TASK = Tasks.CLASSIFICATION CLASSES = DATASET_CLASS(subset="train", batch_size=1).classes MAX_STEPS = 2 SAVE_CHECKPOINT_STEPS = 1 KEEP_CHECKPOINT_MAX = 5 TEST_STEPS = 100 SUMMARISE_STEPS = 100 # pretrain IS_PRETRAIN = False PRETRAIN_VARS = [] PRETRAIN_DIR = "" PRETRAIN_FILE = "" PRE_PROCESSOR = Sequence([Resize(size=IMAGE_SIZE), PerImageStandardization()]) POST_PROCESSOR = None 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.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
# pretrain IS_PRETRAIN = False PRETRAIN_VARS = [] PRETRAIN_DIR = "" PRETRAIN_FILE = "" # for debug # BATCH_SIZE = 2 # SUMMARISE_STEPS = 1 # IS_DEBUG = True PRE_PROCESSOR = Sequence([ Resize(size=IMAGE_SIZE), PerImageStandardization(), ]) POST_PROCESSOR = Sequence([ Bilinear(size=IMAGE_SIZE, data_format=DATA_FORMAT, compatible_tensorflow_v1=True), Softmax(), ]) NETWORK = SmartDict() NETWORK.OPTIMIZER_CLASS = tf.compat.v1.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.WEIGHT_DECAY_RATE = 0. NETWORK.AUXILIARY_LOSS_WEIGHT = 0.5
# pretrain IS_PRETRAIN = False PRETRAIN_VARS = [] PRETRAIN_DIR = "" PRETRAIN_FILE = "" # for debug # BATCH_SIZE = 2 # SUMMARISE_STEPS = 1 # IS_DEBUG = True PRE_PROCESSOR = Sequence([ Resize(size=IMAGE_SIZE), {% if quantize_first_convolution %}DivideBy255(){% else %}PerImageStandardization(){% endif %} ]) POST_PROCESSOR = None NETWORK = SmartDict() NETWORK.OPTIMIZER_CLASS = {{optimizer_class}} NETWORK.OPTIMIZER_KWARGS = {{optimizer_kwargs}} NETWORK.LEARNING_RATE_FUNC = {{learning_rate_func}} NETWORK.LEARNING_RATE_KWARGS = {{learning_rate_kwargs}} 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,