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Batch size can now be changed during training

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This commit is contained in:
Brigham Lysenko 2019-08-13 13:13:36 -06:00
commit d433acc4a0
3 changed files with 276 additions and 168 deletions
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9
mainscripts/Trainer.py
View file

@ -104,14 +104,14 @@ def trainerThread (s2c, c2s, args, device_args):
print("Unable to execute program: %s" % (prog) ) print("Unable to execute program: %s" % (prog) )
if not is_reached_goal: if not is_reached_goal:
iter, iter_time = model.train_one_iter() iter, iter_time, batch_size = model.train_one_iter()
loss_history = model.get_loss_history() loss_history = model.get_loss_history()
time_str = time.strftime("[%H:%M:%S]") time_str = time.strftime("[%H:%M:%S]")
if iter_time >= 10: if iter_time >= 10:
loss_string = "{0}[#{1:06d}][{2:.5s}s]".format ( time_str, iter, '{:0.4f}'.format(iter_time) ) loss_string = "{0}[#{1:06d}][{2:.5s}s]".format ( time_str, iter, '{:0.4f}'.format(iter_time) )
else: else:
loss_string = "{0}[#{1:06d}][{2:04d}ms]".format ( time_str, iter, int(iter_time*1000) ) loss_string = "{0}[#{1:06d}][{2:04d}ms][bs: {3}]".format ( time_str, iter, int(iter_time*1000), batch_size)
if shared_state['after_save']: if shared_state['after_save']:
shared_state['after_save'] = False shared_state['after_save'] = False
@ -186,6 +186,7 @@ def main(args, device_args):
no_preview = args.get('no_preview', False) no_preview = args.get('no_preview', False)
s2c = queue.Queue() s2c = queue.Queue()
c2s = queue.Queue() c2s = queue.Queue()
@ -216,6 +217,7 @@ def main(args, device_args):
is_waiting_preview = False is_waiting_preview = False
show_last_history_iters_count = 0 show_last_history_iters_count = 0
iter = 0 iter = 0
batch_size = 1
while True: while True:
if not c2s.empty(): if not c2s.empty():
input = c2s.get() input = c2s.get()
@ -225,6 +227,7 @@ def main(args, device_args):
loss_history = input['loss_history'] if 'loss_history' in input.keys() else None loss_history = input['loss_history'] if 'loss_history' in input.keys() else None
previews = input['previews'] if 'previews' in input.keys() else None previews = input['previews'] if 'previews' in input.keys() else None
iter = input['iter'] if 'iter' in input.keys() else 0 iter = 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: if previews is not None:
max_w = 0 max_w = 0
max_h = 0 max_h = 0
@ -280,7 +283,7 @@ def main(args, device_args):
else: else:
loss_history_to_show = loss_history[-show_last_history_iters_count:] loss_history_to_show = loss_history[-show_last_history_iters_count:]
lh_img = models.ModelBase.get_loss_history_preview(loss_history_to_show, iter, w, c) lh_img = models.ModelBase.get_loss_history_preview(loss_history_to_show, iter, batch_size, w, c)
final = np.concatenate ( [final, lh_img], axis=0 ) final = np.concatenate ( [final, lh_img], axis=0 )
final = np.concatenate ( [final, selected_preview_rgb], axis=0 ) final = np.concatenate ( [final, selected_preview_rgb], axis=0 )

104
models/ModelBase.py
View file

@ -23,6 +23,7 @@ You can implement your own model. Check examples.
class ModelBase(object): class ModelBase(object):
def __init__(self, model_path, training_data_src_path=None, training_data_dst_path=None, pretraining_data_path=None, debug = False, device_args = None, def __init__(self, model_path, training_data_src_path=None, training_data_dst_path=None, pretraining_data_path=None, debug = False, device_args = None,
ask_enable_autobackup=True, ask_enable_autobackup=True,
ask_write_preview_history=True, ask_write_preview_history=True,
@ -42,7 +43,8 @@ class ModelBase(object):
for idx, name in idxs_names_list: for idx, name in idxs_names_list:
io.log_info("[%d] : %s" % (idx, name)) io.log_info("[%d] : %s" % (idx, name))
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] ) 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])
self.device_args = device_args self.device_args = device_args
self.device_config = nnlib.DeviceConfig(allow_growth=True, **self.device_args) self.device_config = nnlib.DeviceConfig(allow_growth=True, **self.device_args)
@ -65,6 +67,8 @@ class ModelBase(object):
self.debug = debug self.debug = debug
self.is_training_mode = (training_data_src_path is not None and training_data_dst_path is not None) self.is_training_mode = (training_data_src_path is not None and training_data_dst_path is not None)
self.paddle = 'pong'
self.iter = 0 self.iter = 0
self.options = {} self.options = {}
self.loss_history = [] self.loss_history = []
@ -81,7 +85,9 @@ class ModelBase(object):
self.loss_history = model_data.get('loss_history', []) self.loss_history = model_data.get('loss_history', [])
self.sample_for_preview = model_data.get('sample_for_preview', None) self.sample_for_preview = model_data.get('sample_for_preview', None)
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 ) 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)
yn_str = {True: 'y', False: 'n'} yn_str = {True: 'y', False: 'n'}
@ -90,20 +96,36 @@ class ModelBase(object):
if ask_enable_autobackup and (self.iter == 0 or ask_override): if ask_enable_autobackup and (self.iter == 0 or ask_override):
default_autobackup = False if self.iter == 0 else self.options.get('autobackup', False) default_autobackup = False if self.iter == 0 else self.options.get('autobackup', False)
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") 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")
else: else:
self.options['autobackup'] = self.options.get('autobackup', False) self.options['autobackup'] = self.options.get('autobackup', False)
if ask_write_preview_history and (self.iter == 0 or ask_override): if ask_write_preview_history and (self.iter == 0 or ask_override):
default_write_preview_history = False if self.iter == 0 else self.options.get('write_preview_history',False) default_write_preview_history = False if self.iter == 0 else self.options.get('write_preview_history',
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.") False)
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.")
else: else:
self.options['write_preview_history'] = self.options.get('write_preview_history', False) self.options['write_preview_history'] = self.options.get('write_preview_history', False)
if (self.iter == 0 or ask_override) and self.options['write_preview_history'] and io.is_support_windows(): if (self.iter == 0 or ask_override) and self.options['write_preview_history'] and io.is_support_windows():
choose_preview_history = io.input_bool("Choose image for the preview history? (y/n skip:%s) : " % (yn_str[False]) , False) choose_preview_history = io.input_bool("Choose image for the preview history?"
" (y/n skip:%s) : " % (yn_str[False]), False)
elif (self.iter == 0 or ask_override) and self.options['write_preview_history'] and io.is_colab(): elif (self.iter == 0 or ask_override) and self.options['write_preview_history'] and io.is_colab():
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") 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")
else: else:
choose_preview_history = False choose_preview_history = False
@ -117,26 +139,48 @@ class ModelBase(object):
if ask_batch_size and (self.iter == 0 or ask_override): if ask_batch_size and (self.iter == 0 or ask_override):
default_batch_size = 0 if self.iter == 0 else self.options.get('batch_size', 0) default_batch_size = 0 if self.iter == 0 else self.options.get('batch_size', 0)
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.")) 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."))
self.options['ping_pong'] = io.input_bool(
"Enable ping-pong? (y/n ?:help skip:%s) : " % (yn_str[True]),
True,
help_message="Cycles batch size between 1 and chosen batch size, simulating super convergence")
else: else:
self.options['batch_size'] = self.options.get('batch_size', 0) self.options['batch_size'] = self.options.get('batch_size', 0)
self.options['ping_pong'] = self.options.get('ping_pong', True)
if ask_sort_by_yaw: if ask_sort_by_yaw:
if (self.iter == 0 or ask_override): if (self.iter == 0 or ask_override):
default_sort_by_yaw = self.options.get('sort_by_yaw', False) default_sort_by_yaw = self.options.get('sort_by_yaw', False)
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." ) 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")
else: else:
self.options['sort_by_yaw'] = self.options.get('sort_by_yaw', False) self.options['sort_by_yaw'] = self.options.get('sort_by_yaw', False)
if ask_random_flip: if ask_random_flip:
if (self.iter == 0): if (self.iter == 0):
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.") 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.")
else: else:
self.options['random_flip'] = self.options.get('random_flip', True) self.options['random_flip'] = self.options.get('random_flip', True)
if ask_src_scale_mod: if ask_src_scale_mod:
if (self.iter == 0): if self.iter == 0:
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) 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)
else: else:
self.options['src_scale_mod'] = self.options.get('src_scale_mod', 0) self.options['src_scale_mod'] = self.options.get('src_scale_mod', 0)
@ -169,6 +213,7 @@ class ModelBase(object):
self.onInitialize() self.onInitialize()
self.options['batch_size'] = self.batch_size self.options['batch_size'] = self.batch_size
self.options['paddle'] = 'ping'
if self.debug or self.batch_size == 0: if self.debug or self.batch_size == 0:
self.batch_size = 1 self.batch_size = 1
@ -178,8 +223,10 @@ class ModelBase(object):
self.preview_history_path = self.model_path / ('%s_history' % (self.get_model_name())) self.preview_history_path = self.model_path / ('%s_history' % (self.get_model_name()))
self.autobackups_path = self.model_path / ('%s_autobackups' % (self.get_model_name())) self.autobackups_path = self.model_path / ('%s_autobackups' % (self.get_model_name()))
else: else:
self.preview_history_path = self.model_path / ( '%d_%s_history' % (self.device_args['force_gpu_idx'], self.get_model_name()) ) self.preview_history_path = self.model_path / ('%d_%s_history' % (self.device_args['force_gpu_idx'],
self.autobackups_path = self.model_path / ( '%d_%s_autobackups' % (self.device_args['force_gpu_idx'], self.get_model_name()) ) self.get_model_name()))
self.autobackups_path = self.model_path / ('%d_%s_autobackups' % (self.device_args['force_gpu_idx'],
self.get_model_name()))
if self.autobackup: if self.autobackup:
self.autobackup_current_hour = time.localtime().tm_hour self.autobackup_current_hour = time.localtime().tm_hour
@ -215,7 +262,8 @@ class ModelBase(object):
while True: while True:
key_events = io.get_key_events(wnd_name) 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) 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'): if key == ord('\n') or key == ord('\r'):
choosed = True choosed = True
break break
@ -354,7 +402,8 @@ class ModelBase(object):
} }
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 = [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 += [str(summary_path), str(self.model_data_path)]
if self.autobackup: if self.autobackup:
@ -388,7 +437,8 @@ class ModelBase(object):
plist += [(bgr, idx_backup_path / (('preview_%s.jpg') % (name)))] plist += [(bgr, idx_backup_path / (('preview_%s.jpg') % (name)))]
for preview, filepath in plist: for preview, filepath in plist:
preview_lh = ModelBase.get_loss_history_preview(self.loss_history, self.iter, preview.shape[1], preview.shape[2]) 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) img = (np.concatenate([preview_lh, preview], axis=0) * 255).astype(np.uint8)
cv2_imwrite(filepath, img) cv2_imwrite(filepath, img)
@ -415,7 +465,6 @@ class ModelBase(object):
except Exception as e: except Exception as e:
print("Unable to load ", opt_filename) print("Unable to load ", opt_filename)
def save_weights_safe(self, model_filename_list): def save_weights_safe(self, model_filename_list):
for model, filename in model_filename_list: for model, filename in model_filename_list:
filename = self.get_strpath_storage_for_file(filename) filename = self.get_strpath_storage_for_file(filename)
@ -490,14 +539,18 @@ class ModelBase(object):
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: for preview, filepath in plist:
preview_lh = ModelBase.get_loss_history_preview(self.loss_history, self.iter, preview.shape[1], preview.shape[2]) 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) img = (np.concatenate([preview_lh, preview], axis=0) * 255).astype(np.uint8)
cv2_imwrite(filepath, img) cv2_imwrite(filepath, img)
if self.iter % 50 == 0 and self.iter != 0:
self.set_batch_size(self.batch_size + 1)
self.iter += 1 self.iter += 1
return self.iter, iter_time return self.iter, iter_time, self.batch_size
def pass_one_iter(self): def pass_one_iter(self):
self.last_sample = self.generate_next_sample() self.last_sample = self.generate_next_sample()
@ -536,7 +589,8 @@ class ModelBase(object):
if self.device_args['force_gpu_idx'] == -1: 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: 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): 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} # example d = {2:2,3:4,4:8,5:16,6:32,7:32,8:32,9:48}
@ -556,7 +610,7 @@ class ModelBase(object):
self.batch_size = d[keys[-1]] self.batch_size = d[keys[-1]]
@staticmethod @staticmethod
def get_loss_history_preview(loss_history, iter, w, c): def get_loss_history_preview(loss_history, iter,batch_size, w, c):
loss_history = np.array(loss_history.copy()) loss_history = np.array(loss_history.copy())
lh_height = 100 lh_height = 100
@ -611,7 +665,9 @@ class ModelBase(object):
last_line_t = int((lh_lines - 1) * lh_line_height) last_line_t = int((lh_lines - 1) * lh_line_height)
last_line_b = int(lh_lines * 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'
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 ) lh_img[last_line_t:last_line_b, 0:w] += imagelib.get_text_image((last_line_b - last_line_t, w, c), bs_text,
color=[0.8] * c)
return lh_img return lh_img

55
models/Model_SAE/Model.py
View file

@ -8,6 +8,8 @@ from samplelib import *
from interact import interact as io from interact import interact as io
# SAE - Styled AutoEncoder # SAE - Styled AutoEncoder
class SAEModel(ModelBase): class SAEModel(ModelBase):
encoderH5 = 'encoder.h5' encoderH5 = 'encoder.h5'
@ -152,6 +154,7 @@ class SAEModel(ModelBase):
SAEModel.initialize_nn_functions() SAEModel.initialize_nn_functions()
self.set_vram_batch_requirements({1.5: 4}) self.set_vram_batch_requirements({1.5: 4})
global resolution
resolution= self.options['resolution'] resolution= self.options['resolution']
ae_dims = self.options['ae_dims'] ae_dims = self.options['ae_dims']
e_ch_dims = self.options['e_ch_dims'] e_ch_dims = self.options['e_ch_dims']
@ -163,9 +166,10 @@ class SAEModel(ModelBase):
d_residual_blocks = True d_residual_blocks = True
bgr_shape = (resolution, resolution, 3) bgr_shape = (resolution, resolution, 3)
mask_shape = (resolution, resolution, 1) mask_shape = (resolution, resolution, 1)
global ms_count
self.ms_count = ms_count = 3 if (self.options['multiscale_decoder']) else 1 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) apply_random_ct = self.options.get('apply_random_ct', False)
masked_training = True masked_training = True
@ -436,13 +440,19 @@ class SAEModel(ModelBase):
self.src_sample_losses = [] self.src_sample_losses = []
self.dst_sample_losses = [] self.dst_sample_losses = []
global t
t = SampleProcessor.Types t = SampleProcessor.Types
global face_type
face_type = t.FACE_TYPE_FULL if self.options['face_type'] == 'f' else t.FACE_TYPE_HALF 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 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_src_path = self.training_data_src_path
global training_data_dst_path
training_data_dst_path= self.training_data_dst_path training_data_dst_path= self.training_data_dst_path
global sort_by_yaw
sort_by_yaw = self.sort_by_yaw sort_by_yaw = self.sort_by_yaw
if self.pretrain and self.pretraining_data_path is not None: if self.pretrain and self.pretraining_data_path is not None:
@ -458,7 +468,8 @@ class SAEModel(ModelBase):
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, sample_process_options=SampleProcessor.Options(random_flip=self.random_flip,
scale_range=np.array([-0.05, scale_range=np.array([-0.05,
0.05]) + self.src_scale_mod / 100.0), 0.05]) + self.src_scale_mod / 100.0),
output_sample_types=[{'types': (t.IMG_WARPED_TRANSFORMED, face_type, t_mode_bgr), output_sample_types=[{'types': (
t.IMG_WARPED_TRANSFORMED, face_type, t_mode_bgr),
'resolution': resolution, 'apply_ct': apply_random_ct}] + \ 'resolution': resolution, 'apply_ct': apply_random_ct}] + \
[{'types': (t.IMG_TRANSFORMED, face_type, t_mode_bgr), [{'types': (t.IMG_TRANSFORMED, face_type, t_mode_bgr),
'resolution': resolution // (2 ** i), 'resolution': resolution // (2 ** i),
@ -470,7 +481,8 @@ class SAEModel(ModelBase):
SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, ), sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, ),
output_sample_types=[{'types': (t.IMG_WARPED_TRANSFORMED, face_type, t_mode_bgr), output_sample_types=[{'types': (
t.IMG_WARPED_TRANSFORMED, face_type, t_mode_bgr),
'resolution': resolution}] + \ 'resolution': resolution}] + \
[{'types': (t.IMG_TRANSFORMED, face_type, t_mode_bgr), [{'types': (t.IMG_TRANSFORMED, face_type, t_mode_bgr),
'resolution': resolution // (2 ** i)} for i in 'resolution': resolution // (2 ** i)} for i in
@ -514,6 +526,43 @@ class SAEModel(ModelBase):
def onSave(self): def onSave(self):
self.save_weights_safe(self.get_model_filename_list()) 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 # override
def onTrainOneIter(self, generators_samples, generators_list): def onTrainOneIter(self, generators_samples, generators_list):
src_samples = generators_samples[0] src_samples = generators_samples[0]