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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from .basic import UNetBlock
from modules.general.utils import (
append_dims,
ConvNd,
normalization,
zero_module,
)
class ResBlock(UNetBlock):
r"""A residual block that can optionally change the number of channels.
Args:
channels: the number of input channels.
emb_channels: the number of timestep embedding channels.
dropout: the rate of dropout.
out_channels: if specified, the number of out channels.
use_conv: if True and out_channels is specified, use a spatial
convolution instead of a smaller 1x1 convolution to change the
channels in the skip connection.
dims: determines if the signal is 1D, 2D, or 3D.
up: if True, use this block for upsampling.
down: if True, use this block for downsampling.
"""
def __init__(
self,
channels,
emb_channels,
dropout: float = 0.0,
out_channels=None,
use_conv=False,
use_scale_shift_norm=False,
dims=2,
up=False,
down=False,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_scale_shift_norm = use_scale_shift_norm
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
ConvNd(dims, channels, self.out_channels, 3, padding=1),
)
self.updown = up or down
if up:
self.h_upd = Upsample(channels, False, dims)
self.x_upd = Upsample(channels, False, dims)
elif down:
self.h_upd = Downsample(channels, False, dims)
self.x_upd = Downsample(channels, False, dims)
else:
self.h_upd = self.x_upd = nn.Identity()
self.emb_layers = nn.Sequential(
nn.SiLU(),
ConvNd(
dims,
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
1,
),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
ConvNd(dims, self.out_channels, self.out_channels, 3, padding=1)
),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
elif use_conv:
self.skip_connection = ConvNd(
dims, channels, self.out_channels, 3, padding=1
)
else:
self.skip_connection = ConvNd(dims, channels, self.out_channels, 1)
def forward(self, x, emb):
"""
Apply the block to a Tensor, conditioned on a timestep embedding.
x: an [N x C x ...] Tensor of features.
emb: an [N x emb_channels x ...] Tensor of timestep embeddings.
:return: an [N x C x ...] Tensor of outputs.
"""
if self.updown:
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
h = in_rest(x)
h = self.h_upd(h)
x = self.x_upd(x)
h = in_conv(h)
else:
h = self.in_layers(x)
emb_out = self.emb_layers(emb)
emb_out = append_dims(emb_out, h.dim())
if self.use_scale_shift_norm:
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
scale, shift = torch.chunk(emb_out, 2, dim=1)
h = out_norm(h) * (1 + scale) + shift
h = out_rest(h)
else:
h = h + emb_out
h = self.out_layers(h)
return self.skip_connection(x) + h
class Upsample(nn.Module):
r"""An upsampling layer with an optional convolution.
Args:
channels: channels in the inputs and outputs.
dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
upsampling occurs in the inner-two dimensions.
out_channels: if specified, the number of out channels.
"""
def __init__(self, channels, dims=2, out_channels=None):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.dims = dims
self.conv = ConvNd(dims, self.channels, self.out_channels, 3, padding=1)
def forward(self, x):
assert x.shape[1] == self.channels
if self.dims == 3:
x = F.interpolate(
x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
)
else:
x = F.interpolate(x, scale_factor=2, mode="nearest")
x = self.conv(x)
return x
class Downsample(nn.Module):
r"""A downsampling layer with an optional convolution.
Args:
channels: channels in the inputs and outputs.
dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
downsampling occurs in the inner-two dimensions.
out_channels: if specified, the number of output channels.
"""
def __init__(self, channels, dims=2, out_channels=None):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.dims = dims
stride = 2 if dims != 3 else (1, 2, 2)
self.op = ConvNd(
dims, self.channels, self.out_channels, 3, stride=stride, padding=1
)
def forward(self, x):
assert x.shape[1] == self.channels
return self.op(x)
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