def create_generators(args, data, DeepForest_config):
""" Create generators for training and validation.
"""
# create random transform generator for augmenting training data
if args.random_transform:
transform_generator = random_transform_generator(
min_rotation=-0.1,
max_rotation=0.1,
min_translation=(-0.1, -0.1),
max_translation=(0.1, 0.1),
min_shear=-0.1,
max_shear=0.1,
min_scaling=(0.9, 0.9),
max_scaling=(1.1, 1.1),
flip_x_chance=0.5,
flip_y_chance=0.5,
)
else:
transform_generator = random_transform_generator(flip_x_chance=0.5)
#Split training and test data - hardcoded paths set below.
train, test = preprocess.split_training(
data,
DeepForest_config,
single_tile=DeepForest_config["single_tile"],
experiment=experiment)
experiment.log_dataset_hash(data=train)
#Write out for debug
if args.save_path:
train.to_csv(os.path.join(args.save_path, 'training_dict.csv'),
header=False)
#Training Generator
train_generator = OnTheFlyGenerator(data,
train,
batch_size=args.batch_size,
DeepForest_config=DeepForest_config,
group_method="none",
shuffle_tile_epoch=True,
name="training")
#Validation Generator
validation_generator = OnTheFlyGenerator(
data,
test,
batch_size=args.batch_size,
DeepForest_config=DeepForest_config,
group_method="none",
name="validation")
return train_generator, validation_generator
def create_generator(args, main, DeepForest_config):
""" Create generators for training and validation.
"""
# create random transform generator for augmenting training data
if args.random_transform:
transform_generator = random_transform_generator(
min_rotation=-0.1,
max_rotation=0.1,
min_translation=(-0.1, -0.1),
max_translation=(0.1, 0.1),
min_shear=-0.1,
max_shear=0.1,
min_scaling=(0.9, 0.9),
max_scaling=(1.1, 1.1),
flip_x_chance=0.5,
flip_y_chance=0.5,
)
else:
transform_generator = random_transform_generator(flip_x_chance=0.5)
#Split training and test data - hardcoded paths set below.
train, test = preprocess.split_training(
data,
DeepForest_config,
single_tile=DeepForest_config["single_tile"],
experiment=None)
#Training Generator
generator = OnTheFlyGenerator(data,
train,
batch_size=args.batch_size,
DeepForest_config=DeepForest_config,
group_method="none")
return (generator)
def create_NEON_generator(args, site, DeepForest_config):
""" Create generators for training and validation.
"""
annotations, windows = preprocess.NEON_annotations(site, DeepForest_config)
#Training Generator
generator = OnTheFlyGenerator(annotations,
windows,
batch_size=args.batch_size,
DeepForest_config=DeepForest_config,
group_method="none")
return (generator)
def create_NEON_generator(batch_size, DeepForest_config, name="evaluation"):
""" Create generators for training and validation.
"""
annotations, windows = NEON_annotations(DeepForest_config)
#Training Generator
generator = OnTheFlyGenerator(annotations,
windows,
batch_size=batch_size,
DeepForest_config=DeepForest_config,
group_method="none",
name=name,
preprocess_image=image_utils.normalize)
return (generator)
def create_generator(args,data,config):
""" Create generators for training and validation.
"""
#Split training and test data - hardcoded paths set below.
_,test=preprocess.split_training(data,DeepForest_config,single_tile=DeepForest_config["single_tile"],experiment=None)
#Training Generator
generator = OnTheFlyGenerator(
data,
test,
batch_size=args.batch_size,
DeepForest_config=DeepForest_config,
group_method="none")
return(generator)