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crf_loss.py
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crf_loss.py
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import argparse
import os
import numpy as np
import math
import itertools
import time
import datetime
import sys
from math import exp
import random
#from torchvision.utils import save_image
#from torchvision import datasets
from torch.utils.data import DataLoader
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch
kernel_size = 9 #gaussian kernel dimension
dilation = 1 #cheating :) The "real" dimension of the gaussian kernel is kernel size, but the "effective" dimension is (kernel_size*dilation + 1)
padding = (kernel_size // 2) * dilation #do not touch this
bs = 4 #batch size
win = 256 #window size
sigma_X = 3.0 #for distance gaussian
sigma_I = 0.1 #for RGB/grayscale gaussian
sample_interval = 20 # sample image every
class kernel_loss(torch.nn.Module):
def sub_kernel(self):
filters = kernel_size * kernel_size
middle = kernel_size // 2
kernel = Variable(torch.zeros((filters, 1, kernel_size, kernel_size))).cuda()
for i in range(kernel_size):
for j in range(kernel_size):
kernel[i*kernel_size+j, 0, i, j] = -1
kernel[i*kernel_size+j, 0, middle, middle] = kernel[i*kernel_size+j, 0, middle, middle] + 1
return kernel
def dist_kernel(self):
filters = kernel_size * kernel_size
middle = kernel_size // 2
kernel = Variable(torch.zeros((bs, filters, 1, 1))).cuda()
for i in range(kernel_size):
for j in range(kernel_size):
ii = i - middle
jj = j - middle
distance = pow(ii,2) + pow(jj,2)
kernel[:, i*kernel_size+j, 0, 0] = exp(-distance / pow(sigma_X,2))
#print(kernel.view(4,1,kernel_size,kernel_size))
return kernel
def central_kernel(self):
filters = kernel_size * kernel_size
middle = kernel_size // 2
kernel = Variable(torch.zeros((filters, 1, kernel_size, kernel_size))).cuda()
for i in range(kernel_size):
for j in range(kernel_size):
kernel[i*kernel_size+j, 0, middle, middle] = 1
return kernel
def select_kernel(self):
filters = kernel_size * kernel_size
middle = kernel_size // 2
kernel = Variable(torch.zeros((filters, 1, kernel_size, kernel_size))).cuda()
for i in range(kernel_size):
for j in range(kernel_size):
kernel[i*kernel_size+j, 0, i, j] = 1
return kernel
def color_tensor(self, x):
result = Variable(torch.zeros((bs, kernel_size*kernel_size, win-2*padding, win-2*padding))).cuda()
for i in range(x.shape[1]):
channel = x[:,i,:,:].unsqueeze(1)
sub = nn.Conv2d(in_channels=1, out_channels=kernel_size*kernel_size, kernel_size=kernel_size, bias=False, padding=0, dilation=dilation)
sub.weight.data = self.sub_matrix
color = sub(channel)
color = torch.pow(color,2)
result = result + color
result = torch.exp(-result / pow(sigma_I,2))
return result
def probability_tensor(self, y):
conv = nn.Conv2d(in_channels=1, out_channels=kernel_size*kernel_size, kernel_size=kernel_size, bias=False, padding=0, dilation=dilation)
conv.weight.data = self.select_matrix
prob = conv(y)
return prob
#def probability_central(self, y):
# conv = nn.Conv2d(in_channels=1, out_channels=kernel_size*kernel_size, kernel_size=kernel_size, bias=False, padding=padding)
# conv.weight.data = self.one_matrix
# prob = conv(y)
# return prob
def __init__(self):
super(kernel_loss,self).__init__()
#self.softmax = nn.Softmax(dim=1)
self.dist_tensor = self.dist_kernel()
#self.one_matrix = self.central_kernel()
self.select_matrix = self.select_kernel()
self.sub_matrix = self.sub_kernel() #shape: [filters, 1, h, w]
def forward(self,x,y):
"""
x --> Image. It can also have just 1 channel (grayscale). Values between 0 and 1
y --> Mask. Values between 0 and 1
"""
#y = self.softmax(y)
y0 = y[:,0,:,:].unsqueeze(1) #build: 0, background: 1, default 1
y1 = y[:,1,:,:].unsqueeze(1) #build: 1, background: 0, default 0
y0p = y0[:,:,padding:-padding,padding:-padding]
y1p = y1[:,:,padding:-padding,padding:-padding]
W = self.color_tensor(x)
W = (W * self.dist_tensor.expand_as(W))
potts_loss_0 = y0p.expand_as(W) * W * self.probability_tensor(y1)
potts_loss_1 = y1p.expand_as(W) * W * self.probability_tensor(y0)
numel = potts_loss_0.numel()
#ncut_loss_0 = (potts_loss_0 / (self.probability_tensor(y0) * W)).mean()
#ncut_loss_1 = (potts_loss_1 / (self.probability_tensor(y1) * W)).mean()
"""
if random.randint(0,sample_interval) == 0:
r = random.randint(0,20)
img = torch.mean(W, dim=1).unsqueeze(1)
#amin = torch.min(img)
#amax = torch.max(img)
#img = (img - amin) / (amax - amin)
save_image(img, "./debug/%d_img.png" % r, nrow=2)
#img2 = torch.mean(potts_loss_0, dim=1).unsqueeze(1)
#amin = torch.min(img2)
#amax = torch.max(img2)
#img2 = (img2 - amin) / (amax - amin)
#save_image(img2, "./debug/%d_b.png" % r, nrow=2)
img3 = torch.mean(potts_loss_0, dim=1).unsqueeze(1)
#amin = torch.min(img3)
#amax = torch.max(img3)
#img3 = (img3 - amin) / (amax - amin)
save_image(img3, "./debug/%d_loss.png" % r, nrow=2)
#img4 = torch.mean(loss_matrix, dim=1).unsqueeze(1)
##amin = torch.min(img4)
##amax = torch.max(img4)
##img4 = (img4 - amin) / (amax - amin)
#save_image(img4, "./debug/%d_d.png" % r, nrow=2)
save_image(x, "./debug/%d_map.png" % r, nrow=2)
"""
potts_loss_0 = (potts_loss_0).mean()
potts_loss_1 = (potts_loss_1).mean()
potts_loss = potts_loss_0 + potts_loss_1
return potts_loss
"""
#ncut_loss_0 = potts_loss_0 / (self.probability_tensor(y0) * W).mean()
#ncut_loss_1 = potts_loss_1 / (self.probability_tensor(y1) * W).mean()
ncut_loss_0 = potts_loss_0 / (y0p.expand_as(W) * W).mean()
ncut_loss_1 = potts_loss_1 / (y1p.expand_as(W) * W).mean()
#ncut_loss_0 = ncut_loss_0.mean()
#ncut_loss_1 = ncut_loss_1.mean()
ncut_loss = ncut_loss_0 + ncut_loss_1
#potts_loss = potts_loss_0 + potts_loss_1
#ncut_loss = ncut_loss_0 + ncut_loss_1
return (potts_loss, ncut_loss, numel)
"""