def test_quantize_training():
"""Test only that no error raised."""
config = EasyDict()
config.NETWORK_CLASS = FlowNetSV1Quantized
config.DATASET_CLASS = FlyingChairs
config.IS_DEBUG = False
config.IMAGE_SIZE = [384, 512]
config.BATCH_SIZE = 8
config.TEST_STEPS = 1
config.MAX_STEPS = 2
config.SAVE_CHECKPOINT_STEPS = 1
config.KEEP_CHECKPOINT_MAX = 5
config.SUMMARISE_STEPS = 1
config.IS_PRETRAIN = False
config.IS_DISTRIBUTION = False
# network model config
config.NETWORK = EasyDict()
config.NETWORK.OPTIMIZER_CLASS = tf.train.AdamOptimizer
config.NETWORK.OPTIMIZER_KWARGS = {"learning_rate": 0.001}
config.NETWORK.IMAGE_SIZE = config.IMAGE_SIZE
config.NETWORK.BATCH_SIZE = config.BATCH_SIZE
config.NETWORK.DATA_FORMAT = "NHWC"
config.NETWORK.ACTIVATION_QUANTIZER = linear_mid_tread_half_quantizer
config.NETWORK.ACTIVATION_QUANTIZER_KWARGS = {'bit': 2, 'max_value': 2.0}
config.NETWORK.WEIGHT_QUANTIZER = binary_channel_wise_mean_scaling_quantizer
config.NETWORK.WEIGHT_QUANTIZER_KWARGS = {}
# dataset config
config.DATASET = EasyDict()
config.DATASET.PRE_PROCESSOR = None
config.DATASET.BATCH_SIZE = config.BATCH_SIZE
config.DATASET.DATA_FORMAT = "NHWC"
config.DATASET.VALIDATION_RATE = 0.2
config.DATASET.VALIDATION_SEED = 2019
config.DATASET.AUGMENTOR = Sequence([
# Geometric transformation
FlipLeftRight(0.5),
FlipTopBottom(0.5),
Translate(-0.2, 0.2),
Rotate(-17, +17),
Scale(1.0, 2.0),
# Pixel-wise augmentation
Brightness(0.8, 1.2),
Contrast(0.2, 1.4),
Color(0.5, 2.0),
Gamma(0.7, 1.5),
# Hue(-128.0, 128.0),
GaussianNoise(0.0, 10.0)
])
config.DATASET.PRE_PROCESSOR = Sequence([
DevideBy255(),
])
environment.init("test_flownet_s_v1_quantize")
prepare_dirs(recreate=True)
start_training(config)
# rotation U([-17 deg, +17 deg])
# scaling U([0.9, 2.0])
# Pixel-Wise transformation
# Gaussian noise N(0, 1) * U([0.0, 0.04 * (255)])
# contrast U([0.2, 1.4])
# color U([0.5, 2.0])
# gamma U([0.7, 1.5])
# brightness 1 + 0.2 * N(0, 1)
# NOTE (by KI-42) in this setup, I modified the augmentation setup described above a little bit.
# hue U([-128 deg, 128 deg])
# brightness U(0.6, 1.4)
DATASET.AUGMENTOR = Sequence([
# Geometric transformation
FlipLeftRight(0.5),
FlipTopBottom(0.5),
Translate(-0.2, 0.2),
Rotate(-17, +17),
Scale(1.0, 2.0),
# Pixel-wise augmentation
Brightness(0.6, 1.4),
Contrast(0.2, 1.4),
Color(0.5, 2.0),
Gamma(0.7, 1.5),
# Hue(-128.0, 128.0),
GaussianNoise(10.0)
# GaussianBlur(0.0, 2.0)
])
DATASET.PRE_PROCESSOR = PRE_PROCESSOR
# rotation U([-17 deg, +17 deg])
# scaling U([0.9, 2.0])
# Pixel-Wise transformation
# Gaussian noise N(0, 1) * U([0.0, 0.04 * (255)])
# contrast U([0.2, 1.4])
# color U([0.5, 2.0])
# gamma U([0.7, 1.5])
# brightness 1 + 0.2 * N(0, 1)
# NOTE (by KI-42) in this setup, I modified the augmentation setup described above a little bit.
# hue U([-128 deg, 128 deg])
# brightness U(0.6, 1.4)
DATASET.AUGMENTOR = Sequence([
# Geometric transformation
# FlipLeftRight(0.5),
# FlipTopBottom(0.5),
Translate(-0.1, 0.1),
Rotate(-5, +5),
# Scale(1.0, 2.0),
# Pixel-wise augmentation
Brightness(0.6, 1.4),
Contrast(0.2, 1.4),
Color(0.5, 2.0),
Gamma(0.7, 1.5),
# Hue(-128.0, 128.0),
GaussianNoise(10.0)
# GaussianBlur(0.0, 2.0)
])
DATASET.PRE_PROCESSOR = PRE_PROCESSOR