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_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 read_best_svms_from_file(d_train):
all_settings = []
for cls in config.pascal_classes:
filename = config.get_gist_crossval_filename(d_train, cls)
lines = open(filename,'r').readlines()
best_ap = 0
best_line_idx = -1
for line_idx, line in enumerate(lines):
ap = float(line.split()[-1])
if ap > best_ap:
best_ap = ap
best_line_idx = line_idx
best_line = lines[best_line_idx].split()
kernel = best_line[0]
C = int(best_line[1].split('=')[1])
gamma = float(best_line[2].split('=')[1])
all_settings.append([cls, C, kernel, gamma, best_ap])
return all_settings
def read_best_svms_from_file(d_train):
all_settings = []
for cls in config.pascal_classes:
filename = config.get_gist_crossval_filename(d_train, cls)
lines = open(filename, 'r').readlines()
best_ap = 0
best_line_idx = -1
for line_idx, line in enumerate(lines):
ap = float(line.split()[-1])
if ap > best_ap:
best_ap = ap
best_line_idx = line_idx
best_line = lines[best_line_idx].split()
kernel = best_line[0]
C = int(best_line[1].split('=')[1])
gamma = float(best_line[2].split('=')[1])
all_settings.append([cls, C, kernel, gamma, best_ap])
return all_settings
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
