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 NETWORK.ACTIVATION_QUANTIZER_KWARGS = {'bit': 2, 'max_value': 2.0} NETWORK.WEIGHT_QUANTIZER = binary_channel_wise_mean_scaling_quantizer NETWORK.WEIGHT_QUANTIZER_KWARGS = {} NETWORK.QUANTIZE_FIRST_CONVOLUTION = True NETWORK.QUANTIZE_LAST_CONVOLUTION = False # dataset DATASET = SmartDict() DATASET.BATCH_SIZE = BATCH_SIZE DATASET.DATA_FORMAT = DATA_FORMAT DATASET.PRE_PROCESSOR = PRE_PROCESSOR DATASET.AUGMENTOR = Sequence([ FlipLeftRight(), Brightness((0.75, 1.25)), Color((0.75, 1.25)), Contrast((0.75, 1.25)), Hue((-10, 10)), SSDRandomCrop(min_crop_ratio=0.7), ])
NETWORK.OPTIMIZER_CLASS = tf.compat.v1.train.MomentumOptimizer NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9} NETWORK.LEARNING_RATE_FUNC = tf.compat.v1.train.piecewise_constant step_per_epoch = 50000 // 200 NETWORK.LEARNING_RATE_KWARGS = { "values": [0.01, 0.001, 0.0001, 0.00001], "boundaries": [step_per_epoch * 200, step_per_epoch * 300, step_per_epoch * 350], } NETWORK.IMAGE_SIZE = IMAGE_SIZE NETWORK.BATCH_SIZE = BATCH_SIZE NETWORK.DATA_FORMAT = DATA_FORMAT NETWORK.WEIGHT_DECAY_RATE = 0.0005 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 = {} NETWORK.QUANTIZE_FIRST_CONVOLUTION = False NETWORK.QUANTIZE_LAST_CONVOLUTION = False # dataset DATASET = SmartDict() DATASET.BATCH_SIZE = BATCH_SIZE DATASET.DATA_FORMAT = DATA_FORMAT DATASET.PRE_PROCESSOR = PRE_PROCESSOR DATASET.AUGMENTOR = Sequence([ Pad(2), Crop(size=IMAGE_SIZE), FlipLeftRight(), ])