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model.py
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model.py
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import torch
import torch.optim as optim
from torch.autograd import Variable
import math
import sepconv
import sys
from torch.nn import functional as F
def to_variable(x):
if torch.cuda.is_available():
x = x.cuda()
return Variable(x)
class KernelEstimation(torch.nn.Module):
def __init__(self, kernel_size):
super(KernelEstimation, self).__init__()
self.kernel_size = kernel_size
def Basic(input_channel, output_channel):
return torch.nn.Sequential(
torch.nn.Conv2d(in_channels=input_channel, out_channels=output_channel, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=output_channel, out_channels=output_channel, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=output_channel, out_channels=output_channel, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False)
)
def Upsample(channel):
return torch.nn.Sequential(
torch.nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),
torch.nn.Conv2d(in_channels=channel, out_channels=channel, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False)
)
def Subnet(ks):
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=ks, 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=ks, out_channels=ks, kernel_size=3, stride=1, padding=1)
)
self.moduleConv1 = Basic(6, 32)
self.modulePool1 = torch.nn.AvgPool2d(kernel_size=2, stride=2)
self.moduleConv2 = Basic(32, 64)
self.modulePool2 = torch.nn.AvgPool2d(kernel_size=2, stride=2)
self.moduleConv3 = Basic(64, 128)
self.modulePool3 = torch.nn.AvgPool2d(kernel_size=2, stride=2)
self.moduleConv4 = Basic(128, 256)
self.modulePool4 = torch.nn.AvgPool2d(kernel_size=2, stride=2)
self.moduleConv5 = Basic(256, 512)
self.modulePool5 = torch.nn.AvgPool2d(kernel_size=2, stride=2)
self.moduleDeconv5 = Basic(512, 512)
self.moduleUpsample5 = Upsample(512)
self.moduleDeconv4 = Basic(512, 256)
self.moduleUpsample4 = Upsample(256)
self.moduleDeconv3 = Basic(256, 128)
self.moduleUpsample3 = Upsample(128)
self.moduleDeconv2 = Basic(128, 64)
self.moduleUpsample2 = Upsample(64)
self.moduleVertical1 = Subnet(self.kernel_size)
self.moduleVertical2 = Subnet(self.kernel_size)
self.moduleHorizontal1 = Subnet(self.kernel_size)
self.moduleHorizontal2 = Subnet(self.kernel_size)
def forward(self, rfield0, rfield2):
tensorJoin = torch.cat([rfield0, rfield2], 1)
tensorConv1 = self.moduleConv1(tensorJoin)
tensorPool1 = self.modulePool1(tensorConv1)
tensorConv2 = self.moduleConv2(tensorPool1)
tensorPool2 = self.modulePool2(tensorConv2)
tensorConv3 = self.moduleConv3(tensorPool2)
tensorPool3 = self.modulePool3(tensorConv3)
tensorConv4 = self.moduleConv4(tensorPool3)
tensorPool4 = self.modulePool4(tensorConv4)
tensorConv5 = self.moduleConv5(tensorPool4)
tensorPool5 = self.modulePool5(tensorConv5)
tensorDeconv5 = self.moduleDeconv5(tensorPool5)
tensorUpsample5 = self.moduleUpsample5(tensorDeconv5)
tensorCombine = tensorUpsample5 + tensorConv5
tensorDeconv4 = self.moduleDeconv4(tensorCombine)
tensorUpsample4 = self.moduleUpsample4(tensorDeconv4)
tensorCombine = tensorUpsample4 + tensorConv4
tensorDeconv3 = self.moduleDeconv3(tensorCombine)
tensorUpsample3 = self.moduleUpsample3(tensorDeconv3)
tensorCombine = tensorUpsample3 + tensorConv3
tensorDeconv2 = self.moduleDeconv2(tensorCombine)
tensorUpsample2 = self.moduleUpsample2(tensorDeconv2)
tensorCombine = tensorUpsample2 + tensorConv2
Vertical1 = self.moduleVertical1(tensorCombine)
Vertical2 = self.moduleVertical2(tensorCombine)
Horizontal1 = self.moduleHorizontal1(tensorCombine)
Horizontal2 = self.moduleHorizontal2(tensorCombine)
return Vertical1, Horizontal1, Vertical2, Horizontal2
class SepConvNet(torch.nn.Module):
def __init__(self, kernel_size):
super(SepConvNet, self).__init__()
self.kernel_size = kernel_size
self.kernel_pad = int(math.floor(kernel_size / 2.0))
self.epoch = Variable(torch.tensor(0, requires_grad=False))
self.get_kernel = KernelEstimation(self.kernel_size)
self.optimizer = optim.Adam(self.parameters(), lr=0.001)
self.criterion = torch.nn.MSELoss()
self.modulePad = torch.nn.ReplicationPad2d([self.kernel_pad, self.kernel_pad, self.kernel_pad, self.kernel_pad])
def forward(self, frame0, frame2):
h0 = int(list(frame0.size())[2])
w0 = int(list(frame0.size())[3])
h2 = int(list(frame2.size())[2])
w2 = int(list(frame2.size())[3])
if h0 != h2 or w0 != w2:
sys.exit('Frame sizes do not match')
h_padded = False
w_padded = False
if h0 % 32 != 0:
pad_h = 32 - (h0 % 32)
frame0 = F.pad(frame0, (0, 0, 0, pad_h))
frame2 = F.pad(frame2, (0, 0, 0, pad_h))
h_padded = True
if w0 % 32 != 0:
pad_w = 32 - (w0 % 32)
frame0 = F.pad(frame0, (0, pad_w, 0, 0))
frame2 = F.pad(frame2, (0, pad_w, 0, 0))
w_padded = True
Vertical1, Horizontal1, Vertical2, Horizontal2 = self.get_kernel(frame0, frame2)
tensorDot1 = sepconv.FunctionSepconv()(self.modulePad(frame0), Vertical1, Horizontal1)
tensorDot2 = sepconv.FunctionSepconv()(self.modulePad(frame2), Vertical2, Horizontal2)
frame1 = tensorDot1 + tensorDot2
if h_padded:
frame1 = frame1[:, :, 0:h0, :]
if w_padded:
frame1 = frame1[:, :, :, 0:w0]
return frame1
def train_model(self, frame0, frame2, frame1):
self.optimizer.zero_grad()
output = self.forward(frame0, frame2)
loss = self.criterion(output, frame1)
loss.backward()
self.optimizer.step()
return loss
def increase_epoch(self):
self.epoch += 1