mirror of
https://github.com/hanzoai/mugen.git
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717 lines
28 KiB
Python
717 lines
28 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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import logging
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import typing as tp
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from functools import partial
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import os
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from pathlib import Path
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import flashy
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from omegaconf import DictConfig
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import multiprocessing
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import numpy as np
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import torch
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import torch.nn as nn
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from . import base, builders
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from ..models.builders import get_watermark_model
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from ..modules.watermark import pad, mix
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from ..metrics.miou import calculate_miou
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from ..metrics.pesq import PesqMetric
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from ..utils import checkpoint
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from ..utils.audio_effects import (
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compress_with_encodec,
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get_audio_effects,
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select_audio_effects,
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)
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from ..utils.samples.manager import SampleManager
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from ..data.audio import save_spectrograms
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from ..utils.utils import get_pool_executor
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from torchmetrics.audio.snr import ScaleInvariantSignalNoiseRatio
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from torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility
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if tp.TYPE_CHECKING:
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from ..models.watermark import WMModel
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def get_encodec_audio_effect(encodec_cfg: DictConfig, sr: int) -> tp.Dict:
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"""
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Construct encodec-based compression data agumentation. This method is
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is put here instead of in `audiocraft.utils.audio_effects` because
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it depends on the package `audiocraft.solvers`, which is one layer
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higher than `audiocraft.utils`, so we avoid the circle dependency
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from any solvers using `audiocraft.utils.audio_effects` to do the
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augmentation
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"""
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from ..solvers.compression import CompressionSolver
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codec_model = CompressionSolver.model_from_checkpoint(encodec_cfg.ckpt)
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codec_model.train()
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return {
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f"encodec_nq={n_q}": partial(
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compress_with_encodec,
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model=codec_model,
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n_q=n_q,
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sample_rate=sr,
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)
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for n_q in encodec_cfg.n_qs
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}
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def random_message(nbits: int, batch_size: int) -> torch.Tensor:
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"""Return random message as 0/1 tensor."""
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if nbits == 0:
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return torch.tensor([])
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return torch.randint(0, 2, (batch_size, nbits))
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class WatermarkSolver(base.StandardSolver):
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"""Solver for different watermarking models"""
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def __init__(self, cfg: DictConfig):
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super().__init__(cfg)
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self.rng: torch.Generator # set at each epoch
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self.model: WMModel
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if hasattr(cfg, "fsdp"):
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assert not getattr(
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cfg.fsdp, "use", False
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), "FSDP not supported by WatermarkSolver."
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self._init_losses()
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self._init_augmentations()
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self.balancer = builders.get_balancer(self.loss_weights, self.cfg.balancer)
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self.path_specs = os.path.join(self.folder, "spectrograms")
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os.makedirs(self.path_specs, exist_ok=True)
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def _init_losses(self):
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assert hasattr(self.cfg, "losses") and isinstance(
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self.cfg.losses, (DictConfig, tp.Mapping)
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), "WatermarkSolver must declare training losses in the config"
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self.adv_losses = builders.get_adversarial_losses(self.cfg) # noqa
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self.register_stateful("adv_losses")
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self.aux_losses = nn.ModuleDict() # noqa
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self.info_losses = nn.ModuleDict() # noqa
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self.wm_losses = nn.ModuleDict() # noqa
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loss_weights = {}
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for loss_name, weight in self.cfg.losses.items():
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# explicitly skip this loss calculation by setting a -1 as weight
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# if weight == 0 it will be calculated but kept as info
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if weight == -1:
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continue
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if loss_name in ["adv", "feat"]:
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for adv_name, _ in self.adv_losses.items():
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loss_weights[f"{loss_name}_{adv_name}"] = weight
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elif weight > 0:
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if loss_name[:3] == "wm_":
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self.wm_losses[loss_name] = builders.get_loss(
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loss_name, self.cfg
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).to(self.device)
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loss_weights[loss_name] = weight
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else:
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self.aux_losses[loss_name] = builders.get_loss(
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loss_name, self.cfg
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).to(self.device)
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loss_weights[loss_name] = weight
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else:
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self.info_losses[loss_name] = builders.get_loss(loss_name, self.cfg).to(
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self.device
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)
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self.loss_weights = loss_weights # noqa
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def _init_augmentations(self):
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if not hasattr(self.cfg, "aug_weights") or not hasattr(
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self.cfg, "audio_effects"
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):
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return
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aug_weights = {}
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cfg_audio_effects = dict(self.cfg.audio_effects)
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# Handle `encodec` augmentation separately as this requires loading a
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# CompressionSolver checkpoint
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encodec_cfg = cfg_audio_effects.pop("encodec", None)
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if encodec_cfg:
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encodec_effects = get_encodec_audio_effect(
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encodec_cfg, self.cfg.sample_rate
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)
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for aug_name in encodec_effects.keys():
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aug_weights[aug_name] = getattr(self.cfg.aug_weights, "encodec", -1)
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else:
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encodec_effects = {}
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other_effects = get_audio_effects(self.cfg) # noqa
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for name in other_effects.keys():
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aug_weights[name] = self.cfg.aug_weights.get(name, -1)
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self.aug_weights = aug_weights # noqa
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self.augmentations = {**encodec_effects, **other_effects} # noqa
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@property
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def best_metric_name(self) -> tp.Optional[str]:
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# best model is the last for the watermark model for now
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return None
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def build_model(self):
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"""Instantiate model and optimizer."""
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# Model and optimizer
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self.model = get_watermark_model(self.cfg)
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# Need two optimizers ?
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self.optimizer = builders.get_optimizer(self.model.parameters(), self.cfg.optim)
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self.register_stateful("model", "optimizer")
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self.register_best_state("model")
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self.register_ema("model")
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def build_dataloaders(self):
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"""Instantiate audio dataloaders for each stage."""
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self.dataloaders = builders.get_audio_datasets(self.cfg)
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def show(self):
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"""Show the Watermark model and employed adversarial loss."""
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self.log_model_summary(self.model)
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self.logger.info("Sould print losses here:")
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def crop(
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self, signal: torch.Tensor, watermark: torch.Tensor
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) -> tp.Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""
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Applies a transformation to modify the watermarked signal to train localization.
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It can be one of the following:
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- zero padding: add zeros at the begining and the end of the signal
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- crop: crop the watermark apply a watermark only on some parts of the signal
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- shuffle: replace some part of the audio with other non watermarked parts
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from the batch
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In every cases the function returns a mask that contains indicates the parts that are or
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not watermarked
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Args:
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watermark (torch.Tensor): The watermark to apply on the signal.
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signal (torch.Tensor): clean signal
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Returns:
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watermark (torch.Tensor): modified watermark
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signal (torch.Tensor): modified signal
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mask (torch.Tensor): mask indicating which portion is still watermarked
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"""
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assert (
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self.cfg.crop.prob + self.cfg.crop.shuffle_prob + self.cfg.crop.pad_prob
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<= 1
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), f"The sum of the probabilities {self.cfg.crop.prob=} {self.cfg.crop.shuffle_prob=} \
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{self.cfg.crop.pad_prob=} should be less than 1"
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mask = torch.ones_like(watermark)
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p = torch.rand(1)
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if p < self.cfg.crop.pad_prob: # Pad with some probability
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start = int(torch.rand(1) * 0.33 * watermark.size(-1))
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finish = int((0.66 + torch.rand(1) * 0.33) * watermark.size(-1))
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mask[:, :, :start] = 0
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mask[:, :, finish:] = 0
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if torch.rand(1) > 0.5:
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mask = 1 - mask
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signal *= mask # pad signal
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elif (
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p < self.cfg.crop.prob + self.cfg.crop.pad_prob + self.cfg.crop.shuffle_prob
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):
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# Define a mask, then crop or shuffle
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mask_size = round(watermark.shape[-1] * self.cfg.crop.size)
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n_windows = int(
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torch.randint(1, self.cfg.crop.max_n_windows + 1, (1,)).item()
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)
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window_size = int(mask_size / n_windows)
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for _ in range(n_windows): # Create multiple windows in the mask
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mask_start = torch.randint(0, watermark.shape[-1] - window_size, (1,))
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mask[:, :, mask_start: mask_start + window_size] = (
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0 # Apply window to mask
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)
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# inverse the mask half the time
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if torch.rand(1) > 0.5:
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mask = 1 - mask
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if p < self.cfg.crop.pad_prob + self.cfg.crop.shuffle_prob: # shuffle
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# shuffle
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signal_cloned = signal.clone().detach() # detach to be sure
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shuffle_idx = torch.randint(0, signal.size(0), (signal.size(0),))
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signal = signal * mask + signal_cloned[shuffle_idx] * (
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1 - mask
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) # shuffle signal where not wm
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watermark *= mask # Apply mask to the watermark
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return signal, watermark, mask
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def run_step(self, idx: int, batch: torch.Tensor, metrics: dict):
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"""Perform one training or valid step on a given batch."""
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x = batch.to(self.device)
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y = x.clone()
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nbits = getattr(self.model, "nbits")
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message = random_message(nbits, y.shape[0]).to(self.device)
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watermark = self.model.get_watermark(x, message=message)
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y, watermark, mask = self.crop(y, watermark)
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y_wm = y + watermark
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if (
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self.cfg.losses.adv != 0 or self.cfg.losses.feat != 0
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) and self.is_training: # train quality adv
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d_losses: dict = {}
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if (
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len(self.adv_losses) > 0
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and torch.rand(1, generator=self.rng).item()
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<= 1 / self.cfg.adversarial.every
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):
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for adv_name, adversary in self.adv_losses.items():
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disc_loss = adversary.train_adv(y_wm, y)
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d_losses[f"d_{adv_name}"] = disc_loss
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metrics["d_loss"] = torch.sum(torch.stack(list(d_losses.values())))
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metrics.update(d_losses)
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balanced_losses: dict = {}
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other_losses: dict = {}
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# adversarial losses
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if self.cfg.losses.adv != 0 or self.cfg.losses.feat != 0:
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for adv_name, adversary in self.adv_losses.items():
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adv_loss, feat_loss = adversary(y_wm, y)
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balanced_losses[f"adv_{adv_name}"] = adv_loss
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balanced_losses[f"feat_{adv_name}"] = feat_loss
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# auxiliary losses on quality/similarity
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for loss_name, criterion in self.aux_losses.items():
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loss = criterion(y_wm, y)
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balanced_losses[loss_name] = loss
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# apply augmentations
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mode = "all" if self.cfg.select_aug_mode == "all" else "weighted"
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selected_augs = select_audio_effects(
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self.augmentations,
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self.aug_weights,
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mode=mode,
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max_length=self.cfg.n_max_aug,
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)
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N_augs = len(selected_augs)
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for (
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augmentation_name,
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augmentation_method,
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) in selected_augs.items():
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# concatenate to use the augmentation function only once
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y_y_wm = torch.cat([y, y_wm], dim=0)
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aug_cat, mask_aug = augmentation_method(y_y_wm, mask=mask)
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aug_y = aug_cat[: y.size(0)]
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aug_y_wm = aug_cat[y.size(0):]
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positive = self.model.detect_watermark(aug_y_wm)
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negative = self.model.detect_watermark(aug_y)
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for loss_name, criterion in self.wm_losses.items():
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loss = criterion(positive, negative, mask_aug, message)
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other_losses[f"{loss_name}_{augmentation_name}"] = loss
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# weighted losses
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metrics.update(balanced_losses)
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metrics.update(other_losses)
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if self.is_training: # something is weird about the loss balancer not
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other_loss = torch.tensor(0.0, device=self.device)
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for name, o_loss in other_losses.items():
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if "wm_detection" in name:
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# here we include the detection losses for augmentation
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other_loss += (self.loss_weights["wm_detection"] / N_augs) * o_loss
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elif "wm_mb" in name:
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other_loss += (self.loss_weights["wm_mb"] / N_augs) * o_loss
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else:
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other_loss += self.loss_weights[name] * o_loss
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if other_loss.requires_grad:
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other_loss.backward(retain_graph=True)
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ratio1 = sum(
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p.grad.data.norm(p=2).pow(2)
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for p in self.model.parameters()
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if p.grad is not None
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)
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assert isinstance(ratio1, torch.Tensor)
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metrics["ratio1"] = ratio1.sqrt()
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# balancer losses backward, returns effective training loss
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# with effective weights at the current batch.
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metrics["g_loss"] = self.balancer.backward(balanced_losses, y_wm)
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# add metrics corresponding to weight ratios
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metrics.update(self.balancer.metrics)
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ratio2 = sum(
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p.grad.data.norm(p=2).pow(2)
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for p in self.model.parameters()
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if p.grad is not None
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)
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assert isinstance(ratio2, torch.Tensor)
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metrics["ratio2"] = ratio2.sqrt()
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# optim
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flashy.distrib.sync_model(self.model)
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if self.cfg.optim.max_norm:
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torch.nn.utils.clip_grad_norm_(
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self.model.parameters(), self.cfg.optim.max_norm
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)
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self.optimizer.step()
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self.optimizer.zero_grad()
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# informative losses only
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info_losses: dict = {}
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with torch.no_grad():
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for loss_name, criterion in self.info_losses.items():
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loss = criterion(y_wm, y)
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info_losses[loss_name] = loss
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# pesq
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metrics["pesq"] = tensor_pesq(y_wm, y, sr=self.cfg.sample_rate)
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# max allocated memory
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metrics["max_mem"] = torch.cuda.max_memory_allocated() / 1e9
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metrics.update(info_losses)
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if self.cfg.losses.adv != 0 or self.cfg.losses.feat != 0:
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# aggregated GAN losses: this is useful to report adv and feat across different adversarial loss setups
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adv_losses = [
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loss
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for loss_name, loss in metrics.items()
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if loss_name.startswith("adv")
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]
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if len(adv_losses) > 0:
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metrics["adv"] = torch.sum(torch.stack(adv_losses))
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feat_losses = [
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loss
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for loss_name, loss in metrics.items()
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if loss_name.startswith("feat")
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]
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if len(feat_losses) > 0:
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metrics["feat"] = torch.sum(torch.stack(feat_losses))
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return metrics
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def run_epoch(self):
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# reset random seed at the beginning of the epoch
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self.rng = torch.Generator()
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self.rng.manual_seed(1234 + self.epoch)
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# run epoch
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super().run_epoch()
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def evaluate(self) -> dict:
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"""Evaluate stage. Runs audio reconstruction evaluation."""
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self.model.eval()
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evaluate_stage_name = str(self.current_stage)
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loader = self.dataloaders["evaluate"]
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updates = len(loader)
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lp = self.log_progress(
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f"{evaluate_stage_name} inference",
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loader,
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total=updates,
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updates=self.log_updates,
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)
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average = flashy.averager()
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pendings = []
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ctx = multiprocessing.get_context("spawn")
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with get_pool_executor(self.cfg.evaluate.num_workers, mp_context=ctx) as pool:
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for batch in lp:
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x = batch.to(self.device)
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with torch.no_grad():
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message = random_message(self.model.nbits, x.shape[0])
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watermark = self.model.get_watermark(x, message)
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x_wm = x + watermark
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y_pred = x_wm.cpu()
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y = batch.cpu() # should already be on CPU but just in case
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pendings.append(
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pool.submit(
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evaluate_audio_watermark,
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y_pred,
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y,
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self.cfg,
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)
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)
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# evaluate augmentations
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# evaluation is run on all the augmentations
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for (
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augmentation_name,
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augmentation_method,
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) in self.augmentations.items():
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# if (
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# "mp3" in augmentation_name
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# and idx >= 8
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# and self.cfg.evaluate.every <= 2
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# ):
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# # When evaluating often do not compute mp3 on the full eval dset to make things faster
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# continue
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with torch.no_grad():
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aug_positive = self.model.detect_watermark(
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augmentation_method(x_wm)
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)
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aug_negative = self.model.detect_watermark(
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augmentation_method(x)
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)
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pendings.append(
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pool.submit(
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evaluate_augmentations,
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aug_positive.cpu(),
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aug_negative.cpu(),
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augmentation_name,
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message.cpu(),
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)
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)
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# end eval of augmentations
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# evaluate localization cropping
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for window_size in np.linspace(0.1, 0.9, 9):
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mixed, true_predictions = mix(x, x_wm, window_size=window_size)
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model_predictions = self.model.detect_watermark(mixed)
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pendings.append(
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pool.submit(
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evaluate_localizations,
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model_predictions.cpu(),
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true_predictions.cpu(),
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f"crop_{window_size:0.1f}",
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)
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)
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|
mixed, true_predictions = mix(
|
|
x, x_wm, window_size=window_size, shuffle=True
|
|
)
|
|
model_predictions = self.model.detect_watermark(mixed)
|
|
pendings.append(
|
|
pool.submit(
|
|
evaluate_localizations,
|
|
model_predictions.cpu(),
|
|
true_predictions.cpu(),
|
|
f"shuffle_{window_size:0.1f}",
|
|
)
|
|
)
|
|
# evaluate localization padding
|
|
mixed, true_predictions = pad(x_wm)
|
|
model_predictions = self.model.detect_watermark(mixed)
|
|
pendings.append(
|
|
pool.submit(
|
|
evaluate_localizations,
|
|
model_predictions.cpu(),
|
|
true_predictions.cpu(),
|
|
"padding",
|
|
)
|
|
)
|
|
mixed, true_predictions = pad(x_wm, central=True)
|
|
model_predictions = self.model.detect_watermark(mixed)
|
|
pendings.append(
|
|
pool.submit(
|
|
evaluate_localizations,
|
|
model_predictions.cpu(),
|
|
true_predictions.cpu(),
|
|
"central_padding",
|
|
)
|
|
)
|
|
# end of evaluate localization
|
|
|
|
metrics_lp = self.log_progress(
|
|
f"{evaluate_stage_name} metrics", pendings, updates=self.log_updates
|
|
)
|
|
for pending in metrics_lp:
|
|
metrics = pending.result()
|
|
metrics = average(metrics)
|
|
|
|
metrics = flashy.distrib.average_metrics(metrics, len(loader))
|
|
if self.cfg.select_aug_mode == "use_eval_acc":
|
|
# Adjust augmentation weights based on evaluation loss.
|
|
# Higher accuracy results in lower probability of selecting this augmentation.
|
|
for name in self.augmentations.keys():
|
|
if (
|
|
self.aug_weights[name] != -1
|
|
): # keep weight to -1 for unwanted augmentations
|
|
# set to 0.05 to ensure that an augmentation is never completely removed during a full epoch.
|
|
self.aug_weights[name] = max(1 - metrics[f"aug_{name}_acc"], 0.05)
|
|
return metrics
|
|
|
|
def generate(self):
|
|
"""Generate stage."""
|
|
self.model.eval()
|
|
sample_manager = SampleManager(self.xp, map_reference_to_sample_id=True)
|
|
generate_stage_name = str(self.current_stage)
|
|
|
|
loader = self.dataloaders["generate"]
|
|
updates = len(loader)
|
|
lp = self.log_progress(
|
|
generate_stage_name, loader, total=updates, updates=self.log_updates
|
|
)
|
|
path_dir = os.path.join(self.path_specs, f"epoch={self.epoch}")
|
|
os.makedirs(path_dir, exist_ok=True)
|
|
first_batch = True
|
|
for batch in lp:
|
|
reference, _ = batch
|
|
reference = reference.to(self.device)
|
|
with torch.no_grad():
|
|
message = random_message(self.model.nbits, reference.shape[0])
|
|
watermark = self.model.get_watermark(reference, message)
|
|
x_wm = reference + watermark
|
|
|
|
reference = reference.cpu()
|
|
sample_manager.add_samples(
|
|
x_wm.cpu(), self.epoch, ground_truth_wavs=reference
|
|
)
|
|
if first_batch and flashy.distrib.is_rank_zero():
|
|
for i in range(reference.size(0)):
|
|
ys = [
|
|
reference.cpu()[i].squeeze(0).numpy(),
|
|
x_wm.cpu()[i].squeeze(0).numpy(),
|
|
watermark.cpu()[i].squeeze(0).numpy(),
|
|
]
|
|
path = os.path.join(path_dir, f"spec_{i}.pdf")
|
|
save_spectrograms(
|
|
ys,
|
|
names=["Ground Truth", "Audio Watermarked", "Watermark"],
|
|
sr=self.cfg.sample_rate,
|
|
path=path,
|
|
)
|
|
first_batch = False
|
|
flashy.distrib.barrier()
|
|
|
|
def load_from_pretrained(self, name: str) -> dict:
|
|
raise ValueError("No pretrained model")
|
|
|
|
@staticmethod
|
|
def model_from_checkpoint(
|
|
checkpoint_path: tp.Union[Path, str],
|
|
device: tp.Union[torch.device, str] = "cpu",
|
|
) -> "WMModel":
|
|
"""Instantiate a WatermarkModel from a given checkpoint path or dora sig.
|
|
|
|
Args:
|
|
checkpoint_path (Path or str): Path to checkpoint or dora sig from where the checkpoint is resolved.
|
|
device (torch.device or str): Device on which the model is loaded.
|
|
"""
|
|
checkpoint_path = str(checkpoint_path)
|
|
logger = logging.getLogger(__name__)
|
|
logger.info(f"Loading WatermarkModel from checkpoint: {checkpoint_path}")
|
|
_checkpoint_path = checkpoint.resolve_checkpoint_path(
|
|
checkpoint_path, use_fsdp=False
|
|
)
|
|
assert (
|
|
_checkpoint_path is not None
|
|
), f"Could not resolve WatermarkModel checkpoint path: {checkpoint_path}"
|
|
state = checkpoint.load_checkpoint(_checkpoint_path)
|
|
assert (
|
|
state is not None and "xp.cfg" in state
|
|
), f"Could not load WatermarkModel from ckpt: {checkpoint_path}"
|
|
cfg = state["xp.cfg"]
|
|
cfg.device = device
|
|
watermarking_model = get_watermark_model(cfg).to(device)
|
|
|
|
assert "best_state" in state and state["best_state"] != {}
|
|
assert (
|
|
"exported" not in state
|
|
), "When loading an exported checkpoint, use the //pretrained/ prefix."
|
|
watermarking_model.load_state_dict(state["best_state"]["model"])
|
|
watermarking_model.eval()
|
|
logger.info("Watermarking model loaded!")
|
|
return watermarking_model
|
|
|
|
|
|
def evaluate_localizations(predictions, true_predictions, name):
|
|
metrics = {}
|
|
# predictions are output of the detector shape [bsz, 2, frames]
|
|
# true_predictions is output of the mix method shape [bsz, 2, frames]
|
|
metrics[f"localization_acc_{name}"] = (
|
|
((predictions[:, 1, :] > 0.5) == true_predictions[:, 1, :])
|
|
.float()
|
|
.mean()
|
|
.item()
|
|
)
|
|
metrics[f"localization_miou_{name}"] = calculate_miou(
|
|
predictions[:, 1, :], true_predictions[:, 1, :]
|
|
)
|
|
return metrics
|
|
|
|
|
|
def evaluate_augmentations(
|
|
positive: torch.Tensor,
|
|
negative: torch.Tensor,
|
|
augmentation_name: str,
|
|
message: torch.Tensor,
|
|
) -> dict:
|
|
"""calculating evaluation metrics but take name of the augmentation
|
|
method that has been done before getting positive and negative results"""
|
|
metrics = {}
|
|
metrics[f"aug_{augmentation_name}_acc"] = compute_accuracy(positive, negative)
|
|
metrics[f"aug_{augmentation_name}_fpr"] = compute_FPR(negative)
|
|
metrics[f"aug_{augmentation_name}_fnr"] = compute_FNR(positive)
|
|
if message.shape[0] != 0:
|
|
metrics[f"aug_{augmentation_name}_bit_acc"] = compute_bit_acc(positive, message)
|
|
|
|
# add one metric which is average overall score of all augmentations
|
|
metrics["all_aug_acc"] = compute_accuracy(positive, negative)
|
|
|
|
return metrics
|
|
|
|
|
|
def evaluate_audio_watermark(
|
|
y_pred: torch.Tensor,
|
|
y: torch.Tensor,
|
|
cfg: DictConfig,
|
|
) -> dict:
|
|
"""Audio reconstruction evaluation method that can be conveniently pickled."""
|
|
metrics = {}
|
|
if cfg.evaluate.metrics.visqol:
|
|
visqol = builders.get_visqol(cfg.metrics.visqol)
|
|
metrics["visqol"] = visqol(y_pred, y, cfg.sample_rate)
|
|
sisnr = ScaleInvariantSignalNoiseRatio().to(y.device)
|
|
stoi = ShortTimeObjectiveIntelligibility(fs=cfg.sample_rate)
|
|
metrics["sisnr"] = sisnr(y_pred, y)
|
|
metrics["stoi"] = stoi(y_pred, y)
|
|
metrics["pesq"] = tensor_pesq(y_pred, y, sr=cfg.sample_rate)
|
|
return metrics
|
|
|
|
|
|
def tensor_pesq(y_pred: torch.Tensor, y: torch.Tensor, sr: int):
|
|
# pesq returns error if no speech is detected, so we catch it
|
|
return PesqMetric(sr)(y_pred, y).item()
|
|
|
|
|
|
def compute_accuracy(positive, negative):
|
|
N = (positive[:, 1, :].mean(dim=1) > 0.5).sum() + (
|
|
negative[:, 0, :].mean(dim=1) > 0.5
|
|
).sum()
|
|
acc = N / (2 * positive.size(0))
|
|
return acc
|
|
|
|
|
|
def compute_FPR(negative):
|
|
N = (negative[:, 1, :].mean(dim=1) > 0.5).sum()
|
|
fpr = N / (negative.size(0))
|
|
return fpr
|
|
|
|
|
|
def compute_FNR(positive):
|
|
N = (positive[:, 0, :].mean(dim=1) > 0.5).sum()
|
|
fpr = N / (positive.size(0))
|
|
return fpr
|
|
|
|
|
|
def _bit_acc(decoded, original):
|
|
bit_acc = (decoded == original).float().mean()
|
|
return bit_acc
|
|
|
|
|
|
def compute_bit_acc(positive, original, mask=None):
|
|
"""Compute bit accuracy.
|
|
Args:
|
|
positive: detector outputs [bsz, 2+nbits, time_steps]
|
|
original: original message (0 or 1) [bsz, nbits]
|
|
mask: mask of the watermark [bsz, 1, time_steps]
|
|
"""
|
|
decoded = positive[:, 2:, :] # b 2+nbits t -> b nbits t
|
|
if mask is not None:
|
|
# cut last dim of positive to keep only where mask is 1
|
|
new_shape = [*decoded.shape[:-1], -1] # b nbits t -> b nbits -1
|
|
decoded = torch.masked_select(decoded, mask == 1).reshape(new_shape)
|
|
# average decision over time, then threshold
|
|
decoded = decoded.mean(dim=-1) > 0 # b nbits
|
|
return _bit_acc(decoded, original)
|