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import torch
import torch.nn.functional as F
from torch import nn
from networks.layers.basic import DropPath, GroupNorm1D, GNActDWConv2d, seq_to_2d, ScaleOffset, mask_out
from networks.layers.attention import silu, MultiheadAttention, MultiheadLocalAttentionV2, MultiheadLocalAttentionV3, GatedPropagation, LocalGatedPropagation
def _get_norm(indim, type='ln', groups=8):
if type == 'gn':
return GroupNorm1D(indim, groups)
else:
return nn.LayerNorm(indim)
def _get_activation_fn(activation):
"""Return an activation function given a string"""
if activation == "relu":
return F.relu
if activation == "gelu":
return F.gelu
if activation == "glu":
return F.glu
raise RuntimeError(
F"activation should be relu/gele/glu, not {activation}.")
class LongShortTermTransformer(nn.Module):
def __init__(self,
num_layers=2,
d_model=256,
self_nhead=8,
att_nhead=8,
dim_feedforward=1024,
emb_dropout=0.,
droppath=0.1,
lt_dropout=0.,
st_dropout=0.,
droppath_lst=False,
droppath_scaling=False,
activation="gelu",
return_intermediate=False,
intermediate_norm=True,
final_norm=True,
block_version="v1"):
super().__init__()
self.intermediate_norm = intermediate_norm
self.final_norm = final_norm
self.num_layers = num_layers
self.return_intermediate = return_intermediate
self.emb_dropout = nn.Dropout(emb_dropout, True)
self.mask_token = nn.Parameter(torch.randn([1, 1, d_model]))
if block_version == "v1":
block = LongShortTermTransformerBlock
elif block_version == "v2":
block = LongShortTermTransformerBlockV2
elif block_version == "v3":
block = LongShortTermTransformerBlockV3
else:
raise NotImplementedError
layers = []
for idx in range(num_layers):
if droppath_scaling:
if num_layers == 1:
droppath_rate = 0
else:
droppath_rate = droppath * idx / (num_layers - 1)
else:
droppath_rate = droppath
layers.append(
block(d_model, self_nhead, att_nhead, dim_feedforward,
droppath_rate, lt_dropout, st_dropout, droppath_lst,
activation))
self.layers = nn.ModuleList(layers)
num_norms = num_layers - 1 if intermediate_norm else 0
if final_norm:
num_norms += 1
self.decoder_norms = [
_get_norm(d_model, type='ln') for _ in range(num_norms)
] if num_norms > 0 else None
if self.decoder_norms is not None:
self.decoder_norms = nn.ModuleList(self.decoder_norms)
def forward(self,
tgt,
long_term_memories,
short_term_memories,
curr_id_emb=None,
self_pos=None,
size_2d=None):
output = self.emb_dropout(tgt)
# output = mask_out(output, self.mask_token, 0.15, self.training)
intermediate = []
intermediate_memories = []
for idx, layer in enumerate(self.layers):
output, memories = layer(output,
long_term_memories[idx] if
long_term_memories is not None else None,
short_term_memories[idx] if
short_term_memories is not None else None,
curr_id_emb=curr_id_emb,
self_pos=self_pos,
size_2d=size_2d)
if self.return_intermediate:
intermediate.append(output)
intermediate_memories.append(memories)
if self.decoder_norms is not None:
if self.final_norm:
output = self.decoder_norms[-1](output)
if self.return_intermediate:
intermediate.pop()
intermediate.append(output)
if self.intermediate_norm:
for idx in range(len(intermediate) - 1):
intermediate[idx] = self.decoder_norms[idx](
intermediate[idx])
if self.return_intermediate:
return intermediate, intermediate_memories
return output, memories
class DualBranchGPM(nn.Module):
def __init__(self,
num_layers=2,
d_model=256,
self_nhead=8,
att_nhead=8,
dim_feedforward=1024,
emb_dropout=0.,
droppath=0.1,
lt_dropout=0.,
st_dropout=0.,
droppath_lst=False,
droppath_scaling=False,
activation="gelu",
return_intermediate=False,
intermediate_norm=True,
final_norm=True):
super().__init__()
self.intermediate_norm = intermediate_norm
self.final_norm = final_norm
self.num_layers = num_layers
self.return_intermediate = return_intermediate
self.emb_dropout = nn.Dropout(emb_dropout, True)
# self.mask_token = nn.Parameter(torch.randn([1, 1, d_model]))
block = GatedPropagationModule
layers = []
for idx in range(num_layers):
if droppath_scaling:
if num_layers == 1:
droppath_rate = 0
else:
droppath_rate = droppath * idx / (num_layers - 1)
else:
droppath_rate = droppath
layers.append(
block(d_model,
self_nhead,
att_nhead,
dim_feedforward,
droppath_rate,
lt_dropout,
st_dropout,
droppath_lst,
activation,
layer_idx=idx))
self.layers = nn.ModuleList(layers)
num_norms = num_layers - 1 if intermediate_norm else 0
if final_norm:
num_norms += 1
self.decoder_norms = [
_get_norm(d_model * 2, type='gn', groups=2)
for _ in range(num_norms)
] if num_norms > 0 else None
if self.decoder_norms is not None:
self.decoder_norms = nn.ModuleList(self.decoder_norms)
def forward(self,
tgt,
long_term_memories,
short_term_memories,
curr_id_emb=None,
self_pos=None,
size_2d=None):
output = self.emb_dropout(tgt)
# output = mask_out(output, self.mask_token, 0.15, self.training)
intermediate = []
intermediate_memories = []
output_id = None
for idx, layer in enumerate(self.layers):
output, output_id, memories = layer(
output,
output_id,
long_term_memories[idx]
if long_term_memories is not None else None,
short_term_memories[idx]
if short_term_memories is not None else None,
curr_id_emb=curr_id_emb,
self_pos=self_pos,
size_2d=size_2d)
cat_output = torch.cat([output, output_id], dim=2)
if self.return_intermediate:
intermediate.append(cat_output)
intermediate_memories.append(memories)
if self.decoder_norms is not None:
if self.final_norm:
cat_output = self.decoder_norms[-1](cat_output)
if self.return_intermediate:
intermediate.pop()
intermediate.append(cat_output)
if self.intermediate_norm:
for idx in range(len(intermediate) - 1):
intermediate[idx] = self.decoder_norms[idx](
intermediate[idx])
if self.return_intermediate:
return intermediate, intermediate_memories
return cat_output, memories
class LongShortTermTransformerBlock(nn.Module):
def __init__(self,
d_model,
self_nhead,
att_nhead,
dim_feedforward=1024,
droppath=0.1,
lt_dropout=0.,
st_dropout=0.,
droppath_lst=False,
activation="gelu",
local_dilation=1,
enable_corr=True):
super().__init__()
# Long Short-Term Attention
self.norm1 = _get_norm(d_model)
self.linear_Q = nn.Linear(d_model, d_model)
self.linear_V = nn.Linear(d_model, d_model)
self.long_term_attn = MultiheadAttention(d_model,
att_nhead,
use_linear=False,
dropout=lt_dropout)
# MultiheadLocalAttention = MultiheadLocalAttentionV2 if enable_corr else MultiheadLocalAttentionV3
if enable_corr:
try:
import spatial_correlation_sampler
MultiheadLocalAttention = MultiheadLocalAttentionV2
except Exception as inst:
print(inst)
print("Failed to import PyTorch Correlation, For better efficiency, please install it.")
MultiheadLocalAttention = MultiheadLocalAttentionV3
else:
MultiheadLocalAttention = MultiheadLocalAttentionV3
self.short_term_attn = MultiheadLocalAttention(d_model,
att_nhead,
dilation=local_dilation,
use_linear=False,
dropout=st_dropout)
self.lst_dropout = nn.Dropout(max(lt_dropout, st_dropout), True)
self.droppath_lst = droppath_lst
# Self-attention
self.norm2 = _get_norm(d_model)
self.self_attn = MultiheadAttention(d_model, self_nhead)
# Feed-forward
self.norm3 = _get_norm(d_model)
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.activation = GNActDWConv2d(dim_feedforward)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.droppath = DropPath(droppath, batch_dim=1)
self._init_weight()
def with_pos_embed(self, tensor, pos=None):
size = tensor.size()
if len(size) == 4 and pos is not None:
n, c, h, w = size
pos = pos.view(h, w, n, c).permute(2, 3, 0, 1)
return tensor if pos is None else tensor + pos
def forward(self,
tgt,
long_term_memory=None,
short_term_memory=None,
curr_id_emb=None,
self_pos=None,
size_2d=(30, 30)):
# Self-attention
_tgt = self.norm1(tgt)
q = k = self.with_pos_embed(_tgt, self_pos)
v = _tgt
tgt2 = self.self_attn(q, k, v)[0]
tgt = tgt + self.droppath(tgt2)
# Long Short-Term Attention
_tgt = self.norm2(tgt)
curr_Q = self.linear_Q(_tgt)
curr_K = curr_Q
curr_V = _tgt
local_Q = seq_to_2d(curr_Q, size_2d)
if curr_id_emb is not None:
global_K, global_V = self.fuse_key_value_id(
curr_K, curr_V, curr_id_emb)
local_K = seq_to_2d(global_K, size_2d)
local_V = seq_to_2d(global_V, size_2d)
else:
global_K, global_V = long_term_memory
local_K, local_V = short_term_memory
tgt2 = self.long_term_attn(curr_Q, global_K, global_V)[0]
tgt3 = self.short_term_attn(local_Q, local_K, local_V)[0]
if self.droppath_lst:
tgt = tgt + self.droppath(tgt2 + tgt3)
else:
tgt = tgt + self.lst_dropout(tgt2 + tgt3)
# Feed-forward
_tgt = self.norm3(tgt)
tgt2 = self.linear2(self.activation(self.linear1(_tgt), size_2d))
tgt = tgt + self.droppath(tgt2)
return tgt, [[curr_K, curr_V], [global_K, global_V],
[local_K, local_V]]
def fuse_key_value_id(self, key, value, id_emb):
K = key
V = self.linear_V(value + id_emb)
return K, V
def _init_weight(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
class LongShortTermTransformerBlockV2(nn.Module):
def __init__(self,
d_model,
self_nhead,
att_nhead,
dim_feedforward=1024,
droppath=0.1,
lt_dropout=0.,
st_dropout=0.,
droppath_lst=False,
activation="gelu",
local_dilation=1,
enable_corr=True):
super().__init__()
self.d_model = d_model
self.att_nhead = att_nhead
# Self-attention
self.norm1 = _get_norm(d_model)
self.self_attn = MultiheadAttention(d_model, self_nhead)
# Long Short-Term Attention
self.norm2 = _get_norm(d_model)
self.linear_QV = nn.Linear(d_model, 2 * d_model)
self.linear_ID_KV = nn.Linear(d_model, d_model + att_nhead)
self.long_term_attn = MultiheadAttention(d_model,
att_nhead,
use_linear=False,
dropout=lt_dropout)
# MultiheadLocalAttention = MultiheadLocalAttentionV2 if enable_corr else MultiheadLocalAttentionV3
if enable_corr:
try:
import spatial_correlation_sampler
MultiheadLocalAttention = MultiheadLocalAttentionV2
except Exception as inst:
print(inst)
print("Failed to import PyTorch Correlation, For better efficiency, please install it.")
MultiheadLocalAttention = MultiheadLocalAttentionV3
else:
MultiheadLocalAttention = MultiheadLocalAttentionV3
self.short_term_attn = MultiheadLocalAttention(d_model,
att_nhead,
dilation=local_dilation,
use_linear=False,
dropout=st_dropout)
self.lst_dropout = nn.Dropout(max(lt_dropout, st_dropout), True)
self.droppath_lst = droppath_lst
# Feed-forward
self.norm3 = _get_norm(d_model)
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.activation = GNActDWConv2d(dim_feedforward)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.droppath = DropPath(droppath, batch_dim=1)
self._init_weight()
def with_pos_embed(self, tensor, pos=None):
size = tensor.size()
if len(size) == 4 and pos is not None:
n, c, h, w = size
pos = pos.view(h, w, n, c).permute(2, 3, 0, 1)
return tensor if pos is None else tensor + pos
def forward(self,
tgt,
long_term_memory=None,
short_term_memory=None,
curr_id_emb=None,
self_pos=None,
size_2d=(30, 30)):
# Self-attention
_tgt = self.norm1(tgt)
q = k = self.with_pos_embed(_tgt, self_pos)
v = _tgt
tgt2 = self.self_attn(q, k, v)[0]
tgt = tgt + self.droppath(tgt2)
# Long Short-Term Attention
_tgt = self.norm2(tgt)
curr_QV = self.linear_QV(_tgt)
curr_QV = torch.split(curr_QV, self.d_model, dim=2)
curr_Q = curr_K = curr_QV[0]
curr_V = curr_QV[1]
local_Q = seq_to_2d(curr_Q, size_2d)
if curr_id_emb is not None:
global_K, global_V = self.fuse_key_value_id(
curr_K, curr_V, curr_id_emb)
local_K = seq_to_2d(global_K, size_2d)
local_V = seq_to_2d(global_V, size_2d)
else:
global_K, global_V = long_term_memory
local_K, local_V = short_term_memory
tgt2 = self.long_term_attn(curr_Q, global_K, global_V)[0]
tgt3 = self.short_term_attn(local_Q, local_K, local_V)[0]
if self.droppath_lst:
tgt = tgt + self.droppath(tgt2 + tgt3)
else:
tgt = tgt + self.lst_dropout(tgt2 + tgt3)
# Feed-forward
_tgt = self.norm3(tgt)
tgt2 = self.linear2(self.activation(self.linear1(_tgt), size_2d))
tgt = tgt + self.droppath(tgt2)
return tgt, [[curr_K, curr_V], [global_K, global_V],
[local_K, local_V]]
def fuse_key_value_id(self, key, value, id_emb):
ID_KV = self.linear_ID_KV(id_emb)
ID_K, ID_V = torch.split(ID_KV, [self.att_nhead, self.d_model], dim=2)
bs = key.size(1)
K = key.view(-1, bs, self.att_nhead, self.d_model //
self.att_nhead) * (1 + torch.tanh(ID_K)).unsqueeze(-1)
K = K.view(-1, bs, self.d_model)
V = value + ID_V
return K, V
def _init_weight(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
class GatedPropagationModule(nn.Module):
def __init__(self,
d_model,
self_nhead,
att_nhead,
dim_feedforward=1024,
droppath=0.1,
lt_dropout=0.,
st_dropout=0.,
droppath_lst=False,
activation="gelu",
local_dilation=1,
enable_corr=True,
max_local_dis=7,
layer_idx=0,
expand_ratio=2.):
super().__init__()
expand_ratio = expand_ratio
expand_d_model = int(d_model * expand_ratio)
self.expand_d_model = expand_d_model
self.d_model = d_model
self.att_nhead = att_nhead
d_att = d_model // 2 if att_nhead == 1 else d_model // att_nhead
self.d_att = d_att
self.layer_idx = layer_idx
# Long Short-Term Attention
self.norm1 = _get_norm(d_model)
self.linear_QV = nn.Linear(d_model, d_att * att_nhead + expand_d_model)
self.linear_U = nn.Linear(d_model, expand_d_model)
if layer_idx == 0:
self.linear_ID_V = nn.Linear(d_model, expand_d_model)
else:
self.id_norm1 = _get_norm(d_model)
self.linear_ID_V = nn.Linear(d_model * 2, expand_d_model)
self.linear_ID_U = nn.Linear(d_model, expand_d_model)
self.long_term_attn = GatedPropagation(d_qk=self.d_model,
d_vu=self.d_model * 2,
num_head=att_nhead,
use_linear=False,
dropout=lt_dropout,
d_att=d_att,
top_k=-1,
expand_ratio=expand_ratio)
if enable_corr:
try:
import spatial_correlation_sampler
except Exception as inst:
print(inst)
print("Failed to import PyTorch Correlation, For better efficiency, please install it.")
enable_corr = False
self.short_term_attn = LocalGatedPropagation(d_qk=self.d_model,
d_vu=self.d_model * 2,
num_head=att_nhead,
dilation=local_dilation,
use_linear=False,
enable_corr=enable_corr,
dropout=st_dropout,
d_att=d_att,
max_dis=max_local_dis,
expand_ratio=expand_ratio)
self.lst_dropout = nn.Dropout(max(lt_dropout, st_dropout), True)
self.droppath_lst = droppath_lst
# Self-attention
self.norm2 = _get_norm(d_model)
self.id_norm2 = _get_norm(d_model)
self.self_attn = GatedPropagation(d_model * 2,
d_model * 2,
self_nhead,
d_att=d_att)
self.droppath = DropPath(droppath, batch_dim=1)
self._init_weight()
def with_pos_embed(self, tensor, pos=None):
size = tensor.size()
if len(size) == 4 and pos is not None:
n, c, h, w = size
pos = pos.view(h, w, n, c).permute(2, 3, 0, 1)
return tensor if pos is None else tensor + pos
def forward(self,
tgt,
tgt_id=None,
long_term_memory=None,
short_term_memory=None,
curr_id_emb=None,
self_pos=None,
size_2d=(30, 30)):
# Long Short-Term Attention
_tgt = self.norm1(tgt)
curr_QV = self.linear_QV(_tgt)
curr_QV = torch.split(
curr_QV, [self.d_att * self.att_nhead, self.expand_d_model], dim=2)
curr_Q = curr_K = curr_QV[0]
local_Q = seq_to_2d(curr_Q, size_2d)
curr_V = silu(curr_QV[1])
curr_U = self.linear_U(_tgt)
if tgt_id is None:
tgt_id = 0
cat_curr_U = torch.cat(
[silu(curr_U), torch.ones_like(curr_U)], dim=-1)
curr_ID_V = None
else:
_tgt_id = self.id_norm1(tgt_id)
curr_ID_V = _tgt_id
curr_ID_U = self.linear_ID_U(_tgt_id)
cat_curr_U = silu(torch.cat([curr_U, curr_ID_U], dim=-1))
if curr_id_emb is not None:
global_K, global_V = curr_K, curr_V
local_K = seq_to_2d(global_K, size_2d)
local_V = seq_to_2d(global_V, size_2d)
_, global_ID_V = self.fuse_key_value_id(None, curr_ID_V,
curr_id_emb)
local_ID_V = seq_to_2d(global_ID_V, size_2d)
else:
global_K, global_V, _, global_ID_V = long_term_memory
local_K, local_V, _, local_ID_V = short_term_memory
cat_global_V = torch.cat([global_V, global_ID_V], dim=-1)
cat_local_V = torch.cat([local_V, local_ID_V], dim=1)
cat_tgt2, _ = self.long_term_attn(curr_Q, global_K, cat_global_V,
cat_curr_U, size_2d)
cat_tgt3, _ = self.short_term_attn(local_Q, local_K, cat_local_V,
cat_curr_U, size_2d)
tgt2, tgt_id2 = torch.split(cat_tgt2, self.d_model, dim=-1)
tgt3, tgt_id3 = torch.split(cat_tgt3, self.d_model, dim=-1)
if self.droppath_lst:
tgt = tgt + self.droppath(tgt2 + tgt3)
tgt_id = tgt_id + self.droppath(tgt_id2 + tgt_id3)
else:
tgt = tgt + self.lst_dropout(tgt2 + tgt3)
tgt_id = tgt_id + self.lst_dropout(tgt_id2 + tgt_id3)
# Self-attention
_tgt = self.norm2(tgt)
_tgt_id = self.id_norm2(tgt_id)
q = k = v = u = torch.cat([_tgt, _tgt_id], dim=-1)
cat_tgt2, _ = self.self_attn(q, k, v, u, size_2d)
tgt2, tgt_id2 = torch.split(cat_tgt2, self.d_model, dim=-1)
tgt = tgt + self.droppath(tgt2)
tgt_id = tgt_id + self.droppath(tgt_id2)
return tgt, tgt_id, [[curr_K, curr_V, None, curr_ID_V],
[global_K, global_V, None, global_ID_V],
[local_K, local_V, None, local_ID_V]]
def fuse_key_value_id(self, key, value, id_emb):
ID_K = None
if value is not None:
ID_V = silu(self.linear_ID_V(torch.cat([value, id_emb], dim=2)))
else:
ID_V = silu(self.linear_ID_V(id_emb))
return ID_K, ID_V
def _init_weight(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
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