def plot_results():
data = load_data("val", independent=False)
data = make_hierarchical_data(data, lateral=False, latent=True)
logger = SaveLogger("test_latent_2.0001.pickle", save_every=100)
ssvm = logger.load()
plot_results_hierarchy(data, ssvm.predict(data.X),
folder="latent_results_val_50_states_no_lateral")
def plot_results():
data = load_data("val", independent=False)
data = make_hierarchical_data(data, lateral=False, latent=True)
logger = SaveLogger("test_latent_2.0001.pickle", save_every=100)
ssvm = logger.load()
plot_results_hierarchy(data,
ssvm.predict(data.X),
folder="latent_results_val_50_states_no_lateral")
def test_logging():
iris = load_iris()
X, y = iris.data, iris.target
X_ = [(np.atleast_2d(x), np.empty((0, 2), dtype=np.int)) for x in X]
Y = y.reshape(-1, 1)
X_train, X_test, y_train, y_test = train_test_split(X_, Y, random_state=1)
_, file_name = mkstemp()
pbl = GraphCRF(n_features=4, n_states=3, inference_method=inference_method)
logger = SaveLogger(file_name)
svm = NSlackSSVM(pbl, C=100, n_jobs=1, logger=logger)
svm.fit(X_train, y_train)
score_current = svm.score(X_test, y_test)
score_auto_saved = logger.load().score(X_test, y_test)
alt_file_name = file_name + "alt"
logger.save(svm, alt_file_name)
logger.file_name = alt_file_name
logger.load()
score_manual_saved = logger.load().score(X_test, y_test)
assert_less(.97, score_current)
assert_less(.97, score_auto_saved)
assert_less(.97, score_manual_saved)
assert_almost_equal(score_auto_saved, score_manual_saved)
def main(C=1):
dataset = NYUSegmentation()
# load training data
data_train = load_nyu('train', n_sp=500, sp='rgbd')
data_train = add_edges(data_train)
data_train = add_edge_features(dataset,
data_train,
depth_diff=True,
normal_angles=True)
data_train = make_hierarchical_data(dataset, data_train)
data_train = discard_void(dataset, data_train)
n_states = 4.
print("number of samples: %s" % len(data_train.X))
class_weights = 1. / np.bincount(np.hstack(data_train.Y))
class_weights *= n_states / np.sum(class_weights)
#class_weights = np.ones(n_states)
print(class_weights)
#model = crfs.GraphCRF(n_states=n_states,
#n_features=data_train.X[0][0].shape[1],
#inference_method='qpbo', class_weight=class_weights)
model = crfs.EdgeFeatureLatentNodeCRF(n_hidden_states=5,
n_edge_features=5,
inference_method='qpbo',
class_weight=class_weights,
symmetric_edge_features=[0, 1],
latent_node_features=False,
n_labels=4)
experiment_name = "rgbd_normal_angles_fold1_strong_reweight%f" % C
base_ssvm = learners.OneSlackSSVM(model,
verbose=2,
C=C,
max_iter=100000,
n_jobs=1,
tol=0.001,
show_loss_every=100,
inference_cache=50,
cache_tol='auto',
logger=SaveLogger(experiment_name +
".pickle",
save_every=100),
inactive_threshold=1e-5,
break_on_bad=False,
inactive_window=50,
switch_to=("ad3", {
'branch_and_bound': True
}))
latent_logger = SaveLogger("lssvm_" + experiment_name + "_%d.pickle",
save_every=1)
ssvm = learners.LatentSSVM(base_ssvm, logger=latent_logger, latent_iter=3)
ssvm.fit(data_train.X, data_train.Y)
print("fit finished!")
return
def train(trainSetX, trainSetY, testSetX, testSetY):
modelLogger = SaveLogger('imagesegmentation-horse-hog_96_lbp_test.model',
save_every=1)
# Load trained CRF model
print 'Loading trained model for CRF'
#clf = modelLogger.load()
# Uncomment if we want to train from scratch first layer CRF
print 'Training CRF...'
start_time = time.time()
crf = EdgeFeatureGraphCRF() #antisymmetric_edge_features=[1,2]
clf = FrankWolfeSSVM(model=crf,
C=10.,
tol=.1,
verbose=3,
show_loss_every=1,
logger=modelLogger) # #max_iter=50
##clf = OneSlackSSVM(model=crf, verbose=1, show_loss_every=1, logger=modelLogger)
clf.fit(numpy.array(trainSetX), numpy.array(trainSetY))
print 'Training CRF took ' + str(time.time() - start_time) + ' seconds'
#print("Overall super pixelwise accuracy (training set): %f" % clf.score(numpy.array(trainSetX), numpy.array(trainSetY) ))
#print("Overall super pixelwise accuracy (test set): %f" % clf.score(numpy.array(testSetX), numpy.array(testSetY) ))
print 'SUPERPIXELWISE ACCURACY'
print '-----------------------------------------------------------------------'
print ''
print 'TRAINING SET RESULTS'
train_ypred = evaluatePerformance(clf, numpy.array(trainSetX),
numpy.array(trainSetY))
print ''
print 'TEST SET RESULTS'
evaluatePerformance(clf, numpy.array(testSetX), numpy.array(testSetY))
print '-----------------------------------------------------------------------'
def test_n_slack_svm_as_crf_pickling():
iris = load_iris()
X, y = iris.data, iris.target
X_ = [(np.atleast_2d(x), np.empty((0, 2), dtype=np.int)) for x in X]
Y = y.reshape(-1, 1)
X_train, X_test, y_train, y_test = train_test_split(X_, Y, random_state=1)
_, file_name = mkstemp()
pbl = GraphCRF(n_features=4, n_states=3, inference_method='lp')
logger = SaveLogger(file_name)
svm = NSlackSSVM(pbl, C=100, n_jobs=1, logger=logger)
svm.fit(X_train, y_train)
assert_less(.97, svm.score(X_test, y_test))
assert_less(.97, logger.load().score(X_test, y_test))
def fresh_train(self, x, y, iterations=10):
self.model = EdgeFeatureGraphCRF(inference_method="max-product")
self.learner = SubgradientSSVM(
model=self.model,
max_iter=iterations,
logger=SaveLogger(model_file.format(self.userId + "-learner")))
self.learner.fit(x, y, warm_start=False)
self.save()
def test_svm_as_crf_pickling_batch():
iris = load_iris()
X, y = iris.data, iris.target
X_ = [(np.atleast_2d(x), np.empty((0, 2), dtype=np.int)) for x in X]
Y = y.reshape(-1, 1)
X_train, X_test, y_train, y_test = train_test_split(X_, Y, random_state=1)
_, file_name = mkstemp()
pbl = GraphCRF(n_features=4, n_states=3, inference_method='unary')
logger = SaveLogger(file_name)
svm = FrankWolfeSSVM(pbl, C=10, logger=logger, max_iter=50, batch_mode=False)
svm.fit(X_train, y_train)
assert_less(.97, svm.score(X_test, y_test))
assert_less(.97, logger.load().score(X_test, y_test))
def fresh_train(self, x, y, iterations=10):
self.model = ChainCRF(inference_method="max-product")
self.learner = SubgradientSSVM(
model=self.model,
max_iter=iterations,
logger=SaveLogger(
MODEL_PATH_TEMPLATE.format(self.userId + "-learner")),
show_loss_every=50)
self.learner.fit(x, y, warm_start=False)
self.save()
def test_subgradient_svm_as_crf_pickling():
iris = load_iris()
X, y = iris.data, iris.target
X_ = [(np.atleast_2d(x), np.empty((0, 2), dtype=np.int)) for x in X]
Y = y.reshape(-1, 1)
X_train, X_test, y_train, y_test = train_test_split(X_, Y, random_state=1)
_, file_name = mkstemp()
pbl = GraphCRF(n_features=4, n_states=3, inference_method='lp')
logger = SaveLogger(file_name, verbose=1)
svm = SubgradientSSVM(pbl, verbose=0, C=100, n_jobs=1, logger=logger,
max_iter=50, momentum=0, learning_rate=0.01)
svm.fit(X_train, y_train)
assert_less(.97, svm.score(X_test, y_test))
assert_less(.97, logger.load().score(X_test, y_test))
def load(self, expiration_timestamp=None):
"""
Load myself from disk
If an expiration timestamp is given, the model stored on disk must be fresher than timestamp
return self or raise a ModelException
"""
super(Model_SSVM_AD3, self).load(expiration_timestamp)
self.ssvm = self._loadIfFresh(self.getModelFilename(),
expiration_timestamp,
lambda x: SaveLogger(x).load())
try:
self._EdgeBaselineModel = self._loadIfFresh(
self.getEdgeBaselineFilename(), expiration_timestamp,
self.gzip_cPickle_load)
self.setNbClass(
self.ssvm.model.n_states) #required the compute the edge label
except:
self._EdgeBaselineModel = None
return self
def main(C=1, test=False):
# load training data
#independent = True
independent = False
data_train = load_data(which="piecewise")
data_train = add_edges(data_train,
independent=independent,
fully_connected=True)
data_train = add_kraehenbuehl_features(data_train, which="train_30px")
data_train = add_kraehenbuehl_features(data_train, which="train")
#data_train = load_data_global_probs()
if not independent:
data_train = add_edge_features(data_train)
data_train = discard_void(data_train, 21)
if test:
data_val = load_data("val", which="piecewise_train")
data_val = add_edges(data_val, independent=independent)
data_val = add_kraehenbuehl_features(data_val, which="train_30px")
data_val = add_kraehenbuehl_features(data_val, which="train")
data_val = add_edge_features(data_val)
data_val = discard_void(data_val, 21)
data_train = concatenate_datasets(data_train, data_val)
#X_.extend(data_val.X)
#Y_.extend(data_val.Y)
n_states = 21
print("number of samples: %s" % len(data_train.X))
class_weights = 1. / np.bincount(np.hstack(data_train.Y))
#class_weights[21] = 0
class_weights *= 21. / np.sum(class_weights)
#class_weights = np.ones(n_states)
print(class_weights)
#model = crfs.GraphCRF(n_states=n_states,
#n_features=data_train.X[0][0].shape[1],
#inference_method='qpbo', class_weight=class_weights)
model = crfs.EdgeFeatureGraphCRF(n_states=n_states,
n_features=data_train.X[0][0].shape[1],
inference_method='qpbo',
class_weight=class_weights,
n_edge_features=3,
symmetric_edge_features=[0, 1],
antisymmetric_edge_features=[2])
experiment_name = "fully_connected_%f" % C
#warm_start = True
warm_start = False
ssvm = learners.OneSlackSSVM(model,
verbose=2,
C=C,
max_iter=100000,
n_jobs=-1,
tol=0.0001,
show_loss_every=50,
inference_cache=50,
cache_tol='auto',
logger=SaveLogger(experiment_name + ".pickle",
save_every=100),
inactive_threshold=1e-5,
break_on_bad=False,
inactive_window=50,
switch_to_ad3=False)
#ssvm = learners.SubgradientSSVM(
#model, verbose=3, C=C, max_iter=10000, n_jobs=-1, show_loss_every=10,
#logger=SaveLogger(experiment_name + ".pickle", save_every=10),
#momentum=0, learning_rate=0.001, decay_exponent=1)
if warm_start:
ssvm = SaveLogger(experiment_name + ".pickle").load()
ssvm.logger = SaveLogger(file_name=experiment_name + "_refit.pickle",
save_every=10)
ssvm.learning_rate = 0.000001
#ssvm.model.inference_method = 'ad3'
#ssvm.n_jobs = 1
ssvm.fit(data_train.X, data_train.Y, warm_start=warm_start)
print("fit finished!")
return
def svm_on_segments(C=.1, learning_rate=.001, subgradient=True):
# load and prepare data
lateral = True
latent = True
test = False
#data_train = load_data(which="piecewise")
#data_train = add_edges(data_train, independent=False)
#data_train = add_kraehenbuehl_features(data_train, which="train_30px")
#data_train = add_kraehenbuehl_features(data_train, which="train")
#if lateral:
#data_train = add_edge_features(data_train)
data_train = load_data_global_probs(latent=latent)
X_org_ = data_train.X
#data_train = make_hierarchical_data(data_train, lateral=lateral,
#latent=latent, latent_lateral=True)
data_train = discard_void(data_train, 21, latent_features=True)
X_, Y_ = data_train.X, data_train.Y
# remove edges
if not lateral:
X_org_ = [(x[0], np.zeros((0, 2), dtype=np.int)) for x in X_org_]
if test:
data_val = load_data('val', which="piecewise")
data_val = add_edges(data_val, independent=False)
data_val = add_kraehenbuehl_features(data_val)
data_val = make_hierarchical_data(data_val, lateral=lateral,
latent=latent)
data_val = discard_void(data_val, 21)
X_.extend(data_val.X)
Y_.extend(data_val.Y)
n_states = 21
class_weights = 1. / np.bincount(np.hstack(Y_))
class_weights *= 21. / np.sum(class_weights)
experiment_name = ("latent5_features_C%f_top_node" % C)
logger = SaveLogger(experiment_name + ".pickle", save_every=10)
if latent:
model = LatentNodeCRF(n_labels=n_states,
n_features=data_train.X[0][0].shape[1],
n_hidden_states=5, inference_method='qpbo' if
lateral else 'dai', class_weight=class_weights,
latent_node_features=True)
if subgradient:
ssvm = learners.LatentSubgradientSSVM(
model, C=C, verbose=1, show_loss_every=10, logger=logger,
n_jobs=-1, learning_rate=learning_rate, decay_exponent=1,
momentum=0., max_iter=100000)
else:
latent_logger = SaveLogger("lssvm_" + experiment_name +
"_%d.pickle", save_every=1)
base_ssvm = learners.OneSlackSSVM(
model, verbose=2, C=C, max_iter=100000, n_jobs=-1, tol=0.001,
show_loss_every=200, inference_cache=50, logger=logger,
cache_tol='auto', inactive_threshold=1e-5, break_on_bad=False,
switch_to_ad3=True)
ssvm = learners.LatentSSVM(base_ssvm, logger=latent_logger)
warm_start = False
if warm_start:
ssvm = logger.load()
ssvm.logger = SaveLogger(experiment_name + "_retrain.pickle",
save_every=10)
ssvm.max_iter = 100000
ssvm.learning_rate = 0.00001
ssvm.momentum = 0
else:
#model = GraphCRF(n_states=n_states,
#n_features=data_train.X[0][0].shape[1],
#inference_method='qpbo' if lateral else 'dai',
#class_weight=class_weights)
model = EdgeFeatureGraphCRF(n_states=n_states,
n_features=data_train.X[0][0].shape[1],
inference_method='qpbo' if lateral else
'dai', class_weight=class_weights,
n_edge_features=4,
symmetric_edge_features=[0, 1],
antisymmetric_edge_features=[2])
ssvm = learners.OneSlackSSVM(
model, verbose=2, C=C, max_iter=100000, n_jobs=-1,
tol=0.0001, show_loss_every=200, inference_cache=50, logger=logger,
cache_tol='auto', inactive_threshold=1e-5, break_on_bad=False)
#ssvm = logger.load()
X_, Y_ = shuffle(X_, Y_)
#ssvm.fit(data_train.X, data_train.Y)
#ssvm.fit(X_, Y_, warm_start=warm_start)
ssvm.fit(X_, Y_)
print("fit finished!")
def svm_on_segments(C=.1, learning_rate=.001, subgradient=False):
data_file = "data_train_XY.pickle"
ds = PascalSegmentation()
if os.path.exists(data_file):
X_, Y_ = cPickle.load(open(data_file))
else:
# load and prepare data
data_train = load_pascal("train", sp_type="cpmc")
data_train = make_cpmc_hierarchy(ds, data_train)
data_train = discard_void(ds, data_train)
X_, Y_ = data_train.X, data_train.Y
cPickle.dump((X_, Y_), open(data_file, 'wb'), -1)
class_weights = 1. / np.bincount(np.hstack(Y_))
class_weights *= 21. / np.sum(class_weights)
experiment_name = ("latent_25_cpmc_%f_qpbo_n_slack_blub3" % C)
logger = SaveLogger(experiment_name + ".pickle", save_every=10)
model = LatentNodeCRF(n_hidden_states=25,
inference_method='qpbo',
class_weight=class_weights,
latent_node_features=False)
if subgradient:
ssvm = learners.LatentSubgradientSSVM(model,
C=C,
verbose=1,
show_loss_every=10,
logger=logger,
n_jobs=-1,
learning_rate=learning_rate,
decay_exponent=1,
momentum=0.,
max_iter=100000,
decay_t0=100)
else:
latent_logger = SaveLogger("lssvm_" + experiment_name + "_%d.pickle",
save_every=1)
#base_ssvm = learners.OneSlackSSVM(
#model, verbose=2, C=C, max_iter=100, n_jobs=-1, tol=0.001,
#show_loss_every=200, inference_cache=50, logger=logger,
#cache_tol='auto', inactive_threshold=1e-5, break_on_bad=False,
#switch_to=('ogm', {'alg': 'dd'}))
base_ssvm = learners.NSlackSSVM(model,
verbose=4,
C=C,
n_jobs=-1,
tol=0.1,
show_loss_every=20,
logger=logger,
inactive_threshold=1e-8,
break_on_bad=False,
batch_size=36,
inactive_window=10,
switch_to=('ad3', {
'branch_and_bound': True
}))
ssvm = learners.LatentSSVM(base_ssvm,
logger=latent_logger,
latent_iter=3)
#warm_start = True
warm_start = False
if warm_start:
ssvm = logger.load()
ssvm.logger = SaveLogger(experiment_name + "_retrain.pickle",
save_every=10)
ssvm.max_iter = 10000
ssvm.decay_exponent = 1
#ssvm.decay_t0 = 1000
#ssvm.learning_rate = 0.00001
#ssvm.momentum = 0
X_, Y_ = shuffle(X_, Y_)
#ssvm.fit(data_train.X, data_train.Y)
ssvm.fit(X_, Y_)
#H_init = [np.hstack([y, np.random.randint(21, 26)]) for y in Y_]
#ssvm.fit(X_, Y_, H_init=H_init)
print("fit finished!")
def gridsearch(self, lGraph, verbose=0):
"""
do a grid search instead of a normal training
"""
traceln("--- GRID SEARCH FOR CRF MODEL ---")
traceln("\t- computing features on training set")
traceln("\t\t #nodes=%d #edges=%d " %
Graph.getNodeEdgeTotalNumber(lGraph))
chronoOn()
lX, lY = self.get_lX_lY(lGraph)
dPrm = {}
dPrm['C'] = self.C if type(self.C) == list else [self.C]
dPrm['tol'] = self.tol if type(self.tol) == list else [self.tol]
dPrm['inference_cache'] = self.inference_cache if type(
self.inference_cache) == list else [self.inference_cache]
dPrm['max_iter'] = self.max_iter if type(
self.max_iter) == list else [self.max_iter]
traceln("\t- creating a SSVM-trained CRF model")
traceln("\t\t- computing class weight:")
clsWeights = self.computeClassWeight(lY)
traceln("\t\t\t%s" % clsWeights)
crf = self._getCRFModel(clsWeights)
self._ssvm = OneSlackSSVM(
crf
#, inference_cache=self.inference_cache, C=self.C, tol=self.tol
,
n_jobs=self.njobs
#, logger=SaveLogger(sModelFN, save_every=self.save_every)
#, max_iter=self.max_iter
,
show_loss_every=10
# , verbose=verbose)
,
verbose=1)
self._gs_ssvm = GridSearchCV(self._ssvm,
dPrm,
n_jobs=1,
verbose=verbose)
self.ssvm = None
chronoOn()
traceln("\t - training by grid search a graph-based model")
traceln("\t\t solver parameters for grid search:", " inference_cache=",
self.inference_cache, " C=", self.C, " tol=", self.tol,
" n_jobs=", self.njobs, " max_iter=", self.max_iter)
self._gs_ssvm.fit(lX, lY)
traceln(
"\t [%.1fs] done (graph-based model is trained with best parameters, selected by grid search) \n"
% chronoOff())
self.ssvm = self._gs_ssvm.best_estimator_ #Estimator that was chosen by the search
try:
#win32
dBestParams = self._gs_ssvm.best_params_
except:
#do not know how to get this... in
dBestParams = {
'C': self.ssvm.C,
'inference_cache': self.ssvm.inference_cache,
'max_iter': self.ssvm.max_iter,
'tol': self.ssvm.tol
}
self.storeBestParams(dBestParams)
traceln("\t", "- " * 20)
traceln("\tBest parameters: ", dBestParams)
traceln("\t", "- " * 20)
try:
self.ssvm.alphas = None
self.ssvm.constraints_ = None
self.ssvm.inference_cache_ = None
traceln(
"\t\t(model made slimmer. Not sure you can efficiently warm-start it later on. See option -w.)"
)
except Exception as e:
traceln(
"\t\t(COULD NOT make the model slimmer. Got exception: %s" %
str(e))
logger = SaveLogger(self.getModelFilename())
logger(self.ssvm) #save this model!
traceln(self.getModelInfo())
#Also save the details of this grid search
sFN = self.getModelFilename()[:-4] + "GridSearchCV.pkl"
try:
self.gzip_cPickle_dump(sFN, self._gs_ssvm)
traceln("\n\n--- GridSearchCV details: (also in %s)" % sFN)
traceln("--- Best parameters set found on development set:")
traceln(self._gs_ssvm.best_params_)
traceln("--- Grid scores on development set:")
means = self._gs_ssvm.cv_results_['mean_test_score']
stds = self._gs_ssvm.cv_results_['std_test_score']
for mean, std, params in zip(means, stds,
self._gs_ssvm.cv_results_['params']):
traceln("%0.3f (+/-%0.03f) for %r" % (mean, std * 2, params))
traceln("--- ---")
except Exception as e:
traceln(
"WARNING: error while dealing with the GridSearchCV object.")
traceln(e)
#the baseline model(s) if any
self._trainBaselines(lX, lY)
#do some garbage collection
del lX, lY
gc.collect()
return
def train(self,
lGraph_trn,
lGraph_vld,
bWarmStart=True,
expiration_timestamp=None,
verbose=0):
"""
Train a CRF model using the list of labelled graph as training
if bWarmStart if True, try to continue from previous training, IF the stored model is older than expiration_timestamp!!
, otherwise, starts from scratch
return nothing
"""
if self.bGridSearch:
return self.gridsearch(lGraph_trn, verbose=verbose)
traceln("\t- computing features on training set")
traceln("\t\t #nodes=%d #edges=%d " %
Graph.getNodeEdgeTotalNumber(lGraph_trn))
lX, lY = self.get_lX_lY(lGraph_trn)
lX_vld, lY_vld = self.get_lX_lY(lGraph_vld)
bMakeSlim = not bWarmStart # for warm-start mode, we do not make the model slimer!"
traceln("\t- retrieving or creating model...")
self.ssvm = None
sModelFN = self.getModelFilename()
if bWarmStart:
try:
try:
self.ssvm = self._loadIfFresh(
sModelFN + "._last_", expiration_timestamp,
lambda x: SaveLogger(x).load())
traceln(
"\t- warmstarting with last saved model (not necessarily best one)!"
)
except:
self.ssvm = self._loadIfFresh(
sModelFN, expiration_timestamp,
lambda x: SaveLogger(x).load())
traceln("\t- warmstarting from last best model!")
#we allow to change the max_iter of the model
try:
self.ssvm.max_iter #to make sure we do something that makes sense...
if self.ssvm.max_iter != self.max_iter:
traceln(
"\t- changing max_iter value from (stored) %d to %d"
% (self.ssvm.max_iter, self.max_iter))
self.ssvm.max_iter = self.max_iter
except AttributeError:
traceln("\t- cannot access or change the max_iter value")
try:
self.ssvm.n_jobs #to make sure we do something that makes sense...
if self.ssvm.n_jobs != self.njobs:
traceln(
"\t- changing n_jobs value from (stored) %d to %d"
% (self.ssvm.n_jobs, self.njobs))
self.ssvm.n_jobs = self.njobs
except AttributeError:
traceln("\t- cannot access or change the n_jobs value")
except Exception as e:
self.ssvm = None
traceln("\t- Cannot warmstart: %s" % e)
#self.ssvm is either None or containing a nice ssvm model!!
chronoOn("train")
traceln("\t- training graph-based model")
traceln("\t\t solver parameters:", " inference_cache=",
self.inference_cache, " C=", self.C, " tol=", self.tol,
" n_jobs=", self.njobs)
if not self.ssvm:
traceln("\t- creating a new SSVM-trained CRF model")
traceln("\t\t- computing class weight:")
if self.balanced:
traceln("\t\tusing balanced weights")
self.setBalancedWeights()
clsWeights = self.computeClassWeight(lY)
traceln("\t\t\t --> %s" % clsWeights)
#clsWeights = np.array([1, 4.5])
# These weights are tuned for best performance of LR and SVM and hence consistently used here
crf = self._getCRFModel(clsWeights)
self.ssvm = OneSlackSSVM(crf,
inference_cache=self.inference_cache,
C=self.C,
tol=self.tol,
n_jobs=self.njobs,
logger=SaveLogger(
sModelFN, save_every=self.save_every),
max_iter=self.max_iter,
show_loss_every=10,
verbose=verbose)
bWarmStart = False
if lGraph_vld:
self.ssvm.fit_with_valid(lX,
lY,
lX_vld,
lY_vld,
warm_start=bWarmStart,
valid_every=self.save_every)
else:
# old classical method
self.ssvm.fit(lX, lY, warm_start=bWarmStart)
traceln("\t [%.1fs] done (graph-CRF model is trained) \n" %
chronoOff("train"))
#traceln(self.getModelInfo())
#cleaning useless data that takes MB on disk
if bMakeSlim:
self.ssvm.alphas = None
self.ssvm.constraints_ = None
self.ssvm.inference_cache_ = None
traceln(
"\t\t(model made slimmer. Not sure you can efficiently warm-start it later on. See option -w.)"
)
#the baseline model(s) if any
self._trainBaselines(lX, lY)
#do some garbage collection
del lX, lY
gc.collect()
return
n_jobs = 6
C = 0.01
# init CRF model
# model = models.EdgeFeatureGraphCRF(inference_method='qpbo', class_weight=class_weight, symmetric_edge_features=[0, 1], antisymmetric_edge_features=[2, 3])
model = models.EdgeFeatureGraphCRF(inference_method='qpbo',
class_weight=class_weight,
symmetric_edge_features=[0, 1, 2],
antisymmetric_edge_features=[3, 4])
# model = models.EdgeFeatureGraphCRF(class_weight=class_weight, symmetric_edge_features=[0, 1], antisymmetric_edge_features=[2, 3])
# init learner
ssvm = learners.NSlackSSVM(model,
verbose=2,
n_jobs=n_jobs,
C=C,
logger=SaveLogger(args.model_filename,
save_every=50))
# ssvm = learners.NSlackSSVM(model, verbose=2, C=C, max_iter=100000, n_jobs=n_jobs, tol=0.0001, show_loss_every=5, logger=SaveLogger(args.model_filename, save_every=50), inactive_threshold=1e-3, inactive_window=10, batch_size=100)
# train model
ssvm.fit(X_train, y_train)
# predict score on test dataset
y_pred = ssvm.predict(X_test)
y_pred, y_test = np.hstack(y_pred), np.hstack(y_test)
y_pred = y_pred[y_test != 0]
y_test = y_test[y_test != 0]
print("Score on validation set: %f" % np.mean(y_test == y_pred))
# Script to predict edge map
# Importing Libraries
from pystruct.utils import SaveLogger
import scipy.io as sio
# Getting parameters
params = sio.loadmat(
'/home/dell/Desktop/sarvaswa/objectness-release-v2.2/Trial_Pascal/testSet/exp1/salprop-v1.0/matpy/params.mat'
)
matpyfiles = params['params'][0]['matpyfiles'][0]
featureFile = matpyfiles['featureFile'][0][0][0]
edgesFile = matpyfiles['edgesFile'][0][0][0]
predictFile = matpyfiles['predictFile'][0][0][0]
modelFile = params['params'][0]['modelFileCRF'][0][0]
# Loading required Files
modelLogger = SaveLogger(modelFile)
crf = modelLogger.load()
feat = sio.loadmat(featureFile)
feat = feat['feat']
edges = sio.loadmat(edgesFile)
edges = edges['edges'] - 1
# Make Prediction
inData = [(feat, edges)]
prediction = crf.predict(inData)
# Save Prediction;
sio.savemat(predictFile, mdict={'prediction': prediction})
model = crfs.EdgeFeatureGraphCRF(inference_method='qpbo',
class_weight=class_weights,
symmetric_edge_features=[0, 1],
antisymmetric_edge_features=[2])
experiment_name = "edge_features_one_slack_trainval_%f" % C
ssvm = learners.NSlackSSVM(model,
verbose=2,
C=C,
max_iter=100000,
n_jobs=-1,
tol=0.0001,
show_loss_every=5,
logger=SaveLogger(experiment_name + ".pickle",
save_every=100),
inactive_threshold=1e-3,
inactive_window=10,
batch_size=100)
ssvm.fit(data_train['X'], data_train['Y'])
data_val = cPickle.load(open("data_val_dict.pickle"))
y_pred = ssvm.predict(data_val['X'])
# we throw away void superpixels and flatten everything
y_pred, y_true = np.hstack(y_pred), np.hstack(data_val['Y'])
y_pred = y_pred[y_true != 255]
y_true = y_true[y_true != 255]
print("Score on validation set: %f" % np.mean(y_true == y_pred))
# --- MAIN: DISPLAY STORED MODEL INFO ------------------------------------------------------------------
if __name__ == "__main__":
try:
sModelDir, sModelName = sys.argv[1:3]
except:
print("Usage: %s <model-dir> <model-name>"%sys.argv[0])
print("Display some info regarding the stored model")
exit(1)
mdl = Model_SSVM_AD3(sModelName, sModelDir)
print("Loading %s"%mdl.getModelFilename())
if False:
mdl.load() #loads all sub-models!!
else:
mdl.ssvm = mdl._loadIfFresh(mdl.getModelFilename(), None, lambda x: SaveLogger(x).load())
print(mdl.getModelInfo())
fig = plt.figure()
fig.canvas.set_window_title("dir=%s model=%s "%(sModelDir, sModelName))
plt.plot(mdl.ssvm.loss_curve_)
plt.xlabel("Iteration / 10")
plt.ylabel("Loss")
plt.show()
def svm_on_segments(C=.1, learning_rate=.001, subgradient=True):
# load and prepare data
lateral = True
latent = True
test = False
#data_train = load_data(which="piecewise")
#data_train = add_edges(data_train, independent=False)
#data_train = add_kraehenbuehl_features(data_train, which="train_30px")
#data_train = add_kraehenbuehl_features(data_train, which="train")
#if lateral:
#data_train = add_edge_features(data_train)
data_train = load_data_global_probs(latent=latent)
X_org_ = data_train.X
#data_train = make_hierarchical_data(data_train, lateral=lateral,
#latent=latent, latent_lateral=True)
data_train = discard_void(data_train, 21, latent_features=True)
X_, Y_ = data_train.X, data_train.Y
# remove edges
if not lateral:
X_org_ = [(x[0], np.zeros((0, 2), dtype=np.int)) for x in X_org_]
if test:
data_val = load_data('val', which="piecewise")
data_val = add_edges(data_val, independent=False)
data_val = add_kraehenbuehl_features(data_val)
data_val = make_hierarchical_data(data_val,
lateral=lateral,
latent=latent)
data_val = discard_void(data_val, 21)
X_.extend(data_val.X)
Y_.extend(data_val.Y)
n_states = 21
class_weights = 1. / np.bincount(np.hstack(Y_))
class_weights *= 21. / np.sum(class_weights)
experiment_name = ("latent5_features_C%f_top_node" % C)
logger = SaveLogger(experiment_name + ".pickle", save_every=10)
if latent:
model = LatentNodeCRF(n_labels=n_states,
n_features=data_train.X[0][0].shape[1],
n_hidden_states=5,
inference_method='qpbo' if lateral else 'dai',
class_weight=class_weights,
latent_node_features=True)
if subgradient:
ssvm = learners.LatentSubgradientSSVM(model,
C=C,
verbose=1,
show_loss_every=10,
logger=logger,
n_jobs=-1,
learning_rate=learning_rate,
decay_exponent=1,
momentum=0.,
max_iter=100000)
else:
latent_logger = SaveLogger("lssvm_" + experiment_name +
"_%d.pickle",
save_every=1)
base_ssvm = learners.OneSlackSSVM(model,
verbose=2,
C=C,
max_iter=100000,
n_jobs=-1,
tol=0.001,
show_loss_every=200,
inference_cache=50,
logger=logger,
cache_tol='auto',
inactive_threshold=1e-5,
break_on_bad=False,
switch_to_ad3=True)
ssvm = learners.LatentSSVM(base_ssvm, logger=latent_logger)
warm_start = False
if warm_start:
ssvm = logger.load()
ssvm.logger = SaveLogger(experiment_name + "_retrain.pickle",
save_every=10)
ssvm.max_iter = 100000
ssvm.learning_rate = 0.00001
ssvm.momentum = 0
else:
#model = GraphCRF(n_states=n_states,
#n_features=data_train.X[0][0].shape[1],
#inference_method='qpbo' if lateral else 'dai',
#class_weight=class_weights)
model = EdgeFeatureGraphCRF(
n_states=n_states,
n_features=data_train.X[0][0].shape[1],
inference_method='qpbo' if lateral else 'dai',
class_weight=class_weights,
n_edge_features=4,
symmetric_edge_features=[0, 1],
antisymmetric_edge_features=[2])
ssvm = learners.OneSlackSSVM(model,
verbose=2,
C=C,
max_iter=100000,
n_jobs=-1,
tol=0.0001,
show_loss_every=200,
inference_cache=50,
logger=logger,
cache_tol='auto',
inactive_threshold=1e-5,
break_on_bad=False)
#ssvm = logger.load()
X_, Y_ = shuffle(X_, Y_)
#ssvm.fit(data_train.X, data_train.Y)
#ssvm.fit(X_, Y_, warm_start=warm_start)
ssvm.fit(X_, Y_)
print("fit finished!")
from pystruct.models import LatentGridCRF, GridCRF, LatentGraphCRF, GraphCRF, EdgeFeatureGraphCRF
from pystruct.learners import LatentSSVM, OneSlackSSVM, SubgradientSSVM, FrankWolfeSSVM
from pystruct.utils import make_grid_edges, SaveLogger
from skimage import img_as_ubyte
from matplotlib import pyplot as plt
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from skimage.transform import resize
from skimage.filters import threshold_otsu
# Pixel Classes - Black, White
horsePixelClasses = [numpy.array([0., 0., 0.]), numpy.array([1., 1., 1.])]
# Load trained Model
horseModelLogger = SaveLogger('save/imagesegmentation-horse-hog_96_lbp.model',
save_every=1)
horseCRF = horseModelLogger.load()
######################################
# Compute S_0 score
######################################
def foregroundQualityScore(a, b):
TP = TN = FP = FN = 0.0
for i in range(0, len(a)):
if a[i] == b[i]:
if a[i] == 0:
TN += 1
else:
TP += 1
def main():
argv = sys.argv
print("loading %s ..." % argv[1])
ssvm = SaveLogger(file_name=argv[1]).load()
plot_learning(ssvm)
G = [np.empty((0, 2), dtype=np.int) for x in trainDirty[0:n_train]]
else:
edgeList = edges((28, 28), dist=dist, diag=diag)
G = [edgeList for x in trainDirty[0:n_train]]
X_flat = [np.vstack(i) for i in trainDirty[0:n_train]]
Y_flat = np.array(trainLabels[0:n_train])
crf = GraphCRF(inference_method=inference)
svm = NSlackSSVM(model=crf,
max_iter=num_iter,
C=C,
n_jobs=6,
verbose=1,
logger=SaveLogger(folderName + "model_%d.pickle",
save_every=save_every,
verbose=0))
asdf = zip(X_flat, G)
svm.fit(asdf, Y_flat)
#%%####### TESTING ##################
if dist == 0:
G2 = G = [np.empty((0, 2), dtype=np.int) for x in testDirty[0:n_test]]
else:
G2 = [edgeList for x in testDirty[0:n_test]]
X_flat2 = [np.vstack(i) for i in testDirty[0:n_test]]
Y_flat2 = np.array(testLabels[0:n_test])
def train(self, lGraph, bWarmStart=True, expiration_timestamp=None, verbose=0):
"""
Train a CRF model using the list of labelled graph as training
if bWarmStart if True, try to continue from previous training, IF the stored model is older than expiration_timestamp!!
, otherwise, starts from scratch
return nothing
"""
if self.bGridSearch:
return self.gridsearch(lGraph, verbose=verbose)
traceln("\t- computing features on training set")
traceln("\t\t #nodes=%d #edges=%d "%Graph.getNodeEdgeTotalNumber(lGraph))
chronoOn()
lX, lY = self.get_lX_lY(lGraph)
self._computeModelCaracteristics(lX) #we discover here dynamically the number of features of nodes and edges
traceln("\t\t %s"%self._getNbFeatureAsText())
traceln("\t [%.1fs] done\n"%chronoOff())
bMakeSlim = not bWarmStart # for warm-start mode, we do not make the model slimer!"
traceln("\t- retrieving or creating model...")
self.ssvm = None
sModelFN = self.getModelFilename()
if bWarmStart:
try:
self.ssvm = self._loadIfFresh(sModelFN, expiration_timestamp, lambda x: SaveLogger(x).load())
traceln("\t- warmstarting!")
#we allow to change the max_iter of the model
try:
self.ssvm.max_iter #to make sure we do something that makes sense...
if self.ssvm.max_iter != self.max_iter:
traceln("\t- changing max_iter value from (stored) %d to %d"%(self.ssvm.max_iter, self.max_iter))
self.ssvm.max_iter = self.max_iter
except AttributeError:
traceln("\t- cannot access or change the max_iter value")
try:
self.ssvm.n_jobs #to make sure we do something that makes sense...
if self.ssvm.n_jobs != self.njobs:
traceln("\t- changing n_jobs value from (stored) %d to %d"%(self.ssvm.n_jobs, self.njobs))
self.ssvm.n_jobs = self.njobs
except AttributeError:
traceln("\t- cannot access or change the n_jobs value")
except Exception as e:
self.ssvm = None
traceln("\t- Cannot warmstart: %s"%e)
#self.ssvm is either None or containing a nice ssvm model!!
if not self.ssvm:
traceln("\t- creating a new SSVM-trained CRF model")
traceln("\t\t- computing class weight:")
clsWeights = self.computeClassWeight(lY)
traceln("\t\t\t --> %s" % clsWeights)
crf = self._getCRFModel(clsWeights)
self.ssvm = OneSlackSSVM(crf
, inference_cache=self.inference_cache, C=self.C, tol=self.tol, n_jobs=self.njobs
, logger=SaveLogger(sModelFN, save_every=self.save_every)
, max_iter=self.max_iter
, show_loss_every=10, verbose=verbose)
bWarmStart = False
chronoOn()
traceln("\t- training graph-based model")
traceln("\t\t solver parameters:"
, " inference_cache=",self.inference_cache
, " C=",self.C, " tol=",self.tol, " n_jobs=",self.njobs)
self.ssvm.fit(lX, lY, warm_start=bWarmStart)
traceln("\t [%.1fs] done (graph-based model is trained) \n"%chronoOff())
traceln(self.getModelInfo())
#cleaning useless data that takes MB on disk
if bMakeSlim:
self.ssvm.alphas = None
self.ssvm.constraints_ = None
self.ssvm.inference_cache_ = None
traceln("\t\t(model made slimmer. Not sure you can efficiently warm-start it later on. See option -w.)")
#the baseline model(s) if any
self._trainBaselines(lX, lY)
#do some garbage collection
del lX, lY
gc.collect()
return
def main():
tweets_data_train = []
with open('Train\dataset_train.pkl', 'rb') as r:
tweets_set = pickle.load(r)
for i in range(0, len(tweets_set)):
for j in range(0, len(tweets_set[i])):
t = tweets_set[i][j][1].encode('ascii', 'ignore')
tweets_data_train.append(t)
features_train_transformed = get_extra_features(tweets_data_train)
print(features_train_transformed.shape)
features_train_transformed.dump("Train\extra_features_train.pkl")
extra_features_train = numpy.load("Train\extra_features_train.pkl")
print "EXTRA FEATURES FOR TRAIN DATA IS SUCCESSFULLY EXTRACTED"
tweets_data_test = []
with open('Test\dataset_test.pkl', 'rb') as r:
tweets_set = pickle.load(r)
for i in range(0, len(tweets_set)):
for j in range(0, len(tweets_set[i])):
t = tweets_set[i][j][1].encode('ascii', 'ignore')
tweets_data_test.append(t)
features_test_transformed = get_extra_features(tweets_data_test)
features_test_transformed.dump("Test\extra_features_test.pkl")
extra_features_test = numpy.load("Test\extra_features_test.pkl")
print "EXTRA FEATURES FOR TEST DATA IS SUCCESSFULLY EXTRACTED"
#TFIDF VECTORIZER
features_train_tfidf, features_test_tfidf = get_main_features(
tweets_data_train, tweets_data_test)
with open('Train\edges_train.pkl', 'rb') as e:
edges_train = pickle.load(e)
with open('Train\labels_train.pkl', 'rb') as l:
labels_tr = pickle.load(l)
with open('Test\edges_test.pkl', 'rb') as e:
edges_test = pickle.load(e)
with open('Test\labels_test.pkl', 'rb') as l:
labels_te = pickle.load(l)
#edges=numpy.array(edges)
labels_tr = numpy.array(labels_tr)
labels_te = numpy.array(labels_te)
#labels_1D=numpy.zeros(1)
labels_train = array_to_list(labels_tr)
labels_test = array_to_list(labels_te)
labels_test = numpy.array(labels_test)
#labels_1D=numpy.delete(labels_1D,(0),0)
"""
selector=SelectPercentile(f_classif,percentile=70)
selector.fit(features_train_tfidf,labels_1D)
features_train_transformed=selector.transform(features_train_tfidf).toarray()
features_test_transformed=selector.transform(features_test_tfidf).toarray()
print "Features Selection is done successfully """
print features_test_tfidf.shape, extra_features_test.shape
features_train_transformed = numpy.concatenate(
(features_train_tfidf, extra_features_train), axis=1)
features_test_transformed = numpy.concatenate(
(features_test_tfidf, extra_features_test), axis=1)
print "TFIDF FEATURES ARE SUCCESSFULLY CREATED"
features_train = get_features_and_edges(features_train_transformed,
edges_train)
features_test = get_features_and_edges(features_test_transformed,
edges_test)
labels_train = numpy.array(labels_train)
print labels_train.shape
model_name = "GraphCRF_model"
model = GraphCRF(directed=True)
ssvm = FrankWolfeSSVM(model=model,
C=1.0,
max_iter=100,
logger=SaveLogger(model_name + ".pickle",
save_every=100))
start_time = time.time()
final_model = ssvm.fit(features_train, labels_train)
print("--- Time taken to train the classifier is %s seconds " %
(time.time() - start_time))
print "YAAY ! A GRAPH CRF MODEL IS SUCCESSFULLY CREATED AND TRAINED"
print("Charliehedbo event is the Test Data")
pickle.dump(final_model, open('Saved_Model/sdqc_final_model.pkl', 'wb'))
ssvm = pickle.load(open('Saved_Model/sdqc_final_model.pkl', 'rb'))
#ssvm = SaveLogger(model_name+".pickle").load()
X_test = []
y_test = []
for i in range(0, len(features_test)):
if features_test[i][0].shape[0] >= 3:
X_test.append(features_test[i])
y_test.append(labels_test[i])
#print X_test
#print ("Accuracy score with Graph CRF : %f" % ssvm.score(X_test,y_test))
predictions = ssvm.predict(X_test)
#PREDICTIONS AND y_TEST ARE LIST OF ARRAYS
true = numpy.zeros(1)
prediction = numpy.zeros(1)
for i in range(0, len(predictions)):
true = numpy.hstack((true, y_test[i]))
prediction = numpy.hstack((prediction, predictions[i]))
true = numpy.delete(true, (0), axis=0)
prediction = numpy.delete(prediction, (0), axis=0)
print "TOTAL", true.shape[0]
print accuracy_score(true, prediction)
with open('SDQC_Result.pkl', 'wb') as w:
pickle.dump(prediction, w)
print(
classification_report(
true,
prediction,
target_names=["support", "deny", "query", "comment"]))
print confusion_matrix(true, prediction, labels=[0, 1, 2, 3])
plot_cmat(true, prediction)
def validate(self):
""" Tweaks C for the svc. self.validation_set is used for validating """
validation_features = \
bookfunctions.get_features_from_pages_data(self.validation_set,
self.number_of_blocks, self.overlap, self.svm_path)
if self.use_page_classifier:
# FIXME: number of blocks is fixed to what the page classifier has
# learned on in my test case, for now.
page_validation_features = bookfunctions.get_all_features(self.validation_set, \
(5,5))
s = page_validation_features.shape
# Reshape all features to 1 feature vector
page_validation_features.shape = (s[0], s[1] * s[2] * s[3])
validation_labels = bookfunctions.get_all_labels(self.validation_set, \
self.number_of_blocks, overlap=self.overlap)
print """validation set features size after concatenate %s. validation
labels size: %s""" % (str(np.shape(validation_features)), \
str(np.shape(validation_labels)))
best_f = 0
# Count the number of class labels in order to set the class weights
class_weights = 1. / np.bincount(self.train_labels.flatten())
# Normalize class weights in order to have a scalable tolerance
# parameter
class_weights *= float(np.shape(self.train_features)[3]) / np.sum(class_weights)
print "class weights: %s" % str(class_weights)
self.crf = WeightedGridCRF(neighborhood=4, class_weight=class_weights)
for i in range(1, 5):
c = 10**i
self.logger = SaveLogger(get_log_path('model', c, self.use_svm, \
self.overlap, self.use_page_classifier), save_every=15)
print "validating with c = " + str(c)
temp_classifier = ssvm.OneSlackSSVM(model=self.crf, C=c, n_jobs=-1,
verbose=2, logger=self.logger, tol=.01)
# Fit the classifier:
temp_classifier.fit(self.train_features, self.train_labels)
# Write the ssvm parameters!
with open(get_log_path('param', c, self.use_svm, self.overlap,
self.use_page_classifier), 'w') as f:
f.write(str(temp_classifier.get_params()))
print "validation features shape: %s" + str(np.shape(validation_features))
validation_predicted_labels = temp_classifier.predict(validation_features)
validation_predicted_labels = np.array(validation_predicted_labels)
if self.use_page_classifier:
# Get the page predictions, which have pretttyy high accuracy
validation_predicted_pages = self.page_classifier.predict( \
page_validation_features)
for i, page in enumerate(validation_predicted_pages):
if page != 0:
# Replace any page that has no images according to the
# page classifier, with a page that is fully classified
# as 1.
validation_predicted_labels[i] = \
np.ones((validation_predicted_labels.shape[1],
validation_predicted_labels.shape[2]))
print "C = %d" % (c)
prfs = precision_recall_fscore_support(validation_labels.flatten(), \
validation_predicted_labels.flatten())
print """
Precision:
Image: %f
Text: %f
Recall:
Image: %f
Text: %f
Fscore:
Image: %f
Text: %f
Support:
Image: %f
Text: %f
""" % tuple(np.ndarray.flatten(np.array(prfs)))
f = prfs[2][0]
if f > best_f:
best_f = f
self.classifier = temp_classifier
print "F-score for best c: %s" % str(best_f)
return best_f
def main(C=1, test=False):
ds = PascalSegmentation()
# load training data
edge_type = "pairwise"
if test:
which = "train"
else:
which = "kTrain"
data_train = load_pascal(which=which, sp_type="cpmc")
data_train = add_edges(data_train, edge_type)
data_train = add_edge_features(ds, data_train)
data_train = discard_void(ds, data_train, ds.void_label)
print("number of samples: %s" % len(data_train.X))
class_weights = 1. / np.bincount(np.hstack(data_train.Y))
class_weights *= 21. / np.sum(class_weights)
print(class_weights)
#model = crfs.GraphCRF(n_states=n_states,
#n_features=data_train.X[0][0].shape[1],
#inference_method='qpbo', class_weight=class_weights)
model = crfs.EdgeFeatureGraphCRF(inference_method='qpbo',
class_weight=class_weights,
symmetric_edge_features=[0, 1],
antisymmetric_edge_features=[2])
experiment_name = "cpmc_edge_features_trainval_new_%f" % C
#warm_start = True
warm_start = False
ssvm = learners.OneSlackSSVM(
model, verbose=2, C=C, max_iter=100000, n_jobs=-1,
tol=0.0001, show_loss_every=50, inference_cache=50, cache_tol='auto',
logger=SaveLogger(experiment_name + ".pickle", save_every=100),
inactive_threshold=1e-5, break_on_bad=False, inactive_window=50,
switch_to=None)
#ssvm = learners.SubgradientSSVM(
#model, verbose=3, C=C, max_iter=10000, n_jobs=-1, show_loss_every=10,
#logger=SaveLogger(experiment_name + ".pickle", save_every=10),
#momentum=0, learning_rate=0.1, decay_exponent=1, decay_t0=100)
if warm_start:
ssvm = SaveLogger(experiment_name + ".pickle").load()
ssvm.logger = SaveLogger(
file_name=experiment_name + "_refit.pickle",
save_every=10)
#ssvm.learning_rate = 0.000001
ssvm.model.inference_method = 'ad3bb'
#ssvm.n_jobs = 1
ssvm.fit(data_train.X, data_train.Y, warm_start=warm_start)
return
print("fit finished!")
if test:
data_val = load_pascal('val')
else:
data_val = load_pascal('kVal')
data_val = add_edges(data_val, edge_type)
data_val = add_edge_features(ds, data_val, more_colors=True)
eval_on_sp(ds, data_val, ssvm.predict(data_val.X), print_results=True)
def main():
argv = sys.argv
print("loading %s ..." % argv[1])
ssvm1 = SaveLogger(file_name=argv[1]).load()
ssvm2 = SaveLogger(file_name=argv[2]).load()
data_str = 'val'
if len(argv) <= 3:
raise ValueError("Need a folder name for plotting.")
print("loading data...")
data = load_nyu(data_str, n_sp=500)
dataset = NYUSegmentation()
print("done")
data1 = add_edges(data, kind="pairwise")
data2 = add_edges(data, kind="pairwise")
data1 = add_edge_features(dataset, data1)
data2 = add_edge_features(dataset, data2, depth_diff=True)
Y_pred1 = ssvm1.predict(data1.X)
Y_pred2 = ssvm2.predict(data2.X)
folder = argv[3]
if not os.path.exists(folder):
os.mkdir(folder)
np.random.seed(0)
for image_name, superpixels, y_pred1, y_pred2 in zip(data.file_names,
data.superpixels,
Y_pred1, Y_pred2):
if np.all(y_pred1 == y_pred2):
continue
gt = dataset.get_ground_truth(image_name)
perf1 = eval_on_pixels(dataset, [gt], [y_pred1[superpixels]],
print_results=False)[0]
perf1 = np.mean(perf1[np.isfinite(perf1)])
perf2 = eval_on_pixels(dataset, [gt], [y_pred2[superpixels]],
print_results=False)[0]
perf2 = np.mean(perf2[np.isfinite(perf2)])
if np.abs(perf1 - perf2) < 2:
continue
image = dataset.get_image(image_name)
fig, axes = plt.subplots(2, 3, figsize=(12, 6))
axes[0, 0].imshow(image)
axes[0, 0].imshow((y_pred1 != y_pred2)[superpixels], vmin=0, vmax=1,
alpha=.7)
axes[0, 1].set_title("ground truth")
axes[0, 1].imshow(image)
axes[0, 1].imshow(gt, alpha=.7, cmap=dataset.cmap, vmin=0,
vmax=dataset.cmap.N)
axes[1, 0].set_title("%.2f" % perf1)
axes[1, 0].imshow(image)
axes[1, 0].imshow(y_pred1[superpixels], vmin=0, vmax=dataset.cmap.N,
alpha=.7, cmap=dataset.cmap)
axes[1, 1].set_title("%.2f" % perf2)
axes[1, 1].imshow(image)
axes[1, 1].imshow(y_pred2[superpixels], alpha=.7, cmap=dataset.cmap,
vmin=0, vmax=dataset.cmap.N)
present_y = np.unique(np.hstack([y_pred1, y_pred2, np.unique(gt)]))
present_y = np.array([y_ for y_ in present_y if y_ !=
dataset.void_label])
axes[0, 2].imshow(present_y[:, np.newaxis], interpolation='nearest',
cmap=dataset.cmap, vmin=0, vmax=dataset.cmap.N)
for i, c in enumerate(present_y):
axes[0, 2].text(1, i, dataset.classes[c])
for ax in axes.ravel():
ax.set_xticks(())
ax.set_yticks(())
axes[1, 2].set_visible(False)
fig.savefig(folder + "/%s.png" % image_name, bbox_inches="tight")
plt.close(fig)
def svm_on_segments(C=.1, learning_rate=.001, subgradient=False):
data_file = "data_train_XY.pickle"
ds = PascalSegmentation()
if os.path.exists(data_file):
X_, Y_ = cPickle.load(open(data_file))
else:
# load and prepare data
data_train = load_pascal("train", sp_type="cpmc")
data_train = make_cpmc_hierarchy(ds, data_train)
data_train = discard_void(ds, data_train)
X_, Y_ = data_train.X, data_train.Y
cPickle.dump((X_, Y_), open(data_file, 'wb'), -1)
class_weights = 1. / np.bincount(np.hstack(Y_))
class_weights *= 21. / np.sum(class_weights)
experiment_name = ("latent_25_cpmc_%f_qpbo_n_slack_blub3" % C)
logger = SaveLogger(experiment_name + ".pickle", save_every=10)
model = LatentNodeCRF(n_hidden_states=25,
inference_method='qpbo',
class_weight=class_weights,
latent_node_features=False)
if subgradient:
ssvm = learners.LatentSubgradientSSVM(
model, C=C, verbose=1, show_loss_every=10, logger=logger,
n_jobs=-1, learning_rate=learning_rate, decay_exponent=1,
momentum=0., max_iter=100000, decay_t0=100)
else:
latent_logger = SaveLogger("lssvm_" + experiment_name +
"_%d.pickle", save_every=1)
#base_ssvm = learners.OneSlackSSVM(
#model, verbose=2, C=C, max_iter=100, n_jobs=-1, tol=0.001,
#show_loss_every=200, inference_cache=50, logger=logger,
#cache_tol='auto', inactive_threshold=1e-5, break_on_bad=False,
#switch_to=('ogm', {'alg': 'dd'}))
base_ssvm = learners.NSlackSSVM(
model, verbose=4, C=C, n_jobs=-1, tol=0.1,
show_loss_every=20, logger=logger, inactive_threshold=1e-8,
break_on_bad=False, batch_size=36, inactive_window=10,
switch_to=('ad3', {'branch_and_bound': True}))
ssvm = learners.LatentSSVM(base_ssvm, logger=latent_logger,
latent_iter=3)
#warm_start = True
warm_start = False
if warm_start:
ssvm = logger.load()
ssvm.logger = SaveLogger(experiment_name + "_retrain.pickle",
save_every=10)
ssvm.max_iter = 10000
ssvm.decay_exponent = 1
#ssvm.decay_t0 = 1000
#ssvm.learning_rate = 0.00001
#ssvm.momentum = 0
X_, Y_ = shuffle(X_, Y_)
#ssvm.fit(data_train.X, data_train.Y)
ssvm.fit(X_, Y_)
#H_init = [np.hstack([y, np.random.randint(21, 26)]) for y in Y_]
#ssvm.fit(X_, Y_, H_init=H_init)
print("fit finished!")
def main():
argv = sys.argv
print("loading %s ..." % argv[1])
ssvm = SaveLogger(file_name=argv[1]).load()
if hasattr(ssvm, 'problem'):
ssvm.model = ssvm.problem
print(ssvm)
if hasattr(ssvm, 'base_ssvm'):
ssvm = ssvm.base_ssvm
print("Iterations: %d" % len(ssvm.objective_curve_))
print("Objective: %f" % ssvm.objective_curve_[-1])
inference_run = None
if hasattr(ssvm, 'cached_constraint_'):
inference_run = ~np.array(ssvm.cached_constraint_)
print("Gap: %f" %
(np.array(ssvm.primal_objective_curve_)[inference_run][-1] -
ssvm.objective_curve_[-1]))
if len(argv) <= 2:
argv.append("acc")
if len(argv) <= 3:
dataset = 'nyu'
else:
dataset = argv[3]
if argv[2] == 'acc':
ssvm.n_jobs = 1
for data_str, title in zip(["train", "val"],
["TRAINING SET", "VALIDATION SET"]):
print(title)
edge_type = "pairwise"
if dataset == 'msrc':
ds = MSRC21Dataset()
data = msrc_helpers.load_data(data_str, which="piecewise_new")
#data = add_kraehenbuehl_features(data, which="train_30px")
data = msrc_helpers.add_kraehenbuehl_features(data, which="train")
elif dataset == 'pascal':
ds = PascalSegmentation()
data = pascal_helpers.load_pascal(data_str, sp_type="cpmc")
#data = pascal_helpers.load_pascal(data_str)
elif dataset == 'nyu':
ds = NYUSegmentation()
data = nyu_helpers.load_nyu(data_str, n_sp=500, sp='rgbd')
else:
raise ValueError("Excepted dataset to be 'nyu', 'pascal' or 'msrc',"
" got %s." % dataset)
if type(ssvm.model).__name__ == "LatentNodeCRF":
print("making data hierarchical")
data = pascal_helpers.make_cpmc_hierarchy(ds, data)
#data = make_hierarchical_data(
#ds, data, lateral=True, latent=True, latent_lateral=False,
#add_edge_features=False)
else:
data = add_edges(data, edge_type)
if type(ssvm.model).__name__ == 'EdgeFeatureGraphCRF':
data = add_edge_features(ds, data, depth_diff=True, normal_angles=True)
if type(ssvm.model).__name__ == "EdgeFeatureLatentNodeCRF":
data = add_edge_features(ds, data)
data = make_hierarchical_data(
ds, data, lateral=True, latent=True, latent_lateral=False,
add_edge_features=True)
#ssvm.model.inference_method = "qpbo"
Y_pred = ssvm.predict(data.X)
if isinstance(ssvm.model, LatentNodeCRF):
Y_pred = [ssvm.model.label_from_latent(h) for h in Y_pred]
Y_flat = np.hstack(data.Y)
print("superpixel accuracy: %.2f"
% (np.mean((np.hstack(Y_pred) == Y_flat)[Y_flat != ds.void_label]) * 100))
if dataset == 'msrc':
res = msrc_helpers.eval_on_pixels(data, Y_pred,
print_results=True)
print("global: %.2f, average: %.2f" % (res['global'] * 100,
res['average'] * 100))
#msrc_helpers.plot_confusion_matrix(res['confusion'])
else:
hamming, jaccard = eval_on_sp(ds, data, Y_pred,
print_results=True)
print("Jaccard: %.2f, Hamming: %.2f" % (jaccard.mean(),
hamming.mean()))
plt.show()
elif argv[2] == 'plot':
data_str = 'val'
if len(argv) <= 4:
raise ValueError("Need a folder name for plotting.")
if dataset == "msrc":
ds = MSRC21Dataset()
data = msrc_helpers.load_data(data_str, which="piecewise")
data = add_edges(data, independent=False)
data = msrc_helpers.add_kraehenbuehl_features(
data, which="train_30px")
data = msrc_helpers.add_kraehenbuehl_features(
data, which="train")
elif dataset == "pascal":
ds = PascalSegmentation()
data = pascal_helpers.load_pascal("val")
data = add_edges(data)
elif dataset == "nyu":
ds = NYUSegmentation()
data = nyu_helpers.load_nyu("test")
data = add_edges(data)
if type(ssvm.model).__name__ == 'EdgeFeatureGraphCRF':
data = add_edge_features(ds, data, depth_diff=True, normal_angles=True)
Y_pred = ssvm.predict(data.X)
plot_results(ds, data, Y_pred, argv[4])
def main(C=1, test=False):
ds = PascalSegmentation()
# load training data
edge_type = "pairwise"
if test:
which = "train"
else:
which = "kTrain"
data_train = load_pascal(which=which, sp_type="cpmc")
data_train = add_edges(data_train, edge_type)
data_train = add_edge_features(ds, data_train)
data_train = discard_void(ds, data_train, ds.void_label)
print("number of samples: %s" % len(data_train.X))
class_weights = 1. / np.bincount(np.hstack(data_train.Y))
class_weights *= 21. / np.sum(class_weights)
print(class_weights)
#model = crfs.GraphCRF(n_states=n_states,
#n_features=data_train.X[0][0].shape[1],
#inference_method='qpbo', class_weight=class_weights)
model = crfs.EdgeFeatureGraphCRF(inference_method='qpbo',
class_weight=class_weights,
symmetric_edge_features=[0, 1],
antisymmetric_edge_features=[2])
experiment_name = "cpmc_edge_features_trainval_new_%f" % C
#warm_start = True
warm_start = False
ssvm = learners.OneSlackSSVM(model,
verbose=2,
C=C,
max_iter=100000,
n_jobs=-1,
tol=0.0001,
show_loss_every=50,
inference_cache=50,
cache_tol='auto',
logger=SaveLogger(experiment_name + ".pickle",
save_every=100),
inactive_threshold=1e-5,
break_on_bad=False,
inactive_window=50,
switch_to=None)
#ssvm = learners.SubgradientSSVM(
#model, verbose=3, C=C, max_iter=10000, n_jobs=-1, show_loss_every=10,
#logger=SaveLogger(experiment_name + ".pickle", save_every=10),
#momentum=0, learning_rate=0.1, decay_exponent=1, decay_t0=100)
if warm_start:
ssvm = SaveLogger(experiment_name + ".pickle").load()
ssvm.logger = SaveLogger(file_name=experiment_name + "_refit.pickle",
save_every=10)
#ssvm.learning_rate = 0.000001
ssvm.model.inference_method = 'ad3bb'
#ssvm.n_jobs = 1
ssvm.fit(data_train.X, data_train.Y, warm_start=warm_start)
return
print("fit finished!")
if test:
data_val = load_pascal('val')
else:
data_val = load_pascal('kVal')
data_val = add_edges(data_val, edge_type)
data_val = add_edge_features(ds, data_val, more_colors=True)
eval_on_sp(ds, data_val, ssvm.predict(data_val.X), print_results=True)
ssvm_path = '../models_backup/model_c_10_svm_%d_overlap_%d.py' % (int(use_svm), \ int(overlap)) output_folder = '../output_testset' # read all images from input folder #pages_data = bookfunctions.get_pages_and_data_from_book(input_folder) pages_data = bookfunctions.get_pages_and_data_from_folder(input_folder) #pages_data = pages_data[379:381] true_labels = bookfunctions.get_all_labels(pages_data, \ number_of_blocks, overlap) features = bookfunctions.get_features_from_pages_data(pages_data, \ number_of_blocks, overlap, svm_path) # put features in ssvm logger = SaveLogger(ssvm_path) ssvm = logger.load() predicted_labels = np.array(ssvm.predict(features)) prfs = precision_recall_fscore_support(true_labels.flatten(), \ predicted_labels.flatten()) cm = confusion_matrix(true_labels.flatten(), predicted_labels.flatten()) print """ Precision: Image: %f Text: %f Recall: Image: %f Text: %f Fscore: Image: %f
from common.trace import traceln
from crf.Model_SSVM_AD3 import Model_SSVM_AD3
# --- MAIN: DISPLAY STORED MODEL INFO ------------------------------------------------------------------
if __name__ == "__main__":
try:
sModelDir, sModelName = sys.argv[1:3]
except:
print("Usage: %s <model-dir> <model-name>" % sys.argv[0])
print("Display some info regarding the stored model")
exit(1)
mdl = Model_SSVM_AD3(sModelName, sModelDir)
print("Loading %s" % mdl.getModelFilename())
if False:
mdl.load() #loads all sub-models!!
else:
mdl.ssvm = mdl._loadIfFresh(mdl.getModelFilename(), None,
lambda x: SaveLogger(x).load())
print(mdl.getModelInfo())
fig = plt.figure()
fig.canvas.set_window_title("dir=%s model=%s " % (sModelDir, sModelName))
plt.plot(mdl.ssvm.loss_curve_)
plt.xlabel("Iteration / 10")
plt.ylabel("Loss")
plt.show()
ssvm_path = '../models_backup/model_c_10_svm_%d_overlap_%d.py' % (int(use_svm), \
int(overlap))
output_folder = '../output_testset'
# read all images from input folder
#pages_data = bookfunctions.get_pages_and_data_from_book(input_folder)
pages_data = bookfunctions.get_pages_and_data_from_folder(input_folder)
#pages_data = pages_data[379:381]
true_labels = bookfunctions.get_all_labels(pages_data, \
number_of_blocks, overlap)
features = bookfunctions.get_features_from_pages_data(pages_data, \
number_of_blocks, overlap, svm_path)
# put features in ssvm
logger = SaveLogger(ssvm_path)
ssvm = logger.load()
predicted_labels = np.array(ssvm.predict(features))
prfs = precision_recall_fscore_support(true_labels.flatten(), \
predicted_labels.flatten())
cm = confusion_matrix(true_labels.flatten(), predicted_labels.flatten())
print """
Precision:
Image: %f
Text: %f
Recall:
Image: %f
Text: %f
Fscore:
Image: %f
Text: %f
def main(C=1, test=False):
# load training data
#independent = True
independent = False
data_train = load_data(which="piecewise")
data_train = add_edges(data_train, independent=independent,
fully_connected=True)
data_train = add_kraehenbuehl_features(data_train, which="train_30px")
data_train = add_kraehenbuehl_features(data_train, which="train")
#data_train = load_data_global_probs()
if not independent:
data_train = add_edge_features(data_train)
data_train = discard_void(data_train, 21)
if test:
data_val = load_data("val", which="piecewise_train")
data_val = add_edges(data_val, independent=independent)
data_val = add_kraehenbuehl_features(data_val, which="train_30px")
data_val = add_kraehenbuehl_features(data_val, which="train")
data_val = add_edge_features(data_val)
data_val = discard_void(data_val, 21)
data_train = concatenate_datasets(data_train, data_val)
#X_.extend(data_val.X)
#Y_.extend(data_val.Y)
n_states = 21
print("number of samples: %s" % len(data_train.X))
class_weights = 1. / np.bincount(np.hstack(data_train.Y))
#class_weights[21] = 0
class_weights *= 21. / np.sum(class_weights)
#class_weights = np.ones(n_states)
print(class_weights)
#model = crfs.GraphCRF(n_states=n_states,
#n_features=data_train.X[0][0].shape[1],
#inference_method='qpbo', class_weight=class_weights)
model = crfs.EdgeFeatureGraphCRF(n_states=n_states,
n_features=data_train.X[0][0].shape[1],
inference_method='qpbo',
class_weight=class_weights,
n_edge_features=3,
symmetric_edge_features=[0, 1],
antisymmetric_edge_features=[2])
experiment_name = "fully_connected_%f" % C
#warm_start = True
warm_start = False
ssvm = learners.OneSlackSSVM(
model, verbose=2, C=C, max_iter=100000, n_jobs=-1,
tol=0.0001, show_loss_every=50, inference_cache=50, cache_tol='auto',
logger=SaveLogger(experiment_name + ".pickle", save_every=100),
inactive_threshold=1e-5, break_on_bad=False, inactive_window=50,
switch_to_ad3=False)
#ssvm = learners.SubgradientSSVM(
#model, verbose=3, C=C, max_iter=10000, n_jobs=-1, show_loss_every=10,
#logger=SaveLogger(experiment_name + ".pickle", save_every=10),
#momentum=0, learning_rate=0.001, decay_exponent=1)
if warm_start:
ssvm = SaveLogger(experiment_name + ".pickle").load()
ssvm.logger = SaveLogger(
file_name=experiment_name + "_refit.pickle",
save_every=10)
ssvm.learning_rate = 0.000001
#ssvm.model.inference_method = 'ad3'
#ssvm.n_jobs = 1
ssvm.fit(data_train.X, data_train.Y, warm_start=warm_start)
print("fit finished!")
return
rgb = load_data(usc_shadow_path+"Image Pairs","_rgb")
depth = load_data(usc_shadow_path+"Image Pairs","_z")
gt = load_data(usc_shadow_path+"Ground Truth","_gt")
# X, Y = generate_crosses_explicit(n_samples=50, noise=10)
X = rgb
Y = gt / 255
crf = GridCRF(neighborhood=4, inference_method= 'ad3')
# clf = ssvm.OneSlackSSVM(model=crf, C=100, inference_cache=10,
# tol=.1, verbose= True, max_iter= 10,
# logger=SaveLogger("ShadowLogger_%d", save_every=1, verbose=1))
# clf.fit(X, Y)
logger = SaveLogger('ShadowLogger_9')
clf = logger.load()
Y_pred = np.array(clf.predict(X))
print("overall accuracy (training set): %f" % clf.score(X, Y))
# plot one example
for i in range(5):
x, y, y_pred = X[0,:,:,i], Y[0,:,:,i], Y_pred[i]
y_pred = y_pred.reshape(x.shape[:2])
fig, plots = plt.subplots(1, 4, figsize=(12, 4))
plots[0].matshow(y)
plots[0].set_title("ground truth")
plots[1].matshow(x)
plots[1].set_title("input")
plots[2].matshow(y_pred)
