DeepLearningTutorials/code/test.py at master · scyoyo/DeepLearningTutorials · GitHub

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import convolutional_mlp, logistic_cg, logistic_sgd, mlp, SdA, dA, rbm , DBN
from nose.plugins.skip import SkipTest
import numpy, theano
import time, sys


def test_logistic_sgd():
    logistic_sgd.sgd_optimization_mnist(n_epochs=10)


def test_logistic_cg():
    logistic_cg.cg_optimization_mnist(n_epochs=10)


def test_mlp():
    mlp.test_mlp(n_epochs=5)


def test_convolutional_mlp():
    convolutional_mlp.evaluate_lenet5(n_epochs=5,nkerns=[5,5])


def test_dA():
    dA.test_dA(training_epochs = 3, output_folder = 'tmp_dA_plots')


def test_SdA():
    SdA.test_SdA(pretraining_epochs = 2, training_epochs = 3, batch_size = 300)


def test_dbn():
    DBN.test_DBN(pretraining_epochs = 1, training_epochs = 2, batch_size =300)


def test_rbm():
    rbm.test_rbm(training_epochs = 1, batch_size = 300, n_chains = 1, n_samples = 1,
            output_folder =  'tmp_rbm_plots')


def speed():
    """
    This fonction modify the configuration theano and don't restore it!
    I want it to be compatible with python2.4 so using try: finaly: is not an option.
    """

    import theano
    algo=['logistic_sgd','logistic_cg','mlp','convolutional_mlp','dA','SdA','DBN','rbm']
    to_exec=[True]*len(algo)
#    to_exec=[False]*len(algo)
#    to_exec[-1]=True
    do_float64=True
    do_float32=True
    do_gpu=True

    algo_executed = [s for idx,s in enumerate(algo) if to_exec[idx]]

    expected_times_64=numpy.asarray([  12.42313051,   28.09523582,  106.35365391,  116.79225969,  153.12310314,
                                       425.09175086,  642.72824597,  652.52828193])
    expected_times_32=numpy.asarray([  13.29699826,   32.42813158,   68.03559947,  105.54640913,  107.00527334,
                                       242.41721797,  490.40798998, 528.88854146])
    expected_times_gpu=numpy.asarray([   3.07663488,  7.55523491,   18.99226785,   9.58915591,   24.13007045,
                                         24.77524018,  92.66246653,  322.34032917])
    expected_times_64 = [s for idx,s in enumerate(expected_times_64) if to_exec[idx]]
    expected_times_32 = [s for idx,s in enumerate(expected_times_32) if to_exec[idx]]
    expected_times_gpu = [s for idx,s in enumerate(expected_times_gpu) if to_exec[idx]]

    def time_test(m,l,idx,f,**kwargs):
        if not to_exec[idx]:
            return
        print algo[idx]
        ts=m.call_time
        try:
            f(**kwargs)
        except Exception, e:
            print >> sys.stderr, 'test', algo[idx], 'FAILED', e
            l.append(numpy.nan)
            return
        te=m.call_time
        l.append(te-ts)

    def do_tests():
        m=theano.compile.mode.get_default_mode()
        l=[]
        time_test(m,l,0, logistic_sgd.sgd_optimization_mnist,n_epochs=30)
        time_test(m,l,1, logistic_cg.cg_optimization_mnist,n_epochs=30)
        time_test(m,l,2, mlp.test_mlp, n_epochs=5)
        time_test(m,l,3, convolutional_mlp.evaluate_lenet5, n_epochs=5,nkerns=[5,5])
        time_test(m,l,4, dA.test_dA, training_epochs = 2, output_folder = 'tmp_dA_plots')
        time_test(m,l,5, SdA.test_SdA, pretraining_epochs = 1, training_epochs = 2, batch_size = 300)
        time_test(m,l,6, DBN.test_DBN, pretraining_epochs = 1, training_epochs = 2, batch_size = 300)
        time_test(m,l,7, rbm.test_rbm, training_epochs = 1, batch_size = 300, n_chains = 1, n_samples = 1, output_folder =  'tmp_rbm_plots')
        return numpy.asarray(l)


    #test in float64 in FAST_RUN mode on the cpu
    if do_float64:
        theano.config.floatX='float64'
        theano.config.mode='FAST_RUN'
        float64_times=do_tests()
        print >> sys.stderr, algo_executed
        print >> sys.stderr, 'float64 times',float64_times
        print >> sys.stderr, 'float64 expected',expected_times_64
        print >> sys.stderr, 'float64 % expected/get',expected_times_64/float64_times

    #test in float32 in FAST_RUN mode on the cpu
    theano.config.floatX='float32'
    if do_float32:
        float32_times=do_tests()
        print >> sys.stderr, algo_executed
        print >> sys.stderr, 'float32 times',float32_times
        print >> sys.stderr, 'float32 expected',expected_times_32
        print >> sys.stderr, 'float32 % expected/get',expected_times_32/float32_times

        if do_float64:
            print >> sys.stderr, 'float64/float32',float64_times/float32_times
            print >> sys.stderr
            print >> sys.stderr, 'Duplicate the timing to have everything in one place'
            print >> sys.stderr, algo_executed
            print >> sys.stderr, 'float64 times',float64_times
            print >> sys.stderr, 'float64 expected',expected_times_64
            print >> sys.stderr, 'float64 % expected/get',expected_times_64/float64_times
            print >> sys.stderr, 'float32 times',float32_times
            print >> sys.stderr, 'float32 expected',expected_times_32
            print >> sys.stderr, 'float32 % expected/get',expected_times_32/float32_times

            print >> sys.stderr, 'float64/float32',float64_times/float32_times
            print >> sys.stderr, 'expected float64/float32',expected_times_64/float32_times

    #test in float32 in FAST_RUN mode on the gpu
    import theano.sandbox.cuda
    if do_gpu:
        theano.sandbox.cuda.use('gpu')
        gpu_times=do_tests()
        print >> sys.stderr, algo_executed
        print >> sys.stderr, 'gpu times',gpu_times
        print >> sys.stderr, 'gpu expected',expected_times_gpu
        print >> sys.stderr, 'gpu % expected/get',expected_times_gpu/gpu_times
        if do_float64:
            print >> sys.stderr, 'float64/gpu',float64_times/gpu_times

        if (do_float64 + do_float32 + do_gpu) > 1:
            print >> sys.stderr
            print >> sys.stderr, 'Duplicate the timing to have everything in one place'
            print >> sys.stderr, algo_executed
            if do_float64:
                print >> sys.stderr, 'float64 times',float64_times
                print >> sys.stderr, 'float64 expected',expected_times_64
                print >> sys.stderr, 'float64 % expected/get',expected_times_64/float64_times
            if do_float32:
                print >> sys.stderr, 'float32 times',float32_times
                print >> sys.stderr, 'float32 expected',expected_times_32
                print >> sys.stderr, 'float32 % expected/get',expected_times_32/float32_times
            if do_gpu:
                print >> sys.stderr, 'gpu times',gpu_times
                print >> sys.stderr, 'gpu expected',expected_times_gpu
                print >> sys.stderr, 'gpu % expected/get',expected_times_gpu/gpu_times

            if do_float64 and do_float32:
                print >> sys.stderr, 'float64/float32',float64_times/float32_times
                print >> sys.stderr, 'expected float64/float32',expected_times_64/float32_times
            if do_float64 and do_gpu:
                print >> sys.stderr, 'float64/gpu',float64_times/gpu_times
                print >> sys.stderr, 'expected float64/gpu',expected_times_64/gpu_times
            if do_float32 and do_gpu:
                print >> sys.stderr, 'float32/gpu',float32_times/gpu_times
                print >> sys.stderr, 'expected float32/gpu',expected_times_32/gpu_times

    if do_float64:
        print >> sys.stderr, 'speed_failure_float64='+str(sum((expected_times_64/float64_times)>0.95))
    if do_float32:
        print >> sys.stderr, 'speed_failure_float32='+str(sum((expected_times_32/float32_times)>0.95))
    if do_gpu:
        print >> sys.stderr, 'speed_failure_gpu='+str(sum((expected_times_gpu/gpu_times)>0.95))

        assert not numpy.isnan(gpu_times).any()