def train_svm(C=0.1, grid=False):
ds = NYUSegmentation()
data_train = load_nyu("train", n_sp=500, sp='rgbd')
svm = LinearSVC(C=C, dual=False, class_weight='auto')
#N_train = []
#for f, sp in zip(data_train.file_names, data_train.superpixels):
#normals = ds.get_pointcloud_normals(f)[:, :, 3:]
#mean_normals = get_sp_normals(normals, sp)
#N_train.append(mean_normals * .1)
#N_flat_train = np.vstack(N_train)
X, y = np.vstack(data_train.X), np.hstack(data_train.Y)
#X = np.hstack([X, N_flat_train])
svm.fit(X, y)
print(svm.score(X, y))
eval_on_sp(ds, data_train, [svm.predict(x)
for x in data_train.X],
print_results=True)
data_val = load_nyu("val", n_sp=500, sp='rgbd')
#N_val = []
#for f, sp in zip(data_val.file_names, data_val.superpixels):
#normals = ds.get_pointcloud_normals(f)[:, :, 3:]
#mean_normals = get_sp_normals(normals, sp)
#N_val.append(mean_normals * .1)
eval_on_sp(ds, data_val, [svm.predict(x)
for x in data_val.X],
print_results=True)
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 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_svm(C=0.1, grid=False):
ds = NYUSegmentation()
data_train = load_nyu("train", n_sp=500, sp="rgbd")
svm = LinearSVC(C=C, dual=False, class_weight="auto")
# N_train = []
# for f, sp in zip(data_train.file_names, data_train.superpixels):
# normals = ds.get_pointcloud_normals(f)[:, :, 3:]
# mean_normals = get_sp_normals(normals, sp)
# N_train.append(mean_normals * .1)
# N_flat_train = np.vstack(N_train)
X, y = np.vstack(data_train.X), np.hstack(data_train.Y)
# X = np.hstack([X, N_flat_train])
svm.fit(X, y)
print(svm.score(X, y))
eval_on_sp(ds, data_train, [svm.predict(x) for x in data_train.X], print_results=True)
data_val = load_nyu("val", n_sp=500, sp="rgbd")
# N_val = []
# for f, sp in zip(data_val.file_names, data_val.superpixels):
# normals = ds.get_pointcloud_normals(f)[:, :, 3:]
# mean_normals = get_sp_normals(normals, sp)
# N_val.append(mean_normals * .1)
eval_on_sp(ds, data_val, [svm.predict(x) for x in data_val.X], print_results=True)
def eval_sp_prediction():
dataset = NYUSegmentation()
data = load_nyu('val', n_sp=500, sp='rgbd')
predictions = [np.argmax(x, axis=-1) for x in data.X]
#predictions = data.Y
hamming, jaccard = eval_on_sp(dataset, data, predictions, print_results=True)
def eval_sp_prediction():
dataset = NYUSegmentation()
data = load_nyu("val", n_sp=500, sp="rgbd")
predictions = [np.argmax(x, axis=-1) for x in data.X]
# predictions = data.Y
hamming, jaccard = eval_on_sp(dataset, data, predictions, print_results=True)
