badwinner.py

# Input 1×1000×80
# Conv(3×3) 16×998×78
# leaky relu
# ( after ll conv)
# Pool(3×3) 16×332×26
# Conv(3×3) 16×330×24
# Pool(3×3) 16×110×8

# Conv(3×1) 16×108×8
# Pool(3×1) 16×36×8
# Conv(3×1) 16×34×8
# Pool(3×1) 16×11×8
# Dense 256
# leaky relu (after all dense)
# Dense 32
# Dense 1
#
#
#  leaky rectifier
# nonlinearity max(x, x/100)
import sys
import argparse
import logging
import tensorflow as tf


# Worth looking into lme pooling as proposed in  https://github.com/f0k/birdclef2018/blob/master/experiments/model.py
# Research/2018-birdclef.pdf


class MagTransform(tf.keras.layers.Layer):
    def __init__(self):
        super(MagTransform, self).__init__()
        self.a = self.add_weight(
            initializer=tf.keras.initializers.Constant(value=0.0),
            name="a-power",
            dtype="float32",
            shape=(),
            trainable=True,
        )

    def call(self, inputs):
        c = tf.math.pow(inputs, tf.math.sigmoid(self.a))
        return c


def build_model(input_shape, norm_layer, num_labels, multi_label=False):
    input = tf.keras.Input(shape=(*input_shape, 1), name="input")
    # x = norm_layer(input)
    filters = 16
    # if multi_label:
    # filters = 32
    # y = x σ(a) , where σ(a) = 1/ (1 + exp(−a))

    x = MagTransform()(input)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Conv2D(filters, (3, 3), activation=tf.keras.layers.LeakyReLU())(
        x
    )
    x = tf.keras.layers.MaxPool2D((3, 3))(x)
    x = tf.keras.layers.Conv2D(filters, (3, 3), activation=tf.keras.layers.LeakyReLU())(
        x
    )
    x = tf.keras.layers.MaxPool2D((3, 3))(x)

    x = tf.keras.layers.Conv2D(filters, (1, 3), activation=tf.keras.layers.LeakyReLU())(
        x
    )
    x = tf.keras.layers.MaxPool2D((1, 3))(x)
    #
    # x = tf.keras.layers.Conv2D(filters, (1, 3), activation=tf.keras.layers.LeakyReLU())(x)
    # x = tf.keras.layers.MaxPool2D((1, 3))(x)
    x = tf.keras.layers.Dropout(0.5)(x)
    # if num_labels > 1:
    # dense = [1024, 512]
    # else:
    dense = [256, 32]
    for d in dense:
        x = tf.keras.layers.Dense(d, activation=tf.keras.layers.LeakyReLU())(x)
        x = tf.keras.layers.Dropout(0.5)(x)
    # x = tf.keras.layers.Dense(32, activation=tf.keras.layers.LeakyReLU())(x)
    # x = tf.keras.layers.Dropout(0.5)(x)
    x = tf.keras.layers.GlobalAveragePooling2D()(x)
    activation = "softmax"
    if multi_label:
        activation = "sigmoid"
    logging.info("Using %s activation", activation)
    x = tf.keras.layers.Dense(num_labels, activation=activation)(x)

    model = tf.keras.models.Model(input, outputs=x)
    return model


def main():
    init_logging()
    args = parse_args()
    model = build_model((120, 480), None, 2)
    model.summary()


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--confusion", help="Save confusion matrix for model")
    parser.add_argument("-w", "--weights", help="Weights to use")

    parser.add_argument("-c", "--config-file", help="Path to config file to use")

    args = parser.parse_args()
    return args


def init_logging():
    """Set up logging for use by various classifier pipeline scripts.

    Logs will go to stderr.
    """

    fmt = "%(process)d %(thread)s:%(levelname)7s %(message)s"
    logging.basicConfig(
        stream=sys.stderr, level=logging.INFO, format=fmt, datefmt="%Y-%m-%d %H:%M:%S"
    )


if __name__ == "__main__":
    main()