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 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_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 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 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 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 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!")
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
Support:
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
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
else:
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!")
# 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})
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!")
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)
plots[2].set_title("prediction")
