如何将yolo权重转换为tflite文件 [英] How can I convert yolo weights to tflite file
问题描述
我将在android中使用yolo权重,因此我计划将yolo权重文件转换为tflite文件.
I will use yolo weights in android so I plan to convert yolo weights file to tflite file.
我在anaconda提示符中使用此代码,因为我在环境中下载了keras库.
I use this code in anaconda prompt because I downloaded keras library in env.
activate env
python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5
最后做到了,将Keras模型保存到model_data/yolo.h5
Finally, it did.Saved Keras model to model_data/yolo.h5
然后我将使用此代码在jupyter笔记本中将此h5文件转换为tflite
文件.
And I'm going to convert this h5 file to tflite
file in jupyter notebook with this code.
model = tf.keras.models.load_model("./yolo/yolo.h5", compile=False)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
open("keras_model.tflite", "wb").write(tflite_model)
但是会发生此错误.
ValueError Traceback (most recent call last)
<ipython-input-3-964a59978091> in <module>()
1 model = tf.keras.models.load_model("./yolo/yolo.h5", compile=False)
2 converter = tf.lite.TFLiteConverter.from_keras_model(model)
----> 3 tflite_model = converter.convert()
4 open("keras_model.tflite", "wb").write(tflite_model)
~\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow_core\lite\python\lite.py in convert(self)
426 raise ValueError(
427 "None is only supported in the 1st dimension. Tensor '{0}' has "
--> 428 "invalid shape '{1}'.".format(_get_tensor_name(tensor), shape_list))
429 elif shape_list and shape_list[0] is None:
430 # Set the batch size to 1 if undefined.
ValueError: None is only supported in the 1st dimension. Tensor 'input_1' has invalid shape '[None, None, None, 3]'.
我该如何解决?
我们的模型摘要是
型号:"model_1"
Model: "model_1"
input_1(InputLayer)[(无,无,无,0
input_1 (InputLayer) [(None, None, None, 0
conv2d_1(Conv2D)(无,无,无,3 864 input_1 [0] [0]
conv2d_1 (Conv2D) (None, None, None, 3 864 input_1[0][0]
batch_normalization_1(BatchNor(无,无,无,3 128 conv2d_1 [0] [0]
batch_normalization_1 (BatchNor (None, None, None, 3 128 conv2d_1[0][0]
leaky_re_lu_1(LeakyReLU)(无,无,无,3 0 batch_normalization_1 [0] [0]
leaky_re_lu_1 (LeakyReLU) (None, None, None, 3 0 batch_normalization_1[0][0]
zero_padding2d_1(ZeroPadding2D(无,无,无,3 0 Leaky_re_lu_1 [0] [0]
zero_padding2d_1 (ZeroPadding2D (None, None, None, 3 0 leaky_re_lu_1[0][0]
conv2d_2(Conv2D)(无,无,无,6 18432 zero_padding2d_1 [0] [0]
conv2d_2 (Conv2D) (None, None, None, 6 18432 zero_padding2d_1[0][0]
batch_normalization_2(BatchNor(无,无,无,6 256 conv2d_2 [0] [0]
batch_normalization_2 (BatchNor (None, None, None, 6 256 conv2d_2[0][0]
leaky_re_lu_2(LeakyReLU)(无,无,无,6 0 batch_normalization_2 [0] [0]
leaky_re_lu_2 (LeakyReLU) (None, None, None, 6 0 batch_normalization_2[0][0]
conv2d_3(Conv2D)(无,无,无,3 2048 Leaky_re_lu_2 [0] [0]
conv2d_3 (Conv2D) (None, None, None, 3 2048 leaky_re_lu_2[0][0]
. . .
batch_normalization_65(BatchNo(无,无,无,5 2048 conv2d_66 [0] [0]
batch_normalization_65 (BatchNo (None, None, None, 5 2048 conv2d_66[0][0]
batch_normalization_72(BatchNo(无,无,无,2 1024 conv2d_74 [0] [0]
batch_normalization_72 (BatchNo (None, None, None, 2 1024 conv2d_74[0][0]
leaky_re_lu_58(LeakyReLU)(无,无,无,1 0 batch_normalization_58 [0] [0]
leaky_re_lu_58 (LeakyReLU) (None, None, None, 1 0 batch_normalization_58[0][0]
leaky_re_lu_65(LeakyReLU)(无,无,无,5 0 batch_normalization_65 [0] [0]
leaky_re_lu_65 (LeakyReLU) (None, None, None, 5 0 batch_normalization_65[0][0]
leaky_re_lu_72(LeakyReLU)(无,无,无,2 0 batch_normalization_72 [0] [0]
leaky_re_lu_72 (LeakyReLU) (None, None, None, 2 0 batch_normalization_72[0][0]
conv2d_59(Conv2D)(无,无,无,2 261375 lossy_re_lu_58 [0] [0]
conv2d_59 (Conv2D) (None, None, None, 2 261375 leaky_re_lu_58[0][0]
conv2d_67(Conv2D)(无,无,无,2 130815泄漏_re_lu_65 [0] [0]
conv2d_67 (Conv2D) (None, None, None, 2 130815 leaky_re_lu_65[0][0]
总参数:62,001,757 可训练的参数:61,949,149 不可训练的参数:52,608
Total params: 62,001,757 Trainable params: 61,949,149 Non-trainable params: 52,608
推荐答案
我建议这样做:
- 将Darknet权重(
.weights
)转换为TensorFlow冻结图格式(.pb
). - 将此
.pb
文件转换为tflite.
- Convert Darknet weights (
.weights
) to TensorFlow frozen graph format (.pb
). - Convert this
.pb
file to tflite.
此过程更简单.我已经记录了3-4种将Darknet转换为TensorFlow的方法.请在此处找到它们.
This process is simpler. I have documented some 3-4 methods to convert Darknet to TensorFlow. Please find them here.
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