无法在自定义模型上运行nvidia深流

问题描述

我正在尝试使用nvidia深流运行我的.h5 keras分类模型。我浏览了nvidia提供的样本，并进行了以下操作：

1. 将我的模型转换为.engin文件
2. 更改配置文件
3. 提供一个类.txt文件（labels.txt）

代码可以正常工作，但是当我打印frame_Meta.bInferDone时，它给了我0，这意味着在框架上没有进行推断。

配置文件

[property]
gpu-id=0
process-mode=1 #primary
net-scale-factor=1
model-engine-file=model.engine
labelfile-path=labels.txt
force-implicit-batch-dim=1
batch-size=1
network-mode=1
network-type=1 #classifier
num-detected-classes=2
interval=0
gie-unique-id=1
is-classifier=1
classifier-threshold=0.2
output-blob-names=dense_2

Keras模型摘要

Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None,224,3)]     0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None,64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None,64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None,112,64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None,128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None,128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None,56,128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None,256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None,256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None,256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None,28,256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None,512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None,512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None,512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None,14,512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None,512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None,512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None,512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None,7,512)         0         
_________________________________________________________________
flatten (Flatten)            (None,25088)             0         
_________________________________________________________________
fc1 (Dense)                  (None,4096)              102764544 
_________________________________________________________________
fc2 (Dense)                  (None,4096)              16781312  
_________________________________________________________________
dense_1 (Dense)              (None,1000)              4097000   
_________________________________________________________________
dropout_1 (Dropout)          (None,1000)              0         
_________________________________________________________________
dense_2 (Dense)              (None,2)                 2002      
=================================================================
Total params: 138,359,546
Trainable params: 138,546
Non-trainable params: 0
_________________________________________________________________

我在NVIDIA论坛上为此问题创建了一个主题。 Here。

解决方法

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