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"
)