2016-01-12 05:17:31 +08:00
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# OpenFace training tests.
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#
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# 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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import os
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import shutil
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import numpy as np
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np.set_printoptions(precision=2)
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import pandas as pd
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import tempfile
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from subprocess import Popen, PIPE
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openfaceDir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
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modelDir = os.path.join(openfaceDir, 'models')
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exampleImages = os.path.join(openfaceDir, 'images', 'examples')
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lfwSubset = os.path.join(openfaceDir, 'data', 'lfw-subset')
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def test_dnn_training():
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# Get lfw-subset by running ./data/download-lfw-subset.sh
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assert os.path.isdir(lfwSubset)
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cmd = ['python2', os.path.join(openfaceDir, 'util', 'align-dlib.py'),
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os.path.join(lfwSubset, 'raw'), 'align', 'outerEyesAndNose',
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os.path.join(lfwSubset, 'aligned', 'train')]
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p = Popen(cmd, stdout=PIPE, stderr=PIPE)
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(out, err) = p.communicate()
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assert p.returncode == 0
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workDir = tempfile.mkdtemp(prefix='OpenFaceTrainingTest-')
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cmd = ['th', './main.lua',
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'-data', os.path.join(lfwSubset, 'aligned'),
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'-modelDef', '../models/openface/nn4.def.lua',
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'-peoplePerBatch', '3',
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2016-01-12 05:49:05 +08:00
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'-imagesPerPerson', '4',
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2016-01-12 05:17:31 +08:00
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'-nEpochs', '10',
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'-epochSize', '5',
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'-testEpochSize', '0',
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'-cache', workDir,
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'-cuda', '-cudnn',
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'-nDonkeys', '-1']
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p = Popen(cmd, stdout=PIPE, stderr=PIPE, cwd=os.path.join(openfaceDir, 'training'))
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(out, err) = p.communicate()
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# print(out)
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# print(err)
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assert p.returncode == 0
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# Training won't make much progress on lfw-subset, but as a sanity check,
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# make sure the training code runs and doesn't get worse than the initialize
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# loss value of 0.2.
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trainLoss = pd.read_csv(os.path.join(workDir, '1', 'train.log'),
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sep='\t').as_matrix()[:, 0]
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assert trainLoss[-1] < 0.25
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shutil.rmtree(os.path.join(lfwSubset, 'aligned'))
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shutil.rmtree(workDir)
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