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import torch
import torch.nn as nn
from .attention import (
single_head_full_attention,
single_head_full_attention_1d,
single_head_split_window_attention,
single_head_split_window_attention_1d,
)
from .utils import generate_shift_window_attn_mask, generate_shift_window_attn_mask_1d
class TransformerLayer(nn.Module):
def __init__(
self,
d_model=128,
nhead=1,
no_ffn=False,
ffn_dim_expansion=4,
):
super().__init__()
self.dim = d_model
self.nhead = nhead
self.no_ffn = no_ffn
# multi-head attention
self.q_proj = nn.Linear(d_model, d_model, bias=False)
self.k_proj = nn.Linear(d_model, d_model, bias=False)
self.v_proj = nn.Linear(d_model, d_model, bias=False)
self.merge = nn.Linear(d_model, d_model, bias=False)
self.norm1 = nn.LayerNorm(d_model)
# no ffn after self-attn, with ffn after cross-attn
if not self.no_ffn:
in_channels = d_model * 2
self.mlp = nn.Sequential(
nn.Linear(in_channels, in_channels * ffn_dim_expansion, bias=False),
nn.GELU(),
nn.Linear(in_channels * ffn_dim_expansion, d_model, bias=False),
)
self.norm2 = nn.LayerNorm(d_model)
def forward(
self,
source,
target,
height=None,
width=None,
shifted_window_attn_mask=None,
shifted_window_attn_mask_1d=None,
attn_type="swin",
with_shift=False,
attn_num_splits=None,
):
# source, target: [B, L, C]
query, key, value = source, target, target
# for stereo: 2d attn in self-attn, 1d attn in cross-attn
is_self_attn = (query - key).abs().max() < 1e-6
# single-head attention
query = self.q_proj(query) # [B, L, C]
key = self.k_proj(key) # [B, L, C]
value = self.v_proj(value) # [B, L, C]
if attn_type == "swin" and attn_num_splits > 1: # self, cross-attn: both swin 2d
if self.nhead > 1:
# we observe that multihead attention slows down the speed and increases the memory consumption
# without bringing obvious performance gains and thus the implementation is removed
raise NotImplementedError
else:
message = single_head_split_window_attention(
query,
key,
value,
num_splits=attn_num_splits,
with_shift=with_shift,
h=height,
w=width,
attn_mask=shifted_window_attn_mask,
)
elif attn_type == "self_swin2d_cross_1d": # self-attn: swin 2d, cross-attn: full 1d
if self.nhead > 1:
raise NotImplementedError
else:
if is_self_attn:
if attn_num_splits > 1:
message = single_head_split_window_attention(
query,
key,
value,
num_splits=attn_num_splits,
with_shift=with_shift,
h=height,
w=width,
attn_mask=shifted_window_attn_mask,
)
else:
# full 2d attn
message = single_head_full_attention(query, key, value) # [N, L, C]
else:
# cross attn 1d
message = single_head_full_attention_1d(
query,
key,
value,
h=height,
w=width,
)
elif attn_type == "self_swin2d_cross_swin1d": # self-attn: swin 2d, cross-attn: swin 1d
if self.nhead > 1:
raise NotImplementedError
else:
if is_self_attn:
if attn_num_splits > 1:
# self attn shift window
message = single_head_split_window_attention(
query,
key,
value,
num_splits=attn_num_splits,
with_shift=with_shift,
h=height,
w=width,
attn_mask=shifted_window_attn_mask,
)
else:
# full 2d attn
message = single_head_full_attention(query, key, value) # [N, L, C]
else:
if attn_num_splits > 1:
assert shifted_window_attn_mask_1d is not None
# cross attn 1d shift
message = single_head_split_window_attention_1d(
query,
key,
value,
num_splits=attn_num_splits,
with_shift=with_shift,
h=height,
w=width,
attn_mask=shifted_window_attn_mask_1d,
)
else:
message = single_head_full_attention_1d(
query,
key,
value,
h=height,
w=width,
)
else:
message = single_head_full_attention(query, key, value) # [B, L, C]
message = self.merge(message) # [B, L, C]
message = self.norm1(message)
if not self.no_ffn:
message = self.mlp(torch.cat([source, message], dim=-1))
message = self.norm2(message)
return source + message
class TransformerBlock(nn.Module):
"""self attention + cross attention + FFN."""
def __init__(
self,
d_model=128,
nhead=1,
ffn_dim_expansion=4,
):
super().__init__()
self.self_attn = TransformerLayer(
d_model=d_model,
nhead=nhead,
no_ffn=True,
ffn_dim_expansion=ffn_dim_expansion,
)
self.cross_attn_ffn = TransformerLayer(
d_model=d_model,
nhead=nhead,
ffn_dim_expansion=ffn_dim_expansion,
)
def forward(
self,
source,
target,
height=None,
width=None,
shifted_window_attn_mask=None,
shifted_window_attn_mask_1d=None,
attn_type="swin",
with_shift=False,
attn_num_splits=None,
):
# source, target: [B, L, C]
# self attention
source = self.self_attn(
source,
source,
height=height,
width=width,
shifted_window_attn_mask=shifted_window_attn_mask,
attn_type=attn_type,
with_shift=with_shift,
attn_num_splits=attn_num_splits,
)
# cross attention and ffn
source = self.cross_attn_ffn(
source,
target,
height=height,
width=width,
shifted_window_attn_mask=shifted_window_attn_mask,
shifted_window_attn_mask_1d=shifted_window_attn_mask_1d,
attn_type=attn_type,
with_shift=with_shift,
attn_num_splits=attn_num_splits,
)
return source
class FeatureTransformer(nn.Module):
def __init__(
self,
num_layers=6,
d_model=128,
nhead=1,
ffn_dim_expansion=4,
):
super().__init__()
self.d_model = d_model
self.nhead = nhead
self.layers = nn.ModuleList(
[
TransformerBlock(
d_model=d_model,
nhead=nhead,
ffn_dim_expansion=ffn_dim_expansion,
)
for i in range(num_layers)
]
)
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def forward(
self,
feature0,
feature1,
attn_type="swin",
attn_num_splits=None,
**kwargs,
):
b, c, h, w = feature0.shape
assert self.d_model == c
feature0 = feature0.flatten(-2).permute(0, 2, 1) # [B, H*W, C]
feature1 = feature1.flatten(-2).permute(0, 2, 1) # [B, H*W, C]
# 2d attention
if "swin" in attn_type and attn_num_splits > 1:
# global and refine use different number of splits
window_size_h = h // attn_num_splits
window_size_w = w // attn_num_splits
# compute attn mask once
shifted_window_attn_mask = generate_shift_window_attn_mask(
input_resolution=(h, w),
window_size_h=window_size_h,
window_size_w=window_size_w,
shift_size_h=window_size_h // 2,
shift_size_w=window_size_w // 2,
device=feature0.device,
) # [K*K, H/K*W/K, H/K*W/K]
else:
shifted_window_attn_mask = None
# 1d attention
if "swin1d" in attn_type and attn_num_splits > 1:
window_size_w = w // attn_num_splits
# compute attn mask once
shifted_window_attn_mask_1d = generate_shift_window_attn_mask_1d(
input_w=w,
window_size_w=window_size_w,
shift_size_w=window_size_w // 2,
device=feature0.device,
) # [K, W/K, W/K]
else:
shifted_window_attn_mask_1d = None
# concat feature0 and feature1 in batch dimension to compute in parallel
concat0 = torch.cat((feature0, feature1), dim=0) # [2B, H*W, C]
concat1 = torch.cat((feature1, feature0), dim=0) # [2B, H*W, C]
for i, layer in enumerate(self.layers):
concat0 = layer(
concat0,
concat1,
height=h,
width=w,
attn_type=attn_type,
with_shift="swin" in attn_type and attn_num_splits > 1 and i % 2 == 1,
attn_num_splits=attn_num_splits,
shifted_window_attn_mask=shifted_window_attn_mask,
shifted_window_attn_mask_1d=shifted_window_attn_mask_1d,
)
# update feature1
concat1 = torch.cat(concat0.chunk(chunks=2, dim=0)[::-1], dim=0)
feature0, feature1 = concat0.chunk(chunks=2, dim=0) # [B, H*W, C]
# reshape back
feature0 = feature0.view(b, h, w, c).permute(0, 3, 1, 2).contiguous() # [B, C, H, W]
feature1 = feature1.view(b, h, w, c).permute(0, 3, 1, 2).contiguous() # [B, C, H, W]
return feature0, feature1
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