def __init__(self, config="conf/config.yml", saved_model=None, log_dir=None): """ Args: config: path to a config file, defaults to ../conf/config.yml saved_model: Optional, a previous saved AttentionModel .h5 """ self.config = parse_yaml(config) if saved_model: self.model = load_model(saved_model) #Holders self.testing_set = None self.training_set = None if log_dir: self.log_dir = log_dir else: self.log_dir = None #log some helpful data self.height = self.config["train"]["crop_size"] self.width = self.config["train"]["crop_size"] self.channels = self.config["train"]["sensor_channels"] self.weighted_sum = self.config["train"]["weighted_sum"] self.extend_box = self.config["train"]["extend_box"] self.classes_file = os.path.join(self.config["train"]["tfrecords"], "class_labels.csv") self.classes = self.config["train"]["classes"]
def __init__(self, config="conf/config.yml", saved_model=None, log_dir=None): """ Args: config: path to a config file, defaults to ../conf/config.yml saved_model: Optional, a previous saved AttentionModel .h5 """ self.config = parse_yaml(config) if saved_model: self.model = load_model(saved_model) #Holders self.testing_set = None self.training_set = None if log_dir: self.log_dir = log_dir else: self.log_dir = None #log config self.HSI_size = self.config["train"]["HSI"]["crop_size"] self.HSI_channels = self.config["train"]["HSI"]["sensor_channels"] self.HSI_weighted_sum = self.config["train"]["HSI"]["weighted_sum"] self.RGB_size= self.config["train"]["RGB"]["crop_size"] self.RGB_channels = self.config["train"]["RGB"]["sensor_channels"] self.RGB_weighted_sum = self.config["train"]["RGB"]["weighted_sum"] self.extend_box = self.config["train"]["extend_box"] self.classes_file = self.config["train"]["species_class_file"] self.sites = self.config["train"]["sites"]
def __init__(self, config="conf/config.yml", saved_model=None, log_dir=None): """ Args: config: path to a config file, defaults to ../conf/config.yml saved_model: Optional, a previous saved AttentionModel .h5 """ self.config = parse_yaml(config) if saved_model: self.model = load_model(saved_model) #Holders self.testing_set = None self.training_set = None if log_dir: self.log_dir = log_dir else: self.log_dir = None #log config self.HSI_size = self.config["train"]["HSI"]["crop_size"] self.HSI_channels = self.config["train"]["HSI"]["sensor_channels"] self.HSI_weighted_sum = self.config["train"]["HSI"]["weighted_sum"] self.RGB_size = self.config["train"]["RGB"]["crop_size"] self.RGB_channels = self.config["train"]["RGB"]["sensor_channels"] self.RGB_weighted_sum = self.config["train"]["RGB"]["weighted_sum"] self.HSI_extend_box = self.config["train"]["HSI"]["extend_box"] self.classes_file = self.config["train"]["species_class_file"] try: if self.config["train"]["site_class_file"] is not None: self.sites = pd.read_csv( self.config["train"]["site_class_file"]).shape[0] if self.config["train"]["domain_class_file"] is not None: self.domains = pd.read_csv( self.config["train"]["domain_class_file"]).shape[0] except: pass try: self.train_shp = gpd.read_file( self.config["train"]["ground_truth_path"]) except: self.train_shp = None try: self.test_shp = gpd.read_file( self.config["evaluation"]["ground_truth_path"]) except: self.test_shp = None
def __init__(self, config="conf/config.yml", saved_model=None): """ Args: config: path to a config file, defaults to ../conf/config.yml saved_model: Optional, a previous saved AttentionModel .h5 """ self.config = parse_yaml(config) if saved_model: self.model = load_model(saved_model) #Holders self.testing_set = None self.training_set = None
RGB_crops=RGB_crops, labels=numeric_labels, sites=numeric_sites, elevations=elevations, box_index=box_indexes, savedir=savedir, RGB_size=RGB_size, HSI_size=HSI_size, chunk_size=chunk_size) return tfrecords if __name__ == "__main__": #Read config from top level dir ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) config = parse_yaml("{}/conf/tree_config.yml".format(ROOT)) #train data main( field_data=config["train"]["ground_truth_path"], RGB_size=config["train"]["RGB"]["crop_size"], HSI_size=config["train"]["HSI"]["crop_size"], hyperspectral_dir=config["hyperspectral_sensor_pool"], rgb_dir=config["rgb_sensor_pool"], extend_box=config["train"]["extend_box"], hyperspectral_savedir=config["hyperspectral_tif_dir"], savedir=config["train"]["tfrecords"], n_workers=config["cpu_workers"], saved_model="/home/b.weinstein/miniconda3/envs/DeepTreeAttention_DeepForest/lib/python3.7/site-packages/deepforest/data/NEON.h5" )