"""Bilateral Segmentation Network""" import torch import torch.nn as nn import torch.nn.functional as F import numpy as np from core.models.base_models.resnet import resnet18,resnet50 from core.nn import _ConvBNReLU __all__ = ['BiSeNet', 'get_bisenet', 'get_bisenet_resnet18_citys'] class BiSeNet(nn.Module): def __init__(self, nclass, backbone='resnet18', aux=False, jpu=False, pretrained_base=True, **kwargs): super(BiSeNet, self).__init__() self.aux = aux self.spatial_path = SpatialPath(3, 128, **kwargs) self.context_path = ContextPath(backbone, pretrained_base, **kwargs) self.ffm = FeatureFusion(256, 256, 4, **kwargs) self.head = _BiSeHead(256, 64, nclass, **kwargs) if aux: self.auxlayer1 = _BiSeHead(128, 256, nclass, **kwargs) self.auxlayer2 = _BiSeHead(128, 256, nclass, **kwargs) self.__setattr__('exclusive', ['spatial_path', 'context_path', 'ffm', 'head', 'auxlayer1', 'auxlayer2'] if aux else [ 'spatial_path', 'context_path', 'ffm', 'head']) def forward(self, x,outsize=None,test_flag=False): size = x.size()[2:] spatial_out = self.spatial_path(x) context_out = self.context_path(x) fusion_out = self.ffm(spatial_out, context_out[-1]) outputs = [] x = self.head(fusion_out) x = F.interpolate(x, size, mode='bilinear', align_corners=True) if outsize: print('######using torch resize#######',outsize) x = F.interpolate(x, outsize, mode='bilinear', align_corners=True) outputs.append(x) if self.aux: auxout1 = self.auxlayer1(context_out[0]) auxout1 = F.interpolate(auxout1, size, mode='bilinear', align_corners=True) outputs.append(auxout1) auxout2 = self.auxlayer2(context_out[1]) auxout2 = F.interpolate(auxout2, size, mode='bilinear', align_corners=True) outputs.append(auxout2) if test_flag: outputs = [torch.argmax(outputx ,axis=1) for outputx in outputs] #return tuple(outputs) return outputs[0] class BiSeNet_MultiOutput(nn.Module): def __init__(self, nclass, backbone='resnet18', aux=False, jpu=False, pretrained_base=True, **kwargs): super(BiSeNet_MultiOutput, self).__init__() self.aux = aux self.spatial_path = SpatialPath(3, 128, **kwargs) self.context_path = ContextPath(backbone, pretrained_base, **kwargs) self.ffm = FeatureFusion(256, 256, 4, **kwargs) assert isinstance(nclass,list) self.outCnt = len(nclass) for ii,nclassii in enumerate(nclass): setattr(self,'head%d'%(ii) , _BiSeHead(256, 64, nclassii, **kwargs)) if aux: self.auxlayer1 = _BiSeHead(128, 256, nclass, **kwargs) self.auxlayer2 = _BiSeHead(128, 256, nclass, **kwargs) self.__setattr__('exclusive', ['spatial_path', 'context_path', 'ffm', 'head', 'auxlayer1', 'auxlayer2'] if aux else [ 'spatial_path', 'context_path', 'ffm', 'head']) def forward(self, x,outsize=None,test_flag=False,smooth_kernel=0): size = x.size()[2:] spatial_out = self.spatial_path(x) context_out = self.context_path(x) fusion_out = self.ffm(spatial_out, context_out[-1]) outputs = [] for ii in range(self.outCnt): x = getattr(self,'head%d'%(ii))(fusion_out) x = F.interpolate(x, size, mode='bilinear', align_corners=True) outputs.append(x) if self.aux: auxout1 = self.auxlayer1(context_out[0]) auxout1 = F.interpolate(auxout1, size, mode='bilinear', align_corners=True) outputs.append(auxout1) auxout2 = self.auxlayer2(context_out[1]) auxout2 = F.interpolate(auxout2, size, mode='bilinear', align_corners=True) outputs.append(auxout2) if test_flag: outputs = [torch.argmax(outputx ,axis=1) for outputx in outputs] if smooth_kernel>0: gaussian_kernel = torch.from_numpy(np.ones((1,1,smooth_kernel,smooth_kernel)) ) pad = int((smooth_kernel - 1)/2) if not gaussian_kernel.is_cuda: gaussian_kernel = gaussian_kernel.to(x.device) #print(gaussian_kernel.dtype,gaussian_kernel,outputs[0].dtype) outputs = [ x.unsqueeze(1).double() for x in outputs] outputs = [torch.conv2d(x, gaussian_kernel, padding=pad) for x in outputs ] outputs = [ x.squeeze(1).long() for x in outputs] #return tuple(outputs) return outputs class _BiSeHead(nn.Module): def __init__(self, in_channels, inter_channels, nclass, norm_layer=nn.BatchNorm2d, **kwargs): super(_BiSeHead, self).__init__() self.block = nn.Sequential( _ConvBNReLU(in_channels, inter_channels, 3, 1, 1, norm_layer=norm_layer), nn.Dropout(0.1), nn.Conv2d(inter_channels, nclass, 1) ) def forward(self, x): x = self.block(x) return x class SpatialPath(nn.Module): """Spatial path""" def __init__(self, in_channels, out_channels, norm_layer=nn.BatchNorm2d, **kwargs): super(SpatialPath, self).__init__() inter_channels = 64 self.conv7x7 = _ConvBNReLU(in_channels, inter_channels, 7, 2, 3, norm_layer=norm_layer) self.conv3x3_1 = _ConvBNReLU(inter_channels, inter_channels, 3, 2, 1, norm_layer=norm_layer) self.conv3x3_2 = _ConvBNReLU(inter_channels, inter_channels, 3, 2, 1, norm_layer=norm_layer) self.conv1x1 = _ConvBNReLU(inter_channels, out_channels, 1, 1, 0, norm_layer=norm_layer) def forward(self, x): x = self.conv7x7(x) x = self.conv3x3_1(x) x = self.conv3x3_2(x) x = self.conv1x1(x) return x class _GlobalAvgPooling(nn.Module): def __init__(self, in_channels, out_channels, norm_layer, **kwargs): super(_GlobalAvgPooling, self).__init__() self.gap = nn.Sequential( nn.AdaptiveAvgPool2d(1), nn.Conv2d(in_channels, out_channels, 1, bias=False), norm_layer(out_channels), nn.ReLU(True) ) def forward(self, x): size = x.size()[2:] pool = self.gap(x) out = F.interpolate(pool, size, mode='bilinear', align_corners=True) return out class AttentionRefinmentModule(nn.Module): def __init__(self, in_channels, out_channels, norm_layer=nn.BatchNorm2d, **kwargs): super(AttentionRefinmentModule, self).__init__() self.conv3x3 = _ConvBNReLU(in_channels, out_channels, 3, 1, 1, norm_layer=norm_layer) self.channel_attention = nn.Sequential( nn.AdaptiveAvgPool2d(1), _ConvBNReLU(out_channels, out_channels, 1, 1, 0, norm_layer=norm_layer), nn.Sigmoid() ) def forward(self, x): x = self.conv3x3(x) attention = self.channel_attention(x) x = x * attention return x class ContextPath(nn.Module): def __init__(self, backbone='resnet18', pretrained_base=True, norm_layer=nn.BatchNorm2d, **kwargs): super(ContextPath, self).__init__() if backbone == 'resnet18': pretrained = resnet18(pretrained=pretrained_base, **kwargs) elif backbone=='resnet50': pretrained = resnet50(pretrained=pretrained_base, **kwargs) else: raise RuntimeError('unknown backbone: {}'.format(backbone)) self.conv1 = pretrained.conv1 self.bn1 = pretrained.bn1 self.relu = pretrained.relu self.maxpool = pretrained.maxpool self.layer1 = pretrained.layer1 self.layer2 = pretrained.layer2 self.layer3 = pretrained.layer3 self.layer4 = pretrained.layer4 inter_channels = 128 self.global_context = _GlobalAvgPooling(512, inter_channels, norm_layer) self.arms = nn.ModuleList( [AttentionRefinmentModule(512, inter_channels, norm_layer, **kwargs), AttentionRefinmentModule(256, inter_channels, norm_layer, **kwargs)] ) self.refines = nn.ModuleList( [_ConvBNReLU(inter_channels, inter_channels, 3, 1, 1, norm_layer=norm_layer), _ConvBNReLU(inter_channels, inter_channels, 3, 1, 1, norm_layer=norm_layer)] ) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) context_blocks = [] context_blocks.append(x) x = self.layer2(x) context_blocks.append(x) c3 = self.layer3(x) context_blocks.append(c3) c4 = self.layer4(c3) context_blocks.append(c4) context_blocks.reverse() global_context = self.global_context(c4) last_feature = global_context context_outputs = [] for i, (feature, arm, refine) in enumerate(zip(context_blocks[:2], self.arms, self.refines)): feature = arm(feature) feature += last_feature last_feature = F.interpolate(feature, size=context_blocks[i + 1].size()[2:], mode='bilinear', align_corners=True) last_feature = refine(last_feature) context_outputs.append(last_feature) return context_outputs class FeatureFusion(nn.Module): def __init__(self, in_channels, out_channels, reduction=1, norm_layer=nn.BatchNorm2d, **kwargs): super(FeatureFusion, self).__init__() self.conv1x1 = _ConvBNReLU(in_channels, out_channels, 1, 1, 0, norm_layer=norm_layer, **kwargs) self.channel_attention = nn.Sequential( nn.AdaptiveAvgPool2d(1), _ConvBNReLU(out_channels, out_channels // reduction, 1, 1, 0, norm_layer=norm_layer), _ConvBNReLU(out_channels // reduction, out_channels, 1, 1, 0, norm_layer=norm_layer), nn.Sigmoid() ) def forward(self, x1, x2): fusion = torch.cat([x1, x2], dim=1) out = self.conv1x1(fusion) attention = self.channel_attention(out) out = out + out * attention return out def get_bisenet(dataset='citys', backbone='resnet18', 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 ..data.dataloader import datasets model = BiSeNet(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('bisenet_%s_%s' % (backbone, acronyms[dataset]), root=root), map_location=device)) return model def get_bisenet_resnet18_citys(**kwargs): return get_bisenet('citys', 'resnet18', **kwargs) if __name__ == '__main__': # img = torch.randn(2, 3, 224, 224) # model = BiSeNet(19, backbone='resnet18') # print(model.exclusive) input = torch.rand(2, 3, 224, 224) model = BiSeNet(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))