class TestCscClassifier:
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 get_scores_for_img(self, img_ind, cls_ind):
dets = self.dets.filter_on_column('cls_ind',
self.d.classes.index(self.cls),
omit=True)
dets = dets.subset(['score', 'img_ind'])
dets = dets.filter_on_column('img_ind', 0, omit=True)
dets.arr = self.csc.normalize_dpm_scores(dets.arr)
return dets.subset_arr('score')
def test_classify_image(self):
scores = self.get_scores_for_img(0, self.cls)
res = self.csc.classify_image(scores)
assert (round(res, 12) == 1.)
def test_compute_histogram(self):
for img in range(50):
scores = self.get_scores_for_img(0, self.cls)
vector = self.csc.create_vector_from_scores(scores)
class TestCscClassifier:
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 get_scores_for_img(self, img_ind, cls_ind):
dets = self.dets.filter_on_column('cls_ind', self.d.classes.index(self.cls), omit=True)
dets = dets.subset(['score', 'img_ind'])
dets = dets.filter_on_column('img_ind', 0, omit=True)
dets.arr = self.csc.normalize_dpm_scores(dets.arr)
return dets.subset_arr('score')
def test_classify_image(self):
scores = self.get_scores_for_img(0, self.cls)
res = self.csc.classify_image(scores)
assert(round(res,12) == 1.)
def test_compute_histogram(self):
for img in range(50):
scores = self.get_scores_for_img(0, self.cls)
vector = self.csc.create_vector_from_scores(scores)
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 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 __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 __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 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
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
class ExternalDetector(Detector):
"""
A mock interface to the Felzenszwalb DPM, CSC, or the Pendersoli CtF detector.
Actually works by pre-loading all the detections and then returning them as
requested.
"""
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 detect(self, image, astable=False, dets=None):
"""
Return the detections that match that image index in cached dets.
Must return in the same format as the Detector superclass, so we have to
delete a column.
"""
if not dets:
img_ind = self.dataset.get_img_ind(image)
dets = self.dets.filter_on_column('img_ind', img_ind, omit=True)
time_passed = 0
if not dets.arr.shape[0] < 1:
time_passed = np.max(dets.subset_arr('time'))
# Halve the time passed if my may25 DPM detector, to have reasonable times
# Also halve the time passed by csc_half detector, because we halved its AP
if self.detname == 'dpm_may25' or self.detname == 'csc_half':
time_passed /= 2
# TODO: hack that returns time around 1s always
#hist(np.maximum(0.8,1+0.1*np.random.randn(1000)),20)
time_passed = np.maximum(0.8, 1 + 0.1 * np.random.randn())
dets = dets.with_column_omitted('time')
if astable:
return (dets, time_passed)
else:
return (dets.arr, time_passed)
def compute_score(self, image, oracle=False, dets=None):
"""
Return the 0/1 decision of whether the cls of this detector is present in
the image, given the detections table.
If oracle=True, returns the correct answer (look up the ground truth).
"""
if oracle:
return Detector.compute_score(self, image, oracle)
if not dets:
img_ind = self.dataset.get_img_ind(image)
dets = self.dets.filter_on_column('img_ind', img_ind)
scores = dets.subset_arr('score')
score = self.classif.classify_image(scores)
dt = 0
# TODO: figure out the dt situation above
return (score, dt)
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)
class ExternalDetector(Detector):
"""
A mock interface to the Felzenszwalb DPM, CSC, or the Pendersoli CtF detector.
Actually works by pre-loading all the detections and then returning them as
requested.
"""
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 detect(self, image, astable=False, dets=None):
"""
Return the detections that match that image index in cached dets.
Must return in the same format as the Detector superclass, so we have to
delete a column.
"""
if not dets:
img_ind = self.dataset.get_img_ind(image)
dets = self.dets.filter_on_column('img_ind',img_ind,omit=True)
time_passed = 0
if not dets.arr.shape[0]<1:
time_passed = np.max(dets.subset_arr('time'))
# Halve the time passed if my may25 DPM detector, to have reasonable times
# Also halve the time passed by csc_half detector, because we halved its AP
if self.detname=='dpm_may25' or self.detname=='csc_half':
time_passed /= 2
# TODO: hack that returns time around 1s always
#hist(np.maximum(0.8,1+0.1*np.random.randn(1000)),20)
time_passed = np.maximum(0.8,1+0.1*np.random.randn())
dets = dets.with_column_omitted('time')
if astable:
return (dets, time_passed)
else:
return (dets.arr, time_passed)
def compute_score(self, image, oracle=False, dets=None):
"""
Return the 0/1 decision of whether the cls of this detector is present in
the image, given the detections table.
If oracle=True, returns the correct answer (look up the ground truth).
"""
if oracle:
return Detector.compute_score(self, image, oracle)
if not dets:
img_ind = self.dataset.get_img_ind(image)
dets = self.dets.filter_on_column('img_ind',img_ind)
scores = dets.subset_arr('score')
score = self.classif.classify_image(scores)
dt = 0
# TODO: figure out the dt situation above
return (score,dt)