Custom conv_filters not working

I am trying to train a number of decentralized DQN policies on the Battle environment. Any observation size other than 42 x 42 or 84 x 84 (default supported sizes) with my own conv_filters does not work if the number of filters is more than one. for example:

"model": {
          "dim": 15,
           "conv_filters": [
             [32, [5, 5], 1],
             [64, [5, 5], 1],
             [128, [5, 5], 1],
             [256, [3, 3], 1]
            ],
},

does not work, but

"model": {
          "dim": 15,
           "conv_filters": [
             [32, [15, 15], 1]
            ],
},

does. I am using torch which ends up calling visionnet.py, so in principle, there shouldn’t be a problem, but apparently, there is … Any idea what the problem might be ?

Hi @A_M,
and welcome to the forum! This is a known issue (see e.g. here).

The VisionNet needs the right filters. What means right here? It means that at the end of all the filtering there must be a dimension [B, 1, 1, F], where B stands for the batch size (does not get considered in the conv_filters attribute) and F for the number of feature maps (the number of “neurons” the following Dense layer should connect to.

In your last example you do exactly this: applying the [32, [15, 15], 1] to a [B, 15, 15, 3] image results in a [B, 1, 1, 32] output ("valid" is used). The first example does not result in a [B, 1, 1, F] output: Using three filters with kernels [5, 5] and stride 1, results in a size of [B, 15, 15, 128] (note, "same" is used for the convolution in all fConv2D layers, but the last). What you want to do is to set the filters with strides larger 1:

"conv_filters": [
     [32, [5, 5], 2], 
     [64, [5, 5], 2], 
     [128, [5, 5], 2], 
     [256, [4, 4], 1]
],

Hope this helps.
Simon

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