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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 | |