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# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import Optional, Tuple, Union
import torch
import torch.nn.functional as F
from torch import nn
from .activations import get_activation
from .resnet import Downsample1D, ResidualTemporalBlock1D, Upsample1D, rearrange_dims
class DownResnetBlock1D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: Optional[int] = None,
num_layers: int = 1,
conv_shortcut: bool = False,
temb_channels: int = 32,
groups: int = 32,
groups_out: Optional[int] = None,
non_linearity: Optional[str] = None,
time_embedding_norm: str = "default",
output_scale_factor: float = 1.0,
add_downsample: bool = True,
):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.time_embedding_norm = time_embedding_norm
self.add_downsample = add_downsample
self.output_scale_factor = output_scale_factor
if groups_out is None:
groups_out = groups
# there will always be at least one resnet
resnets = [ResidualTemporalBlock1D(in_channels, out_channels, embed_dim=temb_channels)]
for _ in range(num_layers):
resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=temb_channels))
self.resnets = nn.ModuleList(resnets)
if non_linearity is None:
self.nonlinearity = None
else:
self.nonlinearity = get_activation(non_linearity)
self.downsample = None
if add_downsample:
self.downsample = Downsample1D(out_channels, use_conv=True, padding=1)
def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
output_states = ()
hidden_states = self.resnets[0](hidden_states, temb)
for resnet in self.resnets[1:]:
hidden_states = resnet(hidden_states, temb)
output_states += (hidden_states,)
if self.nonlinearity is not None:
hidden_states = self.nonlinearity(hidden_states)
if self.downsample is not None:
hidden_states = self.downsample(hidden_states)
return hidden_states, output_states
class UpResnetBlock1D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: Optional[int] = None,
num_layers: int = 1,
temb_channels: int = 32,
groups: int = 32,
groups_out: Optional[int] = None,
non_linearity: Optional[str] = None,
time_embedding_norm: str = "default",
output_scale_factor: float = 1.0,
add_upsample: bool = True,
):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.time_embedding_norm = time_embedding_norm
self.add_upsample = add_upsample
self.output_scale_factor = output_scale_factor
if groups_out is None:
groups_out = groups
# there will always be at least one resnet
resnets = [ResidualTemporalBlock1D(2 * in_channels, out_channels, embed_dim=temb_channels)]
for _ in range(num_layers):
resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=temb_channels))
self.resnets = nn.ModuleList(resnets)
if non_linearity is None:
self.nonlinearity = None
else:
self.nonlinearity = get_activation(non_linearity)
self.upsample = None
if add_upsample:
self.upsample = Upsample1D(out_channels, use_conv_transpose=True)
def forward(
self,
hidden_states: torch.FloatTensor,
res_hidden_states_tuple: Optional[Tuple[torch.FloatTensor, ...]] = None,
temb: Optional[torch.FloatTensor] = None,
) -> torch.FloatTensor:
if res_hidden_states_tuple is not None:
res_hidden_states = res_hidden_states_tuple[-1]
hidden_states = torch.cat((hidden_states, res_hidden_states), dim=1)
hidden_states = self.resnets[0](hidden_states, temb)
for resnet in self.resnets[1:]:
hidden_states = resnet(hidden_states, temb)
if self.nonlinearity is not None:
hidden_states = self.nonlinearity(hidden_states)
if self.upsample is not None:
hidden_states = self.upsample(hidden_states)
return hidden_states
class ValueFunctionMidBlock1D(nn.Module):
def __init__(self, in_channels: int, out_channels: int, embed_dim: int):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.embed_dim = embed_dim
self.res1 = ResidualTemporalBlock1D(in_channels, in_channels // 2, embed_dim=embed_dim)
self.down1 = Downsample1D(out_channels // 2, use_conv=True)
self.res2 = ResidualTemporalBlock1D(in_channels // 2, in_channels // 4, embed_dim=embed_dim)
self.down2 = Downsample1D(out_channels // 4, use_conv=True)
def forward(self, x: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
x = self.res1(x, temb)
x = self.down1(x)
x = self.res2(x, temb)
x = self.down2(x)
return x
class MidResTemporalBlock1D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
embed_dim: int,
num_layers: int = 1,
add_downsample: bool = False,
add_upsample: bool = False,
non_linearity: Optional[str] = None,
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.add_downsample = add_downsample
# there will always be at least one resnet
resnets = [ResidualTemporalBlock1D(in_channels, out_channels, embed_dim=embed_dim)]
for _ in range(num_layers):
resnets.append(ResidualTemporalBlock1D(out_channels, out_channels, embed_dim=embed_dim))
self.resnets = nn.ModuleList(resnets)
if non_linearity is None:
self.nonlinearity = None
else:
self.nonlinearity = get_activation(non_linearity)
self.upsample = None
if add_upsample:
self.upsample = Downsample1D(out_channels, use_conv=True)
self.downsample = None
if add_downsample:
self.downsample = Downsample1D(out_channels, use_conv=True)
if self.upsample and self.downsample:
raise ValueError("Block cannot downsample and upsample")
def forward(self, hidden_states: torch.FloatTensor, temb: torch.FloatTensor) -> torch.FloatTensor:
hidden_states = self.resnets[0](hidden_states, temb)
for resnet in self.resnets[1:]:
hidden_states = resnet(hidden_states, temb)
if self.upsample:
hidden_states = self.upsample(hidden_states)
if self.downsample:
self.downsample = self.downsample(hidden_states)
return hidden_states
class OutConv1DBlock(nn.Module):
def __init__(self, num_groups_out: int, out_channels: int, embed_dim: int, act_fn: str):
super().__init__()
self.final_conv1d_1 = nn.Conv1d(embed_dim, embed_dim, 5, padding=2)
self.final_conv1d_gn = nn.GroupNorm(num_groups_out, embed_dim)
self.final_conv1d_act = get_activation(act_fn)
self.final_conv1d_2 = nn.Conv1d(embed_dim, out_channels, 1)
def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
hidden_states = self.final_conv1d_1(hidden_states)
hidden_states = rearrange_dims(hidden_states)
hidden_states = self.final_conv1d_gn(hidden_states)
hidden_states = rearrange_dims(hidden_states)
hidden_states = self.final_conv1d_act(hidden_states)
hidden_states = self.final_conv1d_2(hidden_states)
return hidden_states
class OutValueFunctionBlock(nn.Module):
def __init__(self, fc_dim: int, embed_dim: int, act_fn: str = "mish"):
super().__init__()
self.final_block = nn.ModuleList(
[
nn.Linear(fc_dim + embed_dim, fc_dim // 2),
get_activation(act_fn),
nn.Linear(fc_dim // 2, 1),
]
)
def forward(self, hidden_states: torch.FloatTensor, temb: torch.FloatTensor) -> torch.FloatTensor:
hidden_states = hidden_states.view(hidden_states.shape[0], -1)
hidden_states = torch.cat((hidden_states, temb), dim=-1)
for layer in self.final_block:
hidden_states = layer(hidden_states)
return hidden_states
_kernels = {
"linear": [1 / 8, 3 / 8, 3 / 8, 1 / 8],
"cubic": [-0.01171875, -0.03515625, 0.11328125, 0.43359375, 0.43359375, 0.11328125, -0.03515625, -0.01171875],
"lanczos3": [
0.003689131001010537,
0.015056144446134567,
-0.03399861603975296,
-0.066637322306633,
0.13550527393817902,
0.44638532400131226,
0.44638532400131226,
0.13550527393817902,
-0.066637322306633,
-0.03399861603975296,
0.015056144446134567,
0.003689131001010537,
],
}
class Downsample1d(nn.Module):
def __init__(self, kernel: str = "linear", pad_mode: str = "reflect"):
super().__init__()
self.pad_mode = pad_mode
kernel_1d = torch.tensor(_kernels[kernel])
self.pad = kernel_1d.shape[0] // 2 - 1
self.register_buffer("kernel", kernel_1d)
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
hidden_states = F.pad(hidden_states, (self.pad,) * 2, self.pad_mode)
weight = hidden_states.new_zeros([hidden_states.shape[1], hidden_states.shape[1], self.kernel.shape[0]])
indices = torch.arange(hidden_states.shape[1], device=hidden_states.device)
kernel = self.kernel.to(weight)[None, :].expand(hidden_states.shape[1], -1)
weight[indices, indices] = kernel
return F.conv1d(hidden_states, weight, stride=2)
class Upsample1d(nn.Module):
def __init__(self, kernel: str = "linear", pad_mode: str = "reflect"):
super().__init__()
self.pad_mode = pad_mode
kernel_1d = torch.tensor(_kernels[kernel]) * 2
self.pad = kernel_1d.shape[0] // 2 - 1
self.register_buffer("kernel", kernel_1d)
def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
hidden_states = F.pad(hidden_states, ((self.pad + 1) // 2,) * 2, self.pad_mode)
weight = hidden_states.new_zeros([hidden_states.shape[1], hidden_states.shape[1], self.kernel.shape[0]])
indices = torch.arange(hidden_states.shape[1], device=hidden_states.device)
kernel = self.kernel.to(weight)[None, :].expand(hidden_states.shape[1], -1)
weight[indices, indices] = kernel
return F.conv_transpose1d(hidden_states, weight, stride=2, padding=self.pad * 2 + 1)
class SelfAttention1d(nn.Module):
def __init__(self, in_channels: int, n_head: int = 1, dropout_rate: float = 0.0):
super().__init__()
self.channels = in_channels
self.group_norm = nn.GroupNorm(1, num_channels=in_channels)
self.num_heads = n_head
self.query = nn.Linear(self.channels, self.channels)
self.key = nn.Linear(self.channels, self.channels)
self.value = nn.Linear(self.channels, self.channels)
self.proj_attn = nn.Linear(self.channels, self.channels, bias=True)
self.dropout = nn.Dropout(dropout_rate, inplace=True)
def transpose_for_scores(self, projection: torch.Tensor) -> torch.Tensor:
new_projection_shape = projection.size()[:-1] + (self.num_heads, -1)
# move heads to 2nd position (B, T, H * D) -> (B, T, H, D) -> (B, H, T, D)
new_projection = projection.view(new_projection_shape).permute(0, 2, 1, 3)
return new_projection
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
residual = hidden_states
batch, channel_dim, seq = hidden_states.shape
hidden_states = self.group_norm(hidden_states)
hidden_states = hidden_states.transpose(1, 2)
query_proj = self.query(hidden_states)
key_proj = self.key(hidden_states)
value_proj = self.value(hidden_states)
query_states = self.transpose_for_scores(query_proj)
key_states = self.transpose_for_scores(key_proj)
value_states = self.transpose_for_scores(value_proj)
scale = 1 / math.sqrt(math.sqrt(key_states.shape[-1]))
attention_scores = torch.matmul(query_states * scale, key_states.transpose(-1, -2) * scale)
attention_probs = torch.softmax(attention_scores, dim=-1)
# compute attention output
hidden_states = torch.matmul(attention_probs, value_states)
hidden_states = hidden_states.permute(0, 2, 1, 3).contiguous()
new_hidden_states_shape = hidden_states.size()[:-2] + (self.channels,)
hidden_states = hidden_states.view(new_hidden_states_shape)
# compute next hidden_states
hidden_states = self.proj_attn(hidden_states)
hidden_states = hidden_states.transpose(1, 2)
hidden_states = self.dropout(hidden_states)
output = hidden_states + residual
return output
class ResConvBlock(nn.Module):
def __init__(self, in_channels: int, mid_channels: int, out_channels: int, is_last: bool = False):
super().__init__()
self.is_last = is_last
self.has_conv_skip = in_channels != out_channels
if self.has_conv_skip:
self.conv_skip = nn.Conv1d(in_channels, out_channels, 1, bias=False)
self.conv_1 = nn.Conv1d(in_channels, mid_channels, 5, padding=2)
self.group_norm_1 = nn.GroupNorm(1, mid_channels)
self.gelu_1 = nn.GELU()
self.conv_2 = nn.Conv1d(mid_channels, out_channels, 5, padding=2)
if not self.is_last:
self.group_norm_2 = nn.GroupNorm(1, out_channels)
self.gelu_2 = nn.GELU()
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
residual = self.conv_skip(hidden_states) if self.has_conv_skip else hidden_states
hidden_states = self.conv_1(hidden_states)
hidden_states = self.group_norm_1(hidden_states)
hidden_states = self.gelu_1(hidden_states)
hidden_states = self.conv_2(hidden_states)
if not self.is_last:
hidden_states = self.group_norm_2(hidden_states)
hidden_states = self.gelu_2(hidden_states)
output = hidden_states + residual
return output
class UNetMidBlock1D(nn.Module):
def __init__(self, mid_channels: int, in_channels: int, out_channels: Optional[int] = None):
super().__init__()
out_channels = in_channels if out_channels is None else out_channels
# there is always at least one resnet
self.down = Downsample1d("cubic")
resnets = [
ResConvBlock(in_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, out_channels),
]
attentions = [
SelfAttention1d(mid_channels, mid_channels // 32),
SelfAttention1d(mid_channels, mid_channels // 32),
SelfAttention1d(mid_channels, mid_channels // 32),
SelfAttention1d(mid_channels, mid_channels // 32),
SelfAttention1d(mid_channels, mid_channels // 32),
SelfAttention1d(out_channels, out_channels // 32),
]
self.up = Upsample1d(kernel="cubic")
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
hidden_states = self.down(hidden_states)
for attn, resnet in zip(self.attentions, self.resnets):
hidden_states = resnet(hidden_states)
hidden_states = attn(hidden_states)
hidden_states = self.up(hidden_states)
return hidden_states
class AttnDownBlock1D(nn.Module):
def __init__(self, out_channels: int, in_channels: int, mid_channels: Optional[int] = None):
super().__init__()
mid_channels = out_channels if mid_channels is None else mid_channels
self.down = Downsample1d("cubic")
resnets = [
ResConvBlock(in_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, out_channels),
]
attentions = [
SelfAttention1d(mid_channels, mid_channels // 32),
SelfAttention1d(mid_channels, mid_channels // 32),
SelfAttention1d(out_channels, out_channels // 32),
]
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
hidden_states = self.down(hidden_states)
for resnet, attn in zip(self.resnets, self.attentions):
hidden_states = resnet(hidden_states)
hidden_states = attn(hidden_states)
return hidden_states, (hidden_states,)
class DownBlock1D(nn.Module):
def __init__(self, out_channels: int, in_channels: int, mid_channels: Optional[int] = None):
super().__init__()
mid_channels = out_channels if mid_channels is None else mid_channels
self.down = Downsample1d("cubic")
resnets = [
ResConvBlock(in_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, out_channels),
]
self.resnets = nn.ModuleList(resnets)
def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
hidden_states = self.down(hidden_states)
for resnet in self.resnets:
hidden_states = resnet(hidden_states)
return hidden_states, (hidden_states,)
class DownBlock1DNoSkip(nn.Module):
def __init__(self, out_channels: int, in_channels: int, mid_channels: Optional[int] = None):
super().__init__()
mid_channels = out_channels if mid_channels is None else mid_channels
resnets = [
ResConvBlock(in_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, out_channels),
]
self.resnets = nn.ModuleList(resnets)
def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
hidden_states = torch.cat([hidden_states, temb], dim=1)
for resnet in self.resnets:
hidden_states = resnet(hidden_states)
return hidden_states, (hidden_states,)
class AttnUpBlock1D(nn.Module):
def __init__(self, in_channels: int, out_channels: int, mid_channels: Optional[int] = None):
super().__init__()
mid_channels = out_channels if mid_channels is None else mid_channels
resnets = [
ResConvBlock(2 * in_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, out_channels),
]
attentions = [
SelfAttention1d(mid_channels, mid_channels // 32),
SelfAttention1d(mid_channels, mid_channels // 32),
SelfAttention1d(out_channels, out_channels // 32),
]
self.attentions = nn.ModuleList(attentions)
self.resnets = nn.ModuleList(resnets)
self.up = Upsample1d(kernel="cubic")
def forward(
self,
hidden_states: torch.FloatTensor,
res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
temb: Optional[torch.FloatTensor] = None,
) -> torch.FloatTensor:
res_hidden_states = res_hidden_states_tuple[-1]
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
for resnet, attn in zip(self.resnets, self.attentions):
hidden_states = resnet(hidden_states)
hidden_states = attn(hidden_states)
hidden_states = self.up(hidden_states)
return hidden_states
class UpBlock1D(nn.Module):
def __init__(self, in_channels: int, out_channels: int, mid_channels: Optional[int] = None):
super().__init__()
mid_channels = in_channels if mid_channels is None else mid_channels
resnets = [
ResConvBlock(2 * in_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, out_channels),
]
self.resnets = nn.ModuleList(resnets)
self.up = Upsample1d(kernel="cubic")
def forward(
self,
hidden_states: torch.FloatTensor,
res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
temb: Optional[torch.FloatTensor] = None,
) -> torch.FloatTensor:
res_hidden_states = res_hidden_states_tuple[-1]
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
for resnet in self.resnets:
hidden_states = resnet(hidden_states)
hidden_states = self.up(hidden_states)
return hidden_states
class UpBlock1DNoSkip(nn.Module):
def __init__(self, in_channels: int, out_channels: int, mid_channels: Optional[int] = None):
super().__init__()
mid_channels = in_channels if mid_channels is None else mid_channels
resnets = [
ResConvBlock(2 * in_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, mid_channels),
ResConvBlock(mid_channels, mid_channels, out_channels, is_last=True),
]
self.resnets = nn.ModuleList(resnets)
def forward(
self,
hidden_states: torch.FloatTensor,
res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
temb: Optional[torch.FloatTensor] = None,
) -> torch.FloatTensor:
res_hidden_states = res_hidden_states_tuple[-1]
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
for resnet in self.resnets:
hidden_states = resnet(hidden_states)
return hidden_states
DownBlockType = Union[DownResnetBlock1D, DownBlock1D, AttnDownBlock1D, DownBlock1DNoSkip]
MidBlockType = Union[MidResTemporalBlock1D, ValueFunctionMidBlock1D, UNetMidBlock1D]
OutBlockType = Union[OutConv1DBlock, OutValueFunctionBlock]
UpBlockType = Union[UpResnetBlock1D, UpBlock1D, AttnUpBlock1D, UpBlock1DNoSkip]
def get_down_block(
down_block_type: str,
num_layers: int,
in_channels: int,
out_channels: int,
temb_channels: int,
add_downsample: bool,
) -> DownBlockType:
if down_block_type == "DownResnetBlock1D":
return DownResnetBlock1D(
in_channels=in_channels,
num_layers=num_layers,
out_channels=out_channels,
temb_channels=temb_channels,
add_downsample=add_downsample,
)
elif down_block_type == "DownBlock1D":
return DownBlock1D(out_channels=out_channels, in_channels=in_channels)
elif down_block_type == "AttnDownBlock1D":
return AttnDownBlock1D(out_channels=out_channels, in_channels=in_channels)
elif down_block_type == "DownBlock1DNoSkip":
return DownBlock1DNoSkip(out_channels=out_channels, in_channels=in_channels)
raise ValueError(f"{down_block_type} does not exist.")
def get_up_block(
up_block_type: str, num_layers: int, in_channels: int, out_channels: int, temb_channels: int, add_upsample: bool
) -> UpBlockType:
if up_block_type == "UpResnetBlock1D":
return UpResnetBlock1D(
in_channels=in_channels,
num_layers=num_layers,
out_channels=out_channels,
temb_channels=temb_channels,
add_upsample=add_upsample,
)
elif up_block_type == "UpBlock1D":
return UpBlock1D(in_channels=in_channels, out_channels=out_channels)
elif up_block_type == "AttnUpBlock1D":
return AttnUpBlock1D(in_channels=in_channels, out_channels=out_channels)
elif up_block_type == "UpBlock1DNoSkip":
return UpBlock1DNoSkip(in_channels=in_channels, out_channels=out_channels)
raise ValueError(f"{up_block_type} does not exist.")
def get_mid_block(
mid_block_type: str,
num_layers: int,
in_channels: int,
mid_channels: int,
out_channels: int,
embed_dim: int,
add_downsample: bool,
) -> MidBlockType:
if mid_block_type == "MidResTemporalBlock1D":
return MidResTemporalBlock1D(
num_layers=num_layers,
in_channels=in_channels,
out_channels=out_channels,
embed_dim=embed_dim,
add_downsample=add_downsample,
)
elif mid_block_type == "ValueFunctionMidBlock1D":
return ValueFunctionMidBlock1D(in_channels=in_channels, out_channels=out_channels, embed_dim=embed_dim)
elif mid_block_type == "UNetMidBlock1D":
return UNetMidBlock1D(in_channels=in_channels, mid_channels=mid_channels, out_channels=out_channels)
raise ValueError(f"{mid_block_type} does not exist.")
def get_out_block(
*, out_block_type: str, num_groups_out: int, embed_dim: int, out_channels: int, act_fn: str, fc_dim: int
) -> Optional[OutBlockType]:
if out_block_type == "OutConv1DBlock":
return OutConv1DBlock(num_groups_out, out_channels, embed_dim, act_fn)
elif out_block_type == "ValueFunction":
return OutValueFunctionBlock(fc_dim, embed_dim, act_fn)
return None
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