Ejemplos de SmartDict.OPTIMIZER_CLASS en Python

Ejemplo n.º 1

Archivo: object_detection.py Proyecto: kainoj/blueoil

    FormatYoloV2(
        image_size=IMAGE_SIZE,
        classes=CLASSES,
        anchors=anchors,
        data_format=DATA_FORMAT,
    ),
    ExcludeLowScoreBox(threshold=score_threshold),
    NMS(
        iou_threshold=nms_iou_threshold,
        max_output_size=nms_max_output_size,
        classes=CLASSES,
    ),
])

NETWORK = SmartDict()
NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant
_epoch_steps = 16551 // BATCH_SIZE
NETWORK.LEARNING_RATE_KWARGS = {
    "values": [1e-6, 1e-4, 1e-5, 1e-6, 1e-7],
    "boundaries":
    [_epoch_steps, _epoch_steps * 10, _epoch_steps * 60, _epoch_steps * 90],
}
NETWORK.IMAGE_SIZE = IMAGE_SIZE
NETWORK.BATCH_SIZE = BATCH_SIZE
NETWORK.DATA_FORMAT = DATA_FORMAT
NETWORK.ANCHORS = anchors
NETWORK.OBJECT_SCALE = 5.0
NETWORK.NO_OBJECT_SCALE = 1.0
NETWORK.CLASS_SCALE = 1.0

Ejemplo n.º 2

    'learning_rate':
    hp.uniform('learning_rate', 0, 0.01),
    'learning_rate_func':
    hp.choice('learning_rate_func', [
        {
            'scheduler': tf.compat.v1.train.piecewise_constant,
            'scheduler_factor': hp.uniform('scheduler_factor', 0.05, 0.5),
            'scheduler_steps': [25000, 50000, 75000],
        },
    ]),
    'weight_decay_rate':
    0.0001,
}

NETWORK = SmartDict()
NETWORK.OPTIMIZER_CLASS = None
NETWORK.OPTIMIZER_KWARGS = {}
NETWORK.LEARNING_RATE_FUNC = None
NETWORK.LEARNING_RATE_KWARGS = {}
NETWORK.WEIGHT_DECAY_RATE = None
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
DATASET = SmartDict()
DATASET.BATCH_SIZE = BATCH_SIZE

Ejemplo n.º 3

SAVE_CHECKPOINT_STEPS = 1
KEEP_CHECKPOINT_MAX = 5
TEST_STEPS = 100
SUMMARISE_STEPS = 100

# pretrain
IS_PRETRAIN = False
PRETRAIN_VARS = []
PRETRAIN_DIR = ""
PRETRAIN_FILE = ""

PRE_PROCESSOR = Sequence([Resize(size=IMAGE_SIZE), PerImageStandardization()])
POST_PROCESSOR = None

NETWORK = SmartDict()
NETWORK.OPTIMIZER_CLASS = tf.train.AdamOptimizer
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 = 0.0005
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
DATASET = SmartDict()
DATASET.BATCH_SIZE = BATCH_SIZE
DATASET.DATA_FORMAT = DATA_FORMAT
DATASET.PRE_PROCESSOR = PRE_PROCESSOR

Ejemplo n.º 4

Archivo: lm_bisenet_quantize_camvid.py Proyecto: kainoj/blueoil

# BATCH_SIZE = 2
# SUMMARISE_STEPS = 1
# IS_DEBUG = True

PRE_PROCESSOR = Sequence([
    Resize(size=IMAGE_SIZE),
    PerImageStandardization(),
])
POST_PROCESSOR = Sequence([
    Bilinear(size=IMAGE_SIZE, data_format=DATA_FORMAT, compatible_tensorflow_v1=True),
    Softmax(),
])


NETWORK = SmartDict()
NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.AdamOptimizer
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

Ejemplo n.º 5

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

PRETRAIN_DIR = ""
PRETRAIN_FILE = ""

# for debug
# BATCH_SIZE = 2
# SUMMARISE_STEPS = 1
# IS_DEBUG = True

PRE_PROCESSOR = Sequence([
    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 = {}