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pwc_net.py
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#-*- encoding: UTF-8 -*-
import torch
import sys
from functools import partial
import pickle
try:
from pwc.correlation import correlation # the custom cost volume layer
except:
sys.path.insert(0, '../pwc/correlation'); import correlation # you should consider upgrading python
class PWCNET(torch.nn.Module):
def __init__(self):
super(PWCNET, self).__init__()
class Extractor(torch.nn.Module):
def __init__(self):
super(Extractor, self).__init__()
self.netOne = 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.netTwo = 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.netThr = 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.netFou = 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.netFiv = 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.netSix = 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)
)
def forward(self, tenInput):
tenOne = self.netOne(tenInput)
tenTwo = self.netTwo(tenOne)
tenThr = self.netThr(tenTwo)
tenFou = self.netFou(tenThr)
tenFiv = self.netFiv(tenFou)
tenSix = self.netSix(tenFiv)
return [ tenOne, tenTwo, tenThr, tenFou, tenFiv, tenSix ]
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]
self.backwarp_tenGrid = {}
self.backwarp_tenPartial = {}
if intLevel < 6: self.netUpflow = torch.nn.ConvTranspose2d(in_channels=2,
out_channels=2, kernel_size=4, stride=2, padding=1)
if intLevel < 6: self.netUpfeat = 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.fltBackwarp = [ None, None, None, 5.0, 2.5, 1.25, 0.625, None ][intLevel + 1]
self.netOne = 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.netTwo = 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.netThr = 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.netFou = 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.netFiv = 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.netSix = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=intCurrent + 128 + 128 + 96 + 64 + 32, out_channels=2,
kernel_size=3, stride=1, padding=1)
)
def forward(self, tenFirst, tenSecond, objPrevious):
tenFlow = None
tenFeat = None
if objPrevious is None:
tenFlow = None
tenFeat = None
tenVolume = torch.nn.functional.leaky_relu(input=correlation.FunctionCorrelation(
tenFirst=tenFirst, tenSecond=tenSecond), negative_slope=0.1, inplace=False)
tenFeat = torch.cat([ tenVolume ], 1)
elif objPrevious is not None:
tenFlow = self.netUpflow(objPrevious['tenFlow'])
tenFeat = self.netUpfeat(objPrevious['tenFeat'])
tenVolume = torch.nn.functional.leaky_relu(input=correlation.FunctionCorrelation(
tenFirst=tenFirst, tenSecond=self.backwarp(tenInput=tenSecond,
tenFlow=tenFlow * self.fltBackwarp)), negative_slope=0.1, inplace=False)
tenFeat = torch.cat([ tenVolume, tenFirst, tenFlow, tenFeat ], 1)
tenFeat = torch.cat([ self.netOne(tenFeat), tenFeat ], 1)
tenFeat = torch.cat([ self.netTwo(tenFeat), tenFeat ], 1)
tenFeat = torch.cat([ self.netThr(tenFeat), tenFeat ], 1)
tenFeat = torch.cat([ self.netFou(tenFeat), tenFeat ], 1)
tenFeat = torch.cat([ self.netFiv(tenFeat), tenFeat ], 1)
tenFlow = self.netSix(tenFeat)
return {
'tenFlow': tenFlow,
'tenFeat': tenFeat
}
def backwarp(self, tenInput, tenFlow):
index = str(tenFlow.shape) + str(tenInput.device)
if index not in self.backwarp_tenGrid:
tenHor = torch.linspace(-1.0 + (1.0 / tenFlow.shape[3]), 1.0 - (1.0 / tenFlow.shape[3]),
tenFlow.shape[3]).view(1, 1, 1, -1).expand(-1, -1, tenFlow.shape[2], -1)
tenVer = torch.linspace(-1.0 + (1.0 / tenFlow.shape[2]), 1.0 - (1.0 / tenFlow.shape[2]),
tenFlow.shape[2]).view(1, 1, -1, 1).expand(-1, -1, -1, tenFlow.shape[3])
self.backwarp_tenGrid[index] = torch.cat([ tenHor, tenVer ], 1).to(tenInput.device)
if index not in self.backwarp_tenPartial:
self.backwarp_tenPartial[index] = tenFlow.new_ones([ tenFlow.shape[0],
1, tenFlow.shape[2], tenFlow.shape[3] ])
tenFlow = torch.cat([ tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0),
tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0) ], 1)
tenInput = torch.cat([ tenInput, self.backwarp_tenPartial[index] ], 1)
tenOutput = torch.nn.functional.grid_sample(input=tenInput,
grid=(self.backwarp_tenGrid[index] + tenFlow).permute(0, 2, 3, 1),
mode='bilinear', padding_mode='zeros', align_corners=False)
tenMask = tenOutput[:, -1:, :, :]; tenMask[tenMask > 0.999] = 1.0; tenMask[tenMask < 1.0] = 0.0
return tenOutput[:, :-1, :, :] * tenMask
class Refiner(torch.nn.Module):
def __init__(self):
super(Refiner, self).__init__()
self.netMain = 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)
)
def forward(self, tenInput):
return self.netMain(tenInput)
self.netExtractor = Extractor()
self.netTwo = Decoder(2)
self.netThr = Decoder(3)
self.netFou = Decoder(4)
self.netFiv = Decoder(5)
self.netSix = Decoder(6)
self.netRefiner = Refiner()
# pickle.load = partial(pickle.load, encoding="latin1")
# pickle.Unpickler = partial(pickle.Unpickler, encoding="latin1")
# self.load_state_dict({ strKey.replace('module', 'net'): tenWeight for strKey, tenWeight
# in torch.load('./pwc/pwc-net.pth', map_location=lambda storage,
# loc: storage, pickle_module=pickle).items() })
self.load_state_dict({ strKey.replace('module', 'net'): tenWeight for strKey, tenWeight
in torch.load('./pwc/pwc-net.pth').items() })
def forward(self, tenFirst, tenSecond):
tenFirst = self.netExtractor(tenFirst)
tenSecond = self.netExtractor(tenSecond)
objEstimate = self.netSix(tenFirst[-1], tenSecond[-1], None)
objEstimate = self.netFiv(tenFirst[-2], tenSecond[-2], objEstimate)
objEstimate = self.netFou(tenFirst[-3], tenSecond[-3], objEstimate)
objEstimate = self.netThr(tenFirst[-4], tenSecond[-4], objEstimate)
objEstimate = self.netTwo(tenFirst[-5], tenSecond[-5], objEstimate)
return objEstimate['tenFlow'] + self.netRefiner(objEstimate['tenFeat'])