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photo_wct.py
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photo_wct.py
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"""
Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
from models import VGGEncoder, VGGDecoder
class PhotoWCT(nn.Module):
def __init__(self):
super(PhotoWCT, self).__init__()
self.e1 = VGGEncoder(1)
self.d1 = VGGDecoder(1)
self.e2 = VGGEncoder(2)
self.d2 = VGGDecoder(2)
self.e3 = VGGEncoder(3)
self.d3 = VGGDecoder(3)
self.e4 = VGGEncoder(4)
self.d4 = VGGDecoder(4)
def transform(self, cont_img, styl_img, cont_seg, styl_seg):
self.__compute_label_info(cont_seg, styl_seg)
sF4, sF3, sF2, sF1 = self.e4.forward_multiple(styl_img)
cF4, cpool_idx, cpool1, cpool_idx2, cpool2, cpool_idx3, cpool3 = self.e4(cont_img)
sF4 = sF4.data.squeeze(0)
cF4 = cF4.data.squeeze(0)
csF4 = self.__feature_wct(cF4, sF4, cont_seg, styl_seg)
Im4 = self.d4(csF4, cpool_idx, cpool1, cpool_idx2, cpool2, cpool_idx3, cpool3)
cF3, cpool_idx, cpool1, cpool_idx2, cpool2 = self.e3(Im4)
sF3 = sF3.data.squeeze(0)
cF3 = cF3.data.squeeze(0)
csF3 = self.__feature_wct(cF3, sF3, cont_seg, styl_seg)
Im3 = self.d3(csF3, cpool_idx, cpool1, cpool_idx2, cpool2)
cF2, cpool_idx, cpool = self.e2(Im3)
sF2 = sF2.data.squeeze(0)
cF2 = cF2.data.squeeze(0)
csF2 = self.__feature_wct(cF2, sF2, cont_seg, styl_seg)
Im2 = self.d2(csF2, cpool_idx, cpool)
cF1 = self.e1(Im2)
sF1 = sF1.data.squeeze(0)
cF1 = cF1.data.squeeze(0)
csF1 = self.__feature_wct(cF1, sF1, cont_seg, styl_seg)
Im1 = self.d1(csF1)
return Im1
def __compute_label_info(self, cont_seg, styl_seg):
if cont_seg.size == False or styl_seg.size == False:
return
max_label = np.max(cont_seg) + 1
self.label_set = np.unique(cont_seg)
self.label_indicator = np.zeros(max_label)
for l in self.label_set:
# if l==0:
# continue
is_valid = lambda a, b: a > 10 and b > 10 and a / b < 100 and b / a < 100
o_cont_mask = np.where(cont_seg.reshape(cont_seg.shape[0] * cont_seg.shape[1]) == l)
o_styl_mask = np.where(styl_seg.reshape(styl_seg.shape[0] * styl_seg.shape[1]) == l)
self.label_indicator[l] = is_valid(o_cont_mask[0].size, o_styl_mask[0].size)
def __feature_wct(self, cont_feat, styl_feat, cont_seg, styl_seg):
cont_c, cont_h, cont_w = cont_feat.size(0), cont_feat.size(1), cont_feat.size(2)
styl_c, styl_h, styl_w = styl_feat.size(0), styl_feat.size(1), styl_feat.size(2)
cont_feat_view = cont_feat.view(cont_c, -1).clone()
styl_feat_view = styl_feat.view(styl_c, -1).clone()
if cont_seg.size == False or styl_seg.size == False:
target_feature = self.__wct_core(cont_feat_view, styl_feat_view)
else:
target_feature = cont_feat.view(cont_c, -1).clone()
t_cont_seg = np.asarray(Image.fromarray(cont_seg, mode='RGB').resize((cont_w, cont_h), Image.NEAREST))
t_styl_seg = np.asarray(Image.fromarray(styl_seg, mode='RGB').resize((styl_w, styl_h), Image.NEAREST))
for l in self.label_set:
if self.label_indicator[l] == 0:
continue
cont_mask = np.where(t_cont_seg.reshape(t_cont_seg.shape[0] * t_cont_seg.shape[1]) == l)
styl_mask = np.where(t_styl_seg.reshape(t_styl_seg.shape[0] * t_styl_seg.shape[1]) == l)
if cont_mask[0].size <= 0 or styl_mask[0].size <= 0:
continue
cont_indi = torch.LongTensor(cont_mask[0])
styl_indi = torch.LongTensor(styl_mask[0])
if self.is_cuda:
cont_indi = cont_indi.cuda(0)
styl_indi = styl_indi.cuda(0)
cFFG = torch.index_select(cont_feat_view, 1, cont_indi)
sFFG = torch.index_select(styl_feat_view, 1, styl_indi)
tmp_target_feature = self.__wct_core(cFFG, sFFG)
target_feature.index_copy_(1, cont_indi, tmp_target_feature)
target_feature = target_feature.view_as(cont_feat)
ccsF = target_feature.float().unsqueeze(0)
return ccsF
def __wct_core(self, cont_feat, styl_feat):
cFSize = cont_feat.size()
c_mean = torch.mean(cont_feat, 1) # c x (h x w)
c_mean = c_mean.unsqueeze(1).expand_as(cont_feat)
cont_feat = cont_feat - c_mean
iden = torch.eye(cFSize[0]) # .double()
if self.is_cuda:
iden = iden.cuda()
contentConv = torch.mm(cont_feat, cont_feat.t()).div(cFSize[1] - 1) + iden
# del iden
c_u, c_e, c_v = torch.svd(contentConv, some=False)
# c_e2, c_v = torch.eig(contentConv, True)
# c_e = c_e2[:,0]
k_c = cFSize[0]
for i in range(cFSize[0] - 1, -1, -1):
if c_e[i] >= 0.00001:
k_c = i + 1
break
sFSize = styl_feat.size()
s_mean = torch.mean(styl_feat, 1)
styl_feat = styl_feat - s_mean.unsqueeze(1).expand_as(styl_feat)
styleConv = torch.mm(styl_feat, styl_feat.t()).div(sFSize[1] - 1)
s_u, s_e, s_v = torch.svd(styleConv, some=False)
k_s = sFSize[0]
for i in range(sFSize[0] - 1, -1, -1):
if s_e[i] >= 0.00001:
k_s = i + 1
break
c_d = (c_e[0:k_c]).pow(-0.5)
step1 = torch.mm(c_v[:, 0:k_c], torch.diag(c_d))
step2 = torch.mm(step1, (c_v[:, 0:k_c].t()))
whiten_cF = torch.mm(step2, cont_feat)
s_d = (s_e[0:k_s]).pow(0.5)
targetFeature = torch.mm(torch.mm(torch.mm(s_v[:, 0:k_s], torch.diag(s_d)), (s_v[:, 0:k_s].t())), whiten_cF)
targetFeature = targetFeature + s_mean.unsqueeze(1).expand_as(targetFeature)
return targetFeature
@property
def is_cuda(self):
return next(self.parameters()).is_cuda