Python PerImageStandardization 예제들

예제 #1

TASK = Tasks.CLASSIFICATION
CLASSES = DATASET_CLASS(subset="train", batch_size=1).classes

MAX_STEPS = 2
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

예제 #2

파일: lm_bisenet_quantize_camvid.py 프로젝트: kainoj/blueoil

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

# for debug
# 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

예제 #3

파일: semantic_segmentation.tpl.py 프로젝트: kainoj/blueoil

# pretrain
IS_PRETRAIN = False
PRETRAIN_VARS = []
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,