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• Results of Training the Customised Network

  Capt. Flatus O'Flaherty ☠ • 12/12/2018 at 13:57 • 0 comments

  I trained my first customised network, as designed in the previous log, on an Amazon AWS GPU and successfully got convergence, which means the network does work:

  Training took about 3.5 hours and it got an mAP (val) (mean actual precision) of about 10, which is not particularly good, but it's a good start. It's very possible that if I had kept on training that this might have continued to slowly increase, but my budget for this session was $10, so it had to stop!
  

• Baby steps

  Capt. Flatus O'Flaherty ☠ • 12/11/2018 at 16:15 • 0 comments

  After messing about with some networks for a couple of months in an attemt to design computer vision for machines, I started to notice a few re-occurring topics and buzz words such as 'prototext' and 'weights'.

  Curiosity has now got the better of me and I worked out that the various prototext files associated with a network describe it's structure in reasonably simple and human readable terms eg:

  layer {
    name: "pool2/3x3_s2"
    type: "Pooling"
    bottom: "conv2/norm2"
    top: "pool2/3x3_s2"
    pooling_param {
      pool: MAX
      kernel_size: 3
      stride: 2
    }
  }
  

  I presume that this is a python layer, but I'm not sure, but it does not matter for now. 
  

  The fantastic Nvidia Digits software enables print out of a fancy graphical representation of the whole network and, starting with the renowned bvlc_googlenet.caffemodel, I thought I'd try and hack it and learn something through experimentation.

  One of the first thing I looked for was symmetry and repetition, with the desire to simplify what initially look very complicated. I noticed that the above layer describes a 'link' between other blocks of layers that seem to repeat themselves about 6 times:

  ...... in the massive bvlc_googlenet network:

  ..... and in this way I managed to simplify it by removing what looked like about 6 large blocks of repeating layers to this:

  ...... And looking at this diagram very carefully, there's still one big block that repeats and should also be able to be removed. I tried removing it, but unfortunately gave this error:

  Creating layer coverage/sig
  Creating Layer coverage/sig
  coverage/sig <- cvg/classifier
  coverage/sig -> coverage
  Setting up coverage/sig
  Top shape: 2 1 80 80 (12800)
  Memory required for data: 851282688
  Creating layer bbox/regressor
  Creating Layer bbox/regressor
  bbox/regressor <- pool5/drop_s1_pool5/drop_s1_0_split_1
  bbox/regressor -> bboxes
  Setting up bbox/regressor
  Top shape: 2 4 80 80 (51200)
  Memory required for data: 851487488
  Creating layer bbox_mask
  Creating Layer bbox_mask
  bbox_mask <- bboxes
  bbox_mask <- coverage-block
  bbox_mask -> bboxes-masked
  Check failed: bottom[i]->shape() == bottom[0]->shape()      

  ...... So that's it for now ..... and here's my 'simplified' network prototext for object detection:

  # DetectNet network

  # DetectNet network
  
  # Data/Input layers
  name: "DetectNet"
  layer {
    name: "train_data"
    type: "Data"
    top: "data"
    data_param {
      backend: LMDB
      source: "examples/kitti/kitti_train_images.lmdb"
      batch_size: 10
    }
    include: { phase: TRAIN }
  }
  layer {
    name: "train_label"
    type: "Data"
    top: "label"
    data_param {
      backend: LMDB
      source: "examples/kitti/kitti_train_labels.lmdb"
      batch_size: 10
    }
    include: { phase: TRAIN }
  }
  layer {
    name: "val_data"
    type: "Data"
    top: "data"
    data_param {
      backend: LMDB
      source: "examples/kitti/kitti_test_images.lmdb"
      batch_size: 6
    }
    include: { phase: TEST stage: "val" }
  }
  layer {
    name: "val_label"
    type: "Data"
    top: "label"
    data_param {
      backend: LMDB
      source: "examples/kitti/kitti_test_labels.lmdb"
      batch_size: 6
    }
    include: { phase: TEST stage: "val" }
  }
  layer {
    name: "deploy_data"
    type: "Input"
    top: "data"
    input_param {
      shape {
        dim: 1
        dim: 3
        dim: 640
        dim: 640
      }
    }
    include: { phase: TEST not_stage: "val" }
  }
  
  # Data transformation layers
  layer {
    name: "train_transform"
    type: "DetectNetTransformation"
    bottom: "data"
    bottom:...

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