def main(args=None):
if len(sys.argv) is 3:
model_path = str(sys.argv[1])
dataset_path = str(sys.argv[2])
else:
print(
"Pass model path and dataset path in respectively as command line argument"
)
exit()
from generators.pascal import PascalVocGenerator
from model import efficientdet
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
phi = 4
weighted_bifpn = False
common_args = {
'batch_size': 1,
'phi': phi,
}
test_generator = PascalVocGenerator(dataset_path,
'test',
shuffle_groups=False,
skip_truncated=False,
skip_difficult=True,
**common_args)
model_path = model_path
input_shape = (test_generator.image_size, test_generator.image_size)
anchors = test_generator.anchors
num_classes = test_generator.num_classes()
model, prediction_model = efficientdet(phi=phi,
num_classes=num_classes,
weighted_bifpn=weighted_bifpn)
prediction_model.load_weights(model_path, by_name=True)
average_precisions = evaluate(test_generator,
prediction_model,
visualize=False)
# compute per class average precision
total_instances = []
precisions = []
for label, (average_precision,
num_annotations) in average_precisions.items():
print('{:.0f} instances of class'.format(num_annotations),
test_generator.label_to_name(label),
'with average precision: {:.4f}'.format(average_precision))
total_instances.append(num_annotations)
precisions.append(average_precision)
mean_ap = sum(precisions) / sum(x > 0 for x in total_instances)
print('mAP: {:.4f}'.format(mean_ap))
}
test_generator = PascalVocGenerator('datasets/VOC2007',
'test',
shuffle_groups=False,
skip_truncated=False,
skip_difficult=True,
**common_args)
model_path = 'checkpoints/2019-12-03/pascal_05_0.6283_1.1975_0.8029.h5'
input_shape = (test_generator.image_size, test_generator.image_size)
anchors = test_generator.anchors
num_classes = test_generator.num_classes()
model, prediction_model = efficientdet(phi=phi,
num_classes=num_classes,
weighted_bifpn=weighted_bifpn)
prediction_model.load_weights(model_path, by_name=True)
average_precisions = evaluate(test_generator,
prediction_model,
visualize=False)
# compute per class average precision
total_instances = []
precisions = []
for label, (average_precision,
num_annotations) in average_precisions.items():
print('{:.0f} instances of class'.format(num_annotations),
test_generator.label_to_name(label),
'with average precision: {:.4f}'.format(average_precision))
total_instances.append(num_annotations)
precisions.append(average_precision)
mean_ap = sum(precisions) / sum(x > 0 for x in total_instances)
print('mAP: {:.4f}'.format(mean_ap))
weighted_bifpn = False
common_args = {
'batch_size': 1,
'phi': phi,
}
test_generator = PascalVocGenerator(
'datasets/VOC2007',
'test',
shuffle_groups=False,
skip_truncated=False,
skip_difficult=True,
**common_args
)
model_path = 'checkpoints/2019-12-03/pascal_05_0.6283_1.1975_0.8029.h5'
input_shape = (test_generator.image_size, test_generator.image_size)
anchors = test_generator.anchors
num_classes = test_generator.num_classes()
model, prediction_model = efficientdet(phi=phi, num_classes=num_classes, weighted_bifpn=weighted_bifpn)
prediction_model.load_weights(model_path, by_name=True)
average_precisions = evaluate(test_generator, prediction_model, visualize=False)
# compute per class average precision
total_instances = []
precisions = []
for label, (average_precision, num_annotations) in average_precisions.items():
print('{:.0f} instances of class'.format(num_annotations), test_generator.label_to_name(label),
'with average precision: {:.4f}'.format(average_precision))
total_instances.append(num_annotations)
precisions.append(average_precision)
mean_ap = sum(precisions) / sum(x > 0 for x in total_instances)
print('mAP: {:.4f}'.format(mean_ap))