def worker(outdir, fail=None):
client = daisy.Client()
while True:
block = client.acquire_block()
if block is None:
break
TestMultipleTasks.process_block(outdir, block, fail)
client.release_block(block, 0)
def worker(self, outdir, fail=None):
client = daisy.Client()
while True:
block = client.acquire_block()
if block is None:
break
self.process_block(outdir, block, fail)
client.release_block(block, 0)
def run_worker(self):
'''Wrapper for `_worker_impl()`'''
assert 'DAISY_CONTEXT' in os.environ, (
"DAISY_CONTEXT must be defined as an environment variable")
logger.info("WORKER: Running with context %s" %
os.environ['DAISY_CONTEXT'])
database = Database(self.db_host, self.db_id)
client_scheduler = daisy.Client()
while True:
with client_scheduler.acquire_block() as block:
if block is None:
break
logger.info(f'Received block {block}')
self._worker_impl(block)
database.add_finished(block.block_id)
def test_basic(self):
roi = daisy.Roi((0, 0, 0), (10, 10, 10))
task_id = 1
block = daisy.Block(roi, roi, roi, block_id=1, task_id=task_id)
parent_conn, child_conn = mp.Pipe()
server_process = mp.Process(target=self.run_test_server,
args=(block, child_conn))
server_process.start()
host, port = parent_conn.recv()
context = daisy.Context(hostname=host,
port=port,
task_id=task_id,
worker_id=1)
client = daisy.Client(context=context)
with client.acquire_block() as block:
block.status = daisy.BlockStatus.SUCCESS
success = parent_conn.recv()
server_process.join()
self.assertTrue(success)
def solve_in_block(db_host,
db_name,
evidence_factor,
comb_angle_factor,
start_edge_prior,
selection_cost,
time_limit,
solve_number,
graph_number,
selected_attr="selected",
solved_attr="solved",
**kwargs):
print("Solve in block")
graph_provider = MongoDbGraphProvider(
db_name,
db_host,
mode='r+',
position_attribute=['z', 'y', 'x'],
edges_collection="edges_g{}".format(graph_number))
client = daisy.Client()
while True:
print("Acquire block")
block = client.acquire_block()
if not block:
return
logger.debug("Solving in block %s", block)
if check_function(block, 'solve_s{}'.format(solve_number), db_name,
db_host):
client.release_block(block, 0)
continue
start_time = time.time()
graph = graph_provider.get_graph(block.read_roi)
num_nodes = graph.number_of_nodes()
num_edges = graph.number_of_edges()
logger.info(
"Reading graph with %d nodes and %d edges took %s seconds" %
(num_nodes, num_edges, time.time() - start_time))
if num_edges == 0:
logger.info("No edges in roi %s. Skipping" % block.read_roi)
write_done(block, 'solve_s{}'.format(solve_number), db_name,
db_host)
client.release_block(block, 0)
continue
print("solve")
solver = Solver(graph, evidence_factor, comb_angle_factor,
start_edge_prior, selection_cost, time_limit,
selected_attr, solved_attr)
solver.initialize()
solver.solve()
start_time = time.time()
graph.update_edge_attrs(block.write_roi,
attributes=[selected_attr, solved_attr])
graph.update_node_attrs(block.write_roi,
attributes=[selected_attr, solved_attr])
logger.info(
"Updating attributes %s & %s for %d edges took %s seconds" %
(selected_attr, solved_attr, num_edges, time.time() - start_time))
print("Write done")
write_done(block, 'solve_s{}'.format(solve_number), db_name, db_host)
print("Release block")
client.release_block(block, 0)
return 0
def solve_in_block(
db_host,
skeletonization_db,
subsampled_skeletonization_db,
time_limit,
solve_number,
graph_number,
location_attr,
u_name,
v_name,
**kwargs,
):
logger.info("Solve in block")
subsampled_provider = MongoDbGraphProvider(subsampled_skeletonization_db,
db_host,
mode="r+",
directed=True)
skeletonization_provider = MongoDbGraphProvider(skeletonization_db,
db_host,
mode="r+")
client = daisy.Client()
while True:
logger.info("Acquire block")
block = client.acquire_block()
if not block:
return 0
logger.debug("Solving in block %s", block)
if check_function(
block,
"solve_s{}".format(solve_number),
subsampled_skeletonization_db,
db_host,
):
client.release_block(block, 0)
continue
start_time = time.time()
skeletonization = skeletonization_provider.get_graph(
block.read_roi, node_inclusion="dangling", edge_inclusion="both")
# anything in matched was solved previously and must be maintained.
pre_solved = subsampled_provider.get_graph(block.read_roi,
node_inclusion="dangling",
edge_inclusion="both")
# if len(skeletonization.nodes()) > 10_000:
# to_remove = set(skeletonization.nodes()) - set(pre_solved.nodes())
# skeletonization.remove_nodes_from(to_remove)
# logger.info(f"Solving for {len(skeletonization.nodes())} would take too long")
# logger.info(f"Ignoring {len(to_remove)} nodes and skipping this block!")
logger.info(
f"Reading skeletonization with {len(skeletonization.nodes)} nodes and "
+
f"{len(skeletonization.edges)} edges took {time.time() - start_time} seconds"
)
if len(skeletonization.nodes) == 0:
logger.info(
f"No consensus nodes in roi {block.read_roi}. Skipping")
write_done(block, f"solve_s{solve_number}",
subsampled_skeletonization_db, db_host)
client.release_block(block, 0)
continue
logger.info("PreProcess...")
start_time = time.time()
logger.info(
f"Skeletoniation has {len(skeletonization.nodes)} nodes "
f"and {len(skeletonization.edges)} edges before subsampling")
num_removed = remove_line_nodes(skeletonization, location_attr)
logger.info(f"Removed {num_removed} nodes from skeletonization!")
num_nodes, num_edges = write_matched(
db_host,
subsampled_skeletonization_db,
block,
skeletonization,
pre_solved,
location_attr,
u_name,
v_name,
)
logger.info(
f"Writing matched graph with {num_nodes} nodes and {num_edges} edges "
f"took {time.time()-start_time} seconds")
logger.info("Write done")
write_done(
block,
"solve_s{}".format(solve_number),
subsampled_skeletonization_db,
db_host,
)
logger.info("Release block")
client.release_block(block, 0)
return 0
import daisy
import time
import random
import sys
from filelock import FileLock
tmp_path = sys.argv[1]
client = daisy.Client()
with FileLock(f"{tmp_path}/worker_{client.worker_id}.lock"):
while True:
with client.acquire_block() as block:
if block is None:
break
else:
time.sleep(random.random())
def agglomerate_worker(input_config):
logging.info(sys.argv)
with open(input_config, 'r') as f:
config = json.load(f)
logging.info(config)
affs_file = config['affs_file']
affs_dataset = config['affs_dataset']
fragments_file = config['fragments_file']
fragments_dataset = config['fragments_dataset']
db_host = config['db_host']
db_name = config['db_name']
queue = config['queue']
merge_function = config['merge_function']
waterz_merge_function = {
'hist_quant_10': 'OneMinus<HistogramQuantileAffinity<RegionGraphType, 10, ScoreValue, 256, false>>',
'hist_quant_10_initmax': 'OneMinus<HistogramQuantileAffinity<RegionGraphType, 10, ScoreValue, 256, true>>',
'hist_quant_25': 'OneMinus<HistogramQuantileAffinity<RegionGraphType, 25, ScoreValue, 256, false>>',
'hist_quant_25_initmax': 'OneMinus<HistogramQuantileAffinity<RegionGraphType, 25, ScoreValue, 256, true>>',
'hist_quant_50': 'OneMinus<HistogramQuantileAffinity<RegionGraphType, 50, ScoreValue, 256, false>>',
'hist_quant_50_initmax': 'OneMinus<HistogramQuantileAffinity<RegionGraphType, 50, ScoreValue, 256, true>>',
'hist_quant_75': 'OneMinus<HistogramQuantileAffinity<RegionGraphType, 75, ScoreValue, 256, false>>',
'hist_quant_75_initmax': 'OneMinus<HistogramQuantileAffinity<RegionGraphType, 75, ScoreValue, 256, true>>',
'hist_quant_90': 'OneMinus<HistogramQuantileAffinity<RegionGraphType, 90, ScoreValue, 256, false>>',
'hist_quant_90_initmax': 'OneMinus<HistogramQuantileAffinity<RegionGraphType, 90, ScoreValue, 256, true>>',
'mean': 'OneMinus<MeanAffinity<RegionGraphType, ScoreValue>>',
}[merge_function]
logging.info(f"Reading affs from {affs_file}")
affs = daisy.open_ds(affs_file, affs_dataset)
logging.info(f"Reading fragments from {fragments_file}")
fragments = daisy.open_ds(fragments_file, fragments_dataset)
# open RAG DB
logging.info("Opening RAG DB...")
rag_provider = daisy.persistence.MongoDbGraphProvider(
db_name,
host=db_host,
mode='r+',
directed=False,
edges_collection='edges_' + merge_function,
position_attribute=['center_z', 'center_y', 'center_x'])
logging.info("RAG DB opened")
# open block done DB
client = pymongo.MongoClient(db_host)
db = client[db_name]
blocks_agglomerated = db['blocks_agglomerated_' + merge_function]
client = daisy.Client()
while True:
block = client.acquire_block()
if block is None:
break
start = time.time()
lsd.agglomerate_in_block(
affs,
fragments,
rag_provider,
block,
merge_function=waterz_merge_function,
threshold=1.0)
document = {
'num_cpus': 5,
'queue': queue,
'block_id': block.block_id,
'read_roi': (block.read_roi.get_begin(), block.read_roi.get_shape()),
'write_roi': (block.write_roi.get_begin(), block.write_roi.get_shape()),
'start': start,
'duration': time.time() - start
}
blocks_agglomerated.insert(document)
client.release_block(block, ret=0)
def extract_fragments_worker(input_config):
logging.info(sys.argv)
with open(input_config, 'r') as f:
config = json.load(f)
logging.info(config)
affs_file = config['affs_file']
affs_dataset = config['affs_dataset']
fragments_file = config['fragments_file']
fragments_dataset = config['fragments_dataset']
db_name = config['db_name']
db_host = config['db_host']
queue = config['queue']
context = config['context']
num_voxels_in_block = config['num_voxels_in_block']
fragments_in_xy=config['fragments_in_xy']
epsilon_agglomerate=config['epsilon_agglomerate']
filter_fragments=config['filter_fragments']
replace_sections=config['replace_sections']
logging.info(f"Reading affs from {affs_file}")
affs = daisy.open_ds(affs_file, affs_dataset, mode='r')
logging.info(f"Reading fragments from {fragments_file}")
fragments = daisy.open_ds(
fragments_file,
fragments_dataset,
mode='r+')
if config['mask_file']:
logging.info(f"Reading mask from {config['mask_file']}")
mask = daisy.open_ds(config['mask_file'], config['mask_dataset'])
else:
mask = None
# open RAG DB
logging.info("Opening RAG DB...")
rag_provider = daisy.persistence.MongoDbGraphProvider(
db_name,
host=db_host,
mode='r+',
directed=False,
position_attribute=['center_z', 'center_y', 'center_x'])
logging.info("RAG DB opened")
# open block done DB
client = pymongo.MongoClient(db_host)
db = client[db_name]
blocks_extracted = db['blocks_extracted']
client = daisy.Client()
while True:
block = client.acquire_block()
if block is None:
break
start = time.time()
lsd.watershed_in_block(
affs,
block,
context,
rag_provider,
fragments,
num_voxels_in_block=num_voxels_in_block,
mask=mask,
fragments_in_xy=fragments_in_xy,
epsilon_agglomerate=epsilon_agglomerate,
filter_fragments=filter_fragments,
replace_sections=replace_sections)
document = {
'num_cpus': 5,
'queue': queue,
'block_id': block.block_id,
'read_roi': (
block.read_roi.get_begin(),
block.read_roi.get_shape()
),
'write_roi': (
block.write_roi.get_begin(),
block.write_roi.get_shape()
),
'start': start,
'duration': time.time() - start
}
blocks_extracted.insert(document)
client.release_block(block, ret=0)
