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model.py
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import torch # Tensors and Dynamic neural networks in Python with strong GPU acceleration
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models import resnet # torchvision is image and video datasets and models for torch deep learning
def init_weights(m):
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
m.weight.data.normal_(0, 1e-3)
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.ConvTranspose2d):
m.weight.data.normal_(0, 1e-3)
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def conv_bn_relu(in_channels, out_channels, kernel_size, \
stride=1, padding=0, bn=True, relu=True):
bias = not bn
layers = []
layers.append(
nn.Conv2d(in_channels,
out_channels,
kernel_size,
stride,
padding,
bias=bias))
if bn:
layers.append(nn.BatchNorm2d(out_channels))
if relu:
layers.append(nn.LeakyReLU(0.2, inplace=True))
layers = nn.Sequential(*layers)
# initialize the weights
for m in layers.modules():
init_weights(m)
return layers
def convt_bn_relu(in_channels, out_channels, kernel_size, \
stride=1, padding=0, output_padding=0, bn=True, relu=True):
bias = not bn
layers = []
layers.append(
nn.ConvTranspose2d(in_channels,
out_channels,
kernel_size,
stride,
padding,
output_padding,
bias=bias))
if bn:
layers.append(nn.BatchNorm2d(out_channels))
if relu:
layers.append(nn.LeakyReLU(0.2, inplace=True))
layers = nn.Sequential(*layers)
# initialize the weights
for m in layers.modules():
init_weights(m)
return layers
class DepthCompletionNet(nn.Module):
def __init__(self, args):
assert (
args.layers in [18, 34, 50, 101, 152]
), 'Only layers 18, 34, 50, 101, and 152 are defined, but got {}'.format(
args.layers)
super(DepthCompletionNet, self).__init__()
self.modality = args.input
if 'd' in self.modality:
channels = 64 // len(self.modality)
self.conv1_d = conv_bn_relu(1,
channels,
kernel_size=3,
stride=1,
padding=1)
if 'rgb' in self.modality:
channels = 64 * 3 // len(self.modality)
self.conv1_img = conv_bn_relu(3,
channels,
kernel_size=3,
stride=1,
padding=1)
elif 'g' in self.modality:
channels = 64 // len(self.modality)
self.conv1_img = conv_bn_relu(1,
channels,
kernel_size=3,
stride=1,
padding=1)
pretrained_model = resnet.__dict__['resnet{}'.format(
args.layers)](pretrained=args.pretrained)
if not args.pretrained:
pretrained_model.apply(init_weights)
#self.maxpool = pretrained_model._modules['maxpool']
self.conv2 = pretrained_model._modules['layer1']
self.conv3 = pretrained_model._modules['layer2']
self.conv4 = pretrained_model._modules['layer3']
self.conv5 = pretrained_model._modules['layer4']
del pretrained_model # clear memory
# define number of intermediate channels
if args.layers <= 34:
num_channels = 512
elif args.layers >= 50:
num_channels = 2048
self.conv6 = conv_bn_relu(num_channels,
512,
kernel_size=3,
stride=2,
padding=1)
# decoding layers
kernel_size = 3
stride = 2
self.convt5 = convt_bn_relu(in_channels=512,
out_channels=256,
kernel_size=kernel_size,
stride=stride,
padding=1,
output_padding=1)
self.convt4 = convt_bn_relu(in_channels=768,
out_channels=128,
kernel_size=kernel_size,
stride=stride,
padding=1,
output_padding=1)
self.convt3 = convt_bn_relu(in_channels=(256 + 128),
out_channels=64,
kernel_size=kernel_size,
stride=stride,
padding=1,
output_padding=1)
self.convt2 = convt_bn_relu(in_channels=(128 + 64),
out_channels=64,
kernel_size=kernel_size,
stride=stride,
padding=1,
output_padding=1)
self.convt1 = convt_bn_relu(in_channels=128,
out_channels=64,
kernel_size=kernel_size,
stride=1,
padding=1)
self.convtf = conv_bn_relu(in_channels=128,
out_channels=1,
kernel_size=1,
stride=1,
bn=False,
relu=False)
def forward(self, x):
# first layer
if 'd' in self.modality:
conv1_d = self.conv1_d(x['d'])
if 'rgb' in self.modality:
conv1_img = self.conv1_img(x['rgb'])
elif 'g' in self.modality:
conv1_img = self.conv1_img(x['g'])
if self.modality == 'rgbd' or self.modality == 'gd':
conv1 = torch.cat((conv1_d, conv1_img), 1)
else:
conv1 = conv1_d if (self.modality == 'd') else conv1_img
conv2 = self.conv2(conv1)
conv3 = self.conv3(conv2) # batchsize * ? * 176 * 608
conv4 = self.conv4(conv3) # batchsize * ? * 88 * 304
conv5 = self.conv5(conv4) # batchsize * ? * 44 * 152
conv6 = self.conv6(conv5) # batchsize * ? * 22 * 76
# decoder
convt5 = self.convt5(conv6)
y = torch.cat((convt5, conv5), 1)
convt4 = self.convt4(y)
y = torch.cat((convt4, conv4), 1)
convt3 = self.convt3(y)
y = torch.cat((convt3, conv3), 1)
convt2 = self.convt2(y)
y = torch.cat((convt2, conv2), 1)
convt1 = self.convt1(y)
y = torch.cat((convt1, conv1), 1)
y = self.convtf(y)
if self.training:
return 100 * y # output can be nagative & zero
else: # during eval
min_distance = 0.9
return F.relu(
100 * y - min_distance
) + min_distance # the minimum range of Velodyne is around 3 feet ~= 0.9m