diff --git a/src/twomartens/masterthesis/cli.py b/src/twomartens/masterthesis/cli.py new file mode 100644 index 0000000..6af4813 --- /dev/null +++ b/src/twomartens/masterthesis/cli.py @@ -0,0 +1,259 @@ +# -*- coding: utf-8 -*- + +# Copyright 2018 Timon Brüning, Inga Kempfert, Anne Kunstmann, Jim Martens, +# Marius Pierenkemper, Yanneck Reiss +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Provides CLI actions. + +Functions: + train(...): trains a network + test(...): evaluates a network + val(...): runs predictions on the validation data + prepare(...): prepares the SceneNet ground truth data +""" +import argparse + + +def train(args: argparse.Namespace) -> None: + if args.network == "auto_encoder": + _auto_encoder_train(args) + + +def _auto_encoder_train(args: argparse.Namespace) -> None: + from twomartens.masterthesis import data + from twomartens.masterthesis.aae import train + import tensorflow as tf + from tensorflow.python.ops import summary_ops_v2 + + tf.enable_eager_execution() + coco_path = args.coco_path + category = args.category + batch_size = 16 + image_size = 256 + coco_data = data.load_coco_train(coco_path, category, num_epochs=args.num_epochs, batch_size=batch_size, + resized_shape=(image_size, image_size)) + train_summary_writer = summary_ops_v2.create_file_writer( + f"{args.summary_path}/train/category-{category}/{args.iteration}" + ) + if args.debug: + with train_summary_writer.as_default(): + train.train_simple(coco_data, iteration=args.iteration, + weights_prefix=f"{args.weights_path}/category-{category}", + zsize=16, lr=0.0001, verbose=args.verbose, image_size=image_size, + channels=3, train_epoch=args.num_epochs, batch_size=batch_size) + else: + train.train_simple(coco_data, iteration=args.iteration, + weights_prefix=f"{args.weights_path}/category-{category}", + zsize=16, lr=0.0001, verbose=args.verbose, image_size=image_size, + channels=3, train_epoch=args.num_epochs, batch_size=batch_size) + + +def test(args: argparse.Namespace) -> None: + if args.network == "ssd": + _ssd_test(args) + else: + raise NotImplementedError + + +def _ssd_test(args: argparse.Namespace) -> None: + import glob + import os + import pickle + + import numpy as np + import tensorflow as tf + + from twomartens.masterthesis import evaluate + from twomartens.masterthesis import ssd + + tf.enable_eager_execution() + + batch_size = 16 + use_dropout = False if args.network == "ssd" else True + output_path = f"{args.output_path}/val/{args.network}/{args.iteration}" + evaluation_path = f"{args.evaluation_path}/{args.network}" + result_file = f"{evaluation_path}/results-{args.iteration}.bin" + label_file = f"{output_path}/labels.bin" + predictions_file = f"{output_path}/predictions.bin" + predictions_per_class_file = f"{output_path}/predictions_class.bin" + os.makedirs(evaluation_path, exist_ok=True) + + # retrieve labels and un-batch them + files = glob.glob(f"{output_path}/*ssd_labels*") + labels = [] + for filename in files: + with open(filename, "rb") as file: + # get labels per batch + _labels = np.asarray(pickle.load(file)) + # exclude padded label entries + for i in range(_labels.shape[0]): + image_labels = _labels[i] + image_labels = image_labels[image_labels[:, 0] != -1] + labels.append(image_labels) + # store labels for later use + with open(label_file, "wb") as file: + pickle.dump(labels, file) + + number_gt_per_class = evaluate.get_number_gt_per_class(labels, ssd.N_CLASSES) + + # retrieve predictions and un-batch them + files = glob.glob(f"{output_path}/*ssd_predictions*") + predictions = [] + for filename in files: + with open(filename, "rb") as file: + # get predictions per batch + _predictions = pickle.load(file) + predictions.extend(_predictions) + del _predictions + + # prepare predictions for further use + with open(predictions_file, "wb") as file: + pickle.dump(predictions, file) + + predictions_per_class = evaluate.prepare_predictions(predictions, ssd.N_CLASSES) + del predictions + + with open(predictions_per_class_file, "wb") as file: + pickle.dump(predictions_per_class, file) + + # compute matches between predictions and ground truth + true_positives, false_positives, \ + cum_true_positives, cum_false_positives, open_set_error = evaluate.match_predictions(predictions_per_class, + labels, + ssd.N_CLASSES) + del labels + cum_precisions, cum_recalls = evaluate.get_precision_recall(number_gt_per_class, + cum_true_positives, + cum_false_positives, + ssd.N_CLASSES) + f1_scores = evaluate.get_f1_score(cum_precisions, cum_recalls, ssd.N_CLASSES) + average_precisions = evaluate.get_mean_average_precisions(cum_precisions, cum_recalls, ssd.N_CLASSES) + mean_average_precision = evaluate.get_mean_average_precision(average_precisions) + + results = { + "true_positives": true_positives, + "false_positives": false_positives, + "cumulative_true_positives": cum_true_positives, + "cumulative_false_positives": cum_false_positives, + "cumulative_precisions": cum_precisions, + "cumulative_recalls": cum_recalls, + "f1_scores": f1_scores, + "mean_average_precisions": average_precisions, + "mean_average_precision": mean_average_precision, + "open_set_error": open_set_error + } + + with open(result_file, "wb") as file: + pickle.dump(results, file) + + +def val(args: argparse.Namespace) -> None: + if args.network == "ssd" or args.network == "bayesian_ssd": + _ssd_val(args) + elif args.network == "auto_encoder": + _auto_encoder_val(args) + + +def _ssd_val(args: argparse.Namespace) -> None: + import pickle + import os + + import tensorflow as tf + from tensorflow.python.ops import summary_ops_v2 + + from twomartens.masterthesis import data + from twomartens.masterthesis import ssd + + config = tf.ConfigProto() + config.log_device_placement = False + config.gpu_options.allow_growth = False + tf.enable_eager_execution(config=config) + + batch_size = 16 + image_size = 300 + forward_passes_per_image = 10 + use_dropout = False if args.network == "ssd" else True + + weights_file = f"{args.weights_path}/VGG_coco_SSD_300x300_iter_400000.h5" + output_path = f"{args.output_path}/val/{args.network}/{args.iteration}/" + os.makedirs(output_path, exist_ok=True) + + # load prepared ground truth + with open(f"{args.ground_truth_path}/photo_paths.bin", "rb") as file: + file_names_photos = pickle.load(file) + with open(f"{args.ground_truth_path}/instances.bin", "rb") as file: + instances = pickle.load(file) + + scenenet_data, nr_digits = data.load_scenenet_val(file_names_photos, instances, args.coco_path, + batch_size=batch_size, + resized_shape=(image_size, image_size)) + del file_names_photos, instances + + use_summary_writer = summary_ops_v2.create_file_writer( + f"{args.summary_path}/val/{args.network}/{args.iteration}" + ) + if args.debug: + with use_summary_writer.as_default(): + ssd.predict(scenenet_data, use_dropout, output_path, weights_file, nr_digits=nr_digits, + forward_passes_per_image=forward_passes_per_image) + else: + ssd.predict(scenenet_data, use_dropout, output_path, weights_file, nr_digits=nr_digits, + forward_passes_per_image=forward_passes_per_image) + + +def _auto_encoder_val(args: argparse.Namespace) -> None: + from twomartens.masterthesis import data + from twomartens.masterthesis.aae import run + import tensorflow as tf + from tensorflow.python.ops import summary_ops_v2 + + tf.enable_eager_execution() + coco_path = args.coco_path + category = args.category + category_trained = args.category_trained + batch_size = 16 + image_size = 256 + coco_data = data.load_coco_val(coco_path, category, num_epochs=1, + batch_size=batch_size, resized_shape=(image_size, image_size)) + use_summary_writer = summary_ops_v2.create_file_writer( + f"{args.summary_path}/val/category-{category}/{args.iteration}" + ) + if args.debug: + with use_summary_writer.as_default(): + run.run_simple(coco_data, iteration=args.iteration_trained, + weights_prefix=f"{args.weights_path}/category-{category_trained}", + zsize=16, verbose=args.verbose, channels=3, batch_size=batch_size, + image_size=image_size) + else: + run.run_simple(coco_data, iteration=args.iteration_trained, + weights_prefix=f"{args.weights_path}/category-{category_trained}", + zsize=16, verbose=args.verbose, channels=3, batch_size=batch_size, + image_size=image_size) + + +def prepare(args: argparse.Namespace) -> None: + import pickle + + from twomartens.masterthesis import data + + file_names_photos, file_names_instances, instances = data.prepare_scenenet_val(args.scenenet_path, + args.protobuf_path) + with open(f"{args.ground_truth_path}/photo_paths.bin", "wb") as file: + pickle.dump(file_names_photos, file) + with open(f"{args.ground_truth_path}/instance_paths.bin", "wb") as file: + pickle.dump(file_names_instances, file) + with open(f"{args.ground_truth_path}/instances.bin", "wb") as file: + pickle.dump(instances, file) diff --git a/src/twomartens/masterthesis/main.py b/src/twomartens/masterthesis/main.py index ff0cdd7..476115a 100644 --- a/src/twomartens/masterthesis/main.py +++ b/src/twomartens/masterthesis/main.py @@ -22,6 +22,8 @@ Functions: """ import argparse +from twomartens.masterthesis import cli + def main() -> None: """ @@ -51,13 +53,13 @@ def main() -> None: args = parser.parse_args() if args.action == "train": - _train(args) + cli.train(args) elif args.action == "test": - _test(args) + cli.test(args) elif args.action == "val": - _val(args) + cli.val(args) elif args.action == "prepare": - _prepare(args) + cli.prepare(args) def _build_prepare(parser: argparse.ArgumentParser) -> None: @@ -138,237 +140,5 @@ def _build_ssd_test(parser: argparse.ArgumentParser) -> None: parser.add_argument("iteration", type=int, help="the validation iteration to use") -def _train(args: argparse.Namespace) -> None: - if args.network == "auto_encoder": - _auto_encoder_train(args) - - -def _auto_encoder_train(args: argparse.Namespace) -> None: - from twomartens.masterthesis import data - from twomartens.masterthesis.aae import train - import tensorflow as tf - from tensorflow.python.ops import summary_ops_v2 - - tf.enable_eager_execution() - coco_path = args.coco_path - category = args.category - batch_size = 16 - image_size = 256 - coco_data = data.load_coco_train(coco_path, category, num_epochs=args.num_epochs, batch_size=batch_size, - resized_shape=(image_size, image_size)) - train_summary_writer = summary_ops_v2.create_file_writer( - f"{args.summary_path}/train/category-{category}/{args.iteration}" - ) - if args.debug: - with train_summary_writer.as_default(): - train.train_simple(coco_data, iteration=args.iteration, - weights_prefix=f"{args.weights_path}/category-{category}", - zsize=16, lr=0.0001, verbose=args.verbose, image_size=image_size, - channels=3, train_epoch=args.num_epochs, batch_size=batch_size) - else: - train.train_simple(coco_data, iteration=args.iteration, - weights_prefix=f"{args.weights_path}/category-{category}", - zsize=16, lr=0.0001, verbose=args.verbose, image_size=image_size, - channels=3, train_epoch=args.num_epochs, batch_size=batch_size) - - -def _test(args: argparse.Namespace) -> None: - if args.network == "ssd": - _ssd_test(args) - else: - raise NotImplementedError - - -def _ssd_test(args: argparse.Namespace) -> None: - import glob - import os - import pickle - - import numpy as np - import tensorflow as tf - - from twomartens.masterthesis import evaluate - from twomartens.masterthesis import ssd - - tf.enable_eager_execution() - - batch_size = 16 - use_dropout = False if args.network == "ssd" else True - output_path = f"{args.output_path}/val/{args.network}/{args.iteration}" - evaluation_path = f"{args.evaluation_path}/{args.network}" - result_file = f"{evaluation_path}/results-{args.iteration}.bin" - label_file = f"{output_path}/labels.bin" - predictions_file = f"{output_path}/predictions.bin" - predictions_per_class_file = f"{output_path}/predictions_class.bin" - os.makedirs(evaluation_path, exist_ok=True) - - # retrieve labels and un-batch them - files = glob.glob(f"{output_path}/*ssd_labels*") - labels = [] - for filename in files: - with open(filename, "rb") as file: - # get labels per batch - _labels = np.asarray(pickle.load(file)) - # exclude padded label entries - for i in range(_labels.shape[0]): - image_labels = _labels[i] - image_labels = image_labels[image_labels[:, 0] != -1] - labels.append(image_labels) - # store labels for later use - with open(label_file, "wb") as file: - pickle.dump(labels, file) - - number_gt_per_class = evaluate.get_number_gt_per_class(labels, ssd.N_CLASSES) - - # retrieve predictions and un-batch them - files = glob.glob(f"{output_path}/*ssd_predictions*") - predictions = [] - for filename in files: - with open(filename, "rb") as file: - # get predictions per batch - _predictions = pickle.load(file) - predictions.extend(_predictions) - del _predictions - - # prepare predictions for further use - with open(predictions_file, "wb") as file: - pickle.dump(predictions, file) - - predictions_per_class = evaluate.prepare_predictions(predictions, ssd.N_CLASSES) - del predictions - - with open(predictions_per_class_file, "wb") as file: - pickle.dump(predictions_per_class, file) - - # compute matches between predictions and ground truth - true_positives, false_positives, \ - cum_true_positives, cum_false_positives, open_set_error = evaluate.match_predictions(predictions_per_class, - labels, - ssd.N_CLASSES) - del labels - cum_precisions, cum_recalls = evaluate.get_precision_recall(number_gt_per_class, - cum_true_positives, - cum_false_positives, - ssd.N_CLASSES) - f1_scores = evaluate.get_f1_score(cum_precisions, cum_recalls, ssd.N_CLASSES) - average_precisions = evaluate.get_mean_average_precisions(cum_precisions, cum_recalls, ssd.N_CLASSES) - mean_average_precision = evaluate.get_mean_average_precision(average_precisions) - - results = { - "true_positives": true_positives, - "false_positives": false_positives, - "cumulative_true_positives": cum_true_positives, - "cumulative_false_positives": cum_false_positives, - "cumulative_precisions": cum_precisions, - "cumulative_recalls": cum_recalls, - "f1_scores": f1_scores, - "mean_average_precisions": average_precisions, - "mean_average_precision": mean_average_precision, - "open_set_error": open_set_error - } - - with open(result_file, "wb") as file: - pickle.dump(results, file) - - -def _val(args: argparse.Namespace) -> None: - if args.network == "ssd" or args.network == "bayesian_ssd": - _ssd_val(args) - elif args.network == "auto_encoder": - _auto_encoder_val(args) - - -def _ssd_val(args: argparse.Namespace) -> None: - import pickle - import os - - import tensorflow as tf - from tensorflow.python.ops import summary_ops_v2 - - from twomartens.masterthesis import data - from twomartens.masterthesis import ssd - - config = tf.ConfigProto() - config.log_device_placement = False - config.gpu_options.allow_growth = False - tf.enable_eager_execution(config=config) - - batch_size = 16 - image_size = 300 - forward_passes_per_image = 10 - use_dropout = False if args.network == "ssd" else True - - weights_file = f"{args.weights_path}/VGG_coco_SSD_300x300_iter_400000.h5" - output_path = f"{args.output_path}/val/{args.network}/{args.iteration}/" - os.makedirs(output_path, exist_ok=True) - - # load prepared ground truth - with open(f"{args.ground_truth_path}/photo_paths.bin", "rb") as file: - file_names_photos = pickle.load(file) - with open(f"{args.ground_truth_path}/instances.bin", "rb") as file: - instances = pickle.load(file) - - scenenet_data, nr_digits = data.load_scenenet_val(file_names_photos, instances, args.coco_path, - batch_size=batch_size, - resized_shape=(image_size, image_size)) - del file_names_photos, instances - - use_summary_writer = summary_ops_v2.create_file_writer( - f"{args.summary_path}/val/{args.network}/{args.iteration}" - ) - if args.debug: - with use_summary_writer.as_default(): - ssd.predict(scenenet_data, use_dropout, output_path, weights_file, nr_digits=nr_digits, - forward_passes_per_image=forward_passes_per_image) - else: - ssd.predict(scenenet_data, use_dropout, output_path, weights_file, nr_digits=nr_digits, - forward_passes_per_image=forward_passes_per_image) - - -def _auto_encoder_val(args: argparse.Namespace) -> None: - from twomartens.masterthesis import data - from twomartens.masterthesis.aae import run - import tensorflow as tf - from tensorflow.python.ops import summary_ops_v2 - - tf.enable_eager_execution() - coco_path = args.coco_path - category = args.category - category_trained = args.category_trained - batch_size = 16 - image_size = 256 - coco_data = data.load_coco_val(coco_path, category, num_epochs=1, - batch_size=batch_size, resized_shape=(image_size, image_size)) - use_summary_writer = summary_ops_v2.create_file_writer( - f"{args.summary_path}/val/category-{category}/{args.iteration}" - ) - if args.debug: - with use_summary_writer.as_default(): - run.run_simple(coco_data, iteration=args.iteration_trained, - weights_prefix=f"{args.weights_path}/category-{category_trained}", - zsize=16, verbose=args.verbose, channels=3, batch_size=batch_size, - image_size=image_size) - else: - run.run_simple(coco_data, iteration=args.iteration_trained, - weights_prefix=f"{args.weights_path}/category-{category_trained}", - zsize=16, verbose=args.verbose, channels=3, batch_size=batch_size, - image_size=image_size) - - -def _prepare(args: argparse.Namespace) -> None: - import pickle - - from twomartens.masterthesis import data - - file_names_photos, file_names_instances, instances = data.prepare_scenenet_val(args.scenenet_path, - args.protobuf_path) - with open(f"{args.ground_truth_path}/photo_paths.bin", "wb") as file: - pickle.dump(file_names_photos, file) - with open(f"{args.ground_truth_path}/instance_paths.bin", "wb") as file: - pickle.dump(file_names_instances, file) - with open(f"{args.ground_truth_path}/instances.bin", "wb") as file: - pickle.dump(instances, file) - - if __name__ == "__main__": main()