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我有一个简单的网络,由两个卷积层和一个完全连接的网络组成,在 pytorch 中定义如下:

def __init__([...])
    [...]
    self.conv1 = nn.Conv1d(1, channels_conv1, width_conv1)
    self.conv2 = nn.Conv1d(channels_conv1, channels_conv2, width_conv2)
    self.fc1 = nn.Linear(hidden_layer_size, 2)

def forward(self, x):
    x = functional.max_pool1d(functional.relu(self.conv1(x)), 2, stride=2)
    x = functional.max_pool1d(functional.relu(self.conv2(x)), 2, stride=2)
    x = x.view(-1, self.num_flat_features(x))
    x = functional.softmax(self.fc1(x))
    return x

我想将其转换为 tflite。所以首先将其转换为onnx

torch.onnx.export(model, input, "net.onnx",
                  export_params=True,
                  input_names=['input'],
                  output_names=['output'],
                  verbose=true)

然后我将结果转换为 tensorflow 图定义onnx-tf。结果net.pb是好的,因为它产生与原始相同的输出prepare(onnx.load('net.onnx')).run(...)

但是,我有两个问题:一个小问题是net.pb图形不再包含输出节点,我必须寻找输出节点。第二个是当我尝试执行最终转换时

tflite_convert --output_file=net.tflite --graph_def_file=net.pb --input_arrays=input --output_arrays=Softmax

我在类型检查中遇到 TOCO 失败:

tensorflow.lite.python.convert.ConverterError: TOCO failed. See console for info.
2018-11-16 16:11:37.592030: I tensorflow/lite/toco/import_tensorflow.cc:1280] Converting unsupported operation: Where
2018-11-16 16:11:37.601384: I tensorflow/lite/toco/graph_transformations/graph_transformations.cc:39] Before Removing unused ops: 61 operators, 113 arrays (0 quantized)
2018-11-16 16:11:37.602005: I tensorflow/lite/toco/graph_transformations/graph_transformations.cc:39] Before general graph transformations: 61 operators, 113 arrays (0 quantized)
2018-11-16 16:11:37.602311: F tensorflow/lite/toco/graph_transformations/resolve_constant_gather.cc:105] Check failed: coords_array.data_type == ArrayDataType::kInt32 Only int32 indices are supported
Aborted (core dumped)

我尝试在网络中进行挖掘,但似乎找不到令人烦恼的对象,也没有发现与此问题明显相关的问题。任何指向这个过程可能已经脱轨的点的指针都会很棒!

tf-nightly==1.13.0.dev20181116
onnx==1.3.0
torch-nightly==1.0.0.dev201811

onnx-tensorflowmaster的(commit b5fef1b)

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