mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-08-22 14:24:40 -07:00
move flask trainer code into main trainer
This commit is contained in:
Jeremy Hummel
parent
9db0507ef8
commit
6d0b34635c
3 changed files with 149 additions and 439 deletions
2
main.py
2
main.py
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@ -135,7 +135,7 @@ if __name__ == "__main__":
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device_args = {'cpu_only' : arguments.cpu_only,
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'force_gpu_idx' : arguments.force_gpu_idx,
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}
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from mainscripts import FlaskTrainer as Trainer
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from mainscripts import Trainer
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Trainer.main(args, device_args)
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p = subparsers.add_parser( "train", help="Trainer")
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@ -1,379 +0,0 @@
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import traceback
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import queue
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import threading
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import time
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from enum import Enum
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import numpy as np
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import itertools
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from pathlib import Path
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from flaskr.app import create_flask_app
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import imagelib
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import cv2
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import models
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from interact import interact as io
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def trainerThread (s2c, c2s, e, args, device_args, socketio=None):
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while True:
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try:
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start_time = time.time()
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training_data_src_path = Path( args.get('training_data_src_dir', '') )
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training_data_dst_path = Path( args.get('training_data_dst_dir', '') )
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pretraining_data_path = args.get('pretraining_data_dir', '')
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pretraining_data_path = Path(pretraining_data_path) if pretraining_data_path is not None else None
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model_path = Path( args.get('model_path', '') )
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model_name = args.get('model_name', '')
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save_interval_min = 15
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debug = args.get('debug', '')
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execute_programs = args.get('execute_programs', [])
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if not training_data_src_path.exists():
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io.log_err('Training data src directory does not exist.')
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break
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if not training_data_dst_path.exists():
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io.log_err('Training data dst directory does not exist.')
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break
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if not model_path.exists():
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model_path.mkdir(exist_ok=True)
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model = models.import_model(model_name)(
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model_path,
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training_data_src_path=training_data_src_path,
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training_data_dst_path=training_data_dst_path,
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pretraining_data_path=pretraining_data_path,
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debug=debug,
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device_args=device_args)
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is_reached_goal = model.is_reached_iter_goal()
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shared_state = { 'after_save' : False }
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loss_string = ""
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save_iter = model.get_iter()
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def model_save():
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if not debug and not is_reached_goal:
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io.log_info ("Saving....", end='\r')
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model.save()
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shared_state['after_save'] = True
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def send_preview():
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if not debug:
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previews = model.get_previews()
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c2s.put ( {'op':'show', 'previews': previews, 'iter':model.get_iter(), 'loss_history': model.get_loss_history().copy() } )
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else:
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previews = [( 'debug, press update for new', model.debug_one_iter())]
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c2s.put ( {'op':'show', 'previews': previews} )
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e.set() #Set the GUI Thread as Ready
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if model.is_first_run():
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model_save()
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if model.get_target_iter() != 0:
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if is_reached_goal:
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io.log_info('Model already trained to target iteration. You can use preview.')
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else:
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io.log_info('Starting. Target iteration: %d. Press "Enter" to stop training and save model.' % ( model.get_target_iter() ) )
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else:
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io.log_info('Starting. Press "Enter" to stop training and save model.')
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last_save_time = time.time()
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execute_programs = [ [x[0], x[1], time.time() ] for x in execute_programs ]
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for i in itertools.count(0,1):
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if not debug:
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cur_time = time.time()
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for x in execute_programs:
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prog_time, prog, last_time = x
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exec_prog = False
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if prog_time > 0 and (cur_time - start_time) >= prog_time:
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x[0] = 0
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exec_prog = True
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elif prog_time < 0 and (cur_time - last_time) >= -prog_time:
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x[2] = cur_time
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exec_prog = True
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if exec_prog:
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try:
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exec(prog)
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except Exception as e:
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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, 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][bs: {3}]".format ( time_str, iter, '{:0.4f}'.format(iter_time), batch_size )
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else:
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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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last_save_time = time.time() #upd last_save_time only after save+one_iter, because plaidML rebuilds programs after save https://github.com/plaidml/plaidml/issues/274
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mean_loss = np.mean ( [ np.array(loss_history[i]) for i in range(save_iter, iter) ], axis=0)
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for loss_value in mean_loss:
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loss_string += "[%.4f]" % (loss_value)
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io.log_info (loss_string)
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save_iter = iter
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else:
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for loss_value in loss_history[-1]:
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loss_string += "[%.4f]" % (loss_value)
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if io.is_colab():
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io.log_info ('\r' + loss_string, end='')
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else:
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io.log_info (loss_string, end='\r')
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if socketio is not None:
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socketio.emit('loss', loss_string)
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if model.get_target_iter() != 0 and model.is_reached_iter_goal():
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io.log_info ('Reached target iteration.')
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model_save()
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is_reached_goal = True
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io.log_info ('You can use preview now.')
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if not is_reached_goal and (time.time() - last_save_time) >= save_interval_min*60:
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model_save()
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send_preview()
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if i==0:
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if is_reached_goal:
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model.pass_one_iter()
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send_preview()
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if debug:
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time.sleep(0.005)
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while not s2c.empty():
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input = s2c.get()
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op = input['op']
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if op == 'save':
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model_save()
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elif op == 'preview':
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if is_reached_goal:
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model.pass_one_iter()
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send_preview()
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elif op == 'close':
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model_save()
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i = -1
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break
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if i == -1:
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break
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model.finalize()
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except Exception as e:
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print ('Error: %s' % (str(e)))
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traceback.print_exc()
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break
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c2s.put ( {'op':'close'} )
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class Zoom(Enum):
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ZOOM_25 = (1/4, '25%')
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ZOOM_33 = (1/3, '33%')
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ZOOM_50 = (1/2, '50%')
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ZOOM_67 = (2/3, '67%')
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ZOOM_75 = (3/4, '75%')
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ZOOM_80 = (4/5, '80%')
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ZOOM_90 = (9/10, '90%')
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ZOOM_100 = (1, '100%')
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ZOOM_110 = (11/10, '110%')
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ZOOM_125 = (5/4, '125%')
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ZOOM_150 = (3/2, '150%')
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ZOOM_175 = (7/4, '175%')
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ZOOM_200 = (2, '200%')
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ZOOM_250 = (5/2, '250%')
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ZOOM_300 = (3, '300%')
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ZOOM_400 = (4, '400%')
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ZOOM_500 = (5, '500%')
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def __init__(self, scale, label):
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self.scale = scale
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self.label = label
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def prev(self):
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cls = self.__class__
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members = list(cls)
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index = members.index(self) - 1
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if index < 0:
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return self
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return members[index]
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def next(self):
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cls = self.__class__
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members = list(cls)
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index = members.index(self) + 1
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if index >= len(members):
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return self
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return members[index]
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def scale_previews(previews, zoom=Zoom.ZOOM_100):
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scaled = []
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for preview in previews:
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preview_name, preview_rgb = preview
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scale_factor = zoom.scale
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if scale_factor < 1:
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scaled.append((preview_name, cv2.resize(preview_rgb, (0, 0),
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fx=scale_factor,
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fy=scale_factor,
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interpolation=cv2.INTER_AREA)))
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elif scale_factor > 1:
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scaled.append((preview_name, cv2.resize(preview_rgb, (0, 0),
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fx=scale_factor,
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fy=scale_factor,
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interpolation=cv2.INTER_LANCZOS4)))
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else:
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scaled.append((preview_name, preview_rgb))
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return scaled
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def create_preview_pane_image(previews, selected_preview, loss_history,
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show_last_history_iters_count, iteration, batch_size, zoom=Zoom.ZOOM_100):
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scaled_previews = scale_previews(previews, zoom)
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selected_preview_name = scaled_previews[selected_preview][0]
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selected_preview_rgb = scaled_previews[selected_preview][1]
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h, w, c = selected_preview_rgb.shape
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# HEAD
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head_lines = [
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'[s]:save [enter]:exit [-/+]:zoom: %s' % zoom.label,
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'[p]:update [space]:next preview [l]:change history range',
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'Preview: "%s" [%d/%d]' % (selected_preview_name,selected_preview+1, len(previews))
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]
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head_line_height = int(15 * zoom.scale)
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head_height = len(head_lines) * head_line_height
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head = np.ones((head_height, w, c)) * 0.1
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for i in range(0, len(head_lines)):
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t = i * head_line_height
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b = (i+1) * head_line_height
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head[t:b, 0:w] += imagelib.get_text_image((head_line_height, w, c), head_lines[i], color=[0.8]*c)
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final = head
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if loss_history is not None:
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if show_last_history_iters_count == 0:
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loss_history_to_show = loss_history
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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_height = int(100 * zoom.scale)
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lh_img = models.ModelBase.get_loss_history_preview(loss_history_to_show, iteration, batch_size, w, c, lh_height)
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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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final = np.clip(final, 0, 1)
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return (final*255).astype(np.uint8)
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def main(args, device_args):
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io.log_info ("Running trainer.\r\n")
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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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s2flask = queue.Queue()
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socketio, flask_app = create_flask_app(s2c, c2s, s2flask, args)
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e = threading.Event()
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thread = threading.Thread(target=trainerThread, args=(s2c, c2s, e, args, device_args, socketio))
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thread.start()
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e.wait() #Wait for inital load to occur.
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flask_t = threading.Thread(target=socketio.run, args=(flask_app,), kwargs={'debug': True, 'use_reloader': False})
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flask_t.start()
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wnd_name = "Training preview"
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io.named_window(wnd_name)
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io.capture_keys(wnd_name)
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previews = None
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loss_history = None
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selected_preview = 0
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update_preview = False
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is_showing = False
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is_waiting_preview = False
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show_last_history_iters_count = 0
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iteration = 0
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batch_size = 1
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zoom = Zoom.ZOOM_100
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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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op = input['op']
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if op == 'show':
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is_waiting_preview = False
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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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iteration = 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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update_preview = True
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elif op == 'update':
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if not is_waiting_preview:
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is_waiting_preview = True
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s2c.put({'op': 'preview'})
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elif op == 'next_preview':
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selected_preview = (selected_preview + 1) % len(previews)
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update_preview = True
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elif op == 'change_history_range':
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if show_last_history_iters_count == 0:
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show_last_history_iters_count = 5000
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elif show_last_history_iters_count == 5000:
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show_last_history_iters_count = 10000
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elif show_last_history_iters_count == 10000:
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show_last_history_iters_count = 50000
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elif show_last_history_iters_count == 50000:
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show_last_history_iters_count = 100000
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elif show_last_history_iters_count == 100000:
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show_last_history_iters_count = 0
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update_preview = True
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elif op == 'close':
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s2c.put({'op': 'close'})
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break
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if update_preview:
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update_preview = False
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selected_preview = selected_preview % len(previews)
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preview_pane_image = create_preview_pane_image(previews,
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selected_preview,
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loss_history,
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show_last_history_iters_count,
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iteration,
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batch_size,
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zoom)
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# io.show_image(wnd_name, preview_pane_image)
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model_path = Path(args.get('model_path', ''))
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filename = 'preview.jpg'
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preview_file = str(model_path / filename)
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cv2.imwrite(preview_file, preview_pane_image)
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s2flask.put({'op': 'show'})
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socketio.emit('preview', {'iter': iteration, 'loss': loss_history[-1]})
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# cv2.imshow(wnd_name, preview_pane_image)
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is_showing = True
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try:
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io.process_messages(0.01)
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except KeyboardInterrupt:
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s2c.put({'op': 'close'})
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@ -1,20 +1,21 @@
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import sys
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import traceback
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import traceback
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import queue
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import threading
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import time
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from enum import Enum
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import itertools
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import numpy as np
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import itertools
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from pathlib import Path
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from utils import Path_utils
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from flaskr.app import create_flask_app
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import imagelib
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import cv2
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import models
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from interact import interact as io
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def trainerThread (s2c, c2s, e, args, device_args):
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def trainer_thread (s2c, c2s, e, args, device_args, socketio=None):
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while True:
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try:
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start_time = time.time()
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@ -137,6 +138,9 @@ def trainerThread (s2c, c2s, e, args, device_args):
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else:
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io.log_info (loss_string, end='\r')
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if socketio is not None:
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socketio.emit('loss', loss_string)
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if model.get_target_iter() != 0 and model.is_reached_iter_goal():
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io.log_info ('Reached target iteration.')
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model_save()
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@ -287,28 +291,23 @@ def main(args, device_args):
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no_preview = args.get('no_preview', False)
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flask_preview = args.get('flask_preview', False)
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s2c = queue.Queue()
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c2s = queue.Queue()
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e = threading.Event()
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thread = threading.Thread(target=trainerThread, args=(s2c, c2s, e, args, device_args) )
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thread.start()
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e.wait() #Wait for inital load to occur.
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if flask_preview:
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s2flask = queue.Queue()
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socketio, flask_app = create_flask_app(s2c, c2s, s2flask, args)
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e = threading.Event()
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thread = threading.Thread(target=trainer_thread, args=(s2c, c2s, e, args, device_args, socketio))
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thread.start()
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e.wait() #Wait for inital load to occur.
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flask_t = threading.Thread(target=socketio.run, args=(flask_app,), kwargs={'debug': True, 'use_reloader': False})
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flask_t.start()
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if no_preview:
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while True:
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if not c2s.empty():
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input = c2s.get()
|
||||
op = input.get('op','')
|
||||
if op == 'close':
|
||||
break
|
||||
try:
|
||||
io.process_messages(0.1)
|
||||
except KeyboardInterrupt:
|
||||
s2c.put ( {'op': 'close'} )
|
||||
else:
|
||||
wnd_name = "Training preview"
|
||||
io.named_window(wnd_name)
|
||||
io.capture_keys(wnd_name)
|
||||
|
|
@ -336,7 +335,27 @@ def main(args, device_args):
|
|||
#batch_size = input['batch_size'] if 'iter' in input.keys() else 1
|
||||
if previews is not None:
|
||||
update_preview = True
|
||||
elif op == 'update':
|
||||
if not is_waiting_preview:
|
||||
is_waiting_preview = True
|
||||
s2c.put({'op': 'preview'})
|
||||
elif op == 'next_preview':
|
||||
selected_preview = (selected_preview + 1) % len(previews)
|
||||
update_preview = True
|
||||
elif op == 'change_history_range':
|
||||
if show_last_history_iters_count == 0:
|
||||
show_last_history_iters_count = 5000
|
||||
elif show_last_history_iters_count == 5000:
|
||||
show_last_history_iters_count = 10000
|
||||
elif show_last_history_iters_count == 10000:
|
||||
show_last_history_iters_count = 50000
|
||||
elif show_last_history_iters_count == 50000:
|
||||
show_last_history_iters_count = 100000
|
||||
elif show_last_history_iters_count == 100000:
|
||||
show_last_history_iters_count = 0
|
||||
update_preview = True
|
||||
elif op == 'close':
|
||||
s2c.put({'op': 'close'})
|
||||
break
|
||||
|
||||
if update_preview:
|
||||
|
|
@ -349,44 +368,114 @@ def main(args, device_args):
|
|||
iteration,
|
||||
batch_size,
|
||||
zoom)
|
||||
io.show_image(wnd_name, preview_pane_image)
|
||||
# io.show_image(wnd_name, preview_pane_image)
|
||||
model_path = Path(args.get('model_path', ''))
|
||||
filename = 'preview.jpg'
|
||||
preview_file = str(model_path / filename)
|
||||
cv2.imwrite(preview_file, preview_pane_image)
|
||||
s2flask.put({'op': 'show'})
|
||||
socketio.emit('preview', {'iter': iteration, 'loss': loss_history[-1]})
|
||||
is_showing = True
|
||||
|
||||
key_events = io.get_key_events(wnd_name)
|
||||
key, chr_key, ctrl_pressed, alt_pressed, shift_pressed = key_events[-1] if len(key_events) > 0 else (0,0,False,False,False)
|
||||
|
||||
if key == ord('\n') or key == ord('\r'):
|
||||
s2c.put ( {'op': 'close'} )
|
||||
elif key == ord('s'):
|
||||
s2c.put ( {'op': 'save'} )
|
||||
elif key == ord('p'):
|
||||
if not is_waiting_preview:
|
||||
is_waiting_preview = True
|
||||
s2c.put ( {'op': 'preview'} )
|
||||
elif key == ord('l'):
|
||||
if show_last_history_iters_count == 0:
|
||||
show_last_history_iters_count = 5000
|
||||
elif show_last_history_iters_count == 5000:
|
||||
show_last_history_iters_count = 10000
|
||||
elif show_last_history_iters_count == 10000:
|
||||
show_last_history_iters_count = 50000
|
||||
elif show_last_history_iters_count == 50000:
|
||||
show_last_history_iters_count = 100000
|
||||
elif show_last_history_iters_count == 100000:
|
||||
show_last_history_iters_count = 0
|
||||
update_preview = True
|
||||
elif key == ord(' '):
|
||||
selected_preview = (selected_preview + 1) % len(previews)
|
||||
update_preview = True
|
||||
elif key == ord('-'):
|
||||
zoom = zoom.prev()
|
||||
update_preview = True
|
||||
elif key == ord('=') or key == ord('+'):
|
||||
zoom = zoom.next()
|
||||
update_preview = True
|
||||
try:
|
||||
io.process_messages(0.1)
|
||||
io.process_messages(0.01)
|
||||
except KeyboardInterrupt:
|
||||
s2c.put ( {'op': 'close'} )
|
||||
s2c.put({'op': 'close'})
|
||||
else:
|
||||
thread = threading.Thread(target=trainer_thread, args=(s2c, c2s, e, args, device_args))
|
||||
thread.start()
|
||||
|
||||
io.destroy_all_windows()
|
||||
e.wait() #Wait for inital load to occur.
|
||||
|
||||
if no_preview:
|
||||
while True:
|
||||
if not c2s.empty():
|
||||
input = c2s.get()
|
||||
op = input.get('op','')
|
||||
if op == 'close':
|
||||
break
|
||||
try:
|
||||
io.process_messages(0.1)
|
||||
except KeyboardInterrupt:
|
||||
s2c.put ( {'op': 'close'} )
|
||||
else:
|
||||
wnd_name = "Training preview"
|
||||
io.named_window(wnd_name)
|
||||
io.capture_keys(wnd_name)
|
||||
|
||||
previews = None
|
||||
loss_history = None
|
||||
selected_preview = 0
|
||||
update_preview = False
|
||||
is_showing = False
|
||||
is_waiting_preview = False
|
||||
show_last_history_iters_count = 0
|
||||
iteration = 0
|
||||
batch_size = 1
|
||||
zoom = Zoom.ZOOM_100
|
||||
|
||||
while True:
|
||||
if not c2s.empty():
|
||||
input = c2s.get()
|
||||
op = input['op']
|
||||
if op == 'show':
|
||||
is_waiting_preview = False
|
||||
loss_history = input['loss_history'] if 'loss_history' in input.keys() else None
|
||||
previews = input['previews'] if 'previews' in input.keys() else None
|
||||
iteration = input['iter'] if 'iter' in input.keys() else 0
|
||||
#batch_size = input['batch_size'] if 'iter' in input.keys() else 1
|
||||
if previews is not None:
|
||||
update_preview = True
|
||||
elif op == 'close':
|
||||
break
|
||||
|
||||
if update_preview:
|
||||
update_preview = False
|
||||
selected_preview = selected_preview % len(previews)
|
||||
preview_pane_image = create_preview_pane_image(previews,
|
||||
selected_preview,
|
||||
loss_history,
|
||||
show_last_history_iters_count,
|
||||
iteration,
|
||||
batch_size,
|
||||
zoom)
|
||||
io.show_image(wnd_name, preview_pane_image)
|
||||
is_showing = True
|
||||
|
||||
key_events = io.get_key_events(wnd_name)
|
||||
key, chr_key, ctrl_pressed, alt_pressed, shift_pressed = key_events[-1] if len(key_events) > 0 else (0,0,False,False,False)
|
||||
|
||||
if key == ord('\n') or key == ord('\r'):
|
||||
s2c.put ( {'op': 'close'} )
|
||||
elif key == ord('s'):
|
||||
s2c.put ( {'op': 'save'} )
|
||||
elif key == ord('p'):
|
||||
if not is_waiting_preview:
|
||||
is_waiting_preview = True
|
||||
s2c.put ( {'op': 'preview'} )
|
||||
elif key == ord('l'):
|
||||
if show_last_history_iters_count == 0:
|
||||
show_last_history_iters_count = 5000
|
||||
elif show_last_history_iters_count == 5000:
|
||||
show_last_history_iters_count = 10000
|
||||
elif show_last_history_iters_count == 10000:
|
||||
show_last_history_iters_count = 50000
|
||||
elif show_last_history_iters_count == 50000:
|
||||
show_last_history_iters_count = 100000
|
||||
elif show_last_history_iters_count == 100000:
|
||||
show_last_history_iters_count = 0
|
||||
update_preview = True
|
||||
elif key == ord(' '):
|
||||
selected_preview = (selected_preview + 1) % len(previews)
|
||||
update_preview = True
|
||||
elif key == ord('-'):
|
||||
zoom = zoom.prev()
|
||||
update_preview = True
|
||||
elif key == ord('=') or key == ord('+'):
|
||||
zoom = zoom.next()
|
||||
update_preview = True
|
||||
try:
|
||||
io.process_messages(0.1)
|
||||
except KeyboardInterrupt:
|
||||
s2c.put ( {'op': 'close'} )
|
||||
|
||||
io.destroy_all_windows()
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue