logger.info('Predict worker finished')
def check_block(blocks_predicted, block):
done = blocks_predicted.count({'block_id': block.block_id}) >= 1
return done
if __name__ == "__main__":
predict_config = sys.argv[1]
worker_config = sys.argv[2]
data_config = sys.argv[3]
predict_config_dict = read_predict_config(predict_config)
worker_config_dict = read_worker_config(worker_config)
data_config_dict = read_data_config(data_config)
full_config = predict_config_dict
full_config.update(worker_config_dict)
full_config.update(data_config_dict)
start = time.time()
predict_blockwise(**full_config)
end = time.time()
seconds = end - start
logger.info('Total time to predict: %f seconds' % seconds)
def set_up_environment(base_dir, experiment, train_number, predict_number,
graph_number, solve_number, mount_dirs, singularity,
queue, clean_up, reset):
input_params = locals()
train_files = {}
graph_setup_dir = os.path.join(
os.path.join(base_dir, experiment),
"03_graph/setup_t{}_p{}_g{}".format(train_number, predict_number,
graph_number))
solve_setup_dir = os.path.join(
os.path.join(base_dir, experiment),
"04_solve/setup_t{}_p{}_g{}_s{}".format(train_number, predict_number,
graph_number, solve_number))
predict_cfg_dict = read_predict_config(
os.path.join(graph_setup_dir, "predict_config.ini"))
data_cfg_dict = read_data_config(
os.path.join(graph_setup_dir, "data_config.ini"))
roi = Roi(data_cfg_dict["in_offset"], data_cfg_dict["in_size"])
db_name = predict_cfg_dict["db_name"]
db_host = predict_cfg_dict["db_host"]
selected_attr = "selected_{}".format(solve_number)
solved_attr = "solved_{}".format(solve_number)
edge_collection = "edges_g{}".format(graph_number)
node_collection = "nodes"
solved_before = attr_exists(db_name, db_host, edge_collection, solved_attr)
if not solved_before:
print("Graph not solved before, build attributes...")
reset_solve(db_name, db_host, edge_collection, node_collection,
selected_attr, solved_attr)
if clean_up:
if __name__ == "__main__":
if click.confirm(
'Are you sure you want to remove {} and all its contents?'.
format(solve_setup_dir),
default=False):
rmtree(solve_setup_dir)
else:
print("Abort clean up")
else:
rmtree(solve_setup_dir)
if reset:
if __name__ == "__main__":
if click.confirm(
'Are you sure you want to reset solve and selected status in {}_solve_s{}?'
.format(db_name, solve_number),
default=False):
reset_solve(db_name, db_host, edge_collection, node_collection,
selected_attr, solved_attr)
reset_step("solve_s{}_g{}".format(solve_number, graph_number),
db_name, db_host)
else:
print("Abort reset")
else:
reset_solve(db_name, db_host, edge_collection, node_collection,
selected_attr, solved_attr)
reset_step("solve_s{}_g{}".format(solve_number, graph_number),
db_name, db_host)
if not os.path.exists(graph_setup_dir):
raise ValueError("No graph at {}".format(graph_setup_dir))
if not os.path.exists(solve_setup_dir):
os.makedirs(solve_setup_dir)
else:
if __name__ == "__main__":
if click.confirm(
'Solve setup {} exists already, overwrite?'.format(
solve_setup_dir),
default=False):
rmtree(solve_setup_dir)
os.makedirs(solve_setup_dir)
else:
print("Abort.")
return
else:
raise ValueError(
"Solve setup exists already, choose different predict number or clean up."
)
copyfile(os.path.join(graph_setup_dir, "predict_config.ini"),
os.path.join(solve_setup_dir, "predict_config.ini"))
copyfile(os.path.join(graph_setup_dir, "data_config.ini"),
os.path.join(solve_setup_dir, "data_config.ini"))
copyfile(os.path.join(graph_setup_dir, "graph_config.ini"),
os.path.join(solve_setup_dir, "graph_config.ini"))
copyfile(
os.path.join(os.path.abspath(os.path.dirname(__file__)),
"solve/solve.py"),
os.path.join(solve_setup_dir, "solve.py"))
worker_config = create_worker_config(mount_dirs, singularity, queue)
solve_config = create_solve_config(solve_number, selected_attr,
solved_attr)
with open(os.path.join(solve_setup_dir, "worker_config.ini"), "w+") as f:
worker_config.write(f)
with open(os.path.join(solve_setup_dir, "solve_config.ini"), "w+") as f:
solve_config.write(f)
return solve_setup_dir
import os
from subprocess import check_call
from funlib.run import run, run_singularity
import logging
from micron import read_predict_config, read_worker_config, read_data_config
import time
import json
predict_config = read_predict_config("predict_config.ini")
worker_config = read_worker_config("worker_config.ini")
data_config = read_data_config("data_config.ini")
start = time.time()
if worker_config["singularity_container"] != "None":
run_singularity("python mknet.py",
singularity_image=worker_config["singularity_container"],
mount_dirs=worker_config["mount_dirs"],
execute=True)
else:
check_call("python mknet.py", shell=True)
check_call("python {} {} {} {}".format(predict_config["blockwise"],
os.path.abspath("predict_config.ini"),
os.path.abspath("worker_config.ini"),
os.path.abspath("data_config.ini")),
shell=True)
end = time.time()
with open("./time_prediction.json", "w") as f:
def set_up_environment(base_dir,
experiment,
train_number,
predict_number,
graph_number,
solve_number,
eval_number,
tracing_file=None,
tracing_offset=None,
tracing_size=None,
subsample_factor=None,
max_edges=None,
distance_threshold=None,
optimality_gap=0.0,
time_limit=None,
voxel_size=None,
mount_dirs=None,
singularity=None,
queue=None,
num_cpus=5,
num_block_workers=1,
num_cache_workers=5):
input_params = locals()
train_files = {}
graph_setup_dir = os.path.join(
os.path.join(base_dir, experiment),
"03_graph/setup_t{}_p{}_g{}".format(train_number, predict_number,
graph_number))
solve_setup_dir = os.path.join(
os.path.join(base_dir, experiment),
"04_solve/setup_t{}_p{}_g{}_s{}".format(train_number, predict_number,
graph_number, solve_number))
eval_setup_dir = os.path.join(
os.path.join(base_dir, experiment),
"05_eval/setup_t{}_p{}_g{}_s{}_e{}".format(train_number,
predict_number,
graph_number, solve_number,
eval_number))
predict_cfg_dict = read_predict_config(
os.path.join(graph_setup_dir, "predict_config.ini"))
data_cfg_dict = read_data_config(
os.path.join(graph_setup_dir, "data_config.ini"))
roi = Roi(data_cfg_dict["in_offset"], data_cfg_dict["in_size"])
db_name = predict_cfg_dict["db_name"]
db_host = predict_cfg_dict["db_host"]
selected_attr = "selected_{}".format(solve_number)
solved_attr = "solved_{}".format(solve_number)
edge_collection = "edges_g{}".format(graph_number)
node_collection = "nodes"
solved_before = attr_exists(db_name, db_host, edge_collection, solved_attr)
if not solved_before:
raise ValueError("Graph not solved, run solve before evaluation.")
if not os.path.exists(graph_setup_dir):
raise ValueError("No graph at {}".format(graph_setup_dir))
if not os.path.exists(solve_setup_dir):
raise ValueError("No solve setup at {}".format(solve_setup_dir))
if os.path.exists(eval_setup_dir):
raise ValueError(
"Eval setup already exists at {}".format(eval_setup_dir))
os.makedirs(eval_setup_dir)
copyfile(os.path.join(graph_setup_dir, "predict_config.ini"),
os.path.join(eval_setup_dir, "predict_config.ini"))
copyfile(os.path.join(graph_setup_dir, "data_config.ini"),
os.path.join(eval_setup_dir, "data_config.ini"))
copyfile(os.path.join(graph_setup_dir, "graph_config.ini"),
os.path.join(eval_setup_dir, "graph_config.ini"))
copyfile(os.path.join(solve_setup_dir, "solve_config.ini"),
os.path.join(eval_setup_dir, "solve_config.ini"))
copyfile(
os.path.join(os.path.abspath(os.path.dirname(__file__)),
"post/evaluate.py"),
os.path.join(eval_setup_dir, "evaluate.py"))
worker_config = create_worker_config(mount_dirs, singularity, queue,
num_cpus, num_block_workers,
num_cache_workers)
eval_config = create_eval_config(eval_number, tracing_file, tracing_offset,
tracing_size, subsample_factor, max_edges,
distance_threshold, optimality_gap,
time_limit, voxel_size)
with open(os.path.join(eval_setup_dir, "worker_config.ini"), "w+") as f:
worker_config.write(f)
with open(os.path.join(eval_setup_dir, "eval_config.ini"), "w+") as f:
eval_config.write(f)
return eval_setup_dir