def main():
chainer.config.train = False
model = FastStyleNet()
serializers.load_npz(
'./chainer-fast-neuralstyle-models/models/starrynight.model', model)
input = './test1.jpg'
original = Image.open(input).convert('RGB')
print(original.size)
image = np.asarray(original, dtype=np.float32).transpose(2, 0, 1)
image = image.reshape((1, ) + image.shape)
padding = 0 #50
if padding > 0:
image = np.pad(
image, [[0, 0], [0, 0], [padding, padding], [padding, padding]],
'symmetric')
x = image
out = model(x)
out = out.data[0]
print(out.shape)
print('model done.')
postprocess(out)
print('export onnx...')
onnx_chainer.export(model, x, filename='FastStyleNet.onnx')
kanagawa = "../../resources/chainer-fast-neuralstyle-models/models/kanagawa.model"
sys.setrecursionlimit(10000)
parser = argparse.ArgumentParser()
parser.add_argument("--model", default=NSTModelPath.kanagawa.name, choices=[v.name for v in NSTModelPath])
parser.add_argument("--backend", default="webgpu", choices=["webgpu", "webassembly", "fallback"])
parser.add_argument("--encoding")
args = parser.parse_args()
print(f"model: {args.model}")
print(f"backend: {args.backend}")
print(f"encoding: {args.encoding}")
# Load chainer pre-trained model
model = FastStyleNet()
model_path = NSTModelPath[args.model].value
if not path.exists(model_path):
raise FileNotFoundError(f"Model data ({model_path}) is not found. Please clone " +
"'https://github.com/gafr/chainer-fast-neuralstyle-models' under the resource directory. " +
"Clone command takes about a few minute, the repository size is about 200MB.")
chainer.serializers.load_npz(model_path, model)
# Execute forward propagation to construct computation graph
if chainer.__version__ >= "2.":
with chainer.using_config("train", False): # fixes batch normalization
x = chainer.Variable(np.zeros((1, 3, 144, 192), dtype=np.float32))
y = model(x)
else: