yolo.h5文件问题的解决 - 吴恩达深度学习：目标检测之YOLO算法

冷不防 2022-12-30 08:25 181阅读 0赞

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")

*  1

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.weights

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Window

[http://pjreddie.com/media/files/yolo.weights][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][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][https_github.com_allanzelener_YAD2K]  
点击  
![在这里插入图片描述][20181226151107879.png]  
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

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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。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NvbG85NQ_size_16_color_FFFFFF_t_70]

[http_pjreddie.com_media_files_yolo.weights]: http://pjreddie.com/media/files/yolo.weights
[https_github.com_pjreddie_darknet_tree_master_cfg]: https://github.com/pjreddie/darknet/tree/master/cfg
[https_github.com_allanzelener_YAD2K]: https://github.com/allanzelener/YAD2K
[20181226151107879.png]: /images/20221120/e03b743c95d842799a81f49067ac1eb6.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NvbG85NQ_size_16_color_FFFFFF_t_70]: /images/20221120/93c96d23730b4b72ab4798507b0dff72.png