facebookresearch--audiocraft
Alexandre Défossez (autodoc)
1281 行
62 KiB
HTML
1281 行
62 KiB
HTML
<!doctype html>
|
||
<html lang="en">
|
||
<head>
|
||
<meta charset="utf-8">
|
||
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1">
|
||
<meta name="generator" content="pdoc3 0.11.5">
|
||
<title>audiocraft.solvers.watermark API documentation</title>
|
||
<meta name="description" content="">
|
||
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/13.0.0/sanitize.min.css" integrity="sha512-y1dtMcuvtTMJc1yPgEqF0ZjQbhnc/bFhyvIyVNb9Zk5mIGtqVaAB1Ttl28su8AvFMOY0EwRbAe+HCLqj6W7/KA==" crossorigin>
|
||
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/13.0.0/typography.min.css" integrity="sha512-Y1DYSb995BAfxobCkKepB1BqJJTPrOp3zPL74AWFugHHmmdcvO+C48WLrUOlhGMc0QG7AE3f7gmvvcrmX2fDoA==" crossorigin>
|
||
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/styles/default.min.css" crossorigin>
|
||
<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:1.5em;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:2em 0 .50em 0}h3{font-size:1.4em;margin:1.6em 0 .7em 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .2s ease-in-out}a:visited{color:#503}a:hover{color:#b62}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900;font-weight:bold}pre code{font-size:.8em;line-height:1.4em;padding:1em;display:block}code{background:#f3f3f3;font-family:"DejaVu Sans Mono",monospace;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source > summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible;min-width:max-content}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em 1em;margin:1em 0}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
||
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul ul{padding-left:1em}.toc > ul > li{margin-top:.5em}}</style>
|
||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||
<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/highlight.min.js" integrity="sha512-D9gUyxqja7hBtkWpPWGt9wfbfaMGVt9gnyCvYa+jojwwPHLCzUm5i8rpk7vD7wNee9bA35eYIjobYPaQuKS1MQ==" crossorigin></script>
|
||
<script>window.addEventListener('DOMContentLoaded', () => {
|
||
hljs.configure({languages: ['bash', 'css', 'diff', 'graphql', 'ini', 'javascript', 'json', 'plaintext', 'python', 'python-repl', 'rust', 'shell', 'sql', 'typescript', 'xml', 'yaml']});
|
||
hljs.highlightAll();
|
||
/* Collapse source docstrings */
|
||
setTimeout(() => {
|
||
[...document.querySelectorAll('.hljs.language-python > .hljs-string')]
|
||
.filter(el => el.innerHTML.length > 200 && ['"""', "'''"].includes(el.innerHTML.substring(0, 3)))
|
||
.forEach(el => {
|
||
let d = document.createElement('details');
|
||
d.classList.add('hljs-string');
|
||
d.innerHTML = '<summary>"""</summary>' + el.innerHTML.substring(3);
|
||
el.replaceWith(d);
|
||
});
|
||
}, 100);
|
||
})</script>
|
||
</head>
|
||
<body>
|
||
<main>
|
||
<article id="content">
|
||
<header>
|
||
<h1 class="title">Module <code>audiocraft.solvers.watermark</code></h1>
|
||
</header>
|
||
<section id="section-intro">
|
||
</section>
|
||
<section>
|
||
</section>
|
||
<section>
|
||
</section>
|
||
<section>
|
||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||
<dl>
|
||
<dt id="audiocraft.solvers.watermark.compute_FNR"><code class="name flex">
|
||
<span>def <span class="ident">compute_FNR</span></span>(<span>positive)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def compute_FNR(positive):
|
||
N = (positive[:, 0, :].mean(dim=1) > 0.5).sum()
|
||
fpr = N / (positive.size(0))
|
||
return fpr</code></pre>
|
||
</details>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.solvers.watermark.compute_FPR"><code class="name flex">
|
||
<span>def <span class="ident">compute_FPR</span></span>(<span>negative)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def compute_FPR(negative):
|
||
N = (negative[:, 1, :].mean(dim=1) > 0.5).sum()
|
||
fpr = N / (negative.size(0))
|
||
return fpr</code></pre>
|
||
</details>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.solvers.watermark.compute_accuracy"><code class="name flex">
|
||
<span>def <span class="ident">compute_accuracy</span></span>(<span>positive, negative)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">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</code></pre>
|
||
</details>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.solvers.watermark.compute_bit_acc"><code class="name flex">
|
||
<span>def <span class="ident">compute_bit_acc</span></span>(<span>positive, original, mask=None)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">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)</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Compute bit accuracy.</p>
|
||
<h2 id="args">Args</h2>
|
||
<dl>
|
||
<dt><strong><code>positive</code></strong></dt>
|
||
<dd>detector outputs [bsz, 2+nbits, time_steps]</dd>
|
||
<dt><strong><code>original</code></strong></dt>
|
||
<dd>original message (0 or 1) [bsz, nbits]</dd>
|
||
<dt><strong><code>mask</code></strong></dt>
|
||
<dd>mask of the watermark [bsz, 1, time_steps]</dd>
|
||
</dl></div>
|
||
</dd>
|
||
<dt id="audiocraft.solvers.watermark.evaluate_audio_watermark"><code class="name flex">
|
||
<span>def <span class="ident">evaluate_audio_watermark</span></span>(<span>y_pred: torch.Tensor, y: torch.Tensor, cfg: omegaconf.dictconfig.DictConfig) ‑> dict</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">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</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Audio reconstruction evaluation method that can be conveniently pickled.</p></div>
|
||
</dd>
|
||
<dt id="audiocraft.solvers.watermark.evaluate_augmentations"><code class="name flex">
|
||
<span>def <span class="ident">evaluate_augmentations</span></span>(<span>positive: torch.Tensor,<br>negative: torch.Tensor,<br>augmentation_name: str,<br>message: torch.Tensor) ‑> dict</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">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</code></pre>
|
||
</details>
|
||
<div class="desc"><p>calculating evaluation metrics but take name of the augmentation
|
||
method that has been done before getting positive and negative results</p></div>
|
||
</dd>
|
||
<dt id="audiocraft.solvers.watermark.evaluate_localizations"><code class="name flex">
|
||
<span>def <span class="ident">evaluate_localizations</span></span>(<span>predictions, true_predictions, name)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">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</code></pre>
|
||
</details>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.solvers.watermark.get_encodec_audio_effect"><code class="name flex">
|
||
<span>def <span class="ident">get_encodec_audio_effect</span></span>(<span>encodec_cfg: omegaconf.dictconfig.DictConfig, sr: int) ‑> Dict</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def get_encodec_audio_effect(encodec_cfg: DictConfig, sr: int) -> tp.Dict:
|
||
"""
|
||
Construct encodec-based compression data agumentation. This method is
|
||
is put here instead of in `audiocraft.utils.audio_effects` because
|
||
it depends on the package `audiocraft.solvers`, which is one layer
|
||
higher than `audiocraft.utils`, so we avoid the circle dependency
|
||
from any solvers using `audiocraft.utils.audio_effects` to do the
|
||
augmentation
|
||
"""
|
||
from ..solvers.compression import CompressionSolver
|
||
|
||
codec_model = CompressionSolver.model_from_checkpoint(encodec_cfg.ckpt)
|
||
codec_model.train()
|
||
return {
|
||
f"encodec_nq={n_q}": partial(
|
||
compress_with_encodec,
|
||
model=codec_model,
|
||
n_q=n_q,
|
||
sample_rate=sr,
|
||
)
|
||
for n_q in encodec_cfg.n_qs
|
||
}</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Construct encodec-based compression data agumentation. This method is
|
||
is put here instead of in <code><a title="audiocraft.utils.audio_effects" href="../utils/audio_effects.html">audiocraft.utils.audio_effects</a></code> because
|
||
it depends on the package <code><a title="audiocraft.solvers" href="index.html">audiocraft.solvers</a></code>, which is one layer
|
||
higher than <code><a title="audiocraft.utils" href="../utils/index.html">audiocraft.utils</a></code>, so we avoid the circle dependency
|
||
from any solvers using <code><a title="audiocraft.utils.audio_effects" href="../utils/audio_effects.html">audiocraft.utils.audio_effects</a></code> to do the
|
||
augmentation</p></div>
|
||
</dd>
|
||
<dt id="audiocraft.solvers.watermark.random_message"><code class="name flex">
|
||
<span>def <span class="ident">random_message</span></span>(<span>nbits: int, batch_size: int) ‑> torch.Tensor</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def random_message(nbits: int, batch_size: int) -> torch.Tensor:
|
||
"""Return random message as 0/1 tensor."""
|
||
if nbits == 0:
|
||
return torch.tensor([])
|
||
return torch.randint(0, 2, (batch_size, nbits))</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Return random message as 0/1 tensor.</p></div>
|
||
</dd>
|
||
<dt id="audiocraft.solvers.watermark.tensor_pesq"><code class="name flex">
|
||
<span>def <span class="ident">tensor_pesq</span></span>(<span>y_pred: torch.Tensor, y: torch.Tensor, sr: int)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">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()</code></pre>
|
||
</details>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
</dl>
|
||
</section>
|
||
<section>
|
||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||
<dl>
|
||
<dt id="audiocraft.solvers.watermark.WatermarkSolver"><code class="flex name class">
|
||
<span>class <span class="ident">WatermarkSolver</span></span>
|
||
<span>(</span><span>cfg: omegaconf.dictconfig.DictConfig)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">class WatermarkSolver(base.StandardSolver):
|
||
"""Solver for different watermarking models"""
|
||
|
||
def __init__(self, cfg: DictConfig):
|
||
super().__init__(cfg)
|
||
self.rng: torch.Generator # set at each epoch
|
||
self.model: WMModel
|
||
if hasattr(cfg, "fsdp"):
|
||
assert not getattr(
|
||
cfg.fsdp, "use", False
|
||
), "FSDP not supported by WatermarkSolver."
|
||
self._init_losses()
|
||
self._init_augmentations()
|
||
self.balancer = builders.get_balancer(self.loss_weights, self.cfg.balancer)
|
||
self.path_specs = os.path.join(self.folder, "spectrograms")
|
||
os.makedirs(self.path_specs, exist_ok=True)
|
||
|
||
def _init_losses(self):
|
||
assert hasattr(self.cfg, "losses") and isinstance(
|
||
self.cfg.losses, (DictConfig, tp.Mapping)
|
||
), "WatermarkSolver must declare training losses in the config"
|
||
|
||
self.adv_losses = builders.get_adversarial_losses(self.cfg) # noqa
|
||
self.register_stateful("adv_losses")
|
||
|
||
self.aux_losses = nn.ModuleDict() # noqa
|
||
self.info_losses = nn.ModuleDict() # noqa
|
||
self.wm_losses = nn.ModuleDict() # noqa
|
||
loss_weights = {}
|
||
for loss_name, weight in self.cfg.losses.items():
|
||
|
||
# explicitly skip this loss calculation by setting a -1 as weight
|
||
# if weight == 0 it will be calculated but kept as info
|
||
if weight == -1:
|
||
continue
|
||
|
||
if loss_name in ["adv", "feat"]:
|
||
for adv_name, _ in self.adv_losses.items():
|
||
loss_weights[f"{loss_name}_{adv_name}"] = weight
|
||
elif weight > 0:
|
||
if loss_name[:3] == "wm_":
|
||
self.wm_losses[loss_name] = builders.get_loss(
|
||
loss_name, self.cfg
|
||
).to(self.device)
|
||
loss_weights[loss_name] = weight
|
||
else:
|
||
self.aux_losses[loss_name] = builders.get_loss(
|
||
loss_name, self.cfg
|
||
).to(self.device)
|
||
loss_weights[loss_name] = weight
|
||
else:
|
||
self.info_losses[loss_name] = builders.get_loss(loss_name, self.cfg).to(
|
||
self.device
|
||
)
|
||
|
||
self.loss_weights = loss_weights # noqa
|
||
|
||
def _init_augmentations(self):
|
||
if not hasattr(self.cfg, "aug_weights") or not hasattr(
|
||
self.cfg, "audio_effects"
|
||
):
|
||
return
|
||
|
||
aug_weights = {}
|
||
cfg_audio_effects = dict(self.cfg.audio_effects)
|
||
|
||
# Handle `encodec` augmentation separately as this requires loading a
|
||
# CompressionSolver checkpoint
|
||
encodec_cfg = cfg_audio_effects.pop("encodec", None)
|
||
if encodec_cfg:
|
||
encodec_effects = get_encodec_audio_effect(
|
||
encodec_cfg, self.cfg.sample_rate
|
||
)
|
||
for aug_name in encodec_effects.keys():
|
||
aug_weights[aug_name] = getattr(self.cfg.aug_weights, "encodec", -1)
|
||
else:
|
||
encodec_effects = {}
|
||
|
||
other_effects = get_audio_effects(self.cfg) # noqa
|
||
for name in other_effects.keys():
|
||
aug_weights[name] = self.cfg.aug_weights.get(name, -1)
|
||
|
||
self.aug_weights = aug_weights # noqa
|
||
self.augmentations = {**encodec_effects, **other_effects} # noqa
|
||
|
||
@property
|
||
def best_metric_name(self) -> tp.Optional[str]:
|
||
# best model is the last for the watermark model for now
|
||
return None
|
||
|
||
def build_model(self):
|
||
"""Instantiate model and optimizer."""
|
||
# Model and optimizer
|
||
self.model = get_watermark_model(self.cfg)
|
||
# Need two optimizers ?
|
||
self.optimizer = builders.get_optimizer(self.model.parameters(), self.cfg.optim)
|
||
self.register_stateful("model", "optimizer")
|
||
self.register_best_state("model")
|
||
self.register_ema("model")
|
||
|
||
def build_dataloaders(self):
|
||
"""Instantiate audio dataloaders for each stage."""
|
||
self.dataloaders = builders.get_audio_datasets(self.cfg)
|
||
|
||
def show(self):
|
||
"""Show the Watermark model and employed adversarial loss."""
|
||
self.log_model_summary(self.model)
|
||
self.logger.info("Sould print losses here:")
|
||
|
||
def crop(
|
||
self, signal: torch.Tensor, watermark: torch.Tensor
|
||
) -> tp.Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||
"""
|
||
Applies a transformation to modify the watermarked signal to train localization.
|
||
It can be one of the following:
|
||
- zero padding: add zeros at the begining and the end of the signal
|
||
- crop: crop the watermark apply a watermark only on some parts of the signal
|
||
- shuffle: replace some part of the audio with other non watermarked parts
|
||
from the batch
|
||
In every cases the function returns a mask that contains indicates the parts that are or
|
||
not watermarked
|
||
|
||
Args:
|
||
watermark (torch.Tensor): The watermark to apply on the signal.
|
||
signal (torch.Tensor): clean signal
|
||
Returns:
|
||
watermark (torch.Tensor): modified watermark
|
||
signal (torch.Tensor): modified signal
|
||
mask (torch.Tensor): mask indicating which portion is still watermarked
|
||
"""
|
||
assert (
|
||
self.cfg.crop.prob + self.cfg.crop.shuffle_prob + self.cfg.crop.pad_prob
|
||
<= 1
|
||
), f"The sum of the probabilities {self.cfg.crop.prob=} {self.cfg.crop.shuffle_prob=} \
|
||
{self.cfg.crop.pad_prob=} should be less than 1"
|
||
mask = torch.ones_like(watermark)
|
||
p = torch.rand(1)
|
||
if p < self.cfg.crop.pad_prob: # Pad with some probability
|
||
start = int(torch.rand(1) * 0.33 * watermark.size(-1))
|
||
finish = int((0.66 + torch.rand(1) * 0.33) * watermark.size(-1))
|
||
mask[:, :, :start] = 0
|
||
mask[:, :, finish:] = 0
|
||
if torch.rand(1) > 0.5:
|
||
mask = 1 - mask
|
||
signal *= mask # pad signal
|
||
|
||
elif (
|
||
p < self.cfg.crop.prob + self.cfg.crop.pad_prob + self.cfg.crop.shuffle_prob
|
||
):
|
||
# Define a mask, then crop or shuffle
|
||
mask_size = round(watermark.shape[-1] * self.cfg.crop.size)
|
||
n_windows = int(
|
||
torch.randint(1, self.cfg.crop.max_n_windows + 1, (1,)).item()
|
||
)
|
||
window_size = int(mask_size / n_windows)
|
||
for _ in range(n_windows): # Create multiple windows in the mask
|
||
mask_start = torch.randint(0, watermark.shape[-1] - window_size, (1,))
|
||
mask[:, :, mask_start: mask_start + window_size] = (
|
||
0 # Apply window to mask
|
||
)
|
||
# inverse the mask half the time
|
||
if torch.rand(1) > 0.5:
|
||
mask = 1 - mask
|
||
|
||
if p < self.cfg.crop.pad_prob + self.cfg.crop.shuffle_prob: # shuffle
|
||
# shuffle
|
||
signal_cloned = signal.clone().detach() # detach to be sure
|
||
shuffle_idx = torch.randint(0, signal.size(0), (signal.size(0),))
|
||
signal = signal * mask + signal_cloned[shuffle_idx] * (
|
||
1 - mask
|
||
) # shuffle signal where not wm
|
||
|
||
watermark *= mask # Apply mask to the watermark
|
||
return signal, watermark, mask
|
||
|
||
def run_step(self, idx: int, batch: torch.Tensor, metrics: dict):
|
||
"""Perform one training or valid step on a given batch."""
|
||
x = batch.to(self.device)
|
||
y = x.clone()
|
||
nbits = getattr(self.model, "nbits")
|
||
message = random_message(nbits, y.shape[0]).to(self.device)
|
||
watermark = self.model.get_watermark(x, message=message)
|
||
y, watermark, mask = self.crop(y, watermark)
|
||
|
||
y_wm = y + watermark
|
||
|
||
if (
|
||
self.cfg.losses.adv != 0 or self.cfg.losses.feat != 0
|
||
) and self.is_training: # train quality adv
|
||
d_losses: dict = {}
|
||
if (
|
||
len(self.adv_losses) > 0
|
||
and torch.rand(1, generator=self.rng).item()
|
||
<= 1 / self.cfg.adversarial.every
|
||
):
|
||
for adv_name, adversary in self.adv_losses.items():
|
||
disc_loss = adversary.train_adv(y_wm, y)
|
||
d_losses[f"d_{adv_name}"] = disc_loss
|
||
metrics["d_loss"] = torch.sum(torch.stack(list(d_losses.values())))
|
||
metrics.update(d_losses)
|
||
|
||
balanced_losses: dict = {}
|
||
other_losses: dict = {}
|
||
|
||
# adversarial losses
|
||
if self.cfg.losses.adv != 0 or self.cfg.losses.feat != 0:
|
||
for adv_name, adversary in self.adv_losses.items():
|
||
adv_loss, feat_loss = adversary(y_wm, y)
|
||
balanced_losses[f"adv_{adv_name}"] = adv_loss
|
||
balanced_losses[f"feat_{adv_name}"] = feat_loss
|
||
|
||
# auxiliary losses on quality/similarity
|
||
for loss_name, criterion in self.aux_losses.items():
|
||
loss = criterion(y_wm, y)
|
||
balanced_losses[loss_name] = loss
|
||
|
||
# apply augmentations
|
||
mode = "all" if self.cfg.select_aug_mode == "all" else "weighted"
|
||
selected_augs = select_audio_effects(
|
||
self.augmentations,
|
||
self.aug_weights,
|
||
mode=mode,
|
||
max_length=self.cfg.n_max_aug,
|
||
)
|
||
N_augs = len(selected_augs)
|
||
for (
|
||
augmentation_name,
|
||
augmentation_method,
|
||
) in selected_augs.items():
|
||
# concatenate to use the augmentation function only once
|
||
y_y_wm = torch.cat([y, y_wm], dim=0)
|
||
aug_cat, mask_aug = augmentation_method(y_y_wm, mask=mask)
|
||
aug_y = aug_cat[: y.size(0)]
|
||
aug_y_wm = aug_cat[y.size(0):]
|
||
positive = self.model.detect_watermark(aug_y_wm)
|
||
negative = self.model.detect_watermark(aug_y)
|
||
for loss_name, criterion in self.wm_losses.items():
|
||
loss = criterion(positive, negative, mask_aug, message)
|
||
other_losses[f"{loss_name}_{augmentation_name}"] = loss
|
||
|
||
# weighted losses
|
||
metrics.update(balanced_losses)
|
||
metrics.update(other_losses)
|
||
if self.is_training: # something is weird about the loss balancer not
|
||
other_loss = torch.tensor(0.0, device=self.device)
|
||
for name, o_loss in other_losses.items():
|
||
if "wm_detection" in name:
|
||
# here we include the detection losses for augmentation
|
||
other_loss += (self.loss_weights["wm_detection"] / N_augs) * o_loss
|
||
elif "wm_mb" in name:
|
||
other_loss += (self.loss_weights["wm_mb"] / N_augs) * o_loss
|
||
else:
|
||
other_loss += self.loss_weights[name] * o_loss
|
||
if other_loss.requires_grad:
|
||
other_loss.backward(retain_graph=True)
|
||
ratio1 = sum(
|
||
p.grad.data.norm(p=2).pow(2)
|
||
for p in self.model.parameters()
|
||
if p.grad is not None
|
||
)
|
||
assert isinstance(ratio1, torch.Tensor)
|
||
metrics["ratio1"] = ratio1.sqrt()
|
||
|
||
# balancer losses backward, returns effective training loss
|
||
# with effective weights at the current batch.
|
||
metrics["g_loss"] = self.balancer.backward(balanced_losses, y_wm)
|
||
# add metrics corresponding to weight ratios
|
||
metrics.update(self.balancer.metrics)
|
||
ratio2 = sum(
|
||
p.grad.data.norm(p=2).pow(2)
|
||
for p in self.model.parameters()
|
||
if p.grad is not None
|
||
)
|
||
assert isinstance(ratio2, torch.Tensor)
|
||
metrics["ratio2"] = ratio2.sqrt()
|
||
|
||
# optim
|
||
flashy.distrib.sync_model(self.model)
|
||
if self.cfg.optim.max_norm:
|
||
torch.nn.utils.clip_grad_norm_(
|
||
self.model.parameters(), self.cfg.optim.max_norm
|
||
)
|
||
|
||
self.optimizer.step()
|
||
self.optimizer.zero_grad()
|
||
|
||
# informative losses only
|
||
info_losses: dict = {}
|
||
with torch.no_grad():
|
||
for loss_name, criterion in self.info_losses.items():
|
||
loss = criterion(y_wm, y)
|
||
info_losses[loss_name] = loss
|
||
# pesq
|
||
metrics["pesq"] = tensor_pesq(y_wm, y, sr=self.cfg.sample_rate)
|
||
# max allocated memory
|
||
metrics["max_mem"] = torch.cuda.max_memory_allocated() / 1e9
|
||
|
||
metrics.update(info_losses)
|
||
if self.cfg.losses.adv != 0 or self.cfg.losses.feat != 0:
|
||
# aggregated GAN losses: this is useful to report adv and feat across different adversarial loss setups
|
||
adv_losses = [
|
||
loss
|
||
for loss_name, loss in metrics.items()
|
||
if loss_name.startswith("adv")
|
||
]
|
||
if len(adv_losses) > 0:
|
||
metrics["adv"] = torch.sum(torch.stack(adv_losses))
|
||
feat_losses = [
|
||
loss
|
||
for loss_name, loss in metrics.items()
|
||
if loss_name.startswith("feat")
|
||
]
|
||
if len(feat_losses) > 0:
|
||
metrics["feat"] = torch.sum(torch.stack(feat_losses))
|
||
|
||
return metrics
|
||
|
||
def run_epoch(self):
|
||
# reset random seed at the beginning of the epoch
|
||
self.rng = torch.Generator()
|
||
self.rng.manual_seed(1234 + self.epoch)
|
||
# run epoch
|
||
super().run_epoch()
|
||
|
||
def evaluate(self) -> dict:
|
||
"""Evaluate stage. Runs audio reconstruction evaluation."""
|
||
self.model.eval()
|
||
evaluate_stage_name = str(self.current_stage)
|
||
|
||
loader = self.dataloaders["evaluate"]
|
||
updates = len(loader)
|
||
lp = self.log_progress(
|
||
f"{evaluate_stage_name} inference",
|
||
loader,
|
||
total=updates,
|
||
updates=self.log_updates,
|
||
)
|
||
average = flashy.averager()
|
||
|
||
pendings = []
|
||
ctx = multiprocessing.get_context("spawn")
|
||
with get_pool_executor(self.cfg.evaluate.num_workers, mp_context=ctx) as pool:
|
||
for batch in lp:
|
||
x = batch.to(self.device)
|
||
with torch.no_grad():
|
||
message = random_message(self.model.nbits, x.shape[0])
|
||
watermark = self.model.get_watermark(x, message)
|
||
x_wm = x + watermark
|
||
y_pred = x_wm.cpu()
|
||
y = batch.cpu() # should already be on CPU but just in case
|
||
pendings.append(
|
||
pool.submit(
|
||
evaluate_audio_watermark,
|
||
y_pred,
|
||
y,
|
||
self.cfg,
|
||
)
|
||
)
|
||
# evaluate augmentations
|
||
# evaluation is run on all the augmentations
|
||
for (
|
||
augmentation_name,
|
||
augmentation_method,
|
||
) in self.augmentations.items():
|
||
# if (
|
||
# "mp3" in augmentation_name
|
||
# and idx >= 8
|
||
# and self.cfg.evaluate.every <= 2
|
||
# ):
|
||
# # When evaluating often do not compute mp3 on the full eval dset to make things faster
|
||
# continue
|
||
with torch.no_grad():
|
||
aug_positive = self.model.detect_watermark(
|
||
augmentation_method(x_wm)
|
||
)
|
||
aug_negative = self.model.detect_watermark(
|
||
augmentation_method(x)
|
||
)
|
||
|
||
pendings.append(
|
||
pool.submit(
|
||
evaluate_augmentations,
|
||
aug_positive.cpu(),
|
||
aug_negative.cpu(),
|
||
augmentation_name,
|
||
message.cpu(),
|
||
)
|
||
)
|
||
# end eval of augmentations
|
||
|
||
# evaluate localization cropping
|
||
for window_size in np.linspace(0.1, 0.9, 9):
|
||
|
||
mixed, true_predictions = mix(x, x_wm, window_size=window_size)
|
||
model_predictions = self.model.detect_watermark(mixed)
|
||
pendings.append(
|
||
pool.submit(
|
||
evaluate_localizations,
|
||
model_predictions.cpu(),
|
||
true_predictions.cpu(),
|
||
f"crop_{window_size:0.1f}",
|
||
)
|
||
)
|
||
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</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Solver for different watermarking models</p></div>
|
||
<h3>Ancestors</h3>
|
||
<ul class="hlist">
|
||
<li><a title="audiocraft.solvers.base.StandardSolver" href="base.html#audiocraft.solvers.base.StandardSolver">StandardSolver</a></li>
|
||
<li>abc.ABC</li>
|
||
<li>flashy.solver.BaseSolver</li>
|
||
</ul>
|
||
<h3>Static methods</h3>
|
||
<dl>
|
||
<dt id="audiocraft.solvers.watermark.WatermarkSolver.model_from_checkpoint"><code class="name flex">
|
||
<span>def <span class="ident">model_from_checkpoint</span></span>(<span>checkpoint_path: str | pathlib.Path, device: torch.device | str = 'cpu') ‑> WMModel</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">@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</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Instantiate a WatermarkModel from a given checkpoint path or dora sig.</p>
|
||
<h2 id="args">Args</h2>
|
||
<dl>
|
||
<dt><strong><code>checkpoint_path</code></strong> : <code>Path</code> or <code>str</code></dt>
|
||
<dd>Path to checkpoint or dora sig from where the checkpoint is resolved.</dd>
|
||
<dt><strong><code>device</code></strong> : <code>torch.device</code> or <code>str</code></dt>
|
||
<dd>Device on which the model is loaded.</dd>
|
||
</dl></div>
|
||
</dd>
|
||
</dl>
|
||
<h3>Methods</h3>
|
||
<dl>
|
||
<dt id="audiocraft.solvers.watermark.WatermarkSolver.build_dataloaders"><code class="name flex">
|
||
<span>def <span class="ident">build_dataloaders</span></span>(<span>self)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def build_dataloaders(self):
|
||
"""Instantiate audio dataloaders for each stage."""
|
||
self.dataloaders = builders.get_audio_datasets(self.cfg)</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Instantiate audio dataloaders for each stage.</p></div>
|
||
</dd>
|
||
<dt id="audiocraft.solvers.watermark.WatermarkSolver.build_model"><code class="name flex">
|
||
<span>def <span class="ident">build_model</span></span>(<span>self)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def build_model(self):
|
||
"""Instantiate model and optimizer."""
|
||
# Model and optimizer
|
||
self.model = get_watermark_model(self.cfg)
|
||
# Need two optimizers ?
|
||
self.optimizer = builders.get_optimizer(self.model.parameters(), self.cfg.optim)
|
||
self.register_stateful("model", "optimizer")
|
||
self.register_best_state("model")
|
||
self.register_ema("model")</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Instantiate model and optimizer.</p></div>
|
||
</dd>
|
||
<dt id="audiocraft.solvers.watermark.WatermarkSolver.crop"><code class="name flex">
|
||
<span>def <span class="ident">crop</span></span>(<span>self, signal: torch.Tensor, watermark: torch.Tensor) ‑> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def crop(
|
||
self, signal: torch.Tensor, watermark: torch.Tensor
|
||
) -> tp.Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||
"""
|
||
Applies a transformation to modify the watermarked signal to train localization.
|
||
It can be one of the following:
|
||
- zero padding: add zeros at the begining and the end of the signal
|
||
- crop: crop the watermark apply a watermark only on some parts of the signal
|
||
- shuffle: replace some part of the audio with other non watermarked parts
|
||
from the batch
|
||
In every cases the function returns a mask that contains indicates the parts that are or
|
||
not watermarked
|
||
|
||
Args:
|
||
watermark (torch.Tensor): The watermark to apply on the signal.
|
||
signal (torch.Tensor): clean signal
|
||
Returns:
|
||
watermark (torch.Tensor): modified watermark
|
||
signal (torch.Tensor): modified signal
|
||
mask (torch.Tensor): mask indicating which portion is still watermarked
|
||
"""
|
||
assert (
|
||
self.cfg.crop.prob + self.cfg.crop.shuffle_prob + self.cfg.crop.pad_prob
|
||
<= 1
|
||
), f"The sum of the probabilities {self.cfg.crop.prob=} {self.cfg.crop.shuffle_prob=} \
|
||
{self.cfg.crop.pad_prob=} should be less than 1"
|
||
mask = torch.ones_like(watermark)
|
||
p = torch.rand(1)
|
||
if p < self.cfg.crop.pad_prob: # Pad with some probability
|
||
start = int(torch.rand(1) * 0.33 * watermark.size(-1))
|
||
finish = int((0.66 + torch.rand(1) * 0.33) * watermark.size(-1))
|
||
mask[:, :, :start] = 0
|
||
mask[:, :, finish:] = 0
|
||
if torch.rand(1) > 0.5:
|
||
mask = 1 - mask
|
||
signal *= mask # pad signal
|
||
|
||
elif (
|
||
p < self.cfg.crop.prob + self.cfg.crop.pad_prob + self.cfg.crop.shuffle_prob
|
||
):
|
||
# Define a mask, then crop or shuffle
|
||
mask_size = round(watermark.shape[-1] * self.cfg.crop.size)
|
||
n_windows = int(
|
||
torch.randint(1, self.cfg.crop.max_n_windows + 1, (1,)).item()
|
||
)
|
||
window_size = int(mask_size / n_windows)
|
||
for _ in range(n_windows): # Create multiple windows in the mask
|
||
mask_start = torch.randint(0, watermark.shape[-1] - window_size, (1,))
|
||
mask[:, :, mask_start: mask_start + window_size] = (
|
||
0 # Apply window to mask
|
||
)
|
||
# inverse the mask half the time
|
||
if torch.rand(1) > 0.5:
|
||
mask = 1 - mask
|
||
|
||
if p < self.cfg.crop.pad_prob + self.cfg.crop.shuffle_prob: # shuffle
|
||
# shuffle
|
||
signal_cloned = signal.clone().detach() # detach to be sure
|
||
shuffle_idx = torch.randint(0, signal.size(0), (signal.size(0),))
|
||
signal = signal * mask + signal_cloned[shuffle_idx] * (
|
||
1 - mask
|
||
) # shuffle signal where not wm
|
||
|
||
watermark *= mask # Apply mask to the watermark
|
||
return signal, watermark, mask</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Applies a transformation to modify the watermarked signal to train localization.
|
||
It can be one of the following:
|
||
- zero padding: add zeros at the begining and the end of the signal
|
||
- crop: crop the watermark apply a watermark only on some parts of the signal
|
||
- shuffle: replace some part of the audio with other non watermarked parts
|
||
from the batch
|
||
In every cases the function returns a mask that contains indicates the parts that are or
|
||
not watermarked</p>
|
||
<h2 id="args">Args</h2>
|
||
<dl>
|
||
<dt><strong><code>watermark</code></strong> : <code>torch.Tensor</code></dt>
|
||
<dd>The watermark to apply on the signal.</dd>
|
||
<dt><strong><code>signal</code></strong> : <code>torch.Tensor</code></dt>
|
||
<dd>clean signal</dd>
|
||
</dl>
|
||
<h2 id="returns">Returns</h2>
|
||
<p>watermark (torch.Tensor): modified watermark
|
||
signal (torch.Tensor): modified signal
|
||
mask (torch.Tensor): mask indicating which portion is still watermarked</p></div>
|
||
</dd>
|
||
<dt id="audiocraft.solvers.watermark.WatermarkSolver.evaluate"><code class="name flex">
|
||
<span>def <span class="ident">evaluate</span></span>(<span>self) ‑> dict</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def evaluate(self) -> dict:
|
||
"""Evaluate stage. Runs audio reconstruction evaluation."""
|
||
self.model.eval()
|
||
evaluate_stage_name = str(self.current_stage)
|
||
|
||
loader = self.dataloaders["evaluate"]
|
||
updates = len(loader)
|
||
lp = self.log_progress(
|
||
f"{evaluate_stage_name} inference",
|
||
loader,
|
||
total=updates,
|
||
updates=self.log_updates,
|
||
)
|
||
average = flashy.averager()
|
||
|
||
pendings = []
|
||
ctx = multiprocessing.get_context("spawn")
|
||
with get_pool_executor(self.cfg.evaluate.num_workers, mp_context=ctx) as pool:
|
||
for batch in lp:
|
||
x = batch.to(self.device)
|
||
with torch.no_grad():
|
||
message = random_message(self.model.nbits, x.shape[0])
|
||
watermark = self.model.get_watermark(x, message)
|
||
x_wm = x + watermark
|
||
y_pred = x_wm.cpu()
|
||
y = batch.cpu() # should already be on CPU but just in case
|
||
pendings.append(
|
||
pool.submit(
|
||
evaluate_audio_watermark,
|
||
y_pred,
|
||
y,
|
||
self.cfg,
|
||
)
|
||
)
|
||
# evaluate augmentations
|
||
# evaluation is run on all the augmentations
|
||
for (
|
||
augmentation_name,
|
||
augmentation_method,
|
||
) in self.augmentations.items():
|
||
# if (
|
||
# "mp3" in augmentation_name
|
||
# and idx >= 8
|
||
# and self.cfg.evaluate.every <= 2
|
||
# ):
|
||
# # When evaluating often do not compute mp3 on the full eval dset to make things faster
|
||
# continue
|
||
with torch.no_grad():
|
||
aug_positive = self.model.detect_watermark(
|
||
augmentation_method(x_wm)
|
||
)
|
||
aug_negative = self.model.detect_watermark(
|
||
augmentation_method(x)
|
||
)
|
||
|
||
pendings.append(
|
||
pool.submit(
|
||
evaluate_augmentations,
|
||
aug_positive.cpu(),
|
||
aug_negative.cpu(),
|
||
augmentation_name,
|
||
message.cpu(),
|
||
)
|
||
)
|
||
# end eval of augmentations
|
||
|
||
# evaluate localization cropping
|
||
for window_size in np.linspace(0.1, 0.9, 9):
|
||
|
||
mixed, true_predictions = mix(x, x_wm, window_size=window_size)
|
||
model_predictions = self.model.detect_watermark(mixed)
|
||
pendings.append(
|
||
pool.submit(
|
||
evaluate_localizations,
|
||
model_predictions.cpu(),
|
||
true_predictions.cpu(),
|
||
f"crop_{window_size:0.1f}",
|
||
)
|
||
)
|
||
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</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Evaluate stage. Runs audio reconstruction evaluation.</p></div>
|
||
</dd>
|
||
<dt id="audiocraft.solvers.watermark.WatermarkSolver.load_from_pretrained"><code class="name flex">
|
||
<span>def <span class="ident">load_from_pretrained</span></span>(<span>self, name: str) ‑> dict</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def load_from_pretrained(self, name: str) -> dict:
|
||
raise ValueError("No pretrained model")</code></pre>
|
||
</details>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.solvers.watermark.WatermarkSolver.show"><code class="name flex">
|
||
<span>def <span class="ident">show</span></span>(<span>self)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def show(self):
|
||
"""Show the Watermark model and employed adversarial loss."""
|
||
self.log_model_summary(self.model)
|
||
self.logger.info("Sould print losses here:")</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Show the Watermark model and employed adversarial loss.</p></div>
|
||
</dd>
|
||
</dl>
|
||
<h3>Inherited members</h3>
|
||
<ul class="hlist">
|
||
<li><code><b><a title="audiocraft.solvers.base.StandardSolver" href="base.html#audiocraft.solvers.base.StandardSolver">StandardSolver</a></b></code>:
|
||
<ul class="hlist">
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.autocast" href="base.html#audiocraft.solvers.base.StandardSolver.autocast">autocast</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.best_metric_name" href="base.html#audiocraft.solvers.base.StandardSolver.best_metric_name">best_metric_name</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.commit" href="base.html#audiocraft.solvers.base.StandardSolver.commit">commit</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.common_train_valid" href="base.html#audiocraft.solvers.base.StandardSolver.common_train_valid">common_train_valid</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.generate" href="base.html#audiocraft.solvers.base.StandardSolver.generate">generate</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.get_eval_solver_from_sig" href="base.html#audiocraft.solvers.base.StandardSolver.get_eval_solver_from_sig">get_eval_solver_from_sig</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.initialize_ema" href="base.html#audiocraft.solvers.base.StandardSolver.initialize_ema">initialize_ema</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.load_checkpoints" href="base.html#audiocraft.solvers.base.StandardSolver.load_checkpoints">load_checkpoints</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.log_model_summary" href="base.html#audiocraft.solvers.base.StandardSolver.log_model_summary">log_model_summary</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.register_best_state" href="base.html#audiocraft.solvers.base.StandardSolver.register_best_state">register_best_state</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.register_ema" href="base.html#audiocraft.solvers.base.StandardSolver.register_ema">register_ema</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.restore" href="base.html#audiocraft.solvers.base.StandardSolver.restore">restore</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.run" href="base.html#audiocraft.solvers.base.StandardSolver.run">run</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.run_epoch" href="base.html#audiocraft.solvers.base.StandardSolver.run_epoch">run_epoch</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.run_one_stage" href="base.html#audiocraft.solvers.base.StandardSolver.run_one_stage">run_one_stage</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.run_step" href="base.html#audiocraft.solvers.base.StandardSolver.run_step">run_step</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.save_checkpoints" href="base.html#audiocraft.solvers.base.StandardSolver.save_checkpoints">save_checkpoints</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.should_run_stage" href="base.html#audiocraft.solvers.base.StandardSolver.should_run_stage">should_run_stage</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.should_stop_training" href="base.html#audiocraft.solvers.base.StandardSolver.should_stop_training">should_stop_training</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.train" href="base.html#audiocraft.solvers.base.StandardSolver.train">train</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.update_best_state_from_stage" href="base.html#audiocraft.solvers.base.StandardSolver.update_best_state_from_stage">update_best_state_from_stage</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.base.StandardSolver.valid" href="base.html#audiocraft.solvers.base.StandardSolver.valid">valid</a></code></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</dd>
|
||
</dl>
|
||
</section>
|
||
</article>
|
||
<nav id="sidebar">
|
||
<div class="toc">
|
||
<ul></ul>
|
||
</div>
|
||
<ul id="index">
|
||
<li><h3>Super-module</h3>
|
||
<ul>
|
||
<li><code><a title="audiocraft.solvers" href="index.html">audiocraft.solvers</a></code></li>
|
||
</ul>
|
||
</li>
|
||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||
<ul class="">
|
||
<li><code><a title="audiocraft.solvers.watermark.compute_FNR" href="#audiocraft.solvers.watermark.compute_FNR">compute_FNR</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.watermark.compute_FPR" href="#audiocraft.solvers.watermark.compute_FPR">compute_FPR</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.watermark.compute_accuracy" href="#audiocraft.solvers.watermark.compute_accuracy">compute_accuracy</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.watermark.compute_bit_acc" href="#audiocraft.solvers.watermark.compute_bit_acc">compute_bit_acc</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.watermark.evaluate_audio_watermark" href="#audiocraft.solvers.watermark.evaluate_audio_watermark">evaluate_audio_watermark</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.watermark.evaluate_augmentations" href="#audiocraft.solvers.watermark.evaluate_augmentations">evaluate_augmentations</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.watermark.evaluate_localizations" href="#audiocraft.solvers.watermark.evaluate_localizations">evaluate_localizations</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.watermark.get_encodec_audio_effect" href="#audiocraft.solvers.watermark.get_encodec_audio_effect">get_encodec_audio_effect</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.watermark.random_message" href="#audiocraft.solvers.watermark.random_message">random_message</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.watermark.tensor_pesq" href="#audiocraft.solvers.watermark.tensor_pesq">tensor_pesq</a></code></li>
|
||
</ul>
|
||
</li>
|
||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||
<ul>
|
||
<li>
|
||
<h4><code><a title="audiocraft.solvers.watermark.WatermarkSolver" href="#audiocraft.solvers.watermark.WatermarkSolver">WatermarkSolver</a></code></h4>
|
||
<ul class="">
|
||
<li><code><a title="audiocraft.solvers.watermark.WatermarkSolver.build_dataloaders" href="#audiocraft.solvers.watermark.WatermarkSolver.build_dataloaders">build_dataloaders</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.watermark.WatermarkSolver.build_model" href="#audiocraft.solvers.watermark.WatermarkSolver.build_model">build_model</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.watermark.WatermarkSolver.crop" href="#audiocraft.solvers.watermark.WatermarkSolver.crop">crop</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.watermark.WatermarkSolver.evaluate" href="#audiocraft.solvers.watermark.WatermarkSolver.evaluate">evaluate</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.watermark.WatermarkSolver.load_from_pretrained" href="#audiocraft.solvers.watermark.WatermarkSolver.load_from_pretrained">load_from_pretrained</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.watermark.WatermarkSolver.model_from_checkpoint" href="#audiocraft.solvers.watermark.WatermarkSolver.model_from_checkpoint">model_from_checkpoint</a></code></li>
|
||
<li><code><a title="audiocraft.solvers.watermark.WatermarkSolver.show" href="#audiocraft.solvers.watermark.WatermarkSolver.show">show</a></code></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</nav>
|
||
</main>
|
||
<footer id="footer">
|
||
<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.11.5</a>.</p>
|
||
</footer>
|
||
</body>
|
||
</html>
|