| """ Gather-Excite Attention Block |
| |
| Paper: `Gather-Excite: Exploiting Feature Context in CNNs` - https://arxiv.org/abs/1810.12348 |
| |
| Official code here, but it's only partial impl in Caffe: https://github.com/hujie-frank/GENet |
| |
| I've tried to support all of the extent both w/ and w/o params. I don't believe I've seen another |
| impl that covers all of the cases. |
| |
| NOTE: extent=0 + extra_params=False is equivalent to Squeeze-and-Excitation |
| |
| Hacked together by / Copyright 2021 Ross Wightman |
| """ |
| import math |
|
|
| from torch import nn as nn |
| import torch.nn.functional as F |
|
|
| from .create_act import create_act_layer, get_act_layer |
| from .create_conv2d import create_conv2d |
| from .helpers import make_divisible |
| from .mlp import ConvMlp |
|
|
|
|
| class GatherExcite(nn.Module): |
| """ Gather-Excite Attention Module |
| """ |
| def __init__( |
| self, channels, feat_size=None, extra_params=False, extent=0, use_mlp=True, |
| rd_ratio=1./16, rd_channels=None, rd_divisor=1, add_maxpool=False, |
| act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, gate_layer='sigmoid'): |
| super(GatherExcite, self).__init__() |
| self.add_maxpool = add_maxpool |
| act_layer = get_act_layer(act_layer) |
| self.extent = extent |
| if extra_params: |
| self.gather = nn.Sequential() |
| if extent == 0: |
| assert feat_size is not None, 'spatial feature size must be specified for global extent w/ params' |
| self.gather.add_module( |
| 'conv1', create_conv2d(channels, channels, kernel_size=feat_size, stride=1, depthwise=True)) |
| if norm_layer: |
| self.gather.add_module(f'norm1', nn.BatchNorm2d(channels)) |
| else: |
| assert extent % 2 == 0 |
| num_conv = int(math.log2(extent)) |
| for i in range(num_conv): |
| self.gather.add_module( |
| f'conv{i + 1}', |
| create_conv2d(channels, channels, kernel_size=3, stride=2, depthwise=True)) |
| if norm_layer: |
| self.gather.add_module(f'norm{i + 1}', nn.BatchNorm2d(channels)) |
| if i != num_conv - 1: |
| self.gather.add_module(f'act{i + 1}', act_layer(inplace=True)) |
| else: |
| self.gather = None |
| if self.extent == 0: |
| self.gk = 0 |
| self.gs = 0 |
| else: |
| assert extent % 2 == 0 |
| self.gk = self.extent * 2 - 1 |
| self.gs = self.extent |
|
|
| if not rd_channels: |
| rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.) |
| self.mlp = ConvMlp(channels, rd_channels, act_layer=act_layer) if use_mlp else nn.Identity() |
| self.gate = create_act_layer(gate_layer) |
|
|
| def forward(self, x): |
| size = x.shape[-2:] |
| if self.gather is not None: |
| x_ge = self.gather(x) |
| else: |
| if self.extent == 0: |
| |
| x_ge = x.mean(dim=(2, 3), keepdims=True) |
| if self.add_maxpool: |
| |
| x_ge = 0.5 * x_ge + 0.5 * x.amax((2, 3), keepdim=True) |
| else: |
| x_ge = F.avg_pool2d( |
| x, kernel_size=self.gk, stride=self.gs, padding=self.gk // 2, count_include_pad=False) |
| if self.add_maxpool: |
| |
| x_ge = 0.5 * x_ge + 0.5 * F.max_pool2d(x, kernel_size=self.gk, stride=self.gs, padding=self.gk // 2) |
| x_ge = self.mlp(x_ge) |
| if x_ge.shape[-1] != 1 or x_ge.shape[-2] != 1: |
| x_ge = F.interpolate(x_ge, size=size) |
| return x * self.gate(x_ge) |
|
|
