def __init__(self):
self.d = Dataset('full_pascal_trainval')
self.d_val = Dataset('full_pascal_test')
self.cls = 'dog'
suffix = 'default'
self.csc = CSCClassifier(suffix, self.cls, self.d, self.d_val)
csc_test = np.load(config.get_ext_dets_filename(self.d, 'csc_default'))
self.dets = csc_test[()]
def train_csc_svms(d_train, d_val, kernel, C):
# d: trainval
# d_train: train | trainval
# d_val: val | test
dp = DatasetPolicy(d_train, d_train, detectors=['csc_default'])
for cls_idx in range(mpi.comm_rank, len(d_train.classes), mpi.comm_size):
cls = d_train.classes[cls_idx]
ext_detector = dp.actions[cls_idx].obj
csc = CSCClassifier('default', cls, d_train, d_val)
csc.train_for_cls(ext_detector, kernel, C)
def __init__(self, dataset, train_dataset, cls, dets, detname):
"""
Expects cached detections in Table format to be passed in.
The dets should not have the 'cls_ind' column, as they should all be of the
same class.
"""
Detector.__init__(self, dataset, train_dataset, cls, detname)
self.dets = dets
# TODO: hack for csc_X
suffix = detname[4:]
if self.detname == 'dpm':
self.classif = DPMClassifier()
else:
self.classif = CSCClassifier(suffix, cls, train_dataset, dataset)
def test_csc_svm(d_train, d_val):
dp = DatasetPolicy(d_val, d_train, detectors=['csc_default'])
table = np.zeros((len(d_val.images), len(d_val.classes)))
for cls_idx in range(mpi.comm_rank, len(d_val.classes), mpi.comm_size):
cls = d_val.classes[cls_idx]
ext_detector = dp.actions[cls_idx].obj
# Load the classifier we trained in train_csc_svms
csc = CSCClassifier('default', cls, d_train, d_val)
table[:, cls_idx] = csc.eval_cls(ext_detector)
print '%d is at safebarrier' % mpi.comm_rank
safebarrier(comm)
print 'passed safebarrier'
table = comm.reduce(table, op=MPI.SUM, root=0)
if mpi.comm_rank == 0:
print 'save table'
print table
cPickle.dump(table, open('table', 'w'))
print 'saved'
return table
print table
#cPickle.dump(table, open('tab_linear_5','w'))
return table
if __name__ == '__main__':
d_train = Dataset('full_pascal_trainval')
d_val = Dataset('full_pascal_val')
train_gt = d_train.get_cls_ground_truth()
val_gt = d_val.get_cls_ground_truth()
if mpi.comm_rank == 0:
filename = os.path.join(
config.get_classifier_dataset_dirname(
CSCClassifier('default', 'dog', d_train, d_val), d_train),
'crossval.txt')
kernels = ['linear']
Cs = [50]
settings = list(itertools.product(kernels, Cs))
for setin in settings:
kernel = setin[0]
C = setin[1]
#train_csc_svms(d_train, d_val, kernel, C)
safebarrier(comm)
table_arr = test_csc_svm(d_train, d_val)
