Some questions about convert caffe model to torch

Question

I use torch-caffe-binding to convert caffe model to torch. And I want to delete the loss layer in the end and add other torch layers, could I just delete the layer in the .prototxt file and "train" the model to get the .caffemodel file and import in torch?
And the model uses the lmdb type data, when I use net:forward(input) to train the model, the model just uses the data defined in the data layer instead of uses the inputdata. So how to train the model that uses the lmdb data?
the caffe model has some custom layer so I can't use the loadcaffe to load the model in torch

Answer 1

You have 3 issues here -

1. You probably need the loss layer for the training (this is what you want to minimize). So train with it, and after the training is done remove it from your prototxt before converting to torch.

2. In order to use the lmdb rather than using the data layer, connect your input to the first conv layer (assuming your first non-input layer is conv, e.g. say you have

   layer {
     name: "input-data"
     type: "DummyData"
     top: "data"
     top: "im_info"
     dummy_data_param {
       shape { dim: 1 dim: 3 dim: 224 dim: 224 }
     }
   }
   

   and also

   input: "data"
   input_shape: {
   dim: 1
   dim: 3
   dim: 224
   dim: 224
   }
   

   and then

   layer {
     name: "conv1"
     type: "Convolution"
     bottom: "data" --> **here put data instead of input-data**
     top: "conv1"
     convolution_param {
       num_output: 96
       kernel_size: 3
       pad: 1
       stride: 1
     }
   }
   

3. As for the custom layer, you'll have to find an equivalent implementation in torch or implement it on your own