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https://github.com/deepfakes/faceswap
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* Remove tensorflow_probability requirement * setup.py - fix progress bars * requirements.txt: Remove pre python 3.9 packages * update apple requirements.txt * update INSTALL.md * Remove python<3.9 code * setup.py - fix Windows Installer * typing: python3.9 compliant * Update pytest and readthedocs python versions * typing fixes * Python Version updates - Reduce max version to 3.10 - Default to 3.10 in installers - Remove incompatible 3.11 tests * Update dependencies * Downgrade imageio dep for Windows * typing: merge optional unions and fixes * Updates - min python version 3.10 - typing to python 3.10 spec - remove pre-tf2.10 code - Add conda tests * train: re-enable optimizer saving * Update dockerfiles * Update setup.py - Apple Conda deps to setup.py - Better Cuda + dependency handling * bugfix: Patch logging to prevent Autograph errors * Update dockerfiles * Setup.py - Setup.py - stdout to utf-8 * Add more OSes to github Actions * suppress mac-os end to end test
710 lines
30 KiB
Python
710 lines
30 KiB
Python
#!/usr/bin/env python3
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"""
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Settings for the model base plugins.
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The objects in this module should not be called directly, but are called from
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:class:`~plugins.train.model._base.ModelBase`
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Handles configuration of model plugins for:
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- Loss configuration
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- Optimizer settings
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- General global model configuration settings
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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import logging
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import platform
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import typing as T
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from contextlib import nullcontext
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import tensorflow as tf
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# Ignore linting errors from Tensorflow's thoroughly broken import system
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from tensorflow.keras import losses as k_losses # pylint:disable=import-error
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import tensorflow.keras.mixed_precision as mixedprecision # noqa pylint:disable=import-error
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from lib.model import losses, optimizers
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from lib.model.autoclip import AutoClipper
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from lib.utils import get_backend
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if T.TYPE_CHECKING:
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from collections.abc import Callable
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from contextlib import AbstractContextManager as ContextManager
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from argparse import Namespace
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from .model import State
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keras = tf.keras
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K = keras.backend
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logger = logging.getLogger(__name__) # pylint: disable=invalid-name
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@dataclass
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class LossClass:
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""" Typing class for holding loss functions.
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Parameters
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----------
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function: Callable
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The function that takes in the true/predicted images and returns the loss
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init: bool, Optional
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Whether the loss object ``True`` needs to be initialized (i.e. it's a class) or
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``False`` it does not require initialization (i.e. it's a function).
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Default ``True``
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kwargs: dict
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Any keyword arguments to supply to the loss function at initialization.
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"""
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function: Callable[[tf.Tensor, tf.Tensor], tf.Tensor] | T.Any = k_losses.mae
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init: bool = True
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kwargs: dict[str, T.Any] = field(default_factory=dict)
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class Loss():
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""" Holds loss names and functions for an Autoencoder.
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Parameters
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----------
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config: dict
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The configuration options for the current model plugin
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color_order: str
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Color order of the model. One of `"BGR"` or `"RGB"`
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"""
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def __init__(self, config: dict, color_order: T.Literal["bgr", "rgb"]) -> None:
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logger.debug("Initializing %s: (color_order: %s)", self.__class__.__name__, color_order)
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self._config = config
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self._mask_channels = self._get_mask_channels()
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self._inputs: list[tf.keras.layers.Layer] = []
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self._names: list[str] = []
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self._funcs: dict[str, Callable] = {}
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self._loss_dict = {"ffl": LossClass(function=losses.FocalFrequencyLoss),
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"flip": LossClass(function=losses.LDRFLIPLoss,
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kwargs={"color_order": color_order}),
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"gmsd": LossClass(function=losses.GMSDLoss),
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"l_inf_norm": LossClass(function=losses.LInfNorm),
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"laploss": LossClass(function=losses.LaplacianPyramidLoss),
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"logcosh": LossClass(function=k_losses.logcosh, init=False),
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"lpips_alex": LossClass(function=losses.LPIPSLoss,
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kwargs={"trunk_network": "alex"}),
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"lpips_squeeze": LossClass(function=losses.LPIPSLoss,
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kwargs={"trunk_network": "squeeze"}),
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"lpips_vgg16": LossClass(function=losses.LPIPSLoss,
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kwargs={"trunk_network": "vgg16"}),
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"ms_ssim": LossClass(function=losses.MSSIMLoss),
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"mae": LossClass(function=k_losses.mean_absolute_error, init=False),
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"mse": LossClass(function=k_losses.mean_squared_error, init=False),
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"pixel_gradient_diff": LossClass(function=losses.GradientLoss),
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"ssim": LossClass(function=losses.DSSIMObjective),
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"smooth_loss": LossClass(function=losses.GeneralizedLoss)}
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logger.debug("Initialized: %s", self.__class__.__name__)
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@property
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def names(self) -> list[str]:
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""" list: The list of loss names for the model. """
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return self._names
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@property
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def functions(self) -> dict:
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""" dict: The loss functions that apply to each model output. """
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return self._funcs
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@property
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def _mask_inputs(self) -> list | None:
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""" list: The list of input tensors to the model that contain the mask. Returns ``None``
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if there is no mask input to the model. """
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mask_inputs = [inp for inp in self._inputs if inp.name.startswith("mask")]
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return None if not mask_inputs else mask_inputs
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@property
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def _mask_shapes(self) -> list[tuple] | None:
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""" list: The list of shape tuples for the mask input tensors for the model. Returns
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``None`` if there is no mask input. """
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if self._mask_inputs is None:
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return None
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return [K.int_shape(mask_input) for mask_input in self._mask_inputs]
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def configure(self, model: tf.keras.models.Model) -> None:
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""" Configure the loss functions for the given inputs and outputs.
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Parameters
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----------
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model: :class:`keras.models.Model`
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The model that is to be trained
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"""
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self._inputs = model.inputs
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self._set_loss_names(model.outputs)
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self._set_loss_functions(model.output_names)
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self._names.insert(0, "total")
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def _set_loss_names(self, outputs: list[tf.Tensor]) -> None:
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""" Name the losses based on model output.
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This is used for correct naming in the state file, for display purposes only.
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Adds the loss names to :attr:`names`
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Notes
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-----
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TODO Currently there is an issue in Tensorflow that wraps all outputs in an Identity layer
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when running in Eager Execution mode, which means we cannot use the name of the output
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layers to name the losses (https://github.com/tensorflow/tensorflow/issues/32180).
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With this in mind, losses are named based on their shapes
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Parameters
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----------
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outputs: list
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A list of output tensors from the model plugin
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"""
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# TODO Use output names if/when these are fixed upstream
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split_outputs = [outputs[:len(outputs) // 2], outputs[len(outputs) // 2:]]
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for side, side_output in zip(("a", "b"), split_outputs):
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output_names = [output.name for output in side_output]
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output_shapes = [K.int_shape(output)[1:] for output in side_output]
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output_types = ["mask" if shape[-1] == 1 else "face" for shape in output_shapes]
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logger.debug("side: %s, output names: %s, output_shapes: %s, output_types: %s",
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side, output_names, output_shapes, output_types)
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for idx, name in enumerate(output_types):
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suffix = "" if output_types.count(name) == 1 else f"_{idx}"
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self._names.append(f"{name}_{side}{suffix}")
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logger.debug(self._names)
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def _get_function(self, name: str) -> Callable[[tf.Tensor, tf.Tensor], tf.Tensor]:
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""" Obtain the requested Loss function
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Parameters
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----------
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name: str
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The name of the loss function from the training configuration file
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Returns
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-------
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Keras Loss Function
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The requested loss function
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"""
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func = self._loss_dict[name]
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retval = func.function(**func.kwargs) if func.init else func.function # type:ignore
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logger.debug("Obtained loss function `%s` (%s)", name, retval)
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return retval
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def _set_loss_functions(self, output_names: list[str]):
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""" Set the loss functions and their associated weights.
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Adds the loss functions to the :attr:`functions` dictionary.
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Parameters
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----------
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output_names: list
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The output names from the model
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"""
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face_losses = [(lossname, self._config.get(f"loss_weight_{k[-1]}", 100))
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for k, lossname in sorted(self._config.items())
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if k.startswith("loss_function")
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and self._config.get(f"loss_weight_{k[-1]}", 100) != 0
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and lossname is not None]
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for name, output_name in zip(self._names, output_names):
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if name.startswith("mask"):
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loss_func = self._get_function(self._config["mask_loss_function"])
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else:
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loss_func = losses.LossWrapper()
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for func, weight in face_losses:
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self._add_face_loss_function(loss_func, func, weight / 100.)
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logger.debug("%s: (output_name: '%s', function: %s)", name, output_name, loss_func)
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self._funcs[output_name] = loss_func
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logger.debug("functions: %s", self._funcs)
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def _add_face_loss_function(self,
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loss_wrapper: losses.LossWrapper,
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loss_function: str,
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weight: float) -> None:
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""" Add the given face loss function at the given weight and apply any mouth and eye
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multipliers
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Parameters
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----------
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loss_wrapper: :class:`lib.model.losses.LossWrapper`
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The wrapper loss function that holds the face losses
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loss_function: str
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The loss function to add to the loss wrapper
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weight: float
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The amount of weight to apply to the given loss function
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"""
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logger.debug("Adding loss function: %s, weight: %s", loss_function, weight)
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loss_wrapper.add_loss(self._get_function(loss_function),
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weight=weight,
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mask_channel=self._mask_channels[0])
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channel_idx = 1
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for section in ("eye_multiplier", "mouth_multiplier"):
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mask_channel = self._mask_channels[channel_idx]
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multiplier = self._config[section] * 1.
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if multiplier > 1.:
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logger.debug("Adding section loss %s: %s", section, multiplier)
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loss_wrapper.add_loss(self._get_function(loss_function),
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weight=weight * multiplier,
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mask_channel=mask_channel)
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channel_idx += 1
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def _get_mask_channels(self) -> list[int]:
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""" Obtain the channels from the face targets that the masks reside in from the training
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data generator.
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Returns
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-------
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list:
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A list of channel indices that contain the mask for the corresponding config item
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"""
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eye_multiplier = self._config["eye_multiplier"]
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mouth_multiplier = self._config["mouth_multiplier"]
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if not self._config["penalized_mask_loss"] and (eye_multiplier > 1 or
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mouth_multiplier > 1):
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logger.warning("You have selected eye/mouth loss multipliers greater than 1x, but "
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"Penalized Mask Loss is disabled. Disabling all multipliers.")
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eye_multiplier = 1
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mouth_multiplier = 1
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uses_masks = (self._config["penalized_mask_loss"],
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eye_multiplier > 1,
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mouth_multiplier > 1)
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mask_channels = [-1 for _ in range(len(uses_masks))]
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current_channel = 3
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for idx, mask_required in enumerate(uses_masks):
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if mask_required:
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mask_channels[idx] = current_channel
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current_channel += 1
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logger.debug("uses_masks: %s, mask_channels: %s", uses_masks, mask_channels)
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return mask_channels
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class Optimizer(): # pylint:disable=too-few-public-methods
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""" Obtain the selected optimizer with the appropriate keyword arguments.
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Parameters
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----------
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optimizer: str
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The selected optimizer name for the plugin
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learning_rate: float
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The selected learning rate to use
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autoclip: bool
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``True`` if AutoClip should be enabled otherwise ``False``
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epsilon: float
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The value to use for the epsilon of the optimizer
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"""
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def __init__(self,
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optimizer: str,
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learning_rate: float,
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autoclip: bool,
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epsilon: float) -> None:
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logger.debug("Initializing %s: (optimizer: %s, learning_rate: %s, autoclip: %s, "
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", epsilon: %s)", self.__class__.__name__, optimizer, learning_rate,
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autoclip, epsilon)
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valid_optimizers = {"adabelief": (optimizers.AdaBelief,
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{"beta_1": 0.5, "beta_2": 0.99, "epsilon": epsilon}),
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"adam": (optimizers.Adam,
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{"beta_1": 0.5, "beta_2": 0.99, "epsilon": epsilon}),
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"nadam": (optimizers.Nadam,
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{"beta_1": 0.5, "beta_2": 0.99, "epsilon": epsilon}),
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"rms-prop": (optimizers.RMSprop, {"epsilon": epsilon})}
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optimizer_info = valid_optimizers[optimizer]
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self._optimizer: Callable = optimizer_info[0]
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self._kwargs: dict[str, T.Any] = optimizer_info[1]
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self._configure(learning_rate, autoclip)
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logger.verbose("Using %s optimizer", optimizer.title()) # type:ignore[attr-defined]
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logger.debug("Initialized: %s", self.__class__.__name__)
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@property
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def optimizer(self) -> tf.keras.optimizers.Optimizer:
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""" :class:`keras.optimizers.Optimizer`: The requested optimizer. """
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return self._optimizer(**self._kwargs)
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def _configure(self,
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learning_rate: float,
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autoclip: bool) -> None:
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""" Configure the optimizer based on user settings.
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Parameters
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----------
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learning_rate: float
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The selected learning rate to use
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autoclip: bool
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``True`` if AutoClip should be enabled otherwise ``False``
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"""
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self._kwargs["learning_rate"] = learning_rate
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if not autoclip:
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return
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logger.info("Enabling AutoClip")
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self._kwargs["gradient_transformers"] = [AutoClipper(10, history_size=10000)]
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logger.debug("optimizer kwargs: %s", self._kwargs)
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class Settings():
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""" Tensorflow core training settings.
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Sets backend tensorflow settings prior to launching the model.
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Tensorflow 2 uses distribution strategies for multi-GPU/system training. These are context
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managers.
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Parameters
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----------
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arguments: :class:`argparse.Namespace`
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The arguments that were passed to the train or convert process as generated from
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Faceswap's command line arguments
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mixed_precision: bool
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``True`` if Mixed Precision training should be used otherwise ``False``
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allow_growth: bool
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``True`` if the Tensorflow allow_growth parameter should be set otherwise ``False``
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is_predict: bool, optional
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``True`` if the model is being loaded for inference, ``False`` if the model is being loaded
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for training. Default: ``False``
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"""
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def __init__(self,
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arguments: Namespace,
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mixed_precision: bool,
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allow_growth: bool,
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is_predict: bool) -> None:
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logger.debug("Initializing %s: (arguments: %s, mixed_precision: %s, allow_growth: %s, "
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"is_predict: %s)", self.__class__.__name__, arguments, mixed_precision,
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allow_growth, is_predict)
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self._set_tf_settings(allow_growth, arguments.exclude_gpus)
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use_mixed_precision = not is_predict and mixed_precision
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self._use_mixed_precision = self._set_keras_mixed_precision(use_mixed_precision)
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if self._use_mixed_precision:
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logger.info("Enabling Mixed Precision Training.")
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if hasattr(arguments, "distribution_strategy"):
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strategy = arguments.distribution_strategy
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else:
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strategy = "default"
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self._strategy = self._get_strategy(strategy)
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logger.debug("Initialized %s", self.__class__.__name__)
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@property
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def use_mixed_precision(self) -> bool:
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""" bool: ``True`` if mixed precision training has been enabled, otherwise ``False``. """
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return self._use_mixed_precision
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@classmethod
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def loss_scale_optimizer(
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cls,
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optimizer: tf.keras.optimizers.Optimizer) -> mixedprecision.LossScaleOptimizer:
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""" Optimize loss scaling for mixed precision training.
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Parameters
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----------
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optimizer: :class:`tf.keras.optimizers.Optimizer`
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The optimizer instance to wrap
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Returns
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--------
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:class:`tf.keras.mixed_precision.loss_scale_optimizer.LossScaleOptimizer`
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The original optimizer with loss scaling applied
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"""
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return mixedprecision.LossScaleOptimizer(optimizer) # pylint:disable=no-member
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@classmethod
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def _set_tf_settings(cls, allow_growth: bool, exclude_devices: list[int]) -> None:
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""" Specify Devices to place operations on and Allow TensorFlow to manage VRAM growth.
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Enables the Tensorflow allow_growth option if requested in the command line arguments
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Parameters
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----------
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allow_growth: bool
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``True`` if the Tensorflow allow_growth parameter should be set otherwise ``False``
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exclude_devices: list or ``None``
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List of GPU device indices that should not be made available to Tensorflow. Pass
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``None`` if all devices should be made available
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"""
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backend = get_backend()
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if backend == "cpu":
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logger.verbose("Hiding GPUs from Tensorflow") # type:ignore[attr-defined]
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tf.config.set_visible_devices([], "GPU")
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return
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if not exclude_devices and not allow_growth:
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logger.debug("Not setting any specific Tensorflow settings")
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return
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gpus = tf.config.list_physical_devices('GPU')
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if exclude_devices:
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gpus = [gpu for idx, gpu in enumerate(gpus) if idx not in exclude_devices]
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logger.debug("Filtering devices to: %s", gpus)
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tf.config.set_visible_devices(gpus, "GPU")
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if allow_growth and backend == "nvidia":
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logger.debug("Setting Tensorflow 'allow_growth' option")
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for gpu in gpus:
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logger.info("Setting allow growth for GPU: %s", gpu)
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tf.config.experimental.set_memory_growth(gpu, True)
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logger.debug("Set Tensorflow 'allow_growth' option")
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@classmethod
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def _set_keras_mixed_precision(cls, use_mixed_precision: bool) -> bool:
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""" Enable the Keras experimental Mixed Precision API.
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Enables the Keras experimental Mixed Precision API if requested in the user configuration
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file.
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Parameters
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----------
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use_mixed_precision: bool
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``True`` if experimental mixed precision support should be enabled for Nvidia GPUs
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otherwise ``False``.
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Returns
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-------
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bool
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``True`` if mixed precision has been enabled otherwise ``False``
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"""
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logger.debug("use_mixed_precision: %s", use_mixed_precision)
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if not use_mixed_precision:
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policy = mixedprecision.Policy('float32') # pylint:disable=no-member
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mixedprecision.set_global_policy(policy) # pylint:disable=no-member
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logger.debug("Disabling mixed precision. (Compute dtype: %s, variable_dtype: %s)",
|
|
policy.compute_dtype, policy.variable_dtype)
|
|
return False
|
|
|
|
policy = mixedprecision.Policy('mixed_float16') # pylint:disable=no-member
|
|
mixedprecision.set_global_policy(policy) # pylint:disable=no-member
|
|
logger.debug("Enabled mixed precision. (Compute dtype: %s, variable_dtype: %s)",
|
|
policy.compute_dtype, policy.variable_dtype)
|
|
return True
|
|
|
|
def _get_strategy(self,
|
|
strategy: T.Literal["default", "central-storage", "mirrored"]
|
|
) -> tf.distribute.Strategy | None:
|
|
""" If we are running on Nvidia backend and the strategy is not ``None`` then return
|
|
the correct tensorflow distribution strategy, otherwise return ``None``.
|
|
|
|
Notes
|
|
-----
|
|
By default Tensorflow defaults mirrored strategy to use the Nvidia NCCL method for
|
|
reductions, however this is only available in Linux, so the method used falls back to
|
|
`Hierarchical Copy All Reduce` if the OS is not Linux.
|
|
|
|
Central Storage strategy is not compatible with Mixed Precision. However, in testing it
|
|
worked fine when using a single GPU, so we monkey-patch out the tests for Mixed-Precision
|
|
when using this strategy with a single GPU
|
|
|
|
Parameters
|
|
----------
|
|
strategy: str
|
|
One of 'default', 'central-storage' or 'mirrored'.
|
|
|
|
Returns
|
|
-------
|
|
:class:`tensorflow.distribute.Strategy` or `None`
|
|
The request Tensorflow Strategy if the backend is Nvidia and the strategy is not
|
|
`"Default"` otherwise ``None``
|
|
"""
|
|
if get_backend() not in ("nvidia", "directml", "rocm"):
|
|
retval = None
|
|
elif strategy == "mirrored":
|
|
retval = self._get_mirrored_strategy()
|
|
elif strategy == "central-storage":
|
|
retval = self._get_central_storage_strategy()
|
|
else:
|
|
retval = tf.distribute.get_strategy()
|
|
logger.debug("Using strategy: %s", retval)
|
|
return retval
|
|
|
|
@classmethod
|
|
def _get_mirrored_strategy(cls) -> tf.distribute.MirroredStrategy:
|
|
""" Obtain an instance of a Tensorflow Mirrored Strategy, setting the cross device
|
|
operations appropriate for the OS in use.
|
|
|
|
Returns
|
|
-------
|
|
:class:`tensorflow.distribute.MirroredStrategy`
|
|
The Mirrored Distribution Strategy object with correct cross device operations set
|
|
"""
|
|
if platform.system().lower() == "linux":
|
|
cross_device_ops = tf.distribute.NcclAllReduce()
|
|
else:
|
|
cross_device_ops = tf.distribute.HierarchicalCopyAllReduce()
|
|
logger.debug("cross_device_ops: %s", cross_device_ops)
|
|
return tf.distribute.MirroredStrategy(cross_device_ops=cross_device_ops)
|
|
|
|
@classmethod
|
|
def _get_central_storage_strategy(cls) -> tf.distribute.experimental.CentralStorageStrategy:
|
|
""" Obtain an instance of a Tensorflow Central Storage Strategy. If the strategy is being
|
|
run on a single GPU then monkey patch Tensorflows mixed-precision strategy checks to pass
|
|
successfully.
|
|
|
|
Returns
|
|
-------
|
|
:class:`tensorflow.distribute.experimental.CentralStorageStrategy`
|
|
The Central Storage Distribution Strategy object
|
|
"""
|
|
gpus = tf.config.get_visible_devices("GPU")
|
|
if len(gpus) == 1:
|
|
# TODO Remove these monkey patches when Strategy supports mixed-precision
|
|
from keras.mixed_precision import loss_scale_optimizer # noqa pylint:disable=import-outside-toplevel
|
|
|
|
# Force a return of True on Loss Scale Optimizer Stategy check
|
|
loss_scale_optimizer.strategy_supports_loss_scaling = lambda: True
|
|
|
|
# As LossScaleOptimizer aggregates gradients internally, it passes `False` as the value
|
|
# for `experimental_aggregate_gradients` in `OptimizerV2.apply_gradients`. This causes
|
|
# the optimizer to fail when checking against this strategy. We could monkey patch
|
|
# `Optimizer.apply_gradients`, but it is a lot more code to check, so we just switch
|
|
# the `experimental_aggregate_gradients` back to `True`. In brief testing this does not
|
|
# appear to have a negative impact.
|
|
func = lambda s, grads, wvars, name: s._optimizer.apply_gradients( # noqa pylint:disable=protected-access,unnecessary-lambda-assignment
|
|
list(zip(grads, wvars.value)), name, experimental_aggregate_gradients=True)
|
|
loss_scale_optimizer.LossScaleOptimizer._apply_gradients = func # noqa pylint:disable=protected-access
|
|
|
|
return tf.distribute.experimental.CentralStorageStrategy(parameter_device="/cpu:0")
|
|
|
|
def _get_mixed_precision_layers(self, layers: list[dict]) -> list[str]:
|
|
""" Obtain the names of the layers in a mixed precision model that have their dtype policy
|
|
explicitly set to mixed-float16.
|
|
|
|
Parameters
|
|
----------
|
|
layers: List
|
|
The list of layers that appear in a keras's model configuration `dict`
|
|
|
|
Returns
|
|
-------
|
|
list
|
|
A list of layer names within the model that are assigned a float16 policy
|
|
"""
|
|
retval = []
|
|
for layer in layers:
|
|
config = layer["config"]
|
|
|
|
if layer["class_name"] == "Functional": # Recurse into sub-models
|
|
retval.extend(self._get_mixed_precision_layers(config["layers"]))
|
|
continue
|
|
|
|
dtype = config["dtype"]
|
|
if isinstance(dtype, dict) and dtype["config"]["name"] == "mixed_float16":
|
|
logger.debug("Adding supported mixed precision layer: %s %s", layer["name"], dtype)
|
|
retval.append(layer["name"])
|
|
else:
|
|
logger.debug("Skipping unsupported layer: %s %s", layer["name"], dtype)
|
|
return retval
|
|
|
|
def _switch_precision(self, layers: list[dict], compatible: list[str]) -> None:
|
|
""" Switch a model's datatype between mixed-float16 and float32.
|
|
|
|
Parameters
|
|
----------
|
|
layers: List
|
|
The list of layers that appear in a keras's model configuration `dict`
|
|
compatible: List
|
|
A list of layer names that are compatible to have their datatype switched
|
|
"""
|
|
dtype = "mixed_float16" if self.use_mixed_precision else "float32"
|
|
policy = {"class_name": "Policy", "config": {"name": dtype}}
|
|
|
|
for layer in layers:
|
|
config = layer["config"]
|
|
|
|
if layer["class_name"] == "Functional": # Recurse into sub-models
|
|
self._switch_precision(config["layers"], compatible)
|
|
continue
|
|
|
|
if layer["name"] not in compatible:
|
|
logger.debug("Skipping incompatible layer: %s", layer["name"])
|
|
continue
|
|
|
|
logger.debug("Updating dtype for %s from: %s to: %s",
|
|
layer["name"], config["dtype"], policy)
|
|
config["dtype"] = policy
|
|
|
|
def get_mixed_precision_layers(self,
|
|
build_func: Callable[[list[tf.keras.layers.Layer]],
|
|
tf.keras.models.Model],
|
|
inputs: list[tf.keras.layers.Layer]) -> list[str]:
|
|
""" Get and store the mixed precision layers from a full precision enabled model.
|
|
|
|
Parameters
|
|
----------
|
|
build_func: Callable
|
|
The function to be called to compile the newly created model
|
|
inputs:
|
|
The inputs to the model to be compiled
|
|
|
|
Returns
|
|
-------
|
|
list
|
|
The list of layer names within the full precision model that can be switched
|
|
to mixed precision
|
|
"""
|
|
logger.info("Storing Mixed Precision compatible layers. Please ignore any following "
|
|
"warnings about using mixed precision.")
|
|
self._set_keras_mixed_precision(True)
|
|
model = build_func(inputs)
|
|
layers = self._get_mixed_precision_layers(model.get_config()["layers"])
|
|
self._set_keras_mixed_precision(False)
|
|
del model
|
|
return layers
|
|
|
|
def check_model_precision(self,
|
|
model: tf.keras.models.Model,
|
|
state: "State") -> tf.keras.models.Model:
|
|
""" Check the model's precision.
|
|
|
|
If this is a new model, then
|
|
Rewrite an existing model's training precsion mode from mixed-float16 to float32 or
|
|
vice versa.
|
|
|
|
This is not easy to do in keras, so we edit the model's config to change the dtype policy
|
|
for compatible layers. Create a new model from this config, then port the weights from the
|
|
old model to the new model.
|
|
|
|
Parameters
|
|
----------
|
|
model: :class:`keras.models.Model`
|
|
The original saved keras model to rewrite the dtype
|
|
state: ~:class:`plugins.train.model._base.model.State`
|
|
The State information for the model
|
|
|
|
Returns
|
|
-------
|
|
:class:`keras.models.Model`
|
|
The original model with the datatype updated
|
|
"""
|
|
if self.use_mixed_precision and not state.mixed_precision_layers:
|
|
# Switching to mixed precision on a model which was started in FP32 prior to the
|
|
# ability to switch between precisions on a saved model is not supported as we
|
|
# do not have the compatible layer names
|
|
logger.warning("Switching from Full Precision to Mixed Precision is not supported on "
|
|
"older model files. Reverting to Full Precision.")
|
|
return model
|
|
|
|
config = model.get_config()
|
|
|
|
if not self.use_mixed_precision and not state.mixed_precision_layers:
|
|
# Switched to Full Precision, get compatible layers from model if not already stored
|
|
state.add_mixed_precision_layers(self._get_mixed_precision_layers(config["layers"]))
|
|
|
|
self._switch_precision(config["layers"], state.mixed_precision_layers)
|
|
|
|
new_model = keras.models.Model().from_config(config)
|
|
new_model.set_weights(model.get_weights())
|
|
logger.info("Mixed precision has been updated from '%s' to '%s'",
|
|
not self.use_mixed_precision, self.use_mixed_precision)
|
|
del model
|
|
return new_model
|
|
|
|
def strategy_scope(self) -> ContextManager:
|
|
""" Return the strategy scope if we have set a strategy, otherwise return a null
|
|
context.
|
|
|
|
Returns
|
|
-------
|
|
:func:`tensorflow.python.distribute.Strategy.scope` or :func:`contextlib.nullcontext`
|
|
The tensorflow strategy scope if a strategy is valid in the current scenario. A null
|
|
context manager if the strategy is not valid in the current scenario
|
|
"""
|
|
retval = nullcontext() if self._strategy is None else self._strategy.scope()
|
|
logger.debug("Using strategy scope: %s", retval)
|
|
return retval
|