571 lines
19 KiB
Python
571 lines
19 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import copy
|
|
|
|
from .seecoder_utils import with_pos_embed
|
|
|
|
###########
|
|
# helpers #
|
|
###########
|
|
|
|
def _get_clones(module, N):
|
|
return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
|
|
|
|
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/gelu, not {activation}.")
|
|
|
|
def c2_xavier_fill(module):
|
|
# Caffe2 implementation of XavierFill in fact
|
|
nn.init.kaiming_uniform_(module.weight, a=1)
|
|
if module.bias is not None:
|
|
nn.init.constant_(module.bias, 0)
|
|
|
|
def with_pos_embed(x, pos):
|
|
return x if pos is None else x + pos
|
|
|
|
###########
|
|
# Modules #
|
|
###########
|
|
|
|
class Conv2d_Convenience(nn.Conv2d):
|
|
def __init__(self, *args, **kwargs):
|
|
norm = kwargs.pop("norm", None)
|
|
activation = kwargs.pop("activation", None)
|
|
super().__init__(*args, **kwargs)
|
|
self.norm = norm
|
|
self.activation = activation
|
|
|
|
def forward(self, x):
|
|
x = F.conv2d(
|
|
x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
|
|
if self.norm is not None:
|
|
x = self.norm(x)
|
|
if self.activation is not None:
|
|
x = self.activation(x)
|
|
return x
|
|
|
|
class DecoderLayer(nn.Module):
|
|
def __init__(self,
|
|
dim=256,
|
|
feedforward_dim=1024,
|
|
dropout=0.1,
|
|
activation="relu",
|
|
n_heads=8,):
|
|
|
|
super().__init__()
|
|
|
|
self.self_attn = nn.MultiheadAttention(dim, n_heads, dropout=dropout)
|
|
self.dropout1 = nn.Dropout(dropout)
|
|
self.norm1 = nn.LayerNorm(dim)
|
|
|
|
self.linear1 = nn.Linear(dim, feedforward_dim)
|
|
self.activation = _get_activation_fn(activation)
|
|
self.dropout2 = nn.Dropout(dropout)
|
|
self.linear2 = nn.Linear(feedforward_dim, dim)
|
|
self.dropout3 = nn.Dropout(dropout)
|
|
self.norm2 = nn.LayerNorm(dim)
|
|
|
|
def forward(self, x):
|
|
h = x
|
|
h1 = self.self_attn(x, x, x, attn_mask=None)[0]
|
|
h = h + self.dropout1(h1)
|
|
h = self.norm1(h)
|
|
|
|
h2 = self.linear2(self.dropout2(self.activation(self.linear1(h))))
|
|
h = h + self.dropout3(h2)
|
|
h = self.norm2(h)
|
|
return h
|
|
|
|
class DecoderLayerStacked(nn.Module):
|
|
def __init__(self, layer, num_layers, norm=None):
|
|
super().__init__()
|
|
self.layers = _get_clones(layer, num_layers)
|
|
self.num_layers = num_layers
|
|
self.norm = norm
|
|
|
|
def forward(self, x):
|
|
h = x
|
|
for _, layer in enumerate(self.layers):
|
|
h = layer(h)
|
|
if self.norm is not None:
|
|
h = self.norm(h)
|
|
return h
|
|
|
|
class SelfAttentionLayer(nn.Module):
|
|
def __init__(self, channels, nhead, dropout=0.0,
|
|
activation="relu", normalize_before=False):
|
|
super().__init__()
|
|
self.self_attn = nn.MultiheadAttention(channels, nhead, dropout=dropout)
|
|
|
|
self.norm = nn.LayerNorm(channels)
|
|
self.dropout = nn.Dropout(dropout)
|
|
|
|
self.activation = _get_activation_fn(activation)
|
|
self.normalize_before = normalize_before
|
|
|
|
self._reset_parameters()
|
|
|
|
def _reset_parameters(self):
|
|
for p in self.parameters():
|
|
if p.dim() > 1:
|
|
nn.init.xavier_uniform_(p)
|
|
|
|
def forward_post(self,
|
|
qkv,
|
|
qk_pos = None,
|
|
mask = None,):
|
|
h = qkv
|
|
qk = with_pos_embed(qkv, qk_pos).transpose(0, 1)
|
|
v = qkv.transpose(0, 1)
|
|
h1 = self.self_attn(qk, qk, v, attn_mask=mask)[0]
|
|
h1 = h1.transpose(0, 1)
|
|
h = h + self.dropout(h1)
|
|
h = self.norm(h)
|
|
return h
|
|
|
|
def forward_pre(self, tgt,
|
|
tgt_mask = None,
|
|
tgt_key_padding_mask = None,
|
|
query_pos = None):
|
|
# deprecated
|
|
assert False
|
|
tgt2 = self.norm(tgt)
|
|
q = k = self.with_pos_embed(tgt2, query_pos)
|
|
tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
|
|
key_padding_mask=tgt_key_padding_mask)[0]
|
|
tgt = tgt + self.dropout(tgt2)
|
|
return tgt
|
|
|
|
def forward(self, *args, **kwargs):
|
|
if self.normalize_before:
|
|
return self.forward_pre(*args, **kwargs)
|
|
return self.forward_post(*args, **kwargs)
|
|
|
|
class CrossAttentionLayer(nn.Module):
|
|
def __init__(self, channels, nhead, dropout=0.0,
|
|
activation="relu", normalize_before=False):
|
|
super().__init__()
|
|
self.multihead_attn = nn.MultiheadAttention(channels, nhead, dropout=dropout)
|
|
|
|
self.norm = nn.LayerNorm(channels)
|
|
self.dropout = nn.Dropout(dropout)
|
|
|
|
self.activation = _get_activation_fn(activation)
|
|
self.normalize_before = normalize_before
|
|
|
|
self._reset_parameters()
|
|
|
|
def _reset_parameters(self):
|
|
for p in self.parameters():
|
|
if p.dim() > 1:
|
|
nn.init.xavier_uniform_(p)
|
|
|
|
def forward_post(self,
|
|
q,
|
|
kv,
|
|
q_pos = None,
|
|
k_pos = None,
|
|
mask = None,):
|
|
h = q
|
|
q = with_pos_embed(q, q_pos).transpose(0, 1)
|
|
k = with_pos_embed(kv, k_pos).transpose(0, 1)
|
|
v = kv.transpose(0, 1)
|
|
h1 = self.multihead_attn(q, k, v, attn_mask=mask)[0]
|
|
h1 = h1.transpose(0, 1)
|
|
h = h + self.dropout(h1)
|
|
h = self.norm(h)
|
|
return h
|
|
|
|
def forward_pre(self, tgt, memory,
|
|
memory_mask = None,
|
|
memory_key_padding_mask = None,
|
|
pos = None,
|
|
query_pos = None):
|
|
# Deprecated
|
|
assert False
|
|
tgt2 = self.norm(tgt)
|
|
tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),
|
|
key=self.with_pos_embed(memory, pos),
|
|
value=memory, attn_mask=memory_mask,
|
|
key_padding_mask=memory_key_padding_mask)[0]
|
|
tgt = tgt + self.dropout(tgt2)
|
|
return tgt
|
|
|
|
def forward(self, *args, **kwargs):
|
|
if self.normalize_before:
|
|
return self.forward_pre(*args, **kwargs)
|
|
return self.forward_post(*args, **kwargs)
|
|
|
|
class FeedForwardLayer(nn.Module):
|
|
def __init__(self, channels, hidden_channels=2048, dropout=0.0,
|
|
activation="relu", normalize_before=False):
|
|
super().__init__()
|
|
self.linear1 = nn.Linear(channels, hidden_channels)
|
|
self.dropout = nn.Dropout(dropout)
|
|
self.linear2 = nn.Linear(hidden_channels, channels)
|
|
self.norm = nn.LayerNorm(channels)
|
|
self.activation = _get_activation_fn(activation)
|
|
self.normalize_before = normalize_before
|
|
self._reset_parameters()
|
|
|
|
def _reset_parameters(self):
|
|
for p in self.parameters():
|
|
if p.dim() > 1:
|
|
nn.init.xavier_uniform_(p)
|
|
|
|
def forward_post(self, x):
|
|
h = x
|
|
h1 = self.linear2(self.dropout(self.activation(self.linear1(h))))
|
|
h = h + self.dropout(h1)
|
|
h = self.norm(h)
|
|
return h
|
|
|
|
def forward_pre(self, x):
|
|
xn = self.norm(x)
|
|
h = x
|
|
h1 = self.linear2(self.dropout(self.activation(self.linear1(xn))))
|
|
h = h + self.dropout(h1)
|
|
return h
|
|
|
|
def forward(self, *args, **kwargs):
|
|
if self.normalize_before:
|
|
return self.forward_pre(*args, **kwargs)
|
|
return self.forward_post(*args, **kwargs)
|
|
|
|
class MLP(nn.Module):
|
|
def __init__(self, in_channels, channels, out_channels, num_layers):
|
|
super().__init__()
|
|
self.num_layers = num_layers
|
|
h = [channels] * (num_layers - 1)
|
|
self.layers = nn.ModuleList(
|
|
nn.Linear(n, k)
|
|
for n, k in zip([in_channels]+h, h+[out_channels]))
|
|
|
|
def forward(self, x):
|
|
for i, layer in enumerate(self.layers):
|
|
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
|
|
return x
|
|
|
|
class PPE_MLP(nn.Module):
|
|
def __init__(self, freq_num=20, freq_max=None, out_channel=768, mlp_layer=3):
|
|
import math
|
|
super().__init__()
|
|
self.freq_num = freq_num
|
|
self.freq_max = freq_max
|
|
self.out_channel = out_channel
|
|
self.mlp_layer = mlp_layer
|
|
self.twopi = 2 * math.pi
|
|
|
|
mlp = []
|
|
in_channel = freq_num*4
|
|
for idx in range(mlp_layer):
|
|
linear = nn.Linear(in_channel, out_channel, bias=True)
|
|
nn.init.xavier_normal_(linear.weight)
|
|
nn.init.constant_(linear.bias, 0)
|
|
mlp.append(linear)
|
|
if idx != mlp_layer-1:
|
|
mlp.append(nn.SiLU())
|
|
in_channel = out_channel
|
|
self.mlp = nn.Sequential(*mlp)
|
|
nn.init.constant_(self.mlp[-1].weight, 0)
|
|
|
|
def forward(self, x, mask=None):
|
|
assert mask is None, "Mask not implemented"
|
|
h, w = x.shape[-2:]
|
|
minlen = min(h, w)
|
|
|
|
h_embed, w_embed = torch.meshgrid(torch.arange(h), torch.arange(w), indexing='ij')
|
|
if self.training:
|
|
import numpy.random as npr
|
|
pertube_h, pertube_w = npr.uniform(-0.5, 0.5), npr.uniform(-0.5, 0.5)
|
|
else:
|
|
pertube_h, pertube_w = 0, 0
|
|
|
|
h_embed = (h_embed+0.5 - h/2 + pertube_h) / (minlen) * self.twopi
|
|
w_embed = (w_embed+0.5 - w/2 + pertube_w) / (minlen) * self.twopi
|
|
h_embed, w_embed = h_embed.to(x.device).to(x.dtype), w_embed.to(x.device).to(x.dtype)
|
|
|
|
dim_t = torch.linspace(0, 1, self.freq_num, dtype=torch.float32, device=x.device)
|
|
freq_max = self.freq_max if self.freq_max is not None else minlen/2
|
|
dim_t = freq_max ** dim_t.to(x.dtype)
|
|
|
|
pos_h = h_embed[:, :, None] * dim_t
|
|
pos_w = w_embed[:, :, None] * dim_t
|
|
pos = torch.cat((pos_h.sin(), pos_h.cos(), pos_w.sin(), pos_w.cos()), dim=-1)
|
|
pos = self.mlp(pos)
|
|
pos = pos.permute(2, 0, 1)[None]
|
|
return pos
|
|
|
|
def __repr__(self, _repr_indent=4):
|
|
head = "Positional encoding " + self.__class__.__name__
|
|
body = [
|
|
"num_pos_feats: {}".format(self.num_pos_feats),
|
|
"temperature: {}".format(self.temperature),
|
|
"normalize: {}".format(self.normalize),
|
|
"scale: {}".format(self.scale),
|
|
]
|
|
# _repr_indent = 4
|
|
lines = [head] + [" " * _repr_indent + line for line in body]
|
|
return "\n".join(lines)
|
|
|
|
###########
|
|
# Decoder #
|
|
###########
|
|
|
|
class Decoder(nn.Module):
|
|
def __init__(
|
|
self,
|
|
inchannels,
|
|
trans_input_tags,
|
|
trans_num_layers,
|
|
trans_dim,
|
|
trans_nheads,
|
|
trans_dropout,
|
|
trans_feedforward_dim,):
|
|
|
|
super().__init__()
|
|
trans_inchannels = {
|
|
k: v for k, v in inchannels.items() if k in trans_input_tags}
|
|
fpn_inchannels = {
|
|
k: v for k, v in inchannels.items() if k not in trans_input_tags}
|
|
|
|
self.trans_tags = sorted(list(trans_inchannels.keys()))
|
|
self.fpn_tags = sorted(list(fpn_inchannels.keys()))
|
|
self.all_tags = sorted(list(inchannels.keys()))
|
|
|
|
if len(self.trans_tags)==0:
|
|
assert False # Not allowed
|
|
|
|
self.num_trans_lvls = len(self.trans_tags)
|
|
|
|
self.inproj_layers = nn.ModuleDict()
|
|
for tagi in self.trans_tags:
|
|
layeri = nn.Sequential(
|
|
nn.Conv2d(trans_inchannels[tagi], trans_dim, kernel_size=1),
|
|
nn.GroupNorm(32, trans_dim),)
|
|
nn.init.xavier_uniform_(layeri[0].weight, gain=1)
|
|
nn.init.constant_(layeri[0].bias, 0)
|
|
self.inproj_layers[tagi] = layeri
|
|
|
|
tlayer = DecoderLayer(
|
|
dim = trans_dim,
|
|
n_heads = trans_nheads,
|
|
dropout = trans_dropout,
|
|
feedforward_dim = trans_feedforward_dim,
|
|
activation = 'relu',)
|
|
|
|
self.transformer = DecoderLayerStacked(tlayer, trans_num_layers)
|
|
for p in self.transformer.parameters():
|
|
if p.dim() > 1:
|
|
nn.init.xavier_uniform_(p)
|
|
self.level_embed = nn.Parameter(torch.Tensor(len(self.trans_tags), trans_dim))
|
|
nn.init.normal_(self.level_embed)
|
|
|
|
self.lateral_layers = nn.ModuleDict()
|
|
self.output_layers = nn.ModuleDict()
|
|
for tagi in self.all_tags:
|
|
lateral_conv = Conv2d_Convenience(
|
|
inchannels[tagi], trans_dim, kernel_size=1,
|
|
bias=False, norm=nn.GroupNorm(32, trans_dim))
|
|
c2_xavier_fill(lateral_conv)
|
|
self.lateral_layers[tagi] = lateral_conv
|
|
|
|
for tagi in self.fpn_tags:
|
|
output_conv = Conv2d_Convenience(
|
|
trans_dim, trans_dim, kernel_size=3, stride=1, padding=1,
|
|
bias=False, norm=nn.GroupNorm(32, trans_dim), activation=F.relu,)
|
|
c2_xavier_fill(output_conv)
|
|
self.output_layers[tagi] = output_conv
|
|
|
|
def forward(self, features):
|
|
x = []
|
|
spatial_shapes = {}
|
|
for idx, tagi in enumerate(self.trans_tags[::-1]):
|
|
xi = features[tagi]
|
|
xi = self.inproj_layers[tagi](xi)
|
|
bs, _, h, w = xi.shape
|
|
spatial_shapes[tagi] = (h, w)
|
|
xi = xi.flatten(2).transpose(1, 2) + self.level_embed[idx].view(1, 1, -1)
|
|
x.append(xi)
|
|
|
|
x_length = [xi.shape[1] for xi in x]
|
|
x_concat = torch.cat(x, 1)
|
|
y_concat = self.transformer(x_concat)
|
|
y = torch.split(y_concat, x_length, dim=1)
|
|
|
|
out = {}
|
|
for idx, tagi in enumerate(self.trans_tags[::-1]):
|
|
h, w = spatial_shapes[tagi]
|
|
yi = y[idx].transpose(1, 2).view(bs, -1, h, w)
|
|
out[tagi] = yi
|
|
|
|
for idx, tagi in enumerate(self.all_tags[::-1]):
|
|
lconv = self.lateral_layers[tagi]
|
|
if tagi in self.trans_tags:
|
|
out[tagi] = out[tagi] + lconv(features[tagi])
|
|
tag_save = tagi
|
|
else:
|
|
oconv = self.output_layers[tagi]
|
|
h = lconv(features[tagi])
|
|
oprev = out[tag_save]
|
|
h = h + F.interpolate(oconv(oprev), size=h.shape[-2:], mode="bilinear", align_corners=False)
|
|
out[tagi] = h
|
|
|
|
return out
|
|
|
|
#####################
|
|
# Query Transformer #
|
|
#####################
|
|
|
|
class QueryTransformer(nn.Module):
|
|
def __init__(self,
|
|
in_channels,
|
|
hidden_dim,
|
|
num_queries = [8, 144],
|
|
nheads = 8,
|
|
num_layers = 9,
|
|
feedforward_dim = 2048,
|
|
mask_dim = 256,
|
|
pre_norm = False,
|
|
num_feature_levels = 3,
|
|
enforce_input_project = False,
|
|
with_fea2d_pos = True):
|
|
|
|
super().__init__()
|
|
|
|
if with_fea2d_pos:
|
|
self.pe_layer = PPE_MLP(freq_num=20, freq_max=None, out_channel=hidden_dim, mlp_layer=3)
|
|
else:
|
|
self.pe_layer = None
|
|
|
|
if in_channels!=hidden_dim or enforce_input_project:
|
|
self.input_proj = nn.ModuleList()
|
|
for _ in range(num_feature_levels):
|
|
self.input_proj.append(nn.Conv2d(in_channels, hidden_dim, kernel_size=1))
|
|
c2_xavier_fill(self.input_proj[-1])
|
|
else:
|
|
self.input_proj = None
|
|
|
|
self.num_heads = nheads
|
|
self.num_layers = num_layers
|
|
self.transformer_selfatt_layers = nn.ModuleList()
|
|
self.transformer_crossatt_layers = nn.ModuleList()
|
|
self.transformer_feedforward_layers = nn.ModuleList()
|
|
|
|
for _ in range(self.num_layers):
|
|
self.transformer_selfatt_layers.append(
|
|
SelfAttentionLayer(
|
|
channels=hidden_dim,
|
|
nhead=nheads,
|
|
dropout=0.0,
|
|
normalize_before=pre_norm, ))
|
|
|
|
self.transformer_crossatt_layers.append(
|
|
CrossAttentionLayer(
|
|
channels=hidden_dim,
|
|
nhead=nheads,
|
|
dropout=0.0,
|
|
normalize_before=pre_norm, ))
|
|
|
|
self.transformer_feedforward_layers.append(
|
|
FeedForwardLayer(
|
|
channels=hidden_dim,
|
|
hidden_channels=feedforward_dim,
|
|
dropout=0.0,
|
|
normalize_before=pre_norm, ))
|
|
|
|
self.num_queries = num_queries
|
|
num_gq, num_lq = self.num_queries
|
|
self.init_query = nn.Embedding(num_gq+num_lq, hidden_dim)
|
|
self.query_pos_embedding = nn.Embedding(num_gq+num_lq, hidden_dim)
|
|
|
|
self.num_feature_levels = num_feature_levels
|
|
self.level_embed = nn.Embedding(num_feature_levels, hidden_dim)
|
|
|
|
def forward(self, x):
|
|
# x is a list of multi-scale feature
|
|
assert len(x) == self.num_feature_levels
|
|
fea2d = []
|
|
fea2d_pos = []
|
|
size_list = []
|
|
|
|
for i in range(self.num_feature_levels):
|
|
size_list.append(x[i].shape[-2:])
|
|
if self.pe_layer is not None:
|
|
pi = self.pe_layer(x[i], None).flatten(2)
|
|
pi = pi.transpose(1, 2)
|
|
else:
|
|
pi = None
|
|
xi = self.input_proj[i](x[i]) if self.input_proj is not None else x[i]
|
|
xi = xi.flatten(2) + self.level_embed.weight[i][None, :, None]
|
|
xi = xi.transpose(1, 2)
|
|
fea2d.append(xi)
|
|
fea2d_pos.append(pi)
|
|
|
|
bs, _, _ = fea2d[0].shape
|
|
num_gq, num_lq = self.num_queries
|
|
gquery = self.init_query.weight[:num_gq].unsqueeze(0).repeat(bs, 1, 1)
|
|
lquery = self.init_query.weight[num_gq:].unsqueeze(0).repeat(bs, 1, 1)
|
|
|
|
gquery_pos = self.query_pos_embedding.weight[:num_gq].unsqueeze(0).repeat(bs, 1, 1)
|
|
lquery_pos = self.query_pos_embedding.weight[num_gq:].unsqueeze(0).repeat(bs, 1, 1)
|
|
|
|
for i in range(self.num_layers):
|
|
level_index = i % self.num_feature_levels
|
|
|
|
qout = self.transformer_crossatt_layers[i](
|
|
q = lquery,
|
|
kv = fea2d[level_index],
|
|
q_pos = lquery_pos,
|
|
k_pos = fea2d_pos[level_index],
|
|
mask = None,)
|
|
lquery = qout
|
|
|
|
qout = self.transformer_selfatt_layers[i](
|
|
qkv = torch.cat([gquery, lquery], dim=1),
|
|
qk_pos = torch.cat([gquery_pos, lquery_pos], dim=1),)
|
|
|
|
qout = self.transformer_feedforward_layers[i](qout)
|
|
|
|
gquery = qout[:, :num_gq]
|
|
lquery = qout[:, num_gq:]
|
|
|
|
output = torch.cat([gquery, lquery], dim=1)
|
|
|
|
return output
|
|
|
|
##################
|
|
# Main structure #
|
|
##################
|
|
|
|
class SemanticExtractionEncoder(nn.Module):
|
|
def __init__(self,
|
|
imencoder_cfg,
|
|
imdecoder_cfg,
|
|
qtransformer_cfg):
|
|
super().__init__()
|
|
self.imencoder = imencoder_cfg
|
|
self.imdecoder = imdecoder_cfg
|
|
self.qtransformer = qtransformer_cfg
|
|
|
|
def forward(self, x):
|
|
fea = self.imencoder(x)
|
|
hs = {'res3' : fea['res3'],
|
|
'res4' : fea['res4'],
|
|
'res5' : fea['res5'], }
|
|
hs = self.imdecoder(hs)
|
|
hs = [hs['res3'], hs['res4'], hs['res5']]
|
|
q = self.qtransformer(hs)
|
|
return q
|
|
|
|
def encode(self, x):
|
|
return self(x)
|