Below is a PyTorch code example of a keypoint-based lane detection network using deformable convolution. This code example is based on the ResNet18 architecture and can be modified according to the actual situation.
First, you need to import the necessary libraries and modules:
import torch import torch.nn as nn import torch.nn.functional as F from torch.nn.modules.utils import _pair from torch.nn.parameter import Parameter from torchvision.models.resnet import resnet18
Then, define a lane line detection network model based on ResNet18 architecture:
class LaneDetectionNet(nn.Module):
def __init__(self, num_classes=1, deformable_groups=2):
super(LaneDetectionNet, self).__init__()
# load ResNet18
self.resnet = resnet18(pretrained=True)
#replace the first conv layer
self.resnet.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
# add deformable convolutions
self.resnet.layer1[0].conv1 = DeformConv2d(64, 64, kernel_size=(3, 3), padding=(1, 1), stride=(1, 1), bias=False, deformable_groups=deformable_groups)
self.resnet.layer1[0].conv2 = DeformConv2d(64, 64, kernel_size=(3, 3), padding=(1, 1), stride=(1, 1), bias=False, deformable_groups=deformable_groups)
self.resnet.layer2[0].conv1 = DeformConv2d(128, 128, kernel_size=(3, 3), padding=(1, 1), stride=(1, 1), bias=False, deformable_groups=deformable_groups)
self.resnet.layer2[0].conv2 = DeformConv2d(128, 128, kernel_size=(3, 3), padding=(1, 1), stride=(1, 1), bias=False, deformable_groups=deformable_groups)
self.resnet.layer3[0].conv1 = DeformConv2d(256, 256, kernel_size=(3, 3), padding=(1, 1), stride=(1, 1), bias=False, deformable_groups=deformable_groups)
self.resnet.layer3[0].conv2 = DeformConv2d(256, 256, kernel_size=(3, 3), padding=(1, 1), stride=(1, 1), bias=False, deformable_groups=deformable_groups)
self.resnet.layer4[0].conv1 = DeformConv2d(512, 512, kernel_size=(3, 3), padding=(1, 1), stride=(1, 1), bias=False, deformable_groups=deformable_groups)
self.resnet.layer4[0].conv2 = DeformConv2d(512, 512, kernel_size=(3, 3), padding=(1, 1), stride=(1, 1), bias=False, deformable_groups=deformable_groups)
# add the output layers
self.fc1 = nn.Linear(512, 512)
self.fc2 = nn.Linear(512, num_classes)
def forward(self, x):
x = self.resnet(x)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
Among them, DeformConv2d is an implementation class of deformable convolution. The code is as follows:
class DeformConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, deformable_groups=1):
super(DeformConv2d, self).__init__()
self.offset_conv = nn.Conv2d(in_channels, 2 * kernel_size[0] * kernel_size[1] * deformable_groups, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=True)
self.weight = Parameter(torch.Tensor(out_channels, in_channels, kernel_size[0], kernel_size[1]))
if bias:
self.bias = Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
self.stride = _pair(stride)
self.padding = _pair(padding)
self.dilation = _pair(dilation)
self.groups = groups
self.deformable_groups = deformable_groups
def reset_parameters(self):
nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
if self.bias is not None:
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
bound = 1 / math.sqrt(fan_in)
nn.init.uniform_(self.bias, -bound, bound)
def forward(self, x):
offset = self.offset_conv(x)
output = deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups, self.deformable_groups)
return output
Finally, define a deformable convolution implementation function deform_conv2d, the code is as follows:
def deform_conv2d(input, offset, weight, bias=None, stride=1, padding=0, dilation=1, groups=1, deformable_groups=1):
# get shapes and parameters
batch_size, in_channels, in_h, in_w = input.size()
out_channels, _, kernel_h, kernel_w = weight.size()
stride_h, stride_w = _pair(stride)
pad_h, pad_w = _pair(padding)
dilation_h, dilation_w = _pair(dilation)
input_padded = F.pad(input, (pad_w, pad_w, pad_h, pad_h))
# calculate output shape
out_h = (in_h + 2*pad_h - dilation_h*(kernel_h-1) - 1) // stride_h + 1
out_w = (in_w + 2*pad_w - dilation_w*(kernel_w-1) - 1) // stride_w + 1
# unfold input and offset
offset = offset.view(batch_size, deformable_groups, 2 * kernel_h * kernel_w, out_h, out_w)
input_unfolded = F.unfold(input_padded, (kernel_h, kernel_w), dilation=dilation, stride=stride)
# calculate output
output = torch.zeros(batch_size, out_channels, out_h, out_w).to(input.device)
weight = weight.view(1, out_channels, in_channels // groups, kernel_h, kernel_w).repeat(batch_size, 1, 1, 1, 1)
for h in range(out_h):
for w in range(out_w):
input_region = input_unfolded[:, :, h, w].view(batch_size, -1, 1, 1)
offset_region = offset[:, :, :, h, w]
weight_region = weight
output_region = F.conv2d(input_region, weight_region, bias=None, stride=1, padding=0, dilation=1, groups=deformable_groups)
output_region = deformable_conv2d_compute(output_region, offset_region)
output[:, :, h, w] = output_region.squeeze()
if bias is not None:
output + = bias.view(1, -1, 1, 1)
return output
Among them, the deformable_conv2d_compute function is the calculation function of deformable convolution. Its code is as follows:
def deformable_conv2d_compute(input, offset):
# get shapes and parameters
batch_size, out_channels, out_h, out_w = input.size()
in_channels = offset.size(1) // 2
# sample input according to offset
grid_h = torch.linspace(-1, 1, out_h).view(1, 1, out_h, 1).to(input.device)
grid_w = torch.linspace(-1, 1, out_w).view(1, 1, 1, out_w).to(input.device)
offset_h = offset[:, :in_channels, :, :]
offset_w = offset[:, in_channels:, :, :]
sample_h = torch.add(grid_h, offset_h)
sample_w = torch.add(grid_w, offset_w)
sample_h = sample_h.clamp(-1, 1)
sample_w = sample_w.clamp(-1, 1)
sample_h = ((sample_h + 1) / 2) * (out_h - 1)
sample_w = ((sample_w + 1) / 2) * (out_w - 1)
sample_h_floor = sample_h.floor().long()
sample_w_floor = sample_w.floor().long()
sample_h_ceil = sample_h.ceil().long()
sample_w_ceil = sample_w.ceil().long()
sample_h_floor = sample_h_floor.clamp(0, out_h - 1)
sample_w_floor = sample_w_floor.clamp(0, out_w - 1)
sample_h_ceil = sample_h_ceil.clamp(0, out_h - 1)
sample_w_ceil = sample_w_ceil.clamp(0, out_w - 1)
# gather input values according to sampled indices
input_flat = input.view(batch_size, in_channels, out_h * out_w)
index_base = torch.arange(0, batch_size, device=input.device).view(batch_size, 1, 1) * out_h * out_w
index_base = index_base.expand(batch_size, in_channels, out_h * out_w)
index_offset = torch.arange(0, out_h * out_w, device=input.device).view(1, 1, -1)
index_offset = index_offset.expand(batch_size, in_channels, out_h * out_w)
indices_a = (sample_h_floor + index_base + index_offset).view(batch_size, in_channels * out_h * out_w)
indices_b = (sample_w_floor + index_base + index_offset).view(batch_size, in_channels * out_h * out_w)
indices_c = (sample_h_ceil + index_base + index_offset).view(batch_size, in_channels * out_h * out_w)
indices_d = (sample_w_ceil + index_base + index_offset).view(batch_size, in_channels * out_h * out_w)
value_a = input_flat.gather(2, indices_a.unsqueeze(1).repeat(1, out_channels, 1))
value_b = input_flat.gather(2, indices_b.unsqueeze(1).repeat(1, out_channels, 1))
value_c = input_flat.gather(2, indices_c.unsqueeze(1).repeat(1, out_channels, 1))
value_d = input_flat.gather(2, indices_d.unsqueeze(1).repeat(1, out_channels, 1))
# calculate interpolation weights and output
w_a = ((sample_w_ceil - sample_w) * (sample_h_ceil - sample_h)).view(batch_size, 1, out_h, out_w)
w_b = ((sample_w - sample_w_floor) * (sample_h_ceil - sample_h)).view(batch_size, 1, out_h, out_w)
w_c = ((sample_w_ceil - sample_w) * (sample_h - sample_h_floor)).view(batch_size, 1, out_h, out_w)
w_d = ((sample_w - sample_w_floor) * (sample_h - sample_h_floor)).view(batch_size, 1, out_h, out_w)
output = w_a * value_a + w_b * value_b + w_c * value_c + w_d * value_d
return output
Finally, you can use the following code to test the network:
net = LaneDetectionNet(num_classes=1, deformable_groups=2) # create the network input = torch.randn(1, 3, 100, 100) # create a random input tensor output = net(input) # feed it through the network print(output.shape) # print the output shape
The output should be (1, 1, 1, 1). This shows that the network has successfully compressed the 100*100 pixel image into a scalar. It can be adjusted and optimized according to the actual situation to achieve better performance.