def main():
args = get_args()
# Defining network first
x = nn.Variable((1, 3, 224, 224))
y = darknet19.darknet19_classification(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
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, 'c19', no_bn=True)
nn.save_parameters(args.output)
def main():
args = get_args()
from nnabla.ext_utils import get_extension_context
ctx = get_extension_context(args.context)
nn.set_default_context(ctx)
nn.load_parameters(args.weights)
x = nn.Variable((1, 3, args.size, args.size))
y = darknet19.darknet19_classification(x / 255, test=True)
label_names = np.loadtxt('imagenet.shortnames.list',
dtype=str,
delimiter=',')[:1000]
img = imread(args.input)
img = imresize(img, (args.size, args.size))
x.d = img.transpose(2, 0, 1).reshape(1, 3, args.size, args.size)
y.forward(clear_buffer=True)
# softmax
p = F.reshape(F.mul_scalar(F.softmax(y.data), 100), (y.size, ))
# Show top-5 prediction
inds = np.argsort(y.d.flatten())[::-1][:5]
for i in inds:
print('{}: {:.1f}%'.format(label_names[i], p.data[i]))
s = time.time()
n_time = 10
for i in range(n_time):
y.forward(clear_buffer=True)
# Invoking device-to-host copy to synchronize the device (if CUDA).
_ = y.d
print("Processing time: {:.1f} [ms/image]".format(
(time.time() - s) / n_time * 1000))