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 __init__(self,
test_dataset,
train_dataset,
weights_dataset_name=None,
**kwargs):
"""
Initialize the DatasetPolicy, getting it ready to run on the whole dataset
or a single image.
- test, train, weights datasets should be:
- val, train, val: for training the weights
- test, trainval, val: for final run
- **kwargs update the default config
"""
config = copy.copy(DatasetPolicy.default_config)
config.update(kwargs)
self.dataset = test_dataset
self.train_dataset = train_dataset
if not weights_dataset_name:
weights_dataset_name = self.dataset.name
self.weights_dataset_name = weights_dataset_name
self.__dict__.update(config)
print("DatasetPolicy running with:")
pprint(self.__dict__)
self.ev = Evaluation(self)
self.tt = ut.TicToc()
# Determine inference mode:
# fixed_order, random, oracle policy modes get fixed_order inference mode
if re.search('fastinf', self.policy_mode):
self.inference_mode = 'fastinf'
elif self.policy_mode == 'random':
self.inference_mode = 'random'
elif self.policy_mode in ['no_smooth', 'backoff']:
self.inference_mode = self.policy_mode
else:
self.inference_mode = 'fixed_order'
# Determine fastinf model name
self.fastinf_model_name = 'this_is_empty'
if self.inference_mode == 'fastinf':
if self.detectors == ['csc_default']:
self.fastinf_model_name = 'CSC'
elif self.detectors == ['perfect']:
self.fastinf_model_name = 'perfect'
elif self.detectors == ['gist']:
self.fastinf_model_name = 'GIST'
elif self.detectors == ['gist', 'csc_default']:
self.fastinf_model_name = 'GIST_CSC'
else:
raise RuntimeError("""
We don't have Fastinf models for the detector combination you
are running with: %s""" % self.detectors)
# load the actions and the corresponding weights and we're ready to go
self.test_actions = self.init_actions()
self.actions = self.test_actions
self.load_weights()
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)
