soumith · June 21, 2026 14:56 · larismanzanodavid-dev · May 18, 2026 · larismanzanodavid-dev · May 18, 2026
diff --git a/main.lua b/main.lua
 -- Torch Android demo script
 -- Script: main.lua
 -- Copyright (C) 2013 Soumith Chintala

 require 'torch'
 require 'cunn'
 require 'nnx'
 require 'dok'
 require 'image'

 function demoluafn()
   ret = 'Called demo function from inside lua'
   print()
   return ret
 end

 print("Hello from Lua")

 torch.setdefaulttensortype('torch.FloatTensor')
 -- Doing a small benchmark of the convolution module
 in_planes = 3
 out_planes = 16
 imsz_x = 640
 imsz_y = 480
 num_ops = 2 -- 2 ops, i.e one for multiply and one for accumulate

 test_tensor = torch.rand(in_planes,imsz_x,imsz_y):cuda()

 for kernel_sz=1,16 do
   local model = nn.SpatialConvolution(in_planes,out_planes,kernel_sz,kernel_sz):cuda()
   tstart = os.clock()
   output1 = model:forward(test_tensor)
   tend = os.clock()
   print('-------------------------------------------------------------------------------------------')
   print('Input Size: ' .. in_planes .. 'x' .. imsz_x .. 'x' .. imsz_y
 	 .. '\t\tKernel Size: ' .. kernel_sz .. 'x' .. kernel_sz .. '\t\tOutput Planes:' .. out_planes)
   print('Time taken (in seconds): ' .. (tend-tstart))
   total_ops = in_planes*kernel_sz*kernel_sz*out_planes*(imsz_x-kernel_sz+1)*(imsz_y-kernel_sz+1)*num_ops
   print('GOps:' .. total_ops / 1e9)
   print('Gops/s: ' .. (  total_ops/( (tend-tstart) * 1e9)))
   print('-------------------------------------------------------------------------------------------')
 end
	-- Torch Android demo script
	-- Script: main.lua
	-- Copyright (C) 2013 Soumith Chintala

	require 'torch'
	require 'cunn'
	require 'nnx'
	require 'dok'
	require 'image'

	function demoluafn()
	ret = 'Called demo function from inside lua'
	print()
	return ret
	end

	print("Hello from Lua")

	torch.setdefaulttensortype('torch.FloatTensor')
	-- Doing a small benchmark of the convolution module
	in_planes = 3
	out_planes = 16
	imsz_x = 640
	imsz_y = 480
	num_ops = 2 -- 2 ops, i.e one for multiply and one for accumulate

	test_tensor = torch.rand(in_planes,imsz_x,imsz_y):cuda()

	for kernel_sz=1,16 do
	local model = nn.SpatialConvolution(in_planes,out_planes,kernel_sz,kernel_sz):cuda()
	tstart = os.clock()
	output1 = model:forward(test_tensor)
	tend = os.clock()
	print('-------------------------------------------------------------------------------------------')
	print('Input Size: ' .. in_planes .. 'x' .. imsz_x .. 'x' .. imsz_y
	.. '\t\tKernel Size: ' .. kernel_sz .. 'x' .. kernel_sz .. '\t\tOutput Planes:' .. out_planes)
	print('Time taken (in seconds): ' .. (tend-tstart))
	total_ops = in_planeskernel_szkernel_szout_planes(imsz_x-kernel_sz+1)(imsz_y-kernel_sz+1)num_ops
	print('GOps:' .. total_ops / 1e9)
	print('Gops/s: ' .. ( total_ops/( (tend-tstart) * 1e9)))
	print('-------------------------------------------------------------------------------------------')
	end
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