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_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()
