PRETRAIN_FILE = ""
# distributed training
IS_DISTRIBUTION = False
# for debug
# MAX_STEPS = 10
# BATCH_SIZE = 31
# SAVE_STEPS = 2
# TEST_STEPS = 10
# SUMMARISE_STEPS = 2
# IS_DEBUG = True
PRE_PROCESSOR = Sequence([
Resize(size=IMAGE_SIZE),
PerImageStandardization(),
])
POST_PROCESSOR = None
NETWORK = EasyDict()
NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
step_per_epoch = int(50000 / BATCH_SIZE)
NETWORK.LEARNING_RATE_KWARGS = {
"values": [0.01, 0.1, 0.01, 0.001, 0.0001],
"boundaries": [
step_per_epoch, step_per_epoch * 50, step_per_epoch * 100,
step_per_epoch * 198
],
}
# pretrain
IS_PRETRAIN = False
PRETRAIN_VARS = []
PRETRAIN_DIR = ""
PRETRAIN_FILE = ""
# for debug
# MAX_STEPS = 100
# # BATCH_SIZE = 31
# SAVE_CHECKPOINT_STEPS = 10
# KEEP_CHECKPOINT_MAX = 5
# TEST_STEPS = 10
# SUMMARISE_STEPS = 2
# IS_DEBUG = True
PRE_PROCESSOR = Sequence([Resize(size=IMAGE_SIZE), PerImageStandardization()])
POST_PROCESSOR = None
NETWORK = EasyDict()
NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
NETWORK.LEARNING_RATE_KWARGS = {
"values": [0.1, 0.01, 0.001, 0.0001],
"boundaries": [40000, 60000, 80000],
}
NETWORK.IMAGE_SIZE = IMAGE_SIZE
NETWORK.BATCH_SIZE = BATCH_SIZE
NETWORK.DATA_FORMAT = DATA_FORMAT
NETWORK.WEIGHT_DECAY_RATE = 0.0001
NETWORK.ACTIVATION_QUANTIZER = linear_mid_tread_half_quantizer
# 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 = EasyDict()
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,