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)
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)
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)
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)
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)
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)
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)
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
NETWORK.ACTIVATION_QUANTIZER = linear_mid_tread_half_quantizer