加载 ncnn 模型失败

[popper0912]

使用 Darknet 训练模型后，转化为 ncnn 模型，最后使用 ncnn 加载 .param 文件，返回 -1 ，加载失败。不知道何原因？使用caffe 训练的模型，转化后，可以加载成功。

[xiangweizeng]

应该是转换参数有问题， 找到出现错误的操作， 检查一下转换后的参数吧，如果方便可以提供下网络模型文件

[popper0912]

应该是转换参数有问题， 找到出现错误的操作， 检查一下转换后的参数吧，如果方便可以提供下网络模型文件

我使用的是 yolov3-tiny 版的模型训练的自己的数据，darkNet cfg 文件如下： [net]

Testing

batch=1 subdivisions=1

Training

batch=64

subdivisions=8

width=224 height=224 channels=1 momentum=0.9 decay=0.0005 angle=0 saturation = 1.0 exposure = 1.0 hue=0 flip=0

learning_rate=0.01 burn_in=1000 max_batches = 800000 policy=steps steps=6000,20000,50000,100000 scales=.1,.1,.1,.1

[convolutional] batch_normalize=1 filters=16 size=3 stride=1 pad=1 activation=leaky

[maxpool] size=2 stride=2

[convolutional] batch_normalize=1 filters=32 size=3 stride=1 pad=1 activation=leaky

[maxpool] size=2 stride=2

[convolutional] batch_normalize=1 filters=64 size=3 stride=1 pad=1 activation=leaky

[maxpool] size=2 stride=2

[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=leaky

[maxpool] size=2 stride=2

[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky

[maxpool] size=2 stride=2

[convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky

[maxpool] size=2 stride=1

[convolutional] batch_normalize=1 filters=1024 size=3 stride=1 pad=1 activation=leaky

###################

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky

[convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky

[convolutional] size=1 stride=1 pad=1 filters=27 activation=linear

[yolo] mask = 3,4,5 anchors = 21.2742,29.3429, 25.5435,43.5164, 28.0206,60.4466, 39.7142,54.9552, 33.7873,72.1623, 47.8516,66.9072, 45.8434,86.9286, 57.4758,106.0652, 110.5009,147.0255 classes=4 num=9 jitter=.3 ignore_thresh = .7 truth_thresh = 1 random=0

[route] layers = -4

[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky

[upsample] stride=2

[route] layers = -1, 8

[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky

[convolutional] size=1 stride=1 pad=1 filters=27 activation=linear

[yolo] mask = 0,1,2 anchors = 21.2742,29.3429, 25.5435,43.5164, 28.0206,60.4466, 39.7142,54.9552, 33.7873,72.1623, 47.8516,66.9072, 45.8434,86.9286, 57.4758,106.0652, 110.5009,147.0255 classes=4 num=9 jitter=.3 ignore_thresh = .7 truth_thresh = 1 random=0

=========================================================== 转化后的ncnn 模型 param 参数如下: 7767517 50 52 Input data 0 1 data 0=224 1=224 2=3 Convolution conv_0 1 1 data conv_0 0=16 1=3 2=1 3=1 4=1 5=0 6=432 BatchNorm conv_0_batch_norm 1 1 conv_0 conv_0_batch_norm 0=16 1=0.00001 ReLU conv_0_activation 1 1 conv_0_batch_norm conv_0_activation 0=0.1 Pooling maxpool_1 1 1 conv_0_activation maxpool_1 0=0 1=2 2=2 3=0 5=1 13=0 14=1 15=1 Convolution conv_2 1 1 maxpool_1 conv_2 0=32 1=3 2=1 3=1 4=1 5=0 6=4608 BatchNorm conv_2_batch_norm 1 1 conv_2 conv_2_batch_norm 0=32 1=0.00001 ReLU conv_2_activation 1 1 conv_2_batch_norm conv_2_activation 0=0.1 Pooling maxpool_3 1 1 conv_2_activation maxpool_3 0=0 1=2 2=2 3=0 5=1 13=0 14=1 15=1 Convolution conv_4 1 1 maxpool_3 conv_4 0=64 1=3 2=1 3=1 4=1 5=0 6=18432 BatchNorm conv_4_batch_norm 1 1 conv_4 conv_4_batch_norm 0=64 1=0.00001 ReLU conv_4_activation 1 1 conv_4_batch_norm conv_4_activation 0=0.1 Pooling maxpool_5 1 1 conv_4_activation maxpool_5 0=0 1=2 2=2 3=0 5=1 13=0 14=1 15=1 Convolution conv_6 1 1 maxpool_5 conv_6 0=128 1=3 2=1 3=1 4=1 5=0 6=73728 BatchNorm conv_6_batch_norm 1 1 conv_6 conv_6_batch_norm 0=128 1=0.00001 ReLU conv_6_activation 1 1 conv_6_batch_norm conv_6_activation 0=0.1 Pooling maxpool_7 1 1 conv_6_activation maxpool_7 0=0 1=2 2=2 3=0 5=1 13=0 14=1 15=1 Convolution conv_8 1 1 maxpool_7 conv_8 0=256 1=3 2=1 3=1 4=1 5=0 6=294912 BatchNorm conv_8_batch_norm 1 1 conv_8 conv_8_batch_norm 0=256 1=0.00001 ReLU conv_8_activation 1 1 conv_8_batch_norm conv_8_activation 0=0.1 Split conv_8_activation_split 1 2 conv_8_activation conv_8_activation_split_0 conv_8_activation_split_1 Pooling maxpool_9 1 1 conv_8_activation_split_0 maxpool_9 0=0 1=2 2=2 3=0 5=1 13=0 14=1 15=1 Convolution conv_10 1 1 maxpool_9 conv_10 0=512 1=3 2=1 3=1 4=1 5=0 6=1179648 BatchNorm conv_10_batch_norm 1 1 conv_10 conv_10_batch_norm 0=512 1=0.00001 ReLU conv_10_activation 1 1 conv_10_batch_norm conv_10_activation 0=0.1 Pooling maxpool_11 1 1 conv_10_activation maxpool_11 0=0 1=2 2=1 3=0 5=1 13=0 14=1 15=1 Convolution conv_12 1 1 maxpool_11 conv_12 0=1024 1=3 2=1 3=1 4=1 5=0 6=4718592 BatchNorm conv_12_batch_norm 1 1 conv_12 conv_12_batch_norm 0=1024 1=0.00001 ReLU conv_12_activation 1 1 conv_12_batch_norm conv_12_activation 0=0.1 Convolution conv_13 1 1 conv_12_activation conv_13 0=256 1=1 2=1 3=1 4=0 5=0 6=262144 BatchNorm conv_13_batch_norm 1 1 conv_13 conv_13_batch_norm 0=256 1=0.00001 ReLU conv_13_activation 1 1 conv_13_batch_norm conv_13_activation 0=0.1 Split conv_13_activation_split 1 2 conv_13_activation conv_13_activation_split_0 conv_13_activation_split_1 Convolution conv_14 1 1 conv_13_activation_split_0 conv_14 0=512 1=3 2=1 3=1 4=1 5=0 6=1179648 BatchNorm conv_14_batch_norm 1 1 conv_14 conv_14_batch_norm 0=512 1=0.00001 ReLU conv_14_activation 1 1 conv_14_batch_norm conv_14_activation 0=0.1 Convolution conv_15 1 1 conv_14_activation conv_15 0=27 1=1 2=1 3=1 4=0 5=1 6=13824 DarknetActivation conv_15_activation 1 1 conv_15 conv_15_activation 0=3 Concat route_17 1 1 conv_13_activation_split_1 route_17 0=0 Convolution conv_18 1 1 route_17 conv_18 0=128 1=1 2=1 3=1 4=0 5=0 6=32768 BatchNorm conv_18_batch_norm 1 1 conv_18 conv_18_batch_norm 0=128 1=0.00001 ReLU conv_18_activation 1 1 conv_18_batch_norm conv_18_activation 0=0.1 Interp upsample_19 1 1 conv_18_activation upsample_19 0=1 1=2.f 2=2.f Concat route_20 2 1 upsample_19 conv_8_activation_split_1 route_20 0=0 Convolution conv_21 1 1 route_20 conv_21 0=256 1=3 2=1 3=1 4=1 5=0 6=884736 BatchNorm conv_21_batch_norm 1 1 conv_21 conv_21_batch_norm 0=256 1=0.00001 ReLU conv_21_activation 1 1 conv_21_batch_norm conv_21_activation 0=0.1 Convolution conv_22 1 1 conv_21_activation conv_22 0=27 1=1 2=1 3=1 4=0 5=1 6=6912 DarknetActivation conv_22_activation 1 1 conv_22 conv_22_activation 0=3 Yolov3Detection yolo_23 2 1 conv_15_activation conv_22_activation yolo_23 0=4 1=6 2=0 3=0.25f 4=0.45f 5=224 6=224 -23307=12,39.714199,54.955200,33.787300,72.162300,47.851601,66.907204,21.274200,29.342899,25.543501,43.516399,28.020599,60.446602

我尝试注释掉 DarknetActivation 这一层 加载便成功了 不知道如何解决，请教一下

[xiangweizeng]

DarknetActivation 是自定义的网络层， 你需要将其注册到ncnn network中， 具体的你可以参考example/yolo.cpp

  CustomizedNet yolo;
  register_darknet_layer(yolo);
  yolo.load_param(param);

[popper0912]

DarknetActivation 是自定义的网络层， 你需要将其注册到ncnn network中， 具体的你可以参考example/yolo.cpp

  CustomizedNet yolo;
  register_darknet_layer(yolo);
  yolo.load_param(param);

谢谢