def _validate_encoder_architecture(self):
""" Validate that the requested architecture is a valid choice for the running system
configuration.
If the selection is not valid, an error is logged and system exits.
"""
arch = self.config["enc_architecture"].lower()
model = _MODEL_MAPPING.get(arch)
if not model:
raise FaceswapError(
f"'{arch}' is not a valid choice for encoder architecture. Choose "
f"one of {list(_MODEL_MAPPING.keys())}.")
if get_backend() == "amd" and model.get("no_amd"):
valid = [
k for k, v in _MODEL_MAPPING.items() if not v.get('no_amd')
]
raise FaceswapError(
f"'{arch}' is not compatible with the AMD backend. Choose one of "
f"{valid}.")
tf_ver = get_tf_version()
tf_min = model.get("tf_min", 2.0)
if get_backend() != "amd" and tf_ver < tf_min:
raise FaceswapError(
f"{arch}' is not compatible with your version of Tensorflow. The "
f"minimum version required is {tf_min} whilst you have version "
f"{tf_ver} installed.")
def _test_for_tf_version(self):
""" Check that the required Tensorflow version is installed.
Raises
------
FaceswapError
If Tensorflow is not found, or is not between versions 2.4 and 2.8
"""
amd_ver = 2.2
min_ver = 2.4
max_ver = 2.8
try:
# Ensure tensorflow doesn't pin all threads to one core when using Math Kernel Library
os.environ["TF_MIN_GPU_MULTIPROCESSOR_COUNT"] = "4"
os.environ["KMP_AFFINITY"] = "disabled"
import tensorflow as tf # noqa pylint:disable=import-outside-toplevel,unused-import
except ImportError as err:
if "DLL load failed while importing" in str(err):
msg = (
f"A DLL library file failed to load. Make sure that you have Microsoft Visual "
"C++ Redistributable (2015, 2017, 2019) installed for your machine from: "
"https://support.microsoft.com/en-gb/help/2977003. Original error: "
f"{str(err)}")
else:
msg = (
f"There was an error importing Tensorflow. This is most likely because you do "
"not have TensorFlow installed, or you are trying to run tensorflow-gpu on a "
"system without an Nvidia graphics card. Original import "
f"error: {str(err)}")
self._handle_import_error(msg)
tf_ver = get_tf_version()
backend = get_backend()
if backend != "amd" and tf_ver < min_ver:
msg = (
f"The minimum supported Tensorflow is version {min_ver} but you have version "
f"{tf_ver} installed. Please upgrade Tensorflow.")
self._handle_import_error(msg)
if backend != "amd" and tf_ver > max_ver:
msg = (
f"The maximum supported Tensorflow is version {max_ver} but you have version "
f"{tf_ver} installed. Please downgrade Tensorflow.")
self._handle_import_error(msg)
if backend == "amd" and tf_ver != amd_ver:
msg = (
f"The supported Tensorflow version for AMD cards is {amd_ver} but you have "
"version {tf_ver} installed. Please install the correct version."
)
self._handle_import_error(msg)
logger.debug("Installed Tensorflow Version: %s", tf_ver)
def _log_tensorboard(self, loss):
""" Log current loss to Tensorboard log files
Parameters
----------
loss: list
The list of loss ``floats`` output from the model
"""
if not self._tensorboard:
return
logger.trace("Updating TensorBoard log")
logs = {
log[0]: log[1]
for log in zip(self._model.state.loss_names, loss)
}
self._tensorboard.on_train_batch_end(self._model.iterations, logs=logs)
if get_tf_version() == 2.8:
# Bug in TF 2.8 where batch recording got deleted.
# ref: https://github.com/keras-team/keras/issues/16173
for name, value in logs.items():
tf.summary.scalar("batch_" + name,
value,
step=self._model._model._train_counter) # pylint:disable=protected-access
import tensorflow as tf
from lib.model import losses, optimizers
from lib.utils import get_backend, get_tf_version
if get_backend() == "amd":
import keras
from keras import losses as k_losses
from keras import backend as K
else:
# Ignore linting errors from Tensorflow's thoroughly broken import system
from tensorflow import keras
from tensorflow.keras import losses as k_losses # pylint:disable=import-error
from tensorflow.keras import backend as K # pylint:disable=import-error
if get_tf_version() < 2.4:
import tensorflow.keras.mixed_precision.experimental as mixedprecision # noqa pylint:disable=import-error,no-name-in-module
else:
import tensorflow.keras.mixed_precision as mixedprecision # noqa pylint:disable=import-error,no-name-in-module
if TYPE_CHECKING:
from argparse import Namespace
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
@dataclass
class LossClass:
""" Typing class for holding loss functions.
Parameters