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Running
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Zero
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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from einops import rearrange
import torch.fft as fft
class Linear(torch.nn.Linear):
def reset_parameters(self):
return None
class Conv2d(torch.nn.Conv2d):
def reset_parameters(self):
return None
class Attention2D(nn.Module):
def __init__(self, c, nhead, dropout=0.0):
super().__init__()
self.attn = nn.MultiheadAttention(c, nhead, dropout=dropout, bias=True, batch_first=True)
def forward(self, x, kv, self_attn=False):
orig_shape = x.shape
x = x.view(x.size(0), x.size(1), -1).permute(0, 2, 1) # Bx4xHxW -> Bx(HxW)x4
if self_attn:
#print('in line 23 algong self att ', kv.shape, x.shape)
kv = torch.cat([x, kv], dim=1)
#if x.shape[1] >= 72 * 72:
# x = x * math.sqrt(math.log(64*64, 24*24))
x = self.attn(x, kv, kv, need_weights=False)[0]
x = x.permute(0, 2, 1).view(*orig_shape)
return x
class LayerNorm2d(nn.LayerNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, x):
return super().forward(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
class GlobalResponseNorm(nn.Module):
"from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105"
def __init__(self, dim):
super().__init__()
self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim))
self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim))
def forward(self, x):
Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
return self.gamma * (x * Nx) + self.beta + x
class ResBlock(nn.Module):
def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0): # , num_heads=4, expansion=2):
super().__init__()
self.depthwise = Conv2d(c, c, kernel_size=kernel_size, padding=kernel_size // 2, groups=c)
# self.depthwise = SAMBlock(c, num_heads, expansion)
self.norm = LayerNorm2d(c, elementwise_affine=False, eps=1e-6)
self.channelwise = nn.Sequential(
Linear(c + c_skip, c * 4),
nn.GELU(),
GlobalResponseNorm(c * 4),
nn.Dropout(dropout),
Linear(c * 4, c)
)
def forward(self, x, x_skip=None):
x_res = x
x = self.norm(self.depthwise(x))
if x_skip is not None:
x = torch.cat([x, x_skip], dim=1)
x = self.channelwise(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
return x + x_res
class AttnBlock(nn.Module):
def __init__(self, c, c_cond, nhead, self_attn=True, dropout=0.0):
super().__init__()
self.self_attn = self_attn
self.norm = LayerNorm2d(c, elementwise_affine=False, eps=1e-6)
self.attention = Attention2D(c, nhead, dropout)
self.kv_mapper = nn.Sequential(
nn.SiLU(),
Linear(c_cond, c)
)
def forward(self, x, kv):
kv = self.kv_mapper(kv)
res = self.attention(self.norm(x), kv, self_attn=self.self_attn)
#print(torch.unique(res), torch.unique(x), self.self_attn)
#scale = math.sqrt(math.log(x.shape[-2] * x.shape[-1], 24*24))
x = x + res
return x
class FeedForwardBlock(nn.Module):
def __init__(self, c, dropout=0.0):
super().__init__()
self.norm = LayerNorm2d(c, elementwise_affine=False, eps=1e-6)
self.channelwise = nn.Sequential(
Linear(c, c * 4),
nn.GELU(),
GlobalResponseNorm(c * 4),
nn.Dropout(dropout),
Linear(c * 4, c)
)
def forward(self, x):
x = x + self.channelwise(self.norm(x).permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
return x
class TimestepBlock(nn.Module):
def __init__(self, c, c_timestep, conds=['sca']):
super().__init__()
self.mapper = Linear(c_timestep, c * 2)
self.conds = conds
for cname in conds:
setattr(self, f"mapper_{cname}", Linear(c_timestep, c * 2))
def forward(self, x, t):
t = t.chunk(len(self.conds) + 1, dim=1)
a, b = self.mapper(t[0])[:, :, None, None].chunk(2, dim=1)
for i, c in enumerate(self.conds):
ac, bc = getattr(self, f"mapper_{c}")(t[i + 1])[:, :, None, None].chunk(2, dim=1)
a, b = a + ac, b + bc
return x * (1 + a) + b
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