def test(self):
scores = np.ones(self.det_gt.shape[0])
dets = self.det_gt.append_column('score', scores)
scores = np.ones(self.d.get_det_gt_for_class('A').shape[0])
dets_just_A = self.d.get_det_gt_for_class('A')
dets_just_A = dets_just_A.append_column('score', scores)
self.d.set_values('uniform')
assert (np.all(self.d.values == 1. / 3 * np.ones(len(self.classes))))
dp = DatasetPolicy(self.d, self.d, detector='perfect')
ev = Evaluation(dp)
ap = ev.compute_det_map(dets, self.det_gt)
assert (ap == 1)
ap = ev.compute_det_map(dets_just_A, self.det_gt)
print(ap)
assert (ut.fequal(ap, 0.33333333333333))
self.d.set_values('inverse_prior')
assert (np.all(self.d.values == np.array([0.25, 0.25, 0.5])))
dp = DatasetPolicy(self.d, self.d, detector='perfect')
ev = Evaluation(dp)
ap = ev.compute_det_map(dets, self.det_gt)
assert (ap == 1)
ap = ev.compute_det_map(dets_just_A, self.det_gt)
print(ap)
assert (ut.fequal(ap, 0.25))
def test_load_dpm_detections(self):
conf = dict(self.config)
conf['detectors'] = ['dpm']
policy = DatasetPolicy(self.dataset, self.train_dataset, **conf)
assert (policy.detectors == ['dpm'])
dets = policy.load_ext_detections(self.dataset,
'dpm_may25',
force=True)
dets = dets.with_column_omitted('time')
# load the ground truth dets, processed in Matlab
# (timely/data/test_support/concat_dets.m)
filename = os.path.join(config.test_support_dir, 'val_dets.mat')
dets_correct = Table(
scipy.io.loadmat(filename)['dets'], [
'x1', 'y1', 'x2', 'y2', 'dummy', 'dummy', 'dummy', 'dummy',
'score', 'cls_ind', 'img_ind'
], 'dets_correct')
dets_correct = dets_correct.subset(
['x1', 'y1', 'x2', 'y2', 'score', 'cls_ind', 'img_ind'])
dets_correct.arr[:, :4] -= 1
dets_correct.arr[:, :4] = BoundingBox.convert_arr_from_corners(
dets_correct.arr[:, :4])
dets_correct.cols = ['x', 'y', 'w', 'h', 'score', 'cls_ind', 'img_ind']
print('----mine:')
print(dets)
print('----correct:')
print(dets_correct)
assert (dets_correct == dets)
def test_learn_weights(self):
dataset = Dataset('full_pascal_val')
train_dataset = Dataset('full_pascal_train')
dataset.images = dataset.images[:20]
train_dataset.images = train_dataset.images[:20]
dp = DatasetPolicy(dataset, train_dataset, self.weights_dataset_name,
**self.config)
weights = dp.learn_weights()
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):
self.dataset = Dataset('test_pascal_val')
self.train_dataset = Dataset('test_pascal_train')
self.weights_dataset_name = 'test_pascal_val'
self.config = {
'suffix': 'default',
'detectors':
['perfect'], # perfect,perfect_with_noise,dpm,csc_default,csc_half
'policy_mode': 'random',
'bounds': None,
'weights_mode':
'manual_1' # manual_1, manual_2, manual_3, greedy, rl
}
self.dp = DatasetPolicy(self.dataset, self.train_dataset,
self.weights_dataset_name, **self.config)
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 setup(self):
train_dataset = Dataset('test_pascal_train', force=True)
dataset = Dataset('test_pascal_val', force=True)
self.dp = DatasetPolicy(dataset, train_dataset, detector='perfect')
self.evaluation = Evaluation(self.dp)
def main():
parser = argparse.ArgumentParser(
description="Run experiments with the timely detection system.")
parser.add_argument('--test_dataset',
choices=['val','test','trainval'],
default='val',
help="""Dataset to use for testing. Run on val until final runs.
The training dataset is inferred (val->train; test->trainval; trainval->trainval).""")
parser.add_argument('--first_n', type=int,
help='only take the first N images in the test dataset')
parser.add_argument('--first_n_train', type=int,
help='only take the first N images in the train dataset')
parser.add_argument('--config',
help="""Config file name that specifies the experiments to run.
Give name such that the file is configs/#{name}.json or configs/#{name}/
In the latter case, all files within the directory will be loaded.""")
parser.add_argument('--suffix',
help="Overwrites the suffix in the config(s).")
parser.add_argument('--bounds10', action='store_true',
default=False, help='set bounds to [0,10]')
parser.add_argument('--bounds515', action='store_true',
default=False, help='set bounds to [5,15]')
parser.add_argument('--force', action='store_true',
default=False, help='force overwrite')
parser.add_argument('--wholeset_prs', action='store_true',
default=False, help='evaluate in the final p-r regime')
parser.add_argument('--no_apvst', action='store_true',
default=False, help='do NOT evaluate in the ap vs. time regime')
parser.add_argument('--det_configs', action='store_true',
default=False, help='output detector statistics to det_configs')
parser.add_argument('--inverse_prior', action='store_true',
default=False, help='use inverse prior class values')
args = parser.parse_args()
print(args)
# If config file is not given, just run one experiment using default config
if not args.config:
configs = [DatasetPolicy.default_config]
else:
configs = load_configs(args.config)
# Load the dataset
dataset = Dataset('full_pascal_'+args.test_dataset)
if args.first_n:
dataset.images = dataset.images[:args.first_n]
# Infer train_dataset
if args.test_dataset=='test':
train_dataset = Dataset('full_pascal_trainval')
elif args.test_dataset=='val':
train_dataset = Dataset('full_pascal_train')
elif args.test_dataset=='trainval':
train_dataset = Dataset('full_pascal_trainval')
else:
None # impossible by argparse settings
# Only need to set training dataset values; evaluation gets it from there
if args.inverse_prior:
train_dataset.set_values('inverse_prior')
# TODO: hack
if args.first_n_train:
train_dataset.images = train_dataset.images[:args.first_n_train]
# In both the above cases, we use the val dataset for weights
weights_dataset_name = 'full_pascal_val'
dets_tables = []
dets_tables_whole = []
clses_tables_whole = []
all_bounds = []
plot_infos = []
for config_f in configs:
if args.suffix:
config_f['suffix'] = args.suffix
if args.bounds10:
config_f['bounds'] = [0,10]
if args.bounds515:
config_f['bounds'] = [5,15]
assert(not (args.bounds10 and args.bounds515))
if args.inverse_prior:
config_f['suffix'] += '_inverse_prior'
config_f['values'] = 'inverse_prior'
dp = DatasetPolicy(dataset, train_dataset, weights_dataset_name, **config_f)
ev = Evaluation(dp)
all_bounds.append(dp.bounds)
plot_infos.append(dict((k,config_f[k]) for k in ('label','line','color') if k in config_f))
# output the det configs first
if args.det_configs:
dp.output_det_statistics()
# evaluate in the AP vs. Time regime, unless told not to
if not args.no_apvst:
dets_table = ev.evaluate_vs_t(None,None,force=args.force)
#dets_table_whole,clses_table_whole = ev.evaluate_vs_t_whole(None,None,force=args.force)
if comm_rank==0:
dets_tables.append(dets_table)
#dets_tables_whole.append(dets_table_whole)
#clses_tables_whole.append(clses_table_whole)
# optionally, evaluate in the standard PR regime
if args.wholeset_prs:
ev.evaluate_detections_whole(None,force=args.force)
# and plot the comparison if multiple config files were given
if not args.no_apvst and len(configs)>1 and comm_rank==0:
# filename of the final plot is the config file name
dirname = config.get_evals_dir(dataset.get_name())
filename = args.config
if args.inverse_prior:
filename += '_inverse_prior'
# det avg
ff = opjoin(dirname, '%s_det_avg.png'%filename)
ff_nl = opjoin(dirname, '%s_det_avg_nl.png'%filename)
# make sure directory exists
ut.makedirs(os.path.dirname(ff))
Evaluation.plot_ap_vs_t(dets_tables, ff, all_bounds, with_legend=True, force=True, plot_infos=plot_infos)
Evaluation.plot_ap_vs_t(dets_tables, ff_nl, all_bounds, with_legend=False, force=True, plot_infos=plot_infos)
if False:
# det whole
ff = opjoin(dirname, '%s_det_whole.png'%filename)
ff_nl = opjoin(dirname, '%s_det_whole_nl.png'%filename)
Evaluation.plot_ap_vs_t(dets_tables_whole, ff, all_bounds, with_legend=True, force=True, plot_infos=plot_infos)
Evaluation.plot_ap_vs_t(dets_tables_whole, ff_nl, all_bounds, with_legend=False, force=True, plot_infos=plot_infos)
# cls whole
ff = opjoin(dirname, '%s_cls_whole.png'%filename)
ff_nl = opjoin(dirname, '%s_cls_whole_nl.png'%filename)
Evaluation.plot_ap_vs_t(clses_tables_whole, ff, all_bounds, with_legend=True, force=True, plot_infos=plot_infos)
Evaluation.plot_ap_vs_t(clses_tables_whole, ff_nl, all_bounds, with_legend=False, force=True, plot_infos=plot_infos)
def main():
parser = argparse.ArgumentParser(description='Execute different functions of our system')
parser.add_argument('mode',
choices=[
'window_stats', 'evaluate_metaparams', 'evaluate_jw',
'evaluate_get_pos_windows', 'train_svm',
'extract_sift','extract_assignments','extract_codebook',
'evaluate_jw_grid', 'final_metaparams',
'assemble_dpm_dets','ctfdet','assemble_ctf_dets'
])
parser.add_argument('--test_dataset', choices=['val','test','train'],
default='test', help='dataset to use for testing. the training dataset \
is automatically inferred (val->train and test->trainval).')
parser.add_argument('--first_n', type=int,
help='only take the first N images in the datasets')
parser.add_argument('--bounds', type=str,
help='the start_time and deadline_time for the ImagePolicy and corresponding evaluation. ex: (1,5)')
parser.add_argument('--name', help='name for this run')
parser.add_argument('--priors', default='random', help= \
"list of choice for the policy for selecting the next action. choose from random, oracle,fixed_order, no_smooth, backoff. ex: --priors=random,oracle,no_smooth")
parser.add_argument('--compare_evals', action='store_true',
default=False, help='plot all the priors modes given on same plot'),
parser.add_argument('--detector', choices=['perfect','perfect_with_noise', 'dpm','ctf'],
default='perfect', help='detector type')
parser.add_argument('--force', action='store_true',
default=False, help='force overwrite')
parser.add_argument('--gist', action='store_true',
default=False, help='use GIST as one of the actions')
parser.add_argument('--clear_tmp', action='store_true',
default=False, help='clear the cached windows folder before running'),
parser.add_argument('--feature_type', choices=['sift','dsift'],
default='dsift', help='use this feature type'),
parser.add_argument('--kernel', choices=['chi2','rbf'],
default='chi2', help='kernel to train svm on'),
args = parser.parse_args()
if args.priors:
args.priors = args.priors.split(',')
if args.bounds:
args.bounds = [float(x) for x in re.findall(r'\d+', args.bounds)]
assert(len(args.bounds)==2)
print(args)
# Load the dataset
dataset = Dataset('full_pascal_'+args.test_dataset)
if args.first_n:
dataset.images = dataset.images[:args.first_n]
# Infer train_dataset
if args.test_dataset=='test':
train_dataset = Dataset('full_pascal_trainval')
elif args.test_dataset=='val':
train_dataset = Dataset('full_pascal_train')
else:
print("Impossible, setting train_dataset to dataset")
train_dataset = dataset
# Create window generator
sw = SlidingWindows(dataset,train_dataset)
if args.clear_tmp:
dirname = config.get_sliding_windows_cached_dir(train_dataset.get_name())
shutil.rmtree(dirname)
dirname = config.get_sliding_windows_cached_dir(dataset.get_name())
shutil.rmtree(dirname)
if args.mode=='assemble_dpm_dets':
policy = DatasetPolicy(dataset,train_dataset,sw)
dets = policy.load_ext_detections(dataset,suffix='dpm_may25')
if args.mode=='assemble_ctf_dets':
policy = DatasetPolicy(dataset,train_dataset,sw)
dets = policy.load_ext_detections(dataset,'ctf','ctf_default')
dets = policy.load_ext_detections(dataset,'ctf','ctf_nohal')
dets = policy.load_ext_detections(dataset,'ctf', 'ctf_halfsize')
if args.mode=='evaluate_get_pos_windows':
evaluate_get_pos_windows(train_dataset)
return
if args.mode=='window_stats':
"Compute and plot the statistics of ground truth window parameters."
results = SlidingWindows.get_dataset_window_stats(train_dataset,plot=True)
if args.mode=='ctfdet':
"""Run Pedersoli's detector on the dataset and assemble into one Table."""
run_pedersoli(dataset)
if args.mode=='evaluate_jw':
"""
Evaluate the jumping window approach by producing plots of recall vs.
#windows.
"""
# TODO hack: both sw and jw should subclass something like WindowGenerator
jw = JumpingWindowsDetector(use_scale=True)
sw.jw = jw
#classes = dataset.classes
classes = ['car']
# classes = ['bicycle' ,'car','horse', 'sofa',\
# 'bird', 'chair', 'motorbike', 'train',\
# 'boat', 'cow', 'person', 'tvmonitor',\
# 'bottle','diningtable', 'pottedplant',\
# 'bus','dog' ,'sheep']
for cls_idx in range(comm_rank, len(classes), comm_size):
#for cls in dataset.classes:
cls = classes[cls_idx]
dirname = config.get_jumping_windows_dir(dataset.get_name())
filename = os.path.join(dirname,'%s'%cls)
sw.evaluate_recall(cls, filename, metaparams=None, mode='jw', plot=True)
if args.mode=='evaluate_jw_grid':
"""
Evaluate the jumping window approach by producing plots of recall vs.
#windows.
"""
sw = SlidingWindows(dataset,train_dataset)
jw = JumpingWindowsDetectorGrid()
sw.jw = jw
for cls in dataset.classes:
dirname = config.get_jumping_windows_dir(dataset.get_name())
filename = os.path.join(dirname,'%s'%cls)
if os.path.isfile(config.data_dir + 'JumpingWindows/'+cls):
sw.evaluate_recall(cls, filename, metaparams=None, mode='jw', plot=True)
if args.mode=='train_svm':
randomize = not os.path.exists('/home/tobibaum')
d = Dataset('full_pascal_train')
dtest = Dataset('full_pascal_val')
e = Extractor()
classes = config.pascal_classes
num_words = 3000
iters = 5
feature_type = 'dsift'
codebook_samples = 15
num_pos = 'max'
testsize = 'max'
if args.first_n:
num_pos = args.first_n
testsize = 1.5*num_pos
kernel = args.kernel
if comm_rank == 0:
ut.makedirs(config.data_dir + 'features/' + feature_type + '/times/')
ut.makedirs(config.data_dir + 'features/' + feature_type + '/codebooks/times/')
ut.makedirs(config.data_dir + 'features/' + feature_type + '/svms/train_times/')
for cls_idx in range(comm_rank, len(classes), comm_size):
#for cls in classes:
cls = classes[cls_idx]
codebook = e.get_codebook(d, feature_type)
pos_arr = d.get_pos_windows(cls)
neg_arr = d.get_neg_windows(pos_arr.shape[0], cls, max_overlap=0)
if not num_pos == 'max':
if not randomize:
pos_arr = pos_arr[:num_pos]
neg_arr = pos_arr[:num_pos]
else:
rand = np.random.random_integers(0, pos_arr.shape[0] - 1, size=num_pos)
pos_arr = pos_arr[rand]
rand = np.random.random_integers(0, neg_arr.shape[0] - 1, size=num_pos)
neg_arr = neg_arr[rand]
pos_table = Table(pos_arr, ['x','y','w','h','img_ind'])
neg_table = Table(neg_arr, pos_table.cols)
train_with_hard_negatives(d, dtest, num_words,codebook_samples,codebook,\
cls, pos_table, neg_table,feature_type, \
iterations=iters, kernel=kernel, L=2, \
testsize=testsize,randomize=randomize)
if args.mode=='evaluate_metaparams':
"""
Grid search over metaparams values for get_windows_new, with the AUC of
recall vs. # windows evaluation.
"""
sw.grid_search_over_metaparams()
return
if args.mode=='final_metaparams':
dirname = config.get_sliding_windows_metaparams_dir(train_dataset.get_name())
# currently these are the best auc/complexity params
best_params_for_classes = [
(62,15,12,'importance',0), #aeroplane
(83,15,12,'importance',0), #bicycle
(62,15,12,'importance',0), #bird
(62,15,12,'importance',0), #boat
(125,12,12,'importance',0), #bottle
(83,12,9,'importance',0), #bus
(125,15,9,'importance',0), #car
(125,12,12,'linear',0), #cat
(125,15,9,'importance',0), #chair
(125,9,6,'importance',0), #cow
(125,15,6,'linear',0), #diningtable
(62,15,12,'importance',0), #dog
(83,15,6,'importance',0), #horse
(83,12,6,'importance',0), #motorbike
(83,15,12,'importance',0), #person
(83,15,6,'importance',0), #pottedplant
(83,15,12,'importance',0), #sheep
(83,9,6,'importance',0), #sofa
(62,12,6,'importance',0), #train
(62,12,12,'importance',0), #tvmonitor
(125,9,12,'importance',0) #all
]
# ACTUALLY THEY ARE ALL THE SAME!
cheap_params = (62, 9, 6, 'importance', 0)
for i in range(comm_rank,dataset.num_classes(),comm_size):
cls = dataset.classes[i]
best_params = best_params_for_classes[i]
#samples,num_scales,num_ratios,mode,priority,cls = cheap_params
metaparams = {
'samples_per_500px': samples,
'num_scales': num_scales,
'num_ratios': num_ratios,
'mode': mode,
'priority': 0 }
filename = '%s_%d_%d_%d_%s_%d'%(
cls,
metaparams['samples_per_500px'],
metaparams['num_scales'],
metaparams['num_ratios'],
metaparams['mode'],
metaparams['priority'])
filename = os.path.join(dirname,filename)
tables = sw.evaluate_recall(cls,filename,metaparams,'sw',plot=True,force=False)
metaparams = {
'samples_per_500px': samples,
'num_scales': num_scales,
'num_ratios': num_ratios,
'mode': mode,
'priority': 1 }
filename = '%s_%d_%d_%d_%s_%d'%(
cls,
metaparams['samples_per_500px'],
metaparams['num_scales'],
metaparams['num_ratios'],
metaparams['mode'],
metaparams['priority'])
filename = os.path.join(dirname,filename)
tables = sw.evaluate_recall(cls,filename,metaparams,'sw',plot=True,force=False)
return
if args.mode=='extract_sift':
e=Extractor()
e.extract_all(['sift'], ['full_pascal_trainval','full_pascal_test'], 0, 0)
if args.mode=='extract_assignments':
e=Extractor()
feature_type = 'sift'
for image_set in ['full_pascal_trainval','full_pascal_test']:
d = Dataset(image_set)
codebook = e.get_codebook(d, feature_type)
print 'codebook loaded'
for img_ind in range(comm_rank,len(d.images),comm_size):
img = d.images[img_ind]
#for img in d.images:
e.get_assignments(np.array([0,0,img.size[0],img.size[1]]), feature_type, \
codebook, img)
if args.mode=='extract_codebook':
d = Dataset('full_pascal_trainval')
e = Extractor()
codebook = e.get_codebook(d, args.feature_type)
def setup(self):
d = Dataset('test_pascal_trainval', force=True)
d2 = Dataset('test_pascal_test', force=True)
config = {'detectors': ['csc_default']}
self.dp = DatasetPolicy(d, d2, **config)
self.bs = BeliefState(d, self.dp.actions)
