facebookresearch--audiocraft
Alexandre Défossez (autodoc)
810 行
41 KiB
HTML
810 行
41 KiB
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<article id="content">
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<header>
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<h1 class="title">Module <code>audiocraft.modules.conv</code></h1>
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</header>
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<section id="section-intro">
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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<h2 class="section-title" id="header-functions">Functions</h2>
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<dl>
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<dt id="audiocraft.modules.conv.apply_parametrization_norm"><code class="name flex">
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<span>def <span class="ident">apply_parametrization_norm</span></span>(<span>module: torch.nn.modules.module.Module, norm: str = 'none')</span>
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</code></dt>
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<dd>
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">def apply_parametrization_norm(module: nn.Module, norm: str = 'none'):
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assert norm in CONV_NORMALIZATIONS
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if norm == 'weight_norm':
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return weight_norm(module)
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elif norm == 'spectral_norm':
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return spectral_norm(module)
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else:
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# We already check was in CONV_NORMALIZATION, so any other choice
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# doesn't need reparametrization.
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return module</code></pre>
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</details>
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<div class="desc"></div>
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</dd>
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<dt id="audiocraft.modules.conv.get_extra_padding_for_conv1d"><code class="name flex">
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<span>def <span class="ident">get_extra_padding_for_conv1d</span></span>(<span>x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0) ‑> int</span>
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</code></dt>
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<dd>
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int,
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padding_total: int = 0) -> int:
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"""See `pad_for_conv1d`."""
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length = x.shape[-1]
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n_frames = (length - kernel_size + padding_total) / stride + 1
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ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
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return ideal_length - length</code></pre>
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</details>
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<div class="desc"><p>See <code><a title="audiocraft.modules.conv.pad_for_conv1d" href="#audiocraft.modules.conv.pad_for_conv1d">pad_for_conv1d()</a></code>.</p></div>
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</dd>
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<dt id="audiocraft.modules.conv.get_norm_module"><code class="name flex">
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<span>def <span class="ident">get_norm_module</span></span>(<span>module: torch.nn.modules.module.Module,<br>causal: bool = False,<br>norm: str = 'none',<br>**norm_kwargs)</span>
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</code></dt>
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<dd>
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none', **norm_kwargs):
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"""Return the proper normalization module. If causal is True, this will ensure the returned
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module is causal, or return an error if the normalization doesn't support causal evaluation.
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"""
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assert norm in CONV_NORMALIZATIONS
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if norm == 'time_group_norm':
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if causal:
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raise ValueError("GroupNorm doesn't support causal evaluation.")
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assert isinstance(module, nn.modules.conv._ConvNd)
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return nn.GroupNorm(1, module.out_channels, **norm_kwargs)
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else:
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return nn.Identity()</code></pre>
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</details>
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<div class="desc"><p>Return the proper normalization module. If causal is True, this will ensure the returned
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module is causal, or return an error if the normalization doesn't support causal evaluation.</p></div>
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</dd>
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<dt id="audiocraft.modules.conv.pad1d"><code class="name flex">
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<span>def <span class="ident">pad1d</span></span>(<span>x: torch.Tensor,<br>paddings: Tuple[int, int],<br>mode: str = 'constant',<br>value: float = 0.0)</span>
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</code></dt>
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<dd>
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'constant', value: float = 0.):
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"""Tiny wrapper around F.pad, just to allow for reflect padding on small input.
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If this is the case, we insert extra 0 padding to the right before the reflection happen.
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"""
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length = x.shape[-1]
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padding_left, padding_right = paddings
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assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
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if mode == 'reflect':
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max_pad = max(padding_left, padding_right)
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extra_pad = 0
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if length <= max_pad:
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extra_pad = max_pad - length + 1
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x = F.pad(x, (0, extra_pad))
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padded = F.pad(x, paddings, mode, value)
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end = padded.shape[-1] - extra_pad
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return padded[..., :end]
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else:
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return F.pad(x, paddings, mode, value)</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Tiny wrapper around F.pad, just to allow for reflect padding on small input.
|
||
If this is the case, we insert extra 0 padding to the right before the reflection happen.</p></div>
|
||
</dd>
|
||
<dt id="audiocraft.modules.conv.pad_for_conv1d"><code class="name flex">
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<span>def <span class="ident">pad_for_conv1d</span></span>(<span>x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def pad_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0):
|
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"""Pad for a convolution to make sure that the last window is full.
|
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Extra padding is added at the end. This is required to ensure that we can rebuild
|
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an output of the same length, as otherwise, even with padding, some time steps
|
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might get removed.
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For instance, with total padding = 4, kernel size = 4, stride = 2:
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0 0 1 2 3 4 5 0 0 # (0s are padding)
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1 2 3 # (output frames of a convolution, last 0 is never used)
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0 0 1 2 3 4 5 0 # (output of tr. conv., but pos. 5 is going to get removed as padding)
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1 2 3 4 # once you removed padding, we are missing one time step !
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"""
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extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
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return F.pad(x, (0, extra_padding))</code></pre>
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</details>
|
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<div class="desc"><p>Pad for a convolution to make sure that the last window is full.
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Extra padding is added at the end. This is required to ensure that we can rebuild
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an output of the same length, as otherwise, even with padding, some time steps
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might get removed.
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For instance, with total padding = 4, kernel size = 4, stride = 2:
|
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0 0 1 2 3 4 5 0 0
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# (0s are padding)
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1
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2
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3
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# (output frames of a convolution, last 0 is never used)
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0 0 1 2 3 4 5 0
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# (output of tr. conv., but pos. 5 is going to get removed as padding)
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1 2 3 4
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# once you removed padding, we are missing one time step !</p></div>
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</dd>
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<dt id="audiocraft.modules.conv.unpad1d"><code class="name flex">
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<span>def <span class="ident">unpad1d</span></span>(<span>x: torch.Tensor, paddings: Tuple[int, int])</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
|
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"""Remove padding from x, handling properly zero padding. Only for 1d!"""
|
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padding_left, padding_right = paddings
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assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
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assert (padding_left + padding_right) <= x.shape[-1]
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end = x.shape[-1] - padding_right
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return x[..., padding_left: end]</code></pre>
|
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</details>
|
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<div class="desc"><p>Remove padding from x, handling properly zero padding. Only for 1d!</p></div>
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</dd>
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</dl>
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</section>
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<section>
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<h2 class="section-title" id="header-classes">Classes</h2>
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<dl>
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<dt id="audiocraft.modules.conv.NormConv1d"><code class="flex name class">
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<span>class <span class="ident">NormConv1d</span></span>
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<span>(</span><span>*args,<br>causal: bool = False,<br>norm: str = 'none',<br>norm_kwargs: Dict[str, Any] = {},<br>**kwargs)</span>
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||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">class NormConv1d(nn.Module):
|
||
"""Wrapper around Conv1d and normalization applied to this conv
|
||
to provide a uniform interface across normalization approaches.
|
||
"""
|
||
def __init__(self, *args, causal: bool = False, norm: str = 'none',
|
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norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
|
||
super().__init__()
|
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self.conv = apply_parametrization_norm(nn.Conv1d(*args, **kwargs), norm)
|
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self.norm = get_norm_module(self.conv, causal, norm, **norm_kwargs)
|
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self.norm_type = norm
|
||
|
||
def forward(self, x):
|
||
x = self.conv(x)
|
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x = self.norm(x)
|
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return x</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Wrapper around Conv1d and normalization applied to this conv
|
||
to provide a uniform interface across normalization approaches.</p>
|
||
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
|
||
<h3>Ancestors</h3>
|
||
<ul class="hlist">
|
||
<li>torch.nn.modules.module.Module</li>
|
||
</ul>
|
||
<h3>Class variables</h3>
|
||
<dl>
|
||
<dt id="audiocraft.modules.conv.NormConv1d.call_super_init"><code class="name">var <span class="ident">call_super_init</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.modules.conv.NormConv1d.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.modules.conv.NormConv1d.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
</dl>
|
||
<h3>Methods</h3>
|
||
<dl>
|
||
<dt id="audiocraft.modules.conv.NormConv1d.forward"><code class="name flex">
|
||
<span>def <span class="ident">forward</span></span>(<span>self, x) ‑> Callable[..., Any]</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def forward(self, x):
|
||
x = self.conv(x)
|
||
x = self.norm(x)
|
||
return x</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Defines the computation performed at every call.</p>
|
||
<p>Should be overridden by all subclasses.</p>
|
||
<div class="admonition note">
|
||
<p class="admonition-title">Note</p>
|
||
<p>Although the recipe for forward pass needs to be defined within
|
||
this function, one should call the :class:<code>Module</code> instance afterwards
|
||
instead of this since the former takes care of running the
|
||
registered hooks while the latter silently ignores them.</p>
|
||
</div></div>
|
||
</dd>
|
||
</dl>
|
||
</dd>
|
||
<dt id="audiocraft.modules.conv.NormConv2d"><code class="flex name class">
|
||
<span>class <span class="ident">NormConv2d</span></span>
|
||
<span>(</span><span>*args, norm: str = 'none', norm_kwargs: Dict[str, Any] = {}, **kwargs)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">class NormConv2d(nn.Module):
|
||
"""Wrapper around Conv2d and normalization applied to this conv
|
||
to provide a uniform interface across normalization approaches.
|
||
"""
|
||
def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
|
||
super().__init__()
|
||
self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm)
|
||
self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs)
|
||
self.norm_type = norm
|
||
|
||
def forward(self, x):
|
||
x = self.conv(x)
|
||
x = self.norm(x)
|
||
return x</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Wrapper around Conv2d and normalization applied to this conv
|
||
to provide a uniform interface across normalization approaches.</p>
|
||
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
|
||
<h3>Ancestors</h3>
|
||
<ul class="hlist">
|
||
<li>torch.nn.modules.module.Module</li>
|
||
</ul>
|
||
<h3>Class variables</h3>
|
||
<dl>
|
||
<dt id="audiocraft.modules.conv.NormConv2d.call_super_init"><code class="name">var <span class="ident">call_super_init</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.modules.conv.NormConv2d.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.modules.conv.NormConv2d.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
</dl>
|
||
<h3>Methods</h3>
|
||
<dl>
|
||
<dt id="audiocraft.modules.conv.NormConv2d.forward"><code class="name flex">
|
||
<span>def <span class="ident">forward</span></span>(<span>self, x) ‑> Callable[..., Any]</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def forward(self, x):
|
||
x = self.conv(x)
|
||
x = self.norm(x)
|
||
return x</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Defines the computation performed at every call.</p>
|
||
<p>Should be overridden by all subclasses.</p>
|
||
<div class="admonition note">
|
||
<p class="admonition-title">Note</p>
|
||
<p>Although the recipe for forward pass needs to be defined within
|
||
this function, one should call the :class:<code>Module</code> instance afterwards
|
||
instead of this since the former takes care of running the
|
||
registered hooks while the latter silently ignores them.</p>
|
||
</div></div>
|
||
</dd>
|
||
</dl>
|
||
</dd>
|
||
<dt id="audiocraft.modules.conv.NormConvTranspose1d"><code class="flex name class">
|
||
<span>class <span class="ident">NormConvTranspose1d</span></span>
|
||
<span>(</span><span>*args,<br>causal: bool = False,<br>norm: str = 'none',<br>norm_kwargs: Dict[str, Any] = {},<br>**kwargs)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">class NormConvTranspose1d(nn.Module):
|
||
"""Wrapper around ConvTranspose1d and normalization applied to this conv
|
||
to provide a uniform interface across normalization approaches.
|
||
"""
|
||
def __init__(self, *args, causal: bool = False, norm: str = 'none',
|
||
norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
|
||
super().__init__()
|
||
self.convtr = apply_parametrization_norm(nn.ConvTranspose1d(*args, **kwargs), norm)
|
||
self.norm = get_norm_module(self.convtr, causal, norm, **norm_kwargs)
|
||
self.norm_type = norm
|
||
|
||
def forward(self, x):
|
||
x = self.convtr(x)
|
||
x = self.norm(x)
|
||
return x</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Wrapper around ConvTranspose1d and normalization applied to this conv
|
||
to provide a uniform interface across normalization approaches.</p>
|
||
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
|
||
<h3>Ancestors</h3>
|
||
<ul class="hlist">
|
||
<li>torch.nn.modules.module.Module</li>
|
||
</ul>
|
||
<h3>Class variables</h3>
|
||
<dl>
|
||
<dt id="audiocraft.modules.conv.NormConvTranspose1d.call_super_init"><code class="name">var <span class="ident">call_super_init</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.modules.conv.NormConvTranspose1d.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.modules.conv.NormConvTranspose1d.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
</dl>
|
||
<h3>Methods</h3>
|
||
<dl>
|
||
<dt id="audiocraft.modules.conv.NormConvTranspose1d.forward"><code class="name flex">
|
||
<span>def <span class="ident">forward</span></span>(<span>self, x) ‑> Callable[..., Any]</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def forward(self, x):
|
||
x = self.convtr(x)
|
||
x = self.norm(x)
|
||
return x</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Defines the computation performed at every call.</p>
|
||
<p>Should be overridden by all subclasses.</p>
|
||
<div class="admonition note">
|
||
<p class="admonition-title">Note</p>
|
||
<p>Although the recipe for forward pass needs to be defined within
|
||
this function, one should call the :class:<code>Module</code> instance afterwards
|
||
instead of this since the former takes care of running the
|
||
registered hooks while the latter silently ignores them.</p>
|
||
</div></div>
|
||
</dd>
|
||
</dl>
|
||
</dd>
|
||
<dt id="audiocraft.modules.conv.NormConvTranspose2d"><code class="flex name class">
|
||
<span>class <span class="ident">NormConvTranspose2d</span></span>
|
||
<span>(</span><span>*args, norm: str = 'none', norm_kwargs: Dict[str, Any] = {}, **kwargs)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">class NormConvTranspose2d(nn.Module):
|
||
"""Wrapper around ConvTranspose2d and normalization applied to this conv
|
||
to provide a uniform interface across normalization approaches.
|
||
"""
|
||
def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
|
||
super().__init__()
|
||
self.convtr = apply_parametrization_norm(nn.ConvTranspose2d(*args, **kwargs), norm)
|
||
self.norm = get_norm_module(self.convtr, causal=False, norm=norm, **norm_kwargs)
|
||
|
||
def forward(self, x):
|
||
x = self.convtr(x)
|
||
x = self.norm(x)
|
||
return x</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Wrapper around ConvTranspose2d and normalization applied to this conv
|
||
to provide a uniform interface across normalization approaches.</p>
|
||
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
|
||
<h3>Ancestors</h3>
|
||
<ul class="hlist">
|
||
<li>torch.nn.modules.module.Module</li>
|
||
</ul>
|
||
<h3>Class variables</h3>
|
||
<dl>
|
||
<dt id="audiocraft.modules.conv.NormConvTranspose2d.call_super_init"><code class="name">var <span class="ident">call_super_init</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.modules.conv.NormConvTranspose2d.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.modules.conv.NormConvTranspose2d.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
</dl>
|
||
<h3>Methods</h3>
|
||
<dl>
|
||
<dt id="audiocraft.modules.conv.NormConvTranspose2d.forward"><code class="name flex">
|
||
<span>def <span class="ident">forward</span></span>(<span>self, x) ‑> Callable[..., Any]</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def forward(self, x):
|
||
x = self.convtr(x)
|
||
x = self.norm(x)
|
||
return x</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Defines the computation performed at every call.</p>
|
||
<p>Should be overridden by all subclasses.</p>
|
||
<div class="admonition note">
|
||
<p class="admonition-title">Note</p>
|
||
<p>Although the recipe for forward pass needs to be defined within
|
||
this function, one should call the :class:<code>Module</code> instance afterwards
|
||
instead of this since the former takes care of running the
|
||
registered hooks while the latter silently ignores them.</p>
|
||
</div></div>
|
||
</dd>
|
||
</dl>
|
||
</dd>
|
||
<dt id="audiocraft.modules.conv.StreamableConv1d"><code class="flex name class">
|
||
<span>class <span class="ident">StreamableConv1d</span></span>
|
||
<span>(</span><span>in_channels: int,<br>out_channels: int,<br>kernel_size: int,<br>stride: int = 1,<br>dilation: int = 1,<br>groups: int = 1,<br>bias: bool = True,<br>causal: bool = False,<br>norm: str = 'none',<br>norm_kwargs: Dict[str, Any] = {},<br>pad_mode: str = 'reflect')</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">class StreamableConv1d(nn.Module):
|
||
"""Conv1d with some builtin handling of asymmetric or causal padding
|
||
and normalization.
|
||
"""
|
||
def __init__(self, in_channels: int, out_channels: int,
|
||
kernel_size: int, stride: int = 1, dilation: int = 1,
|
||
groups: int = 1, bias: bool = True, causal: bool = False,
|
||
norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {},
|
||
pad_mode: str = 'reflect'):
|
||
super().__init__()
|
||
# warn user on unusual setup between dilation and stride
|
||
if stride > 1 and dilation > 1:
|
||
warnings.warn("StreamableConv1d has been initialized with stride > 1 and dilation > 1"
|
||
f" (kernel_size={kernel_size} stride={stride}, dilation={dilation}).")
|
||
self.conv = NormConv1d(in_channels, out_channels, kernel_size, stride,
|
||
dilation=dilation, groups=groups, bias=bias, causal=causal,
|
||
norm=norm, norm_kwargs=norm_kwargs)
|
||
self.causal = causal
|
||
self.pad_mode = pad_mode
|
||
|
||
def forward(self, x):
|
||
B, C, T = x.shape
|
||
kernel_size = self.conv.conv.kernel_size[0]
|
||
stride = self.conv.conv.stride[0]
|
||
dilation = self.conv.conv.dilation[0]
|
||
kernel_size = (kernel_size - 1) * dilation + 1 # effective kernel size with dilations
|
||
padding_total = kernel_size - stride
|
||
extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
|
||
if self.causal:
|
||
# Left padding for causal
|
||
x = pad1d(x, (padding_total, extra_padding), mode=self.pad_mode)
|
||
else:
|
||
# Asymmetric padding required for odd strides
|
||
padding_right = padding_total // 2
|
||
padding_left = padding_total - padding_right
|
||
x = pad1d(x, (padding_left, padding_right + extra_padding), mode=self.pad_mode)
|
||
return self.conv(x)</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Conv1d with some builtin handling of asymmetric or causal padding
|
||
and normalization.</p>
|
||
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
|
||
<h3>Ancestors</h3>
|
||
<ul class="hlist">
|
||
<li>torch.nn.modules.module.Module</li>
|
||
</ul>
|
||
<h3>Class variables</h3>
|
||
<dl>
|
||
<dt id="audiocraft.modules.conv.StreamableConv1d.call_super_init"><code class="name">var <span class="ident">call_super_init</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.modules.conv.StreamableConv1d.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.modules.conv.StreamableConv1d.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
</dl>
|
||
<h3>Methods</h3>
|
||
<dl>
|
||
<dt id="audiocraft.modules.conv.StreamableConv1d.forward"><code class="name flex">
|
||
<span>def <span class="ident">forward</span></span>(<span>self, x) ‑> Callable[..., Any]</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def forward(self, x):
|
||
B, C, T = x.shape
|
||
kernel_size = self.conv.conv.kernel_size[0]
|
||
stride = self.conv.conv.stride[0]
|
||
dilation = self.conv.conv.dilation[0]
|
||
kernel_size = (kernel_size - 1) * dilation + 1 # effective kernel size with dilations
|
||
padding_total = kernel_size - stride
|
||
extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
|
||
if self.causal:
|
||
# Left padding for causal
|
||
x = pad1d(x, (padding_total, extra_padding), mode=self.pad_mode)
|
||
else:
|
||
# Asymmetric padding required for odd strides
|
||
padding_right = padding_total // 2
|
||
padding_left = padding_total - padding_right
|
||
x = pad1d(x, (padding_left, padding_right + extra_padding), mode=self.pad_mode)
|
||
return self.conv(x)</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Defines the computation performed at every call.</p>
|
||
<p>Should be overridden by all subclasses.</p>
|
||
<div class="admonition note">
|
||
<p class="admonition-title">Note</p>
|
||
<p>Although the recipe for forward pass needs to be defined within
|
||
this function, one should call the :class:<code>Module</code> instance afterwards
|
||
instead of this since the former takes care of running the
|
||
registered hooks while the latter silently ignores them.</p>
|
||
</div></div>
|
||
</dd>
|
||
</dl>
|
||
</dd>
|
||
<dt id="audiocraft.modules.conv.StreamableConvTranspose1d"><code class="flex name class">
|
||
<span>class <span class="ident">StreamableConvTranspose1d</span></span>
|
||
<span>(</span><span>in_channels: int,<br>out_channels: int,<br>kernel_size: int,<br>stride: int = 1,<br>causal: bool = False,<br>norm: str = 'none',<br>trim_right_ratio: float = 1.0,<br>norm_kwargs: Dict[str, Any] = {})</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">class StreamableConvTranspose1d(nn.Module):
|
||
"""ConvTranspose1d with some builtin handling of asymmetric or causal padding
|
||
and normalization.
|
||
"""
|
||
def __init__(self, in_channels: int, out_channels: int,
|
||
kernel_size: int, stride: int = 1, causal: bool = False,
|
||
norm: str = 'none', trim_right_ratio: float = 1.,
|
||
norm_kwargs: tp.Dict[str, tp.Any] = {}):
|
||
super().__init__()
|
||
self.convtr = NormConvTranspose1d(in_channels, out_channels, kernel_size, stride,
|
||
causal=causal, norm=norm, norm_kwargs=norm_kwargs)
|
||
self.causal = causal
|
||
self.trim_right_ratio = trim_right_ratio
|
||
assert self.causal or self.trim_right_ratio == 1., \
|
||
"`trim_right_ratio` != 1.0 only makes sense for causal convolutions"
|
||
assert self.trim_right_ratio >= 0. and self.trim_right_ratio <= 1.
|
||
|
||
def forward(self, x):
|
||
kernel_size = self.convtr.convtr.kernel_size[0]
|
||
stride = self.convtr.convtr.stride[0]
|
||
padding_total = kernel_size - stride
|
||
|
||
y = self.convtr(x)
|
||
|
||
# We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be
|
||
# removed at the very end, when keeping only the right length for the output,
|
||
# as removing it here would require also passing the length at the matching layer
|
||
# in the encoder.
|
||
if self.causal:
|
||
# Trim the padding on the right according to the specified ratio
|
||
# if trim_right_ratio = 1.0, trim everything from right
|
||
padding_right = math.ceil(padding_total * self.trim_right_ratio)
|
||
padding_left = padding_total - padding_right
|
||
y = unpad1d(y, (padding_left, padding_right))
|
||
else:
|
||
# Asymmetric padding required for odd strides
|
||
padding_right = padding_total // 2
|
||
padding_left = padding_total - padding_right
|
||
y = unpad1d(y, (padding_left, padding_right))
|
||
return y</code></pre>
|
||
</details>
|
||
<div class="desc"><p>ConvTranspose1d with some builtin handling of asymmetric or causal padding
|
||
and normalization.</p>
|
||
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
|
||
<h3>Ancestors</h3>
|
||
<ul class="hlist">
|
||
<li>torch.nn.modules.module.Module</li>
|
||
</ul>
|
||
<h3>Class variables</h3>
|
||
<dl>
|
||
<dt id="audiocraft.modules.conv.StreamableConvTranspose1d.call_super_init"><code class="name">var <span class="ident">call_super_init</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.modules.conv.StreamableConvTranspose1d.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.modules.conv.StreamableConvTranspose1d.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
</dl>
|
||
<h3>Methods</h3>
|
||
<dl>
|
||
<dt id="audiocraft.modules.conv.StreamableConvTranspose1d.forward"><code class="name flex">
|
||
<span>def <span class="ident">forward</span></span>(<span>self, x) ‑> Callable[..., Any]</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def forward(self, x):
|
||
kernel_size = self.convtr.convtr.kernel_size[0]
|
||
stride = self.convtr.convtr.stride[0]
|
||
padding_total = kernel_size - stride
|
||
|
||
y = self.convtr(x)
|
||
|
||
# We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be
|
||
# removed at the very end, when keeping only the right length for the output,
|
||
# as removing it here would require also passing the length at the matching layer
|
||
# in the encoder.
|
||
if self.causal:
|
||
# Trim the padding on the right according to the specified ratio
|
||
# if trim_right_ratio = 1.0, trim everything from right
|
||
padding_right = math.ceil(padding_total * self.trim_right_ratio)
|
||
padding_left = padding_total - padding_right
|
||
y = unpad1d(y, (padding_left, padding_right))
|
||
else:
|
||
# Asymmetric padding required for odd strides
|
||
padding_right = padding_total // 2
|
||
padding_left = padding_total - padding_right
|
||
y = unpad1d(y, (padding_left, padding_right))
|
||
return y</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Defines the computation performed at every call.</p>
|
||
<p>Should be overridden by all subclasses.</p>
|
||
<div class="admonition note">
|
||
<p class="admonition-title">Note</p>
|
||
<p>Although the recipe for forward pass needs to be defined within
|
||
this function, one should call the :class:<code>Module</code> instance afterwards
|
||
instead of this since the former takes care of running the
|
||
registered hooks while the latter silently ignores them.</p>
|
||
</div></div>
|
||
</dd>
|
||
</dl>
|
||
</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.modules" href="index.html">audiocraft.modules</a></code></li>
|
||
</ul>
|
||
</li>
|
||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||
<ul class="">
|
||
<li><code><a title="audiocraft.modules.conv.apply_parametrization_norm" href="#audiocraft.modules.conv.apply_parametrization_norm">apply_parametrization_norm</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.get_extra_padding_for_conv1d" href="#audiocraft.modules.conv.get_extra_padding_for_conv1d">get_extra_padding_for_conv1d</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.get_norm_module" href="#audiocraft.modules.conv.get_norm_module">get_norm_module</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.pad1d" href="#audiocraft.modules.conv.pad1d">pad1d</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.pad_for_conv1d" href="#audiocraft.modules.conv.pad_for_conv1d">pad_for_conv1d</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.unpad1d" href="#audiocraft.modules.conv.unpad1d">unpad1d</a></code></li>
|
||
</ul>
|
||
</li>
|
||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||
<ul>
|
||
<li>
|
||
<h4><code><a title="audiocraft.modules.conv.NormConv1d" href="#audiocraft.modules.conv.NormConv1d">NormConv1d</a></code></h4>
|
||
<ul class="">
|
||
<li><code><a title="audiocraft.modules.conv.NormConv1d.call_super_init" href="#audiocraft.modules.conv.NormConv1d.call_super_init">call_super_init</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.NormConv1d.dump_patches" href="#audiocraft.modules.conv.NormConv1d.dump_patches">dump_patches</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.NormConv1d.forward" href="#audiocraft.modules.conv.NormConv1d.forward">forward</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.NormConv1d.training" href="#audiocraft.modules.conv.NormConv1d.training">training</a></code></li>
|
||
</ul>
|
||
</li>
|
||
<li>
|
||
<h4><code><a title="audiocraft.modules.conv.NormConv2d" href="#audiocraft.modules.conv.NormConv2d">NormConv2d</a></code></h4>
|
||
<ul class="">
|
||
<li><code><a title="audiocraft.modules.conv.NormConv2d.call_super_init" href="#audiocraft.modules.conv.NormConv2d.call_super_init">call_super_init</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.NormConv2d.dump_patches" href="#audiocraft.modules.conv.NormConv2d.dump_patches">dump_patches</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.NormConv2d.forward" href="#audiocraft.modules.conv.NormConv2d.forward">forward</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.NormConv2d.training" href="#audiocraft.modules.conv.NormConv2d.training">training</a></code></li>
|
||
</ul>
|
||
</li>
|
||
<li>
|
||
<h4><code><a title="audiocraft.modules.conv.NormConvTranspose1d" href="#audiocraft.modules.conv.NormConvTranspose1d">NormConvTranspose1d</a></code></h4>
|
||
<ul class="">
|
||
<li><code><a title="audiocraft.modules.conv.NormConvTranspose1d.call_super_init" href="#audiocraft.modules.conv.NormConvTranspose1d.call_super_init">call_super_init</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.NormConvTranspose1d.dump_patches" href="#audiocraft.modules.conv.NormConvTranspose1d.dump_patches">dump_patches</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.NormConvTranspose1d.forward" href="#audiocraft.modules.conv.NormConvTranspose1d.forward">forward</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.NormConvTranspose1d.training" href="#audiocraft.modules.conv.NormConvTranspose1d.training">training</a></code></li>
|
||
</ul>
|
||
</li>
|
||
<li>
|
||
<h4><code><a title="audiocraft.modules.conv.NormConvTranspose2d" href="#audiocraft.modules.conv.NormConvTranspose2d">NormConvTranspose2d</a></code></h4>
|
||
<ul class="">
|
||
<li><code><a title="audiocraft.modules.conv.NormConvTranspose2d.call_super_init" href="#audiocraft.modules.conv.NormConvTranspose2d.call_super_init">call_super_init</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.NormConvTranspose2d.dump_patches" href="#audiocraft.modules.conv.NormConvTranspose2d.dump_patches">dump_patches</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.NormConvTranspose2d.forward" href="#audiocraft.modules.conv.NormConvTranspose2d.forward">forward</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.NormConvTranspose2d.training" href="#audiocraft.modules.conv.NormConvTranspose2d.training">training</a></code></li>
|
||
</ul>
|
||
</li>
|
||
<li>
|
||
<h4><code><a title="audiocraft.modules.conv.StreamableConv1d" href="#audiocraft.modules.conv.StreamableConv1d">StreamableConv1d</a></code></h4>
|
||
<ul class="">
|
||
<li><code><a title="audiocraft.modules.conv.StreamableConv1d.call_super_init" href="#audiocraft.modules.conv.StreamableConv1d.call_super_init">call_super_init</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.StreamableConv1d.dump_patches" href="#audiocraft.modules.conv.StreamableConv1d.dump_patches">dump_patches</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.StreamableConv1d.forward" href="#audiocraft.modules.conv.StreamableConv1d.forward">forward</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.StreamableConv1d.training" href="#audiocraft.modules.conv.StreamableConv1d.training">training</a></code></li>
|
||
</ul>
|
||
</li>
|
||
<li>
|
||
<h4><code><a title="audiocraft.modules.conv.StreamableConvTranspose1d" href="#audiocraft.modules.conv.StreamableConvTranspose1d">StreamableConvTranspose1d</a></code></h4>
|
||
<ul class="">
|
||
<li><code><a title="audiocraft.modules.conv.StreamableConvTranspose1d.call_super_init" href="#audiocraft.modules.conv.StreamableConvTranspose1d.call_super_init">call_super_init</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.StreamableConvTranspose1d.dump_patches" href="#audiocraft.modules.conv.StreamableConvTranspose1d.dump_patches">dump_patches</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.StreamableConvTranspose1d.forward" href="#audiocraft.modules.conv.StreamableConvTranspose1d.forward">forward</a></code></li>
|
||
<li><code><a title="audiocraft.modules.conv.StreamableConvTranspose1d.training" href="#audiocraft.modules.conv.StreamableConvTranspose1d.training">training</a></code></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
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|
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