def onClientInitialize(self, client_dict):
self.safe_print('Running on %s.' % (client_dict['device_name']))
self.type = client_dict['type']
self.image_size = client_dict['image_size']
self.face_type = client_dict['face_type']
self.device_idx = client_dict['device_idx']
self.cpu_only = client_dict['device_type'] == 'CPU'
self.output_path = Path(
client_dict['output_dir']) if 'output_dir' in client_dict.keys(
) else None
self.debug = client_dict['debug']
self.detector = client_dict['detector']
self.keras = None
self.tf = None
self.tf_session = None
self.e = None
if self.type == 'rects':
if self.detector is not None:
if self.detector == 'mt':
self.gpu_config = gpufmkmgr.GPUConfig(
cpu_only=self.cpu_only,
force_best_gpu_idx=self.device_idx,
allow_growth=True)
self.tf = gpufmkmgr.import_tf(self.gpu_config)
self.tf_session = gpufmkmgr.get_tf_session()
self.keras = gpufmkmgr.import_keras()
self.e = facelib.MTCExtractor(self.keras, self.tf,
self.tf_session)
elif self.detector == 'dlib':
self.dlib = gpufmkmgr.import_dlib(self.device_idx,
cpu_only=self.cpu_only)
self.e = facelib.DLIBExtractor(self.dlib)
self.e.__enter__()
elif self.type == 'landmarks':
self.gpu_config = gpufmkmgr.GPUConfig(
cpu_only=self.cpu_only,
force_best_gpu_idx=self.device_idx,
allow_growth=True)
self.tf = gpufmkmgr.import_tf(self.gpu_config)
self.tf_session = gpufmkmgr.get_tf_session()
self.keras = gpufmkmgr.import_keras()
self.e = facelib.LandmarksExtractor(self.keras)
self.e.__enter__()
elif self.type == 'final':
pass
return None
def __init__(self,):
import gpufmkmgr
self.tf_module = gpufmkmgr.import_tf()
self.tf_session = gpufmkmgr.get_tf_session()
self.bgr_input_tensor = self.tf_module.placeholder("float", [None, None, 3])
self.lab_input_tensor = self.tf_module.placeholder("float", [None, None, 3])
self.lab_output_tensor = self.rgb_to_lab(self.tf_module, self.bgr_input_tensor)
self.bgr_output_tensor = self.lab_to_rgb(self.tf_module, self.lab_input_tensor)
def __init__(self,
model_path,
training_data_src_path=None,
training_data_dst_path=None,
multi_gpu=False,
force_best_gpu_idx=-1,
force_gpu_idxs=None,
write_preview_history=False,
debug=False,
**in_options):
print("Loading model...")
self.model_path = model_path
self.model_data_path = Path(
self.get_strpath_storage_for_file('data.dat'))
self.training_data_src_path = training_data_src_path
self.training_data_dst_path = training_data_dst_path
self.training_datas = [None] * TrainingDataType.QTY
self.src_images_paths = None
self.dst_images_paths = None
self.src_yaw_images_paths = None
self.dst_yaw_images_paths = None
self.src_data_generator = None
self.dst_data_generator = None
self.is_training_mode = (training_data_src_path is not None
and training_data_dst_path is not None)
self.batch_size = 1
self.write_preview_history = write_preview_history
self.debug = debug
self.supress_std_once = False #True
if self.model_data_path.exists():
model_data = pickle.loads(self.model_data_path.read_bytes())
self.epoch = model_data['epoch']
self.options = model_data['options']
self.loss_history = model_data[
'loss_history'] if 'loss_history' in model_data.keys() else []
self.generator_dict_states = model_data[
'generator_dict_states'] if 'generator_dict_states' in model_data.keys(
) else None
self.sample_for_preview = model_data[
'sample_for_preview'] if 'sample_for_preview' in model_data.keys(
) else None
else:
self.epoch = 0
self.options = {}
self.loss_history = []
self.generator_dict_states = None
self.sample_for_preview = None
if self.write_preview_history:
self.preview_history_path = self.model_path / (
'%s_history' % (self.get_model_name()))
if not self.preview_history_path.exists():
self.preview_history_path.mkdir(exist_ok=True)
else:
if self.epoch == 0:
for filename in Path_utils.get_image_paths(
self.preview_history_path):
Path(filename).unlink()
self.multi_gpu = multi_gpu
gpu_idx = force_best_gpu_idx if (
force_best_gpu_idx >= 0
and gpufmkmgr.isValidDeviceIdx(force_best_gpu_idx)
) else gpufmkmgr.getBestDeviceIdx()
gpu_total_vram_gb = gpufmkmgr.getDeviceVRAMTotalGb(gpu_idx)
is_gpu_low_mem = (gpu_total_vram_gb < 4)
self.gpu_total_vram_gb = gpu_total_vram_gb
if self.epoch == 0:
#first run
self.options['created_vram_gb'] = gpu_total_vram_gb
self.created_vram_gb = gpu_total_vram_gb
else:
#not first run
if 'created_vram_gb' in self.options.keys():
self.created_vram_gb = self.options['created_vram_gb']
else:
self.options['created_vram_gb'] = gpu_total_vram_gb
self.created_vram_gb = gpu_total_vram_gb
if force_gpu_idxs is not None:
self.gpu_idxs = [int(x) for x in force_gpu_idxs.split(',')]
else:
if self.multi_gpu:
self.gpu_idxs = gpufmkmgr.getDeviceIdxsEqualModel(gpu_idx)
if len(self.gpu_idxs) <= 1:
self.multi_gpu = False
else:
self.gpu_idxs = [gpu_idx]
self.tf = gpufmkmgr.import_tf(self.gpu_idxs, allow_growth=False)
self.keras = gpufmkmgr.import_keras()
self.keras_contrib = gpufmkmgr.import_keras_contrib()
self.onInitialize(**in_options)
if self.debug:
self.batch_size = 1
if self.is_training_mode:
if self.generator_list is None:
raise Exception('You didnt set_training_data_generators()')
else:
for i, generator in enumerate(self.generator_list):
if not isinstance(generator, TrainingDataGeneratorBase):
raise Exception(
'training data generator is not subclass of TrainingDataGeneratorBase'
)
if self.generator_dict_states is not None and i < len(
self.generator_dict_states):
generator.set_dict_state(self.generator_dict_states[i])
if self.sample_for_preview is None:
self.sample_for_preview = self.generate_next_sample()
print("===== Model summary =====")
print("== Model name: " + self.get_model_name())
print("==")
print("== Current epoch: " + str(self.epoch))
print("==")
print("== Options:")
print("== |== batch_size : %s " % (self.batch_size))
print("== |== multi_gpu : %s " % (self.multi_gpu))
for key in self.options.keys():
print("== |== %s : %s" % (key, self.options[key]))
print("== Running on:")
for idx in self.gpu_idxs:
print("== |== [%d : %s]" % (idx, gpufmkmgr.getDeviceName(idx)))
if self.gpu_total_vram_gb == 2:
print("==")
print(
"== WARNING: You are using 2GB GPU. If training does not start,"
)
print("== close all programs and try again.")
print(
"== Also you can disable Windows Aero Desktop to get extra free VRAM."
)
print("==")
print("=========================")
def __init__(self,
model_path,
training_data_src_path=None,
training_data_dst_path=None,
batch_size=0,
write_preview_history=False,
debug=False,
**in_options):
print("Loading model...")
self.model_path = model_path
self.model_data_path = Path(
self.get_strpath_storage_for_file('data.dat'))
self.training_data_src_path = training_data_src_path
self.training_data_dst_path = training_data_dst_path
self.src_images_paths = None
self.dst_images_paths = None
self.src_yaw_images_paths = None
self.dst_yaw_images_paths = None
self.src_data_generator = None
self.dst_data_generator = None
self.is_training_mode = (training_data_src_path is not None
and training_data_dst_path is not None)
self.batch_size = batch_size
self.write_preview_history = write_preview_history
self.debug = debug
self.supress_std_once = ('TF_SUPPRESS_STD' in os.environ.keys()
and os.environ['TF_SUPPRESS_STD'] == '1')
if self.model_data_path.exists():
model_data = pickle.loads(self.model_data_path.read_bytes())
self.epoch = model_data['epoch']
self.options = model_data['options']
self.loss_history = model_data[
'loss_history'] if 'loss_history' in model_data.keys() else []
self.sample_for_preview = model_data[
'sample_for_preview'] if 'sample_for_preview' in model_data.keys(
) else None
else:
self.epoch = 0
self.options = {}
self.loss_history = []
self.sample_for_preview = None
if self.write_preview_history:
self.preview_history_path = self.model_path / (
'%s_history' % (self.get_model_name()))
if not self.preview_history_path.exists():
self.preview_history_path.mkdir(exist_ok=True)
else:
if self.epoch == 0:
for filename in Path_utils.get_image_paths(
self.preview_history_path):
Path(filename).unlink()
self.gpu_config = gpufmkmgr.GPUConfig(allow_growth=False, **in_options)
self.gpu_total_vram_gb = self.gpu_config.gpu_total_vram_gb
if self.epoch == 0:
#first run
self.options['created_vram_gb'] = self.gpu_total_vram_gb
self.created_vram_gb = self.gpu_total_vram_gb
else:
#not first run
if 'created_vram_gb' in self.options.keys():
self.created_vram_gb = self.options['created_vram_gb']
else:
self.options['created_vram_gb'] = self.gpu_total_vram_gb
self.created_vram_gb = self.gpu_total_vram_gb
self.tf = gpufmkmgr.import_tf(self.gpu_config)
self.tf_sess = gpufmkmgr.get_tf_session()
self.keras = gpufmkmgr.import_keras()
self.keras_contrib = gpufmkmgr.import_keras_contrib()
self.onInitialize(**in_options)
if self.debug or self.batch_size == 0:
self.batch_size = 1
if self.is_training_mode:
if self.generator_list is None:
raise Exception('You didnt set_training_data_generators()')
else:
for i, generator in enumerate(self.generator_list):
if not isinstance(generator, SampleGeneratorBase):
raise Exception(
'training data generator is not subclass of SampleGeneratorBase'
)
if self.sample_for_preview is None:
self.sample_for_preview = self.generate_next_sample()
print("===== Model summary =====")
print("== Model name: " + self.get_model_name())
print("==")
print("== Current epoch: " + str(self.epoch))
print("==")
print("== Options:")
print("== |== batch_size : %s " % (self.batch_size))
print("== |== multi_gpu : %s " % (self.gpu_config.multi_gpu))
for key in self.options.keys():
print("== |== %s : %s" % (key, self.options[key]))
print("== Running on:")
if self.gpu_config.cpu_only:
print("== |== [CPU]")
else:
for idx in self.gpu_config.gpu_idxs:
print("== |== [%d : %s]" % (idx, gpufmkmgr.getDeviceName(idx)))
if not self.gpu_config.cpu_only and self.gpu_total_vram_gb == 2:
print("==")
print(
"== WARNING: You are using 2GB GPU. Result quality may be significantly decreased."
)
print(
"== If training does not start, close all programs and try again."
)
print(
"== Also you can disable Windows Aero Desktop to get extra free VRAM."
)
print("==")
print("=========================")
def extract_pass_process(sq, cq):
e = None
type = None
device_idx = None
debug = False
output_path = None
detector = None
image_size = None
face_type = None
while True:
obj = sq.get()
obj_op = obj['op']
if obj_op == 'extract':
data = obj['data']
filename_path = Path(data[0])
if not filename_path.exists():
cq.put({
'op':
'error',
'close':
False,
'is_file_not_found':
True,
'data':
obj['data'],
'message':
'Failed to extract %s, reason: file not found.' %
(str(filename_path))
})
else:
try:
image = cv2.imread(str(filename_path))
if type == 'rects':
rects = e.extract_from_bgr(image)
cq.put({
'op': 'extract_success',
'data': obj['data'],
'result': [str(filename_path), rects]
})
elif type == 'landmarks':
rects = data[1]
landmarks = e.extract_from_bgr(image, rects)
cq.put({
'op': 'extract_success',
'data': obj['data'],
'result': [str(filename_path), landmarks]
})
elif type == 'final':
result = []
faces = data[1]
if debug:
debug_output_file = '{}_{}'.format(
str(
Path(str(output_path) + '_debug') /
filename_path.stem), 'debug.png')
debug_image = image.copy()
for (face_idx, face) in enumerate(faces):
rect = face[0]
image_landmarks = np.array(face[1])
image_to_face_mat = facelib.LandmarksProcessor.get_transform_mat(
image_landmarks, image_size, face_type)
output_file = '{}_{}{}'.format(
str(output_path / filename_path.stem),
str(face_idx), '.png')
if debug:
facelib.LandmarksProcessor.draw_rect_landmarks(
debug_image, rect, image_landmarks,
image_size, face_type)
face_image = cv2.warpAffine(
image, image_to_face_mat,
(image_size, image_size))
face_image_landmarks = facelib.LandmarksProcessor.transform_points(
image_landmarks, image_to_face_mat)
cv2.imwrite(output_file, face_image)
a_png = AlignedPNG.load(output_file)
d = {
'type':
'face',
'landmarks':
face_image_landmarks.tolist(),
'yaw_value':
facelib.LandmarksProcessor.calc_face_yaw(
face_image_landmarks),
'pitch_value':
facelib.LandmarksProcessor.calc_face_pitch(
face_image_landmarks),
'source_filename':
filename_path.name,
'source_rect':
rect,
'source_landmarks':
image_landmarks.tolist()
}
a_png.setFaceswapDictData(d)
a_png.save(output_file)
result.append(output_file)
if debug:
cv2.imwrite(debug_output_file, debug_image)
cq.put({
'op': 'extract_success',
'data': obj['data'],
'result': result
})
except Exception as e:
cq.put({
'op':
'error',
'close':
True,
'data':
obj['data'],
'message':
'Failed to extract %s, reason: %s. \r\n%s' %
(str(filename_path), str(e), traceback.format_exc())
})
break
elif obj_op == 'init':
try:
type = obj['type']
image_size = obj['image_size']
face_type = obj['face_type']
device_idx = obj['device_idx']
output_path = Path(
obj['output_dir']) if 'output_dir' in obj.keys() else None
debug = obj['debug']
detector = obj['detector']
if type == 'rects':
if detector is not None:
if detector == 'mt':
tf = gpufmkmgr.import_tf([device_idx],
allow_growth=True)
tf_session = gpufmkmgr.get_tf_session()
keras = gpufmkmgr.import_keras()
e = facelib.MTCExtractor(keras, tf, tf_session)
elif detector == 'dlib':
dlib = gpufmkmgr.import_dlib(device_idx)
e = facelib.DLIBExtractor(dlib)
e.__enter__()
elif type == 'landmarks':
gpufmkmgr.import_tf([device_idx], allow_growth=True)
keras = gpufmkmgr.import_keras()
e = facelib.LandmarksExtractor(keras)
e.__enter__()
elif type == 'final':
pass
cq.put({'op': 'init_ok'})
except Exception as e:
cq.put({
'op':
'error',
'close':
True,
'message':
'Exception while initialization: %s' %
(traceback.format_exc())
})
break
if detector is not None and (type == 'rects' or type == 'landmarks'):
e.__exit__()