mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-07 10:37:19 -04:00
* model_refactor (#571) * original model to new structure * IAE model to new structure * OriginalHiRes to new structure * Fix trainer for different resolutions * Initial config implementation * Configparse library added * improved training data loader * dfaker model working * Add logging to training functions * Non blocking input for cli training * Add error handling to threads. Add non-mp queues to queue_handler * Improved Model Building and NNMeta * refactor lib/models * training refactor. DFL H128 model Implementation * Dfaker - use hashes * Move timelapse. Remove perceptual loss arg * Update INSTALL.md. Add logger formatting. Update Dfaker training * DFL h128 partially ported * Add mask to dfaker (#573) * Remove old models. Add mask to dfaker * dfl mask. Make masks selectable in config (#575) * DFL H128 Mask. Mask type selectable in config. * remove gan_v2_2 * Creating Input Size config for models Creating Input Size config for models Will be used downstream in converters. Also name change of image_shape to input_shape to clarify ( for future models with potentially different output_shapes) * Add mask loss options to config * MTCNN options to config.ini. Remove GAN config. Update USAGE.md * Add sliders for numerical values in GUI * Add config plugins menu to gui. Validate config * Only backup model if loss has dropped. Get training working again * bugfixes * Standardise loss printing * GUI idle cpu fixes. Graph loss fix. * mutli-gpu logging bugfix * Merge branch 'staging' into train_refactor * backup state file * Crash protection: Only backup if both total losses have dropped * Port OriginalHiRes_RC4 to train_refactor (OriginalHiRes) * Load and save model structure with weights * Slight code update * Improve config loader. Add subpixel opt to all models. Config to state * Show samples... wrong input * Remove AE topology. Add input/output shapes to State * Port original_villain (birb/VillainGuy) model to faceswap * Add plugin info to GUI config pages * Load input shape from state. IAE Config options. * Fix transform_kwargs. Coverage to ratio. Bugfix mask detection * Suppress keras userwarnings. Automate zoom. Coverage_ratio to model def. * Consolidation of converters & refactor (#574) * Consolidation of converters & refactor Initial Upload of alpha Items - consolidate convert_mased & convert_adjust into one converter -add average color adjust to convert_masked -allow mask transition blur size to be a fixed integer of pixels and a fraction of the facial mask size -allow erosion/dilation size to be a fixed integer of pixels and a fraction of the facial mask size -eliminate redundant type conversions to avoid multiple round-off errors -refactor loops for vectorization/speed -reorganize for clarity & style changes TODO - bug/issues with warping the new face onto a transparent old image...use a cleanup mask for now - issues with mask border giving black ring at zero erosion .. investigate - remove GAN ?? - test enlargment factors of umeyama standard face .. match to coverage factor - make enlargment factor a model parameter - remove convert_adjusted and referencing code when finished * Update Convert_Masked.py default blur size of 2 to match original... description of enlargement tests breakout matrxi scaling into def * Enlargment scale as a cli parameter * Update cli.py * dynamic interpolation algorithm Compute x & y scale factors from the affine matrix on the fly by QR decomp. Choose interpolation alogrithm for the affine warp based on an upsample or downsample for each image * input size input size from config * fix issues with <1.0 erosion * Update convert.py * Update Convert_Adjust.py more work on the way to merginf * Clean up help note on sharpen * cleanup seamless * Delete Convert_Adjust.py * Update umeyama.py * Update training_data.py * swapping * segmentation stub * changes to convert.str * Update masked.py * Backwards compatibility fix for models Get converter running * Convert: Move masks to class. bugfix blur_size some linting * mask fix * convert fixes - missing facehull_rect re-added - coverage to % - corrected coverage logic - cleanup of gui option ordering * Update cli.py * default for blur * Update masked.py * added preliminary low_mem version of OriginalHighRes model plugin * Code cleanup, minor fixes * Update masked.py * Update masked.py * Add dfl mask to convert * histogram fix & seamless location * update * revert * bugfix: Load actual configuration in gui * Standardize nn_blocks * Update cli.py * Minor code amends * Fix Original HiRes model * Add masks to preview output for mask trainers refactor trainer.__base.py * Masked trainers converter support * convert bugfix * Bugfix: Converter for masked (dfl/dfaker) trainers * Additional Losses (#592) * initial upload * Delete blur.py * default initializer = He instead of Glorot (#588) * Allow kernel_initializer to be overridable * Add ICNR Initializer option for upscale on all models. * Hopefully fixes RSoDs with original-highres model plugin * remove debug line * Original-HighRes model plugin Red Screen of Death fix, take #2 * Move global options to _base. Rename Villain model * clipnorm and res block biases * scale the end of res block * res block * dfaker pre-activation res * OHRES pre-activation * villain pre-activation * tabs/space in nn_blocks * fix for histogram with mask all set to zero * fix to prevent two networks with same name * GUI: Wider tooltips. Improve TQDM capture * Fix regex bug * Convert padding=48 to ratio of image size * Add size option to alignments tool extract * Pass through training image size to convert from model * Convert: Pull training coverage from model * convert: coverage, blur and erode to percent * simplify matrix scaling * ordering of sliders in train * Add matrix scaling to utils. Use interpolation in lib.aligner transform * masked.py Import get_matrix_scaling from utils * fix circular import * Update masked.py * quick fix for matrix scaling * testing thus for now * tqdm regex capture bugfix * Minor ammends * blur size cleanup * Remove coverage option from convert (Now cascades from model) * Implement convert for all model types * Add mask option and coverage option to all existing models * bugfix for model loading on convert * debug print removal * Bugfix for masks in dfl_h128 and iae * Update preview display. Add preview scaling to cli * mask notes * Delete training_data_v2.py errant file * training data variables * Fix timelapse function * Add new config items to state file for legacy purposes * Slight GUI tweak * Raise exception if problem with loaded model * Add Tensorboard support (Logs stored in model directory) * ICNR fix * loss bugfix * convert bugfix * Move ini files to config folder. Make TensorBoard optional * Fix training data for unbalanced inputs/outputs * Fix config "none" test * Keep helptext in .ini files when saving config from GUI * Remove frame_dims from alignments * Add no-flip and warp-to-landmarks cli options * Revert OHR to RC4_fix version * Fix lowmem mode on OHR model * padding to variable * Save models in parallel threads * Speed-up of res_block stability * Automated Reflection Padding * Reflect Padding as a training option Includes auto-calculation of proper padding shapes, input_shapes, output_shapes Flag included in config now * rest of reflect padding * Move TB logging to cli. Session info to state file * Add session iterations to state file * Add recent files to menu. GUI code tidy up * [GUI] Fix recent file list update issue * Add correct loss names to TensorBoard logs * Update live graph to use TensorBoard and remove animation * Fix analysis tab. GUI optimizations * Analysis Graph popup to Tensorboard Logs * [GUI] Bug fix for graphing for models with hypens in name * [GUI] Correctly split loss to tabs during training * [GUI] Add loss type selection to analysis graph * Fix store command name in recent files. Switch to correct tab on open * [GUI] Disable training graph when 'no-logs' is selected * Fix graphing race condition * rename original_hires model to unbalanced
224 lines
8.9 KiB
Python
224 lines
8.9 KiB
Python
#!/usr/bin/env python3
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""" Multithreading/processing utils for faceswap """
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import logging
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import multiprocessing as mp
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import queue as Queue
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import sys
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import threading
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from lib.logger import LOG_QUEUE, set_root_logger
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logger = logging.getLogger(__name__) # pylint: disable=invalid-name
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_launched_processes = set() # pylint: disable=invalid-name
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class PoolProcess():
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""" Pool multiple processes """
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def __init__(self, method, in_queue, out_queue, *args, processes=None, **kwargs):
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self._name = method.__qualname__
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logger.debug("Initializing %s: (target: '%s', processes: %s)",
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self.__class__.__name__, self._name, processes)
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self.procs = self.set_procs(processes)
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ctx = mp.get_context("spawn")
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self.pool = ctx.Pool(processes=self.procs)
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self._method = method
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self._kwargs = self.build_target_kwargs(in_queue, out_queue, kwargs)
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self._args = args
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logger.debug("Initialized %s: '%s'", self.__class__.__name__, self._name)
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@staticmethod
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def build_target_kwargs(in_queue, out_queue, kwargs):
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""" Add standard kwargs to passed in kwargs list """
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kwargs["log_init"] = set_root_logger
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kwargs["log_queue"] = LOG_QUEUE
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kwargs["in_queue"] = in_queue
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kwargs["out_queue"] = out_queue
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return kwargs
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def set_procs(self, processes):
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""" Set the number of processes to use """
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if processes is None:
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running_processes = len(mp.active_children())
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processes = max(mp.cpu_count() - running_processes, 1)
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logger.verbose("Processing '%s' in %s processes", self._name, processes)
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return processes
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def start(self):
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""" Run the processing pool """
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logging.debug("Pooling Processes: (target: '%s', args: %s, kwargs: %s)",
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self._name, self._args, self._kwargs)
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for idx in range(self.procs):
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logger.debug("Adding process %s of %s to mp.Pool '%s'",
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idx + 1, self.procs, self._name)
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self.pool.apply_async(self._method, args=self._args, kwds=self._kwargs)
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logging.debug("Pooled Processes: '%s'", self._name)
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def join(self):
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""" Join the process """
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logger.debug("Joining Pooled Process: '%s'", self._name)
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self.pool.close()
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self.pool.join()
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logger.debug("Joined Pooled Process: '%s'", self._name)
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class SpawnProcess(mp.context.SpawnProcess):
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""" Process in spawnable context
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Must be spawnable to share CUDA across processes """
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def __init__(self, target, in_queue, out_queue, *args, **kwargs):
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name = target.__qualname__
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logger.debug("Initializing %s: (target: '%s', args: %s, kwargs: %s)",
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self.__class__.__name__, name, args, kwargs)
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ctx = mp.get_context("spawn")
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self.event = ctx.Event()
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kwargs = self.build_target_kwargs(in_queue, out_queue, kwargs)
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super().__init__(target=target, name=name, args=args, kwargs=kwargs)
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self.daemon = True
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logger.debug("Initialized %s: '%s'", self.__class__.__name__, name)
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def build_target_kwargs(self, in_queue, out_queue, kwargs):
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""" Add standard kwargs to passed in kwargs list """
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kwargs["event"] = self.event
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kwargs["log_init"] = set_root_logger
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kwargs["log_queue"] = LOG_QUEUE
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kwargs["in_queue"] = in_queue
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kwargs["out_queue"] = out_queue
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return kwargs
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def start(self):
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""" Add logging to start function """
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logger.debug("Spawning Process: (name: '%s', args: %s, kwargs: %s, daemon: %s)",
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self._name, self._args, self._kwargs, self.daemon)
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super().start()
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_launched_processes.add(self)
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logger.debug("Spawned Process: (name: '%s', PID: %s)", self._name, self.pid)
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def join(self, timeout=None):
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""" Add logging to join function """
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logger.debug("Joining Process: (name: '%s', PID: %s)", self._name, self.pid)
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super().join(timeout=timeout)
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_launched_processes.remove(self)
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logger.debug("Joined Process: (name: '%s', PID: %s)", self._name, self.pid)
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class FSThread(threading.Thread):
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""" Subclass of thread that passes errors back to parent """
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def __init__(self, group=None, target=None, name=None, # pylint: disable=too-many-arguments
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args=(), kwargs=None, *, daemon=None):
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super().__init__(group=group, target=target, name=name,
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args=args, kwargs=kwargs, daemon=daemon)
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self.err = None
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def run(self):
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try:
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if self._target:
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self._target(*self._args, **self._kwargs)
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except Exception: # pylint: disable=broad-except
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self.err = sys.exc_info()
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logger.debug("Error in thread (%s): %s", self._name,
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self.err[1].with_traceback(self.err[2]))
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finally:
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# Avoid a refcycle if the thread is running a function with
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# an argument that has a member that points to the thread.
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del self._target, self._args, self._kwargs
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class MultiThread():
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""" Threading for IO heavy ops
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Catches errors in thread and rethrows to parent """
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def __init__(self, target, *args, thread_count=1, name=None, **kwargs):
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self._name = name if name else target.__name__
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logger.debug("Initializing %s: (target: '%s', thread_count: %s)",
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self.__class__.__name__, self._name, thread_count)
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logger.trace("args: %s, kwargs: %s", args, kwargs)
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self.daemon = True
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self._thread_count = thread_count
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self._threads = list()
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self._target = target
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self._args = args
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self._kwargs = kwargs
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logger.debug("Initialized %s: '%s'", self.__class__.__name__, self._name)
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@property
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def has_error(self):
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""" Return true if a thread has errored, otherwise false """
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return any(thread.err for thread in self._threads)
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@property
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def errors(self):
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""" Return a list of thread errors """
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return [thread.err for thread in self._threads]
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def start(self):
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""" Start a thread with the given method and args """
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logger.debug("Starting thread(s): '%s'", self._name)
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for idx in range(self._thread_count):
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name = "{}_{}".format(self._name, idx)
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logger.debug("Starting thread %s of %s: '%s'",
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idx + 1, self._thread_count, name)
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thread = FSThread(name=name,
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target=self._target,
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args=self._args,
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kwargs=self._kwargs)
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thread.daemon = self.daemon
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thread.start()
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self._threads.append(thread)
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logger.debug("Started all threads '%s': %s", self._name, len(self._threads))
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def join(self):
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""" Join the running threads, catching and re-raising any errors """
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logger.debug("Joining Threads: '%s'", self._name)
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for thread in self._threads:
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logger.debug("Joining Thread: '%s'", thread._name) # pylint: disable=protected-access
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thread.join()
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if thread.err:
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logger.error("Caught exception in thread: '%s'",
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thread._name) # pylint: disable=protected-access
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raise thread.err[1].with_traceback(thread.err[2])
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logger.debug("Joined all Threads: '%s'", self._name)
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class BackgroundGenerator(threading.Thread):
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""" Run a queue in the background. From:
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https://stackoverflow.com/questions/7323664/ """
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# See below why prefetch count is flawed
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def __init__(self, generator, prefetch=1):
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threading.Thread.__init__(self)
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self.queue = Queue.Queue(maxsize=prefetch)
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self.generator = generator
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self.daemon = True
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self.start()
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def run(self):
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""" Put until queue size is reached.
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Note: put blocks only if put is called while queue has already
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reached max size => this makes 2 prefetched items! One in the
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queue, one waiting for insertion! """
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for item in self.generator:
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self.queue.put(item)
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self.queue.put(None)
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def iterator(self):
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""" Iterate items out of the queue """
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while True:
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next_item = self.queue.get()
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if next_item is None:
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break
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yield next_item
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def terminate_processes():
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""" Join all active processes on unexpected shutdown
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If the process is doing long running work, make sure you
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have a mechanism in place to terminate this work to avoid
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long blocks
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"""
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logger.debug("Processes to join: %s", [process.name
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for process in _launched_processes
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if process.is_alive()])
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for process in list(_launched_processes):
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if process.is_alive():
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process.join()
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