Initial commit of baseline codebase:

LICENSE.md

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+xView2
+Copyright 2019 Carnegie Mellon University.
+MIT (SEI)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+xView2-update includes and/or can make use of certain third party software ("Third Party Software"). The Third Party Software that is used by xView2-update is dependent upon your system configuration, but typically includes the software identified below. By using xView2-update, You agree to comply with any and all relevant Third Party Software terms and conditions contained in any such Third Party Software or separate license file distributed with such Third Party Software. The parties who own the Third Party Software ("Third Party Licensors") are intended third party beneficiaries to this License with respect to the terms applicable to their Third Party Software. Third Party Software licenses only apply to the Third Party Software and not any other portion of xView2-update or xView2-update as a whole.
+
+This material is based upon work funded and supported by the Department of Defense under Contract No. FA8702-15-D-0002 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center.
+The view, opinions, and/or findings contained in this material are those of the author(s) and should not be construed as an official Government position, policy, or decision, unless designated by other documentation.
+
+References herein to any specific commercial product, process, or service by trade name, trade mark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by Carnegie Mellon University or its Software Engineering Institute.
+
+NO WARRANTY. THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING INSTITUTE MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, TRADEMARK, OR COPYRIGHT INFRINGEMENT.
+[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

model/damage_classification.py

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+#####################################################################################################################################################################
+# xView2                                                                                                                                                            #
+# Copyright 2019 Carnegie Mellon University.                                                                                                                        #
+# NO WARRANTY. THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING INSTITUTE MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO    #
+# WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY,          # 
+# EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, # 
+# TRADEMARK, OR COPYRIGHT INFRINGEMENT.                                                                                                                             #
+# Released under a MIT (SEI)-style license, please see LICENSE.md or contact [email protected] for full terms.                                                 #
+# [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                                                                                                                                                         #
+#####################################################################################################################################################################
+
+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 cv2
+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 keras
+import ast
+from keras import Sequential
+from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Add, Input, Concatenate
+from keras.models import Model
+from keras.applications.resnet50 import ResNet50
+from keras import backend as K
+
+from model import *
+
+logging.basicConfig(level=logging.INFO)
+
+# Configurations
+NUM_WORKERS = 4 
+NUM_CLASSES = 4
+BATCH_SIZE = 64
+NUM_EPOCHS = 100 
+LEARNING_RATE = 0.0001
+RANDOM_SEED = 123
+LOG_STEP = 150
+LOG_DIR = '/path/to/logs' + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
+
+damage_intensity_encoding = dict()
+damage_intensity_encoding[3] = '3'
+damage_intensity_encoding[2] = '2' 
+damage_intensity_encoding[1] = '1' 
+damage_intensity_encoding[0] = '0' 
+
+
+###
+# Function to compute unweighted f1 scores, just for reference
+###
+def f1(y_true, y_pred):
+    def recall(y_true, y_pred):
+        """Recall metric.
+
+        Only computes a batch-wise average of recall.
+
+        Computes the recall, a metric for multi-label classification of
+        how many relevant items are selected.
+        """
+        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
+        recall = true_positives / (possible_positives + K.epsilon())
+        return recall
+
+    def precision(y_true, y_pred):
+        """Precision metric.
+
+        Only computes a batch-wise average of precision.
+
+        Computes the precision, a metric for multi-label classification of
+        how many selected items are relevant.
+        """
+        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
+        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
+        precision = true_positives / (predicted_positives + K.epsilon())
+        return precision
+
+
+    precision = precision(y_true, y_pred)
+    recall = recall(y_true, y_pred)
+    return 2*((precision*recall)/(precision+recall+K.epsilon()))
+
+
+###
+# Creates data generator for validation set
+###
+def validation_generator(test_csv, test_dir):
+    df = pd.read_csv(test_csv)
+    df = df.replace({"labels" : damage_intensity_encoding })
+
+    gen = keras.preprocessing.image.ImageDataGenerator(
+                             rescale=1.4)
+
+
+    return gen.flow_from_dataframe(dataframe=df,
+                                   directory=test_dir,
+                                   x_col='uuid',
+                                   y_col='labels',
+                                   batch_size=BATCH_SIZE,
+                                   shuffle=False,
+                                   seed=RANDOM_SEED,
+                                   class_mode="categorical",
+                                   target_size=(128, 128))
+
+
+###
+# Applies random transformations to training data
+###
+def augment_data(df, in_dir):
+
+    df = df.replace({"labels" : damage_intensity_encoding })
+    gen = keras.preprocessing.image.ImageDataGenerator(horizontal_flip=True,
+                             vertical_flip=True,
+                             width_shift_range=0.1,
+                             height_shift_range=0.1,
+                             rescale=1.4)
+    return gen.flow_from_dataframe(dataframe=df,
+                                   directory=in_dir,
+                                   x_col='uuid',
+                                   y_col='labels',
+                                   batch_size=BATCH_SIZE,
+                                   seed=RANDOM_SEED,
+                                   class_mode="categorical",
+                                   target_size=(128, 128))
+
+
+# Run training and evaluation based on existing or new model
+def train_model(train_data, train_csv, test_data, test_csv, model_in, model_out):
+
+    model = generate_xBD_baseline_model()
+
+    # Add model weights if provided by user
+    if model_in is not None:
+        model.load_weights(model_in)
+
+    df = pd.read_csv(train_csv)
+    class_weights = compute_class_weight('balanced', np.unique(df['labels'].to_list()), df['labels'].to_list());
+    d_class_weights = dict(enumerate(class_weights))
+
+    samples = df['uuid'].count()
+    steps = np.ceil(samples/BATCH_SIZE)
+
+    # Augments the training data
+    train_gen_flow = augment_data(df, train_data)
+
+    #Set up tensorboard logging
+    tensorboard_callbacks = keras.callbacks.TensorBoard(log_dir=LOG_DIR,
+                                                        batch_size=BATCH_SIZE)
+
+
+    #Filepath to save model weights
+    filepath = model_out + "-saved-model-{epoch:02d}-{acc:.2f}.hdf5"
+    checkpoints = keras.callbacks.ModelCheckpoint(filepath,
+                                                    monitor=['loss', 'accuracy'],
+                                                    verbose=1,
+                                                    save_best_only=False,
+                                                    mode='max')
+
+    #Adds adam optimizer
+    adam = keras.optimizers.Adam(lr=LEARNING_RATE,
+                                    beta_1=0.9,
+                                    beta_2=0.999,
+                                    epsilon=None,
+                                    decay=0.0,
+                                    amsgrad=False)
+
+
+    model.compile(loss=ordinal_loss, optimizer=adam, metrics=['accuracy', f1])
+
+    #Training begins
+    model.fit_generator(generator=train_gen_flow,
+                        steps_per_epoch=steps,
+                        epochs=NUM_EPOCHS,
+                        workers=NUM_WORKERS,
+                        use_multiprocessing=True,
+                        class_weight=d_class_weights,
+                        callbacks=[tensorboard_callbacks, checkpoints],
+                        verbose=1)
+
+
+    #Evalulate f1 weighted scores on validation set
+    validation_gen = validation_generator(test_csv, test_data)
+    predictions = model.predict(validation_gen)
+
+    val_trues = validation_gen.classes
+    val_pred = np.argmax(predictions, axis=-1)
+
+    f1_weighted = f1_score(val_trues, val_pred, average='weighted')
+    print(f1_weighted)
+
+
+def main():
+
+    parser = argparse.ArgumentParser(description='Run Building Damage Classification Training & Evaluation')
+    parser.add_argument('--train_data',
+                        required=True,
+                        metavar="/path/to/xBD_train",
+                        help="Full path to the train data directory")
+    parser.add_argument('--train_csv',
+                        required=True,
+                        metavar="/path/to/xBD_split",
+                        help="Full path to the train csv")
+    parser.add_argument('--test_data',
+                        required=True,
+                        metavar="/path/to/xBD_test",
+                        help="Full path to the test data directory")
+    parser.add_argument('--test_csv',
+                        required=True,
+                        metavar="/path/to/xBD_split",
+                        help="Full path to the test csv")
+    parser.add_argument('--model_in',
+                        default=None,
+                        metavar='/path/to/input_model',
+                        help="Path to save model")
+    parser.add_argument('--model_out',
+                        required=True,
+                        metavar='/path/to/save_model',
+                        help="Path to save model")
+
+    args = parser.parse_args()
+
+    train_model(args.train_data, args.train_csv, args.test_data, args.test_csv, args.model_in, args.model_out)
+
+
+if __name__ == '__main__':
+    main()