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from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
# import configigure
# from ldm.modules.diffusionmodules.util import checkpoint, FourierEmbedder
from torch.utils import checkpoint
import os
from torchvision.utils import save_image
iter_att = 0
def exists(val):
return val is not None
def uniq(arr):
return{el: True for el in arr}.keys()
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
def max_neg_value(t):
return -torch.finfo(t.dtype).max
def init_(tensor):
dim = tensor.shape[-1]
std = 1 / math.sqrt(dim)
tensor.uniform_(-std, std)
return tensor
# feedforward
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
return x * F.gelu(gate)
class FeedForward(nn.Module):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
super().__init__()
inner_dim = int(dim * mult)
dim_out = default(dim_out, dim)
project_in = nn.Sequential(
nn.Linear(dim, inner_dim),
nn.GELU()
) if not glu else GEGLU(dim, inner_dim)
self.net = nn.Sequential(
project_in,
nn.Dropout(dropout),
nn.Linear(inner_dim, dim_out)
)
def forward(self, x):
return self.net(x)
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module
def Normalize(in_channels):
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
class LinearAttention(nn.Module):
def __init__(self, dim, heads=4, dim_head=32):
super().__init__()
self.heads = heads
hidden_dim = dim_head * heads
self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
self.to_out = nn.Conv2d(hidden_dim, dim, 1)
def forward(self, x):
b, c, h, w = x.shape
qkv = self.to_qkv(x)
q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
k = k.softmax(dim=-1)
context = torch.einsum('bhdn,bhen->bhde', k, v)
out = torch.einsum('bhde,bhdn->bhen', context, q)
out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
return self.to_out(out)
class CrossAttention(nn.Module):
def __init__(self, query_dim, key_dim, value_dim, heads=8, dim_head=64, dropout=0):
super().__init__()
inner_dim = dim_head * heads
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(key_dim, inner_dim, bias=False)
self.to_v = nn.Linear(value_dim, inner_dim, bias=False)
self.to_out = nn.Sequential( nn.Linear(inner_dim, query_dim), nn.Dropout(dropout) )
def fill_inf_from_mask(self, sim, mask):
if mask is not None:
B,M = mask.shape
mask = mask.unsqueeze(1).repeat(1,self.heads,1).reshape(B*self.heads,1,-1)
max_neg_value = -torch.finfo(sim.dtype).max
sim.masked_fill_(~mask, max_neg_value)
return sim
# def scaled_dot_product(q, k, v, mask=None):
# d_k = q.size()[-1]
# attn_logits = torch.matmul(q, k.transpose(-2, -1))
# attn_logits = attn_logits / math.sqrt(d_k)
# if mask is not None:
# attn_logits = attn_logits.masked_fill(mask == 0, -9e15)
# attention = F.softmax(attn_logits, dim=-1)
# values = torch.matmul(attention, v)
# return values, attention
def forward(self, x, key, value, mask=None):
# import pdb; pdb.set_trace()
q = self.to_q(x) # B*N*(H*C)
k = self.to_k(key) # B*M*(H*C)
v = self.to_v(value) # B*M*(H*C)
B, N, HC = q.shape
_, M, _ = key.shape
H = self.heads
C = HC // H
q = q.view(B,N,H,C).permute(0,2,1,3).reshape(B*H,N,C) # (B*H)*N*C
k = k.view(B,M,H,C).permute(0,2,1,3).reshape(B*H,M,C) # (B*H)*M*C
v = v.view(B,M,H,C).permute(0,2,1,3).reshape(B*H,M,C) # (B*H)*M*C
sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale # (B*H)*N*M
self.fill_inf_from_mask(sim, mask)
attn = sim.softmax(dim=-1) # (B*H)*N*M
# import pdb; pdb.set_trace()
# if attn.shape[1] == 4096:
# self.visual_att(attn)
out = torch.einsum('b i j, b j d -> b i d', attn, v) # (B*H)*N*C
out = out.view(B,H,N,C).permute(0,2,1,3).reshape(B,N,(H*C)) # B*N*(H*C)
return self.to_out(out), attn
def visual_att(self, att):
global iter_att
ll = [0,2,7]
for i in range(12):
kk = torch.sum(att[:,:,i], axis=0)
kk = kk.reshape(64,64)
save_image( (kk-kk.min()) / (kk.max() - kk.min()) , os.path.join('att', str(iter_att) + '_' +str(i) + '.png'))
iter_att += 1
class SelfAttention(nn.Module):
def __init__(self, query_dim, heads=8, dim_head=64, dropout=0.):
super().__init__()
inner_dim = dim_head * heads
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(query_dim, inner_dim, bias=False)
self.to_v = nn.Linear(query_dim, inner_dim, bias=False)
self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout) )
def forward(self, x, gated=False):
q = self.to_q(x) # B*N*(H*C)
k = self.to_k(x) # B*N*(H*C)
v = self.to_v(x) # B*N*(H*C)
B, N, HC = q.shape
H = self.heads
C = HC // H
# if gated: import pdb; pdb.set_trace()
# import pdb; pdb.set_trace()
q = q.view(B,N,H,C).permute(0,2,1,3).reshape(B*H,N,C) # (B*H)*N*C
k = k.view(B,N,H,C).permute(0,2,1,3).reshape(B*H,N,C) # (B*H)*N*C
v = v.view(B,N,H,C).permute(0,2,1,3).reshape(B*H,N,C) # (B*H)*N*C
sim = torch.einsum('b i c, b j c -> b i j', q, k) * self.scale # (B*H)*N*N
attn = sim.softmax(dim=-1) # (B*H)*N*N
# if gated and attn.shape[1] == 4126:
# self.visual_att(attn)
out = torch.einsum('b i j, b j c -> b i c', attn, v) # (B*H)*N*C
out = out.view(B,H,N,C).permute(0,2,1,3).reshape(B,N,(H*C)) # B*N*(H*C)
return self.to_out(out), attn
def visual_att(self, att):
global iter_att
ll = [0,2,7]
for i in range():
kk = torch.sum(att[i],axis=0)
kk = kk[:4096].reshape(64,64)
save_image( (kk-kk.min()) / (kk.max() - kk.min()) , os.path.join('att', str(iter_att) + '_' +str(i) + '.png'))
iter_att += 1
class GatedCrossAttentionDense(nn.Module):
def __init__(self, query_dim, key_dim, value_dim, n_heads, d_head):
super().__init__()
self.attn = CrossAttention(query_dim=query_dim, key_dim=key_dim, value_dim=value_dim, heads=n_heads, dim_head=d_head)
self.ff = FeedForward(query_dim, glu=True)
self.norm1 = nn.LayerNorm(query_dim)
self.norm2 = nn.LayerNorm(query_dim)
self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)) )
self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)) )
# this can be useful: we can externally change magnitude of tanh(alpha)
# for example, when it is set to 0, then the entire model is same as original one
self.scale = 1
def forward(self, x, objs):
x = x + self.scale*torch.tanh(self.alpha_attn) * self.attn( self.norm1(x), objs, objs)
x = x + self.scale*torch.tanh(self.alpha_dense) * self.ff( self.norm2(x) )
return x
class GatedSelfAttentionDense(nn.Module):
def __init__(self, query_dim, context_dim, n_heads, d_head):
super().__init__()
# we need a linear projection since we need cat visual feature and obj feature
self.linear = nn.Linear(context_dim, query_dim)
self.attn = SelfAttention(query_dim=query_dim, heads=n_heads, dim_head=d_head)
self.ff = FeedForward(query_dim, glu=True)
self.norm1 = nn.LayerNorm(query_dim)
self.norm2 = nn.LayerNorm(query_dim)
self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)) )
self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)) )
# this can be useful: we can externally change magnitude of tanh(alpha)
# for example, when it is set to 0, then the entire model is same as original one
self.scale = 1
def forward(self, x, objs,t):
# if t >300:
# self.scale = 1
# elif t > 200:
# self.scale = 0.9
# else:
# self.scale = 0.6
# if t >700:
# self.scale = 1
# elif t > 300:
# self.scale = 0.7
# else:
# self.scale = 0.4
# self.scale = 0
N_visual = x.shape[1]
objs = self.linear(objs)
out, grounding_att = self.attn( self.norm1(torch.cat([x,objs],dim=1)), True )
out = out[:,0:N_visual,:]
x = x + self.scale*torch.tanh(self.alpha_attn) * out
x = x + self.scale*torch.tanh(self.alpha_dense) * self.ff( self.norm2(x) )
return x , grounding_att
class GatedSelfAttentionDense2(nn.Module):
def __init__(self, query_dim, context_dim, n_heads, d_head):
super().__init__()
# we need a linear projection since we need cat visual feature and obj feature
self.linear = nn.Linear(context_dim, query_dim)
self.attn = SelfAttention(query_dim=query_dim, heads=n_heads, dim_head=d_head)
self.ff = FeedForward(query_dim, glu=True)
self.norm1 = nn.LayerNorm(query_dim)
self.norm2 = nn.LayerNorm(query_dim)
self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)) )
self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)) )
# this can be useful: we can externally change magnitude of tanh(alpha)
# for example, when it is set to 0, then the entire model is same as original one
self.scale = 1
def forward(self, x, objs):
B, N_visual, _ = x.shape
B, N_ground, _ = objs.shape
objs = self.linear(objs)
# sanity check
size_v = math.sqrt(N_visual)
size_g = math.sqrt(N_ground)
assert int(size_v) == size_v, "Visual tokens must be square rootable"
assert int(size_g) == size_g, "Grounding tokens must be square rootable"
size_v = int(size_v)
size_g = int(size_g)
# select grounding token and resize it to visual token size as residual
out = self.attn( self.norm1(torch.cat([x,objs],dim=1)) )[:,N_visual:,:]
out = out.permute(0,2,1).reshape( B,-1,size_g,size_g )
out = torch.nn.functional.interpolate(out, (size_v,size_v), mode='bicubic')
residual = out.reshape(B,-1,N_visual).permute(0,2,1)
# add residual to visual feature
x = x + self.scale*torch.tanh(self.alpha_attn) * residual
x = x + self.scale*torch.tanh(self.alpha_dense) * self.ff( self.norm2(x) )
return x
class BasicTransformerBlock(nn.Module):
def __init__(self, query_dim, key_dim, value_dim, n_heads, d_head, fuser_type, use_checkpoint=True):
super().__init__()
self.attn1 = SelfAttention(query_dim=query_dim, heads=n_heads, dim_head=d_head)
self.ff = FeedForward(query_dim, glu=True)
self.attn2 = CrossAttention(query_dim=query_dim, key_dim=key_dim, value_dim=value_dim, heads=n_heads, dim_head=d_head)
self.norm1 = nn.LayerNorm(query_dim)
self.norm2 = nn.LayerNorm(query_dim)
self.norm3 = nn.LayerNorm(query_dim)
self.use_checkpoint = use_checkpoint
if fuser_type == "gatedSA":
# note key_dim here actually is context_dim
self.fuser = GatedSelfAttentionDense(query_dim, key_dim, n_heads, d_head)
elif fuser_type == "gatedSA2":
# note key_dim here actually is context_dim
self.fuser = GatedSelfAttentionDense2(query_dim, key_dim, n_heads, d_head)
elif fuser_type == "gatedCA":
self.fuser = GatedCrossAttentionDense(query_dim, key_dim, value_dim, n_heads, d_head)
else:
assert False
def forward(self, x, context, objs,t):
# return checkpoint(self._forward, (x, context, objs), self.parameters(), self.use_checkpoint)
# import pdb; pdb.set_trace()
# if self.use_checkpoint and x.requires_grad:
# return checkpoint.checkpoint(self._forward, x, context, objs,t)
# else:
return self._forward(x, context, objs,t)
def _forward(self, x, context, objs,t):
# self_att_grounding = []
out, self_prob = self.attn1( self.norm1(x) )
x = x + out
x, self_prob_grounding = self.fuser(x, objs,t) # identity mapping in the beginning
x_1, prob = self.attn2(self.norm2(x), context, context)
x = x + x_1
x = self.ff(self.norm3(x)) + x
# self_att_grounding.append(self_prob)
# self_att_grounding.append(self_prob_grounding)
return x, prob, self_prob
class SpatialTransformer(nn.Module):
def __init__(self, in_channels, key_dim, value_dim, n_heads, d_head, depth=1, fuser_type=None, use_checkpoint=True):
super().__init__()
self.in_channels = in_channels
query_dim = n_heads * d_head
self.norm = Normalize(in_channels)
self.proj_in = nn.Conv2d(in_channels,
query_dim,
kernel_size=1,
stride=1,
padding=0)
self.transformer_blocks = nn.ModuleList(
[BasicTransformerBlock(query_dim, key_dim, value_dim, n_heads, d_head, fuser_type, use_checkpoint=use_checkpoint)
for d in range(depth)]
)
self.proj_out = zero_module(nn.Conv2d(query_dim,
in_channels,
kernel_size=1,
stride=1,
padding=0))
def forward(self, x, context, objs,t):
b, c, h, w = x.shape
x_in = x
x = self.norm(x)
x = self.proj_in(x)
x = rearrange(x, 'b c h w -> b (h w) c')
probs = []
self_prob_list = []
for block in self.transformer_blocks:
x, prob, self_prob = block(x, context, objs,t)
probs.append(prob)
self_prob_list.append(self_prob)
x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
x = self.proj_out(x)
return x + x_in, probs, self_prob_list |