szagoruyko

#!/usr/bin/env python
import subprocess
import re


class bcolors:
    HEADER = '\033[95m'
    OKBLUE = '\033[94m'
    OKGREEN = '\033[92m'
    WARNING = '\033[93m'

szagoruyko

function removeDescriptors(net)
  for i,val in pairs(net.modules) do
    if tostring(val):find('cudnn') then
      for name,field in pairs(val) do
        if name:find('Desc') then
          val[name] = nil
        end
      end
      val.algType = nil
      val.iDesc = nil

szagoruyko

Using 1-th gpu
Loading ./data/ptb.train.txt, size of data = 929589
Loading ./data/ptb.valid.txt, size of data = 73760
Loading ./data/ptb.test.txt, size of data = 82430
Network parameters:
{
  layers : 2
  lr : 1
  max_max_epoch : 13
  max_grad_norm : 5

szagoruyko

Using 1-th gpu
Loading ./data/ptb.train.txt, size of data = 929589
Loading ./data/ptb.valid.txt, size of data = 73760
Loading ./data/ptb.test.txt, size of data = 82430
Network parameters:
{
  layers : 2
  lr : 1
  max_max_epoch : 13
  max_grad_norm : 5

szagoruyko

-- a script to simplify trained net by incorporating every SpatialBatchNormalization to SpatialConvolution
-- and BatchNormalization to Linear

local function BNtoConv(net)
  for i,v in ipairs(net.modules) do
    if v.modules then
      BNtoConv(v)
    else
      if torch.typename(v) == 'nn.SpatialBatchNormalization' and
        (torch.typename(net:get(i-1)):find'SpatialConvolution') then

szagoruyko

local Linear, parent = torch.class('nn.NoBiasLinear', 'nn.Linear')

function Linear:__init(inputSize, outputSize)
   parent.__init(self, inputSize, outputSize)
   self.bias:fill(0)
end

function Linear:accGradParameters(input, gradOutput, scale)
   scale = scale or 1
   if input:dim() == 1 then

szagoruyko

local BidirectionalSequencer, parent = torch.class('nn.BidirectionalSequencer', 'nn.Container')

function BidirectionalSequencer:__init(module_forward, module_backward, nOutputSize)
  parent.__init(self)
  self.module_forward = module_forward
  self.module_backward = module_backward
  self.modules[1] = nn.Sequencer(module_forward)
  self.modules[2] = nn.Sequencer(module_backward)

  self.output = {}

szagoruyko

-- Usage:
-- find . -name model.net -print0 | xargs -0 -n 1 th ~/clearState.lua

require 'cudnn'

local name = arg[1]

assert(paths.filep(name))

print'before'

szagoruyko

require 'cv.highgui'
require 'cv.videoio'
require 'cv.imgproc'
require 'nn'
--require 'clnn'

local cap = cv.VideoCapture{device=0}
if not cap:isOpened() then
  print("Failed to open the default camera")
  os.exit(-1)

szagoruyko

function nn.Module:replace(callback)
  local out = callback(self)
  if self.modules then
    for i, module in ipairs(self.modules) do
      self.modules[i] = module:replace(callback)
    end
  end
  return out
end