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# coding=utf-8
# Copyright 2022 The IDEA Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import torch
import torch.nn as nn
from detrex.layers import FFN, BaseTransformerLayer, MultiheadAttention, TransformerLayerSequence
class PnPDetrTransformerEncoder(TransformerLayerSequence):
def __init__(
self,
embed_dim: int = 256,
num_heads: int = 8,
attn_dropout: float = 0.1,
feedforward_dim: int = 2048,
ffn_dropout: float = 0.1,
num_layers: int = 6,
post_norm: bool = True,
batch_first: bool = False,
):
super(PnPDetrTransformerEncoder, self).__init__(
transformer_layers=BaseTransformerLayer(
attn=MultiheadAttention(
embed_dim=embed_dim,
num_heads=num_heads,
attn_drop=attn_dropout,
batch_first=batch_first,
),
ffn=FFN(
embed_dim=embed_dim,
feedforward_dim=feedforward_dim,
ffn_drop=ffn_dropout,
),
norm=nn.LayerNorm(
normalized_shape=embed_dim,
),
operation_order=("self_attn", "norm", "ffn", "norm"),
),
num_layers=num_layers,
)
self.embed_dim = self.layers[0].embed_dim
self.pre_norm = self.layers[0].pre_norm
if post_norm:
self.post_norm_layer = nn.LayerNorm(self.embed_dim)
else:
self.post_norm_layer = None
def forward(
self,
query,
key,
value,
query_pos=None,
key_pos=None,
attn_masks=None,
query_key_padding_mask=None,
key_padding_mask=None,
**kwargs,
):
for layer in self.layers:
query = layer(
query,
key,
value,
query_pos=query_pos,
key_pos=key_pos,
attn_masks=attn_masks,
query_key_padding_mask=query_key_padding_mask,
key_padding_mask=key_padding_mask,
**kwargs,
)
if self.post_norm_layer is not None:
query = self.post_norm_layer(query)
return query
class PnPDetrTransformerDecoder(TransformerLayerSequence):
def __init__(
self,
embed_dim: int = 256,
num_heads: int = 8,
attn_dropout: float = 0.1,
feedforward_dim: int = 2048,
ffn_dropout: float = 0.1,
num_layers: int = 6,
post_norm: bool = True,
return_intermediate: bool = True,
batch_first: bool = False,
):
super(PnPDetrTransformerDecoder, self).__init__(
transformer_layers=BaseTransformerLayer(
attn=MultiheadAttention(
embed_dim=embed_dim,
num_heads=num_heads,
attn_drop=attn_dropout,
batch_first=batch_first,
),
ffn=FFN(
embed_dim=embed_dim,
feedforward_dim=feedforward_dim,
ffn_drop=ffn_dropout,
),
norm=nn.LayerNorm(
normalized_shape=embed_dim,
),
operation_order=("self_attn", "norm", "cross_attn", "norm", "ffn", "norm"),
),
num_layers=num_layers,
)
self.return_intermediate = return_intermediate
self.embed_dim = self.layers[0].embed_dim
if post_norm:
self.post_norm_layer = nn.LayerNorm(self.embed_dim)
else:
self.post_norm_layer = None
def forward(
self,
query,
key,
value,
query_pos=None,
key_pos=None,
attn_masks=None,
query_key_padding_mask=None,
key_padding_mask=None,
**kwargs,
):
if not self.return_intermediate:
for layer in self.layers:
query = layer(
query,
key,
value,
query_pos=query_pos,
key_pos=key_pos,
attn_masks=attn_masks,
query_key_padding_mask=query_key_padding_mask,
key_padding_mask=key_padding_mask,
**kwargs,
)
if self.post_norm_layer is not None:
query = self.post_norm_layer(query)[None]
return query
# return intermediate
intermediate = []
for layer in self.layers:
query = layer(
query,
key,
value,
query_pos=query_pos,
key_pos=key_pos,
attn_masks=attn_masks,
query_key_padding_mask=query_key_padding_mask,
key_padding_mask=key_padding_mask,
**kwargs,
)
if self.return_intermediate:
if self.post_norm_layer is not None:
intermediate.append(self.post_norm_layer(query))
else:
intermediate.append(query)
return torch.stack(intermediate)
class SortSampler(nn.Module):
def __init__(self, topk_ratio, input_dim, score_pred_net='2layer-fc', kproj_net='2layer-fc', unsample_abstract_number=30,pos_embed_kproj=False):
super().__init__()
self.topk_ratio = topk_ratio
if score_pred_net == '2layer-fc-256':
self.score_pred_net = nn.Sequential(nn.Conv2d(input_dim, input_dim, 1),
nn.ReLU(),
nn.Conv2d(input_dim, 1, 1))
elif score_pred_net == '2layer-fc-16':
self.score_pred_net = nn.Sequential(nn.Conv2d(input_dim, 16, 1),
nn.ReLU(),
nn.Conv2d(16, 1, 1))
elif score_pred_net == '1layer-fc':
self.score_pred_net = nn.Conv2d(input_dim, 1, 1)
else:
raise ValueError
self.norm_feature = nn.LayerNorm(input_dim,elementwise_affine=False)
self.unsample_abstract_number = unsample_abstract_number
if kproj_net == '2layer-fc':
self.k_proj = nn.Sequential(nn.Linear(input_dim, input_dim),
nn.ReLU(),
nn.Linear(input_dim, unsample_abstract_number))
elif kproj_net == '1layer-fc':
self.k_proj = nn.Linear(input_dim, unsample_abstract_number)
else:
raise ValueError
self.v_proj = nn.Linear(input_dim, input_dim)
self.pos_embed_kproj = pos_embed_kproj
def forward(self, src, mask, pos_embed, sample_ratio):
bs,c ,h, w = src.shape
sample_weight = self.score_pred_net(src).sigmoid().view(bs,-1)
# sample_weight[mask] = sample_weight[mask].clone() * 0.
# sample_weight.data[mask] = 0.
sample_weight_clone = sample_weight.clone().detach()
sample_weight_clone[mask] = -1.
if sample_ratio==None:
sample_ratio = self.topk_ratio
##max sample number:
sample_lens = ((~mask).sum(1)*sample_ratio).int()
max_sample_num = sample_lens.max()
mask_topk = torch.arange(max_sample_num).expand(len(sample_lens), max_sample_num).to(sample_lens.device) > (sample_lens-1).unsqueeze(1)
## for sampling remaining unsampled points
min_sample_num = sample_lens.min()
sort_order = sample_weight_clone.sort(descending=True,dim=1)[1]
sort_confidence_topk = sort_order[:,:max_sample_num]
sort_confidence_topk_remaining = sort_order[:,min_sample_num:]
## flatten for gathering
src = src.flatten(2).permute(2, 0, 1)
src = self.norm_feature(src)
src_sample_remaining = src.gather(0, sort_confidence_topk_remaining.permute(1, 0)[..., None].expand(-1, -1, c))
## this will maskout the padding and sampled points
mask_unsampled = torch.arange(mask.size(1)).expand(len(sample_lens), mask.size(1)).to(sample_lens.device) < (sample_lens).unsqueeze(1)
mask_unsampled = mask_unsampled | mask.gather(1, sort_order)
mask_unsampled = mask_unsampled[:,min_sample_num:]
## abstract the unsampled points with attention
if self.pos_embed_kproj:
pos_embed_sample_remaining = pos_embed.gather(0, sort_confidence_topk_remaining.permute(1, 0)[..., None].expand(-1, -1, c))
kproj = self.k_proj(src_sample_remaining+pos_embed_sample_remaining)
else:
kproj = self.k_proj(src_sample_remaining)
kproj = kproj.masked_fill(
mask_unsampled.permute(1,0).unsqueeze(2),
float('-inf'),
).permute(1,2,0).softmax(-1)
abs_unsampled_points = torch.bmm(kproj, self.v_proj(src_sample_remaining).permute(1,0,2)).permute(1,0,2)
abs_unsampled_pos_embed = torch.bmm(kproj, pos_embed.gather(0,sort_confidence_topk_remaining.
permute(1,0)[...,None].expand(-1,-1,c)).permute(1,0,2)).permute(1,0,2)
abs_unsampled_mask = mask.new_zeros(mask.size(0),abs_unsampled_points.size(0))
## reg sample weight to be sparse with l1 loss
sample_reg_loss = sample_weight.gather(1,sort_confidence_topk).mean()
src_sampled = src.gather(0, sort_confidence_topk.permute(1,0)[...,None].expand(-1,-1,c)) *sample_weight.gather(1,sort_confidence_topk).permute(1,0).unsqueeze(-1)
pos_embed_sampled = pos_embed.gather(0,sort_confidence_topk.permute(1,0)[...,None].expand(-1,-1,c))
mask_sampled = mask_topk
src = torch.cat([src_sampled, abs_unsampled_points])
pos_embed = torch.cat([pos_embed_sampled,abs_unsampled_pos_embed])
mask = torch.cat([mask_sampled, abs_unsampled_mask],dim=1)
assert ((~mask).sum(1)==sample_lens+self.unsample_abstract_number).all()
return src, sample_reg_loss, sort_confidence_topk, mask, pos_embed
class PnPDetrTransformer(nn.Module):
def __init__(
self,
encoder=None,
decoder=None,
sample_topk_ratio=1/3.,
score_pred_net='2layer-fc-256',
kproj_net='2layer-fc',
unsample_abstract_number=30,
pos_embed_kproj=False,
):
super(PnPDetrTransformer, self).__init__()
self.encoder = encoder
self.decoder = decoder
self.embed_dim = self.encoder.embed_dim
self.sampler = SortSampler(
sample_topk_ratio,
self.embed_dim,
score_pred_net=score_pred_net,
kproj_net=kproj_net,
unsample_abstract_number=unsample_abstract_number,
pos_embed_kproj=pos_embed_kproj
)
self.init_weights()
def init_weights(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def forward(self, x, mask, query_embed, pos_embed, sample_ratio):
bs, c, h, w = x.shape
pos_embed = pos_embed.view(bs, c, -1).permute(2, 0, 1)
query_embed = query_embed.unsqueeze(1).repeat(
1, bs, 1
) # [num_query, dim] -> [num_query, bs, dim]
mask = mask.view(bs, -1) # [bs, h, w] -> [bs, h*w]
x, sample_reg_loss, sort_confidence_topk, mask, pos_embed = self.sampler(x, mask, pos_embed, sample_ratio)
memory = self.encoder(
query=x,
key=None,
value=None,
query_pos=pos_embed,
query_key_padding_mask=mask,
)
target = torch.zeros_like(query_embed)
decoder_output = self.decoder(
query=target,
key=memory,
value=memory,
key_pos=pos_embed,
query_pos=query_embed,
key_padding_mask=mask,
)
decoder_output = decoder_output.transpose(1, 2)
return decoder_output, sample_reg_loss
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