Ejemplos de SmartDict.ENABLE_PREFETCH en Python

Ejemplo n.º 1

        },
    ]),
}

NETWORK = SmartDict()
NETWORK.OPTIMIZER_CLASS = None
NETWORK.OPTIMIZER_KWARGS = {}
NETWORK.LEARNING_RATE_FUNC = None
NETWORK.LEARNING_RATE_KWARGS = {}
NETWORK.IMAGE_SIZE = IMAGE_SIZE
NETWORK.BATCH_SIZE = BATCH_SIZE
NETWORK.DATA_FORMAT = DATA_FORMAT
NETWORK.ACTIVATION_QUANTIZER = linear_mid_tread_half_quantizer
NETWORK.ACTIVATION_QUANTIZER_KWARGS = {'bit': 2, 'max_value': 2}
NETWORK.WEIGHT_QUANTIZER = binary_mean_scaling_quantizer
NETWORK.WEIGHT_QUANTIZER_KWARGS = {}

DATASET = SmartDict()
DATASET.BATCH_SIZE = BATCH_SIZE
DATASET.DATA_FORMAT = DATA_FORMAT
DATASET.PRE_PROCESSOR = PRE_PROCESSOR
DATASET.AUGMENTOR = Sequence([
    Brightness((0.75, 1.25)),
    Color((0.75, 1.25)),
    Contrast((0.75, 1.25)),
    FlipLeftRight(),
    Hue((-10, 10)),
])

DATASET.ENABLE_PREFETCH = False

Ejemplo n.º 2

Archivo: lm_bisenet_quantize_camvid.py Proyecto: kainoj/blueoil

NETWORK.OPTIMIZER_KWARGS = {"learning_rate": 0.001}
NETWORK.IMAGE_SIZE = IMAGE_SIZE
NETWORK.BATCH_SIZE = BATCH_SIZE
NETWORK.DATA_FORMAT = DATA_FORMAT
NETWORK.WEIGHT_DECAY_RATE = 0.
NETWORK.AUXILIARY_LOSS_WEIGHT = 0.5
NETWORK.USE_FEATURE_FUSION = True
NETWORK.USE_ATTENTION_REFINEMENT = True
NETWORK.USE_TAIL_GAP = True
NETWORK.ACTIVATION_QUANTIZER = linear_mid_tread_half_quantizer
NETWORK.ACTIVATION_QUANTIZER_KWARGS = {
    'bit': 2,
    'max_value': 2
}
NETWORK.WEIGHT_QUANTIZER = binary_channel_wise_mean_scaling_quantizer
NETWORK.WEIGHT_QUANTIZER_KWARGS = {}

DATASET = SmartDict()
DATASET.BATCH_SIZE = BATCH_SIZE
DATASET.DATA_FORMAT = DATA_FORMAT
DATASET.PRE_PROCESSOR = PRE_PROCESSOR
DATASET.AUGMENTOR = Sequence([
    Resize(size=IMAGE_SIZE),
    Brightness((0.75, 1.25)),
    Color((0.75, 1.25)),
    Contrast((0.75, 1.25)),
    FlipLeftRight(),
    Hue((-10, 10)),
])
DATASET.ENABLE_PREFETCH = True

Ejemplo n.º 3

Archivo: semantic_segmentation.tpl.py Proyecto: kainoj/blueoil

    Resize(size=IMAGE_SIZE),
    {% if quantize_first_convolution %}DivideBy255(){% else %}PerImageStandardization(){% endif %}
])
POST_PROCESSOR = None

NETWORK = SmartDict()
NETWORK.OPTIMIZER_CLASS = {{optimizer_class}}
NETWORK.OPTIMIZER_KWARGS = {{optimizer_kwargs}}
NETWORK.LEARNING_RATE_FUNC = {{learning_rate_func}}
NETWORK.LEARNING_RATE_KWARGS = {{learning_rate_kwargs}}

NETWORK.IMAGE_SIZE = IMAGE_SIZE
NETWORK.BATCH_SIZE = BATCH_SIZE
NETWORK.DATA_FORMAT = DATA_FORMAT
NETWORK.ACTIVATION_QUANTIZER = linear_mid_tread_half_quantizer
NETWORK.ACTIVATION_QUANTIZER_KWARGS = {
    'bit': 2,
    'max_value': 2
}
NETWORK.WEIGHT_QUANTIZER = binary_mean_scaling_quantizer
NETWORK.WEIGHT_QUANTIZER_KWARGS = {}

DATASET = SmartDict()
DATASET.BATCH_SIZE = BATCH_SIZE
DATASET.DATA_FORMAT = DATA_FORMAT
DATASET.PRE_PROCESSOR = PRE_PROCESSOR
DATASET.AUGMENTOR = Sequence([{% if data_augmentation %}{% for aug_name, aug_val in data_augmentation.items() %}
    {{ aug_name }}({% for param_name, param_value in aug_val %}{{ param_name }}={{ param_value }}, {% endfor %}),{% endfor %}
{% endif %}])
DATASET.ENABLE_PREFETCH = {{ dataset_prefetch }}