def main():
init_logging()
config = load_config()
build_config = config.build
db = TrackDatabase(os.path.join(config.tracks_folder, "dataset.hdf5"))
dataset = Dataset(db, "dataset", config)
tracks_loaded, total_tracks = dataset.load_tracks()
print(
"Loaded {}/{} tracks, found {:.1f}k segments".format(
tracks_loaded, total_tracks, len(dataset.segments) / 1000
)
)
for key, value in dataset.filtered_stats.items():
if value != 0:
print(" {} filtered {}".format(key, value))
print()
show_tracks_breakdown(dataset)
print()
show_segments_breakdown(dataset)
print()
show_cameras_breakdown(dataset)
print()
print("Splitting data set into train / validation")
datasets = split_dataset_by_cameras(db, dataset, build_config)
# if build_config.use_previous_split:
# split = get_previous_validation_bins(build_config.previous_split)
# datasets = split_dataset(db, dataset, build_config, split)
# else:
# datasets = split_dataset(db, dataset, build_config)
pickle.dump(datasets, open(dataset_db_path(config), "wb"))
def train_model(run_name, conf, hyper_params):
"""Trains a model with the given hyper parameters.
"""
run_name = os.path.join("train", run_name)
# a little bit of a pain, the model needs to know how many classes to classify during initialisation,
# but we don't load the dataset till after that, so we load it here just to count the number of labels...
datasets_filename = dataset_db_path(conf)
with open(datasets_filename, "rb") as f:
dsets = pickle.load(f)
labels = dsets[0].labels
model = ModelCRNN_LQ(labels=len(labels), train_config=conf.train, **hyper_params)
model.import_dataset(datasets_filename)
# display the data set summary
print("Training on labels", labels)
print()
print(
"{:<20} {:<20} {:<20} {:<20} (segments/tracks/bins/weight)".format(
"label", "train", "validation", "test"
)
)
for label in labels:
print(
"{:<20} {:<20} {:<20} {:<20}".format(
label,
"{}/{}/{}/{:.1f}".format(*model.datasets.train.get_counts(label)),
"{}/{}/{}/{:.1f}".format(*model.datasets.validation.get_counts(label)),
"{}/{}/{}/{:.1f}".format(*model.datasets.test.get_counts(label)),
)
)
print()
for dataset in dsets:
print(dataset.labels)
print("Training started")
print("---------------------")
print("Hyper parameters")
print("---------------------")
print(model.hyperparams_string)
print()
print("Found {0:.1f}K training examples".format(model.rows / 1000))
print()
model.train_model(
epochs=conf.train.epochs,
run_name=run_name + " " + datetime.datetime.now().strftime("%Y%m%d-%H%M%S"),
)
model.save()
model.close()
# this shouldn't be nessesary, but unfortunately my model.close isn't cleaning up everything.
# I think it's because i'm adding everything to the default graph?
tf.reset_default_graph()
return model
def save_eval_model(args):
config = Config.load_from_file()
datasets_filename = dataset_db_path(config)
with open(datasets_filename, "rb") as f:
dsets = pickle.load(f)
labels = ["hedgehog", "false-positive", "possum", "rodent", "bird"]
# this needs to be the same as the source model class
model = ModelCRNN_HQ(
labels=len(labels),
train_config=config.train,
training=True,
tflite=True,
**config.train.hyper_params,
)
model.saver = tf.compat.v1.train.Saver(max_to_keep=1000)
model.restore_params(os.path.join(args.model_dir, args.model_name))
model.save(os.path.join(args.model_dir, "eval-model"))
model.setup_summary_writers("convert")
def main():
init_logging()
args = parse_args()
config = load_config(args.config_file)
db = TrackDatabase(os.path.join(config.tracks_folder, "dataset.hdf5"))
dataset = Dataset(
db, "dataset", config, consecutive_segments=args.consecutive_segments
)
tracks_loaded, total_tracks = dataset.load_tracks(before_date=args.date)
print(
"Loaded {}/{} tracks, found {:.1f}k segments".format(
tracks_loaded, total_tracks, len(dataset.segments) / 1000
)
)
for key, value in dataset.filtered_stats.items():
if value != 0:
print(" {} filtered {}".format(key, value))
print()
show_tracks_breakdown(dataset)
print()
show_segments_breakdown(dataset)
print()
show_important_frames_breakdown(dataset)
print()
show_cameras_breakdown(dataset)
print()
print("Splitting data set into train / validation")
datasets = split_dataset_by_cameras(db, dataset, config, args)
if args.date is None:
args.date = datetime.datetime.now(pytz.utc) - datetime.timedelta(days=7)
test = test_dataset(db, config, args.date)
datasets = (*datasets, test)
print_counts(dataset, *datasets)
print_cameras(*datasets)
pickle.dump(datasets, open(dataset_db_path(config), "wb"))