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models_x.py
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models_x.py
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from setuptools import setup
import torch
from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CppExtension
if torch.cuda.is_available():
print('Including CUDA code.')
setup(
name='trilinear',
ext_modules=[
CUDAExtension('trilinear', [
'src/trilinear_cuda.cpp',
'src/trilinear_kernel.cu',
])
],
cmdclass={
'build_ext': BuildExtension
})
else:
print('NO CUDA is found. Fall back to CPU.')
setup(name='trilinear',
ext_modules=[CppExtension('trilinear', ['src/trilinear.cpp'])],
cmdclass={'build_ext': BuildExtension})
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
import torchvision.transforms as transforms
from torch.autograd import Variable
import torch
import numpy as np
import math
import trilinear
def weights_init_normal_classifier(m):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
torch.nn.init.xavier_normal_(m.weight.data)
elif classname.find("BatchNorm2d") != -1 or classname.find("InstanceNorm2d") != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
torch.nn.init.constant_(m.bias.data, 0.0)
class resnet18_224(nn.Module):
def __init__(self, out_dim=5, aug_test=False):
super(resnet18_224, self).__init__()
self.aug_test = aug_test
net = models.resnet18(pretrained=True)
# self.mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).cuda()
# self.std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).cuda()
self.upsample = nn.Upsample(size=(224,224),mode='bilinear')
net.fc = nn.Linear(512, out_dim)
self.model = net
def forward(self, x):
x = self.upsample(x)
if self.aug_test:
# x = torch.cat((x, torch.rot90(x, 1, [2, 3]), torch.rot90(x, 3, [2, 3])), 0)
x = torch.cat((x, torch.flip(x, [3])), 0)
f = self.model(x)
return f
##############################
# Discriminator
##############################
def discriminator_block(in_filters, out_filters, normalization=False):
"""Returns downsampling layers of each discriminator block"""
layers = [nn.Conv2d(in_filters, out_filters, 3, stride=2, padding=1)]
layers.append(nn.LeakyReLU(0.2))
if normalization:
layers.append(nn.InstanceNorm2d(out_filters, affine=True))
#layers.append(nn.BatchNorm2d(out_filters))
return layers
class Discriminator(nn.Module):
def __init__(self, in_channels=3):
super(Discriminator, self).__init__()
self.model = nn.Sequential(
nn.Upsample(size=(256,256),mode='bilinear'),
nn.Conv2d(3, 16, 3, stride=2, padding=1),
nn.LeakyReLU(0.2),
nn.InstanceNorm2d(16, affine=True),
*discriminator_block(16, 32),
*discriminator_block(32, 64),
*discriminator_block(64, 128),
*discriminator_block(128, 128),
#*discriminator_block(128, 128),
nn.Conv2d(128, 1, 8, padding=0)
)
def forward(self, img_input):
return self.model(img_input)
class Classifier(nn.Module):
def __init__(self, in_channels=3):
super(Classifier, self).__init__()
self.model = nn.Sequential(
nn.Upsample(size=(256,256),mode='bilinear'),
nn.Conv2d(3, 16, 3, stride=2, padding=1),
nn.LeakyReLU(0.2),
nn.InstanceNorm2d(16, affine=True),
*discriminator_block(16, 32, normalization=True),
*discriminator_block(32, 64, normalization=True),
*discriminator_block(64, 128, normalization=True),
*discriminator_block(128, 128),
#*discriminator_block(128, 128, normalization=True),
nn.Dropout(p=0.5),
nn.Conv2d(128, 3, 8, padding=0),
)
def forward(self, img_input):
return self.model(img_input)
class Classifier_unpaired(nn.Module):
def __init__(self, in_channels=3):
super(Classifier_unpaired, self).__init__()
self.model = nn.Sequential(
nn.Upsample(size=(256,256),mode='bilinear'),
nn.Conv2d(3, 16, 3, stride=2, padding=1),
nn.LeakyReLU(0.2),
nn.InstanceNorm2d(16, affine=True),
*discriminator_block(16, 32),
*discriminator_block(32, 64),
*discriminator_block(64, 128),
*discriminator_block(128, 128),
#*discriminator_block(128, 128),
nn.Conv2d(128, 3, 8, padding=0),
)
def forward(self, img_input):
return self.model(img_input)
class Generator3DLUT_identity(nn.Module):
def __init__(self, dim=33):
super(Generator3DLUT_identity, self).__init__()
if dim == 33:
file = open("IdentityLUT33.txt", 'r')
elif dim == 64:
file = open("IdentityLUT64.txt", 'r')
lines = file.readlines()
buffer = np.zeros((3,dim,dim,dim), dtype=np.float32)
for i in range(0,dim):
for j in range(0,dim):
for k in range(0,dim):
n = i * dim*dim + j * dim + k
x = lines[n].split()
buffer[0,i,j,k] = float(x[0])
buffer[1,i,j,k] = float(x[1])
buffer[2,i,j,k] = float(x[2])
self.LUT = nn.Parameter(torch.from_numpy(buffer).requires_grad_(True))
self.TrilinearInterpolation = TrilinearInterpolation()
def forward(self, x):
_, output = self.TrilinearInterpolation(self.LUT, x)
#self.LUT, output = self.TrilinearInterpolation(self.LUT, x)
return output
class Generator3DLUT_zero(nn.Module):
def __init__(self, dim=33):
super(Generator3DLUT_zero, self).__init__()
self.LUT = torch.zeros(3,dim,dim,dim, dtype=torch.float)
self.LUT = nn.Parameter(torch.tensor(self.LUT))
self.TrilinearInterpolation = TrilinearInterpolation()
def forward(self, x):
_, output = self.TrilinearInterpolation(self.LUT, x)
return output
class TrilinearInterpolationFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, lut, x):
x = x.contiguous()
output = x.new(x.size())
dim = lut.size()[-1]
shift = dim ** 3
binsize = 1.000001 / (dim-1)
W = x.size(2)
H = x.size(3)
batch = x.size(0)
assert 1 == trilinear.forward(lut,
x,
output,
dim,
shift,
binsize,
W,
H,
batch)
int_package = torch.IntTensor([dim, shift, W, H, batch])
float_package = torch.FloatTensor([binsize])
variables = [lut, x, int_package, float_package]
ctx.save_for_backward(*variables)
return lut, output
@staticmethod
def backward(ctx, lut_grad, x_grad):
lut, x, int_package, float_package = ctx.saved_variables
dim, shift, W, H, batch = int_package
dim, shift, W, H, batch = int(dim), int(shift), int(W), int(H), int(batch)
binsize = float(float_package[0])
assert 1 == trilinear.backward(x,
x_grad,
lut_grad,
dim,
shift,
binsize,
W,
H,
batch)
return lut_grad, x_grad
class TrilinearInterpolation(torch.nn.Module):
def __init__(self):
super(TrilinearInterpolation, self).__init__()
def forward(self, lut, x):
return TrilinearInterpolationFunction.apply(lut, x)
class TV_3D(nn.Module):
def __init__(self, dim=33):
super(TV_3D,self).__init__()
self.weight_r = torch.ones(3,dim,dim,dim-1, dtype=torch.float)
self.weight_r[:,:,:,(0,dim-2)] *= 2.0
self.weight_g = torch.ones(3,dim,dim-1,dim, dtype=torch.float)
self.weight_g[:,:,(0,dim-2),:] *= 2.0
self.weight_b = torch.ones(3,dim-1,dim,dim, dtype=torch.float)
self.weight_b[:,(0,dim-2),:,:] *= 2.0
self.relu = torch.nn.ReLU()
def forward(self, LUT):
dif_r = LUT.LUT[:,:,:,:-1] - LUT.LUT[:,:,:,1:]
dif_g = LUT.LUT[:,:,:-1,:] - LUT.LUT[:,:,1:,:]
dif_b = LUT.LUT[:,:-1,:,:] - LUT.LUT[:,1:,:,:]
tv = torch.mean(torch.mul((dif_r ** 2),self.weight_r)) + torch.mean(torch.mul((dif_g ** 2),self.weight_g)) + torch.mean(torch.mul((dif_b ** 2),self.weight_b))
mn = torch.mean(self.relu(dif_r)) + torch.mean(self.relu(dif_g)) + torch.mean(self.relu(dif_b))
return tv, mn