mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-08-22 06:23:20 -07:00
Batch size can now be changed during training
This commit is contained in:
Brigham Lysenko
parent
f14ce6e313
commit
d433acc4a0
3 changed files with 276 additions and 168 deletions
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@ -104,14 +104,14 @@ def trainerThread (s2c, c2s, args, device_args):
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print("Unable to execute program: %s" % (prog) )
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if not is_reached_goal:
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iter, iter_time = model.train_one_iter()
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iter, iter_time, batch_size = model.train_one_iter()
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loss_history = model.get_loss_history()
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time_str = time.strftime("[%H:%M:%S]")
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if iter_time >= 10:
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loss_string = "{0}[#{1:06d}][{2:.5s}s]".format ( time_str, iter, '{:0.4f}'.format(iter_time) )
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else:
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loss_string = "{0}[#{1:06d}][{2:04d}ms]".format ( time_str, iter, int(iter_time*1000) )
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loss_string = "{0}[#{1:06d}][{2:04d}ms][bs: {3}]".format ( time_str, iter, int(iter_time*1000), batch_size)
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if shared_state['after_save']:
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shared_state['after_save'] = False
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@ -186,6 +186,7 @@ def main(args, device_args):
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no_preview = args.get('no_preview', False)
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s2c = queue.Queue()
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c2s = queue.Queue()
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@ -216,6 +217,7 @@ def main(args, device_args):
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is_waiting_preview = False
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show_last_history_iters_count = 0
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iter = 0
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batch_size = 1
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while True:
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if not c2s.empty():
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input = c2s.get()
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@ -225,6 +227,7 @@ def main(args, device_args):
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loss_history = input['loss_history'] if 'loss_history' in input.keys() else None
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previews = input['previews'] if 'previews' in input.keys() else None
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iter = input['iter'] if 'iter' in input.keys() else 0
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#batch_size = input['batch_size'] if 'iter' in input.keys() else 1
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if previews is not None:
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max_w = 0
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max_h = 0
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@ -280,7 +283,7 @@ def main(args, device_args):
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else:
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loss_history_to_show = loss_history[-show_last_history_iters_count:]
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lh_img = models.ModelBase.get_loss_history_preview(loss_history_to_show, iter, w, c)
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lh_img = models.ModelBase.get_loss_history_preview(loss_history_to_show, iter, batch_size, w, c)
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final = np.concatenate ( [final, lh_img], axis=0 )
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final = np.concatenate ( [final, selected_preview_rgb], axis=0 )
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@ -23,6 +23,7 @@ You can implement your own model. Check examples.
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class ModelBase(object):
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def __init__(self, model_path, training_data_src_path=None, training_data_dst_path=None, pretraining_data_path=None, debug = False, device_args = None,
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ask_enable_autobackup=True,
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ask_write_preview_history=True,
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@ -42,7 +43,8 @@ class ModelBase(object):
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for idx, name in idxs_names_list:
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io.log_info("[%d] : %s" % (idx, name))
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device_args['force_gpu_idx'] = io.input_int("Which GPU idx to choose? ( skip: best GPU ) : ", -1, [ x[0] for x in idxs_names_list] )
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device_args['force_gpu_idx'] = io.input_int("Which GPU idx to choose? ( skip: best GPU ) : ", -1,
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[x[0] for x in idxs_names_list])
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self.device_args = device_args
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self.device_config = nnlib.DeviceConfig(allow_growth=True, **self.device_args)
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@ -65,6 +67,8 @@ class ModelBase(object):
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self.debug = debug
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self.is_training_mode = (training_data_src_path is not None and training_data_dst_path is not None)
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self.paddle = 'pong'
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self.iter = 0
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self.options = {}
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self.loss_history = []
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@ -81,7 +85,9 @@ class ModelBase(object):
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self.loss_history = model_data.get('loss_history', [])
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self.sample_for_preview = model_data.get('sample_for_preview', None)
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ask_override = self.is_training_mode and self.iter != 0 and io.input_in_time ("Press enter in 2 seconds to override model settings.", 5 if io.is_colab() else 2 )
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ask_override = self.is_training_mode and self.iter != 0 and io.input_in_time("Press enter in 2 seconds to"
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" override model settings.",
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5 if io.is_colab() else 2)
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yn_str = {True: 'y', False: 'n'}
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@ -90,20 +96,36 @@ class ModelBase(object):
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if ask_enable_autobackup and (self.iter == 0 or ask_override):
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default_autobackup = False if self.iter == 0 else self.options.get('autobackup', False)
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self.options['autobackup'] = io.input_bool("Enable autobackup? (y/n ?:help skip:%s) : " % (yn_str[default_autobackup]) , default_autobackup, help_message="Autobackup model files with preview every hour for last 15 hours. Latest backup located in model/<>_autobackups/01")
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self.options['autobackup'] = io.input_bool("Enable autobackup? (y/n ?:help skip:%s) : " %
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(yn_str[default_autobackup]), default_autobackup,
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help_message="Autobackup model files with preview every hour for"
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" last 15 hours. Latest backup located in model/<>"
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"_autobackups/01")
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else:
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self.options['autobackup'] = self.options.get('autobackup', False)
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if ask_write_preview_history and (self.iter == 0 or ask_override):
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default_write_preview_history = False if self.iter == 0 else self.options.get('write_preview_history',False)
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self.options['write_preview_history'] = io.input_bool("Write preview history? (y/n ?:help skip:%s) : " % (yn_str[default_write_preview_history]) , default_write_preview_history, help_message="Preview history will be writed to <ModelName>_history folder.")
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default_write_preview_history = False if self.iter == 0 else self.options.get('write_preview_history',
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False)
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self.options['write_preview_history'] = io.input_bool("Write preview history? (y/n ?:help skip:%s) : " %
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(yn_str[default_write_preview_history]),
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default_write_preview_history,
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help_message="Preview history will be writed to"
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" <ModelName>_history folder.")
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else:
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self.options['write_preview_history'] = self.options.get('write_preview_history', False)
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if (self.iter == 0 or ask_override) and self.options['write_preview_history'] and io.is_support_windows():
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choose_preview_history = io.input_bool("Choose image for the preview history? (y/n skip:%s) : " % (yn_str[False]) , False)
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choose_preview_history = io.input_bool("Choose image for the preview history?"
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" (y/n skip:%s) : " % (yn_str[False]), False)
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elif (self.iter == 0 or ask_override) and self.options['write_preview_history'] and io.is_colab():
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choose_preview_history = io.input_bool("Randomly choose new image for preview history? (y/n ?:help skip:%s) : " % (yn_str[False]), False, help_message="Preview image history will stay stuck with old faces if you reuse the same model on different celebs. Choose no unless you are changing src/dst to a new person")
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choose_preview_history = io.input_bool("Randomly choose new image for preview history? (y/n ?:help skip:%s)"
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": " % (yn_str[False]), False,
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help_message="Preview image history will stay stuck with old faces"
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" if you reuse the same model on different celebs."
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" Choose no unless you are changing src/dst to a"
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" new person")
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else:
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choose_preview_history = False
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@ -117,26 +139,48 @@ class ModelBase(object):
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if ask_batch_size and (self.iter == 0 or ask_override):
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default_batch_size = 0 if self.iter == 0 else self.options.get('batch_size', 0)
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self.options['batch_size'] = max(0, io.input_int("Batch_size (?:help skip:%d) : " % (default_batch_size), default_batch_size, help_message="Larger batch size is better for NN's generalization, but it can cause Out of Memory error. Tune this value for your videocard manually."))
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self.options['batch_size'] = max(0, io.input_int("Batch_size (?:help skip:%d) : " % default_batch_size,
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default_batch_size,
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help_message="Larger batch size is better for NN's"
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" generalization, but it can cause Out of"
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" Memory error. Tune this value for your"
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" videocard manually."))
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self.options['ping_pong'] = io.input_bool(
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"Enable ping-pong? (y/n ?:help skip:%s) : " % (yn_str[True]),
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True,
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help_message="Cycles batch size between 1 and chosen batch size, simulating super convergence")
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else:
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self.options['batch_size'] = self.options.get('batch_size', 0)
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self.options['ping_pong'] = self.options.get('ping_pong', True)
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if ask_sort_by_yaw:
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if (self.iter == 0 or ask_override):
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default_sort_by_yaw = self.options.get('sort_by_yaw', False)
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self.options['sort_by_yaw'] = io.input_bool("Feed faces to network sorted by yaw? (y/n ?:help skip:%s) : " % (yn_str[default_sort_by_yaw]), default_sort_by_yaw, help_message="NN will not learn src face directions that don't match dst face directions. Do not use if the dst face has hair that covers the jaw." )
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self.options['sort_by_yaw'] = io.input_bool("Feed faces to network sorted by yaw? (y/n ?:help skip:%s):"
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" " % (yn_str[default_sort_by_yaw]), default_sort_by_yaw,
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help_message="NN will not learn src face directions that"
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" don't match dst face directions. Do not use "
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"if the dst face has hair that covers the jaw")
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else:
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self.options['sort_by_yaw'] = self.options.get('sort_by_yaw', False)
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if ask_random_flip:
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if (self.iter == 0):
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self.options['random_flip'] = io.input_bool("Flip faces randomly? (y/n ?:help skip:y) : ", True, help_message="Predicted face will look more naturally without this option, but src faceset should cover all face directions as dst faceset.")
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self.options['random_flip'] = io.input_bool("Flip faces randomly? (y/n ?:help skip:y) : ", True,
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help_message="Predicted face will look more naturally"
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" without this option, but src faceset should"
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" cover all face directions as dst faceset.")
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else:
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self.options['random_flip'] = self.options.get('random_flip', True)
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if ask_src_scale_mod:
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if (self.iter == 0):
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self.options['src_scale_mod'] = np.clip( io.input_int("Src face scale modifier % ( -30...30, ?:help skip:0) : ", 0, help_message="If src face shape is wider than dst, try to decrease this value to get a better result."), -30, 30)
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if self.iter == 0:
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self.options['src_scale_mod'] = np.clip(io.input_int("Src face scale modifier %"
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" ( -30...30, ?:help skip:0) : ", 0,
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help_message="If src face shape is wider than"
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" dst, try to decrease this value to"
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" get a better result."), -30, 30)
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else:
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self.options['src_scale_mod'] = self.options.get('src_scale_mod', 0)
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@ -169,6 +213,7 @@ class ModelBase(object):
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self.onInitialize()
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self.options['batch_size'] = self.batch_size
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self.options['paddle'] = 'ping'
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if self.debug or self.batch_size == 0:
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self.batch_size = 1
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@ -178,8 +223,10 @@ class ModelBase(object):
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self.preview_history_path = self.model_path / ('%s_history' % (self.get_model_name()))
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self.autobackups_path = self.model_path / ('%s_autobackups' % (self.get_model_name()))
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else:
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self.preview_history_path = self.model_path / ( '%d_%s_history' % (self.device_args['force_gpu_idx'], self.get_model_name()) )
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self.autobackups_path = self.model_path / ( '%d_%s_autobackups' % (self.device_args['force_gpu_idx'], self.get_model_name()) )
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self.preview_history_path = self.model_path / ('%d_%s_history' % (self.device_args['force_gpu_idx'],
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self.get_model_name()))
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self.autobackups_path = self.model_path / ('%d_%s_autobackups' % (self.device_args['force_gpu_idx'],
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self.get_model_name()))
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if self.autobackup:
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self.autobackup_current_hour = time.localtime().tm_hour
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@ -215,7 +262,8 @@ class ModelBase(object):
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while True:
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key_events = io.get_key_events(wnd_name)
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key, chr_key, ctrl_pressed, alt_pressed, shift_pressed = key_events[-1] if len(key_events) > 0 else (0,0,False,False,False)
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key, chr_key, ctrl_pressed, alt_pressed, shift_pressed = key_events[-1] if len(
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key_events) > 0 else (0, 0, False, False, False)
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if key == ord('\n') or key == ord('\r'):
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choosed = True
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break
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@ -354,7 +402,8 @@ class ModelBase(object):
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}
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self.model_data_path.write_bytes(pickle.dumps(model_data))
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bckp_filename_list = [ self.get_strpath_storage_for_file(filename) for _, filename in self.get_model_filename_list() ]
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bckp_filename_list = [self.get_strpath_storage_for_file(filename) for _, filename in
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self.get_model_filename_list()]
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bckp_filename_list += [str(summary_path), str(self.model_data_path)]
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if self.autobackup:
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@ -388,7 +437,8 @@ class ModelBase(object):
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plist += [(bgr, idx_backup_path / (('preview_%s.jpg') % (name)))]
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for preview, filepath in plist:
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preview_lh = ModelBase.get_loss_history_preview(self.loss_history, self.iter, preview.shape[1], preview.shape[2])
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preview_lh = ModelBase.get_loss_history_preview(self.loss_history, self.iter,self.batch_size,
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preview.shape[1], preview.shape[2])
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img = (np.concatenate([preview_lh, preview], axis=0) * 255).astype(np.uint8)
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cv2_imwrite(filepath, img)
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@ -415,7 +465,6 @@ class ModelBase(object):
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except Exception as e:
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print("Unable to load ", opt_filename)
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def save_weights_safe(self, model_filename_list):
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for model, filename in model_filename_list:
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filename = self.get_strpath_storage_for_file(filename)
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@ -490,14 +539,18 @@ class ModelBase(object):
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plist += [(self.get_static_preview(), str(self.preview_history_path / ('%.6d.jpg' % (self.iter))))]
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for preview, filepath in plist:
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preview_lh = ModelBase.get_loss_history_preview(self.loss_history, self.iter, preview.shape[1], preview.shape[2])
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preview_lh = ModelBase.get_loss_history_preview(self.loss_history, self.iter,self.batch_size, preview.shape[1],
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preview.shape[2])
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img = (np.concatenate([preview_lh, preview], axis=0) * 255).astype(np.uint8)
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cv2_imwrite(filepath, img)
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if self.iter % 50 == 0 and self.iter != 0:
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self.set_batch_size(self.batch_size + 1)
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self.iter += 1
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return self.iter, iter_time
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return self.iter, iter_time, self.batch_size
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def pass_one_iter(self):
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self.last_sample = self.generate_next_sample()
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@ -536,7 +589,8 @@ class ModelBase(object):
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if self.device_args['force_gpu_idx'] == -1:
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return str(self.model_path / (self.get_model_name() + '_' + filename))
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else:
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return str( self.model_path / ( str(self.device_args['force_gpu_idx']) + '_' + self.get_model_name() + '_' + filename) )
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return str(self.model_path / (
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str(self.device_args['force_gpu_idx']) + '_' + self.get_model_name() + '_' + filename))
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def set_vram_batch_requirements(self, d):
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# example d = {2:2,3:4,4:8,5:16,6:32,7:32,8:32,9:48}
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@ -556,7 +610,7 @@ class ModelBase(object):
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self.batch_size = d[keys[-1]]
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@staticmethod
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def get_loss_history_preview(loss_history, iter, w, c):
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def get_loss_history_preview(loss_history, iter,batch_size, w, c):
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loss_history = np.array(loss_history.copy())
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lh_height = 100
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@ -611,7 +665,9 @@ class ModelBase(object):
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last_line_t = int((lh_lines - 1) * lh_line_height)
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last_line_b = int(lh_lines * lh_line_height)
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lh_text = 'Iter: %d' % (iter) if iter != 0 else ''
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lh_text = 'Iter: %d' % iter if iter != 0 else ''
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bs_text = 'BS: %d' % batch_size if batch_size is not None else '1'
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lh_img[last_line_t:last_line_b, 0:w] += imagelib.get_text_image ( (last_line_b-last_line_t,w,c), lh_text, color=[0.8]*c )
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lh_img[last_line_t:last_line_b, 0:w] += imagelib.get_text_image((last_line_b - last_line_t, w, c), bs_text,
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color=[0.8] * c)
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return lh_img
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@ -8,6 +8,8 @@ from samplelib import *
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from interact import interact as io
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# SAE - Styled AutoEncoder
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class SAEModel(ModelBase):
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encoderH5 = 'encoder.h5'
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@ -152,6 +154,7 @@ class SAEModel(ModelBase):
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SAEModel.initialize_nn_functions()
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self.set_vram_batch_requirements({1.5: 4})
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global resolution
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resolution= self.options['resolution']
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ae_dims = self.options['ae_dims']
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e_ch_dims = self.options['e_ch_dims']
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@ -163,9 +166,10 @@ class SAEModel(ModelBase):
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d_residual_blocks = True
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bgr_shape = (resolution, resolution, 3)
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mask_shape = (resolution, resolution, 1)
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global ms_count
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self.ms_count = ms_count = 3 if (self.options['multiscale_decoder']) else 1
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global apply_random_ct
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apply_random_ct = self.options.get('apply_random_ct', False)
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masked_training = True
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@ -436,13 +440,19 @@ class SAEModel(ModelBase):
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self.src_sample_losses = []
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self.dst_sample_losses = []
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global t
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t = SampleProcessor.Types
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global face_type
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face_type = t.FACE_TYPE_FULL if self.options['face_type'] == 'f' else t.FACE_TYPE_HALF
|
||||
|
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global t_mode_bgr
|
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t_mode_bgr = t.MODE_BGR if not self.pretrain else t.MODE_BGR_SHUFFLE
|
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|
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global training_data_src_path
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training_data_src_path = self.training_data_src_path
|
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global training_data_dst_path
|
||||
training_data_dst_path= self.training_data_dst_path
|
||||
global sort_by_yaw
|
||||
sort_by_yaw = self.sort_by_yaw
|
||||
|
||||
if self.pretrain and self.pretraining_data_path is not None:
|
||||
|
|
@ -458,7 +468,8 @@ class SAEModel(ModelBase):
|
|||
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip,
|
||||
scale_range=np.array([-0.05,
|
||||
0.05]) + self.src_scale_mod / 100.0),
|
||||
output_sample_types=[{'types': (t.IMG_WARPED_TRANSFORMED, face_type, t_mode_bgr),
|
||||
output_sample_types=[{'types': (
|
||||
t.IMG_WARPED_TRANSFORMED, face_type, t_mode_bgr),
|
||||
'resolution': resolution, 'apply_ct': apply_random_ct}] + \
|
||||
[{'types': (t.IMG_TRANSFORMED, face_type, t_mode_bgr),
|
||||
'resolution': resolution // (2 ** i),
|
||||
|
|
@ -470,7 +481,8 @@ class SAEModel(ModelBase):
|
|||
|
||||
SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
|
||||
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, ),
|
||||
output_sample_types=[{'types': (t.IMG_WARPED_TRANSFORMED, face_type, t_mode_bgr),
|
||||
output_sample_types=[{'types': (
|
||||
t.IMG_WARPED_TRANSFORMED, face_type, t_mode_bgr),
|
||||
'resolution': resolution}] + \
|
||||
[{'types': (t.IMG_TRANSFORMED, face_type, t_mode_bgr),
|
||||
'resolution': resolution // (2 ** i)} for i in
|
||||
|
|
@ -514,6 +526,43 @@ class SAEModel(ModelBase):
|
|||
def onSave(self):
|
||||
self.save_weights_safe(self.get_model_filename_list())
|
||||
|
||||
# override
|
||||
def set_batch_size(self, batch_size):
|
||||
self.batch_size = batch_size
|
||||
self.set_training_data_generators([
|
||||
SampleGeneratorFace(training_data_src_path,
|
||||
sort_by_yaw_target_samples_path=training_data_dst_path if sort_by_yaw else None,
|
||||
random_ct_samples_path=training_data_dst_path if apply_random_ct else None,
|
||||
debug=self.is_debug(), batch_size=self.batch_size,
|
||||
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip,
|
||||
scale_range=np.array([-0.05,
|
||||
0.05]) + self.src_scale_mod / 100.0),
|
||||
output_sample_types=[{'types': (
|
||||
t.IMG_WARPED_TRANSFORMED, face_type, t_mode_bgr),
|
||||
'resolution': resolution, 'apply_ct': apply_random_ct}] + \
|
||||
[{'types': (t.IMG_TRANSFORMED, face_type, t_mode_bgr),
|
||||
'resolution': resolution // (2 ** i),
|
||||
'apply_ct': apply_random_ct} for i in range(ms_count)] + \
|
||||
[{'types': (t.IMG_TRANSFORMED, face_type, t.MODE_M),
|
||||
'resolution': resolution // (2 ** i)} for i in
|
||||
range(ms_count)]
|
||||
),
|
||||
|
||||
SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
|
||||
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, ),
|
||||
output_sample_types=[{'types': (
|
||||
t.IMG_WARPED_TRANSFORMED, face_type, t_mode_bgr),
|
||||
'resolution': resolution}] + \
|
||||
[{'types': (t.IMG_TRANSFORMED, face_type, t_mode_bgr),
|
||||
'resolution': resolution // (2 ** i)} for i in
|
||||
range(ms_count)] + \
|
||||
[{'types': (t.IMG_TRANSFORMED, face_type, t.MODE_M),
|
||||
'resolution': resolution // (2 ** i)} for i in
|
||||
range(ms_count)])
|
||||
])
|
||||
|
||||
|
||||
|
||||
# override
|
||||
def onTrainOneIter(self, generators_samples, generators_list):
|
||||
src_samples = generators_samples[0]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue