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), ])
iou_threshold=0.5, classes=CLASSES, ), ]) NETWORK = SmartDict() NETWORK.OPTIMIZER_CLASS = tf.train.AdamOptimizer NETWORK.OPTIMIZER_KWARGS = {"learning_rate": 0.001} NETWORK.IMAGE_SIZE = IMAGE_SIZE NETWORK.BATCH_SIZE = BATCH_SIZE NETWORK.DATA_FORMAT = DATA_FORMAT NETWORK.ANCHORS = anchors 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_mean_scaling_quantizer NETWORK.WEIGHT_QUANTIZER_KWARGS = {} # dataset DATASET = SmartDict() DATASET.BATCH_SIZE = BATCH_SIZE DATASET.DATA_FORMAT = DATA_FORMAT DATASET.PRE_PROCESSOR = None DATASET.TFDS_PRE_PROCESSOR = TFDS_PRE_PROCESSOR DATASET.TFDS_AUGMENTOR = TFDSProcessorSequence([TFDSFlipLeftRight()]) DATASET.TFDS_KWARGS = { "name": "tfds_object_detection", "data_dir": "tmp/tests/datasets", "image_size": IMAGE_SIZE, }