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import torch
import torch.nn as nn
from ...modules.attention import default, zero_module, checkpoint
from ...modules.diffusionmodules.openaimodel import UNetModel
from ...modules.diffusionmodules.util import timestep_embedding
class DepthAttention(nn.Module):
def __init__(self, query_dim, context_dim, heads, dim_head, output_bias=True):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
self.scale = dim_head ** -0.5
self.heads = heads
self.dim_head = dim_head
self.to_q = nn.Conv2d(query_dim, inner_dim, 1, 1, bias=False)
self.to_k = nn.Conv3d(context_dim, inner_dim, 1, 1, bias=False)
self.to_v = nn.Conv3d(context_dim, inner_dim, 1, 1, bias=False)
if output_bias:
self.to_out = nn.Conv2d(inner_dim, query_dim, 1, 1)
else:
self.to_out = nn.Conv2d(inner_dim, query_dim, 1, 1, bias=False)
def forward(self, x, context):
"""
@param x: b,f0,h,w
@param context: b,f1,d,h,w
@return:
"""
hn, hd = self.heads, self.dim_head
b, _, h, w = x.shape
b, _, d, h, w = context.shape
q = self.to_q(x).reshape(b,hn,hd,h,w) # b,t,h,w
k = self.to_k(context).reshape(b,hn,hd,d,h,w) # b,t,d,h,w
v = self.to_v(context).reshape(b,hn,hd,d,h,w) # b,t,d,h,w
sim = torch.sum(q.unsqueeze(3) * k, 2) * self.scale # b,hn,d,h,w
attn = sim.softmax(dim=2)
# b,hn,hd,d,h,w * b,hn,1,d,h,w
out = torch.sum(v * attn.unsqueeze(2), 3) # b,hn,hd,h,w
out = out.reshape(b,hn*hd,h,w)
return self.to_out(out)
class DepthTransformer(nn.Module):
def __init__(self, dim, n_heads, d_head, context_dim=None, checkpoint=False):
super().__init__()
inner_dim = n_heads * d_head
self.proj_in = nn.Sequential(
nn.Conv2d(dim, inner_dim, 1, 1),
nn.GroupNorm(8, inner_dim),
nn.SiLU(True),
)
self.proj_context = nn.Sequential(
nn.Conv3d(context_dim, context_dim, 1, 1, bias=False), # no bias
nn.GroupNorm(8, context_dim),
nn.ReLU(True), # only relu, because we want input is 0, output is 0
)
self.depth_attn = DepthAttention(query_dim=inner_dim, heads=n_heads, dim_head=d_head, context_dim=context_dim, output_bias=False) # is a self-attention if not self.disable_self_attn
self.proj_out = nn.Sequential(
nn.GroupNorm(8, inner_dim),
nn.ReLU(True),
nn.Conv2d(inner_dim, inner_dim, 3, 1, 1, bias=False),
nn.GroupNorm(8, inner_dim),
nn.ReLU(True),
zero_module(nn.Conv2d(inner_dim, dim, 3, 1, 1, bias=False)),
)
self.checkpoint = checkpoint
def forward(self, x, context=None):
return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
def _forward(self, x, context):
x_in = x
x = self.proj_in(x)
context = self.proj_context(context)
x = self.depth_attn(x, context)
x = self.proj_out(x) + x_in
return x
class DepthWiseAttention(UNetModel):
def __init__(self, volume_dims=(5,16,32,64), *args, **kwargs):
super().__init__(*args, **kwargs)
# num_heads = 4
model_channels = kwargs['model_channels']
channel_mult = kwargs['channel_mult']
d0,d1,d2,d3 = volume_dims
# 4
ch = model_channels*channel_mult[2]
self.middle_conditions = DepthTransformer(ch, 4, d3 // 2, context_dim=d3)
self.output_conditions=nn.ModuleList()
self.output_b2c = {3:0,4:1,5:2,6:3,7:4,8:5,9:6,10:7,11:8}
# 8
ch = model_channels*channel_mult[2]
self.output_conditions.append(DepthTransformer(ch, 4, d2 // 2, context_dim=d2)) # 0
self.output_conditions.append(DepthTransformer(ch, 4, d2 // 2, context_dim=d2)) # 1
# 16
self.output_conditions.append(DepthTransformer(ch, 4, d1 // 2, context_dim=d1)) # 2
ch = model_channels*channel_mult[1]
self.output_conditions.append(DepthTransformer(ch, 4, d1 // 2, context_dim=d1)) # 3
self.output_conditions.append(DepthTransformer(ch, 4, d1 // 2, context_dim=d1)) # 4
# 32
self.output_conditions.append(DepthTransformer(ch, 4, d0 // 2, context_dim=d0)) # 5
ch = model_channels*channel_mult[0]
self.output_conditions.append(DepthTransformer(ch, 4, d0 // 2, context_dim=d0)) # 6
self.output_conditions.append(DepthTransformer(ch, 4, d0 // 2, context_dim=d0)) # 7
self.output_conditions.append(DepthTransformer(ch, 4, d0 // 2, context_dim=d0)) # 8
def forward(self, x, timesteps=None, context=None, down_block_additional_residuals=None, mid_block_additional_residual=None, source_dict=None, **kwargs):
hs = []
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
emb = self.time_embed(t_emb)
h = x.type(self.dtype)
for index, module in enumerate(self.input_blocks):
h = module(h, emb, context)
hs.append(h)
h = self.middle_block(h, emb, context)
h = self.middle_conditions(h, context=source_dict[h.shape[-1]])
is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
if is_controlnet:
new_hs = []
for down_block_res_sample, down_block_additional_residual in zip(
hs, down_block_additional_residuals
):
down_block_res_sample = down_block_res_sample + down_block_additional_residual
new_hs = new_hs + [down_block_res_sample]
hs = new_hs
if is_controlnet:
h = h + mid_block_additional_residual
for index, module in enumerate(self.output_blocks):
h = torch.cat([h, hs.pop()], dim=1)
h = module(h, emb, context)
if index in self.output_b2c:
layer = self.output_conditions[self.output_b2c[index]]
h = layer(h, context=source_dict[h.shape[-1]])
h = h.type(x.dtype)
return self.out(h)
def get_trainable_parameters(self):
paras = [para for para in self.middle_conditions.parameters()] + [para for para in self.output_conditions.parameters()]
# paras = [para for para in self.output_conditions.parameters()]
return paras
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