예제 #1
0
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)
예제 #2
0
# 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