def main(argv):
try:
opts, args = getopt.getopt(argv,"frt")
except getopt.GetoptError:
print('pptGen.py -option where option [-f|-r|-t] f=read and extract features r=read existent features t=test')
sys.exit(2)
(pap, pres) = readData2()
for opt, arg in opts:
if opt == '-f':
featureExtractor = FeatureExtractor(pap, pres)
features = featureExtractor.extractFeatures()
pickle.dump(features, open(feature_file, 'wb'))
scores = featureExtractor.getImportanceMetrics()
pickle.dump(scores, open(score_file, 'wb'))
print(features)
print(scores)
if opt == '-r':
features = pickle.load(open( feature_file, "rb" ))
scores = pickle.load(open(score_file, "rb"))
models = torch.load(os.path.join(output_dir, 'regressor_detector'))
stats = torch.load(os.path.join(output_dir, 'stats_detector'))
# Detector Region Classifier initialization
classifier = falkon.FALKONWrapper(cfg_path=cfg_online_path)
regionClassifier = ocr.OnlineRegionClassifier(classifier,
None,
None,
stats,
cfg_path=cfg_online_path)
region_refiner = RegionRefiner(cfg_online_path)
else:
# Extract detector features for the train set
if not args.save_detector_features and not args.load_detector_features:
negatives, positives, COXY = feature_extractor.extractFeatures(
is_train=True,
output_dir=output_dir,
save_features=args.save_detector_features)
else:
if args.save_detector_features:
feature_extractor.extractFeatures(
is_train=True,
output_dir=output_dir,
save_features=args.save_detector_features)
positives, negatives = load_features_classifier(
features_dir=os.path.join(output_dir, 'features_detector'))
stats = computeFeatStatistics_torch(positives,
negatives,
features_dim=positives[0].size()[1],
cpu_tensor=args.CPU)
# Detector Region Classifier initialization
if not os.path.exists(output_dir):
os.mkdir(output_dir)
# Initialize feature extractor
feature_extractor = FeatureExtractor(cfg_target_task, train_in_cpu=args.CPU)
# Load detector models if requested, else train them
if args.load_detector_models:
model = torch.load(os.path.join(output_dir, 'classifier_detector'))
models = torch.load(os.path.join(output_dir, 'regressor_detector'))
stats = torch.load(os.path.join(output_dir, 'stats_detector'))
else:
# Extract detector features for the train set
if not args.save_detector_features and not args.load_detector_features:
negatives, positives, COXY, negatives_segmentation, positives_segmentation = feature_extractor.extractFeatures(is_train=True, output_dir=output_dir, save_features=args.save_detector_features, extract_features_segmentation=True, use_only_gt_positives_detection=args.use_only_gt_positives_detection)
del feature_extractor
torch.cuda.empty_cache()
# Detector Region Refiner initialization
region_refiner = RegionRefiner(cfg_online_path)
if not args.normalize_features_regressor_detector:
models = region_refiner.trainRegionRefiner(COXY, output_dir=output_dir)
del region_refiner
torch.cuda.empty_cache()
if not args.use_only_gt_positives_detection:
positives = load_positives_from_COXY(COXY)
# Delete already used data
