Spaces:
Running
Running
File size: 6,346 Bytes
5b557cf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 | import torch.nn as nn
import torch
import torch.nn.functional as F
import numpy as np
import cv2
from torch.autograd import Variable
import torchvision.utils as vutils
class CNN(nn.Module):
def __init__(self,layer,matrixSize=32):
super(CNN,self).__init__()
# 256x64x64
if(layer == 'r31'):
self.convs = nn.Sequential(nn.Conv2d(256,128,3,1,1),
nn.ReLU(inplace=True),
nn.Conv2d(128,64,3,1,1),
nn.ReLU(inplace=True),
nn.Conv2d(64,matrixSize,3,1,1))
elif(layer == 'r41'):
# 512x32x32
self.convs = nn.Sequential(nn.Conv2d(512,256,3,1,1),
nn.ReLU(inplace=True),
nn.Conv2d(256,128,3,1,1),
nn.ReLU(inplace=True),
nn.Conv2d(128,matrixSize,3,1,1))
self.fc = nn.Linear(32*32,32*32)
def forward(self,x,masks,style=False):
color_code_number = 9
xb,xc,xh,xw = x.size()
x = x.view(xc,-1)
feature_sub_mean = x.clone()
for i in range(color_code_number):
mask = masks[i].clone().squeeze(0)
mask = cv2.resize(mask.numpy(),(xw,xh),interpolation=cv2.INTER_NEAREST)
mask = torch.FloatTensor(mask)
mask = mask.long()
if(torch.sum(mask) >= 10):
mask = mask.view(-1)
# dilation here
"""
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(5,5))
mask = mask.cpu().numpy()
mask = cv2.dilate(mask.astype(np.float32), kernel)
mask = torch.from_numpy(mask)
mask = mask.squeeze()
"""
fgmask = (mask>0).nonzero().squeeze(1)
fgmask = fgmask.cuda()
selectFeature = torch.index_select(x,1,fgmask) # 32x96
# subtract mean
f_mean = torch.mean(selectFeature,1)
f_mean = f_mean.unsqueeze(1).expand_as(selectFeature)
selectFeature = selectFeature - f_mean
feature_sub_mean.index_copy_(1,fgmask,selectFeature)
feature = self.convs(feature_sub_mean.view(xb,xc,xh,xw))
# 32x16x16
b,c,h,w = feature.size()
transMatrices = {}
feature = feature.view(c,-1)
for i in range(color_code_number):
mask = masks[i].clone().squeeze(0)
mask = cv2.resize(mask.numpy(),(w,h),interpolation=cv2.INTER_NEAREST)
mask = torch.FloatTensor(mask)
mask = mask.long()
if(torch.sum(mask) >= 10):
mask = mask.view(-1)
fgmask = Variable((mask==1).nonzero().squeeze(1))
fgmask = fgmask.cuda()
selectFeature = torch.index_select(feature,1,fgmask) # 32x96
tc,tN = selectFeature.size()
covMatrix = torch.mm(selectFeature,selectFeature.transpose(0,1)).div(tN)
transmatrix = self.fc(covMatrix.view(-1))
transMatrices[i] = transmatrix
return transMatrices,feature_sub_mean
class MulLayer(nn.Module):
def __init__(self,layer,matrixSize=32):
super(MulLayer,self).__init__()
self.snet = CNN(layer)
self.cnet = CNN(layer)
self.matrixSize = matrixSize
if(layer == 'r41'):
self.compress = nn.Conv2d(512,matrixSize,1,1,0)
self.unzip = nn.Conv2d(matrixSize,512,1,1,0)
elif(layer == 'r31'):
self.compress = nn.Conv2d(256,matrixSize,1,1,0)
self.unzip = nn.Conv2d(matrixSize,256,1,1,0)
def forward(self,cF,sF,cmasks,smasks):
sb,sc,sh,sw = sF.size()
sMatrices,sF_sub_mean = self.snet(sF,smasks,style=True)
cMatrices,cF_sub_mean = self.cnet(cF,cmasks,style=False)
compress_content = self.compress(cF_sub_mean.view(cF.size()))
cb,cc,ch,cw = compress_content.size()
compress_content = compress_content.view(cc,-1)
transfeature = compress_content.clone()
color_code_number = 9
finalSMean = Variable(torch.zeros(cF.size()).cuda(0))
finalSMean = finalSMean.view(sc,-1)
for i in range(color_code_number):
cmask = cmasks[i].clone().squeeze(0)
smask = smasks[i].clone().squeeze(0)
cmask = cv2.resize(cmask.numpy(),(cw,ch),interpolation=cv2.INTER_NEAREST)
cmask = torch.FloatTensor(cmask)
cmask = cmask.long()
smask = cv2.resize(smask.numpy(),(sw,sh),interpolation=cv2.INTER_NEAREST)
smask = torch.FloatTensor(smask)
smask = smask.long()
if(torch.sum(cmask) >= 10 and torch.sum(smask) >= 10
and (i in sMatrices) and (i in cMatrices)):
cmask = cmask.view(-1)
fgcmask = Variable((cmask==1).nonzero().squeeze(1))
fgcmask = fgcmask.cuda()
smask = smask.view(-1)
fgsmask = Variable((smask==1).nonzero().squeeze(1))
fgsmask = fgsmask.cuda()
sFF = sF.view(sc,-1)
sFF_select = torch.index_select(sFF,1,fgsmask)
sMean = torch.mean(sFF_select,dim=1,keepdim=True)
sMean = sMean.view(1,sc,1,1)
sMean = sMean.expand_as(cF)
sMatrix = sMatrices[i]
cMatrix = cMatrices[i]
sMatrix = sMatrix.view(self.matrixSize,self.matrixSize)
cMatrix = cMatrix.view(self.matrixSize,self.matrixSize)
transmatrix = torch.mm(sMatrix,cMatrix) # (C*C)
compress_content_select = torch.index_select(compress_content,1,fgcmask)
transfeatureFG = torch.mm(transmatrix,compress_content_select)
transfeature.index_copy_(1,fgcmask,transfeatureFG)
sMean = sMean.contiguous()
sMean_select = torch.index_select(sMean.view(sc,-1),1,fgcmask)
finalSMean.index_copy_(1,fgcmask,sMean_select)
out = self.unzip(transfeature.view(cb,cc,ch,cw))
return out + finalSMean.view(out.size())
|