kopia lustrzana https://github.com/OpenDroneMap/ODM
509 wiersze
21 KiB
Python
509 wiersze
21 KiB
Python
import time
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import datetime
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import os
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import sys
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import threading
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import signal
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import zipfile
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import glob
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from opendm import log
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from opendm import system
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from pyodm import Node, exceptions
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from pyodm.utils import AtomicCounter
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from pyodm.types import TaskStatus
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from osfm import OSFMContext, get_submodel_args_dict, get_submodel_argv
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from pipes import quote
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try:
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import queue
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except ImportError:
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import Queue as queue
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class LocalRemoteExecutor:
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"""
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A class for performing OpenSfM reconstructions and full ODM pipeline executions
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using a mix of local and remote processing. Tasks are executed locally one at a time
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and remotely until a node runs out of available slots for processing. This allows us
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to use the processing power of the current machine as well as offloading tasks to a
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network node.
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"""
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def __init__(self, nodeUrl, rerun = False):
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self.node = Node.from_url(nodeUrl)
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self.params = {
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'tasks': [],
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'threads': [],
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'rerun': rerun
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}
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self.node_online = True
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log.ODM_INFO("LRE: Initializing using cluster node %s:%s" % (self.node.host, self.node.port))
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try:
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info = self.node.info()
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log.ODM_INFO("LRE: Node is online and running %s version %s" % (info.engine, info.engine_version))
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except exceptions.NodeConnectionError:
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log.ODM_WARNING("LRE: The node seems to be offline! We'll still process the dataset, but it's going to run entirely locally.")
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self.node_online = False
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except Exception as e:
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log.ODM_ERROR("LRE: An unexpected problem happened while opening the node connection: %s" % str(e))
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exit(1)
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def set_projects(self, paths):
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self.project_paths = paths
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def run_reconstruction(self):
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self.run(ReconstructionTask)
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def run_toolchain(self):
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self.run(ToolchainTask)
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def run(self, taskClass):
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if not self.project_paths:
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return
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# Shared variables across threads
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class nonloc:
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error = None
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local_processing = False
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max_remote_tasks = None
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calculate_task_limit_lock = threading.Lock()
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finished_tasks = AtomicCounter(0)
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remote_running_tasks = AtomicCounter(0)
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# Create queue
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q = queue.Queue()
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for pp in self.project_paths:
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log.ODM_INFO("LRE: Adding to queue %s" % pp)
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q.put(taskClass(pp, self.node, self.params))
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def remove_task_safe(task):
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try:
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removed = task.remove()
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except exceptions.OdmError:
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removed = False
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return removed
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def cleanup_remote_tasks():
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if self.params['tasks']:
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log.ODM_WARNING("LRE: Attempting to cleanup remote tasks")
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else:
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log.ODM_INFO("LRE: No remote tasks left to cleanup")
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for task in self.params['tasks']:
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log.ODM_INFO("LRE: Removing remote task %s... %s" % (task.uuid, 'OK' if remove_task_safe(task) else 'NO'))
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def handle_result(task, local, error = None, partial=False):
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def cleanup_remote():
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if not partial and task.remote_task:
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log.ODM_INFO("LRE: Cleaning up remote task (%s)... %s" % (task.remote_task.uuid, 'OK' if remove_task_safe(task.remote_task) else 'NO'))
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self.params['tasks'].remove(task.remote_task)
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task.remote_task = None
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if error:
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log.ODM_WARNING("LRE: %s failed with: %s" % (task, str(error)))
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# Special case in which the error is caused by a SIGTERM signal
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# this means a local processing was terminated either by CTRL+C or
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# by canceling the task.
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if str(error) == "Child was terminated by signal 15":
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system.exit_gracefully()
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task_limit_reached = isinstance(error, NodeTaskLimitReachedException)
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if task_limit_reached:
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# Estimate the maximum number of tasks based on how many tasks
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# are currently running
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with calculate_task_limit_lock:
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if nonloc.max_remote_tasks is None:
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node_task_limit = 0
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for t in self.params['tasks']:
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try:
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info = t.info(with_output=-3)
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if info.status == TaskStatus.RUNNING and info.processing_time >= 0 and len(info.output) >= 3:
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node_task_limit += 1
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except exceptions.OdmError:
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pass
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nonloc.max_remote_tasks = max(1, node_task_limit)
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log.ODM_INFO("LRE: Node task limit reached. Setting max remote tasks to %s" % node_task_limit)
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# Retry, but only if the error is not related to a task failure
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if task.retries < task.max_retries and not isinstance(error, exceptions.TaskFailedError):
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# Put task back in queue
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# Don't increment the retry counter if this task simply reached the task
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# limit count.
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if not task_limit_reached:
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task.retries += 1
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task.wait_until = datetime.datetime.now() + datetime.timedelta(seconds=task.retries * task.retry_timeout)
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cleanup_remote()
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q.task_done()
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log.ODM_INFO("LRE: Re-queueing %s (retries: %s)" % (task, task.retries))
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q.put(task)
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if not local: remote_running_tasks.increment(-1)
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return
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else:
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nonloc.error = error
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finished_tasks.increment()
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if not local: remote_running_tasks.increment(-1)
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else:
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if not partial:
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log.ODM_INFO("LRE: %s finished successfully" % task)
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finished_tasks.increment()
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if not local: remote_running_tasks.increment(-1)
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cleanup_remote()
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if not partial: q.task_done()
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def local_worker():
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while True:
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# Block until a new queue item is available
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task = q.get()
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if task is None or nonloc.error is not None:
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q.task_done()
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break
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# Process local
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try:
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nonloc.local_processing = True
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task.process(True, handle_result)
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except Exception as e:
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handle_result(task, True, e)
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finally:
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nonloc.local_processing = False
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def remote_worker():
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while True:
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# Block until a new queue item is available
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task = q.get()
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if task is None or nonloc.error is not None:
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q.task_done()
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break
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# Yield to local processing
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if not nonloc.local_processing:
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log.ODM_INFO("LRE: Yielding to local processing, sending %s back to the queue" % task)
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q.put(task)
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q.task_done()
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time.sleep(0.05)
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continue
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# If we've found an estimate of the limit on the maximum number of tasks
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# a node can process, we block until some tasks have completed
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if nonloc.max_remote_tasks is not None and remote_running_tasks.value >= nonloc.max_remote_tasks:
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q.put(task)
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q.task_done()
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time.sleep(2)
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continue
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# Process remote
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try:
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remote_running_tasks.increment()
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task.process(False, handle_result)
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except Exception as e:
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handle_result(task, False, e)
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# Create queue thread
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local_thread = threading.Thread(target=local_worker)
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if self.node_online:
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remote_thread = threading.Thread(target=remote_worker)
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system.add_cleanup_callback(cleanup_remote_tasks)
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# Start workers
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local_thread.start()
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if self.node_online:
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remote_thread.start()
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# block until all tasks are done (or CTRL+C)
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try:
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while finished_tasks.value < len(self.project_paths) and nonloc.error is None:
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time.sleep(0.5)
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except KeyboardInterrupt:
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log.ODM_WARNING("LRE: CTRL+C")
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system.exit_gracefully()
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# stop workers
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q.put(None)
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if self.node_online:
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q.put(None)
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# Wait for queue thread
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local_thread.join()
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if self.node_online:
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remote_thread.join()
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# Wait for all remains threads
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for thrds in self.params['threads']:
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thrds.join()
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system.remove_cleanup_callback(cleanup_remote_tasks)
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cleanup_remote_tasks()
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if nonloc.error is not None:
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# Try not to leak access token
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if isinstance(nonloc.error, exceptions.NodeConnectionError):
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raise exceptions.NodeConnectionError("A connection error happened. Check the connection to the processing node and try again.")
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else:
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raise nonloc.error
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class NodeTaskLimitReachedException(Exception):
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pass
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class Task:
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def __init__(self, project_path, node, params, max_retries=5, retry_timeout=10):
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self.project_path = project_path
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self.node = node
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self.params = params
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self.wait_until = datetime.datetime.now() # Don't run this task until a certain time
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self.max_retries = max_retries
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self.retries = 0
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self.retry_timeout = retry_timeout
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self.remote_task = None
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def process(self, local, done):
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def handle_result(error = None, partial=False):
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done(self, local, error, partial)
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log.ODM_INFO("LRE: About to process %s %s" % (self, 'locally' if local else 'remotely'))
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if local:
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self._process_local(handle_result) # Block until complete
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else:
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now = datetime.datetime.now()
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if self.wait_until > now:
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wait_for = (self.wait_until - now).seconds + 1
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log.ODM_INFO("LRE: Waiting %s seconds before processing %s" % (wait_for, self))
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time.sleep(wait_for)
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# TODO: we could consider uploading multiple tasks
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# in parallel. But since we are using the same node
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# perhaps this wouldn't be a big speedup.
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self._process_remote(handle_result) # Block until upload is complete
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def path(self, *paths):
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return os.path.join(self.project_path, *paths)
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def touch(self, file):
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with open(file, 'w') as fout:
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fout.write("Done!\n")
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def create_seed_payload(self, paths, touch_files=[]):
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paths = filter(os.path.exists, map(lambda p: self.path(p), paths))
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outfile = self.path("seed.zip")
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with zipfile.ZipFile(outfile, "w", compression=zipfile.ZIP_DEFLATED, allowZip64=True) as zf:
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for p in paths:
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if os.path.isdir(p):
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for root, _, filenames in os.walk(p):
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for filename in filenames:
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filename = os.path.join(root, filename)
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filename = os.path.normpath(filename)
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zf.write(filename, os.path.relpath(filename, self.project_path))
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else:
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zf.write(p, os.path.relpath(p, self.project_path))
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for tf in touch_files:
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zf.writestr(tf, "")
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return outfile
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def _process_local(self, done):
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try:
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self.process_local()
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done()
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except Exception as e:
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done(e)
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def _process_remote(self, done):
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try:
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self.process_remote(done)
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done(error=None, partial=True) # Upload is completed, but processing is not (partial)
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except Exception as e:
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done(e)
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def execute_remote_task(self, done, seed_files = [], seed_touch_files = [], outputs = [], ):
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"""
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Run a task by creating a seed file with all files in seed_files, optionally
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creating empty files (for flag checks) specified in seed_touch_files
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and returning the results specified in outputs. Yeah it's pretty cool!
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"""
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seed_file = self.create_seed_payload(seed_files, touch_files=seed_touch_files)
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# Find all images
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images = glob.glob(self.path("images/**"))
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# Add GCP (optional)
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if os.path.exists(self.path("gcp_list.txt")):
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images.append(self.path("gcp_list.txt"))
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# Add seed file
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images.append(seed_file)
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class nonloc:
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last_update = 0
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def print_progress(percentage):
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if (time.time() - nonloc.last_update >= 2) or int(percentage) == 100:
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log.ODM_INFO("LRE: Upload of %s at [%s%%]" % (self, int(percentage)))
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nonloc.last_update = time.time()
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# Upload task
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task = self.node.create_task(images,
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get_submodel_args_dict(),
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progress_callback=print_progress,
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skip_post_processing=True,
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outputs=outputs)
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self.remote_task = task
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# Cleanup seed file
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os.remove(seed_file)
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# Keep track of tasks for cleanup
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self.params['tasks'].append(task)
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# Check status
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info = task.info()
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if info.status in [TaskStatus.RUNNING, TaskStatus.COMPLETED]:
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def monitor():
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class nonloc:
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status_callback_calls = 0
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last_update = 0
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def status_callback(info):
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# If a task switches from RUNNING to QUEUED, then we need to
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# stop the process and re-add the task to the queue.
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if info.status == TaskStatus.QUEUED:
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log.ODM_WARNING("LRE: %s (%s) turned from RUNNING to QUEUED. Re-adding to back of the queue." % (self, task.uuid))
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raise NodeTaskLimitReachedException("Delayed task limit reached")
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elif info.status == TaskStatus.RUNNING:
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# Print a status message once in a while
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nonloc.status_callback_calls += 1
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if nonloc.status_callback_calls > 30:
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log.ODM_INFO("LRE: %s (%s) is still running" % (self, task.uuid))
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nonloc.status_callback_calls = 0
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try:
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def print_progress(percentage):
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if (time.time() - nonloc.last_update >= 2) or int(percentage) == 100:
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log.ODM_INFO("LRE: Download of %s at [%s%%]" % (self, int(percentage)))
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nonloc.last_update = time.time()
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task.wait_for_completion(status_callback=status_callback)
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log.ODM_INFO("LRE: Downloading assets for %s" % self)
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task.download_assets(self.project_path, progress_callback=print_progress)
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log.ODM_INFO("LRE: Downloaded and extracted assets for %s" % self)
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done()
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except exceptions.TaskFailedError as e:
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# Try to get output
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try:
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output_lines = task.output()
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# Save to file
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error_log_path = self.path("error.log")
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with open(error_log_path, 'w') as f:
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f.write('\n'.join(output_lines) + '\n')
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msg = "(%s) failed with task output: %s\nFull log saved at %s" % (task.uuid, "\n".join(output_lines[-10:]), error_log_path)
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done(exceptions.TaskFailedError(msg))
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except:
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log.ODM_WARNING("LRE: Could not retrieve task output for %s (%s)" % (self, task.uuid))
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done(e)
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except Exception as e:
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done(e)
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# Launch monitor thread and return
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t = threading.Thread(target=monitor)
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self.params['threads'].append(t)
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t.start()
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elif info.status == TaskStatus.QUEUED:
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raise NodeTaskLimitReachedException("Task limit reached")
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else:
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raise Exception("Could not send task to node, task status is %s" % str(info.status))
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|
|
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def process_local(self):
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raise NotImplementedError()
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def process_remote(self, done):
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raise NotImplementedError()
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def __str__(self):
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return os.path.basename(self.project_path)
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class ReconstructionTask(Task):
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def process_local(self):
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octx = OSFMContext(self.path("opensfm"))
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log.ODM_INFO("==================================")
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log.ODM_INFO("Local Reconstruction %s" % octx.name())
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log.ODM_INFO("==================================")
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octx.feature_matching(self.params['rerun'])
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octx.reconstruct(self.params['rerun'])
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|
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def process_remote(self, done):
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octx = OSFMContext(self.path("opensfm"))
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if not octx.is_feature_matching_done() or not octx.is_reconstruction_done() or self.params['rerun']:
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self.execute_remote_task(done, seed_files=["opensfm/exif",
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"opensfm/camera_models.json",
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"opensfm/reference_lla.json"],
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seed_touch_files=["opensfm/split_merge_stop_at_reconstruction.txt"],
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outputs=["opensfm/matches", "opensfm/features",
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"opensfm/reconstruction.json",
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"opensfm/tracks.csv",
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"cameras.json"])
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else:
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log.ODM_INFO("Already processed feature matching and reconstruction for %s" % octx.name())
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done()
|
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|
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class ToolchainTask(Task):
|
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def process_local(self):
|
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completed_file = self.path("toolchain_completed.txt")
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submodel_name = os.path.basename(self.project_path)
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if not os.path.exists(completed_file) or self.params['rerun']:
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log.ODM_INFO("=============================")
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log.ODM_INFO("Local Toolchain %s" % self)
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log.ODM_INFO("=============================")
|
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submodels_path = os.path.abspath(self.path(".."))
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project_name = os.path.basename(os.path.abspath(os.path.join(submodels_path, "..")))
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argv = get_submodel_argv(project_name, submodels_path, submodel_name)
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|
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# Re-run the ODM toolchain on the submodel
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system.run(" ".join(map(quote, argv)), env_vars=os.environ.copy())
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|
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# This will only get executed if the command above succeeds
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self.touch(completed_file)
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else:
|
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log.ODM_INFO("Already processed toolchain for %s" % submodel_name)
|
|
|
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def process_remote(self, done):
|
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completed_file = self.path("toolchain_completed.txt")
|
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submodel_name = os.path.basename(self.project_path)
|
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|
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def handle_result(error = None):
|
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# Mark task as completed if no error
|
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if error is None:
|
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self.touch(completed_file)
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done(error=error)
|
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|
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if not os.path.exists(completed_file) or self.params['rerun']:
|
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self.execute_remote_task(handle_result, seed_files=["opensfm/camera_models.json",
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"opensfm/reference_lla.json",
|
|
"opensfm/reconstruction.json",
|
|
"opensfm/tracks.csv"],
|
|
seed_touch_files=["opensfm/features/empty",
|
|
"opensfm/matches/empty",
|
|
"opensfm/exif/empty"],
|
|
outputs=["odm_orthophoto/odm_orthophoto.tif",
|
|
"odm_orthophoto/cutline.gpkg",
|
|
"odm_orthophoto/odm_orthophoto_cut.tif",
|
|
"odm_dem",
|
|
"odm_georeferencing"])
|
|
else:
|
|
log.ODM_INFO("Already processed toolchain for %s" % submodel_name)
|
|
handle_result() |