test_model.py
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__author__ = 'chunk'
from sklearn import cross_validation
from ..common import *
from ..mdata import CV, ILSVRC, ILSVRC_S
from mmodel.svm import SVM
from mmodel.theano import THEANO
timer = Timer()
def test_SVM_CV():
timer.mark()
dcv = CV.DataCV()
X, Y = dcv.load_data(mode='local') # 90.468586s -> 5.392520s
# X, Y = dcv.load_data(mode='hbase') # 21.682754s
# X, Y = dcv.load_data(mode='spark') # 29.549597s
timer.report()
timer.mark()
# msvm = SVM.ModelSVM(toolset='sklearn') # 3.030380s
# msvm = SVM.ModelSVM(toolset='opencv') # 8.939880s
# msvm = SVM.ModelSVM(toolset='libsvm') # 185.524023s
msvm = SVM.ModelSVM(toolset='spark')
msvm.train(X, Y)
timer.report()
timer.mark()
for path, subdirs, files in os.walk('data/467/'):
for name in files:
imgpath = os.path.join(path, name)
feat = dcv.get_feat(imgpath, 'hog')
print name, msvm.predict(feat)
timer.report()
timer.mark()
print msvm.test(X, Y) # 0.948892561983 for svm_cv, 0.989024793388 for svm_sk, 0.9900826446280992 for svm_lib
timer.report() # 27.421949s for svm_lib
def test_SVM_ILSVRC():
timer.mark()
dil = ILSVRC.DataILSVRC(base_dir='/data/hadoop/ImageNet/ILSVRC/ILSVRC2013_DET_val', category='Test.0.2')
X, Y = dil.load_data(mode='local') #
# X, Y = dil.load_data(mode='hbase') #
# X, Y = dil.load_data(mode='spark') #
X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(X, Y, test_size=0.4, random_state=0)
print np.array(Y).shape, np.array(X).shape
print np.array(X_train).shape, np.array(Y_train).shape
print np.array(X_test).shape, np.array(Y_test).shape
timer.report()
timer.mark()
msvm = SVM.ModelSVM(toolset='sklearn') # 4.884247s 0.777853030816
# msvm = SVM.ModelSVM(toolset='opencv') #
# msvm = SVM.ModelSVM(toolset='libsvm') #
# msvm = SVM.ModelSVM(toolset='spark')
msvm.train(X_train, Y_train)
timer.report()
timer.mark()
print msvm.test(X_test, Y_test) #
timer.report() #
# timer.mark()
# print 'or like this:'
# scores = cross_validation.cross_val_score(msvm.model, X, Y)
# print scores
# timer.report()
def test_SVM_ILSVRC_HBASE():
timer.mark()
# dil = ILSVRC.DataILSVRC(base_dir='ILSVRC2013_DET_val', category='Train_3')
# X, Y = dil.load_data(mode='hbase') # pass
dils = ILSVRC_S.DataILSVRC_S(base='ILSVRC2013_DET_val', category='Test_1')
X, Y = dils.load_data(mode='hbase') # pass
dil = ILSVRC.DataILSVRC(base_dir='/data/hadoop/ImageNet/ILSVRC/ILSVRC2013_DET_val', category='Test_1')
X1, Y1 = dil.load_data(mode='local')
X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(X, Y, test_size=0.4, random_state=0)
print Y,np.sum(np.array(Y)==0),np.sum(np.array(Y)==1)
print np.array(Y).shape, np.array(X).shape
print np.array(X_train).shape, np.array(Y_train).shape
print np.array(X_test).shape, np.array(Y_test).shape
timer.report()
timer.mark()
msvm = SVM.ModelSVM(toolset='sklearn') # 4.884247s 0.777853030816
# msvm = SVM.ModelSVM(toolset='opencv') #
# msvm = SVM.ModelSVM(toolset='libsvm') #
# msvm = SVM.ModelSVM(toolset='spark')
msvm.train(X_train, Y_train)
timer.report()
timer.mark()
print msvm.test(X_test, Y_test) #
timer.report() #
timer.mark()
print msvm.test(X1, Y1) #
timer.report() #
# timer.mark()
# print 'or like this:'
# scores = cross_validation.cross_val_score(msvm.model, X, Y)
# print scores
# timer.report()
def test_SVM_ILSVRC_SPARK():
timer.mark()
dils = ILSVRC_S.DataILSVRC_S(base='ILSVRC2013_DET_val', category='Test_1')
rdd_dataset = dils.load_data(mode='spark') # pass
timer.report()
timer.mark()
# msvm = SVM.ModelSVM(toolset='sklearn') #
# msvm = SVM.ModelSVM(toolset='opencv') #
# msvm = SVM.ModelSVM(toolset='libsvm') #
msvm = SVM.ModelSVM(toolset='spark', sc=dils.sparker)
msvm.train(rdd_dataset)
timer.report()
dataset = rdd_dataset.collect()
length = len(dataset)
X_test, Y_test = [dataset[i].features for i in range(length)], [dataset[i].label for i in range(length)]
timer.mark()
print msvm.test(dils.sparker.sc.parallelize(X_test), Y_test) #
timer.report() #
def test_SVM_ILSVRC_S():
test_SVM_ILSVRC_HBASE()
# test_SVM_ILSVRC_SPARK()
def test_THEANO_crop():
timer.mark()
dilc = ILSVRC.DataILSVRC(base='/data/hadoop/ImageNet/ILSVRC/ILSVRC2013_DET_val', category='Test_crop_pil')
X, Y = dilc.load_data(mode='local', feattype='coef')
timer.report()
timer.mark()
mtheano = THEANO.ModelTHEANO(toolset='cnn')
mtheano.train(X,Y)
timer.report()
if __name__ == '__main__':
# test_SVM_CV()
test_SVM_ILSVRC()
print 'helllo'