Merge branch 'staging'

deepfakes · Dec 19, 2022 · d1e09f7 · d1e09f7
2 parents dd64ed5 + 84ae600
commit d1e09f7
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Showing 4 changed files with 44 additions and 26 deletions.
diff --git a/lib/cli/launcher.py b/lib/cli/launcher.py
@@ -87,9 +87,9 @@ def _test_for_tf_version(self) -> None:
         FaceswapError
             If Tensorflow is not found, or is not between versions 2.4 and 2.9
         """
-        amd_ver = 2.2
-        min_ver = 2.7
-        max_ver = 2.9
+        amd_ver = (2, 2)
+        min_ver = (2, 7)
+        max_ver = (2, 9)
         try:
             import tensorflow as tf  # noqa pylint:disable=import-outside-toplevel,unused-import
         except ImportError as err:

diff --git a/lib/utils.py b/lib/utils.py
@@ -15,7 +15,7 @@
 from socket import timeout as socket_timeout, error as socket_error
 from threading import get_ident
 from time import time
-from typing import cast, Dict, List, Optional, Union, TYPE_CHECKING
+from typing import cast, Dict, List, Optional, Union, Tuple, TYPE_CHECKING
 
 import numpy as np
 from tqdm import tqdm
@@ -34,7 +34,7 @@
 _video_extensions = [  # pylint:disable=invalid-name
     ".avi", ".flv", ".mkv", ".mov", ".mp4", ".mpeg", ".mpg", ".webm", ".wmv",
     ".ts", ".vob"]
-_TF_VERS: Optional[float] = None
+_TF_VERS: Optional[Tuple[int, int]] = None
 ValidBackends = Literal["amd", "nvidia", "cpu", "apple_silicon"]
 
 
@@ -149,7 +149,7 @@ def set_backend(backend: str) -> None:
     _FS_BACKEND = backend
 
 
-def get_tf_version() -> float:
+def get_tf_version() -> Tuple[int, int]:
     """ Obtain the major.minor version of currently installed Tensorflow.
 
     Returns
@@ -160,7 +160,8 @@ def get_tf_version() -> float:
     global _TF_VERS  # pylint:disable=global-statement
     if _TF_VERS is None:
         import tensorflow as tf  # pylint:disable=import-outside-toplevel
-        _TF_VERS = float(".".join(tf.__version__.split(".")[:2]))  # pylint:disable=no-member
+        split = tf.__version__.split(".")[:2]
+        _TF_VERS = (int(split[0]), int(split[1]))
     return _TF_VERS
 
 

diff --git a/plugins/train/model/phaze_a.py b/plugins/train/model/phaze_a.py
@@ -80,7 +80,7 @@ class _EncoderInfo:
     keras_name: str
     default_size: int
     no_amd: bool = False
-    tf_min: float = 2.0
+    tf_min: Tuple[int, int] = (2, 0)
     scaling: Tuple[int, int] = (0, 1)
     min_size: int = 32
     enforce_for_weights: bool = False
@@ -95,35 +95,50 @@ class _EncoderInfo:
     densenet201=_EncoderInfo(
         keras_name="DenseNet201", default_size=224),
     efficientnet_b0=_EncoderInfo(
-        keras_name="EfficientNetB0", no_amd=True, tf_min=2.3, scaling=(0, 255), default_size=224),
+        keras_name="EfficientNetB0",
+        no_amd=True, tf_min=(2, 3), scaling=(0, 255), default_size=224),
     efficientnet_b1=_EncoderInfo(
-        keras_name="EfficientNetB1", no_amd=True, tf_min=2.3, scaling=(0, 255), default_size=240),
+        keras_name="EfficientNetB1",
+        no_amd=True, tf_min=(2, 3), scaling=(0, 255), default_size=240),
     efficientnet_b2=_EncoderInfo(
-        keras_name="EfficientNetB2", no_amd=True, tf_min=2.3, scaling=(0, 255), default_size=260),
+        keras_name="EfficientNetB2",
+        no_amd=True, tf_min=(2, 3), scaling=(0, 255), default_size=260),
     efficientnet_b3=_EncoderInfo(
-        keras_name="EfficientNetB3", no_amd=True, tf_min=2.3, scaling=(0, 255), default_size=300),
+        keras_name="EfficientNetB3",
+        no_amd=True, tf_min=(2, 3), scaling=(0, 255), default_size=300),
     efficientnet_b4=_EncoderInfo(
-        keras_name="EfficientNetB4", no_amd=True, tf_min=2.3, scaling=(0, 255), default_size=380),
+        keras_name="EfficientNetB4",
+        no_amd=True, tf_min=(2, 3), scaling=(0, 255), default_size=380),
     efficientnet_b5=_EncoderInfo(
-        keras_name="EfficientNetB5", no_amd=True, tf_min=2.3, scaling=(0, 255), default_size=456),
+        keras_name="EfficientNetB5",
+        no_amd=True, tf_min=(2, 3), scaling=(0, 255), default_size=456),
     efficientnet_b6=_EncoderInfo(
-        keras_name="EfficientNetB6", no_amd=True, tf_min=2.3, scaling=(0, 255), default_size=528),
+        keras_name="EfficientNetB6",
+        no_amd=True, tf_min=(2, 3), scaling=(0, 255), default_size=528),
     efficientnet_b7=_EncoderInfo(
-        keras_name="EfficientNetB7", no_amd=True, tf_min=2.3, scaling=(0, 255), default_size=600),
+        keras_name="EfficientNetB7",
+        no_amd=True, tf_min=(2, 3), scaling=(0, 255), default_size=600),
     efficientnet_v2_b0=_EncoderInfo(
-        keras_name="EfficientNetV2B0", no_amd=True, tf_min=2.8, scaling=(-1, 1), default_size=224),
+        keras_name="EfficientNetV2B0",
+        no_amd=True, tf_min=(2, 8), scaling=(-1, 1), default_size=224),
     efficientnet_v2_b1=_EncoderInfo(
-        keras_name="EfficientNetV2B1", no_amd=True, tf_min=2.8, scaling=(-1, 1), default_size=240),
+        keras_name="EfficientNetV2B1",
+        no_amd=True, tf_min=(2, 8), scaling=(-1, 1), default_size=240),
     efficientnet_v2_b2=_EncoderInfo(
-        keras_name="EfficientNetV2B2", no_amd=True, tf_min=2.8, scaling=(-1, 1), default_size=260),
+        keras_name="EfficientNetV2B2",
+        no_amd=True, tf_min=(2, 8), scaling=(-1, 1), default_size=260),
     efficientnet_v2_b3=_EncoderInfo(
-        keras_name="EfficientNetV2B3", no_amd=True, tf_min=2.8, scaling=(-1, 1), default_size=300),
+        keras_name="EfficientNetV2B3",
+        no_amd=True, tf_min=(2, 8), scaling=(-1, 1), default_size=300),
     efficientnet_v2_s=_EncoderInfo(
-        keras_name="EfficientNetV2S", no_amd=True, tf_min=2.8, scaling=(-1, 1), default_size=384),
+        keras_name="EfficientNetV2S",
+        no_amd=True, tf_min=(2, 8), scaling=(-1, 1), default_size=384),
     efficientnet_v2_m=_EncoderInfo(
-        keras_name="EfficientNetV2M", no_amd=True, tf_min=2.8, scaling=(-1, 1), default_size=480),
+        keras_name="EfficientNetV2M",
+        no_amd=True, tf_min=(2, 8), scaling=(-1, 1), default_size=480),
     efficientnet_v2_l=_EncoderInfo(
-        keras_name="EfficientNetV2L", no_amd=True, tf_min=2.8, scaling=(-1, 1), default_size=480),
+        keras_name="EfficientNetV2L",
+        no_amd=True, tf_min=(2, 8), scaling=(-1, 1), default_size=480),
     inception_resnet_v2=_EncoderInfo(
         keras_name="InceptionResNetV2", scaling=(-1, 1), min_size=75, default_size=299),
     inception_v3=_EncoderInfo(
@@ -133,9 +148,11 @@ class _EncoderInfo:
     mobilenet_v2=_EncoderInfo(
         keras_name="MobileNetV2", scaling=(-1, 1), default_size=224),
     mobilenet_v3_large=_EncoderInfo(
-        keras_name="MobileNetV3Large", no_amd=True, tf_min=2.4, scaling=(-1, 1), default_size=224),
+        keras_name="MobileNetV3Large",
+        no_amd=True, tf_min=(2, 4), scaling=(-1, 1), default_size=224),
     mobilenet_v3_small=_EncoderInfo(
-        keras_name="MobileNetV3Small", no_amd=True, tf_min=2.4, scaling=(-1, 1), default_size=224),
+        keras_name="MobileNetV3Small",
+        no_amd=True, tf_min=(2, 4), scaling=(-1, 1), default_size=224),
     nasnet_large=_EncoderInfo(
         keras_name="NASNetLarge", scaling=(-1, 1), default_size=331, enforce_for_weights=True),
     nasnet_mobile=_EncoderInfo(

diff --git a/plugins/train/trainer/_base.py b/plugins/train/trainer/_base.py
@@ -259,7 +259,7 @@ def _log_tensorboard(self, loss: List[float]) -> None:
         logs = {log[0]: log[1]
                 for log in zip(self._model.state.loss_names, loss)}
 
-        if get_tf_version() > 2.7:
+        if get_tf_version() > (2, 7):
             # Bug in TF 2.8/2.9 where batch recording got deleted.
             # ref: https://github.com/keras-team/keras/issues/16173
             with tf.summary.record_if(True), self._tensorboard._train_writer.as_default():  # noqa pylint:disable=protected-access,not-context-manager