コード例 #1
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def load_dataset(dataset, hyperparams):
    datasets = {
        "canevet-icml2016-jittered": partial(CanevetICML2016, smooth=10),
        "canevet-icml2016": CanevetICML2016,
        "canevet-icml2016-smooth": partial(CanevetICML2016, smooth=15000),
        "cifar-sanity-check": CIFARSanityCheck,
        "mnist": MNIST,
        "cifar10": CIFAR10,
        "cifar100": CIFAR100,
        "cifar10-augmented": compose(
            partial(OntheflyAugmentedImages, ___, dict(
                featurewise_center=False,
                samplewise_center=False,
                featurewise_std_normalization=False,
                samplewise_std_normalization=False,
                zca_whitening=False,
                rotation_range=0,
                width_shift_range=0.1,
                height_shift_range=0.1,
                horizontal_flip=True,
                vertical_flip=False
            )),
            CIFAR10
        ),
        "cifar10-whitened-augmented": compose(
            partial(OntheflyAugmentedImages, ___, dict(
                featurewise_center=False,
                samplewise_center=False,
                featurewise_std_normalization=False,
                samplewise_std_normalization=False,
                zca_whitening=True,
                rotation_range=0,
                width_shift_range=0.1,
                height_shift_range=0.1,
                horizontal_flip=True,
                vertical_flip=False
            )),
            CIFAR10
        ),
        "cifar100-augmented": compose(
            partial(OntheflyAugmentedImages, ___, dict(
                featurewise_center=False,
                samplewise_center=False,
                featurewise_std_normalization=False,
                samplewise_std_normalization=False,
                zca_whitening=False,
                rotation_range=0,
                width_shift_range=0.1,
                height_shift_range=0.1,
                horizontal_flip=True,
                vertical_flip=False
            )),
            CIFAR100
        ),
        "ptb": partial(PennTreeBank, 20)
    }

    return datasets[dataset]()
コード例 #2
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def get_samplers_dictionary(model, hyperparams={}, reweighting=None):
    # Add some base samplers
    samplers = {
        "uniform":
        partial(UniformSampler, ___, reweighting),
        "model":
        partial(ModelSampler,
                ___,
                reweighting,
                model,
                large_batch=hyperparams.get("presample", 1024)),
        "lstm":
        partial(LSTMSampler,
                ___,
                reweighting,
                log=hyperparams.get("lstm_log", 0) != 0,
                warmup=hyperparams.get("lstm_warmup", 100),
                presample=hyperparams.get("presample", 2048)),
        "pcg":
        partial(PerClassGaussian,
                ___,
                reweighting,
                alpha=hyperparams.get("alpha", 0.9),
                presample=hyperparams.get("presample", 2048)),
        "obs":
        partial(OnlineBatchSelectionSampler,
                ___,
                reweighting,
                model,
                steps_per_epoch=hyperparams.get("steps_per_epoch", 300),
                recompute=hyperparams.get("obs_recompute", 2),
                s_e=(hyperparams.get("obs_se0",
                                     1), hyperparams.get("obs_seend", 1)),
                n_epochs=hyperparams.get("obs_epochs", 1))
    }

    samplers["lstm-comparison"] = lambda x: LSTMComparisonSampler(
        x,
        samplers["lstm"](x),
        samplers["model"](x),
        subset=hyperparams.get("lstm_comparison", 1024))

    # Add some decorated samplers
    samplers_for_decoration = ["model", "lstm", "pcg"]
    for sampler in samplers_for_decoration:
        samplers["smooth-" + sampler] = compose(
            partial(AdditiveSmoothingSampler,
                    c=hyperparams.get("smooth", 1.0)), samplers[sampler])
        samplers["adaptive-smooth-" + sampler] = compose(
            partial(AdaptiveAdditiveSmoothingSampler,
                    percentage=hyperparams.get("smooth", 0.5)),
            samplers[sampler])
        samplers["power-smooth-" + sampler] = compose(
            partial(PowerSmoothingSampler,
                    power=hyperparams.get("smooth", 0.5)), samplers[sampler])

    return samplers
コード例 #3
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    def test_datasets(self):
        datasets = [(CIFAR10, 50000, 10000, (32, 32, 3), 10),
                    (CIFARSanityCheck, 40000, 2000, (32, 32, 3), 2),
                    (MNIST, 60000, 10000, (28, 28, 1), 10),
                    (partial(CanevetICML2016,
                             N=256), int(256**2 - 256**2 * 0.33) + 1,
                     int(256**2 * 0.33), (2, ), 2),
                    (compose(
                        partial(OntheflyAugmentedImages,
                                augmentation_params=dict(
                                    featurewise_center=False,
                                    samplewise_center=False,
                                    featurewise_std_normalization=False,
                                    samplewise_std_normalization=False,
                                    zca_whitening=False,
                                    rotation_range=0,
                                    width_shift_range=0.1,
                                    height_shift_range=0.1,
                                    horizontal_flip=True,
                                    vertical_flip=False)),
                        CIFAR10), 500000, 10000, (32, 32, 3), 10),
                    (partial(PennTreeBank, 20), 887521, 70390, (20, ), 10000)]

        for args in datasets:
            self._test_dset(*args)
コード例 #4
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def load_dataset(dataset):
    datasets = {
        "mnist":
        MNIST,
        "cifar10":
        CIFAR10,
        "cifar100":
        CIFAR100,
        "cifar10-augmented":
        compose(
            partial(
                OntheflyAugmentedImages, ___,
                dict(featurewise_center=False,
                     samplewise_center=False,
                     featurewise_std_normalization=False,
                     samplewise_std_normalization=False,
                     zca_whitening=False,
                     rotation_range=0,
                     width_shift_range=0.1,
                     height_shift_range=0.1,
                     horizontal_flip=True,
                     vertical_flip=False)), CIFAR10),
        "cifar100-augmented":
        compose(
            partial(
                OntheflyAugmentedImages, ___,
                dict(featurewise_center=False,
                     samplewise_center=False,
                     featurewise_std_normalization=False,
                     samplewise_std_normalization=False,
                     zca_whitening=False,
                     rotation_range=0,
                     width_shift_range=0.1,
                     height_shift_range=0.1,
                     horizontal_flip=True,
                     vertical_flip=False)), CIFAR100),
        "ptb":
        partial(PennTreeBank, 20)
    }

    return datasets[dataset]()
コード例 #5
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def get_samplers_dictionary(model, hyperparams={}, reweighting=None):
    # Add some base samplers
    samplers = {
        "uniform":
        partial(UniformSampler, ___, reweighting),
        "model":
        partial(ModelSampler,
                ___,
                reweighting,
                model,
                large_batch=hyperparams.get("presample", 1024)),
        "lstm":
        partial(LSTMSampler,
                ___,
                reweighting,
                log=hyperparams.get("lstm_log", 0) != 0,
                presample=hyperparams.get("presample", 2048)),
        "pcg":
        partial(PerClassGaussian,
                ___,
                reweighting,
                alpha=hyperparams.get("alpha", 0.9),
                presample=hyperparams.get("presample", 2048)),
        "obs":
        partial(OnlineBatchSelectionSampler,
                ___,
                reweighting,
                model,
                steps_per_epoch=hyperparams.get("steps_per_epoch", 300),
                recompute=hyperparams.get("obs_recompute", 2),
                s_e=(hyperparams.get("obs_se0",
                                     1), hyperparams.get("obs_seend", 1)),
                n_epochs=hyperparams.get("obs_epochs", 1)),
        "history":
        partial(HistorySampler,
                ___,
                reweighting,
                model,
                recompute=hyperparams.get("history_recompute", 600)),
        "cache":
        partial(CacheSampler,
                ___,
                reweighting,
                staleness=hyperparams.get("staleness", 3),
                cache_prob=hyperparams.get("cache_prob", 0.5),
                smooth=hyperparams.get("smooth", 0.2))
    }

    samplers["lstm-comparison"] = lambda x: LSTMComparisonSampler(
        x,
        samplers["lstm"](x),
        samplers["model"](x),
        subset=hyperparams.get("lstm_comparison", 1024))

    # Add some decorated samplers
    samplers_for_decoration = ["model", "lstm", "pcg", "history"]
    for sampler in samplers_for_decoration:
        samplers["smooth-" + sampler] = compose(
            partial(AdditiveSmoothingSampler,
                    c=hyperparams.get("smooth", 1.0)), samplers[sampler])
        samplers["adaptive-smooth-" + sampler] = compose(
            partial(AdaptiveAdditiveSmoothingSampler,
                    percentage=hyperparams.get("smooth", 0.5)),
            samplers[sampler])
        samplers["power-smooth-" + sampler] = compose(
            partial(PowerSmoothingSampler,
                    power=hyperparams.get("smooth", 0.5)), samplers[sampler])

    for sampler in samplers.keys():
        if "model" in sampler or "lstm" in sampler:
            samplers["warmup-" + sampler] = compose(
                partial(ConditionalStartSampler, ___,
                        WarmupCondition(hyperparams.get("warmup", 100))),
                samplers[sampler])
            samplers["exp-" + sampler] = compose(
                partial(ConditionalStartSampler, ___,
                        ExpCondition(hyperparams.get("exp_th", 2.0))),
                samplers[sampler])
            samplers["tv-" + sampler] = compose(
                partial(ConditionalStartSampler, ___,
                        TotalVariationCondition(hyperparams.get("tv_th",
                                                                0.5))),
                samplers[sampler])
            samplers["vr-" + sampler] = compose(
                partial(
                    ConditionalStartSampler, ___,
                    VarianceReductionCondition(hyperparams.get("vr_th", 0.5))),
                samplers[sampler])

    return samplers
コード例 #6
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def load_dataset(dataset, hyperparams):
    datasets = {
        "canevet-icml2016-jittered":
        partial(CanevetICML2016, smooth=10),
        "canevet-icml2016":
        CanevetICML2016,
        "canevet-icml2016-smooth":
        partial(CanevetICML2016, smooth=15000),
        "cifar-sanity-check":
        CIFARSanityCheck,
        "mnist":
        MNIST,
        "cifar10":
        CIFAR10,
        "cifar100":
        CIFAR100,
        "cifar10-augmented":
        compose(
            partial(
                OntheflyAugmentedImages, ___,
                dict(featurewise_center=False,
                     samplewise_center=False,
                     featurewise_std_normalization=False,
                     samplewise_std_normalization=False,
                     zca_whitening=False,
                     rotation_range=0,
                     width_shift_range=0.1,
                     height_shift_range=0.1,
                     horizontal_flip=True,
                     vertical_flip=False)), CIFAR10),
        "cifar10-whitened-augmented":
        compose(
            partial(OntheflyAugmentedImages,
                    ___,
                    dict(featurewise_center=False,
                         samplewise_center=False,
                         featurewise_std_normalization=False,
                         samplewise_std_normalization=False,
                         zca_whitening=False,
                         rotation_range=0,
                         width_shift_range=0.1,
                         height_shift_range=0.1,
                         horizontal_flip=True,
                         vertical_flip=False),
                    N=15 * 10**5), ZCAWhitening, CIFAR10),
        "cifar100-augmented":
        compose(
            partial(
                OntheflyAugmentedImages, ___,
                dict(featurewise_center=False,
                     samplewise_center=False,
                     featurewise_std_normalization=False,
                     samplewise_std_normalization=False,
                     zca_whitening=False,
                     rotation_range=0,
                     width_shift_range=0.1,
                     height_shift_range=0.1,
                     horizontal_flip=True,
                     vertical_flip=False)), CIFAR100),
        "cifar100-whitened-augmented":
        compose(
            partial(OntheflyAugmentedImages,
                    ___,
                    dict(featurewise_center=False,
                         samplewise_center=False,
                         featurewise_std_normalization=False,
                         samplewise_std_normalization=False,
                         zca_whitening=False,
                         rotation_range=0,
                         width_shift_range=0.1,
                         height_shift_range=0.1,
                         horizontal_flip=True,
                         vertical_flip=False),
                    N=15 * 10**5), ZCAWhitening, CIFAR100),
        "ptb":
        partial(PennTreeBank, 20),
        "imagenet-32x32":
        partial(ImageNetDownsampled,
                hyperparams.get("imagenet", os.getenv("IMAGENET")),
                size=32),
        "imagenet-64x64":
        partial(ImageNetDownsampled,
                hyperparams.get("imagenet", os.getenv("IMAGENET")),
                size=64),
        "timit":
        partial(TIMIT, 20, hyperparams.get("timit", os.getenv("TIMIT"))),
        "mit-67":
        partial(MIT67, hyperparams.get("mit67", os.getenv("MIT67"))),
        "casia":
        partial(CASIAWebFace,
                hyperparams.get("casia", os.getenv("CASIA")),
                alpha=hyperparams.get("triplet_alpha", 0.2),
                embedding=hyperparams.get("triplet_embedding", 128)),
        "casia-cls":
        partial(
            CASIAWebFace2,
            hyperparams.get("casia", os.getenv("CASIA")),
        ),
        "pixel-mnist":
        PixelByPixelMNIST
    }

    return datasets[dataset]()
コード例 #7
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def load_dataset(dataset):
    datasets = {
        "mnist":
        MNIST,
        "cifar10":
        CIFAR10,
        "cifar100":
        CIFAR100,
        "cifar10-augmented":
        compose(
            partial(
                OntheflyAugmentedImages, ___,
                dict(featurewise_center=False,
                     samplewise_center=False,
                     featurewise_std_normalization=False,
                     samplewise_std_normalization=False,
                     zca_whitening=False,
                     rotation_range=0,
                     width_shift_range=0.1,
                     height_shift_range=0.1,
                     horizontal_flip=True,
                     vertical_flip=False)), CIFAR10),
        "cifar10-whitened-augmented":
        compose(
            partial(OntheflyAugmentedImages,
                    ___,
                    dict(featurewise_center=False,
                         samplewise_center=False,
                         featurewise_std_normalization=False,
                         samplewise_std_normalization=False,
                         zca_whitening=False,
                         rotation_range=0,
                         width_shift_range=0.1,
                         height_shift_range=0.1,
                         horizontal_flip=True,
                         vertical_flip=False),
                    N=15 * 10**5), ZCAWhitening, CIFAR10),
        "cifar100-augmented":
        compose(
            partial(
                OntheflyAugmentedImages, ___,
                dict(featurewise_center=False,
                     samplewise_center=False,
                     featurewise_std_normalization=False,
                     samplewise_std_normalization=False,
                     zca_whitening=False,
                     rotation_range=0,
                     width_shift_range=0.1,
                     height_shift_range=0.1,
                     horizontal_flip=True,
                     vertical_flip=False)), CIFAR100),
        "cifar100-whitened-augmented":
        compose(
            partial(OntheflyAugmentedImages,
                    ___,
                    dict(featurewise_center=False,
                         samplewise_center=False,
                         featurewise_std_normalization=False,
                         samplewise_std_normalization=False,
                         zca_whitening=False,
                         rotation_range=0,
                         width_shift_range=0.1,
                         height_shift_range=0.1,
                         horizontal_flip=True,
                         vertical_flip=False),
                    N=15 * 10**5), ZCAWhitening, CIFAR100),
        "imagenet-32x32":
        partial(ImageNetDownsampled, os.getenv("IMAGENET"), size=32),
        "ptb":
        partial(PennTreeBank, 20),
    }

    return datasets[dataset]()