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import torch
import torch.nn as nn
import torch.nn.functional as F
from .utils import split_feature, merge_splits
def single_head_full_attention(q, k, v):
# q, k, v: [B, L, C]
assert q.dim() == k.dim() == v.dim() == 3
scores = torch.matmul(q, k.permute(0, 2, 1)) / (q.size(2) ** .5) # [B, L, L]
attn = torch.softmax(scores, dim=2) # [B, L, L]
out = torch.matmul(attn, v) # [B, L, C]
return out
def generate_shift_window_attn_mask(input_resolution, window_size_h, window_size_w,
shift_size_h, shift_size_w, device=torch.device('cuda')):
# Ref: https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py
# calculate attention mask for SW-MSA
h, w = input_resolution
img_mask = torch.zeros((1, h, w, 1)).to(device) # 1 H W 1
h_slices = (slice(0, -window_size_h),
slice(-window_size_h, -shift_size_h),
slice(-shift_size_h, None))
w_slices = (slice(0, -window_size_w),
slice(-window_size_w, -shift_size_w),
slice(-shift_size_w, None))
cnt = 0
for h in h_slices:
for w in w_slices:
img_mask[:, h, w, :] = cnt
cnt += 1
mask_windows = split_feature(img_mask, num_splits=input_resolution[-1] // window_size_w, channel_last=True)
mask_windows = mask_windows.view(-1, window_size_h * window_size_w)
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
return attn_mask
def single_head_split_window_attention(q, k, v,
num_splits=1,
with_shift=False,
h=None,
w=None,
attn_mask=None,
):
# Ref: https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py
# q, k, v: [B, L, C]
assert q.dim() == k.dim() == v.dim() == 3
assert h is not None and w is not None
assert q.size(1) == h * w
b, _, c = q.size()
b_new = b * num_splits * num_splits
window_size_h = h // num_splits
window_size_w = w // num_splits
q = q.view(b, h, w, c) # [B, H, W, C]
k = k.view(b, h, w, c)
v = v.view(b, h, w, c)
scale_factor = c ** 0.5
if with_shift:
assert attn_mask is not None # compute once
shift_size_h = window_size_h // 2
shift_size_w = window_size_w // 2
q = torch.roll(q, shifts=(-shift_size_h, -shift_size_w), dims=(1, 2))
k = torch.roll(k, shifts=(-shift_size_h, -shift_size_w), dims=(1, 2))
v = torch.roll(v, shifts=(-shift_size_h, -shift_size_w), dims=(1, 2))
q = split_feature(q, num_splits=num_splits, channel_last=True) # [B*K*K, H/K, W/K, C]
k = split_feature(k, num_splits=num_splits, channel_last=True)
v = split_feature(v, num_splits=num_splits, channel_last=True)
scores = torch.matmul(q.view(b_new, -1, c), k.view(b_new, -1, c).permute(0, 2, 1)
) / scale_factor # [B*K*K, H/K*W/K, H/K*W/K]
if with_shift:
scores += attn_mask.repeat(b, 1, 1)
attn = torch.softmax(scores, dim=-1)
out = torch.matmul(attn, v.view(b_new, -1, c)) # [B*K*K, H/K*W/K, C]
out = merge_splits(out.view(b_new, h // num_splits, w // num_splits, c),
num_splits=num_splits, channel_last=True) # [B, H, W, C]
# shift back
if with_shift:
out = torch.roll(out, shifts=(shift_size_h, shift_size_w), dims=(1, 2))
out = out.view(b, -1, c)
return out
class TransformerLayer(nn.Module):
def __init__(self,
d_model=256,
nhead=1,
attention_type='swin',
no_ffn=False,
ffn_dim_expansion=4,
with_shift=False,
**kwargs,
):
super(TransformerLayer, self).__init__()
self.dim = d_model
self.nhead = nhead
self.attention_type = attention_type
self.no_ffn = no_ffn
self.with_shift = with_shift
# 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,
attn_num_splits=None,
**kwargs,
):
# source, target: [B, L, C]
query, key, value = source, target, target
# 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 self.attention_type == 'swin' and attn_num_splits > 1:
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=self.with_shift,
h=height,
w=width,
attn_mask=shifted_window_attn_mask,
)
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=256,
nhead=1,
attention_type='swin',
ffn_dim_expansion=4,
with_shift=False,
**kwargs,
):
super(TransformerBlock, self).__init__()
self.self_attn = TransformerLayer(d_model=d_model,
nhead=nhead,
attention_type=attention_type,
no_ffn=True,
ffn_dim_expansion=ffn_dim_expansion,
with_shift=with_shift,
)
self.cross_attn_ffn = TransformerLayer(d_model=d_model,
nhead=nhead,
attention_type=attention_type,
ffn_dim_expansion=ffn_dim_expansion,
with_shift=with_shift,
)
def forward(self, source, target,
height=None,
width=None,
shifted_window_attn_mask=None,
attn_num_splits=None,
**kwargs,
):
# 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_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,
attn_num_splits=attn_num_splits,
)
return source
class FeatureTransformer(nn.Module):
def __init__(self,
num_layers=6,
d_model=128,
nhead=1,
attention_type='swin',
ffn_dim_expansion=4,
**kwargs,
):
super(FeatureTransformer, self).__init__()
self.attention_type = attention_type
self.d_model = d_model
self.nhead = nhead
self.layers = nn.ModuleList([
TransformerBlock(d_model=d_model,
nhead=nhead,
attention_type=attention_type,
ffn_dim_expansion=ffn_dim_expansion,
with_shift=True if attention_type == 'swin' and i % 2 == 1 else False,
)
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_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]
if self.attention_type == 'swin' 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
# 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 layer in self.layers:
concat0 = layer(concat0, concat1,
height=h,
width=w,
shifted_window_attn_mask=shifted_window_attn_mask,
attn_num_splits=attn_num_splits,
)
# 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
class FeatureFlowAttention(nn.Module):
"""
flow propagation with self-attention on feature
query: feature0, key: feature0, value: flow
"""
def __init__(self, in_channels,
**kwargs,
):
super(FeatureFlowAttention, self).__init__()
self.q_proj = nn.Linear(in_channels, in_channels)
self.k_proj = nn.Linear(in_channels, in_channels)
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def forward(self, feature0, flow,
local_window_attn=False,
local_window_radius=1,
**kwargs,
):
# q, k: feature [B, C, H, W], v: flow [B, 2, H, W]
if local_window_attn:
return self.forward_local_window_attn(feature0, flow,
local_window_radius=local_window_radius)
b, c, h, w = feature0.size()
query = feature0.view(b, c, h * w).permute(0, 2, 1) # [B, H*W, C]
# a note: the ``correct'' implementation should be:
# ``query = self.q_proj(query), key = self.k_proj(query)''
# this problem is observed while cleaning up the code
# however, this doesn't affect the performance since the projection is a linear operation,
# thus the two projection matrices for key can be merged
# so I just leave it as is in order to not re-train all models :)
query = self.q_proj(query) # [B, H*W, C]
key = self.k_proj(query) # [B, H*W, C]
value = flow.view(b, flow.size(1), h * w).permute(0, 2, 1) # [B, H*W, 2]
scores = torch.matmul(query, key.permute(0, 2, 1)) / (c ** 0.5) # [B, H*W, H*W]
prob = torch.softmax(scores, dim=-1)
out = torch.matmul(prob, value) # [B, H*W, 2]
out = out.view(b, h, w, value.size(-1)).permute(0, 3, 1, 2) # [B, 2, H, W]
return out
def forward_local_window_attn(self, feature0, flow,
local_window_radius=1,
):
assert flow.size(1) == 2
assert local_window_radius > 0
b, c, h, w = feature0.size()
feature0_reshape = self.q_proj(feature0.view(b, c, -1).permute(0, 2, 1)
).reshape(b * h * w, 1, c) # [B*H*W, 1, C]
kernel_size = 2 * local_window_radius + 1
feature0_proj = self.k_proj(feature0.view(b, c, -1).permute(0, 2, 1)).permute(0, 2, 1).reshape(b, c, h, w)
feature0_window = F.unfold(feature0_proj, kernel_size=kernel_size,
padding=local_window_radius) # [B, C*(2R+1)^2), H*W]
feature0_window = feature0_window.view(b, c, kernel_size ** 2, h, w).permute(
0, 3, 4, 1, 2).reshape(b * h * w, c, kernel_size ** 2) # [B*H*W, C, (2R+1)^2]
flow_window = F.unfold(flow, kernel_size=kernel_size,
padding=local_window_radius) # [B, 2*(2R+1)^2), H*W]
flow_window = flow_window.view(b, 2, kernel_size ** 2, h, w).permute(
0, 3, 4, 2, 1).reshape(b * h * w, kernel_size ** 2, 2) # [B*H*W, (2R+1)^2, 2]
scores = torch.matmul(feature0_reshape, feature0_window) / (c ** 0.5) # [B*H*W, 1, (2R+1)^2]
prob = torch.softmax(scores, dim=-1)
out = torch.matmul(prob, flow_window).view(b, h, w, 2).permute(0, 3, 1, 2).contiguous() # [B, 2, H, W]
return out
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