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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@ -23,39 +23,41 @@ 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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ask_target_iter=True,
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ask_batch_size=True,
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ask_write_preview_history=True,
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ask_target_iter=True,
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ask_batch_size=True,
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ask_sort_by_yaw=True,
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ask_random_flip=True,
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ask_random_flip=True,
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ask_src_scale_mod=True):
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device_args['force_gpu_idx'] = device_args.get('force_gpu_idx',-1)
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device_args['cpu_only'] = device_args.get('cpu_only',False)
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device_args['force_gpu_idx'] = device_args.get('force_gpu_idx', -1)
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device_args['cpu_only'] = device_args.get('cpu_only', False)
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if device_args['force_gpu_idx'] == -1 and not device_args['cpu_only']:
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idxs_names_list = nnlib.device.getValidDevicesIdxsWithNamesList()
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if len(idxs_names_list) > 1:
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io.log_info ("You have multi GPUs in a system: ")
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io.log_info("You have multi GPUs in a system: ")
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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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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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io.log_info ("Loading model...")
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io.log_info("Loading model...")
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self.model_path = model_path
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self.model_data_path = Path( self.get_strpath_storage_for_file('data.dat') )
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self.model_data_path = Path(self.get_strpath_storage_for_file('data.dat'))
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self.training_data_src_path = training_data_src_path
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self.training_data_dst_path = training_data_dst_path
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self.pretraining_data_path = pretraining_data_path
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self.src_images_paths = None
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self.dst_images_paths = None
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self.src_yaw_images_paths = None
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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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@ -72,8 +76,8 @@ class ModelBase(object):
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model_data = {}
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if self.model_data_path.exists():
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model_data = pickle.loads ( self.model_data_path.read_bytes() )
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self.iter = max( model_data.get('iter',0), model_data.get('epoch',0) )
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model_data = pickle.loads(self.model_data_path.read_bytes())
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self.iter = max(model_data.get('iter', 0), model_data.get('epoch', 0))
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if 'epoch' in self.options:
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self.options.pop('epoch')
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if self.iter != 0:
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@ -81,65 +85,105 @@ 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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yn_str = {True: 'y', False: 'n'}
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if self.iter == 0:
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io.log_info ("\nModel first run. Enter model options as default for each run.")
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io.log_info("\nModel first run. Enter model options as default for each run.")
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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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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) : " %
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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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if ask_target_iter:
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if (self.iter == 0 or ask_override):
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self.options['target_iter'] = max(0, io.input_int("Target iteration (skip:unlimited/default) : ", 0))
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else:
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self.options['target_iter'] = max(model_data.get('target_iter',0), self.options.get('target_epoch',0))
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self.options['target_iter'] = max(model_data.get('target_iter', 0), self.options.get('target_epoch', 0))
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if 'target_epoch' in self.options:
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self.options.pop('target_epoch')
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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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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,
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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 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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self.autobackup = self.options.get('autobackup', False)
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if not self.autobackup and 'autobackup' in self.options:
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self.options.pop('autobackup')
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@ -148,15 +192,15 @@ class ModelBase(object):
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if not self.write_preview_history and 'write_preview_history' in self.options:
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self.options.pop('write_preview_history')
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self.target_iter = self.options.get('target_iter',0)
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self.target_iter = self.options.get('target_iter', 0)
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if self.target_iter == 0 and 'target_iter' in self.options:
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self.options.pop('target_iter')
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self.batch_size = self.options.get('batch_size',0)
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self.sort_by_yaw = self.options.get('sort_by_yaw',False)
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self.random_flip = self.options.get('random_flip',True)
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self.batch_size = self.options.get('batch_size', 0)
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self.sort_by_yaw = self.options.get('sort_by_yaw', False)
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self.random_flip = self.options.get('random_flip', True)
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self.src_scale_mod = self.options.get('src_scale_mod',0)
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self.src_scale_mod = self.options.get('src_scale_mod', 0)
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if self.src_scale_mod == 0 and 'src_scale_mod' in self.options:
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self.options.pop('src_scale_mod')
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@ -169,21 +213,24 @@ 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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if self.is_training_mode:
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if self.device_args['force_gpu_idx'] == -1:
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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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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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if not self.autobackups_path.exists():
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self.autobackups_path.mkdir(exist_ok=True)
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@ -196,13 +243,13 @@ class ModelBase(object):
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Path(filename).unlink()
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if self.generator_list is None:
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raise ValueError( 'You didnt set_training_data_generators()')
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raise ValueError('You didnt set_training_data_generators()')
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else:
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for i, generator in enumerate(self.generator_list):
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if not isinstance(generator, SampleGeneratorBase):
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raise ValueError('training data generator is not subclass of SampleGeneratorBase')
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if self.sample_for_preview is None or choose_preview_history:
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if self.sample_for_preview is None or choose_preview_history:
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if choose_preview_history and io.is_support_windows():
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wnd_name = "[p] - next. [enter] - confirm."
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io.named_window(wnd_name)
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@ -211,25 +258,26 @@ class ModelBase(object):
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while not choosed:
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self.sample_for_preview = self.generate_next_sample()
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preview = self.get_static_preview()
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io.show_image( wnd_name, (preview*255).astype(np.uint8) )
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io.show_image(wnd_name, (preview * 255).astype(np.uint8))
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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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elif key == ord('p'):
|
||||
break
|
||||
|
||||
|
||||
try:
|
||||
io.process_messages(0.1)
|
||||
except KeyboardInterrupt:
|
||||
choosed = True
|
||||
|
||||
|
||||
io.destroy_window(wnd_name)
|
||||
else:
|
||||
self.sample_for_preview = self.generate_next_sample()
|
||||
else:
|
||||
self.sample_for_preview = self.generate_next_sample()
|
||||
self.last_sample = self.sample_for_preview
|
||||
model_summary_text = []
|
||||
|
||||
|
|
@ -260,15 +308,15 @@ class ModelBase(object):
|
|||
model_summary_text += ["=="]
|
||||
|
||||
model_summary_text += ["========================="]
|
||||
model_summary_text = "\r\n".join (model_summary_text)
|
||||
model_summary_text = "\r\n".join(model_summary_text)
|
||||
self.model_summary_text = model_summary_text
|
||||
io.log_info(model_summary_text)
|
||||
|
||||
#overridable
|
||||
# overridable
|
||||
def onInitializeOptions(self, is_first_run, ask_override):
|
||||
pass
|
||||
|
||||
#overridable
|
||||
# overridable
|
||||
def onInitialize(self):
|
||||
'''
|
||||
initialize your keras models
|
||||
|
|
@ -279,36 +327,36 @@ class ModelBase(object):
|
|||
'''
|
||||
pass
|
||||
|
||||
#overridable
|
||||
# overridable
|
||||
def onSave(self):
|
||||
#save your keras models here
|
||||
# save your keras models here
|
||||
pass
|
||||
|
||||
#overridable
|
||||
# overridable
|
||||
def onTrainOneIter(self, sample, generator_list):
|
||||
#train your keras models here
|
||||
# train your keras models here
|
||||
|
||||
#return array of losses
|
||||
return ( ('loss_src', 0), ('loss_dst', 0) )
|
||||
# return array of losses
|
||||
return (('loss_src', 0), ('loss_dst', 0))
|
||||
|
||||
#overridable
|
||||
# overridable
|
||||
def onGetPreview(self, sample):
|
||||
#you can return multiple previews
|
||||
#return [ ('preview_name',preview_rgb), ... ]
|
||||
# you can return multiple previews
|
||||
# return [ ('preview_name',preview_rgb), ... ]
|
||||
return []
|
||||
|
||||
#overridable if you want model name differs from folder name
|
||||
# overridable if you want model name differs from folder name
|
||||
def get_model_name(self):
|
||||
return Path(inspect.getmodule(self).__file__).parent.name.rsplit("_", 1)[1]
|
||||
|
||||
#overridable , return [ [model, filename],... ] list
|
||||
# overridable , return [ [model, filename],... ] list
|
||||
def get_model_filename_list(self):
|
||||
return []
|
||||
|
||||
#overridable
|
||||
# overridable
|
||||
def get_converter(self):
|
||||
raise NotImplementedError
|
||||
#return existing or your own converter which derived from base
|
||||
# return existing or your own converter which derived from base
|
||||
|
||||
def get_target_iter(self):
|
||||
return self.target_iter
|
||||
|
|
@ -316,8 +364,8 @@ class ModelBase(object):
|
|||
def is_reached_iter_goal(self):
|
||||
return self.target_iter != 0 and self.iter >= self.target_iter
|
||||
|
||||
#multi gpu in keras actually is fake and doesn't work for training https://github.com/keras-team/keras/issues/11976
|
||||
#def to_multi_gpu_model_if_possible (self, models_list):
|
||||
# multi gpu in keras actually is fake and doesn't work for training https://github.com/keras-team/keras/issues/11976
|
||||
# def to_multi_gpu_model_if_possible (self, models_list):
|
||||
# if len(self.device_config.gpu_idxs) > 1:
|
||||
# #make batch_size to divide on GPU count without remainder
|
||||
# self.batch_size = int( self.batch_size / len(self.device_config.gpu_idxs) )
|
||||
|
|
@ -336,61 +384,63 @@ class ModelBase(object):
|
|||
# return models_list
|
||||
|
||||
def get_previews(self):
|
||||
return self.onGetPreview ( self.last_sample )
|
||||
return self.onGetPreview(self.last_sample)
|
||||
|
||||
def get_static_preview(self):
|
||||
return self.onGetPreview (self.sample_for_preview)[0][1] #first preview, and bgr
|
||||
return self.onGetPreview(self.sample_for_preview)[0][1] # first preview, and bgr
|
||||
|
||||
def save(self):
|
||||
summary_path = self.get_strpath_storage_for_file('summary.txt')
|
||||
Path( summary_path ).write_text(self.model_summary_text)
|
||||
Path(summary_path).write_text(self.model_summary_text)
|
||||
self.onSave()
|
||||
|
||||
model_data = {
|
||||
'iter': self.iter,
|
||||
'options': self.options,
|
||||
'loss_history': self.loss_history,
|
||||
'sample_for_preview' : self.sample_for_preview
|
||||
'sample_for_preview': self.sample_for_preview
|
||||
}
|
||||
self.model_data_path.write_bytes( pickle.dumps(model_data) )
|
||||
self.model_data_path.write_bytes(pickle.dumps(model_data))
|
||||
|
||||
bckp_filename_list = [self.get_strpath_storage_for_file(filename) for _, filename in
|
||||
self.get_model_filename_list()]
|
||||
bckp_filename_list += [str(summary_path), str(self.model_data_path)]
|
||||
|
||||
bckp_filename_list = [ self.get_strpath_storage_for_file(filename) for _, filename in self.get_model_filename_list() ]
|
||||
bckp_filename_list += [ str(summary_path), str(self.model_data_path) ]
|
||||
|
||||
if self.autobackup:
|
||||
current_hour = time.localtime().tm_hour
|
||||
if self.autobackup_current_hour != current_hour:
|
||||
self.autobackup_current_hour = current_hour
|
||||
|
||||
for i in range(15,0,-1):
|
||||
for i in range(15, 0, -1):
|
||||
idx_str = '%.2d' % i
|
||||
next_idx_str = '%.2d' % (i+1)
|
||||
|
||||
next_idx_str = '%.2d' % (i + 1)
|
||||
|
||||
idx_backup_path = self.autobackups_path / idx_str
|
||||
next_idx_packup_path = self.autobackups_path / next_idx_str
|
||||
|
||||
|
||||
if idx_backup_path.exists():
|
||||
if i == 15:
|
||||
if i == 15:
|
||||
Path_utils.delete_all_files(idx_backup_path)
|
||||
else:
|
||||
next_idx_packup_path.mkdir(exist_ok=True)
|
||||
Path_utils.move_all_files (idx_backup_path, next_idx_packup_path)
|
||||
|
||||
Path_utils.move_all_files(idx_backup_path, next_idx_packup_path)
|
||||
|
||||
if i == 1:
|
||||
idx_backup_path.mkdir(exist_ok=True)
|
||||
for filename in bckp_filename_list:
|
||||
shutil.copy ( str(filename), str(idx_backup_path / Path(filename).name) )
|
||||
idx_backup_path.mkdir(exist_ok=True)
|
||||
for filename in bckp_filename_list:
|
||||
shutil.copy(str(filename), str(idx_backup_path / Path(filename).name))
|
||||
|
||||
previews = self.get_previews()
|
||||
plist = []
|
||||
for i in range(len(previews)):
|
||||
name, bgr = previews[i]
|
||||
plist += [ (bgr, idx_backup_path / ( ('preview_%s.jpg') % (name)) ) ]
|
||||
plist += [(bgr, idx_backup_path / (('preview_%s.jpg') % (name)))]
|
||||
|
||||
for preview, filepath in plist:
|
||||
preview_lh = ModelBase.get_loss_history_preview(self.loss_history, self.iter, preview.shape[1], preview.shape[2])
|
||||
img = (np.concatenate ( [preview_lh, preview], axis=0 ) * 255).astype(np.uint8)
|
||||
cv2_imwrite (filepath, img )
|
||||
preview_lh = ModelBase.get_loss_history_preview(self.loss_history, self.iter,self.batch_size,
|
||||
preview.shape[1], preview.shape[2])
|
||||
img = (np.concatenate([preview_lh, preview], axis=0) * 255).astype(np.uint8)
|
||||
cv2_imwrite(filepath, img)
|
||||
|
||||
def load_weights_safe(self, model_filename_list, optimizer_filename_list=[]):
|
||||
for model, filename in model_filename_list:
|
||||
|
|
@ -413,16 +463,15 @@ class ModelBase(object):
|
|||
opt.set_weights(weights)
|
||||
print("set ok")
|
||||
except Exception as e:
|
||||
print ("Unable to load ", opt_filename)
|
||||
|
||||
print("Unable to load ", opt_filename)
|
||||
|
||||
def save_weights_safe(self, model_filename_list):
|
||||
for model, filename in model_filename_list:
|
||||
filename = self.get_strpath_storage_for_file(filename)
|
||||
model.save_weights( filename + '.tmp' )
|
||||
model.save_weights(filename + '.tmp')
|
||||
|
||||
rename_list = model_filename_list
|
||||
|
||||
|
||||
"""
|
||||
#unused
|
||||
, optimizer_filename_list=[]
|
||||
|
|
@ -446,24 +495,24 @@ class ModelBase(object):
|
|||
except Exception as e:
|
||||
print ("Unable to save ", opt_filename)
|
||||
"""
|
||||
|
||||
|
||||
for _, filename in rename_list:
|
||||
filename = self.get_strpath_storage_for_file(filename)
|
||||
source_filename = Path(filename+'.tmp')
|
||||
source_filename = Path(filename + '.tmp')
|
||||
if source_filename.exists():
|
||||
target_filename = Path(filename)
|
||||
if target_filename.exists():
|
||||
target_filename.unlink()
|
||||
source_filename.rename ( str(target_filename) )
|
||||
|
||||
source_filename.rename(str(target_filename))
|
||||
|
||||
def debug_one_iter(self):
|
||||
images = []
|
||||
for generator in self.generator_list:
|
||||
for i,batch in enumerate(next(generator)):
|
||||
for i, batch in enumerate(next(generator)):
|
||||
if len(batch.shape) == 4:
|
||||
images.append( batch[0] )
|
||||
images.append(batch[0])
|
||||
|
||||
return imagelib.equalize_and_stack_square (images)
|
||||
return imagelib.equalize_and_stack_square(images)
|
||||
|
||||
def generate_next_sample(self):
|
||||
return [next(generator) for generator in self.generator_list]
|
||||
|
|
@ -475,7 +524,7 @@ class ModelBase(object):
|
|||
iter_time = time.time() - iter_time
|
||||
self.last_sample = sample
|
||||
|
||||
self.loss_history.append ( [float(loss[1]) for loss in losses] )
|
||||
self.loss_history.append([float(loss[1]) for loss in losses])
|
||||
|
||||
if self.iter % 10 == 0:
|
||||
plist = []
|
||||
|
|
@ -484,20 +533,24 @@ class ModelBase(object):
|
|||
previews = self.get_previews()
|
||||
for i in range(len(previews)):
|
||||
name, bgr = previews[i]
|
||||
plist += [ (bgr, self.get_strpath_storage_for_file('preview_%s.jpg' % (name) ) ) ]
|
||||
plist += [(bgr, self.get_strpath_storage_for_file('preview_%s.jpg' % (name)))]
|
||||
|
||||
if self.write_preview_history:
|
||||
plist += [ (self.get_static_preview(), str (self.preview_history_path / ('%.6d.jpg' % (self.iter))) ) ]
|
||||
plist += [(self.get_static_preview(), str(self.preview_history_path / ('%.6d.jpg' % (self.iter))))]
|
||||
|
||||
for preview, filepath in plist:
|
||||
preview_lh = ModelBase.get_loss_history_preview(self.loss_history, self.iter, preview.shape[1], preview.shape[2])
|
||||
img = (np.concatenate ( [preview_lh, preview], axis=0 ) * 255).astype(np.uint8)
|
||||
cv2_imwrite (filepath, img )
|
||||
preview_lh = ModelBase.get_loss_history_preview(self.loss_history, self.iter,self.batch_size, preview.shape[1],
|
||||
preview.shape[2])
|
||||
img = (np.concatenate([preview_lh, preview], axis=0) * 255).astype(np.uint8)
|
||||
cv2_imwrite(filepath, img)
|
||||
|
||||
if self.iter % 50 == 0 and self.iter != 0:
|
||||
|
||||
self.set_batch_size(self.batch_size + 1)
|
||||
|
||||
self.iter += 1
|
||||
|
||||
return self.iter, iter_time
|
||||
return self.iter, iter_time, self.batch_size
|
||||
|
||||
def pass_one_iter(self):
|
||||
self.last_sample = self.generate_next_sample()
|
||||
|
|
@ -523,10 +576,10 @@ class ModelBase(object):
|
|||
def get_loss_history(self):
|
||||
return self.loss_history
|
||||
|
||||
def set_training_data_generators (self, generator_list):
|
||||
def set_training_data_generators(self, generator_list):
|
||||
self.generator_list = generator_list
|
||||
|
||||
def get_training_data_generators (self):
|
||||
def get_training_data_generators(self):
|
||||
return self.generator_list
|
||||
|
||||
def get_model_root_path(self):
|
||||
|
|
@ -534,12 +587,13 @@ class ModelBase(object):
|
|||
|
||||
def get_strpath_storage_for_file(self, filename):
|
||||
if self.device_args['force_gpu_idx'] == -1:
|
||||
return str( self.model_path / ( self.get_model_name() + '_' + filename) )
|
||||
return str(self.model_path / (self.get_model_name() + '_' + filename))
|
||||
else:
|
||||
return str( self.model_path / ( str(self.device_args['force_gpu_idx']) + '_' + self.get_model_name() + '_' + filename) )
|
||||
return str(self.model_path / (
|
||||
str(self.device_args['force_gpu_idx']) + '_' + self.get_model_name() + '_' + filename))
|
||||
|
||||
def set_vram_batch_requirements (self, d):
|
||||
#example d = {2:2,3:4,4:8,5:16,6:32,7:32,8:32,9:48}
|
||||
def set_vram_batch_requirements(self, d):
|
||||
# example d = {2:2,3:4,4:8,5:16,6:32,7:32,8:32,9:48}
|
||||
keys = [x for x in d.keys()]
|
||||
|
||||
if self.device_config.cpu_only:
|
||||
|
|
@ -553,65 +607,67 @@ class ModelBase(object):
|
|||
break
|
||||
|
||||
if self.batch_size == 0:
|
||||
self.batch_size = d[ keys[-1] ]
|
||||
self.batch_size = d[keys[-1]]
|
||||
|
||||
@staticmethod
|
||||
def get_loss_history_preview(loss_history, iter, w, c):
|
||||
loss_history = np.array (loss_history.copy())
|
||||
def get_loss_history_preview(loss_history, iter,batch_size, w, c):
|
||||
loss_history = np.array(loss_history.copy())
|
||||
|
||||
lh_height = 100
|
||||
lh_img = np.ones ( (lh_height,w,c) ) * 0.1
|
||||
|
||||
if len(loss_history) != 0:
|
||||
lh_img = np.ones((lh_height, w, c)) * 0.1
|
||||
|
||||
if len(loss_history) != 0:
|
||||
loss_count = len(loss_history[0])
|
||||
lh_len = len(loss_history)
|
||||
|
||||
l_per_col = lh_len / w
|
||||
plist_max = [ [ max (0.0, loss_history[int(col*l_per_col)][p],
|
||||
*[ loss_history[i_ab][p]
|
||||
for i_ab in range( int(col*l_per_col), int((col+1)*l_per_col) )
|
||||
]
|
||||
)
|
||||
for p in range(loss_count)
|
||||
]
|
||||
for col in range(w)
|
||||
]
|
||||
plist_max = [[max(0.0, loss_history[int(col * l_per_col)][p],
|
||||
*[loss_history[i_ab][p]
|
||||
for i_ab in range(int(col * l_per_col), int((col + 1) * l_per_col))
|
||||
]
|
||||
)
|
||||
for p in range(loss_count)
|
||||
]
|
||||
for col in range(w)
|
||||
]
|
||||
|
||||
plist_min = [ [ min (plist_max[col][p], loss_history[int(col*l_per_col)][p],
|
||||
*[ loss_history[i_ab][p]
|
||||
for i_ab in range( int(col*l_per_col), int((col+1)*l_per_col) )
|
||||
]
|
||||
)
|
||||
for p in range(loss_count)
|
||||
]
|
||||
for col in range(w)
|
||||
]
|
||||
plist_min = [[min(plist_max[col][p], loss_history[int(col * l_per_col)][p],
|
||||
*[loss_history[i_ab][p]
|
||||
for i_ab in range(int(col * l_per_col), int((col + 1) * l_per_col))
|
||||
]
|
||||
)
|
||||
for p in range(loss_count)
|
||||
]
|
||||
for col in range(w)
|
||||
]
|
||||
|
||||
plist_abs_max = np.mean(loss_history[ len(loss_history) // 5 : ]) * 2
|
||||
plist_abs_max = np.mean(loss_history[len(loss_history) // 5:]) * 2
|
||||
|
||||
for col in range(0, w):
|
||||
for p in range(0,loss_count):
|
||||
point_color = [1.0]*c
|
||||
point_color[0:3] = colorsys.hsv_to_rgb ( p * (1.0/loss_count), 1.0, 1.0 )
|
||||
for p in range(0, loss_count):
|
||||
point_color = [1.0] * c
|
||||
point_color[0:3] = colorsys.hsv_to_rgb(p * (1.0 / loss_count), 1.0, 1.0)
|
||||
|
||||
ph_max = int ( (plist_max[col][p] / plist_abs_max) * (lh_height-1) )
|
||||
ph_max = np.clip( ph_max, 0, lh_height-1 )
|
||||
ph_max = int((plist_max[col][p] / plist_abs_max) * (lh_height - 1))
|
||||
ph_max = np.clip(ph_max, 0, lh_height - 1)
|
||||
|
||||
ph_min = int ( (plist_min[col][p] / plist_abs_max) * (lh_height-1) )
|
||||
ph_min = np.clip( ph_min, 0, lh_height-1 )
|
||||
ph_min = int((plist_min[col][p] / plist_abs_max) * (lh_height - 1))
|
||||
ph_min = np.clip(ph_min, 0, lh_height - 1)
|
||||
|
||||
for ph in range(ph_min, ph_max+1):
|
||||
lh_img[ (lh_height-ph-1), col ] = point_color
|
||||
for ph in range(ph_min, ph_max + 1):
|
||||
lh_img[(lh_height - ph - 1), col] = point_color
|
||||
|
||||
lh_lines = 5
|
||||
lh_line_height = (lh_height-1)/lh_lines
|
||||
for i in range(0,lh_lines+1):
|
||||
lh_img[ int(i*lh_line_height), : ] = (0.8,)*c
|
||||
lh_line_height = (lh_height - 1) / lh_lines
|
||||
for i in range(0, lh_lines + 1):
|
||||
lh_img[int(i * lh_line_height), :] = (0.8,) * c
|
||||
|
||||
last_line_t = int((lh_lines-1)*lh_line_height)
|
||||
last_line_b = int(lh_lines*lh_line_height)
|
||||
last_line_t = int((lh_lines - 1) * lh_line_height)
|
||||
last_line_b = int(lh_lines * lh_line_height)
|
||||
|
||||
lh_text = 'Iter: %d' % (iter) if iter != 0 else ''
|
||||
lh_text = 'Iter: %d' % iter if iter != 0 else ''
|
||||
bs_text = 'BS: %d' % batch_size if batch_size is not None else '1'
|
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|
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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 *
|
|||
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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|
|
@ -152,7 +154,8 @@ class SAEModel(ModelBase):
|
|||
SAEModel.initialize_nn_functions()
|
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self.set_vram_batch_requirements({1.5: 4})
|
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|
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resolution = self.options['resolution']
|
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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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d_ch_dims = self.options['d_ch_dims']
|
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|
|
@ -163,9 +166,10 @@ class SAEModel(ModelBase):
|
|||
d_residual_blocks = True
|
||||
bgr_shape = (resolution, resolution, 3)
|
||||
mask_shape = (resolution, resolution, 1)
|
||||
|
||||
global ms_count
|
||||
self.ms_count = ms_count = 3 if (self.options['multiscale_decoder']) else 1
|
||||
|
||||
global apply_random_ct
|
||||
apply_random_ct = self.options.get('apply_random_ct', False)
|
||||
masked_training = True
|
||||
|
||||
|
|
@ -436,18 +440,24 @@ class SAEModel(ModelBase):
|
|||
self.src_sample_losses = []
|
||||
self.dst_sample_losses = []
|
||||
|
||||
global t
|
||||
t = SampleProcessor.Types
|
||||
global face_type
|
||||
face_type = t.FACE_TYPE_FULL if self.options['face_type'] == 'f' else t.FACE_TYPE_HALF
|
||||
|
||||
global t_mode_bgr
|
||||
t_mode_bgr = t.MODE_BGR if not self.pretrain else t.MODE_BGR_SHUFFLE
|
||||
|
||||
global training_data_src_path
|
||||
training_data_src_path = self.training_data_src_path
|
||||
training_data_dst_path = self.training_data_dst_path
|
||||
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:
|
||||
training_data_src_path = self.pretraining_data_path
|
||||
training_data_dst_path = self.pretraining_data_path
|
||||
training_data_dst_path= self.pretraining_data_path
|
||||
sort_by_yaw = False
|
||||
|
||||
self.set_training_data_generators([
|
||||
|
|
@ -458,8 +468,9 @@ 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),
|
||||
'resolution': resolution, 'apply_ct': apply_random_ct}] + \
|
||||
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)] + \
|
||||
|
|
@ -470,8 +481,9 @@ 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),
|
||||
'resolution': resolution}] + \
|
||||
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)] + \
|
||||
|
|
@ -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