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run.py
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run.py
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#!/usr/bin/env python
import torch
import torch.utils.serialization
import getopt
import math
import numpy
import os
import PIL
import PIL.Image
import sys
try:
from correlation import correlation # the custom cost volume layer
except:
sys.path.insert(0, './correlation'); import correlation # you should consider upgrading python
# end
##########################################################
assert(int(torch.__version__.replace('.', '')) >= 40) # requires at least pytorch version 0.4.0
torch.set_grad_enabled(False) # make sure to not compute gradients for computational performance
torch.cuda.device(1) # change this if you have a multiple graphics cards and you want to utilize them
torch.backends.cudnn.enabled = True # make sure to use cudnn for computational performance
##########################################################
arguments_strModel = 'default'
arguments_strFirst = './images/first.png'
arguments_strSecond = './images/second.png'
arguments_strOut = './out.flo'
for strOption, strArgument in getopt.getopt(sys.argv[1:], '', [ strParameter[2:] + '=' for strParameter in sys.argv[1::2] ])[0]:
if strOption == '--model' and strArgument != '': arguments_strModel = strArgument # which model to use
if strOption == '--first' and strArgument != '': arguments_strFirst = strArgument # path to the first frame
if strOption == '--second' and strArgument != '': arguments_strSecond = strArgument # path to the second frame
if strOption == '--out' and strArgument != '': arguments_strOut = strArgument # path to where the output should be stored
# end
##########################################################
class Network(torch.nn.Module):
def __init__(self):
super(Network, self).__init__()
class Extractor(torch.nn.Module):
def __init__(self):
super(Extractor, self).__init__()
self.moduleOne = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=2, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, stride=1, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, stride=1, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
)
self.moduleTwo = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=2, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
)
self.moduleThr = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=2, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
)
self.moduleFou = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=64, out_channels=96, kernel_size=3, stride=2, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=96, out_channels=96, kernel_size=3, stride=1, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=96, out_channels=96, kernel_size=3, stride=1, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
)
self.moduleFiv = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=96, out_channels=128, kernel_size=3, stride=2, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
)
self.moduleSix = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=128, out_channels=196, kernel_size=3, stride=2, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=196, out_channels=196, kernel_size=3, stride=1, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=196, out_channels=196, kernel_size=3, stride=1, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
)
# end
def forward(self, tensorInput):
tensorOne = self.moduleOne(tensorInput)
tensorTwo = self.moduleTwo(tensorOne)
tensorThr = self.moduleThr(tensorTwo)
tensorFou = self.moduleFou(tensorThr)
tensorFiv = self.moduleFiv(tensorFou)
tensorSix = self.moduleSix(tensorFiv)
return [ tensorOne, tensorTwo, tensorThr, tensorFou, tensorFiv, tensorSix ]
# end
# end
class Backward(torch.nn.Module):
def __init__(self):
super(Backward, self).__init__()
# end
def forward(self, tensorInput, tensorFlow):
if hasattr(self, 'tensorPartial') == False or self.tensorPartial.size(0) != tensorFlow.size(0) or self.tensorPartial.size(2) != tensorFlow.size(2) or self.tensorPartial.size(3) != tensorFlow.size(3):
self.tensorPartial = tensorFlow.new_ones(tensorFlow.size(0), 1, tensorFlow.size(2), tensorFlow.size(3))
# end
if hasattr(self, 'tensorGrid') == False or self.tensorGrid.size(0) != tensorFlow.size(0) or self.tensorGrid.size(2) != tensorFlow.size(2) or self.tensorGrid.size(3) != tensorFlow.size(3):
tensorHorizontal = torch.linspace(-1.0, 1.0, tensorFlow.size(3)).view(1, 1, 1, tensorFlow.size(3)).expand(tensorFlow.size(0), -1, tensorFlow.size(2), -1)
tensorVertical = torch.linspace(-1.0, 1.0, tensorFlow.size(2)).view(1, 1, tensorFlow.size(2), 1).expand(tensorFlow.size(0), -1, -1, tensorFlow.size(3))
self.tensorGrid = torch.cat([ tensorHorizontal, tensorVertical ], 1).cuda()
# end
tensorInput = torch.cat([ tensorInput, self.tensorPartial ], 1)
tensorFlow = torch.cat([ tensorFlow[:, 0:1, :, :] / ((tensorInput.size(3) - 1.0) / 2.0), tensorFlow[:, 1:2, :, :] / ((tensorInput.size(2) - 1.0) / 2.0) ], 1)
tensorOutput = torch.nn.functional.grid_sample(input=tensorInput, grid=(self.tensorGrid + tensorFlow).permute(0, 2, 3, 1), mode='bilinear', padding_mode='zeros')
tensorMask = tensorOutput[:, -1:, :, :]; tensorMask[tensorMask > 0.999] = 1.0; tensorMask[tensorMask < 1.0] = 0.0
return tensorOutput[:, :-1, :, :] * tensorMask
# end
# end
class Decoder(torch.nn.Module):
def __init__(self, intLevel):
super(Decoder, self).__init__()
intPrevious = [ None, None, 81 + 32 + 2 + 2, 81 + 64 + 2 + 2, 81 + 96 + 2 + 2, 81 + 128 + 2 + 2, 81, None ][intLevel + 1]
intCurrent = [ None, None, 81 + 32 + 2 + 2, 81 + 64 + 2 + 2, 81 + 96 + 2 + 2, 81 + 128 + 2 + 2, 81, None ][intLevel + 0]
if intLevel < 6: self.moduleUpflow = torch.nn.ConvTranspose2d(in_channels=2, out_channels=2, kernel_size=4, stride=2, padding=1)
if intLevel < 6: self.moduleUpfeat = torch.nn.ConvTranspose2d(in_channels=intPrevious + 128 + 128 + 96 + 64 + 32, out_channels=2, kernel_size=4, stride=2, padding=1)
if intLevel < 6: self.dblBackward = [ None, None, None, 5.0, 2.5, 1.25, 0.625, None ][intLevel + 1]
if intLevel < 6: self.moduleBackward = Backward()
self.moduleCorrelation = correlation.ModuleCorrelation()
self.moduleCorreleaky = torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
self.moduleOne = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=intCurrent, out_channels=128, kernel_size=3, stride=1, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
)
self.moduleTwo = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=intCurrent + 128, out_channels=128, kernel_size=3, stride=1, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
)
self.moduleThr = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=intCurrent + 128 + 128, out_channels=96, kernel_size=3, stride=1, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
)
self.moduleFou = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=intCurrent + 128 + 128 + 96, out_channels=64, kernel_size=3, stride=1, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
)
self.moduleFiv = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=intCurrent + 128 + 128 + 96 + 64, out_channels=32, kernel_size=3, stride=1, padding=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
)
self.moduleSix = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=intCurrent + 128 + 128 + 96 + 64 + 32, out_channels=2, kernel_size=3, stride=1, padding=1)
)
# end
def forward(self, tensorFirst, tensorSecond, objectPrevious):
tensorFlow = None
tensorFeat = None
if objectPrevious is None:
tensorFlow = None
tensorFeat = None
tensorVolume = self.moduleCorreleaky(self.moduleCorrelation(tensorFirst, tensorSecond))
tensorFeat = torch.cat([ tensorVolume ], 1)
elif objectPrevious is not None:
tensorFlow = self.moduleUpflow(objectPrevious['tensorFlow'])
tensorFeat = self.moduleUpfeat(objectPrevious['tensorFeat'])
tensorVolume = self.moduleCorreleaky(self.moduleCorrelation(tensorFirst, self.moduleBackward(tensorSecond, tensorFlow * self.dblBackward)))
tensorFeat = torch.cat([ tensorVolume, tensorFirst, tensorFlow, tensorFeat ], 1)
# end
tensorFeat = torch.cat([ self.moduleOne(tensorFeat), tensorFeat ], 1)
tensorFeat = torch.cat([ self.moduleTwo(tensorFeat), tensorFeat ], 1)
tensorFeat = torch.cat([ self.moduleThr(tensorFeat), tensorFeat ], 1)
tensorFeat = torch.cat([ self.moduleFou(tensorFeat), tensorFeat ], 1)
tensorFeat = torch.cat([ self.moduleFiv(tensorFeat), tensorFeat ], 1)
tensorFlow = self.moduleSix(tensorFeat)
return {
'tensorFlow': tensorFlow,
'tensorFeat': tensorFeat
}
# end
# end
class Refiner(torch.nn.Module):
def __init__(self):
super(Refiner, self).__init__()
self.moduleMain = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=81 + 32 + 2 + 2 + 128 + 128 + 96 + 64 + 32, out_channels=128, kernel_size=3, stride=1, padding=1, dilation=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=2, dilation=2),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=4, dilation=4),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=128, out_channels=96, kernel_size=3, stride=1, padding=8, dilation=8),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=96, out_channels=64, kernel_size=3, stride=1, padding=16, dilation=16),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=64, out_channels=32, kernel_size=3, stride=1, padding=1, dilation=1),
torch.nn.LeakyReLU(inplace=False, negative_slope=0.1),
torch.nn.Conv2d(in_channels=32, out_channels=2, kernel_size=3, stride=1, padding=1, dilation=1)
)
# end
def forward(self, tensorInput):
return self.moduleMain(tensorInput)
# end
# end
self.moduleExtractor = Extractor()
self.moduleTwo = Decoder(2)
self.moduleThr = Decoder(3)
self.moduleFou = Decoder(4)
self.moduleFiv = Decoder(5)
self.moduleSix = Decoder(6)
self.moduleRefiner = Refiner()
self.load_state_dict(torch.load('./network-' + arguments_strModel + '.pytorch'))
# end
def forward(self, tensorFirst, tensorSecond):
tensorFirst = self.moduleExtractor(tensorFirst)
tensorSecond = self.moduleExtractor(tensorSecond)
objectEstimate = self.moduleSix(tensorFirst[-1], tensorSecond[-1], None)
objectEstimate = self.moduleFiv(tensorFirst[-2], tensorSecond[-2], objectEstimate)
objectEstimate = self.moduleFou(tensorFirst[-3], tensorSecond[-3], objectEstimate)
objectEstimate = self.moduleThr(tensorFirst[-4], tensorSecond[-4], objectEstimate)
objectEstimate = self.moduleTwo(tensorFirst[-5], tensorSecond[-5], objectEstimate)
return objectEstimate['tensorFlow'] + self.moduleRefiner(objectEstimate['tensorFeat'])
# end
# end
moduleNetwork = Network().cuda().eval()
##########################################################
def estimate(tensorFirst, tensorSecond):
tensorOutput = torch.FloatTensor()
assert(tensorFirst.size(1) == tensorSecond.size(1))
assert(tensorFirst.size(2) == tensorSecond.size(2))
intWidth = tensorFirst.size(2)
intHeight = tensorFirst.size(1)
assert(intWidth == 1024) # remember that there is no guarantee for correctness, comment this line out if you acknowledge this and want to continue
assert(intHeight == 436) # remember that there is no guarantee for correctness, comment this line out if you acknowledge this and want to continue
if True:
tensorFirst = tensorFirst.cuda()
tensorSecond = tensorSecond.cuda()
tensorOutput = tensorOutput.cuda()
# end
if True:
tensorPreprocessedFirst = tensorFirst.view(1, 3, intHeight, intWidth)
tensorPreprocessedSecond = tensorSecond.view(1, 3, intHeight, intWidth)
intPreprocessedWidth = int(math.floor(math.ceil(intWidth / 64.0) * 64.0))
intPreprocessedHeight = int(math.floor(math.ceil(intHeight / 64.0) * 64.0))
tensorPreprocessedFirst = torch.nn.functional.interpolate(input=tensorPreprocessedFirst, size=(intPreprocessedHeight, intPreprocessedWidth), mode='bilinear', align_corners=False)
tensorPreprocessedSecond = torch.nn.functional.interpolate(input=tensorPreprocessedSecond, size=(intPreprocessedHeight, intPreprocessedWidth), mode='bilinear', align_corners=False)
tensorFlow = 20.0 * torch.nn.functional.interpolate(input=moduleNetwork(tensorPreprocessedFirst, tensorPreprocessedSecond), size=(intHeight, intWidth), mode='bilinear', align_corners=False)
tensorFlow[:, 0, :, :] *= float(intWidth) / float(intPreprocessedWidth)
tensorFlow[:, 1, :, :] *= float(intHeight) / float(intPreprocessedHeight)
tensorOutput.resize_(2, intHeight, intWidth).copy_(tensorFlow[0, :, :, :])
# end
if True:
tensorFirst = tensorFirst.cpu()
tensorSecond = tensorSecond.cpu()
tensorOutput = tensorOutput.cpu()
# end
return tensorOutput
# end
##########################################################
if __name__ == '__main__':
tensorFirst = torch.FloatTensor(numpy.array(PIL.Image.open(arguments_strFirst))[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32) * (1.0 / 255.0))
tensorSecond = torch.FloatTensor(numpy.array(PIL.Image.open(arguments_strSecond))[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32) * (1.0 / 255.0))
tensorOutput = estimate(tensorFirst, tensorSecond)
objectOutput = open(arguments_strOut, 'wb')
numpy.array([ 80, 73, 69, 72 ], numpy.uint8).tofile(objectOutput)
numpy.array([ tensorOutput.size(2), tensorOutput.size(1) ], numpy.int32).tofile(objectOutput)
numpy.array(tensorOutput.numpy().transpose(1, 2, 0), numpy.float32).tofile(objectOutput)
objectOutput.close()
# end