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
NETWORK.LOSS_WARMUP_STEPS = int(8000 / BATCH_SIZE)
# quantize
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 = False
NETWORK.QUANTIZE_LAST_CONVOLUTION = False
# dataset
DATASET = EasyDict()
DATASET.BATCH_SIZE = BATCH_SIZE
DATASET.DATA_FORMAT = DATA_FORMAT
DATASET.PRE_PROCESSOR = PRE_PROCESSOR
DATASET.AUGMENTOR = Sequence([
Brightness(value=(0.75, 1.25), ),
Color(value=(0.75, 1.25), ),
FlipLeftRight(probability=0.5, ),
Hue(value=(-10, 10), ),
SSDRandomCrop(min_crop_ratio=0.3, ),
])
DATASET.ENABLE_PREFETCH = True
PRE_PROCESSOR = Sequence([
Resize(size=IMAGE_SIZE),
PerImageStandardization(),
])
POST_PROCESSOR = None
NETWORK = EasyDict()
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 * 50, step_per_epoch * 100, step_per_epoch * 198],
}
NETWORK.IMAGE_SIZE = IMAGE_SIZE
NETWORK.BATCH_SIZE = BATCH_SIZE
NETWORK.DATA_FORMAT = DATA_FORMAT
NETWORK.WEIGHT_DECAY_RATE = 0.0005
# dataset
DATASET = EasyDict()
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(),
])
if self.enable_prefetch:
self.prefetcher.terminate = True
self.prefetcher.pool.close()
self.prefetcher.pool.join()
if __name__ == '__main__':
from blueoil.datasets.cifar10 import Cifar10
from blueoil.data_processor import Sequence
from blueoil.data_augmentor import FlipLeftRight, Hue, Blur
cifar10 = Cifar10()
augmentor = Sequence([
FlipLeftRight(0.5),
Hue((-10, 10)),
Blur(),
])
dataset_iterator = DatasetIterator(dataset=cifar10,
enable_prefetch=True,
augmentor=augmentor)
time.sleep(2)
import time
t0 = time.time()
data_batch = next(dataset_iterator)
t1 = time.time()
print("time of prefetch: {}".format(t1 - t0))
dataset_iterator2 = DatasetIterator(dataset=cifar10,