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refactoring

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iperov 2021-11-16 15:28:42 +04:00
parent 99462a1a17
commit 5d011368a9
2 changed files with 101 additions and 90 deletions
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134
apps/trainers/FaceAligner/FaceAlignerTrainerApp.py
View file

@ -43,6 +43,7 @@ class FaceAlignerTrainerApp:
self._is_previewing_samples = False self._is_previewing_samples = False
self._new_preview_data : 'PreviewData' = None self._new_preview_data : 'PreviewData' = None
self._last_save_time = None self._last_save_time = None
self._req_is_training = None
# settings / params # settings / params
self._model_data = None self._model_data = None
@ -52,7 +53,7 @@ class FaceAlignerTrainerApp:
self._resolution = None self._resolution = None
self._iteration = None self._iteration = None
self._autosave_period = None self._autosave_period = None
self._is_training = None self._is_training = False
self._loss_history = {} self._loss_history = {}
# Generators # Generators
@ -96,14 +97,9 @@ class FaceAlignerTrainerApp:
def set_autosave_period(self, mins : int): def set_autosave_period(self, mins : int):
self._autosave_period = mins self._autosave_period = mins
def get_is_training(self) -> bool: return self._is_training def get_is_training(self) -> bool: return self._req_is_training if self._req_is_training is not None else self._is_training
def set_is_training(self, training : bool): def set_is_training(self, training : bool):
if self._is_training != training: self._req_is_training = training
if training:
self._last_save_time = time.time()
else:
self._last_save_time = None
self._is_training = training
def get_loss_history(self): return self._loss_history def get_loss_history(self): return self._loss_history
def set_loss_history(self, lh): self._loss_history = lh def set_loss_history(self, lh): self._loss_history = lh
@ -238,71 +234,36 @@ class FaceAlignerTrainerApp:
self._model.to(self._device) self._model.to(self._device)
self._model_optimizer.load_state_dict(self._model_optimizer.state_dict()) self._model_optimizer.load_state_dict(self._model_optimizer.state_dict())
if self._is_training or \ if self._req_is_training is not None:
self._is_previewing_samples or \ if self._req_is_training != self._is_training:
self._ev_request_preview.is_set(): if self._req_is_training:
training_data = self._training_generator.get_next_data(wait=False) self._last_save_time = time.time()
if training_data is not None and \ else:
training_data.resolution == self.get_resolution(): # Skip if resolution is different, due to delay self._last_save_time = None
torch.cuda.empty_cache()
if self._is_training: self._is_training = self._req_is_training
self._model_optimizer.zero_grad() self._req_is_training = None
if self._ev_request_preview.is_set() or \ self.main_loop_training()
self._is_training:
# Inference for both preview and training
img_aligned_shifted_t = torch.tensor(training_data.img_aligned_shifted).to(self._device)
shift_uni_mats_pred_t = self._model(img_aligned_shifted_t).view( (-1,2,3) )
if self._is_training:
# Training optimization step
shift_uni_mats_t = torch.tensor(training_data.shift_uni_mats).to(self._device)
loss_t = (shift_uni_mats_pred_t-shift_uni_mats_t).square().mean()*10.0
loss_t.backward()
self._model_optimizer.step()
loss = loss_t.detach().cpu().numpy()
rec_loss_history = self._loss_history.get('reconstruct', None)
if rec_loss_history is None:
rec_loss_history = self._loss_history['reconstruct'] = []
rec_loss_history.append(float(loss))
self.set_iteration( self.get_iteration() + 1 )
if self._ev_request_preview.is_set():
self._ev_request_preview.clear()
# Preview request
pd = PreviewData()
pd.training_data = training_data
pd.shift_uni_mats_pred = shift_uni_mats_pred_t.detach().cpu().numpy()
self._new_preview_data = pd
if self._is_previewing_samples:
self._new_viewing_data = training_data
if self._is_training:
if self._last_save_time is not None:
while (time.time()-self._last_save_time)/60 >= self._autosave_period:
self._last_save_time += self._autosave_period*60
self._ev_request_save.set()
if self._is_training and self._last_save_time is not None:
while (time.time()-self._last_save_time)/60 >= self._autosave_period:
self._last_save_time += self._autosave_period*60
self._ev_request_save.set()
if self._ev_request_export_model.is_set(): if self._ev_request_export_model.is_set():
self._ev_request_export_model.clear() self._ev_request_export_model.clear()
print('Exporting...') print('Exporting...')
self.export() self.export()
print('Exporting done.') print('Exporting done.')
dc.Diacon.update_dlg() dc.Diacon.get_current_dlg().recreate().set_current()
if self._ev_request_save.is_set(): if self._ev_request_save.is_set():
self._ev_request_save.clear() self._ev_request_save.clear()
print('Saving...') print('Saving...')
self.save() self.save()
print('Saving done.') print('Saving done.')
dc.Diacon.get_current_dlg().recreate().set_current()
dc.Diacon.update_dlg()
if self._ev_request_quit.is_set(): if self._ev_request_quit.is_set():
self._ev_request_quit.clear() self._ev_request_quit.clear()
@ -310,6 +271,53 @@ class FaceAlignerTrainerApp:
time.sleep(0.005) time.sleep(0.005)
def main_loop_training(self):
"""
separated function, because torch tensors refences must be freed from python locals
"""
if self._is_training or \
self._is_previewing_samples or \
self._ev_request_preview.is_set():
training_data = self._training_generator.get_next_data(wait=False)
if training_data is not None and \
training_data.resolution == self.get_resolution(): # Skip if resolution is different, due to delay
if self._is_training:
self._model_optimizer.zero_grad()
if self._ev_request_preview.is_set() or \
self._is_training:
# Inference for both preview and training
img_aligned_shifted_t = torch.tensor(training_data.img_aligned_shifted).to(self._device)
shift_uni_mats_pred_t = self._model(img_aligned_shifted_t).view( (-1,2,3) )
if self._is_training:
# Training optimization step
shift_uni_mats_t = torch.tensor(training_data.shift_uni_mats).to(self._device)
loss_t = (shift_uni_mats_pred_t-shift_uni_mats_t).square().mean()*10.0
loss_t.backward()
self._model_optimizer.step()
loss = loss_t.detach().cpu().numpy()
rec_loss_history = self._loss_history.get('reconstruct', None)
if rec_loss_history is None:
rec_loss_history = self._loss_history['reconstruct'] = []
rec_loss_history.append(float(loss))
self.set_iteration( self.get_iteration() + 1 )
if self._ev_request_preview.is_set():
self._ev_request_preview.clear()
# Preview request
pd = PreviewData()
pd.training_data = training_data
pd.shift_uni_mats_pred = shift_uni_mats_pred_t.detach().cpu().numpy()
self._new_preview_data = pd
if self._is_previewing_samples:
self._new_viewing_data = training_data
def get_main_dlg(self): def get_main_dlg(self):
last_loss = 0 last_loss = 0
@ -334,7 +342,7 @@ class FaceAlignerTrainerApp:
dc.DlgChoice(short_name='p', row_def='| Show current preview.', dc.DlgChoice(short_name='p', row_def='| Show current preview.',
on_choose=lambda dlg: (self._ev_request_preview.set(), dlg.recreate().set_current())), on_choose=lambda dlg: (self._ev_request_preview.set(), dlg.recreate().set_current())),
dc.DlgChoice(short_name='t', row_def=f'| Training | {self._is_training}', dc.DlgChoice(short_name='t', row_def=f'| Training | {self.get_is_training()}',
on_choose=lambda dlg: (self.set_is_training(not self.get_is_training()), dlg.recreate().set_current()) ), on_choose=lambda dlg: (self.set_is_training(not self.get_is_training()), dlg.recreate().set_current()) ),
dc.DlgChoice(short_name='reset', row_def='| Reset model.', dc.DlgChoice(short_name='reset', row_def='| Reset model.',
@ -371,8 +379,8 @@ class FaceAlignerTrainerApp:
lh_ar = np.array(lh[-d*max_lines:], np.float32) lh_ar = np.array(lh[-d*max_lines:], np.float32)
lh_ar = lh_ar.reshape( (max_lines, d)) lh_ar = lh_ar.reshape( (max_lines, d))
lh_ar_max, lh_ar_min, lh_ar_mean, lh_ar_median = lh_ar.max(-1), lh_ar.min(-1), lh_ar.mean(-1), np.median(lh_ar, -1) lh_ar_max, lh_ar_min, lh_ar_mean, lh_ar_median = lh_ar.max(-1), lh_ar.min(-1), lh_ar.mean(-1), np.median(lh_ar, -1)
print( '\n'.join( f'max:[{max_value:.5f}] min:[{min_value:.5f}] mean:[{mean_value:.5f}] median:[{median_value:.5f}]' for max_value, min_value, mean_value, median_value in zip(lh_ar_max, lh_ar_min, lh_ar_mean, lh_ar_median) ) ) print( '\n'.join( f'max:[{max_value:.5f}] min:[{min_value:.5f}] mean:[{mean_value:.5f}] median:[{median_value:.5f}]' for max_value, min_value, mean_value, median_value in zip(lh_ar_max, lh_ar_min, lh_ar_mean, lh_ar_median) ) )
dlg.recreate().set_current() dlg.recreate().set_current()

57
xlib/console/diacon/Diacon.py
View file

@ -16,8 +16,8 @@ class EDlgMode(IntEnum):
class DlgChoice: class DlgChoice:
def __init__(self, short_name : str = None, def __init__(self, short_name : str = None,
row_def : str = None, row_def : str = None,
on_choose : Callable = None): on_choose : Callable = None):
if len(short_name) == 0: if len(short_name) == 0:
raise ValueError('Zero len short_name is not valid.') raise ValueError('Zero len short_name is not valid.')
@ -32,18 +32,18 @@ class DlgChoice:
class Dlg: class Dlg:
def __init__(self, on_recreate : Callable[ [], 'Dlg'] = None, def __init__(self, on_recreate : Callable[ [], 'Dlg'] = None,
on_back : Callable = None, on_back : Callable = None,
top_rows_def : Union[str, List[str]] = None, top_rows_def : Union[str, List[str]] = None,
bottom_rows_def : Union[str, List[str]] = None, bottom_rows_def : Union[str, List[str]] = None,
): ):
""" """
base class for Diacon dialogs. base class for Diacon dialogs.
""" """
self._on_recreate = on_recreate self._on_recreate = on_recreate
self._on_back = on_back self._on_back = on_back
self._top_rows_def = top_rows_def self._top_rows_def = top_rows_def
self._bottom_rows_def = bottom_rows_def self._bottom_rows_def = bottom_rows_def
def recreate(self): def recreate(self):
""" """
""" """
@ -51,27 +51,27 @@ class Dlg:
return self._on_recreate(self) return self._on_recreate(self)
else: else:
raise Exception('on_recreate() is not defined.') raise Exception('on_recreate() is not defined.')
def set_current(self, print=True): def set_current(self, print=True):
Diacon.update_dlg(self, print=print) Diacon.update_dlg(self, print=print)
def handle_user_input(self, s : str): def handle_user_input(self, s : str):
""" """
""" """
mode = self.on_user_input(s.strip()) mode = self.on_user_input(s.strip())
if mode == EDlgMode.UNHANDLED: if mode == EDlgMode.UNHANDLED:
mode = EDlgMode.RELOAD mode = EDlgMode.RELOAD
if mode == EDlgMode.WRONG_INPUT: if mode == EDlgMode.WRONG_INPUT:
print('\nWrong input') print('\nWrong input')
mode = EDlgMode.RELOAD mode = EDlgMode.RELOAD
if mode == EDlgMode.RELOAD: if mode == EDlgMode.RELOAD:
self.recreate().set_current() self.recreate().set_current()
if mode == EDlgMode.BACK: if mode == EDlgMode.BACK:
if self._on_back is not None: if self._on_back is not None:
self._on_back(self) self._on_back(self)
#overridable #overridable
def on_user_input(self, s : str) -> EDlgMode: def on_user_input(self, s : str) -> EDlgMode:
if len(s) == 0: if len(s) == 0:
@ -80,13 +80,13 @@ class Dlg:
if s == '<': if s == '<':
return EDlgMode.BACK return EDlgMode.BACK
return EDlgMode.UNHANDLED return EDlgMode.UNHANDLED
def print(self, table_width_max=80, col_spacing = 3): def print(self, table_width_max=80, col_spacing = 3):
""" """
print dialog print dialog
""" """
table_def : List[str]= [] table_def : List[str]= []
trd = self._top_rows_def trd = self._top_rows_def
brd = self._bottom_rows_def brd = self._bottom_rows_def
if trd is not None: if trd is not None:
@ -99,7 +99,7 @@ class Dlg:
table_def.append('|99') table_def.append('|99')
table_def = self.on_print(table_def) table_def = self.on_print(table_def)
if brd is not None: if brd is not None:
if not isinstance(brd, (list,tuple)): if not isinstance(brd, (list,tuple)):
brd = [brd] brd = [brd]
@ -119,7 +119,7 @@ class Dlg:
def on_print(self, table_lines : List[Tuple[str,str]]): def on_print(self, table_lines : List[Tuple[str,str]]):
return table_lines return table_lines
class DlgNumber(Dlg): class DlgNumber(Dlg):
def __init__(self, is_float : bool, def __init__(self, is_float : bool,
@ -131,7 +131,7 @@ class DlgNumber(Dlg):
on_value : Callable[ [Dlg, Number], None] = None, on_value : Callable[ [Dlg, Number], None] = None,
on_recreate : Callable[ [], 'Dlg'] = None, on_recreate : Callable[ [], 'Dlg'] = None,
on_back : Callable = None, on_back : Callable = None,
top_rows_def : Union[str, List[str]] = None, top_rows_def : Union[str, List[str]] = None,
bottom_rows_def : Union[str, List[str]] = None, ): bottom_rows_def : Union[str, List[str]] = None, ):
super().__init__(on_recreate=on_recreate, on_back=on_back, top_rows_def=top_rows_def, bottom_rows_def=bottom_rows_def) super().__init__(on_recreate=on_recreate, on_back=on_back, top_rows_def=top_rows_def, bottom_rows_def=bottom_rows_def)
@ -139,7 +139,7 @@ class DlgNumber(Dlg):
raise ValueError('min_value > max_value') raise ValueError('min_value > max_value')
if clip_min_value is not None and clip_max_value is not None and clip_min_value > clip_max_value: if clip_min_value is not None and clip_max_value is not None and clip_min_value > clip_max_value:
raise ValueError('clip_min_value > clip_max_value') raise ValueError('clip_min_value > clip_max_value')
self._is_float = is_float self._is_float = is_float
self._current_value = current_value self._current_value = current_value
self._min_value = min_value self._min_value = min_value
@ -152,21 +152,21 @@ class DlgNumber(Dlg):
def on_print(self, table_def : List[str]): def on_print(self, table_def : List[str]):
minv, maxv = self._min_value, self._max_value minv, maxv = self._min_value, self._max_value
if self._is_float: if self._is_float:
line = '| * | Enter float number' line = '| * | Enter float number'
else: else:
line = '| * | Enter integer number' line = '| * | Enter integer number'
if minv is not None and maxv is None: if minv is not None and maxv is None:
line += f' in range: [{minv} ... )' line += f' in range: [{minv} ... )'
elif minv is None and maxv is not None: elif minv is None and maxv is not None:
line += f' in range: ( ... {maxv} ]' line += f' in range: ( ... {maxv} ]'
elif minv is not None and maxv is not None: elif minv is not None and maxv is not None:
line += f' in range: [{minv} ... {maxv} ]' line += f' in range: [{minv} ... {maxv} ]'
table_def.append(line) table_def.append(line)
return table_def return table_def
#overridable #overridable
@ -192,7 +192,7 @@ class DlgNumber(Dlg):
if self._clip_max_value is not None: if self._clip_max_value is not None:
if v > self._clip_max_value: if v > self._clip_max_value:
v = self._clip_max_value v = self._clip_max_value
if self._on_value is not None: if self._on_value is not None:
self._on_value(self, v) self._on_value(self, v)
return EDlgMode.HANDLED return EDlgMode.HANDLED
@ -206,7 +206,7 @@ class DlgChoices(Dlg):
on_multi_choice : Callable[ [ List[DlgChoice] ], None] = None, on_multi_choice : Callable[ [ List[DlgChoice] ], None] = None,
on_recreate : Callable[ [Dlg], Dlg] = None, on_recreate : Callable[ [Dlg], Dlg] = None,
on_back : Callable = None, on_back : Callable = None,
top_rows_def : Union[str, List[str]] = None, top_rows_def : Union[str, List[str]] = None,
bottom_rows_def : Union[str, List[str]] = None, bottom_rows_def : Union[str, List[str]] = None,
): ):
super().__init__(on_recreate=on_recreate, on_back=on_back, top_rows_def=top_rows_def, bottom_rows_def=bottom_rows_def) super().__init__(on_recreate=on_recreate, on_back=on_back, top_rows_def=top_rows_def, bottom_rows_def=bottom_rows_def)
@ -252,7 +252,7 @@ class DlgChoices(Dlg):
else: else:
id = x[0] id = x[0]
choices_id.append(id) choices_id.append(id)
if len(set(choices_id)) != len(choices_id): if len(set(choices_id)) != len(choices_id):
# Duplicate input # Duplicate input
return EDlgMode.WRONG_INPUT return EDlgMode.WRONG_INPUT
@ -261,7 +261,7 @@ class DlgChoices(Dlg):
on_choose = self._choices[id].get_on_choose() on_choose = self._choices[id].get_on_choose()
if on_choose is not None: if on_choose is not None:
on_choose(self) on_choose(self)
if self._on_multi_choice is not None: if self._on_multi_choice is not None:
self._on_multi_choice(choices_id) self._on_multi_choice(choices_id)
@ -304,6 +304,9 @@ class _Diacon:
self._dialog_t = None self._dialog_t = None
self._input_t = None self._input_t = None
def get_current_dlg(self) -> Union[Dlg, None]:
return self._current_dlg
def _input_thread(self,): def _input_thread(self,):
while self._started: while self._started:
if self._input_request: if self._input_request:
@ -335,7 +338,7 @@ class _Diacon:
if input_result is not None: if input_result is not None:
if self._current_dlg is not None: if self._current_dlg is not None:
self._current_dlg.handle_user_input(input_result) self._current_dlg.handle_user_input(input_result)
continue continue
time.sleep(0.005) time.sleep(0.005)
@ -360,7 +363,7 @@ class _Diacon:
""" """
if not self._started: if not self._started:
self.start() self.start()
self._new_dlg = (new_dlg, print) self._new_dlg = (new_dlg, print)
Diacon = _Diacon() Diacon = _Diacon()