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import torch
import torch.nn as nn
from .attention import Attention3D, SpatialAttention, TemporalAttention
from .common import ResidualBlock3D
def get_mid_block(
mid_block_type: str,
in_channels: int,
num_layers: int,
act_fn: str,
norm_num_groups: int = 32,
norm_eps: float = 1e-6,
dropout: float = 0.0,
add_attention: bool = True,
attention_type: str = "3d",
num_attention_heads: int = 1,
output_scale_factor: float = 1.0,
) -> nn.Module:
if mid_block_type == "MidBlock3D":
return MidBlock3D(
in_channels=in_channels,
num_layers=num_layers,
act_fn=act_fn,
norm_num_groups=norm_num_groups,
norm_eps=norm_eps,
dropout=dropout,
add_attention=add_attention,
attention_type=attention_type,
attention_head_dim=in_channels // num_attention_heads,
output_scale_factor=output_scale_factor,
)
else:
raise ValueError(f"Unknown mid block type: {mid_block_type}")
class MidBlock3D(nn.Module):
"""
A 3D UNet mid-block [`MidBlock3D`] with multiple residual blocks and optional attention blocks.
Args:
in_channels (`int`): The number of input channels.
num_layers (`int`, *optional*, defaults to 1): The number of residual blocks.
act_fn (`str`, *optional*, defaults to `swish`): The activation function for the resnet blocks.
norm_num_groups (`int`, *optional*, defaults to 32):
The number of groups to use in the group normalization layers of the resnet blocks.
norm_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks.
dropout (`float`, *optional*, defaults to 0.0): The dropout rate.
add_attention (`bool`, *optional*, defaults to `True`): Whether to add attention blocks.
attention_type: (`str`, *optional*, defaults to `3d`): The type of attention to use. Defaults to `3d`.
attention_head_dim (`int`, *optional*, defaults to 1):
Dimension of a single attention head. The number of attention heads is determined based on this value and
the number of input channels.
output_scale_factor (`float`, *optional*, defaults to 1.0): The output scale factor.
Returns:
`torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size,
in_channels, temporal_length, height, width)`.
"""
def __init__(
self,
in_channels: int,
num_layers: int = 1,
act_fn: str = "silu",
norm_num_groups: int = 32,
norm_eps: float = 1e-6,
dropout: float = 0.0,
add_attention: bool = True,
attention_type: str = "3d",
attention_head_dim: int = 1,
output_scale_factor: float = 1.0,
):
super().__init__()
self.attention_type = attention_type
norm_num_groups = norm_num_groups if norm_num_groups is not None else min(in_channels // 4, 32)
self.convs = nn.ModuleList([
ResidualBlock3D(
in_channels=in_channels,
out_channels=in_channels,
non_linearity=act_fn,
norm_num_groups=norm_num_groups,
norm_eps=norm_eps,
dropout=dropout,
output_scale_factor=output_scale_factor,
)
])
self.attentions = nn.ModuleList([])
for _ in range(num_layers - 1):
if add_attention:
if attention_type == "3d":
self.attentions.append(
Attention3D(
in_channels,
nheads=in_channels // attention_head_dim,
head_dim=attention_head_dim,
bias=True,
upcast_softmax=True,
norm_num_groups=norm_num_groups,
eps=norm_eps,
rescale_output_factor=output_scale_factor,
residual_connection=True,
)
)
elif attention_type == "spatial_temporal":
self.attentions.append(
nn.ModuleList([
SpatialAttention(
in_channels,
nheads=in_channels // attention_head_dim,
head_dim=attention_head_dim,
bias=True,
upcast_softmax=True,
norm_num_groups=norm_num_groups,
eps=norm_eps,
rescale_output_factor=output_scale_factor,
residual_connection=True,
),
TemporalAttention(
in_channels,
nheads=in_channels // attention_head_dim,
head_dim=attention_head_dim,
bias=True,
upcast_softmax=True,
norm_num_groups=norm_num_groups,
eps=norm_eps,
rescale_output_factor=output_scale_factor,
residual_connection=True,
),
])
)
elif attention_type == "spatial":
self.attentions.append(
SpatialAttention(
in_channels,
nheads=in_channels // attention_head_dim,
head_dim=attention_head_dim,
bias=True,
upcast_softmax=True,
norm_num_groups=norm_num_groups,
eps=norm_eps,
rescale_output_factor=output_scale_factor,
residual_connection=True,
)
)
elif attention_type == "temporal":
self.attentions.append(
TemporalAttention(
in_channels,
nheads=in_channels // attention_head_dim,
head_dim=attention_head_dim,
bias=True,
upcast_softmax=True,
norm_num_groups=norm_num_groups,
eps=norm_eps,
rescale_output_factor=output_scale_factor,
residual_connection=True,
)
)
else:
raise ValueError(f"Unknown attention type: {attention_type}")
else:
self.attentions.append(None)
self.convs.append(
ResidualBlock3D(
in_channels=in_channels,
out_channels=in_channels,
non_linearity=act_fn,
norm_num_groups=norm_num_groups,
norm_eps=norm_eps,
dropout=dropout,
output_scale_factor=output_scale_factor,
)
)
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
hidden_states = self.convs[0](hidden_states)
for attn, resnet in zip(self.attentions, self.convs[1:]):
if attn is not None:
if self.attention_type == "spatial_temporal":
spatial_attn, temporal_attn = attn
hidden_states = spatial_attn(hidden_states)
hidden_states = temporal_attn(hidden_states)
else:
hidden_states = attn(hidden_states)
hidden_states = resnet(hidden_states)
return hidden_states
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