Пример #1
0
            "target_lr": 5e-10,
            "update_trigger": (15, 'epoch'),
            "stop_trigger": (90, 'epoch'),
        },
    ]

    # num_epochs = sum([phase["stop_trigger"][0] for phase in learning_rate_schedule])
    #
    # lr_shifter = TwoStateLearningRateShifter(args.learning_rate, learning_rate_schedule)

    trainer = get_trainer(
        net,
        updater,
        log_dir,
        fields_to_print,
        epochs=args.epochs,
        snapshot_interval=args.snapshot_interval,
        print_interval=args.log_interval,
        extra_extensions=(
            evaluator,
            model_snapshotter,
            bbox_plotter,
            # lr_shifter,
        ))

    if args.resume is not None:
        print("resuming training from {}".format(args.resume))
        chainer.serializers.load_npz(args.resume, trainer)

    trainer.run()
Пример #2
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        visualization_anchors=[["localization_net", "vis_anchor"], ["recognition_net", "vis_anchor"]]
    ), (1, 'iteration'))

    # create the trainer object and inject all extensions
    trainer = get_trainer(
        net,
        updater,
        log_dir,
        fields_to_print,
        epochs=args.epochs,
        snapshot_interval=args.snapshot_interval,
        print_interval=args.log_interval,
        extra_extensions=(
            evaluator,
            epoch_evaluator,
            model_snapshotter,
            bbox_plotter,
            (curriculum, (args.test_interval, 'iteration')),
        ),
        postprocess=log_postprocess,
        do_logging=args.no_log,
        model_files=[
            get_definition_filepath(localization_net),
            get_definition_filepath(recognition_net),
            get_definition_filepath(net),
        ]
    )

    # create interactive prompt that can be used to issue commands while the training is in progress
    open_interactive_prompt(
        bbox_plotter=bbox_plotter[0],
Пример #3
0
        send_bboxes=args.send_bboxes,
        upstream_port=args.port,
        visualization_anchors=[["localization_net", "vis_anchor"],
                               ["recognition_net",
                                "vis_anchor"]]), (1, 'iteration'))

    trainer = get_trainer(
        net,
        updater,
        log_dir,
        fields_to_print,
        epochs=args.epochs,
        snapshot_interval=args.snapshot_interval,
        print_interval=args.log_interval,
        extra_extensions=(
            evaluator,
            # epoch_evaluator,
            model_snapshotter,
            bbox_plotter,
            (curriculum, (args.test_interval, 'iteration')),
            # lr_shifter,
        ),
        postprocess=log_postprocess,
        do_logging=args.no_log,
    )

    open_interactive_prompt(
        bbox_plotter=bbox_plotter[0],
        curriculum=curriculum,
        # lr_shifter=lr_shifter[0],
    )