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 main(): ds = PascalSegmentation() # load training data edge_type = "pairwise" 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) X, Y = data_train.X, data_train.Y class_weights = 1. / np.bincount(np.hstack(Y)) class_weights *= 21. / np.sum(class_weights) model = crfs.EdgeFeatureGraphCRF(class_weight=class_weights, symmetric_edge_features=[0, 1], antisymmetric_edge_features=[2], inference_method='qpbo') ssvm = learners.NSlackSSVM(model, C=0.01, n_jobs=-1) ssvm.fit(X, Y)
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(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)