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import torch
import torch.nn as nn
import torch.nn.functional as F
import random
import math
from inspect import isfunction
from einops import rearrange, repeat
from torch import nn, einsum
def exists(val):
return val is not None
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
class CrossAttention(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
self.scale = dim_head ** -0.5
self.heads = heads
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
self.to_out = nn.Sequential(
nn.Linear(inner_dim, query_dim),
nn.Dropout(dropout)
)
def forward(self, x, context=None, mask=None, return_attn=False, need_softmax=True, guidance_mask=None,
forward_layout_guidance=False):
h = self.heads
b = x.shape[0]
q = self.to_q(x)
context = default(context, x)
k = self.to_k(context)
v = self.to_v(context)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
if forward_layout_guidance:
# sim: (B * phase_num * h, HW, 77), b = B * phase_num
# guidance_mask: (B, phase_num, 64, 64)
HW = sim.shape[1]
H = W = int(math.sqrt(HW))
guidance_mask = F.interpolate(guidance_mask, size=(H, W), mode='nearest') # (B, phase_num, H, W)
sim = sim.view(b, h, HW, 77)
guidance_mask = guidance_mask.view(b, 1, HW, 1)
guidance_mask[guidance_mask == 1] = 5.0
guidance_mask[guidance_mask == 0] = 0.1
sim[:, :, :, 1:] = sim[:, :, :, 1:] * guidance_mask
sim = sim.view(b * h, HW, 77)
if exists(mask):
mask = rearrange(mask, 'b ... -> b (...)')
max_neg_value = -torch.finfo(sim.dtype).max
mask = repeat(mask, 'b j -> (b h) () j', h=h)
sim.masked_fill_(~mask, max_neg_value)
if need_softmax:
attn = sim.softmax(dim=-1)
else:
attn = sim
out = einsum('b i j, b j d -> b i d', attn, v)
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
if return_attn:
attn = attn.view(b, h, attn.shape[-2], attn.shape[-1])
return self.to_out(out), attn
else:
return self.to_out(out)
class LayoutAttention(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., use_lora=False):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
self.use_lora = use_lora
self.scale = dim_head ** -0.5
self.heads = heads
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
self.to_out = nn.Sequential(
nn.Linear(inner_dim, query_dim),
nn.Dropout(dropout)
)
def forward(self, x, context=None, mask=None, return_attn=False, need_softmax=True, guidance_mask=None):
h = self.heads
b = x.shape[0]
q = self.to_q(x)
context = default(context, x)
k = self.to_k(context)
v = self.to_v(context)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
_, phase_num, H, W = guidance_mask.shape
HW = H * W
guidance_mask_o = guidance_mask.view(b * phase_num, HW, 1)
guidance_mask_t = guidance_mask.view(b * phase_num, 1, HW)
guidance_mask_sim = torch.bmm(guidance_mask_o, guidance_mask_t) # (B * phase_num, HW, HW)
guidance_mask_sim = guidance_mask_sim.view(b, phase_num, HW, HW).sum(dim=1)
guidance_mask_sim[guidance_mask_sim > 1] = 1 # (B, HW, HW)
guidance_mask_sim = guidance_mask_sim.view(b, 1, HW, HW)
guidance_mask_sim = guidance_mask_sim.repeat(1, self.heads, 1, 1)
guidance_mask_sim = guidance_mask_sim.view(b * self.heads, HW, HW) # (B * head, HW, HW)
sim[:, :, :HW][guidance_mask_sim == 0] = -torch.finfo(sim.dtype).max
if exists(mask):
mask = rearrange(mask, 'b ... -> b (...)')
max_neg_value = -torch.finfo(sim.dtype).max
mask = repeat(mask, 'b j -> (b h) () j', h=h)
sim.masked_fill_(~mask, max_neg_value)
# attention, what we cannot get enough of
if need_softmax:
attn = sim.softmax(dim=-1)
else:
attn = sim
out = einsum('b i j, b j d -> b i d', attn, v)
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
if return_attn:
attn = attn.view(b, h, attn.shape[-2], attn.shape[-1])
return self.to_out(out), attn
else:
return self.to_out(out)
class BasicConv(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=False, bias=False):
super(BasicConv, self).__init__()
self.out_channels = out_planes
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
self.bn = nn.BatchNorm2d(out_planes,eps=1e-5, momentum=0.01, affine=True) if bn else None
self.relu = nn.ReLU() if relu else None
def forward(self, x):
x = self.conv(x)
if self.bn is not None:
x = self.bn(x)
if self.relu is not None:
x = self.relu(x)
return x
class Flatten(nn.Module):
def forward(self, x):
return x.view(x.size(0), -1)
class ChannelGate(nn.Module):
def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max']):
super(ChannelGate, self).__init__()
self.gate_channels = gate_channels
self.mlp = nn.Sequential(
Flatten(),
nn.Linear(gate_channels, gate_channels // reduction_ratio),
nn.ReLU(),
nn.Linear(gate_channels // reduction_ratio, gate_channels)
)
self.pool_types = pool_types
def forward(self, x):
channel_att_sum = None
for pool_type in self.pool_types:
if pool_type=='avg':
avg_pool = F.avg_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))
channel_att_raw = self.mlp( avg_pool )
elif pool_type=='max':
max_pool = F.max_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))
channel_att_raw = self.mlp( max_pool )
elif pool_type=='lp':
lp_pool = F.lp_pool2d( x, 2, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3)))
channel_att_raw = self.mlp( lp_pool )
elif pool_type=='lse':
# LSE pool only
lse_pool = logsumexp_2d(x)
channel_att_raw = self.mlp( lse_pool )
if channel_att_sum is None:
channel_att_sum = channel_att_raw
else:
channel_att_sum = channel_att_sum + channel_att_raw
scale = F.sigmoid( channel_att_sum ).unsqueeze(2).unsqueeze(3).expand_as(x)
return x * scale
def logsumexp_2d(tensor):
tensor_flatten = tensor.view(tensor.size(0), tensor.size(1), -1)
s, _ = torch.max(tensor_flatten, dim=2, keepdim=True)
outputs = s + (tensor_flatten - s).exp().sum(dim=2, keepdim=True).log()
return outputs
class ChannelPool(nn.Module):
def forward(self, x):
return torch.cat( (torch.max(x,1)[0].unsqueeze(1), torch.mean(x,1).unsqueeze(1)), dim=1 )
class SpatialGate(nn.Module):
def __init__(self):
super(SpatialGate, self).__init__()
kernel_size = 7
self.compress = ChannelPool()
self.spatial = BasicConv(2, 1, kernel_size, stride=1, padding=(kernel_size-1) // 2, relu=False)
def forward(self, x):
x_compress = self.compress(x)
x_out = self.spatial(x_compress)
scale = F.sigmoid(x_out) # broadcasting
return x * scale
class CBAM(nn.Module):
def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'], no_spatial=False):
super(CBAM, self).__init__()
self.ChannelGate = ChannelGate(gate_channels, reduction_ratio, pool_types)
self.no_spatial=no_spatial
if not no_spatial:
self.SpatialGate = SpatialGate()
def forward(self, x):
x_out = self.ChannelGate(x)
if not self.no_spatial:
x_out = self.SpatialGate(x_out)
return x_out |