You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
68 lines
2.4 KiB
68 lines
2.4 KiB
4 years ago
|
""" Global Context Attention Block
|
||
|
|
||
|
Paper: `GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond`
|
||
|
- https://arxiv.org/abs/1904.11492
|
||
|
|
||
|
Official code consulted as reference: https://github.com/xvjiarui/GCNet
|
||
|
|
||
|
Hacked together by / Copyright 2021 Ross Wightman
|
||
|
"""
|
||
|
from torch import nn as nn
|
||
|
import torch.nn.functional as F
|
||
|
|
||
|
from .create_act import create_act_layer, get_act_layer
|
||
|
from .helpers import make_divisible
|
||
|
from .mlp import ConvMlp
|
||
|
from .norm import LayerNorm2d
|
||
|
|
||
|
|
||
|
class GlobalContext(nn.Module):
|
||
|
|
||
3 years ago
|
def __init__(self, channels, use_attn=True, fuse_add=False, fuse_scale=True, init_last_zero=False,
|
||
4 years ago
|
rd_ratio=1./8, rd_channels=None, rd_divisor=1, act_layer=nn.ReLU, gate_layer='sigmoid'):
|
||
|
super(GlobalContext, self).__init__()
|
||
|
act_layer = get_act_layer(act_layer)
|
||
|
|
||
|
self.conv_attn = nn.Conv2d(channels, 1, kernel_size=1, bias=True) if use_attn else None
|
||
|
|
||
|
if rd_channels is None:
|
||
|
rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
|
||
|
if fuse_add:
|
||
|
self.mlp_add = ConvMlp(channels, rd_channels, act_layer=act_layer, norm_layer=LayerNorm2d)
|
||
|
else:
|
||
|
self.mlp_add = None
|
||
|
if fuse_scale:
|
||
|
self.mlp_scale = ConvMlp(channels, rd_channels, act_layer=act_layer, norm_layer=LayerNorm2d)
|
||
|
else:
|
||
|
self.mlp_scale = None
|
||
|
|
||
|
self.gate = create_act_layer(gate_layer)
|
||
|
self.init_last_zero = init_last_zero
|
||
|
self.reset_parameters()
|
||
|
|
||
|
def reset_parameters(self):
|
||
|
if self.conv_attn is not None:
|
||
|
nn.init.kaiming_normal_(self.conv_attn.weight, mode='fan_in', nonlinearity='relu')
|
||
|
if self.mlp_add is not None:
|
||
|
nn.init.zeros_(self.mlp_add.fc2.weight)
|
||
|
|
||
|
def forward(self, x):
|
||
|
B, C, H, W = x.shape
|
||
|
|
||
|
if self.conv_attn is not None:
|
||
|
attn = self.conv_attn(x).reshape(B, 1, H * W) # (B, 1, H * W)
|
||
|
attn = F.softmax(attn, dim=-1).unsqueeze(3) # (B, 1, H * W, 1)
|
||
|
context = x.reshape(B, C, H * W).unsqueeze(1) @ attn
|
||
|
context = context.view(B, C, 1, 1)
|
||
|
else:
|
||
|
context = x.mean(dim=(2, 3), keepdim=True)
|
||
|
|
||
|
if self.mlp_scale is not None:
|
||
|
mlp_x = self.mlp_scale(context)
|
||
|
x = x * self.gate(mlp_x)
|
||
|
if self.mlp_add is not None:
|
||
|
mlp_x = self.mlp_add(context)
|
||
|
x = x + mlp_x
|
||
|
|
||
|
return x
|