使用SNPE转换tensorflow稠密层时的误差

Question

在转换自定义tensorflow图时，我看到了与将密集层从pb转换为DLC格式有关的错误：

2017-11-02 13:43:35,260 - 305 - WARNING - WARNING_TF_SCOPE_OP_NOT_CONSUMED: Operation (dense/Tensordot/transpose) not consumed by converter: Transpose.
2017-11-02 13:43:35,261 - 305 - WARNING - WARNING_TF_SCOPE_OP_NOT_CONSUMED: Operation (dense/Tensordot/transpose_1) not consumed by converter: Transpose.
2017-11-02 13:43:35,261 - 305 - WARNING - WARNING_TF_SCOPE_OP_NOT_CONSUMED: Operation (dense/Tensordot/MatMul) not consumed by converter: MatMul.
2017-11-02 13:43:35,261 - 305 - WARNING - WARNING_TF_SCOPE_OP_NOT_CONSUMED: Operation (dense/BiasAdd) not consumed by converter: BiasAdd.
2017-11-02 13:43:35,261 - 305 - WARNING - WARNING_TF_SCOPE_OP_NOT_CONSUMED: Operation (logit/Tensordot/transpose) not consumed by converter: Transpose.
2017-11-02 13:43:35,262 - 305 - WARNING - WARNING_TF_SCOPE_OP_NOT_CONSUMED: Operation (logit/Tensordot/transpose_1) not consumed by converter: Transpose.
2017-11-02 13:43:35,262 - 305 - WARNING - WARNING_TF_SCOPE_OP_NOT_CONSUMED: Operation (logit/Tensordot/MatMul) not consumed by converter: MatMul.
2017-11-02 13:43:35,262 - 305 - WARNING - WARNING_TF_SCOPE_OP_NOT_CONSUMED: Operation (logit/BiasAdd) not consumed by converter: BiasAdd.
2017-11-02 13:43:35,263 - 123 - ERROR - Conversion failed: Some operations in the Tensorflow graph were not resolved to a layer!

我对此感到有点困惑，因为这个层只是一个2D卷积之后的致密层，我确信SNPE支持它。错误的原因是什么？

图的拓扑如下：

0 input_layer Placeholder
1 conv2d/kernel Const
2 conv2d/kernel/read Identity
└─── Input0 ─ conv2d/kernel
3 conv2d/bias Const
4 conv2d/bias/read Identity
└─── Input0 ─ conv2d/bias
5 conv2d/convolution Conv2D
└─── Input0 ─ input_layer
└─── Input1 ─ conv2d/kernel/read
6 conv2d/BiasAdd BiasAdd
└─── Input0 ─ conv2d/convolution
└─── Input1 ─ conv2d/bias/read
7 conv2d/Relu Relu
└─── Input0 ─ conv2d/BiasAdd
8 max_pooling2d/MaxPool MaxPool
└─── Input0 ─ conv2d/Relu
9 conv2d_1/kernel Const
10 conv2d_1/kernel/read Identity
└─── Input0 ─ conv2d_1/kernel
11 conv2d_1/bias Const
12 conv2d_1/bias/read Identity
└─── Input0 ─ conv2d_1/bias
13 conv2d_2/convolution Conv2D
└─── Input0 ─ max_pooling2d/MaxPool
└─── Input1 ─ conv2d_1/kernel/read
14 conv2d_2/BiasAdd BiasAdd
└─── Input0 ─ conv2d_2/convolution
└─── Input1 ─ conv2d_1/bias/read
15 conv2d_2/Relu Relu
└─── Input0 ─ conv2d_2/BiasAdd
16 max_pooling2d_2/MaxPool MaxPool
└─── Input0 ─ conv2d_2/Relu
17 conv2d_2/kernel Const
18 conv2d_2/kernel/read Identity
└─── Input0 ─ conv2d_2/kernel
19 conv2d_2/bias Const
20 conv2d_2/bias/read Identity
└─── Input0 ─ conv2d_2/bias
21 conv2d_3/convolution Conv2D
└─── Input0 ─ max_pooling2d_2/MaxPool
└─── Input1 ─ conv2d_2/kernel/read
22 conv2d_3/BiasAdd BiasAdd
└─── Input0 ─ conv2d_3/convolution
└─── Input1 ─ conv2d_2/bias/read
23 conv2d_3/Relu Relu

注意:我也把这个问题发到高通开发人员网络上，但它似乎没有出现，可能是因为一个慢速队列。

Answer 1

在使用密集层(tf.layers.dense API)时，我也遇到了同样的问题。造成这一问题的原因是，对权重(由tf.layer.dense API引入)进行了整形操作。转换器将其误解为模型执行的一部分，因此试图将其转换为无法转换的层，因为它没有输入层。

您可以使用整形(tf.reshape API)之间的卷积和完全连接，以扁平张量，它将工作良好。