def main():
sys.setrecursionlimit(10000) # workaround for deep copying large graph
parser = argparse.ArgumentParser()
parser.add_argument("--model",
default="resnet50",
choices=["vgg16", "resnet50"])
parser.add_argument("--backend", default="webgpu,webassembly,fallback")
parser.add_argument("--encoding")
parser.add_argument('--out',
'-o',
default='output_chainer',
help='Directory to output the graph descriptor')
args = parser.parse_args()
os.makedirs(args.out, exist_ok=True)
sample_image = np.zeros((224, 224, 3),
dtype=np.uint8) # PIL.Image.open("")
if args.model == "vgg16":
link = chainer.links.model.vision.vgg.VGG16Layers()
prepared_image = chainer.links.model.vision.vgg.prepare(
sample_image) # BGR, CHW
out_layer_name = "fc8"
elif args.model == "resnet50":
link = chainer.links.model.vision.resnet.ResNet50Layers()
prepared_image = chainer.links.model.vision.resnet.prepare(
sample_image)
out_layer_name = "fc6"
nn_input = chainer.Variable(np.array([prepared_image], dtype=np.float32))
nn_output = link(nn_input, layers=[
out_layer_name
])[out_layer_name] # 'prob' is also possible (uses softmax)
chainer_cg = chainer.computational_graph.build_computational_graph(
[nn_output])
converter = ChainerConverter()
graph = converter.convert(chainer_cg, [nn_input],
[nn_output]) # type: Graph
any_backend_failed = False
last_backend_exception = None
for backend in args.backend.split(","):
try:
graph_exec_data = generate_descriptor(
backend, graph, constant_encoder_name=args.encoding)
graph_exec_data.save(args.out)
except Exception as ex:
any_backend_failed = True
last_backend_exception = ex
console.error(
f"Failed generating descriptor for backend {backend}: {str(ex)}\n"
)
if any_backend_failed:
raise last_backend_exception
def main():
sys.setrecursionlimit(10000) # workaround for deep copying large graph
parser = argparse.ArgumentParser()
# default is Caffenet of Caffe example
parser.add_argument("caffemodel")
parser.add_argument("--backend",
default="webgpu,webassembly,fallback",
help="comma-separated list of backends")
parser.add_argument("--input_name", help="blob name for input (mandatory)")
parser.add_argument(
"--input_shape",
help="shape of blobs for inputs (example: '(1,3,224,224)')")
parser.add_argument("--input_npy",
help="npy file containing sample inputs")
parser.add_argument(
"--output_names",
required=True,
help="comma-separated blob name for output (mandatory)")
parser.add_argument(
"--out",
help="output directory (default: <model>/webdnn_graph_descriptor)")
parser.add_argument("--encoding", help="name of weight encoder")
args = parser.parse_args()
# multiple blob input can be easily implemented, but command-line arguments becomes complicated.
input_blob, input_filled = parse_input_blob(args)
output_names = args.output_names.split(",")
console.stderr(
"[convert_caffe] Loading caffe model... (usually takes several minutes)"
)
link = chainer.links.caffe.CaffeFunction(args.caffemodel)
console.stderr("[convert_caffe] Generating feedforward graph")
if chainer.__version__ >= "2.":
chainer.using_config("train", False)
output_blobs = list(
link(inputs={args.input_name: input_blob},
outputs=output_names)) # list of Variable
else:
output_blobs = list(
link(inputs={args.input_name: input_blob},
outputs=output_names,
train=False)) # list of Variable
chainer_cg = chainer.computational_graph.build_computational_graph(
output_blobs)
converter = ChainerConverter()
graph = converter.convert(chainer_cg, [input_blob],
output_blobs) # type: Graph
if args.out:
output_dir = args.out
else:
output_dir = path.join(path.dirname(args.caffemodel),
"webdnn_graph_descriptor")
os.makedirs(output_dir, exist_ok=True)
if input_filled:
# save output of Caffe Network (not required for inference)
output_arrays = {
output_name: output_blob.data
for output_name, output_blob in zip(output_names, output_blobs)
}
np.savez(path.join(output_dir, "example_output.npz"), **output_arrays)
console.stderr("[convert_caffe] Generating descriptors")
any_backend_failed = False
for backend in args.backend.split(","):
try:
graph_exec_data = generate_descriptor(
backend, graph, constant_encoder_name=args.encoding)
graph_exec_data.save(output_dir)
except Exception as ex:
any_backend_failed = True
console.error(
f"[convert_caffe] Failed generating descriptor for backend {backend}: {str(ex)}"
)
if any_backend_failed:
sys.exit(1)
def generate_graph_model1(caption_net):
resnet_in, resnet_out = get_resnet_io_variable()
image_vec_out = caption_net.image_vec(resnet_out)
converter = ChainerConverter()
graph = converter.convert([resnet_in], [image_vec_out])
return graph
