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modelNetS.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import math
W = 256
H = 256
x0 = W/2
y0 = H/2
batchSize = 32
class BasicEncoderPlainBlock(nn.Module):
def __init__(self, inChannel, outChannel, stride):
super(BasicEncoderPlainBlock, self).__init__()
self.conv1 = nn.Conv2d(inChannel, outChannel, kernel_size=3, stride=stride, padding=1)
self.bn1 = nn.BatchNorm2d(outChannel)
self.conv2 = nn.Conv2d(outChannel, outChannel, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(outChannel)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
return x
class BasicEncoderBlock(nn.Module):
def __init__(self, inChannel, outChannel, stride):
super(BasicEncoderBlock, self).__init__()
self.conv1 = nn.Conv2d(inChannel, outChannel, kernel_size=3, stride=stride, padding=1)
self.bn1 = nn.BatchNorm2d(outChannel)
self.conv2 = nn.Conv2d(outChannel, outChannel, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(outChannel)
self.downsample = None
if stride != 1:
self.downsample = nn.Sequential(
nn.Conv2d(inChannel, outChannel, kernel_size=1, stride=stride),
nn.BatchNorm2d(outChannel))
def forward(self, x):
residual = x
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = F.relu(out)
return out
class GenerateLenFlow(torch.autograd.Function):
@staticmethod
def forward(self, Input):
self.save_for_backward(Input)
Input = Input.cuda()
flow = torch.Tensor(batchSize, 2, H, W).cuda()
i, j = np.meshgrid(np.arange(H), np.arange(W), indexing='xy')
i = torch.from_numpy(i).float().cuda()
j = torch.from_numpy(j).float().cuda()
for s in range(batchSize):
coeff = 1 + Input[s] * (-1e-5) * ((i - x0)**2 + (j - y0)**2)
flow[s,0] = (i-x0)/coeff + x0 - i
flow[s,1] = (j-y0)/coeff + y0 - j
return flow
@staticmethod
def backward(self, grad_output):
Input, = self.saved_tensors
Input = Input.cuda()
grad_output = grad_output.cuda()
grad_input = Variable(torch.ones(batchSize, 1), requires_grad=False).cuda()
grad_current = Variable(torch.ones(batchSize, 2, H, W), requires_grad=False).cuda()
i, j = np.meshgrid(np.arange(H), np.arange(W), indexing='xy')
i = torch.from_numpy(i).float().cuda()
j = torch.from_numpy(j).float().cuda()
for s in range(batchSize):
r2 = (i - x0)**2 + (j - y0)**2
temp = (1+r2*Input[s]*(-1e-5))**2
grad_current[s,0] = (i - x0)*(-1)*r2*(-1e-5) / temp
grad_current[s,1] = (j - y0)*(-1)*r2*(-1e-5) / temp
grad = grad_output * grad_current
for s in range(batchSize):
grad_input[s,0] = torch.sum(grad[s,:,:,:])
return grad_input
class GenerateRotFlow(torch.autograd.Function):
@staticmethod
def forward(self, Input):
self.save_for_backward(Input)
Input = Input.cuda()
flow = torch.Tensor(batchSize, 2, H, W).cuda()
sina = torch.sin(Input)
cosa = torch.cos(Input)
i, j = np.meshgrid(np.arange(H), np.arange(W), indexing='xy')
i = torch.from_numpy(i).float().cuda()
j = torch.from_numpy(j).float().cuda()
for s in range(batchSize):
flow[s,0] = cosa[s]*i + sina[s]*j + (1 - sina[s] - cosa[s])*W/2 - i
flow[s,1] = -sina[s]*i + cosa[s]*j + (1 + sina[s] - cosa[s])*H/2 - j
return flow
@staticmethod
def backward(self, grad_output):
Input, = self.saved_tensors
Input = Input.cuda()
grad_output = grad_output.cuda()
grad_input = Variable(torch.ones(batchSize, 1), requires_grad=False).cuda()
grad_current = Variable(torch.ones(batchSize, 2, H, W), requires_grad=False).cuda()
sina = torch.sin(Input)
cosa = torch.cos(Input)
i, j = np.meshgrid(np.arange(H), np.arange(W), indexing='xy')
i = torch.from_numpy(i).float().cuda()
j = torch.from_numpy(j).float().cuda()
for s in range(batchSize):
grad_current[s,0] = -sina[s]*i + cosa[s]*j + (sina[s] - cosa[s])*W/2
grad_current[s,1] = -cosa[s]*i - sina[s]*j + (cosa[s] + sina[s])*H/2
grad = grad_output * grad_current
for s in range(batchSize):
grad_input[s,0] = torch.sum(grad[s,:,:,:])
return grad_input
SheFactor = 1.0/5
class GenerateSheFlow(torch.autograd.Function):
@staticmethod
def forward(self, Input):
self.save_for_backward(Input)
Input = Input.cuda()
flow = torch.Tensor(batchSize, 2, H, W).cuda()
i, j = np.meshgrid(np.arange(H), np.arange(W), indexing='xy')
i = torch.from_numpy(i).float().cuda()
j = torch.from_numpy(j).float().cuda()
for s in range(batchSize):
flow[s,0] = (Input[s]*j - Input[s]*W/2.0)*SheFactor
flow[s,1] = 0
return flow
@staticmethod
def backward(self, grad_output):
Input, = self.saved_tensors
Input = Input.cuda()
grad_output = grad_output.cuda()
grad_input = Variable(torch.ones(batchSize, 1), requires_grad=False).cuda()
grad_current = Variable(torch.ones(batchSize, 2, H, W), requires_grad=False).cuda()
i, j = np.meshgrid(np.arange(H), np.arange(W), indexing='xy')
i = torch.from_numpy(i).float().cuda()
j = torch.from_numpy(j).float().cuda()
for s in range(batchSize):
grad_current[s,0] = (j - W/2.0)*SheFactor
grad_current[s,1] = 0
grad = grad_output * grad_current
for s in range(batchSize):
grad_input[s,0] = torch.sum(grad[s,:,:,:])
return grad_input
ProFactor = 0.1
class GenerateProFlow(torch.autograd.Function):
@staticmethod
def forward(self, Input):
self.save_for_backward(Input)
Input = Input.cuda()
flow = torch.Tensor(batchSize, 2, H, W).cuda()
x4 = Input * ProFactor
a31 = 0
a32 = 2*x4/(1-2*x4)
a11 = 1
a12 = x4/(1-2*x4)
a13 = 0
a21 = 0
a22 = 0.99 + a32*0.995
a23 = 0.005
i, j = np.meshgrid(np.arange(H), np.arange(W), indexing='xy')
i = torch.from_numpy(i).float().cuda()
j = torch.from_numpy(j).float().cuda()
for s in range(batchSize):
im = i/(W - 1.0)
jm = j/(H - 1.0)
flow[s,0] = (W - 1.0)*(a11*im + a12[s]*jm +a13)/(a31*im + a32[s]*jm + 1) - i
flow[s,1] = (H - 1.0)*(a21*im + a22[s]*jm +a23)/(a31*im + a32[s]*jm + 1) - j
return flow
@staticmethod
def backward(self, grad_output):
Input, = self.saved_tensors
Input = Input.cuda()
grad_output = grad_output.cuda()
grad_input = Variable(torch.ones(batchSize, 1), requires_grad=False).cuda()
grad_current = Variable(torch.ones(batchSize, 2, H, W), requires_grad=False).cuda()
i, j = np.meshgrid(np.arange(H), np.arange(W), indexing='xy')
i = torch.from_numpy(i).float().cuda()
j = torch.from_numpy(j).float().cuda()
for s in range(batchSize):
x = Input[s]*ProFactor
t00 = -1.0*((2*H-2)*i+(1-H)*W+H-1)*j
t01 = (4*j*j+(8-8*H)*j+4*H*H-8*H+4)*x*x+((4*H-4)*j-4*H*H+8*H-4)*x+H*H-2*H+1
grad_current[s,0] = ProFactor*t00/t01
t10 = -1.0*((99*H-99)*j*j+(-99*H*H+198*H-99)*j)
t11 = (200*j*j+(400-400*H)*j+200*H*H-400*H+200)*x*x+((200*H-200)*j-200*H*H+400*H-200)*x+50*H*H-100*H+50
grad_current[s,1] = ProFactor*t10/t11
grad = grad_output * grad_current
for s in range(batchSize):
grad_input[s,0] = torch.sum(grad[s,:,:,:])
return grad_input
class GenerateWavFlow(torch.autograd.Function):
@staticmethod
def forward(self, Input):
self.save_for_backward(Input)
Input = Input.cuda()
flow = torch.Tensor(batchSize, 2, H, W).cuda()
temp = torch.ones(batchSize, 1).cuda()
i, j = np.meshgrid(np.arange(H), np.arange(W), indexing='xy')
i = torch.from_numpy(i).float().cuda()
j = torch.from_numpy(j).float().cuda()
for s in range(batchSize):
flow[s,0] = Input[s]*torch.sin(math.pi*4*j/(W*2))
flow[s,1] = 0
return flow
@staticmethod
def backward(self, grad_output):
Input, = self.saved_tensors
Input = Input.cuda()
grad_output = grad_output.cuda()
grad_input = Variable(torch.ones(batchSize, 1), requires_grad=False).cuda()
grad_current = Variable(torch.ones(batchSize, 2, H, W), requires_grad=False).cuda()
temp = torch.ones(batchSize, 1).cuda()
i, j = np.meshgrid(np.arange(H), np.arange(W), indexing='xy')
i = torch.from_numpy(i).float().cuda()
j = torch.from_numpy(j).float().cuda()
for s in range(batchSize):
grad_current[s,0] = torch.sin(math.pi*4*j/(W*2))
grad_current[s,1] = 0
grad = grad_output * grad_current
for s in range(batchSize):
grad_input[s,0] = torch.sum(grad[s,:,:,:])
return grad_input
class EncoderNet(nn.Module):
def __init__(self, layers):
super(EncoderNet, self).__init__()
self.conv = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.bn = nn.BatchNorm2d(64)
self.en_layer1 = self.make_encoder_layer(BasicEncoderPlainBlock, 64, 64, layers[0], stride=1)
self.en_layer2 = self.make_encoder_layer(BasicEncoderBlock, 64, 128, layers[1], stride=2)
self.en_layer3 = self.make_encoder_layer(BasicEncoderBlock, 128, 256, layers[2], stride=2)
self.en_layer4 = self.make_encoder_layer(BasicEncoderBlock, 256, 512, layers[3], stride=2)
self.en_layer5 = self.make_encoder_layer(BasicEncoderBlock, 512, 512, layers[4], stride=2)
self.conv1 = nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1)
self.bn1 = nn.BatchNorm2d(512)
self.conv2 = nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1)
self.bn2 = nn.BatchNorm2d(512)
self.fc = nn.Linear(512 * 4 * 4, 1)
def make_encoder_layer(self, block, inChannel, outChannel, block_num, stride):
layers = []
layers.append(block(inChannel, outChannel, stride=stride))
for i in range(1, block_num):
layers.append(block(outChannel, outChannel, stride=1))
return nn.Sequential(*layers)
def forward(self, x):
x = F.relu(self.bn(self.conv(x)))
x = self.en_layer1(x) #128
x = self.en_layer2(x) #64
x = self.en_layer3(x) #32
x = self.en_layer4(x) #16
x = self.en_layer5(x) #8
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = x.view(x.size(0), -1)
para = self.fc(x)
return para
class ModelNet(nn.Module):
def __init__(self, types):
super(ModelNet, self).__init__()
self.types = types
def forward(self, x):
para = x
if (self.types == 'barrel' or self.types == 'pincushion'):
OBJflow = GenerateLenFlow.apply
flow = OBJflow(para)
elif (self.types == 'rotation'):
OBJflow = GenerateRotFlow.apply
flow = OBJflow(para)
elif (self.types == 'shear'):
OBJflow = GenerateSheFlow.apply
flow = OBJflow(para)
elif (self.types == 'projective'):
OBJflow = GenerateProFlow.apply
flow = OBJflow(para)
elif (self.types == 'wave'):
OBJflow = GenerateWavFlow.apply
flow = OBJflow(para)
return flow
class EPELoss(nn.Module):
def __init__(self):
super(EPELoss, self).__init__()
def forward(self, output, target):
lossvalue = torch.norm(output - target + 1e-16, p=2, dim=1).mean()
return lossvalue