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RedNet_model.py
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RedNet_model.py
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import torch
from torch import nn
import math
import torch.utils.model_zoo as model_zoo
from utils import utils
from torch.utils.checkpoint import checkpoint
class RedNet(nn.Module):
def __init__(self, num_classes=37, pretrained=False):
super(RedNet, self).__init__()
block = Bottleneck
transblock = TransBasicBlock
layers = [3, 4, 6, 3]
# original resnet
self.inplanes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
# resnet for depth channel
self.inplanes = 64
self.conv1_d = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1_d = nn.BatchNorm2d(64)
self.layer1_d = self._make_layer(block, 64, layers[0])
self.layer2_d = self._make_layer(block, 128, layers[1], stride=2)
self.layer3_d = self._make_layer(block, 256, layers[2], stride=2)
self.layer4_d = self._make_layer(block, 512, layers[3], stride=2)
self.inplanes = 512
self.deconv1 = self._make_transpose(transblock, 256, 6, stride=2)
self.deconv2 = self._make_transpose(transblock, 128, 4, stride=2)
self.deconv3 = self._make_transpose(transblock, 64, 3, stride=2)
self.deconv4 = self._make_transpose(transblock, 64, 3, stride=2)
self.agant0 = self._make_agant_layer(64, 64)
self.agant1 = self._make_agant_layer(64 * 4, 64)
self.agant2 = self._make_agant_layer(128 * 4, 128)
self.agant3 = self._make_agant_layer(256 * 4, 256)
self.agant4 = self._make_agant_layer(512 * 4, 512)
# final block
self.inplanes = 64
self.final_conv = self._make_transpose(transblock, 64, 3)
self.final_deconv = nn.ConvTranspose2d(self.inplanes, num_classes, kernel_size=2,
stride=2, padding=0, bias=True)
self.out5_conv = nn.Conv2d(256, num_classes, kernel_size=1, stride=1, bias=True)
self.out4_conv = nn.Conv2d(128, num_classes, kernel_size=1, stride=1, bias=True)
self.out3_conv = nn.Conv2d(64, num_classes, kernel_size=1, stride=1, bias=True)
self.out2_conv = nn.Conv2d(64, num_classes, kernel_size=1, stride=1, bias=True)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
if pretrained:
self._load_resnet_pretrained()
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def _make_transpose(self, block, planes, blocks, stride=1):
upsample = None
if stride != 1:
upsample = nn.Sequential(
nn.ConvTranspose2d(self.inplanes, planes,
kernel_size=2, stride=stride,
padding=0, bias=False),
nn.BatchNorm2d(planes),
)
elif self.inplanes != planes:
upsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes),
)
layers = []
for i in range(1, blocks):
layers.append(block(self.inplanes, self.inplanes))
layers.append(block(self.inplanes, planes, stride, upsample))
self.inplanes = planes
return nn.Sequential(*layers)
def _make_agant_layer(self, inplanes, planes):
layers = nn.Sequential(
nn.Conv2d(inplanes, planes, kernel_size=1,
stride=1, padding=0, bias=False),
nn.BatchNorm2d(planes),
nn.ReLU(inplace=True)
)
return layers
def _load_resnet_pretrained(self):
pretrain_dict = model_zoo.load_url(utils.model_urls['resnet50'])
model_dict = {}
state_dict = self.state_dict()
for k, v in pretrain_dict.items():
if k in state_dict:
if k.startswith('conv1'): # the first conv_op
model_dict[k] = v
model_dict[k.replace('conv1', 'conv1_d')] = torch.mean(v, 1).data. \
view_as(state_dict[k.replace('conv1', 'conv1_d')])
elif k.startswith('bn1'):
model_dict[k] = v
model_dict[k.replace('bn1', 'bn1_d')] = v
elif k.startswith('layer'):
model_dict[k] = v
model_dict[k[:6] + '_d' + k[6:]] = v
state_dict.update(model_dict)
self.load_state_dict(state_dict)
def forward_downsample(self, rgb, depth):
x = self.conv1(rgb)
x = self.bn1(x)
x = self.relu(x)
depth = self.conv1_d(depth)
depth = self.bn1_d(depth)
depth = self.relu(depth)
fuse0 = x + depth
x = self.maxpool(fuse0)
depth = self.maxpool(depth)
# block 1
x = self.layer1(x)
depth = self.layer1_d(depth)
fuse1 = x + depth
# block 2
x = self.layer2(fuse1)
depth = self.layer2_d(depth)
fuse2 = x + depth
# block 3
x = self.layer3(fuse2)
depth = self.layer3_d(depth)
fuse3 = x + depth
# block 4
x = self.layer4(fuse3)
depth = self.layer4_d(depth)
fuse4 = x + depth
return fuse0, fuse1, fuse2, fuse3, fuse4
def forward_upsample(self, fuse0, fuse1, fuse2, fuse3, fuse4):
agant4 = self.agant4(fuse4)
# upsample 1
x = self.deconv1(agant4)
if self.training:
out5 = self.out5_conv(x)
x = x + self.agant3(fuse3)
# upsample 2
x = self.deconv2(x)
if self.training:
out4 = self.out4_conv(x)
x = x + self.agant2(fuse2)
# upsample 3
x = self.deconv3(x)
if self.training:
out3 = self.out3_conv(x)
x = x + self.agant1(fuse1)
# upsample 4
x = self.deconv4(x)
if self.training:
out2 = self.out2_conv(x)
x = x + self.agant0(fuse0)
# final
x = self.final_conv(x)
out = self.final_deconv(x)
if self.training:
return out, out2, out3, out4, out5
return out
def forward(self, rgb, depth, phase_checkpoint=False):
if phase_checkpoint:
depth.requires_grad_()
fuses = checkpoint(self.forward_downsample, rgb, depth)
out = checkpoint(self.forward_upsample, *fuses)
else:
fuses = self.forward_downsample(rgb, depth)
out = self.forward_upsample(*fuses)
return out
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class TransBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, upsample=None, **kwargs):
super(TransBasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, inplanes)
self.bn1 = nn.BatchNorm2d(inplanes)
self.relu = nn.ReLU(inplace=True)
if upsample is not None and stride != 1:
self.conv2 = nn.ConvTranspose2d(inplanes, planes,
kernel_size=3, stride=stride, padding=1,
output_padding=1, bias=False)
else:
self.conv2 = conv3x3(inplanes, planes, stride)
self.bn2 = nn.BatchNorm2d(planes)
self.upsample = upsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.upsample is not None:
residual = self.upsample(x)
out += residual
out = self.relu(out)
return out