = RKNN(verbose=True) 34 35 # The multiple sets of input shapes specified by the user, to simulate the function of dynamic input. 36 # Please make sure the model can be dynamic when enable 'config.dynamic_input', and shape in dynamic_input are correctly! 37 dynamic_input = [ 38 [[1,3,224,224]], # set 0: [input0_224] 39 [[1,3,192,192]], # set 1: [input0_192] 40 [[1,3,160,160]], # set 2: [input0_160] 41 ] 42 43 # Pre-process config 44 print('--> Config model') 45 rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[58.82, 58.82, 58.82], quant_img_RGB2BGR=True, dynamic_input=dynamic_input) 46 print('done') 47 48 # Load model 49 print('--> Loading model') 50 ret = rknn.load_caffe(model='../../caffe/mobilenet_v2/mobilenet_v2.prototxt', 51 blobs='../../caffe/mobilenet_v2/mobilenet_v2.caffemodel') 52 if ret != 0: 53 print('Load model failed!') 54 exit(ret) 55 print('done') 56 57 # Build model 58 print('--> Building model') 59 ret = rknn.build(do_quantization=True, dataset='../../caffe/mobilenet_v2/dataset.txt') 60 if ret != 0: 61 print('Build model failed!') 62 exit(ret) 63 print('done') 64 65 # Export rknn model 66 print('--> Export rknn model') 67 ret = rknn.export_rknn('./mobilenet_v2.rknn') 68 if ret != 0: 69 print('Export rknn model failed!') 70 exit(ret) 71 print('done') 72 73 # Init runtime environment 74 print('--> Init runtime environment') 75 ret = rknn.init_runtime() 76 if ret != 0: 77 print('Init runtime environment failed!') 78 exit(ret) 79 print('done') 80 81 # Set inputs 82 img = cv2.imread('./dog_224x224.jpg') 83 84 # Inference 85 print('--> Running model') 86 img2 = cv2.resize(img, (224,224)) 87 img2 = np.expand_dims(img2, 0) 88 img2 = np.transpose(img2, (0,3,1,2)) # [1,3,224,224] 89 outputs = rknn.inference(inputs=[img2], data_format=['nchw']) 90 np.save('./functions_dynamic_input_0.npy', outputs[0]) 91 show_outputs(outputs) 92 93 img3 = cv2.resize(img, (160,160)) 94 img3 = np.expand_dims(img3, 0) 95 img3 = np.transpose(img3, (0,3,1,2)) # [1,3,160,160] 96 outputs = rknn.inference(inputs=[img3], data_format=['nchw']) 97 np.save('./functions_dynamic_input_1.npy', outputs[0]) 98 show_outputs(outputs) 99 print('done') 100 101 rknn.release() 102