def __init__(self, num_classes=1, label_dict={"Tree": 0}):
"""
Args:
num_classes (int): number of classes in the model
Returns:
self: a deepforest pytorch ligthning module
"""
super().__init__()
# Read config file - if a config file exists in local dir use it,
# if not use installed.
if os.path.exists("deepforest_config.yml"):
config_path = "deepforest_config.yml"
else:
try:
config_path = get_data("deepforest_config.yml")
except Exception as e:
raise ValueError(
"No deepforest_config.yml found either in local "
"directory or in installed package location. {}".format(e))
print("Reading config file: {}".format(config_path))
self.config = utilities.read_config(config_path)
# release version id to flag if release is being used
self.__release_version__ = None
self.num_classes = num_classes
self.create_model()
#Label encoder and decoder
self.label_dict = label_dict
self.numeric_to_label_dict = {v: k for k, v in label_dict.items()}
def __init__(self, weights=None):
self.weights = weights
#Read config file
self.config = utilities.read_config()
#Load model weights if needed
if self.weights is not None:
self.model = utilities.read_model(self.weights, self.config)
else:
self.model = None
def __init__(self, num_classes=1, label_dict = {"Tree":0}, transforms=None, config_file='deepforest_config.yml'):
"""
Args:
num_classes (int): number of classes in the model
config_file (str): path to deepforest config file
Returns:
self: a deepforest pytorch lightning module
"""
super().__init__()
#Pytorch lightning handles the device, but we need one for adhoc methods like predict_image.
if torch.cuda.is_available():
self.current_device = torch.device("cuda")
else:
self.current_device = torch.device("cpu")
# Read config file. Defaults to deepforest_config.yml in working directory.
# Falls back to default installed version
if os.path.exists(config_file):
config_path = config_file
else:
try:
config_path = get_data("deepforest_config.yml")
except Exception as e:
raise ValueError(
"No config file provided and deepforest_config.yml not found either in local "
"directory or in installed package location. {}".format(e))
print("Reading config file: {}".format(config_path))
self.config = utilities.read_config(config_path)
# release version id to flag if release is being used
self.__release_version__ = None
self.num_classes = num_classes
self.create_model()
#Label encoder and decoder
if not len(label_dict) == num_classes:
raise ValueError(
'label_dict {} does not match requested number of classes {}, please supply a label_dict argument {{"label1":0, "label2":1, "label3":2 ... etc}} for each label in the dataset'.format(label_dict, num_classes)
)
self.label_dict = label_dict
self.numeric_to_label_dict = {v: k for k, v in label_dict.items()}
#Add user supplied transforms
if transforms is None:
self.transforms = dataset.get_transform
else:
self.transforms = transforms
self.save_hyperparameters()
def config():
config = utilities.read_config(get_data("deepforest_config.yml"))
config["patch_size"] = 200
config["patch_overlap"] = 0.25
config["annotations_xml"] = get_data("OSBS_029.xml")
config["rgb_dir"] = "data"
config["annotations_file"] = "tests/data/OSBS_029.csv"
config["path_to_raster"] = get_data("OSBS_029.tif")
# Create a clean config test data
annotations = utilities.xml_to_annotations(xml_path=config["annotations_xml"])
annotations.to_csv("tests/data/OSBS_029.csv", index=False)
return config
def __init__(self, weights=None, saved_model=None):
self.weights = weights
self.saved_model = saved_model
# Read config file - if a config file exists in local dir use it,
# if not use installed.
if os.path.exists("deepforest_config.yml"):
config_path = "deepforest_config.yml"
else:
try:
config_path = get_data("deepforest_config.yml")
except Exception as e:
raise ValueError(
"No deepforest_config.yml found either in local "
"directory or in installed package location. {}".format(e))
print("Reading config file: {}".format(config_path))
self.config = utilities.read_config(config_path)
# Create a label dict, defaults to "Tree"
self.read_classes()
# release version id to flag if release is being used
self.__release_version__ = None
# Load saved model if needed
if self.saved_model:
print("Loading saved model")
# Capture user warning, not relevant here
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=UserWarning)
self.model = models.load_model(saved_model)
self.prediction_model = convert_model(self.model)
elif self.weights:
print("Creating model from weights")
backbone = models.backbone(self.config["backbone"])
self.model, self.training_model, self.prediction_model = create_models(
backbone.retinanet, num_classes=1, weights=self.weights)
else:
print(
"A blank deepforest object created. "
"To perform prediction, either train or load an existing model."
)
self.model = None
def config():
config = utilities.read_config("deepforest_config.yml")
return config
def config():
config = utilities.read_config(get_data("deepforest_config.yml"))
return config
local_annotations = future.result()
except Exception as e:
print("future {} failed with {}".format(future, e))
if __name__=="__main__":
#Generate anchor objects for each image and wrap in tfrecords
DEBUG = False
#Number of images per tfrecord
SIZE = 50
#Set paths
if DEBUG:
BASE_PATH = "/Users/ben/Documents/DeepForest_Model/"
FILEPATH = "/Users/ben/Documents/DeepForest_Model/"
BENCHMARK_PATH = "/Users/ben/Documents/NeonTreeEvaluation/"
dask_client = None
else:
BASE_PATH = "/orange/ewhite/b.weinstein/NeonTreeEvaluation/"
FILEPATH = "/orange/ewhite/b.weinstein/NeonTreeEvaluation/"
BENCHMARK_PATH = "/home/b.weinstein/NeonTreeEvaluation/"
dask_client = start_dask_cluster(number_of_workers=15, mem_size="5GB")
#Read config
config = read_config("deepforest_config.yml")
#generate_hand_annotations(DEBUG, BASE_PATH, FILEPATH, SIZE, config, dask_client)
#generate_pretraining(DEBUG, BASE_PATH, FILEPATH, SIZE, config, dask_client)
generate_benchmark(DEBUG, BENCHMARK_PATH, BENCHMARK_PATH, SIZE, config, dask_client)
