mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
544 lines
22 KiB
Python
544 lines
22 KiB
Python
import os
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import json
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import time
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import inspect
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import pickle
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import colorsys
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import imagelib
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from pathlib import Path
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from utils import Path_utils
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from utils import std_utils
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from utils.cv2_utils import *
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import numpy as np
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import cv2
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from samplelib import SampleGeneratorBase
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from nnlib import nnlib
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from interact import interact as io
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'''
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You can implement your own model. Check examples.
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'''
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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_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_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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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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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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self.device_args = device_args
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self.device_config = nnlib.DeviceConfig(allow_growth=False, **self.device_args)
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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.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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self.dst_yaw_images_paths = None
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self.src_data_generator = None
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self.dst_data_generator = None
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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.iter = 0
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self.options = {}
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self.loss_history = []
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self.sample_for_preview = None
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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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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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self.options = model_data['options']
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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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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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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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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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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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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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else:
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self.options['batch_size'] = self.options.get('batch_size', 0)
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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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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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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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else:
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self.options['src_scale_mod'] = self.options.get('src_scale_mod', 0)
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self.write_preview_history = self.options.get('write_preview_history', False)
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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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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.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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self.onInitializeOptions(self.iter == 0, ask_override)
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nnlib.import_all(self.device_config)
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self.keras = nnlib.keras
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self.K = nnlib.keras.backend
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self.onInitialize()
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self.options['batch_size'] = self.batch_size
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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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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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if self.write_preview_history or io.is_colab():
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if not self.preview_history_path.exists():
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self.preview_history_path.mkdir(exist_ok=True)
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else:
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if self.iter == 0:
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for filename in Path_utils.get_image_paths(self.preview_history_path):
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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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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 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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io.capture_keys(wnd_name)
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choosed = False
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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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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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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'):
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break
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try:
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io.process_messages(0.1)
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except KeyboardInterrupt:
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choosed = True
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io.destroy_window(wnd_name)
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else:
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self.sample_for_preview = self.generate_next_sample()
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model_summary_text = []
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model_summary_text += ["===== Model summary ====="]
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model_summary_text += ["== Model name: " + self.get_model_name()]
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model_summary_text += ["=="]
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model_summary_text += ["== Current iteration: " + str(self.iter)]
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model_summary_text += ["=="]
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model_summary_text += ["== Model options:"]
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for key in self.options.keys():
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model_summary_text += ["== |== %s : %s" % (key, self.options[key])]
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if self.device_config.multi_gpu:
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model_summary_text += ["== |== multi_gpu : True "]
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model_summary_text += ["== Running on:"]
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if self.device_config.cpu_only:
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model_summary_text += ["== |== [CPU]"]
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else:
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for idx in self.device_config.gpu_idxs:
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model_summary_text += ["== |== [%d : %s]" % (idx, nnlib.device.getDeviceName(idx))]
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if not self.device_config.cpu_only and self.device_config.gpu_vram_gb[0] == 2:
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model_summary_text += ["=="]
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model_summary_text += ["== WARNING: You are using 2GB GPU. Result quality may be significantly decreased."]
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model_summary_text += ["== If training does not start, close all programs and try again."]
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model_summary_text += ["== Also you can disable Windows Aero Desktop to get extra free VRAM."]
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model_summary_text += ["=="]
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model_summary_text += ["========================="]
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model_summary_text = "\r\n".join (model_summary_text)
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self.model_summary_text = model_summary_text
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io.log_info(model_summary_text)
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#overridable
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def onInitializeOptions(self, is_first_run, ask_override):
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pass
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#overridable
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def onInitialize(self):
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'''
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initialize your keras models
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store and retrieve your model options in self.options['']
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check example
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'''
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pass
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#overridable
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def onSave(self):
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#save your keras models here
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pass
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#overridable
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def onTrainOneIter(self, sample, generator_list):
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#train your keras models here
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#return array of losses
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return ( ('loss_src', 0), ('loss_dst', 0) )
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#overridable
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def onGetPreview(self, sample):
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#you can return multiple previews
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#return [ ('preview_name',preview_rgb), ... ]
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return []
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#overridable if you want model name differs from folder name
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def get_model_name(self):
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return Path(inspect.getmodule(self).__file__).parent.name.rsplit("_", 1)[1]
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#overridable
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def get_converter(self):
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raise NotImplementedError
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#return existing or your own converter which derived from base
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def get_target_iter(self):
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return self.target_iter
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def is_reached_iter_goal(self):
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return self.target_iter != 0 and self.iter >= self.target_iter
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#multi gpu in keras actually is fake and doesn't work for training https://github.com/keras-team/keras/issues/11976
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#def to_multi_gpu_model_if_possible (self, models_list):
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# if len(self.device_config.gpu_idxs) > 1:
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# #make batch_size to divide on GPU count without remainder
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# self.batch_size = int( self.batch_size / len(self.device_config.gpu_idxs) )
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# if self.batch_size == 0:
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# self.batch_size = 1
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# self.batch_size *= len(self.device_config.gpu_idxs)
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#
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# result = []
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# for model in models_list:
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# for i in range( len(model.output_names) ):
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# model.output_names = 'output_%d' % (i)
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# result += [ nnlib.keras.utils.multi_gpu_model( model, self.device_config.gpu_idxs ) ]
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#
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# return result
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# else:
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# return models_list
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def get_previews(self):
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return self.onGetPreview ( self.last_sample )
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def get_static_preview(self):
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return self.onGetPreview (self.sample_for_preview)[0][1] #first preview, and bgr
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def save(self):
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Path( self.get_strpath_storage_for_file('summary.txt') ).write_text(self.model_summary_text)
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self.onSave()
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model_data = {
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'iter': self.iter,
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'options': self.options,
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'loss_history': self.loss_history,
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'sample_for_preview' : self.sample_for_preview
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}
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self.model_data_path.write_bytes( pickle.dumps(model_data) )
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def load_weights_safe(self, model_filename_list, optimizer_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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if Path(filename).exists():
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model.load_weights(filename)
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if len(optimizer_filename_list) != 0:
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opt_filename = self.get_strpath_storage_for_file('opt.h5')
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if Path(opt_filename).exists():
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try:
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with open(opt_filename, "rb") as f:
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d = pickle.loads(f.read())
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for x in optimizer_filename_list:
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opt, filename = x
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if filename in d:
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weights = d[filename].get('weights', None)
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if weights:
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opt.set_weights(weights)
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print("set ok")
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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, optimizer_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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model.save_weights( filename + '.tmp' )
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rename_list = model_filename_list
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if len(optimizer_filename_list) != 0:
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opt_filename = self.get_strpath_storage_for_file('opt.h5')
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try:
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d = {}
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for opt, filename in optimizer_filename_list:
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fd = {}
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symbolic_weights = getattr(opt, 'weights')
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if symbolic_weights:
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fd['weights'] = self.K.batch_get_value(symbolic_weights)
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d[filename] = fd
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with open(opt_filename+'.tmp', 'wb') as f:
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f.write( pickle.dumps(d) )
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rename_list += [('', 'opt.h5')]
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except Exception as e:
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print ("Unable to save ", opt_filename)
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for _, filename in rename_list:
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filename = self.get_strpath_storage_for_file(filename)
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source_filename = Path(filename+'.tmp')
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if source_filename.exists():
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target_filename = Path(filename)
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if target_filename.exists():
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target_filename.unlink()
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source_filename.rename ( str(target_filename) )
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def debug_one_iter(self):
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images = []
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for generator in self.generator_list:
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for i,batch in enumerate(next(generator)):
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if len(batch.shape) == 4:
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images.append( batch[0] )
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return imagelib.equalize_and_stack_square (images)
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def generate_next_sample(self):
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return [next(generator) for generator in self.generator_list]
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def train_one_iter(self):
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sample = self.generate_next_sample()
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iter_time = time.time()
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losses = self.onTrainOneIter(sample, self.generator_list)
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iter_time = time.time() - iter_time
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self.last_sample = sample
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self.loss_history.append ( [float(loss[1]) for loss in losses] )
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if self.iter % 10 == 0:
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plist = []
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if io.is_colab():
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previews = self.get_previews()
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for i in range(len(previews)):
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name, bgr = previews[i]
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plist += [ (bgr, self.get_strpath_storage_for_file('preview_%s.jpg' % (name) ) ) ]
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if self.write_preview_history:
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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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img = (np.concatenate ( [preview_lh, preview], axis=0 ) * 255).astype(np.uint8)
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cv2_imwrite (filepath, img )
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self.iter += 1
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return self.iter, iter_time
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def pass_one_iter(self):
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self.last_sample = self.generate_next_sample()
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def finalize(self):
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nnlib.finalize_all()
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def is_first_run(self):
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return self.iter == 0
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def is_debug(self):
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return self.debug
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def set_batch_size(self, batch_size):
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self.batch_size = batch_size
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def get_batch_size(self):
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return self.batch_size
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def get_iter(self):
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return self.iter
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def get_loss_history(self):
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return self.loss_history
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def set_training_data_generators (self, generator_list):
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self.generator_list = generator_list
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def get_training_data_generators (self):
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return self.generator_list
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def get_model_root_path(self):
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return self.model_path
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|
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def get_strpath_storage_for_file(self, filename):
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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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|
|
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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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keys = [x for x in d.keys()]
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|
|
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if self.device_config.cpu_only:
|
|
if self.batch_size == 0:
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|
self.batch_size = 2
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|
else:
|
|
if self.batch_size == 0:
|
|
for x in keys:
|
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if self.device_config.gpu_vram_gb[0] <= x:
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|
self.batch_size = d[x]
|
|
break
|
|
|
|
if self.batch_size == 0:
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|
self.batch_size = d[ keys[-1] ]
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|
|
|
@staticmethod
|
|
def get_loss_history_preview(loss_history, iter, w, c):
|
|
loss_history = np.array (loss_history.copy())
|
|
|
|
lh_height = 100
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|
lh_img = np.ones ( (lh_height,w,c) ) * 0.1
|
|
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],
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|
*[ 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
|
|
|
|
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 )
|
|
|
|
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 )
|
|
|
|
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
|
|
|
|
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_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 )
|
|
return lh_img
|