"""Context Guided Network for Semantic Segmentation""" import torch import torch.nn as nn import torch.nn.functional as F from core.nn import _ConvBNPReLU, _BNPReLU __all__ = ['CGNet', 'get_cgnet', 'get_cgnet_citys'] class CGNet(nn.Module): r"""CGNet Parameters ---------- nclass : int Number of categories for the training dataset. norm_layer : object Normalization layer used in backbone network (default: :class:`nn.BatchNorm`; for Synchronized Cross-GPU BachNormalization). aux : bool Auxiliary loss. Reference: Tianyi Wu, et al. "CGNet: A Light-weight Context Guided Network for Semantic Segmentation." arXiv preprint arXiv:1811.08201 (2018). """ def __init__(self, nclass, backbone='', aux=False, jpu=False, pretrained_base=True, M=3, N=21, **kwargs): super(CGNet, self).__init__() # stage 1 self.stage1_0 = _ConvBNPReLU(3, 32, 3, 2, 1, **kwargs) self.stage1_1 = _ConvBNPReLU(32, 32, 3, 1, 1, **kwargs) self.stage1_2 = _ConvBNPReLU(32, 32, 3, 1, 1, **kwargs) self.sample1 = _InputInjection(1) self.sample2 = _InputInjection(2) self.bn_prelu1 = _BNPReLU(32 + 3, **kwargs) # stage 2 self.stage2_0 = ContextGuidedBlock(32 + 3, 64, dilation=2, reduction=8, down=True, residual=False, **kwargs) self.stage2 = nn.ModuleList() for i in range(0, M - 1): self.stage2.append(ContextGuidedBlock(64, 64, dilation=2, reduction=8, **kwargs)) self.bn_prelu2 = _BNPReLU(128 + 3, **kwargs) # stage 3 self.stage3_0 = ContextGuidedBlock(128 + 3, 128, dilation=4, reduction=16, down=True, residual=False, **kwargs) self.stage3 = nn.ModuleList() for i in range(0, N - 1): self.stage3.append(ContextGuidedBlock(128, 128, dilation=4, reduction=16, **kwargs)) self.bn_prelu3 = _BNPReLU(256, **kwargs) self.head = nn.Sequential( nn.Dropout2d(0.1, False), nn.Conv2d(256, nclass, 1)) self.__setattr__('exclusive', ['stage1_0', 'stage1_1', 'stage1_2', 'sample1', 'sample2', 'bn_prelu1', 'stage2_0', 'stage2', 'bn_prelu2', 'stage3_0', 'stage3', 'bn_prelu3', 'head']) def forward(self, x): size = x.size()[2:] # stage1 out0 = self.stage1_0(x) out0 = self.stage1_1(out0) out0 = self.stage1_2(out0) inp1 = self.sample1(x) inp2 = self.sample2(x) # stage 2 out0_cat = self.bn_prelu1(torch.cat([out0, inp1], dim=1)) out1_0 = self.stage2_0(out0_cat) for i, layer in enumerate(self.stage2): if i == 0: out1 = layer(out1_0) else: out1 = layer(out1) out1_cat = self.bn_prelu2(torch.cat([out1, out1_0, inp2], dim=1)) # stage 3 out2_0 = self.stage3_0(out1_cat) for i, layer in enumerate(self.stage3): if i == 0: out2 = layer(out2_0) else: out2 = layer(out2) out2_cat = self.bn_prelu3(torch.cat([out2_0, out2], dim=1)) outputs = [] out = self.head(out2_cat) out = F.interpolate(out, size, mode='bilinear', align_corners=True) outputs.append(out) #return tuple(outputs) return outputs[0] class _ChannelWiseConv(nn.Module): def __init__(self, in_channels, out_channels, dilation=1, **kwargs): super(_ChannelWiseConv, self).__init__() self.conv = nn.Conv2d(in_channels, out_channels, 3, 1, dilation, dilation, groups=in_channels, bias=False) def forward(self, x): x = self.conv(x) return x class _FGlo(nn.Module): def __init__(self, in_channels, reduction=16, **kwargs): super(_FGlo, self).__init__() self.gap = nn.AdaptiveAvgPool2d(1) self.fc = nn.Sequential( nn.Linear(in_channels, in_channels // reduction), nn.ReLU(True), nn.Linear(in_channels // reduction, in_channels), nn.Sigmoid()) def forward(self, x): n, c, _, _ = x.size() out = self.gap(x).view(n, c) out = self.fc(out).view(n, c, 1, 1) return x * out class _InputInjection(nn.Module): def __init__(self, ratio): super(_InputInjection, self).__init__() self.pool = nn.ModuleList() for i in range(0, ratio): self.pool.append(nn.AvgPool2d(3, 2, 1)) def forward(self, x): for pool in self.pool: x = pool(x) return x class _ConcatInjection(nn.Module): def __init__(self, in_channels, norm_layer=nn.BatchNorm2d, **kwargs): super(_ConcatInjection, self).__init__() self.bn = norm_layer(in_channels) self.prelu = nn.PReLU(in_channels) def forward(self, x1, x2): out = torch.cat([x1, x2], dim=1) out = self.bn(out) out = self.prelu(out) return out class ContextGuidedBlock(nn.Module): def __init__(self, in_channels, out_channels, dilation=2, reduction=16, down=False, residual=True, norm_layer=nn.BatchNorm2d, **kwargs): super(ContextGuidedBlock, self).__init__() inter_channels = out_channels // 2 if not down else out_channels if down: self.conv = _ConvBNPReLU(in_channels, inter_channels, 3, 2, 1, norm_layer=norm_layer, **kwargs) self.reduce = nn.Conv2d(inter_channels * 2, out_channels, 1, bias=False) else: self.conv = _ConvBNPReLU(in_channels, inter_channels, 1, 1, 0, norm_layer=norm_layer, **kwargs) self.f_loc = _ChannelWiseConv(inter_channels, inter_channels, **kwargs) self.f_sur = _ChannelWiseConv(inter_channels, inter_channels, dilation, **kwargs) self.bn = norm_layer(inter_channels * 2) self.prelu = nn.PReLU(inter_channels * 2) self.f_glo = _FGlo(out_channels, reduction, **kwargs) self.down = down self.residual = residual def forward(self, x): out = self.conv(x) loc = self.f_loc(out) sur = self.f_sur(out) joi_feat = torch.cat([loc, sur], dim=1) joi_feat = self.prelu(self.bn(joi_feat)) if self.down: joi_feat = self.reduce(joi_feat) out = self.f_glo(joi_feat) if self.residual: out = out + x return out def get_cgnet(dataset='citys', backbone='', pretrained=False, root='~/.torch/models', pretrained_base=True, **kwargs): acronyms = { 'pascal_voc': 'pascal_voc', 'pascal_aug': 'pascal_aug', 'ade20k': 'ade', 'coco': 'coco', 'citys': 'citys', } from core.data.dataloader import datasets model = CGNet(datasets[dataset].NUM_CLASS, backbone=backbone, pretrained_base=pretrained_base, **kwargs) if pretrained: from .model_store import get_model_file device = torch.device(kwargs['local_rank']) model.load_state_dict(torch.load(get_model_file('cgnet_%s' % (acronyms[dataset]), root=root), map_location=device)) return model def get_cgnet_citys(**kwargs): return get_cgnet('citys', '', **kwargs) if __name__ == '__main__': # model = get_cgnet_citys() # print(model) input = torch.rand(2, 3, 224, 224) model = CGNet(4, pretrained_base=True) # target = torch.zeros(4, 512, 512).cuda() # model.eval() # print(model) loss = model(input) print(loss, loss.shape) # from torchsummary import summary # # summary(model, (3, 224, 224)) # 打印表格，按顺序输出每层的输出形状和参数 import torch from thop import profile from torchsummary import summary flop, params = profile(model, input_size=(1, 3, 512, 512)) print('flops:{:.3f}G\nparams:{:.3f}M'.format(flop / 1e9, params / 1e6))