yolo.h5文件问题的解决 - 吴恩达深度学习：目标检测之YOLO算法
yolo.h5文件问题的解决 - 吴恩达深度学习：目标检测之YOLO算法
Steve Wang
发布于 2019-05-27 14:07:51
发布于 2019-05-27 14:07:51
版权声明：本文为博主原创文章，未经博主允许不得转载。 https://cloud.tencent.com/developer/article/1436586
在吴恩达深度学习系列视频：卷积神经网络第三周作业中，加载预训练模型时：
yolo_model = load_model("model_data/yolo.h5")你会遇到yolo.h5文件找不到的情况，而当你去网上下载了一个yolo.h5文件时，可能会遇到unknown opcode等其他错误。
下面让我们来自己生成一个全新的yolo.h5文件。
下载预训练的权重：
Linux
wget http://pjreddie.com/media/files/yolo.weightsWindow
http://pjreddie.com/media/files/yolo.weights
点击上面链接之后自动下载
下载配置文件：
https://github.com/pjreddie/darknet/tree/master/cfg
找到yolov2.cfg下载下来，并改名成yolo.cfg(调用该文件时，使用的名字是这个)。
下载需要脚本
https://github.com/allanzelener/YAD2K
点击
下载zip。
当然你使用git命令下载下来也可以。
准备工作
复制或剪切yolo.weights和yolo.cfg以及yad2k.py三个文件，以及一个文件夹yad2k到我的文档(桌面上那个我的文档，它是命令行执行的默认路径，这样你就不需要cd进下载目录执行操作了)。
注意文件夹吴恩达提供的作业里已经有yad2k文件夹，所有windows会提示你是否替换原有文件，点击替换。
打开Anaconda Prompt
(tensorflow) C:\Users\wangh>python yad2k.py yolo.cfg yolo.weights model_data/yolo.h5注意，我是在tensorflow虚拟环境下进行的，你在什么环境下运行都可以，它本质上是调用yad2k.py使用yolo.weights和yolo.cfg去生成yolo.py，需要有keras包。
结果
(tensorflow) C:\Users\wangh>python yad2k.py yolo.cfg yolo.weights model_data/yolo.h5
Using TensorFlow backend.
Loading weights.
Weights Header: [ 0 1 0 32013312]
Parsing Darknet config.
Creating Keras model.
Parsing section net_0
Parsing section convolutional_0
conv2d bn leaky (3, 3, 3, 32)
2018-12-26 14:39:43.768410: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
Parsing section maxpool_0
Parsing section convolutional_1
conv2d bn leaky (3, 3, 32, 64)
Parsing section maxpool_1
Parsing section convolutional_2
conv2d bn leaky (3, 3, 64, 128)
Parsing section convolutional_3
conv2d bn leaky (1, 1, 128, 64)
Parsing section convolutional_4
conv2d bn leaky (3, 3, 64, 128)
Parsing section maxpool_2
Parsing section convolutional_5
conv2d bn leaky (3, 3, 128, 256)
Parsing section convolutional_6
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_7
conv2d bn leaky (3, 3, 128, 256)
Parsing section maxpool_3
Parsing section convolutional_8
conv2d bn leaky (3, 3, 256, 512)
Parsing section convolutional_9
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_10
conv2d bn leaky (3, 3, 256, 512)
Parsing section convolutional_11
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_12
conv2d bn leaky (3, 3, 256, 512)
Parsing section maxpool_4
Parsing section convolutional_13
conv2d bn leaky (3, 3, 512, 1024)
Parsing section convolutional_14
conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_15
conv2d bn leaky (3, 3, 512, 1024)
Parsing section convolutional_16
conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_17
conv2d bn leaky (3, 3, 512, 1024)
Parsing section convolutional_18
conv2d bn leaky (3, 3, 1024, 1024)
Parsing section convolutional_19
conv2d bn leaky (3, 3, 1024, 1024)
Parsing section route_0
Parsing section convolutional_20
conv2d bn leaky (1, 1, 512, 64)
Parsing section reorg_0
Parsing section route_1
Concatenating route layers: [<tf.Tensor 'space_to_depth_x2/SpaceToDepth:0' shape=(?, 19, 19, 256) dtype=float32>, <tf.Tensor 'leaky_re_lu_20/LeakyRelu:0' shape=(?, 19, 19, 1024) dtype=float32>]
Parsing section convolutional_21
conv2d bn leaky (3, 3, 1280, 1024)
Parsing section convolutional_22
conv2d linear (1, 1, 1024, 425)
Parsing section region_0
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 608, 608, 3) 0
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 608, 608, 32) 864 input_1[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 608, 608, 32) 128 conv2d_1[0][0]
__________________________________________________________________________________________________
leaky_re_lu_1 (LeakyReLU) (None, 608, 608, 32) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 304, 304, 32) 0 leaky_re_lu_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 304, 304, 64) 18432 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 304, 304, 64) 256 conv2d_2[0][0]
__________________________________________________________________________________________________
leaky_re_lu_2 (LeakyReLU) (None, 304, 304, 64) 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 152, 152, 64) 0 leaky_re_lu_2[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 152, 152, 128 73728 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 152, 152, 128 512 conv2d_3[0][0]
__________________________________________________________________________________________________
leaky_re_lu_3 (LeakyReLU) (None, 152, 152, 128 0 batch_normalization_3[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 152, 152, 64) 8192 leaky_re_lu_3[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 152, 152, 64) 256 conv2d_4[0][0]
__________________________________________________________________________________________________
leaky_re_lu_4 (LeakyReLU) (None, 152, 152, 64) 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 152, 152, 128 73728 leaky_re_lu_4[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 152, 152, 128 512 conv2d_5[0][0]
__________________________________________________________________________________________________
leaky_re_lu_5 (LeakyReLU) (None, 152, 152, 128 0 batch_normalization_5[0][0]
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, 76, 76, 128) 0 leaky_re_lu_5[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 76, 76, 256) 294912 max_pooling2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 76, 76, 256) 1024 conv2d_6[0][0]
__________________________________________________________________________________________________
leaky_re_lu_6 (LeakyReLU) (None, 76, 76, 256) 0 batch_normalization_6[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 76, 76, 128) 32768 leaky_re_lu_6[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 76, 76, 128) 512 conv2d_7[0][0]
__________________________________________________________________________________________________
leaky_re_lu_7 (LeakyReLU) (None, 76, 76, 128) 0 batch_normalization_7[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 76, 76, 256) 294912 leaky_re_lu_7[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 76, 76, 256) 1024 conv2d_8[0][0]
__________________________________________________________________________________________________
leaky_re_lu_8 (LeakyReLU) (None, 76, 76, 256) 0 batch_normalization_8[0][0]
__________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D) (None, 38, 38, 256) 0 leaky_re_lu_8[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 38, 38, 512) 1179648 max_pooling2d_4[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 38, 38, 512) 2048 conv2d_9[0][0]
__________________________________________________________________________________________________
leaky_re_lu_9 (LeakyReLU) (None, 38, 38, 512) 0 batch_normalization_9[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 38, 38, 256) 131072 leaky_re_lu_9[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 38, 38, 256) 1024 conv2d_10[0][0]
__________________________________________________________________________________________________
leaky_re_lu_10 (LeakyReLU) (None, 38, 38, 256) 0 batch_normalization_10[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 38, 38, 512) 1179648 leaky_re_lu_10[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 38, 38, 512) 2048 conv2d_11[0][0]
__________________________________________________________________________________________________
leaky_re_lu_11 (LeakyReLU) (None, 38, 38, 512) 0 batch_normalization_11[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 38, 38, 256) 131072 leaky_re_lu_11[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 38, 38, 256) 1024 conv2d_12[0][0]
__________________________________________________________________________________________________
leaky_re_lu_12 (LeakyReLU) (None, 38, 38, 256) 0 batch_normalization_12[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 38, 38, 512) 1179648 leaky_re_lu_12[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 38, 38, 512) 2048 conv2d_13[0][0]
__________________________________________________________________________________________________
leaky_re_lu_13 (LeakyReLU) (None, 38, 38, 512) 0 batch_normalization_13[0][0]
__________________________________________________________________________________________________
max_pooling2d_5 (MaxPooling2D) (None, 19, 19, 512) 0 leaky_re_lu_13[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 19, 19, 1024) 4718592 max_pooling2d_5[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 19, 19, 1024) 4096 conv2d_14[0][0]
__________________________________________________________________________________________________
leaky_re_lu_14 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_14[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 19, 19, 512) 524288 leaky_re_lu_14[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 19, 19, 512) 2048 conv2d_15[0][0]
__________________________________________________________________________________________________
leaky_re_lu_15 (LeakyReLU) (None, 19, 19, 512) 0 batch_normalization_15[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 19, 19, 1024) 4718592 leaky_re_lu_15[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 19, 19, 1024) 4096 conv2d_16[0][0]
__________________________________________________________________________________________________
leaky_re_lu_16 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_16[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 19, 19, 512) 524288 leaky_re_lu_16[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 19, 19, 512) 2048 conv2d_17[0][0]
__________________________________________________________________________________________________
leaky_re_lu_17 (LeakyReLU) (None, 19, 19, 512) 0 batch_normalization_17[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 19, 19, 1024) 4718592 leaky_re_lu_17[0][0]
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 19, 19, 1024) 4096 conv2d_18[0][0]
__________________________________________________________________________________________________
leaky_re_lu_18 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_18[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, 19, 19, 1024) 9437184 leaky_re_lu_18[0][0]
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 19, 19, 1024) 4096 conv2d_19[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D) (None, 38, 38, 64) 32768 leaky_re_lu_13[0][0]
__________________________________________________________________________________________________
leaky_re_lu_19 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_19[0][0]
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 38, 38, 64) 256 conv2d_21[0][0]
__________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, 19, 19, 1024) 9437184 leaky_re_lu_19[0][0]
__________________________________________________________________________________________________
leaky_re_lu_21 (LeakyReLU) (None, 38, 38, 64) 0 batch_normalization_21[0][0]
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 19, 19, 1024) 4096 conv2d_20[0][0]
__________________________________________________________________________________________________
space_to_depth_x2 (Lambda) (None, 19, 19, 256) 0 leaky_re_lu_21[0][0]
__________________________________________________________________________________________________
leaky_re_lu_20 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_20[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 19, 19, 1280) 0 space_to_depth_x2[0][0]
leaky_re_lu_20[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D) (None, 19, 19, 1024) 11796480 concatenate_1[0][0]
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 19, 19, 1024) 4096 conv2d_22[0][0]
__________________________________________________________________________________________________
leaky_re_lu_22 (LeakyReLU) (None, 19, 19, 1024) 0 batch_normalization_22[0][0]
__________________________________________________________________________________________________
conv2d_23 (Conv2D) (None, 19, 19, 425) 435625 leaky_re_lu_22[0][0]
==================================================================================================
Total params: 50,983,561
Trainable params: 50,962,889
Non-trainable params: 20,672
__________________________________________________________________________________________________
None
Saved Keras model to model_data/yolo.h5
Read 50983561 of 50983561.0 from Darknet weights.然后model_data文件下多出了一个文件yolo.h5，就可以正常使用了。
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原始发表：2018年12月26日，如有侵权请联系 cloudcommunity@tencent.com 删除
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