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| import torch | |
| import torch.nn as nn | |
| from annotator.uniformer.mmcv.cnn import (ConvModule, DepthwiseSeparableConvModule, constant_init, | |
| kaiming_init) | |
| from torch.nn.modules.batchnorm import _BatchNorm | |
| from annotator.uniformer.mmseg.models.decode_heads.psp_head import PPM | |
| from annotator.uniformer.mmseg.ops import resize | |
| from ..builder import BACKBONES | |
| from ..utils.inverted_residual import InvertedResidual | |
| class LearningToDownsample(nn.Module): | |
| """Learning to downsample module. | |
| Args: | |
| in_channels (int): Number of input channels. | |
| dw_channels (tuple[int]): Number of output channels of the first and | |
| the second depthwise conv (dwconv) layers. | |
| out_channels (int): Number of output channels of the whole | |
| 'learning to downsample' module. | |
| conv_cfg (dict | None): Config of conv layers. Default: None | |
| norm_cfg (dict | None): Config of norm layers. Default: | |
| dict(type='BN') | |
| act_cfg (dict): Config of activation layers. Default: | |
| dict(type='ReLU') | |
| """ | |
| def __init__(self, | |
| in_channels, | |
| dw_channels, | |
| out_channels, | |
| conv_cfg=None, | |
| norm_cfg=dict(type='BN'), | |
| act_cfg=dict(type='ReLU')): | |
| super(LearningToDownsample, self).__init__() | |
| self.conv_cfg = conv_cfg | |
| self.norm_cfg = norm_cfg | |
| self.act_cfg = act_cfg | |
| dw_channels1 = dw_channels[0] | |
| dw_channels2 = dw_channels[1] | |
| self.conv = ConvModule( | |
| in_channels, | |
| dw_channels1, | |
| 3, | |
| stride=2, | |
| conv_cfg=self.conv_cfg, | |
| norm_cfg=self.norm_cfg, | |
| act_cfg=self.act_cfg) | |
| self.dsconv1 = DepthwiseSeparableConvModule( | |
| dw_channels1, | |
| dw_channels2, | |
| kernel_size=3, | |
| stride=2, | |
| padding=1, | |
| norm_cfg=self.norm_cfg) | |
| self.dsconv2 = DepthwiseSeparableConvModule( | |
| dw_channels2, | |
| out_channels, | |
| kernel_size=3, | |
| stride=2, | |
| padding=1, | |
| norm_cfg=self.norm_cfg) | |
| def forward(self, x): | |
| x = self.conv(x) | |
| x = self.dsconv1(x) | |
| x = self.dsconv2(x) | |
| return x | |
| class GlobalFeatureExtractor(nn.Module): | |
| """Global feature extractor module. | |
| Args: | |
| in_channels (int): Number of input channels of the GFE module. | |
| Default: 64 | |
| block_channels (tuple[int]): Tuple of ints. Each int specifies the | |
| number of output channels of each Inverted Residual module. | |
| Default: (64, 96, 128) | |
| out_channels(int): Number of output channels of the GFE module. | |
| Default: 128 | |
| expand_ratio (int): Adjusts number of channels of the hidden layer | |
| in InvertedResidual by this amount. | |
| Default: 6 | |
| num_blocks (tuple[int]): Tuple of ints. Each int specifies the | |
| number of times each Inverted Residual module is repeated. | |
| The repeated Inverted Residual modules are called a 'group'. | |
| Default: (3, 3, 3) | |
| strides (tuple[int]): Tuple of ints. Each int specifies | |
| the downsampling factor of each 'group'. | |
| Default: (2, 2, 1) | |
| pool_scales (tuple[int]): Tuple of ints. Each int specifies | |
| the parameter required in 'global average pooling' within PPM. | |
| Default: (1, 2, 3, 6) | |
| conv_cfg (dict | None): Config of conv layers. Default: None | |
| norm_cfg (dict | None): Config of norm layers. Default: | |
| dict(type='BN') | |
| act_cfg (dict): Config of activation layers. Default: | |
| dict(type='ReLU') | |
| align_corners (bool): align_corners argument of F.interpolate. | |
| Default: False | |
| """ | |
| def __init__(self, | |
| in_channels=64, | |
| block_channels=(64, 96, 128), | |
| out_channels=128, | |
| expand_ratio=6, | |
| num_blocks=(3, 3, 3), | |
| strides=(2, 2, 1), | |
| pool_scales=(1, 2, 3, 6), | |
| conv_cfg=None, | |
| norm_cfg=dict(type='BN'), | |
| act_cfg=dict(type='ReLU'), | |
| align_corners=False): | |
| super(GlobalFeatureExtractor, self).__init__() | |
| self.conv_cfg = conv_cfg | |
| self.norm_cfg = norm_cfg | |
| self.act_cfg = act_cfg | |
| assert len(block_channels) == len(num_blocks) == 3 | |
| self.bottleneck1 = self._make_layer(in_channels, block_channels[0], | |
| num_blocks[0], strides[0], | |
| expand_ratio) | |
| self.bottleneck2 = self._make_layer(block_channels[0], | |
| block_channels[1], num_blocks[1], | |
| strides[1], expand_ratio) | |
| self.bottleneck3 = self._make_layer(block_channels[1], | |
| block_channels[2], num_blocks[2], | |
| strides[2], expand_ratio) | |
| self.ppm = PPM( | |
| pool_scales, | |
| block_channels[2], | |
| block_channels[2] // 4, | |
| conv_cfg=self.conv_cfg, | |
| norm_cfg=self.norm_cfg, | |
| act_cfg=self.act_cfg, | |
| align_corners=align_corners) | |
| self.out = ConvModule( | |
| block_channels[2] * 2, | |
| out_channels, | |
| 1, | |
| conv_cfg=self.conv_cfg, | |
| norm_cfg=self.norm_cfg, | |
| act_cfg=self.act_cfg) | |
| def _make_layer(self, | |
| in_channels, | |
| out_channels, | |
| blocks, | |
| stride=1, | |
| expand_ratio=6): | |
| layers = [ | |
| InvertedResidual( | |
| in_channels, | |
| out_channels, | |
| stride, | |
| expand_ratio, | |
| norm_cfg=self.norm_cfg) | |
| ] | |
| for i in range(1, blocks): | |
| layers.append( | |
| InvertedResidual( | |
| out_channels, | |
| out_channels, | |
| 1, | |
| expand_ratio, | |
| norm_cfg=self.norm_cfg)) | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| x = self.bottleneck1(x) | |
| x = self.bottleneck2(x) | |
| x = self.bottleneck3(x) | |
| x = torch.cat([x, *self.ppm(x)], dim=1) | |
| x = self.out(x) | |
| return x | |
| class FeatureFusionModule(nn.Module): | |
| """Feature fusion module. | |
| Args: | |
| higher_in_channels (int): Number of input channels of the | |
| higher-resolution branch. | |
| lower_in_channels (int): Number of input channels of the | |
| lower-resolution branch. | |
| out_channels (int): Number of output channels. | |
| conv_cfg (dict | None): Config of conv layers. Default: None | |
| norm_cfg (dict | None): Config of norm layers. Default: | |
| dict(type='BN') | |
| act_cfg (dict): Config of activation layers. Default: | |
| dict(type='ReLU') | |
| align_corners (bool): align_corners argument of F.interpolate. | |
| Default: False | |
| """ | |
| def __init__(self, | |
| higher_in_channels, | |
| lower_in_channels, | |
| out_channels, | |
| conv_cfg=None, | |
| norm_cfg=dict(type='BN'), | |
| act_cfg=dict(type='ReLU'), | |
| align_corners=False): | |
| super(FeatureFusionModule, self).__init__() | |
| self.conv_cfg = conv_cfg | |
| self.norm_cfg = norm_cfg | |
| self.act_cfg = act_cfg | |
| self.align_corners = align_corners | |
| self.dwconv = ConvModule( | |
| lower_in_channels, | |
| out_channels, | |
| 1, | |
| conv_cfg=self.conv_cfg, | |
| norm_cfg=self.norm_cfg, | |
| act_cfg=self.act_cfg) | |
| self.conv_lower_res = ConvModule( | |
| out_channels, | |
| out_channels, | |
| 1, | |
| conv_cfg=self.conv_cfg, | |
| norm_cfg=self.norm_cfg, | |
| act_cfg=None) | |
| self.conv_higher_res = ConvModule( | |
| higher_in_channels, | |
| out_channels, | |
| 1, | |
| conv_cfg=self.conv_cfg, | |
| norm_cfg=self.norm_cfg, | |
| act_cfg=None) | |
| self.relu = nn.ReLU(True) | |
| def forward(self, higher_res_feature, lower_res_feature): | |
| lower_res_feature = resize( | |
| lower_res_feature, | |
| size=higher_res_feature.size()[2:], | |
| mode='bilinear', | |
| align_corners=self.align_corners) | |
| lower_res_feature = self.dwconv(lower_res_feature) | |
| lower_res_feature = self.conv_lower_res(lower_res_feature) | |
| higher_res_feature = self.conv_higher_res(higher_res_feature) | |
| out = higher_res_feature + lower_res_feature | |
| return self.relu(out) | |
| class FastSCNN(nn.Module): | |
| """Fast-SCNN Backbone. | |
| Args: | |
| in_channels (int): Number of input image channels. Default: 3. | |
| downsample_dw_channels (tuple[int]): Number of output channels after | |
| the first conv layer & the second conv layer in | |
| Learning-To-Downsample (LTD) module. | |
| Default: (32, 48). | |
| global_in_channels (int): Number of input channels of | |
| Global Feature Extractor(GFE). | |
| Equal to number of output channels of LTD. | |
| Default: 64. | |
| global_block_channels (tuple[int]): Tuple of integers that describe | |
| the output channels for each of the MobileNet-v2 bottleneck | |
| residual blocks in GFE. | |
| Default: (64, 96, 128). | |
| global_block_strides (tuple[int]): Tuple of integers | |
| that describe the strides (downsampling factors) for each of the | |
| MobileNet-v2 bottleneck residual blocks in GFE. | |
| Default: (2, 2, 1). | |
| global_out_channels (int): Number of output channels of GFE. | |
| Default: 128. | |
| higher_in_channels (int): Number of input channels of the higher | |
| resolution branch in FFM. | |
| Equal to global_in_channels. | |
| Default: 64. | |
| lower_in_channels (int): Number of input channels of the lower | |
| resolution branch in FFM. | |
| Equal to global_out_channels. | |
| Default: 128. | |
| fusion_out_channels (int): Number of output channels of FFM. | |
| Default: 128. | |
| out_indices (tuple): Tuple of indices of list | |
| [higher_res_features, lower_res_features, fusion_output]. | |
| Often set to (0,1,2) to enable aux. heads. | |
| Default: (0, 1, 2). | |
| conv_cfg (dict | None): Config of conv layers. Default: None | |
| norm_cfg (dict | None): Config of norm layers. Default: | |
| dict(type='BN') | |
| act_cfg (dict): Config of activation layers. Default: | |
| dict(type='ReLU') | |
| align_corners (bool): align_corners argument of F.interpolate. | |
| Default: False | |
| """ | |
| def __init__(self, | |
| in_channels=3, | |
| downsample_dw_channels=(32, 48), | |
| global_in_channels=64, | |
| global_block_channels=(64, 96, 128), | |
| global_block_strides=(2, 2, 1), | |
| global_out_channels=128, | |
| higher_in_channels=64, | |
| lower_in_channels=128, | |
| fusion_out_channels=128, | |
| out_indices=(0, 1, 2), | |
| conv_cfg=None, | |
| norm_cfg=dict(type='BN'), | |
| act_cfg=dict(type='ReLU'), | |
| align_corners=False): | |
| super(FastSCNN, self).__init__() | |
| if global_in_channels != higher_in_channels: | |
| raise AssertionError('Global Input Channels must be the same \ | |
| with Higher Input Channels!') | |
| elif global_out_channels != lower_in_channels: | |
| raise AssertionError('Global Output Channels must be the same \ | |
| with Lower Input Channels!') | |
| self.in_channels = in_channels | |
| self.downsample_dw_channels1 = downsample_dw_channels[0] | |
| self.downsample_dw_channels2 = downsample_dw_channels[1] | |
| self.global_in_channels = global_in_channels | |
| self.global_block_channels = global_block_channels | |
| self.global_block_strides = global_block_strides | |
| self.global_out_channels = global_out_channels | |
| self.higher_in_channels = higher_in_channels | |
| self.lower_in_channels = lower_in_channels | |
| self.fusion_out_channels = fusion_out_channels | |
| self.out_indices = out_indices | |
| self.conv_cfg = conv_cfg | |
| self.norm_cfg = norm_cfg | |
| self.act_cfg = act_cfg | |
| self.align_corners = align_corners | |
| self.learning_to_downsample = LearningToDownsample( | |
| in_channels, | |
| downsample_dw_channels, | |
| global_in_channels, | |
| conv_cfg=self.conv_cfg, | |
| norm_cfg=self.norm_cfg, | |
| act_cfg=self.act_cfg) | |
| self.global_feature_extractor = GlobalFeatureExtractor( | |
| global_in_channels, | |
| global_block_channels, | |
| global_out_channels, | |
| strides=self.global_block_strides, | |
| conv_cfg=self.conv_cfg, | |
| norm_cfg=self.norm_cfg, | |
| act_cfg=self.act_cfg, | |
| align_corners=self.align_corners) | |
| self.feature_fusion = FeatureFusionModule( | |
| higher_in_channels, | |
| lower_in_channels, | |
| fusion_out_channels, | |
| conv_cfg=self.conv_cfg, | |
| norm_cfg=self.norm_cfg, | |
| act_cfg=self.act_cfg, | |
| align_corners=self.align_corners) | |
| def init_weights(self, pretrained=None): | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| kaiming_init(m) | |
| elif isinstance(m, (_BatchNorm, nn.GroupNorm)): | |
| constant_init(m, 1) | |
| def forward(self, x): | |
| higher_res_features = self.learning_to_downsample(x) | |
| lower_res_features = self.global_feature_extractor(higher_res_features) | |
| fusion_output = self.feature_fusion(higher_res_features, | |
| lower_res_features) | |
| outs = [higher_res_features, lower_res_features, fusion_output] | |
| outs = [outs[i] for i in self.out_indices] | |
| return tuple(outs) | |