def yolov2(x, num_anchors, num_classes, test=False, feature_dict=None):
feature_dict_d = {}
c18 = darknet19_feature(x, test=test, feature_dict=feature_dict_d)
c13 = feature_dict_d['c13']
c21 = yolov2_feature(c13, c18, test=test, feature_dict=feature_dict)
det = yolov2_detection_layer(c21, num_anchors, num_classes)
if feature_dict is not None:
feature_dict.update(feature_dict_d)
feature_dict['c21'] = c21
feature_dict['det'] = det
return det
def main():
args = get_args()
# Defining network first
x = nn.Variable((1, 3, 224, 224))
y = darknet19.darknet19_feature(x / 255, test=True)
# Get NNabla parameters
params = nn.get_parameters(grad_only=False)
# Parse Darknet weights and store them into NNabla params
dn_weights = parser.load_weights_raw(args.input)
cursor = 0
for i in range(1, 19): # 1 to 18
print("Layer", i)
cursor = parser.load_convolutional_and_get_next_cursor(
dn_weights, cursor, params, 'c{}'.format(i))
nn.save_parameters(args.output)