""" BSD 3-Clause License Copyright (c) Soumith Chintala 2016, All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ import torch from torch import nn from torch.nn import functional as F __all__ = ["DeepLabV3Decoder"] class DeepLabV3Decoder(nn.Sequential): def __init__(self, in_channels, out_channels=256, atrous_rates=(12, 24, 36)): super().__init__( ASPP(in_channels, out_channels, atrous_rates), nn.Conv2d(out_channels, out_channels, 3, padding=1, bias=False), nn.BatchNorm2d(out_channels), nn.ReLU(), ) self.out_channels = out_channels def forward(self, *features): return super().forward(features[-1]) class DeepLabV3PlusDecoder(nn.Module): def __init__( self, encoder_channels, out_channels=256, atrous_rates=(12, 24, 36), output_stride=16, ): super().__init__() if output_stride not in {8, 16}: raise ValueError("Output stride should be 8 or 16, got {}.".format(output_stride)) self.out_channels = out_channels self.output_stride = output_stride self.aspp = nn.Sequential( ASPP(encoder_channels[-1], out_channels, atrous_rates, separable=True), SeparableConv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(out_channels), nn.ReLU(), ) scale_factor = 2 if output_stride == 8 else 4 self.up = nn.UpsamplingBilinear2d(scale_factor=scale_factor) highres_in_channels = encoder_channels[-4] highres_out_channels = 48 # proposed by authors of paper self.block1 = nn.Sequential( nn.Conv2d(highres_in_channels, highres_out_channels, kernel_size=1, bias=False), nn.BatchNorm2d(highres_out_channels), nn.ReLU(), ) self.block2 = nn.Sequential( SeparableConv2d( highres_out_channels + out_channels, out_channels, kernel_size=3, padding=1, bias=False, ), nn.BatchNorm2d(out_channels), nn.ReLU(), ) def forward(self, *features): aspp_features = self.aspp(features[-1]) aspp_features = self.up(aspp_features) high_res_features = self.block1(features[-4]) concat_features = torch.cat([aspp_features, high_res_features], dim=1) fused_features = self.block2(concat_features) return fused_features class ASPPConv(nn.Sequential): def __init__(self, in_channels, out_channels, dilation): super().__init__( nn.Conv2d( in_channels, out_channels, kernel_size=3, padding=dilation, dilation=dilation, bias=False, ), nn.BatchNorm2d(out_channels), nn.ReLU(), ) class ASPPSeparableConv(nn.Sequential): def __init__(self, in_channels, out_channels, dilation): super().__init__( SeparableConv2d( in_channels, out_channels, kernel_size=3, padding=dilation, dilation=dilation, bias=False, ), nn.BatchNorm2d(out_channels), nn.ReLU(), ) class ASPPPooling(nn.Sequential): def __init__(self, in_channels, out_channels): super().__init__( nn.AdaptiveAvgPool2d(1), nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), nn.BatchNorm2d(out_channels), nn.ReLU(), ) def forward(self, x): size = x.shape[-2:] for mod in self: x = mod(x) return F.interpolate(x, size=size, mode='bilinear', align_corners=False) class ASPP(nn.Module): def __init__(self, in_channels, out_channels, atrous_rates, separable=False): super(ASPP, self).__init__() modules = [] modules.append( nn.Sequential( nn.Conv2d(in_channels, out_channels, 1, bias=False), nn.BatchNorm2d(out_channels), nn.ReLU(), ) ) rate1, rate2, rate3 = tuple(atrous_rates) ASPPConvModule = ASPPConv if not separable else ASPPSeparableConv modules.append(ASPPConvModule(in_channels, out_channels, rate1)) modules.append(ASPPConvModule(in_channels, out_channels, rate2)) modules.append(ASPPConvModule(in_channels, out_channels, rate3)) modules.append(ASPPPooling(in_channels, out_channels)) self.convs = nn.ModuleList(modules) self.project = nn.Sequential( nn.Conv2d(5 * out_channels, out_channels, kernel_size=1, bias=False), nn.BatchNorm2d(out_channels), nn.ReLU(), nn.Dropout(0.5), ) def forward(self, x): res = [] for conv in self.convs: res.append(conv(x)) res = torch.cat(res, dim=1) return self.project(res) class SeparableConv2d(nn.Sequential): def __init__( self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, bias=True, ): dephtwise_conv = nn.Conv2d( in_channels, in_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=in_channels, bias=False, ) pointwise_conv = nn.Conv2d( in_channels, out_channels, kernel_size=1, bias=bias, ) super().__init__(dephtwise_conv, pointwise_conv)