24bf232753b4b34b6d4164d5a647dd17d16348b2,example/convert_resnet/convert.py,,main,#,23

Before Change

    parser.add_argument("--encoding")
    args = parser.parse_args()

    sample_image = PIL.Image.open("../../resources/imagenet/ILSVRC2012_val_00000001.JPEG")
    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 = ChainerGraphConverter()
    graph = converter.convert(chainer_cg, [nn_input], [nn_output])  // type: Graph

    graph_exec_data = generate_descriptor(args.backend, graph, constant_encoder_name=args.encoding)

    os.makedirs(OUTPUT_DIR, exist_ok=True)

    graph_exec_data.save(OUTPUT_DIR)

    with open(path.join(OUTPUT_DIR, "image_nhwc.json".format()), "w") as f:
        image_nhwc = np.transpose(prepared_image, (1, 2, 0))
        json.dump(image_nhwc.flatten().tolist(), f)

    with open(path.join(OUTPUT_DIR, "fc6.json".format()), "w") as f:
        json.dump(nn_output.data.tolist(), f)

After Change

    parser.add_argument("--encoding")
    args = parser.parse_args()

    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

Instances