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 __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 __init__(self, args):
super(PhotoWCT, self).__init__()
self.args = args
if "16x" in self.args.mode:
if "JointED" not in self.args.mode:
### 16x model trained for pwct
e1 = '../KD/Experiments/Small16xEncoder_pwct/e1/weights/12-20181020-1610_1SE_E25S0-2.pth'
e2 = '../KD/Experiments/Small16xEncoder_pwct/e2/weights/12-20181020-1602_2SE_E25S0-2.pth'
e3 = '../KD/Experiments/Small16xEncoder_pwct/e3/weights/12-20181019-0420_3SE_E25S0-2.pth'
e4 = '../KD/Experiments/Small16xEncoder_pwct/e4/weights/12-20181019-0349_4SE_E25S0-2.pth'
d1 = '../KD/Experiments/Small16xDecoder_pwct/e1/weights/12-20181021-0913_1SD_E25S0-3.pth'
d2 = '../KD/Experiments/Small16xDecoder_pwct/e2/weights/12-20181021-1418_2SD_E25S0-3.pth'
d3 = '../KD/Experiments/Small16xDecoder_pwct/e3/weights/12-20181020-1638_3SD_E25S0-3.pth'
d4 = '../KD/Experiments/Small16xDecoder_pwct/e4/weights/12-20181020-1637_4SD_E25S0-3.pth'
else:
### 16x model trained for pwct, JointED
e1 = '../KD/Experiments/Small16xEncoder_pwct/e1_JointED/weights/12-20181026-0259_1SED_E25S0-2.pth'
d1 = '../KD/Experiments/Small16xEncoder_pwct/e1_JointED/weights/12-20181026-0259_1SED_E25S0-3.pth'
e2 = '../KD/Experiments/Small16xEncoder_pwct/e2_JointED/weights/12-20181026-0256_2SED_E25S0-2.pth'
d2 = '../KD/Experiments/Small16xEncoder_pwct/e2_JointED/weights/12-20181026-0256_2SED_E25S0-3.pth'
e3 = '../KD/Experiments/Small16xEncoder_pwct/e3_JointED/weights/12-20181026-0255_3SED_E25S0-2.pth'
d3 = '../KD/Experiments/Small16xEncoder_pwct/e3_JointED/weights/12-20181026-0255_3SED_E25S0-3.pth'
e4 = '../KD/Experiments/Small16xEncoder_pwct/e4_JointED/weights/12-20181026-0255_4SED_E25S0-2.pth'
d4 = '../KD/Experiments/Small16xEncoder_pwct/e4_JointED/weights/12-20181026-0255_4SED_E25S0-3.pth'
if self.args.mode == "" or self.args.mode == "original":
#### original model
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)
elif "16x" in self.args.mode:
self.e1 = SmallEncoder_16x_plus(1, e1)
self.d1 = SmallDecoder_16x(1, d1)
self.e2 = SmallEncoder_16x_plus(2, e2)
self.d2 = SmallDecoder_16x(2, d2)
self.e3 = SmallEncoder_16x_plus(3, e3)
self.d3 = SmallDecoder_16x(3, d3)
self.e4 = SmallEncoder_16x_plus(4, e4)
self.d4 = SmallDecoder_16x(4, d4)
else:
print("wrong mode")
exit(1)
p_wct.d1.load_state_dict(torch.load('pth_models/feature_invertor_conv1.pth'))
p_wct.e2.load_state_dict(torch.load('pth_models/vgg_normalised_conv2.pth'))
p_wct.d2.load_state_dict(torch.load('pth_models/feature_invertor_conv2.pth'))
p_wct.e3.load_state_dict(torch.load('pth_models/vgg_normalised_conv3.pth'))
p_wct.d3.load_state_dict(torch.load('pth_models/feature_invertor_conv3.pth'))
p_wct.e4.load_state_dict(torch.load('pth_models/vgg_normalised_conv4.pth'))
p_wct.d4.load_state_dict(torch.load('pth_models/feature_invertor_conv4.pth'))
if __name__ == '__main__':
if not os.path.exists('pth_models'):
os.mkdir('pth_models')
## VGGEncoder1
vgg1 = load_lua('models/vgg_normalised_conv1_1_mask.t7')
e1 = VGGEncoder(1)
weight_assign(vgg1, e1, {
'conv0': 0,
'conv1_1': 2,
})
torch.save(e1.state_dict(), 'pth_models/vgg_normalised_conv1.pth')
## VGGDecoder1
inv1 = load_lua('models/feature_invertor_conv1_1_mask.t7')
d1 = VGGDecoder(1)
weight_assign(inv1, d1, {
'conv1_1': 1,
})
torch.save(d1.state_dict(), 'pth_models/feature_invertor_conv1.pth')
## VGGEncoder2
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)
# print(cont_seg)
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()
if len(cont_seg.shape) == 2:
t_cont_seg = np.asarray(
Image.fromarray(cont_seg).resize((cont_w, cont_h),
Image.NEAREST))
else:
t_cont_seg = np.asarray(
Image.fromarray(cont_seg, mode='RGB').resize(
(cont_w, cont_h), Image.NEAREST))
if len(styl_seg.shape) == 2:
t_styl_seg = np.asarray(
Image.fromarray(styl_seg).resize((styl_w, styl_h),
Image.NEAREST))
else:
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)
# print(len(cont_indi))
# print(len(styl_indi))
tmp_target_feature = self.__wct_core(cFFG, sFFG)
# print(tmp_target_feature.size())
if torch.__version__ >= "0.4.0":
# This seems to be a bug in PyTorch 0.4.0 to me.
new_target_feature = torch.transpose(target_feature, 1, 0)
new_target_feature.index_copy_(0, cont_indi, \
torch.transpose(tmp_target_feature,1,0))
target_feature = torch.transpose(new_target_feature, 1, 0)
else:
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
def forward(self, *input):
pass
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,
label_weight_list):
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) #(1,channel,weight,height) -> (channel,weight,height)
cF4 = cF4.data.squeeze(0)
csF4 = self.__feature_wct(cF4, sF4, cont_seg, styl_seg,
label_weight_list)
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,
label_weight_list)
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,
label_weight_list)
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,
label_weight_list)
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 #label 개수
self.label_set = np.unique(cont_seg) #[0,1,2,..]
self.label_indicator = np.zeros(max_label) #[0,0,0,0,..]
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)
#레이블의 위치 저장
self.label_indicator[l] = is_valid(o_cont_mask[0].size,
o_styl_mask[0].size)
#조건 부합하는지에 따라 1.0, 0.0 저장
def __feature_wct(self, cont_feat, styl_feat, cont_seg, styl_seg,
label_weight_list):
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() # 3차원을 2차원으로 조정
styl_feat_view = styl_feat.view(
styl_c, -1).clone() # ex. (512,x,y) -> (512,x*y)
if cont_seg.size == False or styl_seg.size == False:
target_feature = self.__wct_core(cont_feat_view, styl_feat_view)
else:
# print(cont_feat)
target_feature = cont_feat.view(cont_c, -1).clone() # 512 * z
# print(target_feature)
if len(cont_seg.shape) == 2: # 2차원인 경우
t_cont_seg = np.asarray(
Image.fromarray(cont_seg).resize((cont_w, cont_h),
Image.NEAREST)
) #content segment image를 feature의 width, height크기에 맞춰서 조정 후 numpy로 전환해서 저장
else: #그 이상인 경우
t_cont_seg = np.asarray(
Image.fromarray(cont_seg, mode='RGB').resize(
(cont_w, cont_h), Image.NEAREST))
if len(styl_seg.shape) == 2: #style segment도 마찬가지로 크기조정
t_styl_seg = np.asarray(
Image.fromarray(styl_seg).resize((styl_w, styl_h),
Image.NEAREST))
else:
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 #indicator[l]이 false면 즉 변환할 필요가 없는 레이블이면 continue
cont_mask = np.where(
t_cont_seg.reshape(t_cont_seg.shape[0] *
t_cont_seg.shape[1]) ==
l) #l과 label값이 같은 곳의 위치 저장하는 배열
styl_mask = np.where(
t_styl_seg.reshape(
t_styl_seg.shape[0] *
t_styl_seg.shape[1]) == l) # z = x*y 중 label이 같은 일부
if cont_mask[0].size <= 0 or styl_mask[0].size <= 0:
continue
cont_indi = torch.LongTensor(
cont_mask[0]) #마스크를 long으로 자료형 변환 #(1,z<=x*y)
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) #마스크와 인덱스가 같은 픽셀들 선택 c * z
sFFG = torch.index_select(styl_feat_view, 1, styl_indi)
# print(len(cont_indi))
# print(len(styl_indi))
tmp_target_feature = self.__wct_core(cFFG, sFFG) #실질적인 전환
if torch.__version__ >= "0.4.0":
# This seems to be a bug in PyTorch 0.4.0 to me.
if label_weight_list[l] == 0:
tmp_target_feature = cFFG
else:
tmp_target_feature = label_weight_list[
l] * tmp_target_feature
#######################################label_weight에 맞춰서 변환정도 조절#######################################
new_target_feature = torch.transpose(target_feature, 1, 0)
new_target_feature.index_copy_(0, cont_indi, \
torch.transpose(tmp_target_feature,1,0))
target_feature = torch.transpose(new_target_feature, 1, 0)
else:
target_feature.index_copy_(1, cont_indi,
tmp_target_feature)
target_feature = target_feature.view_as(
cont_feat) # c x (hxw) -> c x h x w
ccsF = target_feature.float().unsqueeze(
0) # c x h x w -> 1 x c x h x w
return ccsF
def __wct_core(self, cont_feat, styl_feat):
cFSize = cont_feat.size() # c x z (ex. 512 * 7214)
c_mean = torch.mean(cont_feat, 1) # 행의 평균을 구한 1차원 배열 [c, ]
c_mean = c_mean.unsqueeze(1).expand_as(
cont_feat) # 1차원 배열을 다시 2차원 c x z로 확장
# [1,2,3]이었으면 [[1,1,1],[2,2,2],[3,3,3]]으로, 즉 계산을 위한 차원 확장
cont_feat = cont_feat - c_mean # feature에 행(c)의 평균값 제거
iden = torch.eye(cFSize[0]) # .double()
# c크기대로 대각선만 1.0 인 행렬 [[1,0,0],[0,1,0],[0,0,1]], 즉 단위행렬
if self.is_cuda:
iden = iden.cuda()
contentConv = torch.mm(
cont_feat,
cont_feat.t()).div(cFSize[1] - 1) + iden # 고유벡터와 고유값을 구하기 위한 과정
# (CFFG * CFFG.T) / (h*w -1) + iden
# 크기는 c x c
# 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
#style image도 content와 같은 과정 계산
sFSize = styl_feat.size() # c x (hxw)
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)
epsilon = 0.001 # 0으로 나뉘는걸 방지하는 hyper parameter
ep_mat = torch.ones(k_c, dtype=torch.float32, device='cuda') * epsilon
c_d = c_d + ep_mat
step1 = torch.mm(c_v[:, 0:k_c], torch.diag(c_d))
step2 = torch.mm(step1,
(c_v[:, 0:k_c].t())) #P_C = c_v * 1/root(c_d) * c_v.T
whiten_cF = torch.mm(step2, cont_feat) #P_c * H_c
s_d = (s_e[0:k_s]).pow(0.5)
epsilon2 = 0.001 # 0으로 나뉘는걸 방지하는 hyper parameter
ep_mat2 = torch.ones(k_s, dtype=torch.float32,
device='cuda') * epsilon2
s_d = s_d + ep_mat2
targetFeature = torch.mm(
torch.mm(torch.mm(s_v[:, 0:k_s], torch.diag(s_d)),
(s_v[:, 0:k_s].t())),
whiten_cF) # P_S = s_c * root(s_v) * s_c.T
targetFeature = targetFeature + s_mean.unsqueeze(1).expand_as(
targetFeature) #featue = P_s * P_c * H_c
return targetFeature
@property
def is_cuda(self):
return next(self.parameters()).is_cuda
def forward(self, *input):
pass
import torch
import torch.nn as nn
from torch.utils.serialization import load_lua
from models import VGGEncoder, VGGDecoder
def weight_assign(lua, pth, maps):
for k, v in maps.items():
getattr(pth, k).weight = nn.Parameter(lua.get(v).weight.float())
getattr(pth, k).bias = nn.Parameter(lua.get(v).bias.float())
if __name__ == '__main__':
## VGGEncoder4
vgg4 = load_lua('pretrained/encoder.t7', long_size=8)
e4 = VGGEncoder()
weight_assign(vgg4, e4, {
'conv0': 0,
'conv1_1': 2,
'conv1_2': 5,
'conv2_1': 9,
'conv2_2': 12,
'conv3_1': 16,
'conv3_2': 19,
'conv3_3': 22,
'conv3_4': 25,
'conv4_1': 29,
})
torch.save(e4.state_dict(), 'pretrained/encoder_pretrained.pth')
## VGGDecoder4
torch.load('pth_models/feature_invertor_conv2.pth'))
p_wct.e3.load_state_dict(torch.load('pth_models/vgg_normalised_conv3.pth'))
p_wct.d3.load_state_dict(
torch.load('pth_models/feature_invertor_conv3.pth'))
p_wct.e4.load_state_dict(torch.load('pth_models/vgg_normalised_conv4.pth'))
p_wct.d4.load_state_dict(
torch.load('pth_models/feature_invertor_conv4.pth'))
if __name__ == '__main__':
if not os.path.exists('pth_models'):
os.mkdir('pth_models')
## VGGEncoder1
vgg1 = load_lua('models/vgg_normalised_conv1_1_mask.t7')
e1 = VGGEncoder(1)
weight_assign(vgg1, e1, {
'conv0': 0,
'conv1_1': 2,
})
torch.save(e1.state_dict(), 'pth_models/vgg_normalised_conv1.pth')
## VGGDecoder1
inv1 = load_lua('models/feature_invertor_conv1_1_mask.t7')
d1 = VGGDecoder(1)
weight_assign(inv1, d1, {
'conv1_1': 1,
})
torch.save(d1.state_dict(), 'pth_models/feature_invertor_conv1.pth')
## VGGEncoder2
class PhotoWCT(nn.Module):
def __init__(self, args):
super(PhotoWCT, self).__init__()
self.args = args
if "16x" in self.args.mode:
if "JointED" not in self.args.mode:
### 16x model trained for pwct
e1 = '../KD/Experiments/Small16xEncoder_pwct/e1/weights/12-20181020-1610_1SE_E25S0-2.pth'
e2 = '../KD/Experiments/Small16xEncoder_pwct/e2/weights/12-20181020-1602_2SE_E25S0-2.pth'
e3 = '../KD/Experiments/Small16xEncoder_pwct/e3/weights/12-20181019-0420_3SE_E25S0-2.pth'
e4 = '../KD/Experiments/Small16xEncoder_pwct/e4/weights/12-20181019-0349_4SE_E25S0-2.pth'
d1 = '../KD/Experiments/Small16xDecoder_pwct/e1/weights/12-20181021-0913_1SD_E25S0-3.pth'
d2 = '../KD/Experiments/Small16xDecoder_pwct/e2/weights/12-20181021-1418_2SD_E25S0-3.pth'
d3 = '../KD/Experiments/Small16xDecoder_pwct/e3/weights/12-20181020-1638_3SD_E25S0-3.pth'
d4 = '../KD/Experiments/Small16xDecoder_pwct/e4/weights/12-20181020-1637_4SD_E25S0-3.pth'
else:
### 16x model trained for pwct, JointED
e1 = '../KD/Experiments/Small16xEncoder_pwct/e1_JointED/weights/12-20181026-0259_1SED_E25S0-2.pth'
d1 = '../KD/Experiments/Small16xEncoder_pwct/e1_JointED/weights/12-20181026-0259_1SED_E25S0-3.pth'
e2 = '../KD/Experiments/Small16xEncoder_pwct/e2_JointED/weights/12-20181026-0256_2SED_E25S0-2.pth'
d2 = '../KD/Experiments/Small16xEncoder_pwct/e2_JointED/weights/12-20181026-0256_2SED_E25S0-3.pth'
e3 = '../KD/Experiments/Small16xEncoder_pwct/e3_JointED/weights/12-20181026-0255_3SED_E25S0-2.pth'
d3 = '../KD/Experiments/Small16xEncoder_pwct/e3_JointED/weights/12-20181026-0255_3SED_E25S0-3.pth'
e4 = '../KD/Experiments/Small16xEncoder_pwct/e4_JointED/weights/12-20181026-0255_4SED_E25S0-2.pth'
d4 = '../KD/Experiments/Small16xEncoder_pwct/e4_JointED/weights/12-20181026-0255_4SED_E25S0-3.pth'
if self.args.mode == "" or self.args.mode == "original":
#### original model
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)
elif "16x" in self.args.mode:
self.e1 = SmallEncoder_16x_plus(1, e1)
self.d1 = SmallDecoder_16x(1, d1)
self.e2 = SmallEncoder_16x_plus(2, e2)
self.d2 = SmallDecoder_16x(2, d2)
self.e3 = SmallEncoder_16x_plus(3, e3)
self.d3 = SmallDecoder_16x(3, d3)
self.e4 = SmallEncoder_16x_plus(4, e4)
self.d4 = SmallDecoder_16x(4, d4)
else:
print("wrong mode")
exit(1)
@torch.no_grad()
def transform(self, cont_img, styl_img, cont_seg, styl_seg):
self.__compute_label_info(cont_seg, styl_seg)
if self.args.mode == "" or self.args.mode == "original":
#### original model
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)
elif "16x" in self.args.mode:
#### slimmed model
sF4 = self.e4(styl_img)
torch.cuda.empty_cache()
cF4, cpool_idx, cpool1, cpool_idx2, cpool2, cpool_idx3, cpool3 = self.e4.forward_stem(
cont_img)[3:]
torch.cuda.empty_cache()
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)
torch.cuda.empty_cache()
sF3 = self.e3(styl_img)
torch.cuda.empty_cache()
cF3, cpool_idx, cpool1, cpool_idx2, cpool2 = self.e3.forward_stem(
cont_img)[2:]
torch.cuda.empty_cache()
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)
torch.cuda.empty_cache()
sF2 = self.e2(styl_img)
torch.cuda.empty_cache()
cF2, cpool_idx, cpool = self.e2.forward_stem(Im3)[1:]
torch.cuda.empty_cache()
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)
torch.cuda.empty_cache()
sF1 = self.e1(styl_img)
torch.cuda.empty_cache()
cF1 = self.e1(Im2)
torch.cuda.empty_cache()
sF1 = sF1.data.squeeze(0)
cF1 = cF1.data.squeeze(0)
csF1 = self.__feature_wct(cF1, sF1, cont_seg, styl_seg)
Im1 = self.d1(csF1)
torch.cuda.empty_cache()
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()
if len(cont_seg.shape) == 2:
t_cont_seg = np.asarray(
Image.fromarray(cont_seg).resize((cont_w, cont_h),
Image.NEAREST))
else:
t_cont_seg = np.asarray(
Image.fromarray(cont_seg, mode='RGB').resize(
(cont_w, cont_h), Image.NEAREST))
if len(styl_seg.shape) == 2:
t_styl_seg = np.asarray(
Image.fromarray(styl_seg).resize((styl_w, styl_h),
Image.NEAREST))
else:
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)
# print(len(cont_indi))
# print(len(styl_indi))
tmp_target_feature = self.__wct_core(cFFG, sFFG)
# print(tmp_target_feature.size())
if torch.__version__ >= "0.4.0":
# This seems to be a bug in PyTorch 0.4.0 to me.
new_target_feature = torch.transpose(target_feature, 1, 0)
new_target_feature.index_copy_(0, cont_indi, \
torch.transpose(tmp_target_feature,1,0))
target_feature = torch.transpose(new_target_feature, 1, 0)
else:
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
def forward(self, *input):
pass
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
