content
2. Convert facenet to rknn model and reason
1. Model download
First, you need to download the facenet model. The github download URL is: https://github.com/davidsandberg/facenet
Pre-trained models
| Model name | LFW accuracy | Training dataset | Architecture |
|---|---|---|---|
| 20180408-102900 | 0.9905 | CASIA-WebFace | Inception ResNet v1 |
| 20180402-114759 | 0.9965 | VGGFace2 | Inception ResNet v1 |
NOTE: If you use any of the models, please do not forget to give proper credit to those providing the training dataset as well.
2. Convert facenet to rknn model and reason
import numpy as np
import cv2
from rknn.api import RKNN
INPUT_SIZE = 160
if __name__ == '__main__':
# Create RKNN object
rknn = RKNN(verbose=False, verbose_file='./test1.log')
# Config for Model Input PreProcess
#rknn.config(channel_mean_value='0 0 0 1', reorder_channel='0 1 2',target_platform=['rv1126'])
rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], reorder_channel='0 1 2', target_platform='rv1126',
quantized_dtype='asymmetric_affine-u8', optimization_level=3, output_optimize=1)
print('config done')
# load tensorflow model
print('--> Loading model')
rknn.load_tensorflow(tf_pb='./20180402-114759.pb',
# inputs=['input', 'phase_train'],
inputs=['input'],
outputs=['InceptionResnetV1/Bottleneck/BatchNorm/Reshape_1'],
#outputs=['embeddings'],
input_size_list=[[INPUT_SIZE, INPUT_SIZE, 3]])
print('done')
# Build Model
print('--> Building model')
# rknn.build(do_quantization=False,do_quantization=True, dataset='dataset.txt')
rknn.build(do_quantization=True, dataset='dataset.txt')
print('done')
# Export RKNN Model
rknn.export_rknn('./facenet_Reshape_1.rknn')
# Set inputs
img = cv2.imread('./ldh.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img,(INPUT_SIZE, INPUT_SIZE))
# init runtime environment
print('--> Init runtime environment')
#ret = rknn.init_runtime(target='rv1126')
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])
print('len(outputs[0][0])::', len(outputs[0][0]))
print("outputs::", outputs)
print('done')
# perf
print('--> Begin evaluate model performance')
perf_results = rknn.eval_perf(inputs=[img])
print('done')
print("perf_results:", perf_results)
rknn.release()The output layer taken here is outputs=['InceptionResnetV1/Bottleneck/BatchNorm/Reshape_1'], the result is 512 floats, and some screenshots are as follows
If the output is taken as #outputs=['embeddings'], then the output obtained is
3 View the network model
We use netron to view the transferred rknn model and see where the output layer we took is
references:
Toybrick - open source community - artificial intelligence - facenet model conversion
Toybrick - open source community - artificial intelligence - questions about facenet