Python SmartDict.IMAGE_SIZE示例

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)

示例#2

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

    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 = 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

    # daasegt config
    config.DATASET = SmartDict()
    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, profile_step=1)

示例#3

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

    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 = SmartDict()
    config.NETWORK.IMAGE_SIZE = config.IMAGE_SIZE
    config.NETWORK.BATCH_SIZE = config.BATCH_SIZE

    # daasegt config
    config.DATASET = SmartDict()
    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, profile_step=1)

示例#4

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文件： test_yolo_v2.py 项目： kainoj/blueoil

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

    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 = 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"

    # dataset config
    config.DATASET = SmartDict()
    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)

示例#5

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

    config = SmartDict()

    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 = 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 = 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)

示例#6

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

    config.NETWORK_CLASS = SampleNetworkQuantize
    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 = 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}
    config.NETWORK.WEIGHT_QUANTIZER = binary_mean_scaling_quantizer
    config.NETWORK.WEIGHT_QUANTIZER_KWARGS = {}
    config.NETWORK.DATA_FORMAT = "NHWC"

    # dataset 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_example_quantize")
    prepare_dirs(recreate=True)
    start_training(config, profile_step=1)

示例#7

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文件： test_yolo_v2_quantize.py 项目： 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)

示例#8

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文件： object_detection.py 项目： kainoj/blueoil

        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
NETWORK.LOSS_WARMUP_STEPS = 1280 // BATCH_SIZE

# quantization

示例#9

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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
DATASET.BATCH_SIZE = BATCH_SIZE
DATASET.DATA_FORMAT = DATA_FORMAT
DATASET.PRE_PROCESSOR = PRE_PROCESSOR