def process(options, testCollection, trainCollection, annotationName, feature, outputpkl):
rootpath = options.rootpath
k = options.k
distance = options.distance
variant = options.variant
overwrite = options.overwrite
testset = testCollection
forcetrainmodel = options.trainmodel
modelName = "tagprop"
nnName = distance + "knn"
printStatus(INFO, "Starting TagProp %s,%s,%s,%s,%s" % (variant, trainCollection, testCollection, annotationName, feature))
resultfile = os.path.join(outputpkl)
resultfile_tagprop = os.path.join(rootpath, testCollection, 'TagProp-Prediction', testset, trainCollection, annotationName, modelName, '%s,%s,%s,%d'%(feature,nnName,variant,k), 'prediction.mat')
if checkToSkip(resultfile, overwrite) or checkToSkip(resultfile_tagprop, overwrite):
return 0
tagmatrix_file = os.path.join(rootpath, trainCollection, 'TextData', 'lemm_wordnet_freq_tags.h5')
if not os.path.exists(tagmatrix_file):
printStatus(INFO, "Tag matrix file not found at %s Did you run wordnet_frequency_tags.py?" % (tagmatrix_file))
sys.exit(1)
train_neighs_file = os.path.join(rootpath, trainCollection, 'TagProp-data', trainCollection, '%s,%s,%d'%(feature,nnName,k), 'nn_train.h5')
if not os.path.exists(train_neighs_file):
printStatus(INFO, "Matlab train neighbors file not found at %s Did you run prepare_tagprop_data.py?" % (train_neighs_file))
sys.exit(1)
# do we need to perform learning?
train_model_file = os.path.join(rootpath, trainCollection, 'TagProp-models', '%s,%s,%s,%d'%(feature,nnName,variant,k), 'model.mat')
if os.path.exists(train_model_file) and not forcetrainmodel:
printStatus(INFO, "model for %s available at %s" % (trainCollection, train_model_file))
else:
printStatus(INFO, "starting learning model for %s" % (trainCollection))
makedirsforfile(train_model_file)
script = """
tagprop_path = 'model_based/tagprop/TagProp/';
addpath(tagprop_path);
tagmatrix = h5read('%s', '/tagmatrix') > 0.5;
tagmatrix = sparse(tagmatrix);
NN = h5read('%s', '/NN');
NN = NN(2:end, :);
NN = double(NN);
""" % (tagmatrix_file, train_neighs_file)
if variant == 'dist' or variant == 'distsigmoids':
script += """
NND = h5read('%s', '/NND');
NND = NND(2:end, :);
NND = reshape(NND, 1, size(NND,1), size(NND,2));
NND = double(NND);
""" % train_neighs_file
if variant == 'rank':
script += """
m = tagprop_learn(NN,[],tagmatrix);
"""
elif variant == 'ranksigmoids':
script += """
m = tagprop_learn(NN,[],tagmatrix,'sigmoids',true);
"""
elif variant == 'dist':
script += """
m = tagprop_learn(NN,NND,tagmatrix,'type','dist');
"""
elif variant == 'distsigmoids':
script += """
m = tagprop_learn(NN,NND,tagmatrix,'type','dist','sigmoids',true);
"""
script += """
save('%s', 'm', '-v7.3');
exit;
""" % train_model_file
call_matlab(script)
# we perform prediction
printStatus(INFO, "starting prediction")
test_neighs_file = os.path.join(rootpath, testCollection, 'TagProp-data', testset, trainCollection, annotationName, '%s,%s,%d'%(feature,nnName,k), 'nn_test.h5')
if not os.path.exists(test_neighs_file):
printStatus(INFO, "Matlab test neighbors file not found at %s Did you run prepare_tagprop_data.py?" % (test_neighs_file))
sys.exit(1)
script = """
tagprop_path = 'model_based/tagprop/TagProp/';
addpath(tagprop_path);
load('%s');
tagmatrix = h5read('%s', '/tagmatrix') > 0.5;
tagmatrix = sparse(tagmatrix);
NNT = h5read('%s', '/NNT');
NNT = double(NNT);
""" % (train_model_file, tagmatrix_file, test_neighs_file)
if variant == 'dist' or variant == 'distsigmoids':
script += """
NNDT = h5read('%s', '/NNDT');
NNDT = reshape(NNDT, 1, size(NNDT,1), size(NNDT,2));
NNDT = double(NNDT);
""" % test_neighs_file
script += """
P = tagprop_predict(NNT,[],m)';
save('%s', '-v7.3');
exit;
""" % resultfile_tagprop
makedirsforfile(resultfile_tagprop)
call_matlab(script)
# save results in pkl format
printStatus(INFO, "Dump results in pkl format at %s" % resultfile)
concepts = readConcepts(testCollection, annotationName, rootpath)
id_images = readImageSet(testCollection, testset, rootpath)
id_images.sort()
# id_images = map(int, id_images)
# concepts mapping
tagprop_output = h5py.File(resultfile_tagprop, 'r')
tagprop_input = h5py.File(tagmatrix_file, 'r')
mapping = getVocabMap(list(tagprop_input['vocab'][:]),concepts)
final_tagmatrix = tagprop_output['P'][:][:,mapping]
with open(resultfile, 'w') as f:
pickle.dump({'concepts':concepts, 'id_images':id_images, 'scores':final_tagmatrix}, f, pickle.HIGHEST_PROTOCOL)
def process(options, testCollection, trainCollection, annotationName, feature, outputpkl):
rootpath = options.rootpath
k = options.k
distance = options.distance
variant = options.variant
overwrite = options.overwrite
testset = testCollection
forcetrainmodel = options.trainmodel
modelName = "tagprop"
nnName = distance + "knn"
printStatus(INFO, "Starting TagProp %s,%s,%s,%s,%s" % (variant, trainCollection, testCollection, annotationName, feature))
resultfile = os.path.join(outputpkl)
resultfile_tagprop = os.path.join(rootpath, testCollection, 'TagProp-Prediction', testset, trainCollection, annotationName, modelName, '%s,%s,%s,%d'%(feature,nnName,variant,k), 'prediction.mat')
# if checkToSkip(resultfile, overwrite) or checkToSkip(resultfile_tagprop, overwrite):
# return 0
tagmatrix_file = os.path.join(rootpath, trainCollection, 'TextData', 'lemm_wordnet_freq_tags.h5')
if not os.path.exists(tagmatrix_file):
printStatus(INFO, "Tag matrix file not found at %s Did you run wordnet_frequency_tags.py?" % (tagmatrix_file))
sys.exit(1)
train_neighs_file = os.path.join(rootpath, trainCollection, 'TagProp-data', trainCollection, '%s,%s,%d'%(feature,nnName,k), 'nn_train.h5')
if not os.path.exists(train_neighs_file):
printStatus(INFO, "Matlab train neighbors file not found at %s Did you run prepare_tagprop_data.py?" % (train_neighs_file))
sys.exit(1)
# do we need to perform learning?
train_model_file = os.path.join(rootpath, trainCollection, 'TagProp-models', '%s,%s,%s,%d'%(feature,nnName,variant,k), 'model.mat')
# if os.path.exists(train_model_file) and not forcetrainmodel:
if False:
printStatus(INFO, "model for %s available at %s" % (trainCollection, train_model_file))
else:
printStatus(INFO, "starting learning model for %s" % (trainCollection))
makedirsforfile(train_model_file)
# print(tagmatrix_file, train_neighs_file)
# exit()
script = """
tagprop_path = '%s/model_based/tagprop/TagProp/';
addpath(tagprop_path);
tagmatrix = h5read('%s', '/tagmatrix') > 0.5;
tagmatrix = sparse(tagmatrix);
NN = h5read('%s', '/NN');
NN = NN(2:end, :);
NN = double(NN);
""" % (survey_code, tagmatrix_file, train_neighs_file)
if variant == 'dist' or variant == 'distsigmoids':
script += """
NND = h5read('%s', '/NND');
NND = NND(2:end, :);
NND = reshape(NND, 1, size(NND,1), size(NND,2));
NND = double(NND);
""" % train_neighs_file
if variant == 'rank':
script += """
m = tagprop_learn(NN,[],tagmatrix);
"""
elif variant == 'ranksigmoids':
script += """
m = tagprop_learn(NN,[],tagmatrix,'sigmoids',true);
"""
elif variant == 'dist':
script += """
m = tagprop_learn(NN,NND,tagmatrix,'type','dist');
"""
elif variant == 'distsigmoids':
script += """
m = tagprop_learn(NN,NND,tagmatrix,'type','dist','sigmoids',true);
"""
script += """
save('%s', 'm', '-v7.3');
""" % train_model_file
# call_matlab(script)
# print(script)
# exit()
# we perform prediction
printStatus(INFO, "starting prediction")
test_neighs_file = os.path.join(rootpath, testCollection, 'TagProp-data', testset, trainCollection, annotationName, '%s,%s,%d'%(feature,nnName,k), 'nn_test.h5')
if not os.path.exists(test_neighs_file):
printStatus(INFO, "Matlab test neighbors file not found at %s Did you run prepare_tagprop_data.py?" % (test_neighs_file))
sys.exit(1)
script += """
tagprop_path = '%s/model_based/tagprop/TagProp/';
addpath(tagprop_path);
load('%s');
tagmatrix = h5read('%s', '/tagmatrix') > 0.5;
tagmatrix = sparse(tagmatrix);
NNT = h5read('%s', '/NNT');
NNT = double(NNT);
""" % (survey_code, train_model_file, tagmatrix_file, test_neighs_file)
if variant == 'dist' or variant == 'distsigmoids':
script += """
NNDT = h5read('%s', '/NNDT');
NNDT = reshape(NNDT, 1, size(NNDT,1), size(NNDT,2));
NNDT = double(NNDT);
""" % test_neighs_file
script += """
P = tagprop_predict(NNT,[],m)';
save('%s', '-v7.3');
exit;
""" % resultfile_tagprop
# print(script)
makedirsforfile(resultfile_tagprop)
call_matlab(script)
# exit()
# save results in pkl format
printStatus(INFO, "Dump results in pkl format at %s" % resultfile)
concepts = readConcepts(testCollection, annotationName, rootpath)
id_images = readImageSet(testCollection, testset, rootpath)
id_images.sort()
# id_images = map(int, id_images)
# concepts mapping
tagprop_output = h5py.File(resultfile_tagprop, 'r')
tagprop_input = h5py.File(tagmatrix_file, 'r')
mapping = getVocabMap(list(tagprop_input['vocab'][:]),concepts)
final_tagmatrix = tagprop_output['P'][:][:,mapping]
with open(resultfile, 'w') as f:
pickle.dump({'concepts':concepts, 'id_images':id_images, 'scores':final_tagmatrix}, f, pickle.HIGHEST_PROTOCOL)
def process(options, workingCollection, annotationName, feature, outputpkl):
rootpath = options.rootpath
distance = options.distance
overwrite = options.overwrite
k_ratio = options.kratio
ratio_cs = options.ratiocs
lambda1 = options.lambda1
lambda2 = options.lambda2
outputonlytest = options.outputonlytest
rawtagmatrix = options.rawtagmatrix
modelName = "robustpca"
nnName = distance + "knn"
printStatus(INFO, "Starting RobustPCA %s,%s,%s,%s,%f,%f,%f" % (workingCollection, annotationName, feature, nnName, k_ratio, lambda1, lambda2))
if rawtagmatrix:
printStatus(INFO, "Using raw tag matrix.")
else:
printStatus(INFO, "Using preprocessed tag matrix.")
resultfile = os.path.join(outputpkl)
resultfile_robustpca = os.path.join(rootpath, workingCollection, 'RobustPCA-Prediction', '%s,%s,%f,%f,%f,%d'%(feature,nnName,lambda1,lambda2,k_ratio,rawtagmatrix), 'prediction.mat')
if checkToSkip(resultfile_robustpca, overwrite):
only_dump = True
else:
only_dump = False
if not rawtagmatrix:
tagmatrix_file = os.path.join(rootpath, workingCollection, 'RobustPCA', '%s,%s,%f'%(feature,nnName,DEFAULT_K_PROP), 'tagmatrix.h5')
if not os.path.exists(tagmatrix_file):
printStatus(INFO, "Tag matrix file not found at %s Did you run robustpca_preprocessing.py?" % (tagmatrix_file))
sys.exit(1)
else:
tagmatrix_file = os.path.join(rootpath, workingCollection, 'TextData', "lemm_wordnet_freq_tags.h5")
if not os.path.exists(tagmatrix_file):
printStatus(INFO, 'Tag matrix file not found in %s Did you run wordnet_frequency_tags.py?' % (tagmatrix_file))
sys.exit(1)
laplacianI_file = os.path.join(rootpath, workingCollection, 'LaplacianI', workingCollection, '%s,%s,%f'%(feature,nnName,k_ratio), 'laplacianI.mat')
if not os.path.exists(laplacianI_file):
printStatus(INFO, "LaplacianI file not found at %s Did you run laplacian_images.py?" % (laplacianI_file))
sys.exit(1)
laplacianT_file = os.path.join(rootpath, workingCollection, 'LaplacianT', '%f'%(ratio_cs), 'laplacianT.mat')
if not os.path.exists(laplacianT_file):
printStatus(INFO, "LaplacianT file not found at %s Did you run laplacian_tags.py?" % (laplacianT_file))
sys.exit(1)
# being learning
script = """
rpca_path = 'transduction_based/robustpca/';
addpath(rpca_path);
addpath([rpca_path, 'fast_svd/']);
tagmatrix = sparse(double(h5read('%s', '/tagmatrix')));
load('%s');
load('%s');
lambda1 = %f;
lambda2 = %f;
maxIters = 50;
precision = 1e-4;
mu_start = 1.;
parpool('local', 4);
[P,E]=robustpca(tagmatrix, lambda1, lambda2, tag_similarity, im_similarity, maxIters, precision, mu_start);
""" % (tagmatrix_file, laplacianI_file, laplacianT_file, lambda1, lambda2)
script += """
delete(gcp);
save('%s', 'P', 'E', 'lambda1', 'lambda2', '-v7.3');
exit;
""" % resultfile_robustpca
if not only_dump:
printStatus(INFO, "starting learning")
makedirsforfile(resultfile_robustpca)
call_matlab(script)
if checkToSkip(resultfile, overwrite):
return 0
# save results in pkl format
printStatus(INFO, "Dump results in pkl format at %s" % resultfile)
concepts = readConcepts(workingCollection, annotationName, rootpath)
if outputonlytest:
testset_id_images = readImageSet(workingCollection.split('+')[1], workingCollection.split('+')[1], rootpath)
testset_id_images.sort()
id_images = readImageSet(workingCollection, workingCollection, rootpath)
id_images.sort()
# concepts mapping
robustpca_output = h5py.File(resultfile_robustpca, 'r')
tagprop_input = h5py.File(tagmatrix_file, 'r')
mapping = getVocabMap(list(tagprop_input['vocab'][:]),concepts)
predicted_tagmatrix = robustpca_output['P'][:,mapping]
if outputonlytest:
idx = np.array([bisect_index(id_images, x) for x in testset_id_images])
final_tagmatrix = predicted_tagmatrix[idx, :]
assert(final_tagmatrix.shape[0] == idx.shape[0])
id_images = testset_id_images
else:
final_tagmatrix = predicted_tagmatrix
makedirsforfile(resultfile)
with open(resultfile, 'w') as f:
pickle.dump({'concepts':concepts, 'id_images': id_images, 'scores':final_tagmatrix}, f, pickle.HIGHEST_PROTOCOL)
def process(options, workingCollection, annotationName, feature, outputpkl):
rootpath = options.rootpath
distance = options.distance
overwrite = options.overwrite
k_ratio = options.kratio
ratio_cs = options.ratiocs
lambda1 = options.lambda1
lambda2 = options.lambda2
outputonlytest = options.outputonlytest
rawtagmatrix = options.rawtagmatrix
modelName = "robustpca"
nnName = distance + "knn"
printStatus(
INFO, "Starting RobustPCA %s,%s,%s,%s,%f,%f,%f" %
(workingCollection, annotationName, feature, nnName, k_ratio, lambda1,
lambda2))
if rawtagmatrix:
printStatus(INFO, "Using raw tag matrix.")
else:
printStatus(INFO, "Using preprocessed tag matrix.")
resultfile = os.path.join(outputpkl)
resultfile_robustpca = os.path.join(
rootpath, workingCollection, 'RobustPCA-Prediction',
'%s,%s,%f,%f,%f,%d' %
(feature, nnName, lambda1, lambda2, k_ratio, rawtagmatrix),
'prediction.mat')
if checkToSkip(resultfile_robustpca, overwrite):
only_dump = True
else:
only_dump = False
if not rawtagmatrix:
tagmatrix_file = os.path.join(
rootpath, workingCollection, 'RobustPCA',
'%s,%s,%f' % (feature, nnName, DEFAULT_K_PROP), 'tagmatrix.h5')
if not os.path.exists(tagmatrix_file):
printStatus(
INFO,
"Tag matrix file not found at %s Did you run robustpca_preprocessing.py?"
% (tagmatrix_file))
sys.exit(1)
else:
tagmatrix_file = os.path.join(rootpath, workingCollection, 'TextData',
"lemm_wordnet_freq_tags.h5")
if not os.path.exists(tagmatrix_file):
printStatus(
INFO,
'Tag matrix file not found in %s Did you run wordnet_frequency_tags.py?'
% (tagmatrix_file))
sys.exit(1)
laplacianI_file = os.path.join(rootpath, workingCollection, 'LaplacianI',
workingCollection,
'%s,%s,%f' % (feature, nnName, k_ratio),
'laplacianI.mat')
if not os.path.exists(laplacianI_file):
printStatus(
INFO,
"LaplacianI file not found at %s Did you run laplacian_images.py?"
% (laplacianI_file))
sys.exit(1)
laplacianT_file = os.path.join(rootpath, workingCollection, 'LaplacianT',
'%f' % (ratio_cs), 'laplacianT.mat')
if not os.path.exists(laplacianT_file):
printStatus(
INFO,
"LaplacianT file not found at %s Did you run laplacian_tags.py?" %
(laplacianT_file))
sys.exit(1)
# being learning
script = """
rpca_path = 'transduction_based/robustpca/';
addpath(rpca_path);
addpath([rpca_path, 'fast_svd/']);
tagmatrix = sparse(double(h5read('%s', '/tagmatrix')));
load('%s');
load('%s');
lambda1 = %f;
lambda2 = %f;
maxIters = 50;
precision = 1e-4;
mu_start = 1.;
parpool('local', 4);
[P,E]=robustpca(tagmatrix, lambda1, lambda2, tag_similarity, im_similarity, maxIters, precision, mu_start);
""" % (tagmatrix_file, laplacianI_file, laplacianT_file, lambda1,
lambda2)
script += """
delete(gcp);
save('%s', 'P', 'E', 'lambda1', 'lambda2', '-v7.3');
exit;
""" % resultfile_robustpca
if not only_dump:
printStatus(INFO, "starting learning")
makedirsforfile(resultfile_robustpca)
call_matlab(script)
if checkToSkip(resultfile, overwrite):
return 0
# save results in pkl format
printStatus(INFO, "Dump results in pkl format at %s" % resultfile)
concepts = readConcepts(workingCollection, annotationName, rootpath)
if outputonlytest:
testset_id_images = readImageSet(
workingCollection.split('+')[1],
workingCollection.split('+')[1], rootpath)
testset_id_images.sort()
id_images = readImageSet(workingCollection, workingCollection, rootpath)
id_images.sort()
# concepts mapping
robustpca_output = h5py.File(resultfile_robustpca, 'r')
tagprop_input = h5py.File(tagmatrix_file, 'r')
mapping = getVocabMap(list(tagprop_input['vocab'][:]), concepts)
predicted_tagmatrix = robustpca_output['P'][:, mapping]
if outputonlytest:
idx = np.array([bisect_index(id_images, x) for x in testset_id_images])
final_tagmatrix = predicted_tagmatrix[idx, :]
assert (final_tagmatrix.shape[0] == idx.shape[0])
id_images = testset_id_images
else:
final_tagmatrix = predicted_tagmatrix
makedirsforfile(resultfile)
with open(resultfile, 'w') as f:
pickle.dump(
{
'concepts': concepts,
'id_images': id_images,
'scores': final_tagmatrix
}, f, pickle.HIGHEST_PROTOCOL)
pkl_file = open(sys.argv[2], 'w')
workingCollection = sys.argv[3]
annotationName = sys.argv[4]
rootpath = ROOT_PATH
id_images = tagmatrix_file['id_images']
concepts = readConcepts(workingCollection, annotationName, rootpath)
testset_id_images = readImageSet(workingCollection.split('+')[1], workingCollection.split('+')[1], rootpath)
testset_id_images.sort()
if not type(id_images[0]) is str:
id_images = map(str, id_images)
if not type(testset_id_images[0]) is str:
testset_id_images = map(str, testset_id_images)
mapping = getVocabMap(list(tagmatrix_file['vocab'][:]),concepts)
predicted_tagmatrix = tagmatrix_file['tagmatrix'][:,mapping]
print "predicted_tagmatrix.shape = ", predicted_tagmatrix.shape
print "len(id_images) = ", len(id_images)
print "len(testset_id_images) = ", len(testset_id_images)
idx = np.array([bisect_index(id_images, x) for x in testset_id_images])
final_tagmatrix = predicted_tagmatrix[idx, :]
id_images = testset_id_images
print "dumping %d elements..." % len(id_images)
pickle.dump({'concepts':concepts, 'id_images':map(int, id_images), 'scores':final_tagmatrix}, pkl_file, pickle.HIGHEST_PROTOCOL)