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
DATASET.DATA_FORMAT = DATA_FORMAT
DATASET.PRE_PROCESSOR = PRE_PROCESSOR
DATASET.AUGMENTOR = Sequence([
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
NETWORK.COORDINATE_SCALE = 1.0
NETWORK.LOSS_IOU_THRESHOLD = 0.6
NETWORK.WEIGHT_DECAY_RATE = 0.0005
NETWORK.SCORE_THRESHOLD = score_threshold
NETWORK.NMS_IOU_THRESHOLD = nms_iou_threshold
NETWORK.NMS_MAX_OUTPUT_SIZE = nms_max_output_size
# pretrain
IS_PRETRAIN = False
PRETRAIN_VARS = []
PRETRAIN_DIR = ""
PRETRAIN_FILE = ""
PRE_PROCESSOR = Sequence([Resize(size=IMAGE_SIZE), DivideBy255()])
POST_PROCESSOR = None
NETWORK = SmartDict()
NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.cosine_decay
# Train data num is 28709
step_per_epoch = 28709 // BATCH_SIZE
NETWORK.LEARNING_RATE_KWARGS = {'learning_rate': 0.1, 'decay_steps': 100000}
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
DATASET.AUGMENTOR = Sequence([
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
# In the origianl yolov2 Paper, with a starting learning rate of 10−3, dividing it by 10 at 60 and 90 epochs.
# Train data num per epoch is 16551
step_per_epoch = 16551 // BATCH_SIZE
NETWORK.LEARNING_RATE_KWARGS = {
"values": [5e-4, 2e-2, 5e-3, 5e-4, 5e-5],
"boundaries": [
step_per_epoch * 5, step_per_epoch * 80, step_per_epoch * 120,
step_per_epoch * 140
],
}
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
NETWORK.COORDINATE_SCALE = 1.0
NETWORK.LOSS_IOU_THRESHOLD = 0.6
NETWORK.WEIGHT_DECAY_RATE = 0.0005
NETWORK.SCORE_THRESHOLD = score_threshold
# SUMMARISE_STEPS = 2
# IS_DEBUG = True
PRE_PROCESSOR = Sequence([
Resize(size=IMAGE_SIZE),
DivideBy255(),
])
POST_PROCESSOR = None
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
step_per_epoch = 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 * 100, step_per_epoch * 150, step_per_epoch * 175],
}
NETWORK.IMAGE_SIZE = IMAGE_SIZE
NETWORK.BATCH_SIZE = BATCH_SIZE
NETWORK.DATA_FORMAT = DATA_FORMAT
NETWORK.WEIGHT_DECAY_RATE = 0.00004
# dataset
DATASET = SmartDict()
DATASET.BATCH_SIZE = BATCH_SIZE
DATASET.DATA_FORMAT = DATA_FORMAT
DATASET.PRE_PROCESSOR = PRE_PROCESSOR
DATASET.AUGMENTOR = Sequence([
Pad(2),
Crop(size=IMAGE_SIZE),
FlipLeftRight(),
# TEST_STEPS = 10
# SUMMARISE_STEPS = 2
# IS_DEBUG = True
PRE_PROCESSOR = Sequence([
Resize(size=IMAGE_SIZE),
PerImageStandardization()
])
POST_PROCESSOR = None
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
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
# dataset
DATASET = SmartDict()
DATASET.BATCH_SIZE = BATCH_SIZE
DATASET.DATA_FORMAT = DATA_FORMAT
DATASET.PRE_PROCESSOR = PRE_PROCESSOR
DATASET.AUGMENTOR = Sequence([
Pad(2),
Crop(size=IMAGE_SIZE),
FlipLeftRight(),
])
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
# In the origianl yolov2 Paper, with a starting learning rate of 10−3, dividing it by 10 at 60 and 90 epochs.
# Train data num per epoch is 69863
step_per_epoch = 69863 // BATCH_SIZE
NETWORK.LEARNING_RATE_KWARGS = {
"values": [1e-4, 2e-3, 5e-4, 5e-5, 5e-6],
"boundaries": [
step_per_epoch,
step_per_epoch * 60,
step_per_epoch * 90,
step_per_epoch * 120,
],
}
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
NETWORK.COORDINATE_SCALE = 1.0
NETWORK.LOSS_IOU_THRESHOLD = 0.6
NETWORK.WEIGHT_DECAY_RATE = 0.0005
NETWORK.SCORE_THRESHOLD = score_threshold
# SUMMARISE_STEPS = 2
# IS_DEBUG = True
PRE_PROCESSOR = Sequence([
Resize(size=IMAGE_SIZE),
DivideBy255()
])
POST_PROCESSOR = None
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
step_per_epoch = 50000 // 200
NETWORK.LEARNING_RATE_KWARGS = {
"values": [0.01, 0.001, 0.0001, 0.00001],
"boundaries": [step_per_epoch * 200, step_per_epoch * 300, step_per_epoch * 350],
}
NETWORK.IMAGE_SIZE = IMAGE_SIZE
NETWORK.BATCH_SIZE = BATCH_SIZE
NETWORK.DATA_FORMAT = DATA_FORMAT
NETWORK.WEIGHT_DECAY_RATE = 0.0005
# dataset
DATASET = SmartDict()
DATASET.BATCH_SIZE = BATCH_SIZE
DATASET.DATA_FORMAT = DATA_FORMAT
DATASET.PRE_PROCESSOR = PRE_PROCESSOR
DATASET.AUGMENTOR = Sequence([
Pad(2),
Crop(size=IMAGE_SIZE),
FlipLeftRight(),
# SAVE_CHECKPOINT_STEPS = 2
# TEST_STEPS = 10
# SUMMARISE_STEPS = 2
# IS_DEBUG = True
PRE_PROCESSOR = Sequence([Resize(size=IMAGE_SIZE), DivideBy255()])
POST_PROCESSOR = None
NETWORK = SmartDict()
NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer
NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.polynomial_decay
# TODO(wakiska): It is same as original yolov2 paper (batch size = 128).
NETWORK.LEARNING_RATE_KWARGS = {
"learning_rate": 1e-1,
"decay_steps": 1600000,
"power": 4.0,
"end_learning_rate": 0.0
}
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': 2.0,
}
NETWORK.WEIGHT_QUANTIZER = binary_channel_wise_mean_scaling_quantizer
NETWORK.WEIGHT_QUANTIZER_KWARGS = {}
NETWORK.QUANTIZE_FIRST_CONVOLUTION = True
NETWORK.QUANTIZE_LAST_CONVOLUTION = False
# 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 = {}
DATASET = SmartDict()
DATASET.BATCH_SIZE = BATCH_SIZE
DATASET.DATA_FORMAT = DATA_FORMAT
JointsToGaussianHeatmap(image_size=IMAGE_SIZE,
stride=STRIDE, sigma=2),
DivideBy255()
])
POST_PROCESSOR = Sequence([
GaussianHeatmapToJoints(num_dimensions=2, stride=STRIDE, confidence_threshold=0.1)
])
step_per_epoch = 149813 // BATCH_SIZE
NETWORK = SmartDict()
NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.AdamOptimizer
NETWORK.OPTIMIZER_KWARGS = {}
NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant
NETWORK.LEARNING_RATE_KWARGS = {
"values": [1e-4, 1e-3, 1e-4, 1e-5],
"boundaries": [5000, step_per_epoch * 5, step_per_epoch * 10],
}
NETWORK.STRIDE = STRIDE
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_channel_wise_mean_scaling_quantizer
NETWORK.WEIGHT_QUANTIZER_KWARGS = {}
DATASET = SmartDict()
DATASET.IMAGE_SIZE = IMAGE_SIZE