82 lines
2.4 KiB
Lua
82 lines
2.4 KiB
Lua
-- Copyright 2015 Carnegie Mellon University
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--
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-- Licensed under the Apache License, Version 2.0 (the "License");
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-- you may not use this file except in compliance with the License.
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-- You may obtain a copy of the License at
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--
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-- http://www.apache.org/licenses/LICENSE-2.0
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--
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-- Unless required by applicable law or agreed to in writing, software
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-- distributed under the License is distributed on an "AS IS" BASIS,
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-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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-- See the License for the specific language governing permissions and
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-- limitations under the License.
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testLogger = optim.Logger(paths.concat(opt.save, 'test.log'))
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local batchNumber
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local triplet_loss
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local timer = torch.Timer()
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function test()
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print('==> doing epoch on validation data:')
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print("==> online epoch # " .. epoch)
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batchNumber = 0
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cutorch.synchronize()
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timer:reset()
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model:evaluate()
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model:cuda()
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triplet_loss = 0
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for i=1,opt.testEpochSize do
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donkeys:addjob(
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function()
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local inputs, _ = testLoader:sampleTriplet(opt.batchSize)
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inputs = inputs:float()
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return sendTensor(inputs)
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end,
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testBatch
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)
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if i % 5 == 0 then
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donkeys:synchronize()
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collectgarbage()
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end
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end
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donkeys:synchronize()
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cutorch.synchronize()
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triplet_loss = triplet_loss / opt.testEpochSize
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testLogger:add{
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['avg triplet loss (test set)'] = triplet_loss
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}
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print(string.format('Epoch: [%d][TESTING SUMMARY] Total Time(s): %.2f \t'
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.. 'average triplet loss (per batch): %.2f',
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epoch, timer:time().real, triplet_loss))
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print('\n')
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end
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local inputsCPU = torch.FloatTensor()
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local inputs = torch.CudaTensor()
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function testBatch(inputsThread)
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receiveTensor(inputsThread, inputsCPU)
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inputs:resize(inputsCPU:size()):copy(inputsCPU)
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local embeddings = model:forward({
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inputs:sub(1,opt.batchSize),
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inputs:sub(opt.batchSize+1, 2*opt.batchSize),
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inputs:sub(2*opt.batchSize+1, 3*opt.batchSize)})
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local err = criterion:forward(embeddings)
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cutorch.synchronize()
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triplet_loss = triplet_loss + err
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print(('Epoch: Testing [%d][%d/%d] Triplet Loss: %.2f'):format(epoch, batchNumber,
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opt.testEpochSize, err))
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batchNumber = batchNumber + 1
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end
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