File size: 2,265 Bytes
477da44 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
import numpy as np
import cv2
from rknn.api import RKNN
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 = 'mobilenet_v1\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 dequantize(outputs, scale, zp):
outputs[0] = (outputs[0] - zp) * scale
return outputs
if __name__ == '__main__':
# Create RKNN object
rknn = RKNN(verbose=True)
# Pre-process config
print('--> Config model')
rknn.config(mean_values=[128, 128, 128], std_values=[128, 128, 128], target_platform='rk3566')
print('done')
# Load model (from https://www.tensorflow.org/lite/examples/image_classification/overview?hl=zh-cn)
print('--> Loading model')
ret = rknn.load_tflite(model='mobilenet_v1_1.0_224_quant.tflite')
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('./mobilenet_v1.rknn')
if ret != 0:
print('Export rknn model failed!')
exit(ret)
print('done')
# Set inputs
img = cv2.imread('./dog_224x224.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('./tflite_mobilenet_v1_qat_0.npy', outputs[0])
show_outputs(dequantize(outputs, scale=0.00390625, zp=0))
print('done')
rknn.release()
|