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require 'itorch'
require 'torch'
require 'nn'
local pl = require 'pl.import_into'()
local class = require 'class'
local itorch_utils = {}
-- Itorch utilities.
-- Channels:
function itorch_utils.showResp(chns, baselines)
assert(chns:nDimension() == 3)
local chnTable = {}
for i = 1, chns:size(1) do
if baselines then
chnTable[i] = chns[i] - baselines[i];
else
chnTable[i] = chns[i]
end;
end
itorch.image(chnTable)
end
function itorch_utils.showConvw(layer, outputIdx)
if class.istype(layer, 'nn.SpatialConvolution') == nil then
print("Input is " .. torch.classname(layer) .. " no weights can be shown.");
return
end
local v
if layer.weight:nDimension() == 2 then
v = layer.weight[outputIdx]:view(layer.nInputPlane, layer.kH, layer.kW);
else
v = layer.weight[outputIdx]
end
itorch_utils.showResp(v)
end
function itorch_utils.showImMask(im, mask)
local inputDup = im:sub(1, 3):clone()
inputDup[1]:add(mask)
itorch.image(inputDup)
end
function itorch_utils.showImOverlay(input)
local inputDup = input:sub(1, 3):clone()
inputDup[1]:add(input[4])
itorch.image(inputDup)
end
function itorch_utils.showImOverlays(inputs, extractor)
-- Input is a table with nbatch element, each is 4 * h * w
local vis = pl.tablex.map(function(x)
if extractor ~= nil then
x = extractor(x)
end
local inputDup = x:sub(1, 3):clone()
inputDup[1]:add(x[4])
return inputDup;
end,
inputs);
itorch.image(vis)
end
-- Show a list of images. Inputs are nimage * 3 * h * w, convert them into
function itorch_utils.compareIms(...)
-- Input is a set of images, each is nimage * 3 * h * w
local ims = {}
local args = {...}
local nIms = args[1]:size(1)
for i = 1, nIms do
for _, input in ipairs(args) do
table.insert(ims, input[i])
end
end
itorch.image(ims)
end
return itorch_utils
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