def main():
'''Main.
'''
# Parse arguments
args = get_args()
# Create YOLOv2 detection network
x = nn.Variable((1, 3, args.width, args.width))
y = yolov2.yolov2(x, args.anchors, args.classes, test=True)
params = nn.get_parameters(grad_only=False)
# Parse network parameters
dn_weights = parser.load_weights_raw(args.input)
cursor = 0
for i in range(1, 19): # 1 to 18
cursor = parser.load_convolutional_and_get_next_cursor(
dn_weights, cursor, params, 'c{}'.format(i))
cursor = parser.load_convolutional_and_get_next_cursor(
dn_weights, cursor, params, 'c18_19')
cursor = parser.load_convolutional_and_get_next_cursor(
dn_weights, cursor, params, 'c18_20')
cursor = parser.load_convolutional_and_get_next_cursor(
dn_weights, cursor, params, 'c13_14')
cursor = parser.load_convolutional_and_get_next_cursor(
dn_weights, cursor, params, 'c21')
cursor = parser.load_convolutional_and_get_next_cursor(dn_weights,
cursor,
params,
'detection',
no_bn=True)
assert cursor == dn_weights.size
# Save to a h5 file
nn.save_parameters(args.output)
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)