| | """ 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): |
| |
|
| | def __init__(self, channels, use_attn=True, fuse_add=True, fuse_scale=False, init_last_zero=False, |
| | 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) |
| | attn = F.softmax(attn, dim=-1).unsqueeze(3) |
| | 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 |
| |
|
