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Extracted cli actions into separate module

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Signed-off-by: Jim Martens <github@2martens.de>
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Jim Martens
2019-06-05 10:57:46 +02:00
parent f1fe8ddfc5
commit db9ab462ef
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src/twomartens/masterthesis/cli.py Normal file
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# -*- 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)

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src/twomartens/masterthesis/main.py
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@ -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()