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mcnExtraLayers

Notice: This repo is no longer actively maintained. You are very welcome to use it, but I am unable to respond to issues and provide support.

This repo contains a collection of common MatConvNet functions and DagNN layers which are shared across a number of classification and object detection frameworks.

Layers:

  • vl_nnmax - element-wise maximum across tensors
  • vl_nnsum - element-wise sum across tensors
  • vl_nninterp - a wrapper for bilinear interpolation
  • vl_nnslice - slicing along a given dimension
  • vl_nnspatialsoftmax - spatial application of the softmax operator
  • vl_nnreshape - tensor reshaping
  • vl_nnchannelshuffle - channel shuffling (introduced in ShuffleNet)
  • vl_nnflatten - flatten along a given dimension
  • vl_nnglobalpool - global pooling
  • vl_nnsoftmaxt - softmax along a given dimension
  • vl_nncrop_wrapper - autonn function wrapper for vl_nncrop.m
  • vl_nnaxpy - vector op y <- a*x + y (BLAS Level One style naming convention)
  • vl_nngnorm - group normalization (an alternative to batch norm)
  • vl_nnhuberloss - computation of the Huber (L1-smooth) loss
  • vl_nneuclidenaloss - computation of the Euclidean (L2-smooth) loss
  • vl_nntukeyloss - computation of Tukey's Biweight (robust) loss
  • vl_nnsoftmaxceloss - soft-target cross entropy loss (operates on logits)
  • vl_nncaffepool - "caffe-style" pooling (applies padding before pooling kernel)
  • vl_nnl2norm - l2 feature normalisation

Dependencies

mcnExtraLayers requires the following modules:

  • autonn - automatic differentiation

Utilities

The module also contains some additional utilities which may be useful during network training:

  • findBestCheckpoint - function to rank and prune network checkpoints saved during training (useful for saving space automatically at the end of a training run
  • checkLearningParams - compare mcn network against a caffe prototxt

Install

The module is easiest to install with the vl_contrib package manager:

vl_contrib('install', 'mcnExtraLayers') ;
vl_contrib('setup', 'mcnExtraLayers') ;
vl_contrib('test', 'mcnExtraLayers') ; % optional

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Extra layers and utilities for matconvnet

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