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# Copyright (c) OpenMMLab. All rights reserved.
from abc import abstractmethod
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..cnn import ConvModule
class BaseMergeCell(nn.Module):
"""The basic class for cells used in NAS-FPN and NAS-FCOS.
BaseMergeCell takes 2 inputs. After applying convolution
on them, they are resized to the target size. Then,
they go through binary_op, which depends on the type of cell.
If with_out_conv is True, the result of output will go through
another convolution layer.
Args:
in_channels (int): number of input channels in out_conv layer.
out_channels (int): number of output channels in out_conv layer.
with_out_conv (bool): Whether to use out_conv layer
out_conv_cfg (dict): Config dict for convolution layer, which should
contain "groups", "kernel_size", "padding", "bias" to build
out_conv layer.
out_norm_cfg (dict): Config dict for normalization layer in out_conv.
out_conv_order (tuple): The order of conv/norm/activation layers in
out_conv.
with_input1_conv (bool): Whether to use convolution on input1.
with_input2_conv (bool): Whether to use convolution on input2.
input_conv_cfg (dict): Config dict for building input1_conv layer and
input2_conv layer, which is expected to contain the type of
convolution.
Default: None, which means using conv2d.
input_norm_cfg (dict): Config dict for normalization layer in
input1_conv and input2_conv layer. Default: None.
upsample_mode (str): Interpolation method used to resize the output
of input1_conv and input2_conv to target size. Currently, we
support ['nearest', 'bilinear']. Default: 'nearest'.
"""
def __init__(self,
fused_channels=256,
out_channels=256,
with_out_conv=True,
out_conv_cfg=dict(
groups=1, kernel_size=3, padding=1, bias=True),
out_norm_cfg=None,
out_conv_order=('act', 'conv', 'norm'),
with_input1_conv=False,
with_input2_conv=False,
input_conv_cfg=None,
input_norm_cfg=None,
upsample_mode='nearest'):
super(BaseMergeCell, self).__init__()
assert upsample_mode in ['nearest', 'bilinear']
self.with_out_conv = with_out_conv
self.with_input1_conv = with_input1_conv
self.with_input2_conv = with_input2_conv
self.upsample_mode = upsample_mode
if self.with_out_conv:
self.out_conv = ConvModule(
fused_channels,
out_channels,
**out_conv_cfg,
norm_cfg=out_norm_cfg,
order=out_conv_order)
self.input1_conv = self._build_input_conv(
out_channels, input_conv_cfg,
input_norm_cfg) if with_input1_conv else nn.Sequential()
self.input2_conv = self._build_input_conv(
out_channels, input_conv_cfg,
input_norm_cfg) if with_input2_conv else nn.Sequential()
def _build_input_conv(self, channel, conv_cfg, norm_cfg):
return ConvModule(
channel,
channel,
3,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
bias=True)
@abstractmethod
def _binary_op(self, x1, x2):
pass
def _resize(self, x, size):
if x.shape[-2:] == size:
return x
elif x.shape[-2:] < size:
return F.interpolate(x, size=size, mode=self.upsample_mode)
else:
assert x.shape[-2] % size[-2] == 0 and x.shape[-1] % size[-1] == 0
kernel_size = x.shape[-1] // size[-1]
x = F.max_pool2d(x, kernel_size=kernel_size, stride=kernel_size)
return x
def forward(self, x1, x2, out_size=None):
assert x1.shape[:2] == x2.shape[:2]
assert out_size is None or len(out_size) == 2
if out_size is None: # resize to larger one
out_size = max(x1.size()[2:], x2.size()[2:])
x1 = self.input1_conv(x1)
x2 = self.input2_conv(x2)
x1 = self._resize(x1, out_size)
x2 = self._resize(x2, out_size)
x = self._binary_op(x1, x2)
if self.with_out_conv:
x = self.out_conv(x)
return x
class SumCell(BaseMergeCell):
def __init__(self, in_channels, out_channels, **kwargs):
super(SumCell, self).__init__(in_channels, out_channels, **kwargs)
def _binary_op(self, x1, x2):
return x1 + x2
class ConcatCell(BaseMergeCell):
def __init__(self, in_channels, out_channels, **kwargs):
super(ConcatCell, self).__init__(in_channels * 2, out_channels,
**kwargs)
def _binary_op(self, x1, x2):
ret = torch.cat([x1, x2], dim=1)
return ret
class GlobalPoolingCell(BaseMergeCell):
def __init__(self, in_channels=None, out_channels=None, **kwargs):
super().__init__(in_channels, out_channels, **kwargs)
self.global_pool = nn.AdaptiveAvgPool2d((1, 1))
def _binary_op(self, x1, x2):
x2_att = self.global_pool(x2).sigmoid()
return x2 + x2_att * x1
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