def add_rendering_args(parser):
group = parser.add_argument_group("Rendering")
options.add_common_eval_args(group)
group.add_argument("--render-beam",
default=5,
type=int,
metavar="N",
help="beam size for parallel rendering")
group.add_argument("--render-resolution",
default="512x512",
type=str,
metavar="N",
help='if provide two numbers, means H x W')
group.add_argument("--render-angular-speed",
default=1,
type=float,
metavar="D",
help="angular speed when rendering around the object")
group.add_argument("--render-num-frames",
default=500,
type=int,
metavar="N")
group.add_argument("--render-path-style",
default="circle",
choices=["circle", "zoomin_circle", "zoomin_line"],
type=str)
group.add_argument("--render-path-args",
default="{'radius': 2.5, 'h': 0.0}",
help="specialized arguments for rendering paths")
group.add_argument("--render-output", default=None, type=str)
group.add_argument("--render-at-vector", default="(0,0,0)", type=str)
group.add_argument("--render-up-vector", default="(0,0,-1)", type=str)
group.add_argument("--render-output-types",
nargs="+",
type=str,
default=["color"],
choices=[
"target", "color", "depth", "normal", "voxel",
"predn", "point", "featn2", "vcolors"
])
group.add_argument("--render-raymarching-steps", default=None, type=int)
group.add_argument("--render-save-fps", default=24, type=int)
group.add_argument("--render-combine-output",
action='store_true',
help="if set, concat the images into one file.")
group.add_argument("--render-camera-poses",
default=None,
type=str,
help="text file saved for the testing trajectories")
group.add_argument("--render-camera-intrinsics", default=None, type=str)
group.add_argument(
"--render-views",
type=str,
default=None,
help=
"views sampled for rendering, you can set specific view id, or a range"
)
def get_lm_scorer_parser(default_task='language_modeling'):
parser = options.get_parser('Evaluate Language Model', default_task)
options.add_dataset_args(parser, gen=True)
options.add_common_eval_args(parser)
add_lm_scorer_args(parser)
return parser
for arg in vars(parsed_args).keys():
setattr(args, arg, getattr(parsed_args, arg))
task = tasks.setup_task(args)
# Load dataset splits
task.load_dataset(args.gen_subset)
print('| {} {} {} examples'.format(args.data, args.gen_subset,
len(task.dataset(args.gen_subset))))
eval_dataset(task, model, task.dataset(args.gen_subset), args.out_file,
args.thresholds, args.compute_metrics, use_cuda)
if __name__ == '__main__':
parser = options.get_parser('Evaluate Single Sentence Classifier',
'sentence_classification')
options.add_common_eval_args(parser)
options.add_dataset_args(parser, gen=True)
parser.add_argument('--out-file', type=str, help='output filename')
parser.add_argument('--thresholds',
nargs='+',
type=float,
help='thresholds to try or use')
parser.add_argument(
'--compute-metrics',
action='store_true',
help='if set, uses the labels to compute metrics for each threshold')
args = options.parse_args_and_arch(parser)
main(args)
def add_generation_args(parser):
group = parser.add_argument_group('Generation')
add_common_eval_args(group)
# fmt: off
#### bert sampling options ####
group.add_argument('--decoding-strategy',
default='left_to_right',
choices=STRATEGY_REGISTRY.keys())
group.add_argument('--gold-target-len',
action='store_true',
help='use gold target length')
group.add_argument('--dehyphenate',
action='store_true',
help='turn hyphens into independent tokens')
parser.add_argument('--decoding-iterations',
default=None,
type=int,
metavar='N',
help='number of decoding iterations in mask-predict')
group.add_argument('--length-beam',
default=5,
type=int,
metavar='N',
help='length beam size')
#### other generation options ####
group.add_argument('--beam',
default=5,
type=int,
metavar='N',
help='beam size')
group.add_argument('--nbest',
default=1,
type=int,
metavar='N',
help='number of hypotheses to output')
group.add_argument('--max-len-a',
default=0,
type=float,
metavar='N',
help=('generate sequences of maximum length ax + b, '
'where x is the source length'))
group.add_argument('--max-len-b',
default=200,
type=int,
metavar='N',
help=('generate sequences of maximum length ax + b, '
'where x is the source length'))
group.add_argument('--min-len',
default=1,
type=float,
metavar='N',
help=('minimum generation length'))
group.add_argument('--match-source-len',
default=False,
action='store_true',
help=('generations should match the source length'))
group.add_argument('--no-early-stop',
action='store_true',
help=('continue searching even after finalizing k=beam '
'hypotheses; this is more correct, but increases '
'generation time by 50%%'))
group.add_argument('--unnormalized',
action='store_true',
help='compare unnormalized hypothesis scores')
group.add_argument('--no-beamable-mm',
action='store_true',
help='don\'t use BeamableMM in attention layers')
group.add_argument(
'--lenpen',
default=1,
type=float,
help='length penalty: <1.0 favors shorter, >1.0 favors longer sentences'
)
group.add_argument(
'--unkpen',
default=0,
type=float,
help='unknown word penalty: <0 produces more unks, >0 produces fewer')
group.add_argument(
'--replace-unk',
nargs='?',
const=True,
default=None,
help=
'perform unknown replacement (optionally with alignment dictionary)')
group.add_argument('--sacrebleu',
action='store_true',
help='score with sacrebleu')
group.add_argument('--score-reference',
action='store_true',
help='just score the reference translation')
group.add_argument(
'--prefix-size',
default=0,
type=int,
metavar='PS',
help='initialize generation by target prefix of given length')
group.add_argument(
'--no-repeat-ngram-size',
default=0,
type=int,
metavar='N',
help=
'ngram blocking such that this size ngram cannot be repeated in the generation'
)
group.add_argument('--sampling',
action='store_true',
help='sample hypotheses instead of using beam search')
group.add_argument(
'--sampling-topk',
default=-1,
type=int,
metavar='PS',
help='sample from top K likely next words instead of all words')
group.add_argument(
'--sampling-topp',
default=-1.0,
type=float,
metavar='PS',
help=
'sample from the smallest set whose cumulative probability mass exceeds p for next words'
)
group.add_argument('--temperature',
default=1.,
type=float,
metavar='N',
help='temperature for generation')
group.add_argument('--diverse-beam-groups',
default=-1,
type=int,
metavar='N',
help='number of groups for Diverse Beam Search')
group.add_argument(
'--diverse-beam-strength',
default=0.5,
type=float,
metavar='N',
help='strength of diversity penalty for Diverse Beam Search')
group.add_argument(
'--print-alignment',
action='store_true',
help=
'if set, uses attention feedback to compute and print alignment to source tokens'
)
# fmt: on
return group