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psmnet.py
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psmnet.py
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import math
import cv2
import torch
import numpy as np
from PIL import Image
import torch.nn as nn
import torch.utils.data
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision.transforms as transforms
def convbn(in_planes, out_planes, kernel_size, stride, pad, dilation):
return nn.Sequential(nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=dilation if dilation > 1 else pad, dilation = dilation, bias=False),
nn.BatchNorm2d(out_planes))
def convbn_3d(in_planes, out_planes, kernel_size, stride, pad):
return nn.Sequential(nn.Conv3d(in_planes, out_planes, kernel_size=kernel_size, padding=pad, stride=stride,bias=False),
nn.BatchNorm3d(out_planes))
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride, downsample, pad, dilation):
super(BasicBlock, self).__init__()
self.conv1 = nn.Sequential(convbn(inplanes, planes, 3, stride, pad, dilation),
nn.ReLU(inplace=True))
self.conv2 = convbn(planes, planes, 3, 1, pad, dilation)
self.downsample = downsample
self.stride = stride
def forward(self, x):
out = self.conv1(x)
out = self.conv2(out)
if self.downsample is not None:
x = self.downsample(x)
out += x
return out
class disparityregression(nn.Module):
def __init__(self, maxdisp):
super(disparityregression, self).__init__()
self.disp = torch.Tensor(np.reshape(np.array(range(maxdisp)),[1, maxdisp,1,1])).cuda()
def forward(self, x):
out = torch.sum(x*self.disp.data,1, keepdim=True)
return out
class feature_extraction(nn.Module):
def __init__(self):
super(feature_extraction, self).__init__()
self.inplanes = 32
self.firstconv = nn.Sequential(convbn(3, 32, 3, 2, 1, 1),
nn.ReLU(inplace=True),
convbn(32, 32, 3, 1, 1, 1),
nn.ReLU(inplace=True),
convbn(32, 32, 3, 1, 1, 1),
nn.ReLU(inplace=True))
self.layer1 = self._make_layer(BasicBlock, 32, 3, 1,1,1)
self.layer2 = self._make_layer(BasicBlock, 64, 16, 2,1,1)
self.layer3 = self._make_layer(BasicBlock, 128, 3, 1,1,1)
self.layer4 = self._make_layer(BasicBlock, 128, 3, 1,1,2)
self.branch1 = nn.Sequential(nn.AvgPool2d((64, 64), stride=(64,64)),
convbn(128, 32, 1, 1, 0, 1),
nn.ReLU(inplace=True))
self.branch2 = nn.Sequential(nn.AvgPool2d((32, 32), stride=(32,32)),
convbn(128, 32, 1, 1, 0, 1),
nn.ReLU(inplace=True))
self.branch3 = nn.Sequential(nn.AvgPool2d((16, 16), stride=(16,16)),
convbn(128, 32, 1, 1, 0, 1),
nn.ReLU(inplace=True))
self.branch4 = nn.Sequential(nn.AvgPool2d((8, 8), stride=(8,8)),
convbn(128, 32, 1, 1, 0, 1),
nn.ReLU(inplace=True))
self.lastconv = nn.Sequential(convbn(320, 128, 3, 1, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(128, 32, kernel_size=1, padding=0, stride = 1, bias=False))
def _make_layer(self, block, planes, blocks, stride, pad, dilation):
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, pad, dilation))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes,1,None,pad,dilation))
return nn.Sequential(*layers)
def forward(self, x):
output = self.firstconv(x)
output = self.layer1(output)
output_raw = self.layer2(output)
output = self.layer3(output_raw)
output_skip = self.layer4(output)
output_branch1 = self.branch1(output_skip)
output_branch1 = F.upsample(output_branch1, (output_skip.size()[2],output_skip.size()[3]),mode='bilinear')
output_branch2 = self.branch2(output_skip)
output_branch2 = F.upsample(output_branch2, (output_skip.size()[2],output_skip.size()[3]),mode='bilinear')
output_branch3 = self.branch3(output_skip)
output_branch3 = F.upsample(output_branch3, (output_skip.size()[2],output_skip.size()[3]),mode='bilinear')
output_branch4 = self.branch4(output_skip)
output_branch4 = F.upsample(output_branch4, (output_skip.size()[2],output_skip.size()[3]),mode='bilinear')
output_feature = torch.cat((output_raw, output_skip, output_branch4, output_branch3, output_branch2, output_branch1), 1)
output_feature = self.lastconv(output_feature)
return output_feature
class hourglass(nn.Module):
def __init__(self, inplanes):
super(hourglass, self).__init__()
self.conv1 = nn.Sequential(
convbn_3d(inplanes, inplanes*2, kernel_size=3, stride=2, pad=1),
nn.ReLU(inplace=True))
self.conv2 = convbn_3d(inplanes*2, inplanes*2, kernel_size=3, stride=1, pad=1)
self.conv3 = nn.Sequential(
convbn_3d(inplanes*2, inplanes*2, kernel_size=3, stride=2, pad=1),
nn.ReLU(inplace=True))
self.conv4 = nn.Sequential(
convbn_3d(inplanes*2, inplanes*2, kernel_size=3, stride=1, pad=1),
nn.ReLU(inplace=True))
self.conv5 = nn.Sequential(
nn.ConvTranspose3d(inplanes*2, inplanes*2, kernel_size=3, padding=1, output_padding=1, stride=2,bias=False),
nn.BatchNorm3d(inplanes*2)) #+conv2
self.conv6 = nn.Sequential(
nn.ConvTranspose3d(inplanes*2, inplanes, kernel_size=3, padding=1, output_padding=1, stride=2,bias=False),
nn.BatchNorm3d(inplanes)) #+x
def forward(self, x ,presqu, postsqu):
out = self.conv1(x) #in:1/4 out:1/8
pre = self.conv2(out) #in:1/8 out:1/8
if postsqu is not None:
pre = F.relu(pre + postsqu, inplace=True)
else:
pre = F.relu(pre, inplace=True)
out = self.conv3(pre) #in:1/8 out:1/16
out = self.conv4(out) #in:1/16 out:1/16
if presqu is not None:
post = F.relu(self.conv5(out)+presqu, inplace=True) #in:1/16 out:1/8
else:
post = F.relu(self.conv5(out)+pre, inplace=True)
out = self.conv6(post) #in:1/8 out:1/4
return out, pre, post
class PSMNet(nn.Module):
def __init__(self, maxdisp):
super(PSMNet, self).__init__()
self.maxdisp = maxdisp
self.feature_extraction = feature_extraction()
self.dres0 = nn.Sequential(convbn_3d(64, 32, 3, 1, 1),
nn.ReLU(inplace=True),
convbn_3d(32, 32, 3, 1, 1),
nn.ReLU(inplace=True))
self.dres1 = nn.Sequential(convbn_3d(32, 32, 3, 1, 1),
nn.ReLU(inplace=True),
convbn_3d(32, 32, 3, 1, 1))
self.dres2 = hourglass(32)
self.dres3 = hourglass(32)
self.dres4 = hourglass(32)
self.classif1 = nn.Sequential(convbn_3d(32, 32, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv3d(32, 1, kernel_size=3, padding=1, stride=1,bias=False))
self.classif2 = nn.Sequential(convbn_3d(32, 32, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv3d(32, 1, kernel_size=3, padding=1, stride=1,bias=False))
self.classif3 = nn.Sequential(convbn_3d(32, 32, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv3d(32, 1, kernel_size=3, padding=1, stride=1,bias=False))
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.Conv3d):
n = m.kernel_size[0] * m.kernel_size[1]*m.kernel_size[2] * 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_()
elif isinstance(m, nn.BatchNorm3d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
m.bias.data.zero_()
def forward(self, left, right):
refimg_fea = self.feature_extraction(left)
targetimg_fea = self.feature_extraction(right)
# matching
cost = Variable(torch.FloatTensor(refimg_fea.size()[0], refimg_fea.size()[1]*2, self.maxdisp//4, refimg_fea.size()[2], refimg_fea.size()[3]).zero_()).cuda()
for i in range(self.maxdisp//4):
if i > 0 :
cost[:, :refimg_fea.size()[1], i, :,i:] = refimg_fea[:,:,:,i:]
cost[:, refimg_fea.size()[1]:, i, :,i:] = targetimg_fea[:,:,:,:-i]
else:
cost[:, :refimg_fea.size()[1], i, :,:] = refimg_fea
cost[:, refimg_fea.size()[1]:, i, :,:] = targetimg_fea
cost = cost.contiguous()
cost0 = self.dres0(cost)
cost0 = self.dres1(cost0) + cost0
out1, pre1, post1 = self.dres2(cost0, None, None)
out1 = out1+cost0
out2, pre2, post2 = self.dres3(out1, pre1, post1)
out2 = out2+cost0
out3, pre3, post3 = self.dres4(out2, pre1, post2)
out3 = out3+cost0
cost1 = self.classif1(out1)
cost2 = self.classif2(out2) + cost1
cost3 = self.classif3(out3) + cost2
if self.training:
cost1 = F.upsample(cost1, [self.maxdisp,left.size()[2],left.size()[3]], mode='trilinear')
cost2 = F.upsample(cost2, [self.maxdisp,left.size()[2],left.size()[3]], mode='trilinear')
cost1 = torch.squeeze(cost1,1)
pred1 = F.softmax(cost1,dim=1)
pred1 = disparityregression(self.maxdisp)(pred1)
cost2 = torch.squeeze(cost2,1)
pred2 = F.softmax(cost2,dim=1)
pred2 = disparityregression(self.maxdisp)(pred2)
cost3 = F.upsample(cost3, [self.maxdisp,left.size()[2],left.size()[3]], mode='trilinear')
cost3 = torch.squeeze(cost3,1)
pred3 = F.softmax(cost3,dim=1)
#For your information: This formulation 'softmax(c)' learned "similarity"
#while 'softmax(-c)' learned 'matching cost' as mentioned in the paper.
#However, sign does not affect the performance because feature-based cost volume provided flexibility.
pred3 = disparityregression(self.maxdisp)(pred3)
if self.training:
return pred1, pred2, pred3
else:
return pred3
def predict(model, imgL, imgR):
"""
Make a predicition of disparity map using provided model.
"""
model.eval()
imgL = imgL.cuda()
imgR = imgR.cuda()
with torch.no_grad():
disp = model(imgL,imgR)
disp = torch.squeeze(disp)
pred_disp = disp.data.cpu().numpy()
return pred_disp
def run_psmnet(model, leftimg, rightimg, outputimg):
"""
Running PSMNet given the params. Write and return answer.
"""
normal_mean_var = {'mean': [0.485, 0.456, 0.406],
'std': [0.229, 0.224, 0.225]}
infer_transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize(**normal_mean_var)])
imgL_o = Image.open(leftimg).convert('RGB')
imgR_o = Image.open(rightimg).convert('RGB')
imgL = infer_transform(imgL_o)
imgR = infer_transform(imgR_o)
# pad to width and hight to 16 times
if imgL.shape[1] % 16 != 0:
times = imgL.shape[1]//16
top_pad = (times+1)*16 -imgL.shape[1]
else:
top_pad = 0
if imgL.shape[2] % 16 != 0:
times = imgL.shape[2]//16
right_pad = (times+1)*16-imgL.shape[2]
else:
right_pad = 0
imgL = F.pad(imgL,(0,right_pad, top_pad,0)).unsqueeze(0)
imgR = F.pad(imgR,(0,right_pad, top_pad,0)).unsqueeze(0)
pred_disp = predict(model, imgL,imgR)
if top_pad !=0 and right_pad != 0:
img = pred_disp[top_pad:,:-right_pad]
elif top_pad ==0 and right_pad != 0:
img = pred_disp[:,:-right_pad]
elif top_pad !=0 and right_pad == 0:
img = pred_disp[top_pad:,:]
else:
img = pred_disp
img = (img*192).astype('uint16')
img = Image.fromarray(img)
img = cv2.normalize(img, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)
img.save(outputimg)
return cv2.imread(outputimg, cv2.IMREAD_GRAYSCALE)