当前位置: 首页>>代码示例>>Python>>正文


Python tensorpack.QueueInput方法代码示例

本文整理汇总了Python中tensorpack.QueueInput方法的典型用法代码示例。如果您正苦于以下问题:Python tensorpack.QueueInput方法的具体用法?Python tensorpack.QueueInput怎么用?Python tensorpack.QueueInput使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在tensorpack的用法示例。


在下文中一共展示了tensorpack.QueueInput方法的6个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: get_config

# 需要导入模块: import tensorpack [as 别名]
# 或者: from tensorpack import QueueInput [as 别名]
def get_config(model, fake=False, data_aug=True):
    nr_tower = max(get_nr_gpu(), 1)
    batch = TOTAL_BATCH_SIZE // nr_tower

    if fake:
        logger.info("For benchmark, batch size is fixed to 64 per tower.")
        dataset_train = FakeData(
            [[64, 224, 224, 3], [64]], 1000, random=False, dtype='uint8')
        callbacks = []
    else:
        logger.info("Running on {} towers. Batch size per tower: {}".format(nr_tower, batch))
        dataset_train = get_data('train', batch, data_aug)
        dataset_val = get_data('val', batch, data_aug)
        callbacks = [
            ModelSaver(),
        ]
        if data_aug:
            callbacks.append(ScheduledHyperParamSetter('learning_rate',
                                                       [(30, 1e-2), (60, 1e-3), (85, 1e-4), (95, 1e-5), (105, 1e-6)]))
        callbacks.append(HumanHyperParamSetter('learning_rate'))
        infs = [ClassificationError('wrong-top1', 'val-error-top1'),
                ClassificationError('wrong-top5', 'val-error-top5')]
        if nr_tower == 1:
            # single-GPU inference with queue prefetch
            callbacks.append(InferenceRunner(QueueInput(dataset_val), infs))
        else:
            # multi-GPU inference (with mandatory queue prefetch)
            callbacks.append(DataParallelInferenceRunner(
                dataset_val, infs, list(range(nr_tower))))

    return AutoResumeTrainConfig(
        model=model,
        dataflow=dataset_train,
        callbacks=callbacks,
        steps_per_epoch=5000 if TOTAL_BATCH_SIZE == 256 else 10000,
        max_epoch=110 if data_aug else 64,
        nr_tower=nr_tower
    ) 
开发者ID:microsoft,项目名称:LQ-Nets,代码行数:40,代码来源:imagenet.py

示例2: get_config

# 需要导入模块: import tensorpack [as 别名]
# 或者: from tensorpack import QueueInput [as 别名]
def get_config(model, option):
    dataset_train = get_data('train', option)
    dataset_val = get_data('val', option)
    callbacks = get_callbacks(dataset_val, option)
    steps_per_epoch = get_steps_per_epoch(option)

    return TrainConfig(
        model=model,
        data=StagingInput(QueueInput(dataset_train), nr_stage=1),
        callbacks=callbacks,
        steps_per_epoch=steps_per_epoch,
        max_epoch=option.epoch,
    ) 
开发者ID:junsukchoe,项目名称:ADL,代码行数:15,代码来源:train.py

示例3: get_config

# 需要导入模块: import tensorpack [as 别名]
# 或者: from tensorpack import QueueInput [as 别名]
def get_config(model, fake=False):
    nr_tower = max(get_nr_gpu(), 1)
    assert args.batch % nr_tower == 0
    batch = args.batch // nr_tower

    if fake:
        logger.info("For benchmark, batch size is fixed to 64 per tower.")
        dataset_train = FakeData(
            [[64, 224, 224, 3], [64]], 1000, random=False, dtype='uint8')
        callbacks = []
    else:
        logger.info("Running on {} towers. Batch size per tower: {}".format(nr_tower, batch))
        dataset_train = get_data('train', batch)
        dataset_val = get_data('val', batch)

        BASE_LR = 0.1 * (args.batch / 256.0)
        callbacks = [
            ModelSaver(),
            ScheduledHyperParamSetter(
                'learning_rate', [(0, BASE_LR), (30, BASE_LR * 1e-1), (60, BASE_LR * 1e-2),
                                  (90, BASE_LR * 1e-3)]),
        ]
        if BASE_LR > 0.1:
            callbacks.append(
                ScheduledHyperParamSetter(
                    'learning_rate', [(0, 0.1), (3, BASE_LR)], interp='linear'))

        infs = [ClassificationError('wrong-top1', 'val-error-top1'),
                ClassificationError('wrong-top5', 'val-error-top5')]
        if nr_tower == 1:
            # single-GPU inference with queue prefetch
            callbacks.append(InferenceRunner(QueueInput(dataset_val), infs))
        else:
            # multi-GPU inference (with mandatory queue prefetch)
            callbacks.append(DataParallelInferenceRunner(
                dataset_val, infs, list(range(nr_tower))))

    return TrainConfig(
        model=model,
        dataflow=dataset_train,
        callbacks=callbacks,
        steps_per_epoch=100 if args.fake else 1280000 // args.batch,
        max_epoch=110,
    ) 
开发者ID:qinenergy,项目名称:webvision-2.0-benchmarks,代码行数:46,代码来源:imagenet-resnet.py

示例4: get_config

# 需要导入模块: import tensorpack [as 别名]
# 或者: from tensorpack import QueueInput [as 别名]
def get_config(model, fake=False):
    nr_tower = max(get_num_gpu(), 1)
    assert args.batch % nr_tower == 0
    batch = args.batch // nr_tower

    if fake:
        logger.info("For benchmark, batch size is fixed to 64 per tower.")
        dataset_train = FakeData(
            [[64, 224, 224, 3], [64]], 1000, random=False, dtype='uint8')
        callbacks = []
        steps_per_epoch = 100
    else:
        logger.info("Running on {} towers. Batch size per tower: {}".format(nr_tower, batch))

        dataset_train = get_imagenet_dataflow(args.data, 'train', batch)
        dataset_val = get_imagenet_dataflow(args.data, 'val', min(64, batch))
        steps_per_epoch = 1281167 // args.batch

        BASE_LR = 0.1 * args.batch / 256.0
        logger.info("BASELR: {}".format(BASE_LR))
        callbacks = [
            ModelSaver(),
            EstimatedTimeLeft(),
            GPUUtilizationTracker(),
            ScheduledHyperParamSetter(
                'learning_rate', [(0, BASE_LR), (30, BASE_LR * 1e-1), (60, BASE_LR * 1e-2),
                                  (90, BASE_LR * 1e-3)]),
        ]
        if BASE_LR > 0.1:
            callbacks.append(
                ScheduledHyperParamSetter(
                    'learning_rate', [(0, 0.1), (5 * steps_per_epoch, BASE_LR)],
                    interp='linear', step_based=True))

        infs = [ClassificationError('wrong-top1', 'val-error-top1'),
                ClassificationError('wrong-top5', 'val-error-top5')]
        if nr_tower == 1:
            # single-GPU inference with queue prefetch
            callbacks.append(InferenceRunner(QueueInput(dataset_val), infs))
        else:
            # multi-GPU inference (with mandatory queue prefetch)
            callbacks.append(DataParallelInferenceRunner(
                dataset_val, infs, list(range(nr_tower))))

    return TrainConfig(
        model=model,
        dataflow=dataset_train,
        callbacks=callbacks,
        steps_per_epoch=steps_per_epoch,
        max_epoch=100,
    ) 
开发者ID:ppwwyyxx,项目名称:GroupNorm-reproduce,代码行数:53,代码来源:imagenet-resnet-gn.py

示例5: get_config

# 需要导入模块: import tensorpack [as 别名]
# 或者: from tensorpack import QueueInput [as 别名]
def get_config(model, fake=False):
    nr_tower = max(get_num_gpu(), 1)
    assert args.batch % nr_tower == 0
    batch = args.batch // nr_tower

    logger.info("Running on {} towers. Batch size per tower: {}".format(nr_tower, batch))
    if batch < 32 or batch > 64:
        logger.warn("Batch size per tower not in [32, 64]. This probably will lead to worse accuracy than reported.")
    if fake:
        data = QueueInput(FakeData(
            [[batch, 224, 224, 3], [batch],[batch, 224, 224, 3], [batch]], 1000, random=False, dtype='uint8'))
        callbacks = []
    else:
        data = QueueInput(get_data('train', batch))

        START_LR = 0.1
        BASE_LR = START_LR * (args.batch / 256.0)
        callbacks = [
            ModelSaver(),
            EstimatedTimeLeft(),
            ScheduledHyperParamSetter(
                'learning_rate', [
                    (0, min(START_LR, BASE_LR)), (30, BASE_LR * 1e-1), (45, BASE_LR * 1e-2),
                    (55, BASE_LR * 1e-3)]),
        ]
        if BASE_LR > START_LR:
            callbacks.append(
                ScheduledHyperParamSetter(
                    'learning_rate', [(0, START_LR), (5, BASE_LR)], interp='linear'))

        infs = [ClassificationError('wrong-top1', 'val-error-top1'),
                ClassificationError('wrong-top5', 'val-error-top5')]
        dataset_val = get_data('val', batch)
        if nr_tower == 1:
            # single-GPU inference with queue prefetch
            callbacks.append(InferenceRunner(QueueInput(dataset_val), infs))
        else:
            # multi-GPU inference (with mandatory queue prefetch)
            callbacks.append(DataParallelInferenceRunner(
                dataset_val, infs, list(range(nr_tower))))

    return AutoResumeTrainConfig(
        model=model,
        data=data,
        callbacks=callbacks,
        steps_per_epoch=100 if args.fake else 1280000 // args.batch,
        max_epoch=60,
    ) 
开发者ID:qinenergy,项目名称:adanet,代码行数:50,代码来源:adanet-resnet.py

示例6: get_config

# 需要导入模块: import tensorpack [as 别名]
# 或者: from tensorpack import QueueInput [as 别名]
def get_config(model):
    nr_tower = max(get_num_gpu(), 1)
    assert args.batch % nr_tower == 0
    batch = args.batch // nr_tower

    logger.info("Running on {} towers. Batch size per tower: {}".format(nr_tower, batch))
    if batch < 32 or batch > 64:
        logger.warn("Batch size per tower not in [32, 64]. This probably will lead to worse accuracy than reported.")
    if args.fake:
        data = QueueInput(FakeData(
            [[batch, 224, 224, 3], [batch]], 1000, random=False, dtype='uint8'))
        callbacks = []
    else:
        if args.symbolic:
            data = TFDatasetInput(get_imagenet_tfdata(args.data, 'train', batch))
        else:
            data = QueueInput(get_imagenet_dataflow(args.data, 'train', batch))

        START_LR = 0.1
        BASE_LR = START_LR * (args.batch / 256.0)
        callbacks = [
            ModelSaver(),
            EstimatedTimeLeft(),
            ScheduledHyperParamSetter(
                'learning_rate', [
                    (0, min(START_LR, BASE_LR)), (30, BASE_LR * 1e-1), (60, BASE_LR * 1e-2),
                    (90, BASE_LR * 1e-3), (100, BASE_LR * 1e-4)]),
        ]
        if BASE_LR > START_LR:
            callbacks.append(
                ScheduledHyperParamSetter(
                    'learning_rate', [(0, START_LR), (5, BASE_LR)], interp='linear'))

        infs = [ClassificationError('wrong-top1', 'val-error-top1'),
                ClassificationError('wrong-top5', 'val-error-top5')]
        dataset_val = get_imagenet_dataflow(args.data, 'val', batch)
        if nr_tower == 1:
            # single-GPU inference with queue prefetch
            callbacks.append(InferenceRunner(QueueInput(dataset_val), infs))
        else:
            # multi-GPU inference (with mandatory queue prefetch)
            callbacks.append(DataParallelInferenceRunner(
                dataset_val, infs, list(range(nr_tower))))

    if get_num_gpu() > 0:
        callbacks.append(GPUUtilizationTracker())

    return TrainConfig(
        model=model,
        data=data,
        callbacks=callbacks,
        steps_per_epoch=100 if args.fake else 1281167 // args.batch,
        max_epoch=105,
    ) 
开发者ID:tensorpack,项目名称:tensorpack,代码行数:56,代码来源:imagenet-resnet.py


注:本文中的tensorpack.QueueInput方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。