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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) | |
| 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 | |