FisheyeMODNet: Moving Object detection on Surround-view Cameras for Autonomous Driving
论文链接: /pdf/1908.11789v1.pdf
PyTorch代码: /shanglianlm0525/PyTorch-Networks
环视鱼眼数据:
PyTorch代码:
import torchimport torch.nn as nndef Conv3x3BNReLU(in_channels,out_channels,stride,groups):return nn.Sequential(nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=stride, padding=1,groups=groups),nn.BatchNorm2d(out_channels),nn.ReLU6(inplace=True))def Conv1x1BNReLU(in_channels,out_channels,groups):return nn.Sequential(nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1,groups=groups),nn.BatchNorm2d(out_channels),nn.ReLU6(inplace=True))def Conv1x1BN(in_channels,out_channels,groups):return nn.Sequential(nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1,groups=groups),nn.BatchNorm2d(out_channels))class ChannelShuffle(nn.Module):def __init__(self, groups):super(ChannelShuffle, self).__init__()self.groups = groupsdef forward(self, x):'''Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]'''N, C, H, W = x.size()g = self.groupsreturn x.view(N, g, int(C / g), H, W).permute(0, 2, 1, 3, 4).contiguous().view(N, C, H, W)class ShuffleNetUnits(nn.Module):def __init__(self, in_channels, out_channels, stride, groups):super(ShuffleNetUnits, self).__init__()self.stride = strideout_channels = out_channels - in_channels if self.stride>1 else out_channelsmid_channels = out_channels // 4self.bottleneck = nn.Sequential(Conv1x1BNReLU(in_channels, mid_channels,groups),ChannelShuffle(groups),Conv3x3BNReLU(mid_channels, mid_channels, stride,groups),Conv1x1BN(mid_channels, out_channels,groups))if self.stride>1:self.shortcut = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)self.relu = nn.ReLU6(inplace=True)def forward(self, x):out = self.bottleneck(x)out = torch.cat([self.shortcut(x), out], dim=1) if self.stride > 1 else (out + x)return self.relu(out)class FisheyeMODNet(nn.Module):def __init__(self, groups=1, num_classes=2):super(FisheyeMODNet, self).__init__()layers = [4, 8, 4]self.stage1a = nn.Sequential(nn.Conv2d(in_channels=3, out_channels=24, kernel_size=3,stride=2, padding=1),nn.MaxPool2d(kernel_size=2,stride=2),)self.stage2a = self._make_layer(24, 120, groups, layers[0])self.stage1b = nn.Sequential(nn.Conv2d(in_channels=3, out_channels=24, kernel_size=3, stride=2, padding=1),nn.MaxPool2d(kernel_size=2, stride=2),)self.stage2b = self._make_layer(24, 120, groups, layers[0])self.stage3 = self._make_layer(240, 480, groups, layers[1])self.stage4 = self._make_layer(480, 960, groups, layers[2])self.adapt_conv3 = nn.Conv2d(960, num_classes, kernel_size=1)self.adapt_conv2 = nn.Conv2d(480, num_classes, kernel_size=1)self.adapt_conv1 = nn.Conv2d(240, num_classes, kernel_size=1)self.up_sampling3 = nn.ConvTranspose2d(in_channels=num_classes, out_channels=num_classes, kernel_size=4, stride=2, padding=1)self.up_sampling2 = nn.ConvTranspose2d(in_channels=num_classes, out_channels=num_classes, kernel_size=4, stride=2, padding=1)self.up_sampling1 = nn.ConvTranspose2d(in_channels=num_classes, out_channels=num_classes, kernel_size=16, stride=8, padding=4)self.softmax = nn.Softmax(dim=1)self.init_params()def _make_layer(self, in_channels, out_channels, groups, block_num):layers = []layers.append(ShuffleNetUnits(in_channels=in_channels, out_channels=out_channels, stride=2, groups=groups))for idx in range(1, block_num):layers.append(ShuffleNetUnits(in_channels=out_channels, out_channels=out_channels, stride=1, groups=groups))return nn.Sequential(*layers)def init_params(self):for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight)nn.init.constant_(m.bias, 0)elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.Linear):nn.init.constant_(m.weight, 1)nn.init.constant_(m.bias, 0)def forward(self, x, y):x = self.stage2a(self.stage1a(x))y = self.stage2b(self.stage1b(y))feature1 = torch.cat([x, y], dim=1)feature2 = self.stage3(feature1)feature3 = self.stage4(feature2)out3 = self.up_sampling3(self.adapt_conv3(feature3))out2 = self.up_sampling2(self.adapt_conv2(feature2) + out3)out1 = self.up_sampling1(self.adapt_conv1(feature1) + out2)out = self.softmax(out1)return outif __name__ == '__main__':model = FisheyeMODNet()input1 = torch.randn(1, 3, 640, 640)input2 = torch.randn(1, 3, 640, 640)out = model(input1, input2)print(out.shape)
