76 lines
2.0 KiB
Lua
76 lines
2.0 KiB
Lua
local ffi = require 'ffi'
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local batchNumber, nImgs = 0
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torch.setdefaulttensortype('torch.FloatTensor')
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function batchRepresent()
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local loadSize = {3, opt.imgDim, opt.imgDim}
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print(opt.data)
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local cacheFile = paths.concat(opt.data, 'cache.t7')
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print('cache lotation: ', cacheFile)
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local dumpLoader
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if paths.filep(cacheFile) then
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print('Loading metadata from cache.')
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print('If your dataset has changed, delete the cache file.')
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dumpLoader = torch.load(cacheFile)
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else
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print('Creating metadata for cache.')
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dumpLoader = dataLoader{
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paths = {opt.data},
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loadSize = loadSize,
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sampleSize = loadSize,
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split = 0,
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verbose = true
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}
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torch.save(cacheFile, dumpLoader)
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end
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collectgarbage()
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nImgs = dumpLoader:sizeTest()
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print('nImgs: ', nImgs)
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assert(nImgs > 0, "Failed to get nImgs")
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batchNumber = 0
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for i=1,math.ceil(nImgs/opt.batchSize) do
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local indexStart = (i-1) * opt.batchSize + 1
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local indexEnd = math.min(nImgs, indexStart + opt.batchSize - 1)
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local inputs, labels = dumpLoader:get(indexStart, indexEnd)
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local paths = {}
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for j=indexStart,indexEnd do
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table.insert(paths,
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ffi.string(dumpLoader.imagePath[dumpLoader.testIndices[j]]:data()))
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end
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repBatch(paths, inputs, labels)
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if i % 5 == 0 then
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collectgarbage()
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end
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end
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if opt.cuda then
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cutorch.synchronize()
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end
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end
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function repBatch(paths, inputs, labels)
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-- labels:size(1) is equal to batchSize except for the last iteration if
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-- the number of images isn't equal to the batch size.
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local n = labels:size(1)
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batchNumber = batchNumber + n
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if opt.cuda then
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inputs = inputs:cuda()
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end
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local embeddings = model:forward(inputs):float()
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if opt.cuda then
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cutorch.synchronize()
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end
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for i=1,n do
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labelsCSV:write({labels[i], paths[i]})
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repsCSV:write(embeddings[i]:totable())
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end
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print(('Represent: %d/%d'):format(batchNumber, nImgs))
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end
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