def with_aureliens_potentials_svm(test=False):
data = load_data('train', independent=True)
data = add_kraehenbuehl_features(data)
features = [x[0] for x in data.X]
y = np.hstack(data.Y)
if test:
data_ = load_data('val', independent=True)
data_ = add_kraehenbuehl_features(data_)
features.extend([x[0] for x in data.X])
y = np.hstack([y, np.hstack(data_.Y)])
new_features_flat = np.vstack(features)
from sklearn.svm import LinearSVC
print("training svm")
svm = LinearSVC(C=.001, dual=False, class_weight='auto')
svm.fit(new_features_flat[y != 21], y[y != 21])
print(svm.score(new_features_flat[y != 21], y[y != 21]))
print("evaluating")
eval_on_pixels(data, [svm.predict(x) for x in features])
if test:
print("test data")
data_val = load_data('test', independent=True)
else:
data_val = load_data('val', independent=True)
data_val = add_kraehenbuehl_features(data_val)
features_val = [x[0] for x in data_val.X]
eval_on_pixels(data_val, [svm.predict(x) for x in features_val])
def with_aureliens_potentials_svm(test=False):
data = load_data('train', independent=True)
data = add_kraehenbuehl_features(data)
features = [x[0] for x in data.X]
y = np.hstack(data.Y)
if test:
data_ = load_data('val', independent=True)
data_ = add_kraehenbuehl_features(data_)
features.extend([x[0] for x in data.X])
y = np.hstack([y, np.hstack(data_.Y)])
new_features_flat = np.vstack(features)
from sklearn.svm import LinearSVC
print("training svm")
svm = LinearSVC(C=.001, dual=False, class_weight='auto')
svm.fit(new_features_flat[y != 21], y[y != 21])
print(svm.score(new_features_flat[y != 21], y[y != 21]))
print("evaluating")
eval_on_pixels(data, [svm.predict(x) for x in features])
if test:
print("test data")
data_val = load_data('test', independent=True)
else:
data_val = load_data('val', independent=True)
data_val = add_kraehenbuehl_features(data_val)
features_val = [x[0] for x in data_val.X]
eval_on_pixels(data_val, [svm.predict(x) for x in features_val])
def train_svm(test=False, C=0.01, gamma=.1, grid=False):
which = "piecewise"
data_train = load_data(which=which)
data_train = add_kraehenbuehl_features(data_train, which="train_30px")
data_train = add_kraehenbuehl_features(data_train, which="train")
data_train_novoid = discard_void(data_train, 21)
if grid and test:
raise ValueError("Don't you dare grid-search on the test-set!")
svm = LinearSVC(C=C, class_weight='auto', multi_class='crammer_singer',
dual=False)
#svm = LogisticRegression(C=C, class_weight='auto')
data_val = load_data('val', which=which)
data_val = add_kraehenbuehl_features(data_val, which="train_30px")
data_val = add_kraehenbuehl_features(data_val, which="train")
data_val_novoid = discard_void(data_val, 21)
if grid:
n_samples_train = len(np.hstack(data_train_novoid.Y))
n_samples_val = len(np.hstack(data_val_novoid.Y))
cv = SimpleSplitCV(n_samples_train, n_samples_val)
data_trainval = concatenate_datasets(data_train_novoid,
data_val_novoid)
from sklearn.grid_search import GridSearchCV
#from sklearn.grid_search import RandomizedSearchCV
#from scipy.stats import expon, gamma
#param_grid = {'C': 10. ** np.arange(1, 4), 'gamma': 10. **
#np.arange(-3, 1)}
param_grid = {'C': 10. ** np.arange(-6, 2)}
scorer = PixelwiseScorer(data=data_val)
grid = GridSearchCV(svm, param_grid=param_grid, verbose=10, n_jobs=-1,
cv=cv, scoring=scorer, refit=False)
grid.fit(np.vstack(data_trainval.X),
np.hstack(data_trainval.Y))
print(grid.best_params_)
print(grid.best_score_)
else:
print(svm)
if test:
data_train_novoid = concatenate_datasets(data_train_novoid,
data_val_novoid)
print(np.vstack(data_train_novoid.X).shape)
svm.fit(np.vstack(data_train_novoid.X), np.hstack(data_train_novoid.Y))
if test:
data_test = load_data("test", which=which)
else:
data_test = load_data("val", which=which)
data_test = add_kraehenbuehl_features(data_test, which="train_30px")
data_test = add_kraehenbuehl_features(data_test, which="train")
scorer = PixelwiseScorer(data=data_test)
scorer(svm, None, None)
return svm
def train_car():
car_idx = np.where(classes == "car")[0]
data_train = load_data("train", independent=False)
car_images = np.array(
[i for i, y in enumerate(data_train.Y) if np.any(y == car_idx)])
n_states_per_label = np.ones(22, dtype=np.int)
n_states_per_label[car_idx] = 6
X, Y, file_names, images, all_superpixels = zip(
*[(data_train.X[i], data_train.Y[i], data_train.file_names[i],
data_train.images[i], data_train.superpixels[i])
for i in car_images])
problem = GraphCRF(n_states=22, inference_method='ad3', n_features=21 * 6)
ssvm = learners.SubgradientStructuredSVM(problem,
verbose=2,
C=.001,
max_iter=5000,
n_jobs=-1,
show_loss_every=10,
learning_rate=0.0001,
decay_exponent=0.5)
ssvm.fit(X, Y)
Y_pred = ssvm.predict(X)
plot_results(images,
file_names,
Y,
Y_pred,
all_superpixels,
folder="cars_only")
data_val = load_data("val", independent=False)
car_images_val = np.array(
[i for i, y in enumerate(data_val.Y) if np.any(y == car_idx)])
X_val, Y_val, file_names_val, images_val, all_superpixels_val = \
zip(*[(data_val.X[i], data_val.Y[i], data_val.file_names[i],
data_val.images[i], data_val.superpixels[i]) for i in
car_images_val])
Y_pred_val = ssvm.predict(X_val)
plot_results(images_val,
file_names_val,
Y_val,
Y_pred_val,
all_superpixels_val,
folder="cars_only_val")
# C=10
## train:
#0.92743060939680566V
#> ssvm.score(X_val, Y_val)
#0.52921719955898561
# test 0.61693548387096775
tracer()
def plot_init():
data = load_data("train", independent=False)
data = make_hierarchical_data(data, lateral=False, latent=True)
#X, Y = discard_void(data.X, data.Y, 21)
#data.X, data.Y = X, Y
H = kmeans_init(data.X, data.Y, n_labels=22, n_hidden_states=22)
plot_results_hierarchy(data, H)
def plot_parts():
from pystruct.problems.latent_graph_crf import kmeans_init
car_idx = np.where(classes == "car")[0]
data = load_data("train", independent=False)
car_images = np.array(
[i for i, y in enumerate(data.Y) if np.any(y == car_idx)])
flat_X = [x[0] for x in data.X]
edges = [[x[1]] for x in data.X]
n_states_per_label = np.ones(22, dtype=np.int)
n_states_per_label[car_idx] = 6
H = kmeans_init(flat_X,
data.Y,
edges,
n_labels=22,
n_states_per_label=n_states_per_label,
symmetric=True)
X, Y, file_names, images, all_superpixels, H = zip(
*[(data.X[i], data.Y[i], data.file_names[i], data.images[i],
data.all_superpixels[i], H[i]) for i in car_images])
plot_results(images,
file_names,
Y,
H,
all_superpixels,
folder="test_parts",
use_colors_predict=False)
def on_slic_superpixels():
data = load_data('train', independent=True)
probs = get_kraehenbuehl_pot_sp(data)
results = eval_on_pixels(data, [np.argmax(prob, axis=-1) for prob in
probs])
plt.matshow(results['confusion'])
plt.show()
def on_slic_superpixels():
data = load_data('train', independent=True)
probs = get_kraehenbuehl_pot_sp(data)
results = eval_on_pixels(data,
[np.argmax(prob, axis=-1) for prob in probs])
plt.matshow(results['confusion'])
plt.show()
def train_car_parts():
car_idx = np.where(classes == "car")[0]
data = load_data("train", independent=False)
car_images = np.array([i for i, y in enumerate(data.Y)
if np.any(y == car_idx)])
n_states_per_label = np.ones(22, dtype=np.int)
n_states_per_label[car_idx] = 6
X, Y, file_names, images, all_superpixels = zip(*[
(data.X[i], data.Y[i], data.file_names[i], data.images[i],
data.all_superpixels[i]) for i in car_images])
problem = LatentGraphCRF(n_states_per_label=n_states_per_label,
n_labels=22, inference_method='ad3',
n_features=21 * 6)
ssvm = learners.LatentSSVM(
problem, verbose=2, C=10, max_iter=5000, n_jobs=-1, tol=0.0001,
show_loss_every=10, base_svm='subgradient', inference_cache=50,
latent_iter=5, learning_rate=0.001, decay_exponent=0.5)
ssvm.fit(X, Y)
plot_results(images, file_names, Y, ssvm.H_init_, all_superpixels,
folder="parts_init", use_colors_predict=False)
H = ssvm.predict_latent(X)
plot_results(images, file_names, Y, H, all_superpixels,
folder="parts_prediction", use_colors_predict=False)
H_final = [problem.latent(x, y, ssvm.w) for x, y in zip(X, Y)]
plot_results(images, file_names, Y, H_final, all_superpixels,
folder="parts_final", use_colors_predict=False)
tracer()
def plot_init():
data = load_data("train", independent=False)
data = make_hierarchical_data(data, lateral=False, latent=True)
#X, Y = discard_void(data.X, data.Y, 21)
#data.X, data.Y = X, Y
H = kmeans_init(data.X, data.Y, n_labels=22, n_hidden_states=22)
plot_results_hierarchy(data, H)
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 load_data_global_probs(dataset="train", latent=False):
def padded_vstack(blub):
a, b = blub
if a.shape[0] > b.shape[0]:
b = np.hstack([b, np.zeros((b.shape[0], a.shape[1] - b.shape[1]))])
return np.vstack([a, b])
data = load_data(dataset=dataset, which="piecewise")
data = add_kraehenbuehl_features(data, which="train_30px")
data = add_kraehenbuehl_features(data, which="train")
data = add_edges(data)
if latent:
data = add_top_node(data)
descs = np.load("/home/user/amueller/checkout/superpixel_crf/"
"global_probs_%s.npy" % dataset)
X = []
for x, glob_desc in zip(data.X, descs):
if latent:
x_ = padded_vstack([x[0],
np.repeat(sigm(glob_desc)[np.newaxis, 1:],
x[2], axis=0)])
else:
x_ = np.hstack([x[0], np.repeat(sigm(glob_desc)[np.newaxis, :],
x[0].shape[0], axis=0)])
# add features for latent node
if len(x) == 3:
X.append((x_, x[1], x[2]))
else:
X.append((x_, x[1]))
return DataBunch(X, data.Y, data.file_names, data.superpixels)
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 train_car_parts():
car_idx = np.where(classes == "car")[0]
data = load_data("train", independent=False)
car_images = np.array(
[i for i, y in enumerate(data.Y) if np.any(y == car_idx)])
n_states_per_label = np.ones(22, dtype=np.int)
n_states_per_label[car_idx] = 6
X, Y, file_names, images, all_superpixels = zip(*[(data.X[i], data.Y[i],
data.file_names[i],
data.images[i],
data.all_superpixels[i])
for i in car_images])
problem = LatentGraphCRF(n_states_per_label=n_states_per_label,
n_labels=22,
inference_method='ad3',
n_features=21 * 6)
ssvm = learners.LatentSSVM(problem,
verbose=2,
C=10,
max_iter=5000,
n_jobs=-1,
tol=0.0001,
show_loss_every=10,
base_svm='subgradient',
inference_cache=50,
latent_iter=5,
learning_rate=0.001,
decay_exponent=0.5)
ssvm.fit(X, Y)
plot_results(images,
file_names,
Y,
ssvm.H_init_,
all_superpixels,
folder="parts_init",
use_colors_predict=False)
H = ssvm.predict_latent(X)
plot_results(images,
file_names,
Y,
H,
all_superpixels,
folder="parts_prediction",
use_colors_predict=False)
H_final = [problem.latent(x, y, ssvm.w) for x, y in zip(X, Y)]
plot_results(images,
file_names,
Y,
H_final,
all_superpixels,
folder="parts_final",
use_colors_predict=False)
tracer()
def train_car():
car_idx = np.where(classes == "car")[0]
data_train = load_data("train", independent=False)
car_images = np.array([i for i, y in enumerate(data_train.Y)
if np.any(y == car_idx)])
n_states_per_label = np.ones(22, dtype=np.int)
n_states_per_label[car_idx] = 6
X, Y, file_names, images, all_superpixels = zip(*[
(data_train.X[i], data_train.Y[i], data_train.file_names[i],
data_train.images[i], data_train.superpixels[i])
for i in car_images])
problem = GraphCRF(n_states=22, inference_method='ad3', n_features=21 * 6)
ssvm = learners.SubgradientStructuredSVM(
problem, verbose=2, C=.001, max_iter=5000, n_jobs=-1,
show_loss_every=10, learning_rate=0.0001, decay_exponent=0.5)
ssvm.fit(X, Y)
Y_pred = ssvm.predict(X)
plot_results(images, file_names, Y, Y_pred, all_superpixels,
folder="cars_only")
data_val = load_data("val", independent=False)
car_images_val = np.array([i for i, y in enumerate(data_val.Y)
if np.any(y == car_idx)])
X_val, Y_val, file_names_val, images_val, all_superpixels_val = \
zip(*[(data_val.X[i], data_val.Y[i], data_val.file_names[i],
data_val.images[i], data_val.superpixels[i]) for i in
car_images_val])
Y_pred_val = ssvm.predict(X_val)
plot_results(images_val, file_names_val, Y_val, Y_pred_val,
all_superpixels_val, folder="cars_only_val")
# C=10
## train:
#0.92743060939680566V
#> ssvm.score(X_val, Y_val)
#0.52921719955898561
# test 0.61693548387096775
tracer()
def plot_parts():
from pystruct.problems.latent_graph_crf import kmeans_init
car_idx = np.where(classes == "car")[0]
data = load_data("train", independent=False)
car_images = np.array([i for i, y in enumerate(data.Y)
if np.any(y == car_idx)])
flat_X = [x[0] for x in data.X]
edges = [[x[1]] for x in data.X]
n_states_per_label = np.ones(22, dtype=np.int)
n_states_per_label[car_idx] = 6
H = kmeans_init(flat_X, data.Y, edges, n_labels=22,
n_states_per_label=n_states_per_label, symmetric=True)
X, Y, file_names, images, all_superpixels, H = zip(*[
(data.X[i], data.Y[i], data.file_names[i], data.images[i],
data.all_superpixels[i], H[i])
for i in car_images])
plot_results(images, file_names, Y, H, all_superpixels,
folder="test_parts", use_colors_predict=False)
def load_data_global_probs(dataset="train", latent=False):
def padded_vstack(blub):
a, b = blub
if a.shape[0] > b.shape[0]:
b = np.hstack([b, np.zeros((b.shape[0], a.shape[1] - b.shape[1]))])
return np.vstack([a, b])
data = load_data(dataset=dataset, which="piecewise")
data = add_kraehenbuehl_features(data, which="train_30px")
data = add_kraehenbuehl_features(data, which="train")
data = add_edges(data)
if latent:
data = add_top_node(data)
descs = np.load("/home/user/amueller/checkout/superpixel_crf/"
"global_probs_%s.npy" % dataset)
X = []
for x, glob_desc in zip(data.X, descs):
if latent:
x_ = padded_vstack([
x[0],
np.repeat(sigm(glob_desc)[np.newaxis, 1:], x[2], axis=0)
])
else:
x_ = np.hstack([
x[0],
np.repeat(sigm(glob_desc)[np.newaxis, :],
x[0].shape[0],
axis=0)
])
# add features for latent node
if len(x) == 3:
X.append((x_, x[1], x[2]))
else:
X.append((x_, x[1]))
return DataBunch(X, data.Y, data.file_names, data.superpixels)
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=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 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