def gist_evaluate_svms(d_train, d_val): gist_scores = np.zeros((len(d_val.images), len(d_val.classes))) gist_table = np.load(config.get_gist_dict_filename(d_train.name)) kernels = ['rbf', 'linear', 'poly'] Cs = [1, 10, 100] gammas = [0, 0.3, 1] setts = list(itertools.product(kernels, Cs, gammas)) val_gt = d_val.get_cls_ground_truth() for cls_idx in range(len(d_val.classes)): cls = d_val.classes[cls_idx] gist = GistClassifier(cls, d_train, gist_table=gist_table, d_val=d_val) filename = config.get_gist_crossval_filename(d_train, cls) # doing some crossval right here!!! for set_idx in range(mpi.comm_rank, len(setts), mpi.comm_size): sett = setts[len(setts) - 1 - set_idx] kernel = sett[0] C = sett[1] gamma = sett[2] train_ap = gist.train_svm(d_train, kernel, C, gamma) val_gist_table = np.load(config.get_gist_dict_filename(d_val.name)) gist_scores = gist.svm.predict_proba(val_gist_table)[:, 1] val_ap, _, _ = Evaluation.compute_cls_pr(gist_scores, val_gt.subset_arr(cls)) w = open(filename, 'a') w.write('%s C=%d gamma=%f - train: %f, val: %f\n' % (kernel, C, gamma, train_ap, val_ap)) w.close() print 'ap on val: %f' % val_ap print '%d at safebarrier' % mpi.comm_rank safebarrier(comm) gist_scores = comm.reduce(gist_scores) if mpi.comm_rank == 0: print gist_scores filename = config.get_gist_classifications_filename(d_val) cPickle.dump(gist_scores, open(filename, 'w')) res = Evaluation.compute_cls_pr(gist_scores, val_gt.arr) print res
def gist_train_good_svms(all_settings, d_train): gist_table = np.load(config.get_gist_dict_filename(d_train.name)) for sett_idx in range(mpi.comm_rank, len(all_settings), mpi.comm_size): sett = all_settings[sett_idx] cls = sett[0] C = sett[1] kernel = sett[2] gamma = sett[3] gist = GistClassifier(cls, d_train, gist_table) filename = config.get_gist_crossval_filename(d_train, cls) gist.train_svm(d_train, kernel, C, gamma)
def gist_evaluate_svms(d_train, d_val): gist_scores = np.zeros((len(d_val.images), len(d_val.classes))) gist_table = np.load(config.get_gist_dict_filename(d_train.name)) kernels = ['rbf', 'linear', 'poly'] Cs = [1,10,100] gammas = [0,0.3,1] setts = list(itertools.product(kernels, Cs, gammas)) val_gt = d_val.get_cls_ground_truth() for cls_idx in range(len(d_val.classes)): cls = d_val.classes[cls_idx] gist = GistClassifier(cls, d_train, gist_table=gist_table, d_val=d_val) filename = config.get_gist_crossval_filename(d_train, cls) # doing some crossval right here!!! for set_idx in range(mpi.comm_rank, len(setts), mpi.comm_size): sett = setts[len(setts)-1-set_idx] kernel = sett[0] C = sett[1] gamma = sett[2] train_ap = gist.train_svm(d_train, kernel, C, gamma) val_gist_table = np.load(config.get_gist_dict_filename(d_val.name)) gist_scores = gist.svm.predict_proba(val_gist_table)[:,1] val_ap,_,_ = Evaluation.compute_cls_pr(gist_scores, val_gt.subset_arr(cls)) w = open(filename, 'a') w.write('%s C=%d gamma=%f - train: %f, val: %f\n'%(kernel, C, gamma, train_ap, val_ap)) w.close() print 'ap on val: %f'%val_ap print '%d at safebarrier'%mpi.comm_rank safebarrier(comm) gist_scores = comm.reduce(gist_scores) if mpi.comm_rank == 0: print gist_scores filename = config.get_gist_classifications_filename(d_val) cPickle.dump(gist_scores, open(filename,'w')) res = Evaluation.compute_cls_pr(gist_scores, val_gt.arr) print res
def __init__(self, cls, train_d, gist_table=None, val_d=None): """ Load all gist features right away """ self.train_d = train_d self.val_d = val_d Classifier.__init__(self) self.tt.tic() if gist_table == None: print("Started loading GIST") self.gist_table = np.load(config.get_gist_dict_filename(train_d)) print("Time spent loading gist: %.3f"%self.tt.qtoc()) else: self.gist_table = gist_table self.cls = cls self.svm = self.load_svm()
def __init__(self, cls, train_d, gist_table=None, val_d=None): """ Load all gist features right away """ self.train_d = train_d self.val_d = val_d Classifier.__init__(self) self.tt.tic() if gist_table == None: print("Started loading GIST") self.gist_table = np.load(config.get_gist_dict_filename(train_d)) print("Time spent loading gist: %.3f" % self.tt.qtoc()) else: self.gist_table = gist_table self.cls = cls self.svm = self.load_svm()