70 lines
2.4 KiB
Python
70 lines
2.4 KiB
Python
# -*- coding: utf-8 -*-
|
|
# @Author: Peng Xiang
|
|
|
|
import torch
|
|
from torch import nn, einsum
|
|
from .utils import MLP_Res, grouping_operation, query_knn
|
|
|
|
|
|
class SkipTransformer(nn.Module):
|
|
def __init__(self, in_channel, dim=256, n_knn=16, pos_hidden_dim=64, attn_hidden_multiplier=4):
|
|
super(SkipTransformer, self).__init__()
|
|
self.mlp_v = MLP_Res(in_dim=in_channel*2, hidden_dim=in_channel, out_dim=in_channel)
|
|
self.n_knn = n_knn
|
|
self.conv_key = nn.Conv1d(in_channel, dim, 1)
|
|
self.conv_query = nn.Conv1d(in_channel, dim, 1)
|
|
self.conv_value = nn.Conv1d(in_channel, dim, 1)
|
|
|
|
self.pos_mlp = nn.Sequential(
|
|
nn.Conv2d(3, pos_hidden_dim, 1),
|
|
nn.BatchNorm2d(pos_hidden_dim),
|
|
nn.ReLU(),
|
|
nn.Conv2d(pos_hidden_dim, dim, 1)
|
|
)
|
|
|
|
self.attn_mlp = nn.Sequential(
|
|
nn.Conv2d(dim, dim * attn_hidden_multiplier, 1),
|
|
nn.BatchNorm2d(dim * attn_hidden_multiplier),
|
|
nn.ReLU(),
|
|
nn.Conv2d(dim * attn_hidden_multiplier, dim, 1)
|
|
)
|
|
|
|
self.conv_end = nn.Conv1d(dim, in_channel, 1)
|
|
|
|
def forward(self, pos, key, query, include_self=True):
|
|
"""
|
|
Args:
|
|
pos: (B, 3, N)
|
|
key: (B, in_channel, N)
|
|
query: (B, in_channel, N)
|
|
include_self: boolean
|
|
|
|
Returns:
|
|
Tensor: (B, in_channel, N), shape context feature
|
|
"""
|
|
value = self.mlp_v(torch.cat([key, query], 1))
|
|
identity = value
|
|
key = self.conv_key(key)
|
|
query = self.conv_query(query)
|
|
value = self.conv_value(value)
|
|
b, dim, n = value.shape
|
|
|
|
pos_flipped = pos.permute(0, 2, 1).contiguous()
|
|
idx_knn = query_knn(self.n_knn, pos_flipped, pos_flipped, include_self=include_self)
|
|
|
|
key = grouping_operation(key, idx_knn) # b, dim, n, n_knn
|
|
qk_rel = query.reshape((b, -1, n, 1)) - key
|
|
|
|
pos_rel = pos.reshape((b, -1, n, 1)) - grouping_operation(pos, idx_knn) # b, 3, n, n_knn
|
|
pos_embedding = self.pos_mlp(pos_rel)
|
|
|
|
attention = self.attn_mlp(qk_rel + pos_embedding) # b, dim, n, n_knn
|
|
attention = torch.softmax(attention, -1)
|
|
|
|
value = value.reshape((b, -1, n, 1)) + pos_embedding #
|
|
|
|
agg = einsum('b c i j, b c i j -> b c i', attention, value) # b, dim, n
|
|
y = self.conv_end(agg)
|
|
|
|
return y + identity
|