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# Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch import nn
from ..utils import constant_init, kaiming_init
from .registry import PLUGIN_LAYERS
def last_zero_init(m):
if isinstance(m, nn.Sequential):
constant_init(m[-1], val=0)
else:
constant_init(m, val=0)
@PLUGIN_LAYERS.register_module()
class ContextBlock(nn.Module):
"""ContextBlock module in GCNet.
See 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
(https://arxiv.org/abs/1904.11492) for details.
Args:
in_channels (int): Channels of the input feature map.
ratio (float): Ratio of channels of transform bottleneck
pooling_type (str): Pooling method for context modeling.
Options are 'att' and 'avg', stand for attention pooling and
average pooling respectively. Default: 'att'.
fusion_types (Sequence[str]): Fusion method for feature fusion,
Options are 'channels_add', 'channel_mul', stand for channelwise
addition and multiplication respectively. Default: ('channel_add',)
"""
_abbr_ = 'context_block'
def __init__(self,
in_channels,
ratio,
pooling_type='att',
fusion_types=('channel_add', )):
super(ContextBlock, self).__init__()
assert pooling_type in ['avg', 'att']
assert isinstance(fusion_types, (list, tuple))
valid_fusion_types = ['channel_add', 'channel_mul']
assert all([f in valid_fusion_types for f in fusion_types])
assert len(fusion_types) > 0, 'at least one fusion should be used'
self.in_channels = in_channels
self.ratio = ratio
self.planes = int(in_channels * ratio)
self.pooling_type = pooling_type
self.fusion_types = fusion_types
if pooling_type == 'att':
self.conv_mask = nn.Conv2d(in_channels, 1, kernel_size=1)
self.softmax = nn.Softmax(dim=2)
else:
self.avg_pool = nn.AdaptiveAvgPool2d(1)
if 'channel_add' in fusion_types:
self.channel_add_conv = nn.Sequential(
nn.Conv2d(self.in_channels, self.planes, kernel_size=1),
nn.LayerNorm([self.planes, 1, 1]),
nn.ReLU(inplace=True), # yapf: disable
nn.Conv2d(self.planes, self.in_channels, kernel_size=1))
else:
self.channel_add_conv = None
if 'channel_mul' in fusion_types:
self.channel_mul_conv = nn.Sequential(
nn.Conv2d(self.in_channels, self.planes, kernel_size=1),
nn.LayerNorm([self.planes, 1, 1]),
nn.ReLU(inplace=True), # yapf: disable
nn.Conv2d(self.planes, self.in_channels, kernel_size=1))
else:
self.channel_mul_conv = None
self.reset_parameters()
def reset_parameters(self):
if self.pooling_type == 'att':
kaiming_init(self.conv_mask, mode='fan_in')
self.conv_mask.inited = True
if self.channel_add_conv is not None:
last_zero_init(self.channel_add_conv)
if self.channel_mul_conv is not None:
last_zero_init(self.channel_mul_conv)
def spatial_pool(self, x):
batch, channel, height, width = x.size()
if self.pooling_type == 'att':
input_x = x
# [N, C, H * W]
input_x = input_x.view(batch, channel, height * width)
# [N, 1, C, H * W]
input_x = input_x.unsqueeze(1)
# [N, 1, H, W]
context_mask = self.conv_mask(x)
# [N, 1, H * W]
context_mask = context_mask.view(batch, 1, height * width)
# [N, 1, H * W]
context_mask = self.softmax(context_mask)
# [N, 1, H * W, 1]
context_mask = context_mask.unsqueeze(-1)
# [N, 1, C, 1]
context = torch.matmul(input_x, context_mask)
# [N, C, 1, 1]
context = context.view(batch, channel, 1, 1)
else:
# [N, C, 1, 1]
context = self.avg_pool(x)
return context
def forward(self, x):
# [N, C, 1, 1]
context = self.spatial_pool(x)
out = x
if self.channel_mul_conv is not None:
# [N, C, 1, 1]
channel_mul_term = torch.sigmoid(self.channel_mul_conv(context))
out = out * channel_mul_term
if self.channel_add_conv is not None:
# [N, C, 1, 1]
channel_add_term = self.channel_add_conv(context)
out = out + channel_add_term
return out
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