#save time for logging
dirname = datetime.now().strftime("%Y%m%d_%H%M%S")
#Load DeepForest_config and data file based on training or retraining mode
DeepForest_config = load_config("train")
data = preprocess.load_data(DeepForest_config["training_csvs"],
DeepForest_config["rgb_res"],
DeepForest_config["lidar_path"])
#Log site
site = DeepForest_config["evaluation_site"]
##Preprocess Filters##
if DeepForest_config['preprocess']['zero_area']:
data = preprocess.zero_area(data)
#pass an args object instead of using command line
args = [
"--epochs",
str(DeepForest_config["epochs"]), "--batch-size",
str(DeepForest_config['batch_size']), "--backbone",
str(DeepForest_config["backbone"]), "--score-threshold",
str(DeepForest_config["score_threshold"])
]
#Create log directory if saving snapshots
if not DeepForest_config["save_snapshot_path"] == "None":
snappath = DeepForest_config["save_snapshot_path"] + dirname
os.mkdir(snappath)
project_name='deepforest-retinanet')
##Set seed for reproducibility##
np.random.seed(2)
#Load data and combine into a large frame
#Training
data = preprocess.load_data(data_dir=config['training_csvs'])
#Evaluation
evaluation = preprocess.load_data(data_dir=config['evaluation_csvs'])
##Preprocess Filters##
if config['preprocess']['zero_area']:
data = preprocess.zero_area(data)
evaluation = preprocess.zero_area(evaluation)
#Write training and evaluation data to file for annotations
data.to_csv("data/training/detection.csv")
evaluation.to_csv("data/training/evaluation.csv")
#log data size and set number of steps
if not config["subsample"] == "None":
experiment.log_parameter("training_samples", config["subsample"])
steps = config["subsample"]
else:
experiment.log_parameter("training_samples", data.shape[0])
steps = data.shape[0]
#Create log directory if saving snapshots