Python start_training Examples

Example #1

File: test_darknet.py Project: ytfksw/blueoil

def test_training():
    """Test only no error raised."""
    config = EasyDict()

    config.NETWORK_CLASS = Darknet
    config.DATASET_CLASS = Dummy

    config.IS_DEBUG = False
    config.IMAGE_SIZE = [28, 14]
    config.BATCH_SIZE = 2
    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.TASK = Tasks.CLASSIFICATION

    # 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

    # daasegt config
    config.DATASET = EasyDict()
    config.DATASET.PRE_PROCESSOR = Resize(config.IMAGE_SIZE)
    config.DATASET.BATCH_SIZE = config.BATCH_SIZE

    environment.init("test_darknet")
    prepare_dirs(recreate=True)
    start_training(config)

Example #2

def test_training():
    """Test only that no error raised."""
    config = EasyDict()

    config.NETWORK_CLASS = YoloV2
    config.DATASET_CLASS = Pascalvoc2007

    config.IS_DEBUG = False
    config.IMAGE_SIZE = [128, 160]
    config.BATCH_SIZE = 2
    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.TASK = Tasks.OBJECT_DETECTION

    # 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"

    # dataset config
    config.DATASET = EasyDict()
    config.DATASET.PRE_PROCESSOR = ResizeWithGtBoxes(config.IMAGE_SIZE)
    config.DATASET.BATCH_SIZE = config.BATCH_SIZE
    config.DATASET.DATA_FORMAT = "NHWC"

    environment.init("test_yolo_v2")
    prepare_dirs(recreate=True)
    start_training(config, profile_step=1)

Example #3

def test_training():
    """Test only that no error raised."""
    config = EasyDict()

    config.NETWORK_CLASS = YoloV1
    config.DATASET_CLASS = Pascalvoc2007

    config.IS_DEBUG = False
    config.IMAGE_SIZE = [70, 70]
    config.BATCH_SIZE = 4
    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.TASK = Tasks.OBJECT_DETECTION

    # network model config
    config.NETWORK = EasyDict()
    config.NETWORK.IMAGE_SIZE = config.IMAGE_SIZE
    config.NETWORK.BATCH_SIZE = config.BATCH_SIZE

    # daasegt config
    config.DATASET = EasyDict()
    config.DATASET.PRE_PROCESSOR = ResizeWithGtBoxes(config.IMAGE_SIZE)
    config.DATASET.BATCH_SIZE = config.BATCH_SIZE

    environment.init("test_yolov_1")
    prepare_dirs(recreate=True)
    start_training(config)

Example #4

File: test_lm_bisenet.py Project: kainoj/blueoil

def test_training():
    """Verify only that no error raised."""
    config = SmartDict()

    config.NETWORK_CLASS = LMBiSeNet
    config.DATASET_CLASS = DummyCamvid

    config.IS_DEBUG = False
    config.IMAGE_SIZE = [128, 160]
    config.BATCH_SIZE = 2
    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.TASK = Tasks.SEMANTIC_SEGMENTATION

    # network model config
    config.NETWORK = SmartDict()
    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"

    # daasegt config
    config.DATASET = SmartDict()
    config.DATASET.PRE_PROCESSOR = Resize(config.IMAGE_SIZE)
    config.DATASET.BATCH_SIZE = config.BATCH_SIZE
    config.DATASET.DATA_FORMAT = "NHWC"

    environment.init("test_lm_bisenet")
    prepare_dirs(recreate=True)
    start_training(config, profile_step=1)

Example #5

File: test_lm_single_pose_v1.py Project: riple/blueoil

def test_training():
    """Test only no error raised."""

    config = EasyDict()

    config.NETWORK_CLASS = LmSinglePoseV1Quantize
    config.DATASET_CLASS = MscocoSinglePersonKeypoints

    config.IS_DEBUG = False
    config.IMAGE_SIZE = [160, 160]
    config.BATCH_SIZE = 2
    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
    config.TASK = Tasks.KEYPOINT_DETECTION

    # 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.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 = {}

    # daasegt config
    config.DATASET = EasyDict()
    config.DATASET.PRE_PROCESSOR = Sequence([
        ResizeWithJoints(image_size=config.IMAGE_SIZE),
        JointsToGaussianHeatmap(image_size=config.IMAGE_SIZE, stride=2),
        DivideBy255()])
    config.DATASET.BATCH_SIZE = config.BATCH_SIZE

    environment.init("test_lm_single_pose_v1")
    prepare_dirs(recreate=True)
    start_training(config, profile_step=1)

Example #6

def test_training():
    """Test only that no error raised."""
    config = EasyDict()

    config.NETWORK_CLASS = LmResnetQuantize
    config.DATASET_CLASS = Dummy

    config.IS_DEBUG = False
    config.IMAGE_SIZE = [32, 32]
    config.BATCH_SIZE = 2
    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.TASK = Tasks.CLASSIFICATION

    # 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.ACTIVATION_QUANTIZER = linear_mid_tread_half_quantizer
    config.NETWORK.ACTIVATION_QUANTIZER_KWARGS = {
        'bit': 2,
        'max_value': 2
    }
    config.NETWORK.WEIGHT_QUANTIZER = binary_mean_scaling_quantizer
    config.NETWORK.WEIGHT_QUANTIZER_KWARGS = {}

    # dataset config
    config.DATASET = EasyDict()
    config.DATASET.PRE_PROCESSOR = Resize(config.IMAGE_SIZE)
    config.DATASET.BATCH_SIZE = config.BATCH_SIZE

    environment.init("test_lm_resnet_quantize")
    prepare_dirs(recreate=True)
    start_training(config)

Example #7

File: test_yolo_v2_quantize.py Project: kainoj/blueoil

def test_training():
    """Test only no error raised."""

    config = SmartDict()

    config.NETWORK_CLASS = YoloV2Quantize
    config.DATASET_CLASS = Pascalvoc2007

    config.IS_DEBUG = False
    config.IMAGE_SIZE = [128, 160]
    config.BATCH_SIZE = 2
    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.TASK = Tasks.OBJECT_DETECTION

    # network model config
    config.NETWORK = SmartDict()
    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.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 = {}

    # daasegt config
    config.DATASET = SmartDict()
    config.DATASET.PRE_PROCESSOR = ResizeWithGtBoxes(config.IMAGE_SIZE)
    config.DATASET.BATCH_SIZE = config.BATCH_SIZE

    environment.init("test_yolov_2_quantize")
    prepare_dirs(recreate=True)
    start_training(config, profile_step=1)