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_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 __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():
dataset = Dataset('full_pascal_trainval')
fm = FastinfModel(dataset, 'perfect', 20)
# NOTE: just took values from a run of the thing
prior_correct = [
float(x) for x in
"0.050543 0.053053 0.073697 0.038331 0.050954 0.041879 0.16149\
0.068721 0.10296 0.026837 0.043779 0.087683 0.063447 0.052205\
0.41049 0.051664 0.014211 0.068361 0.056969 0.05046".split()
]
np.testing.assert_almost_equal(fm.p_c, prior_correct, 4)
observations = np.zeros(20)
taken = np.zeros(20)
fm.update_with_observations(taken, observations)
np.testing.assert_almost_equal(fm.p_c, prior_correct, 4)
observations[5] = 1
taken[5] = 1
fm.update_with_observations(taken, observations)
print fm.p_c
correct = [
float(x) for x in
"0.027355 0.11855 0.027593 0.026851 0.012569 0.98999 0.52232\
0.017783 0.010806 0.015199 0.0044641 0.02389 0.033602 0.089089\
0.50297 0.0083272 0.0088274 0.0098522 0.034259 0.0086298".split()
]
np.testing.assert_almost_equal(fm.p_c, correct, 4)
observations[15] = 0
taken[15] = 1
fm.update_with_observations(taken, observations)
correct = [
float(x) for x in
"2.73590000e-02 1.19030000e-01 2.75500000e-02 2.68760000e-02 \
1.23920000e-02 9.90200000e-01 5.25320000e-01 1.76120000e-02 \
1.05030000e-02 1.52130000e-02 4.26410000e-03 2.38250000e-02 \
3.36870000e-02 8.96450000e-02 5.04300000e-01 8.71880000e-05 \
8.82630000e-03 9.55290000e-03 3.43240000e-02 8.44510000e-03".split()
]
np.testing.assert_almost_equal(fm.p_c, correct)
# reinit_marginals
fm.reset()
np.testing.assert_equal(fm.p_c, prior_correct)
print(fm.cache)
def simply_run_it(dataset):
parser = argparse.ArgumentParser(
description="Run fastInf experiments.")
parser.add_argument('-m',type=int,
default=0,
choices=[0,1,2,3,4,5],
help="""optimization method 0-FR, 1-PR, 2-BFGS, 3-STEEP, 4-NEWTON, 5-GRADIENT (0).""")
parser.add_argument('-r',type=int,
default=1,
help="""parameter of L1 regularization.""")
args = parser.parse_args()
m = args.m
r = args.r
d = Dataset(dataset)
suffixs = ['CSC_regions']
run_fastinf_different_settings(d, [m], [r], suffixs)
return table
def conv(d_train, table_arr):
table = Table()
#table_arr = cPickle.load(open('table_linear_5','r'))
table.arr = np.hstack(
(table_arr, np.array(np.arange(table_arr.shape[0]), ndmin=2).T))
table.cols = d_train.classes + ['img_ind']
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))
return np.zeros((1,intervals+1))
dpm = feats.subset(['score', 'cls_ind', 'img_ind'])
img_dpm = dpm.filter_on_column('img_ind', img, omit=True)
if img_dpm.arr.size == 0:
print 'empty vector'
return np.zeros((1,intervals+1))
cls_dpm = img_dpm.filter_on_column('cls_ind', cls, omit=True)
hist = self.compute_histogram(cls_dpm.arr, intervals, lower, upper)
vector = np.zeros((1, intervals+1))
vector[0,0:-1] = hist
vector[0,-1] = img_dpm.shape[0]
return vector
if __name__=='__main__':
train_set = 'full_pascal_train'
train_dataset = Dataset(train_set)
dpm_dir = os.path.join(config.res_dir, 'dpm_dets')
filename = os.path.join(dpm_dir, train_set + '_dets_all_may25_DP.npy')
dpm_train = np.load(filename)
dpm_train = dpm_train[()]
dpm_train = dpm_train.subset(['score', 'cls_ind', 'img_ind'])
dpm_classif = DPMClassifier()
dpm_train.arr = dpm_classif.normalize_dpm_scores(dpm_train.arr)
val_set = 'full_pascal_val'
test_dataset = Dataset(val_set)
dpm_test_dir = os.path.join(config.res_dir, 'dpm_dets')
filename = os.path.join(dpm_dir, val_set + '_dets_all_may25_DP.npy')
dpm_test = np.load(filename)
dpm_test = dpm_test[()]
dpm_test = dpm_test.subset(['score', 'cls_ind', 'img_ind'])
""" Runner script to output cooccurrence statistics for the synthetic and PASCAL datasets. """ from skvisutils import Dataset datasets = [ 'synthetic', 'full_pascal_train','full_pascal_trainval', 'full_pascal_val','full_pascal_test'] for dataset in datasets: d = Dataset(dataset) f = d.plot_coocurrence() f = d.plot_coocurrence(second_order=True) f = d.plot_distribution()
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(config, 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 mpi.mpi.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 mpi.mpi.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 = os.path.join(dirname, '%s_det_avg.png' % filename)
ff_nl = os.path.join(dirname, '%s_det_avg_nl.png' % filename)
# make sure directory exists
skutil.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 = os.path.join(dirname, '%s_det_whole.png' % filename)
ff_nl = os.path.join(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 = os.path.join(dirname, '%s_cls_whole.png' % filename)
ff_nl = os.path.join(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 setUp(self):
self.train_dataset = Dataset(
test_config, 'test_pascal_train').load_from_pascal('train')
self.dataset = Dataset(
test_config, 'test_pascal_val').load_from_pascal('val')
def setUp(self):
d = Dataset(test_config, 'test_pascal_trainval').load_from_pascal('trainval', force=True)
d2 = Dataset(test_config, 'test_pascal_test').load_from_pascal('test', force=True)
config = {'detectors': ['csc_default']}
self.dp = DatasetPolicy(test_config, d, d2, **config)
self.bs = BeliefState(d, self.dp.actions)
