def main(C=1):
dataset = NYUSegmentation()
# load training data
data_train = load_nyu(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 = 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.EdgeFeatureGraphCRF(inference_method='qpbo',
class_weight=class_weights,
n_edge_features=5,
symmetric_edge_features=[0, 1])
experiment_name = "rgbd_test%f" % C
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}))
ssvm.fit(data_train.X, data_train.Y)
print("fit finished!")
return
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(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 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=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!")
