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T4
| #!/usr/bin/python | |
| # -*- encoding: utf-8 -*- | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torchvision | |
| from model.bisenet.resnet import Resnet18 | |
| # from modules.bn import InPlaceABNSync as BatchNorm2d | |
| class ConvBNReLU(nn.Module): | |
| def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs): | |
| super(ConvBNReLU, self).__init__() | |
| self.conv = nn.Conv2d(in_chan, | |
| out_chan, | |
| kernel_size = ks, | |
| stride = stride, | |
| padding = padding, | |
| bias = False) | |
| self.bn = nn.BatchNorm2d(out_chan) | |
| self.init_weight() | |
| def forward(self, x): | |
| x = self.conv(x) | |
| x = F.relu(self.bn(x)) | |
| return x | |
| def init_weight(self): | |
| for ly in self.children(): | |
| if isinstance(ly, nn.Conv2d): | |
| nn.init.kaiming_normal_(ly.weight, a=1) | |
| if not ly.bias is None: nn.init.constant_(ly.bias, 0) | |
| class BiSeNetOutput(nn.Module): | |
| def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs): | |
| super(BiSeNetOutput, self).__init__() | |
| self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1) | |
| self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False) | |
| self.init_weight() | |
| def forward(self, x): | |
| x = self.conv(x) | |
| x = self.conv_out(x) | |
| return x | |
| def init_weight(self): | |
| for ly in self.children(): | |
| if isinstance(ly, nn.Conv2d): | |
| nn.init.kaiming_normal_(ly.weight, a=1) | |
| if not ly.bias is None: nn.init.constant_(ly.bias, 0) | |
| def get_params(self): | |
| wd_params, nowd_params = [], [] | |
| for name, module in self.named_modules(): | |
| if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): | |
| wd_params.append(module.weight) | |
| if not module.bias is None: | |
| nowd_params.append(module.bias) | |
| elif isinstance(module, nn.BatchNorm2d): | |
| nowd_params += list(module.parameters()) | |
| return wd_params, nowd_params | |
| class AttentionRefinementModule(nn.Module): | |
| def __init__(self, in_chan, out_chan, *args, **kwargs): | |
| super(AttentionRefinementModule, self).__init__() | |
| self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1) | |
| self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False) | |
| self.bn_atten = nn.BatchNorm2d(out_chan) | |
| self.sigmoid_atten = nn.Sigmoid() | |
| self.init_weight() | |
| def forward(self, x): | |
| feat = self.conv(x) | |
| atten = F.avg_pool2d(feat, feat.size()[2:]) | |
| atten = self.conv_atten(atten) | |
| atten = self.bn_atten(atten) | |
| atten = self.sigmoid_atten(atten) | |
| out = torch.mul(feat, atten) | |
| return out | |
| def init_weight(self): | |
| for ly in self.children(): | |
| if isinstance(ly, nn.Conv2d): | |
| nn.init.kaiming_normal_(ly.weight, a=1) | |
| if not ly.bias is None: nn.init.constant_(ly.bias, 0) | |
| class ContextPath(nn.Module): | |
| def __init__(self, *args, **kwargs): | |
| super(ContextPath, self).__init__() | |
| self.resnet = Resnet18() | |
| self.arm16 = AttentionRefinementModule(256, 128) | |
| self.arm32 = AttentionRefinementModule(512, 128) | |
| self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) | |
| self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) | |
| self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0) | |
| self.init_weight() | |
| def forward(self, x): | |
| H0, W0 = x.size()[2:] | |
| feat8, feat16, feat32 = self.resnet(x) | |
| H8, W8 = feat8.size()[2:] | |
| H16, W16 = feat16.size()[2:] | |
| H32, W32 = feat32.size()[2:] | |
| avg = F.avg_pool2d(feat32, feat32.size()[2:]) | |
| avg = self.conv_avg(avg) | |
| avg_up = F.interpolate(avg, (H32, W32), mode='nearest') | |
| feat32_arm = self.arm32(feat32) | |
| feat32_sum = feat32_arm + avg_up | |
| feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest') | |
| feat32_up = self.conv_head32(feat32_up) | |
| feat16_arm = self.arm16(feat16) | |
| feat16_sum = feat16_arm + feat32_up | |
| feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest') | |
| feat16_up = self.conv_head16(feat16_up) | |
| return feat8, feat16_up, feat32_up # x8, x8, x16 | |
| def init_weight(self): | |
| for ly in self.children(): | |
| if isinstance(ly, nn.Conv2d): | |
| nn.init.kaiming_normal_(ly.weight, a=1) | |
| if not ly.bias is None: nn.init.constant_(ly.bias, 0) | |
| def get_params(self): | |
| wd_params, nowd_params = [], [] | |
| for name, module in self.named_modules(): | |
| if isinstance(module, (nn.Linear, nn.Conv2d)): | |
| wd_params.append(module.weight) | |
| if not module.bias is None: | |
| nowd_params.append(module.bias) | |
| elif isinstance(module, nn.BatchNorm2d): | |
| nowd_params += list(module.parameters()) | |
| return wd_params, nowd_params | |
| ### This is not used, since I replace this with the resnet feature with the same size | |
| class SpatialPath(nn.Module): | |
| def __init__(self, *args, **kwargs): | |
| super(SpatialPath, self).__init__() | |
| self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3) | |
| self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1) | |
| self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1) | |
| self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0) | |
| self.init_weight() | |
| def forward(self, x): | |
| feat = self.conv1(x) | |
| feat = self.conv2(feat) | |
| feat = self.conv3(feat) | |
| feat = self.conv_out(feat) | |
| return feat | |
| def init_weight(self): | |
| for ly in self.children(): | |
| if isinstance(ly, nn.Conv2d): | |
| nn.init.kaiming_normal_(ly.weight, a=1) | |
| if not ly.bias is None: nn.init.constant_(ly.bias, 0) | |
| def get_params(self): | |
| wd_params, nowd_params = [], [] | |
| for name, module in self.named_modules(): | |
| if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): | |
| wd_params.append(module.weight) | |
| if not module.bias is None: | |
| nowd_params.append(module.bias) | |
| elif isinstance(module, nn.BatchNorm2d): | |
| nowd_params += list(module.parameters()) | |
| return wd_params, nowd_params | |
| class FeatureFusionModule(nn.Module): | |
| def __init__(self, in_chan, out_chan, *args, **kwargs): | |
| super(FeatureFusionModule, self).__init__() | |
| self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0) | |
| self.conv1 = nn.Conv2d(out_chan, | |
| out_chan//4, | |
| kernel_size = 1, | |
| stride = 1, | |
| padding = 0, | |
| bias = False) | |
| self.conv2 = nn.Conv2d(out_chan//4, | |
| out_chan, | |
| kernel_size = 1, | |
| stride = 1, | |
| padding = 0, | |
| bias = False) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.sigmoid = nn.Sigmoid() | |
| self.init_weight() | |
| def forward(self, fsp, fcp): | |
| fcat = torch.cat([fsp, fcp], dim=1) | |
| feat = self.convblk(fcat) | |
| atten = F.avg_pool2d(feat, feat.size()[2:]) | |
| atten = self.conv1(atten) | |
| atten = self.relu(atten) | |
| atten = self.conv2(atten) | |
| atten = self.sigmoid(atten) | |
| feat_atten = torch.mul(feat, atten) | |
| feat_out = feat_atten + feat | |
| return feat_out | |
| def init_weight(self): | |
| for ly in self.children(): | |
| if isinstance(ly, nn.Conv2d): | |
| nn.init.kaiming_normal_(ly.weight, a=1) | |
| if not ly.bias is None: nn.init.constant_(ly.bias, 0) | |
| def get_params(self): | |
| wd_params, nowd_params = [], [] | |
| for name, module in self.named_modules(): | |
| if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): | |
| wd_params.append(module.weight) | |
| if not module.bias is None: | |
| nowd_params.append(module.bias) | |
| elif isinstance(module, nn.BatchNorm2d): | |
| nowd_params += list(module.parameters()) | |
| return wd_params, nowd_params | |
| class BiSeNet(nn.Module): | |
| def __init__(self, n_classes, *args, **kwargs): | |
| super(BiSeNet, self).__init__() | |
| self.cp = ContextPath() | |
| ## here self.sp is deleted | |
| self.ffm = FeatureFusionModule(256, 256) | |
| self.conv_out = BiSeNetOutput(256, 256, n_classes) | |
| self.conv_out16 = BiSeNetOutput(128, 64, n_classes) | |
| self.conv_out32 = BiSeNetOutput(128, 64, n_classes) | |
| self.init_weight() | |
| def forward(self, x): | |
| H, W = x.size()[2:] | |
| feat_res8, feat_cp8, feat_cp16 = self.cp(x) # here return res3b1 feature | |
| feat_sp = feat_res8 # use res3b1 feature to replace spatial path feature | |
| feat_fuse = self.ffm(feat_sp, feat_cp8) | |
| feat_out = self.conv_out(feat_fuse) | |
| feat_out16 = self.conv_out16(feat_cp8) | |
| feat_out32 = self.conv_out32(feat_cp16) | |
| feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True) | |
| feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True) | |
| feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True) | |
| return feat_out, feat_out16, feat_out32 | |
| def init_weight(self): | |
| for ly in self.children(): | |
| if isinstance(ly, nn.Conv2d): | |
| nn.init.kaiming_normal_(ly.weight, a=1) | |
| if not ly.bias is None: nn.init.constant_(ly.bias, 0) | |
| def get_params(self): | |
| wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], [] | |
| for name, child in self.named_children(): | |
| child_wd_params, child_nowd_params = child.get_params() | |
| if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput): | |
| lr_mul_wd_params += child_wd_params | |
| lr_mul_nowd_params += child_nowd_params | |
| else: | |
| wd_params += child_wd_params | |
| nowd_params += child_nowd_params | |
| return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params | |
| if __name__ == "__main__": | |
| net = BiSeNet(19) | |
| net.cuda() | |
| net.eval() | |
| in_ten = torch.randn(16, 3, 640, 480).cuda() | |
| out, out16, out32 = net(in_ten) | |
| print(out.shape) | |
| net.get_params() | |