def main()

in baselines/gist_baseline.py [0:0]

• 90 lines of code
• 16 McCabe index (conditional complexity)

def main():

    parser = argparse.ArgumentParser()

    def aa(*args, **kwargs):
        group.add_argument(*args, **kwargs)

    group = parser.add_argument_group('runtime options')
    aa('--nproc', default=0, type=int, help="number of subprocesses to use")
    aa('--giststream_exec',
        default="lear_gist-1.2/compute_gist_stream",
        help="executable that extracts GIST features")

    group = parser.add_argument_group('feature extraction options')
    aa('--transpose', default=-1, type=int, help="one of the 7 PIL transpose options ")
    aa('--train_pca', default=False, action="store_true", help="run PCA training")
    aa('--pca_file', default="", help="File with PCA descriptors")
    aa('--pca_dim', default=256, type=int, help="output dimension for PCA")
    aa('--pca_white', default=0.0, type=float, help="set to -0.5 to whiten PCA")

    group = parser.add_argument_group('dataset options')
    aa('--file_list', required=True, help="CSV file with image filenames")
    aa('--image_dir', default="", help="search image files in this directory")
    aa('--n_train_pca', default=10000, type=int, help="nb of training vectors for the PCA")
    aa('--i0', default=0, type=int, help="first image to process")
    aa('--i1', default=-1, type=int, help="last image to process + 1")

    group = parser.add_argument_group('output options')
    aa('--o', default="/tmp/desc.hdf5", help="write trained features to this file")

    args = parser.parse_args()
    print("args=", args)

    print("reading image names from", args.file_list)

    if 'Linux' in platform.platform():
        os.system(
            'echo hardware_image_description: '
            '$( cat /proc/cpuinfo | grep ^"model name" | tail -1 ), '
            '$( cat /proc/cpuinfo | grep ^processor | wc -l ) cores'
        )
    else:
        print("hardware_image_description:", platform.machine(), "nb of threads:", args.nproc)

    image_ids = [l.strip() for l in open(args.file_list, "r")]

    if args.i1 == -1:
        args.i1 = len(image_ids)
    image_ids = image_ids[args.i0:args.i1]

    # full path name for the image
    image_dir = args.image_dir
    if not image_dir.endswith('/'):
        image_dir += "/"

    # add jpg suffix if there is none
    image_list = [
        image_dir + fname if "." in fname else image_dir + fname + ".jpg"
        for fname in image_ids
    ]

    print(f"  found {len(image_list)} images")

    if args.train_pca:
        rs = np.random.RandomState(123)
        image_list = [
            image_list[i]
            for i in rs.choice(len(image_list), size=args.n_train_pca, replace=False)
        ]
        print(f"subsampled {args.n_train_pca} vectors")

    print("computing features")

    gist = GISTFeatures(
        execname=args.giststream_exec,
        transpose=args.transpose
    )

    t0 = time.time()

    if args.nproc == 0:
        all_desc = gist.compute_features(image_list)
    else:
        pool = ProcessPool(args.nproc)
        n = len(image_list)
        nproc = args.nproc
        sub_lists = [
            image_list[i * n // nproc : (i + 1) * n // nproc]
            for i in range(nproc)
        ]
        all_desc = list(pool.map(gist.compute_features, sub_lists))
        all_desc = np.vstack(all_desc)

    # normalization is important
    faiss.normalize_L2(all_desc)

    t1 = time.time()
    print()
    print(f"image_description_time: {(t1 - t0) / len(image_list):.5f} s per image")

    if args.train_pca:
        d = all_desc.shape[1]
        pca = faiss.PCAMatrix(d, args.pca_dim, args.pca_white)
        print(f"Train PCA {pca.d_in} -> {pca.d_out}")
        pca.train(all_desc)
        print(f"Storing PCA to {args.pca_file}")
        faiss.write_VectorTransform(pca, args.pca_file)
    elif args.pca_file:
        print("Load PCA matrix", args.pca_file)
        pca = faiss.read_VectorTransform(args.pca_file)
        print(f"Apply PCA {pca.d_in} -> {pca.d_out}")
        all_desc = pca.apply_py(all_desc)


    if not args.train_pca:
        print(f"writing descriptors to {args.o}")
        write_hdf5_descriptors(all_desc, image_ids, args.o)