def test_training(): """Test only that no error raised.""" config = EasyDict() config.NETWORK_CLASS = YoloV1 config.DATASET_CLASS = LmThingsOnATable 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)
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)
def test_training(): """Verify only that no error raised.""" config = EasyDict() 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_STEPS = 1 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" # daasegt config config.DATASET = EasyDict() 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)
def test_training(): """Test only that no error raised.""" config = EasyDict() config.NETWORK_CLASS = YoloV2 config.DATASET_CLASS = LmThingsOnATable config.IS_DEBUG = False config.IMAGE_SIZE = [128, 160] config.BATCH_SIZE = 2 config.TEST_STEPS = 1 config.MAX_STEPS = 2 config.SAVE_STEPS = 1 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 = "NCHW" # daasegt config config.DATASET = EasyDict() config.DATASET.PRE_PROCESSOR = ResizeWithGtBoxes(config.IMAGE_SIZE) config.DATASET.BATCH_SIZE = config.BATCH_SIZE config.DATASET.DATA_FORMAT = "NCHW" environment.init("test_yolo_v2") prepare_dirs(recreate=True) start_training(config)
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)
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)
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)
def run(network, dataset, config_file, experiment_id, recreate): environment.init(experiment_id) config = config_util.load(config_file) if network: network_class = module_loader.load_network_class(network) config.NETWORK_CLASS = network_class if dataset: dataset_class = module_loader.load_dataset_class(dataset) config.DATASET_CLASS = dataset_class config_util.display(config) executor.init_logging(config) executor.prepare_dirs(recreate) config_util.copy_to_experiment_dir(config_file) config_util.save_yaml(environment.EXPERIMENT_DIR, config) start_training(config)
def test_training(): """Test only no error raised.""" config = EasyDict() 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 = 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 = ResizeWithGtBoxes(config.IMAGE_SIZE) config.DATASET.BATCH_SIZE = config.BATCH_SIZE environment.init("test_yolov_2_quantize") prepare_dirs(recreate=True) start_training(config)
def main(model): if model == "yolov2": weight_file = 'inputs/yolo-voc.weights' experiment_id = "convert_weight_from_darknet/yolo_v2" config_file = "configs/convert_weight_from_darknet/yolo_v2.py" if model == "darknet19": weight_file = 'inputs/darknet19_448.weights' experiment_id = "convert_weight_from_darknet/darknet19" config_file = "configs/convert_weight_from_darknet/darknet19.py" recreate = True environment.init(experiment_id) executor.prepare_dirs(recreate) config = config_util.load(config_file) config_util.display(config) config_util.copy_to_experiment_dir(config_file) convert(config, weight_file)
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)