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run_multi.py
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#!/usr/bin/env python
import torch
import getopt
import math
import numpy
import os
import PIL
import PIL.Image
import sys
import shutil
try:
from sepconv import sepconv # the custom separable convolution layer
except:
sys.path.insert(0, './sepconv');
import sepconv # you should consider upgrading python
# end
##########################################################
assert (int(str('').join(torch.__version__.split('.')[0:3])) >= 41) # requires at least pytorch version 0.4.1
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 = 'lf'
arguments_strFirst = r'F:\xxx\yyy\1\1\00001.jpg'
arguments_strSecond = r'F:\xxx\yyy\1\1\00003.jpg'
arguments_strOut = r'F:\xxx\yyy\1\1\out2.jpg'
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, l1 or lf, please see our paper for more details
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__()
def Basic(intInput, intOutput):
return torch.nn.Sequential(
torch.nn.Conv2d(in_channels=intInput, out_channels=intOutput, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=intOutput, out_channels=intOutput, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=intOutput, out_channels=intOutput, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False)
)
# end
def Upsample(intInput, intOutput):
return torch.nn.Sequential(
torch.nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
torch.nn.Conv2d(in_channels=intOutput, out_channels=intOutput, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False)
)
# end
def Subnet():
return torch.nn.Sequential(
torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=64, out_channels=51, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
torch.nn.Conv2d(in_channels=51, out_channels=51, kernel_size=3, stride=1, padding=1)
)
# end
self.moduleConv1 = Basic(6, 32)
self.moduleConv2 = Basic(32, 64)
self.moduleConv3 = Basic(64, 128)
self.moduleConv4 = Basic(128, 256)
self.moduleConv5 = Basic(256, 512)
self.moduleDeconv5 = Basic(512, 512)
self.moduleDeconv4 = Basic(512, 256)
self.moduleDeconv3 = Basic(256, 128)
self.moduleDeconv2 = Basic(128, 64)
self.moduleUpsample5 = Upsample(512, 512)
self.moduleUpsample4 = Upsample(256, 256)
self.moduleUpsample3 = Upsample(128, 128)
self.moduleUpsample2 = Upsample(64, 64)
self.moduleVertical1 = Subnet()
self.moduleVertical2 = Subnet()
self.moduleHorizontal1 = Subnet()
self.moduleHorizontal2 = Subnet()
self.load_state_dict(torch.load(r'F:\xxx\pretrain_model\network-lf.pytorch'))
# end
def forward(self, tensorFirst, tensorSecond):
tensorConv1 = self.moduleConv1(torch.cat([tensorFirst, tensorSecond], 1))
tensorConv2 = self.moduleConv2(torch.nn.functional.avg_pool2d(input=tensorConv1, kernel_size=2, stride=2))
tensorConv3 = self.moduleConv3(torch.nn.functional.avg_pool2d(input=tensorConv2, kernel_size=2, stride=2))
tensorConv4 = self.moduleConv4(torch.nn.functional.avg_pool2d(input=tensorConv3, kernel_size=2, stride=2))
tensorConv5 = self.moduleConv5(torch.nn.functional.avg_pool2d(input=tensorConv4, kernel_size=2, stride=2))
tensorDeconv5 = self.moduleUpsample5(
self.moduleDeconv5(torch.nn.functional.avg_pool2d(input=tensorConv5, kernel_size=2, stride=2)))
tensorDeconv4 = self.moduleUpsample4(self.moduleDeconv4(tensorDeconv5 + tensorConv5))
tensorDeconv3 = self.moduleUpsample3(self.moduleDeconv3(tensorDeconv4 + tensorConv4))
tensorDeconv2 = self.moduleUpsample2(self.moduleDeconv2(tensorDeconv3 + tensorConv3))
tensorCombine = tensorDeconv2 + tensorConv2
tensorFirst = torch.nn.functional.pad(input=tensorFirst,
pad=[int(math.floor(51 / 2.0)), int(math.floor(51 / 2.0)),
int(math.floor(51 / 2.0)), int(math.floor(51 / 2.0))],
mode='replicate')
tensorSecond = torch.nn.functional.pad(input=tensorSecond,
pad=[int(math.floor(51 / 2.0)), int(math.floor(51 / 2.0)),
int(math.floor(51 / 2.0)), int(math.floor(51 / 2.0))],
mode='replicate')
tensorDot1 = sepconv.FunctionSepconv(tensorInput=tensorFirst,
tensorVertical=self.moduleVertical1(tensorCombine),
tensorHorizontal=self.moduleHorizontal1(tensorCombine))
tensorDot2 = sepconv.FunctionSepconv(tensorInput=tensorSecond,
tensorVertical=self.moduleVertical2(tensorCombine),
tensorHorizontal=self.moduleHorizontal2(tensorCombine))
return tensorDot1 + tensorDot2
# end
# end
moduleNetwork = Network().cuda().eval()
##########################################################
def estimate(tensorFirst, tensorSecond):
assert (tensorFirst.size(1) == tensorSecond.size(1))
assert (tensorFirst.size(2) == tensorSecond.size(2))
intWidth = tensorFirst.size(2)
intHeight = tensorFirst.size(1)
# assert(intWidth <= 1280) # while our approach works with larger images, we do not recommend it unless you are aware of the implications
# assert(intHeight <= 720) # while our approach works with larger images, we do not recommend it unless you are aware of the implications
tensorPreprocessedFirst = tensorFirst.cuda().view(1, 3, intHeight, intWidth)
tensorPreprocessedSecond = tensorSecond.cuda().view(1, 3, intHeight, intWidth)
intPreprocessedWidth = int(math.floor(51 / 2.0)) + intWidth + int(math.floor(51 / 2.0))
intPreprocessedHeight = int(math.floor(51 / 2.0)) + intHeight + int(math.floor(51 / 2.0))
if intPreprocessedWidth != ((intPreprocessedWidth >> 7) << 7):
intPreprocessedWidth = (((intPreprocessedWidth >> 7) + 1) << 7) - intPreprocessedWidth # more than necessary
# end
if intPreprocessedHeight != ((intPreprocessedHeight >> 7) << 7):
intPreprocessedHeight = (((intPreprocessedHeight >> 7) + 1) << 7) - intPreprocessedHeight # more than necessary
# end
tensorPreprocessedFirst = torch.nn.functional.pad(input=tensorPreprocessedFirst, pad=[int(math.floor(51 / 2.0)),
int(math.floor(
51 / 2.0)) + intPreprocessedWidth,
int(math.floor(51 / 2.0)),
int(math.floor(
51 / 2.0)) + intPreprocessedHeight],
mode='replicate')
tensorPreprocessedSecond = torch.nn.functional.pad(input=tensorPreprocessedSecond, pad=[int(math.floor(51 / 2.0)),
int(math.floor(
51 / 2.0)) + intPreprocessedWidth,
int(math.floor(51 / 2.0)),
int(math.floor(
51 / 2.0)) + intPreprocessedHeight],
mode='replicate')
return torch.nn.functional.pad(input=moduleNetwork(tensorPreprocessedFirst, tensorPreprocessedSecond),
pad=[0 - int(math.floor(51 / 2.0)),
0 - int(math.floor(51 / 2.0)) - intPreprocessedWidth,
0 - int(math.floor(51 / 2.0)),
0 - int(math.floor(51 / 2.0)) - intPreprocessedHeight], mode='replicate')[0, :,
:, :].cpu()
# end
##########################################################
if __name__ == '__main__':
##解析txt文件
testfile =r'F:\xxx\yyy\test.txt'
with open(testfile, 'r') as document:
index = 0
for line in document:
line = line.split()
if not line: # empty line?
continue
if not (os.path.isdir(line[0])):
continue
record = [int(el) for el in line[2:]]
inframe =[]
for idx in range(2):
picname = "%05d.jpg" % (record[idx])
imagename = os.path.join(line[0], picname)
dstimagename =os.path.join(line[0], "%04d.jpg" % (record[idx]))
inframe.append(imagename)
shutil.copyfile(imagename, dstimagename)
picname = "%04d.jpg" % (record[1] -1 )
outname = os.path.join(line[0],picname)
index += 1
pic1 = PIL.Image.open(inframe[0])
pic2 = PIL.Image.open(inframe[1])
h1 = pic1.height
w1 = pic1.width
iflag = 0
if w1 > 1280 or h1 >720 :
iflag = 1
nh = 720
nw = 960
npic1 = pic1.resize((nw,nh), PIL.Image.ANTIALIAS)
npic2 = pic2.resize((nw, nh), PIL.Image.ANTIALIAS)
print("resize {0}\n".format(outname))
if iflag == 0:
tensorFirst = torch.FloatTensor(
numpy.array(pic1)[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32) * (
1.0 / 255.0))
tensorSecond = torch.FloatTensor(
numpy.array(pic2)[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32) * (
1.0 / 255.0))
tensorOutput = estimate(tensorFirst, tensorSecond)
PIL.Image.fromarray(
(tensorOutput.clamp(0.0, 1.0).numpy().transpose(1, 2, 0)[:, :, ::-1] * 255.0).astype(numpy.uint8)).save(
outname)
else:
tensorFirst = torch.FloatTensor(
numpy.array(npic1)[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32) * (
1.0 / 255.0))
tensorSecond = torch.FloatTensor(
numpy.array(npic2)[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32) * (
1.0 / 255.0))
tensorOutput = estimate(tensorFirst, tensorSecond)
nout0 = PIL.Image.fromarray((tensorOutput.clamp(0.0, 1.0).numpy().transpose(1, 2, 0)[:, :, ::-1] * 255.0).astype(numpy.uint8))
nout = nout0.resize((w1, h1), PIL.Image.ANTIALIAS)
nout.save(outname)
print(outname)
# end