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# pytorch_diffusion + derived encoder decoder
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from beartype import beartype
from beartype.typing import Union, Tuple, Optional, List
from einops import rearrange
def cast_tuple(t, length=1):
return t if isinstance(t, tuple) else ((t,) * length)
def divisible_by(num, den):
return (num % den) == 0
def is_odd(n):
return not divisible_by(n, 2)
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
"""
assert len(timesteps.shape) == 1
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
emb = emb.to(device=timesteps.device)
emb = timesteps.float()[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero pad
emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
return emb
def nonlinearity(x):
# swish
return x * torch.sigmoid(x)
class CausalConv3d(nn.Module):
@beartype
def __init__(
self, chan_in, chan_out, kernel_size: Union[int, Tuple[int, int, int]], pad_mode="constant", **kwargs
):
super().__init__()
kernel_size = cast_tuple(kernel_size, 3)
time_kernel_size, height_kernel_size, width_kernel_size = kernel_size
assert is_odd(height_kernel_size) and is_odd(width_kernel_size)
dilation = kwargs.pop("dilation", 1)
stride = kwargs.pop("stride", 1)
self.pad_mode = pad_mode
time_pad = dilation * (time_kernel_size - 1) + (1 - stride)
height_pad = height_kernel_size // 2
width_pad = width_kernel_size // 2
self.height_pad = height_pad
self.width_pad = width_pad
self.time_pad = time_pad
self.time_causal_padding = (width_pad, width_pad, height_pad, height_pad, time_pad, 0)
stride = (stride, 1, 1)
dilation = (dilation, 1, 1)
self.conv = nn.Conv3d(chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs)
def forward(self, x):
if self.pad_mode == "constant":
causal_padding_3d = (self.time_pad, 0, self.width_pad, self.width_pad, self.height_pad, self.height_pad)
x = F.pad(x, causal_padding_3d, mode="constant", value=0)
elif self.pad_mode == "first":
pad_x = torch.cat([x[:, :, :1]] * self.time_pad, dim=2)
x = torch.cat([pad_x, x], dim=2)
causal_padding_2d = (self.width_pad, self.width_pad, self.height_pad, self.height_pad)
x = F.pad(x, causal_padding_2d, mode="constant", value=0)
elif self.pad_mode == "reflect":
# reflect padding
reflect_x = x[:, :, 1 : self.time_pad + 1, :, :].flip(dims=[2])
if reflect_x.shape[2] < self.time_pad:
reflect_x = torch.cat(
[torch.zeros_like(x[:, :, :1, :, :])] * (self.time_pad - reflect_x.shape[2]) + [reflect_x], dim=2
)
x = torch.cat([reflect_x, x], dim=2)
causal_padding_2d = (self.width_pad, self.width_pad, self.height_pad, self.height_pad)
x = F.pad(x, causal_padding_2d, mode="constant", value=0)
else:
raise ValueError("Invalid pad mode")
return self.conv(x)
def Normalize3D(in_channels): # same for 3D and 2D
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
class Upsample3D(nn.Module):
def __init__(self, in_channels, with_conv, compress_time=False):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
self.compress_time = compress_time
def forward(self, x):
if self.compress_time:
if x.shape[2] > 1:
# split first frame
x_first, x_rest = x[:, :, 0], x[:, :, 1:]
x_first = torch.nn.functional.interpolate(x_first, scale_factor=2.0, mode="nearest")
x_rest = torch.nn.functional.interpolate(x_rest, scale_factor=2.0, mode="nearest")
x = torch.cat([x_first[:, :, None, :, :], x_rest], dim=2)
else:
x = x.squeeze(2)
x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
x = x[:, :, None, :, :]
else:
# only interpolate 2D
t = x.shape[2]
x = rearrange(x, "b c t h w -> (b t) c h w")
x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
x = rearrange(x, "(b t) c h w -> b c t h w", t=t)
if self.with_conv:
t = x.shape[2]
x = rearrange(x, "b c t h w -> (b t) c h w")
x = self.conv(x)
x = rearrange(x, "(b t) c h w -> b c t h w", t=t)
return x
class DownSample3D(nn.Module):
def __init__(self, in_channels, with_conv, compress_time=False, out_channels=None):
super().__init__()
self.with_conv = with_conv
if out_channels is None:
out_channels = in_channels
if self.with_conv:
# no asymmetric padding in torch conv, must do it ourselves
self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=0)
self.compress_time = compress_time
def forward(self, x):
if self.compress_time:
h, w = x.shape[-2:]
x = rearrange(x, "b c t h w -> (b h w) c t")
# split first frame
x_first, x_rest = x[..., 0], x[..., 1:]
if x_rest.shape[-1] > 0:
x_rest = torch.nn.functional.avg_pool1d(x_rest, kernel_size=2, stride=2)
x = torch.cat([x_first[..., None], x_rest], dim=-1)
x = rearrange(x, "(b h w) c t -> b c t h w", h=h, w=w)
if self.with_conv:
pad = (0, 1, 0, 1)
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
t = x.shape[2]
x = rearrange(x, "b c t h w -> (b t) c h w")
x = self.conv(x)
x = rearrange(x, "(b t) c h w -> b c t h w", t=t)
else:
t = x.shape[2]
x = rearrange(x, "b c t h w -> (b t) c h w")
x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
x = rearrange(x, "(b t) c h w -> b c t h w", t=t)
return x
class ResnetBlock3D(nn.Module):
def __init__(
self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512, pad_mode="constant"
):
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.norm1 = Normalize3D(in_channels)
# self.conv1 = torch.nn.Conv3d(in_channels,
# out_channels,
# kernel_size=3,
# stride=1,
# padding=1)
self.conv1 = CausalConv3d(in_channels, out_channels, kernel_size=3, pad_mode=pad_mode)
if temb_channels > 0:
self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
self.norm2 = Normalize3D(out_channels)
self.dropout = torch.nn.Dropout(dropout)
# self.conv2 = torch.nn.Conv3d(out_channels,
# out_channels,
# kernel_size=3,
# stride=1,
# padding=1)
self.conv2 = CausalConv3d(out_channels, out_channels, kernel_size=3, pad_mode=pad_mode)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
# self.conv_shortcut = torch.nn.Conv3d(in_channels,
# out_channels,
# kernel_size=3,
# stride=1,
# padding=1)
self.conv_shortcut = CausalConv3d(in_channels, out_channels, kernel_size=3, pad_mode=pad_mode)
else:
self.nin_shortcut = torch.nn.Conv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
# self.nin_shortcut = CausalConv3d(in_channels, out_channels, kernel_size=1, pad_mode=pad_mode)
def forward(self, x, temb):
h = x
h = self.norm1(h)
h = nonlinearity(h)
h = self.conv1(h)
if temb is not None:
h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None, None]
h = self.norm2(h)
h = nonlinearity(h)
h = self.dropout(h)
h = self.conv2(h)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
x = self.conv_shortcut(x)
else:
x = self.nin_shortcut(x)
return x + h
class AttnBlock2D(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize3D(in_channels)
self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
def forward(self, x):
h_ = x
h_ = self.norm(h_)
t = h_.shape[2]
h_ = rearrange(h_, "b c t h w -> (b t) c h w")
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b, c, h, w = q.shape
q = q.reshape(b, c, h * w)
q = q.permute(0, 2, 1) # b,hw,c
k = k.reshape(b, c, h * w) # b,c,hw
# # original version, nan in fp16
# w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
# w_ = w_ * (int(c)**(-0.5))
# # implement c**-0.5 on q
q = q * (int(c) ** (-0.5))
w_ = torch.bmm(q, k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
v = v.reshape(b, c, h * w)
w_ = w_.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
h_ = torch.bmm(v, w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
h_ = h_.reshape(b, c, h, w)
h_ = self.proj_out(h_)
h_ = rearrange(h_, "(b t) c h w -> b c t h w", t=t)
return x + h_
class Encoder3D(nn.Module):
def __init__(
self,
*,
ch,
out_ch,
ch_mult=(1, 2, 4, 8),
num_res_blocks,
attn_resolutions,
dropout=0.0,
resamp_with_conv=True,
in_channels,
resolution,
z_channels,
double_z=True,
pad_mode="first",
temporal_compress_times=4,
**ignore_kwargs,
):
super().__init__()
self.ch = ch
self.temb_ch = 0
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.in_channels = in_channels
# log2 of temporal_compress_times
self.temporal_compress_level = int(np.log2(temporal_compress_times))
# downsampling
# self.conv_in = torch.nn.Conv3d(in_channels,
# self.ch,
# kernel_size=3,
# stride=1,
# padding=1)
self.conv_in = CausalConv3d(in_channels, self.ch, kernel_size=3, pad_mode=pad_mode)
curr_res = resolution
in_ch_mult = (1,) + tuple(ch_mult)
self.down = nn.ModuleList()
for i_level in range(self.num_resolutions):
block = nn.ModuleList()
attn = nn.ModuleList()
block_in = ch * in_ch_mult[i_level]
block_out = ch * ch_mult[i_level]
for i_block in range(self.num_res_blocks):
block.append(
ResnetBlock3D(
in_channels=block_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout,
pad_mode=pad_mode,
)
)
block_in = block_out
if curr_res in attn_resolutions:
attn.append(AttnBlock2D(block_in))
down = nn.Module()
down.block = block
down.attn = attn
if i_level != self.num_resolutions - 1:
if i_level < self.temporal_compress_level:
down.downsample = DownSample3D(block_in, resamp_with_conv, compress_time=True)
else:
down.downsample = DownSample3D(block_in, resamp_with_conv, compress_time=False)
curr_res = curr_res // 2
self.down.append(down)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock3D(
in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout, pad_mode=pad_mode
)
# remove attention block
# self.mid.attn_1 = AttnBlock2D(block_in)
self.mid.block_2 = ResnetBlock3D(
in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout, pad_mode=pad_mode
)
# end
self.norm_out = Normalize3D(block_in)
# self.conv_out = torch.nn.Conv3d(block_in,
# 2*z_channels if double_z else z_channels,
# kernel_size=3,
# stride=1,
# padding=1)
self.conv_out = CausalConv3d(
block_in, 2 * z_channels if double_z else z_channels, kernel_size=3, pad_mode=pad_mode
)
def forward(self, x, use_cp=False):
# assert x.shape[2] == x.shape[3] == self.resolution, "{}, {}, {}".format(x.shape[2], x.shape[3], self.resolution)
# timestep embedding
temb = None
# downsampling
hs = [self.conv_in(x)]
for i_level in range(self.num_resolutions):
for i_block in range(self.num_res_blocks):
h = self.down[i_level].block[i_block](hs[-1], temb)
if len(self.down[i_level].attn) > 0:
h = self.down[i_level].attn[i_block](h)
hs.append(h)
if i_level != self.num_resolutions - 1:
hs.append(self.down[i_level].downsample(hs[-1]))
# middle
h = hs[-1]
h = self.mid.block_1(h, temb)
# h = self.mid.attn_1(h)
h = self.mid.block_2(h, temb)
# end
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
return h
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