model.py

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# xView2                                                                                                                                                            #
# Copyright 2019 Carnegie Mellon University.                                                                                                                        #
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# [DISTRIBUTION STATEMENT A] This material has been approved for public release and unlimited distribution.  Please see Copyright notice for non-US Government use  #
# and distribution.                                                                                                                                                 #
# This Software includes and/or makes use of the following Third-Party Software subject to its own license:                                                         #
# 1. SpaceNet (https://github.com/motokimura/spacenet_building_detection/blob/master/LICENSE) Copyright 2017 Motoki Kimura.                                         #
# DM19-0988                                                                                                                                                         #
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from PIL import Image
import time
import numpy as np
import pandas as pd
from tqdm import tqdm
import os
import math
import random
import argparse
import logging
import json
import datetime

from sklearn.metrics import f1_score
from sklearn.utils.class_weight import compute_class_weight
import shapely.wkt
import shapely
from shapely.geometry import Polygon
from collections import defaultdict

import tensorflow as tf
import tensorflow.keras
import ast
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Add, Input, Concatenate
from tensorflow.keras.models import Model
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras import backend as K


###
# Loss function for ordinal loss from https://github.com/JHart96/keras_ordinal_categorical_crossentropy
###
def ordinal_loss(y_true, y_pred):
    weights = K.cast(K.abs(K.argmax(y_true, axis=1) - K.argmax(y_pred, axis=1))/(K.int_shape(y_pred)[1] - 1), dtype='float32')
    return (1.0 + weights) * tensorflow.keras.losses.categorical_crossentropy(y_true, y_pred )


###
# Generate a simple CNN
###
def generate_xBD_baseline_model():
  weights = 'imagenet'
  inputs = Input(shape=(128, 128, 3))

  base_model = ResNet50(include_top=False, weights=weights, input_shape=(128, 128, 3))

  for layer in base_model.layers:
    layer.trainable = False

  x = Conv2D(32, (5, 5), strides=(1, 1), padding='same', activation='relu', input_shape=(128, 128, 3))(inputs)
  x = MaxPooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None)(x)

  x = Conv2D(64, (3, 3), strides=(1, 1), padding='same', activation='relu')(x)
  x = MaxPooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None)(x)

  x = Conv2D(64, (3, 3), strides=(1, 1), padding='same', activation='relu')(x)
  x = MaxPooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None)(x)

  x = Flatten()(x)

  base_resnet = base_model(inputs)
  base_resnet = Flatten()(base_resnet)

  concated_layers = Concatenate()([x, base_resnet])

  concated_layers = Dense(2024, activation='relu')(concated_layers)
  concated_layers = Dense(524, activation='relu')(concated_layers)
  concated_layers = Dense(124, activation='relu')(concated_layers)
  output = Dense(4, activation='relu')(concated_layers)

  model = Model(inputs=inputs, outputs=output)
  return model