def get_current_visuals(self):
real_A = util.tensor2im(self.real_A.data)
fake_B = util.tensor2im(self.fake_B.data)
real_B = util.tensor2im(self.real_B.data)
edge = util.atten2im(self.edge_out.data)
self.output_A_I_3 = torch.cat([self.gray, self.gray, self.gray], 1)
out_A_I = util.tensor2im(self.output_A_I_3.data)
if self.opt.skip > 0:
latent_real_A = util.tensor2im(self.latent_real_A.data)
latent_show = util.latent2im(self.latent_real_A.data)
if self.opt.patchD:
fake_patch = util.tensor2im(self.fake_patch.data)
real_patch = util.tensor2im(self.real_patch.data)
if self.opt.patch_vgg:
input_patch = util.tensor2im(self.input_patch.data)
if not self.opt.self_attention:
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
('latent_show', latent_show), ('real_B', real_B),
('real_patch', real_patch),
('fake_patch', fake_patch), ('input_patch', input_patch)])
else:
self_attention = util.atten2im(self.real_A_gray_o.data)
return OrderedDict(
[('real_A', real_A), ('fake_B', fake_B), ('real_B', real_B), ('real_patch', real_patch),
('fake_patch', fake_patch), ('input_patch', input_patch), ('input_gray', self_attention),
('latent', latent_show), ('out_A_I', out_A_I), ('edge', edge)])
else:
if not self.opt.self_attention:
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
('latent_show', latent_show), ('real_B', real_B),
('real_patch', real_patch),
('fake_patch', fake_patch)])
else:
self_attention = util.atten2im(self.real_A_gray.data)
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
('latent_show', latent_show), ('real_B', real_B),
('real_patch', real_patch),
('fake_patch', fake_patch), ('self_attention', self_attention)])
else:
if not self.opt.self_attention:
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
('latent_show', latent_show), ('real_B', real_B)])
else:
self_attention = util.atten2im(self.real_A_gray_o.data)
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('real_B', real_B),
('latent_real_A', latent_real_A), ('latent_show', latent_show),
('self_attention', self_attention)])
else:
if not self.opt.self_attention:
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('real_B', real_B)])
else:
self_attention = util.atten2im(self.real_A_gray.data)
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('real_B', real_B),
('self_attention', self_attention)])
def predict(self):
self.real_A = Variable(self.input_A, volatile=True)
self.real_A_gray = Variable(self.input_A_gray, volatile=True)
if self.opt.noise > 0:
self.noise = Variable(
torch.cuda.FloatTensor(self.real_A.size()).normal_(
mean=0, std=self.opt.noise / 255.))
self.real_A = self.real_A + self.noise
if self.opt.input_linear:
self.real_A = (self.real_A - torch.min(self.real_A)) / (
torch.max(self.real_A) - torch.min(self.real_A))
# print(np.transpose(self.real_A.data[0].cpu().float().numpy(),(1,2,0))[:2][:2][:])
if self.opt.skip == 1:
self.fake_B, self.latent_real_A = self.netG_A.forward(
self.real_A, self.real_A_gray)
else:
self.fake_B = self.netG_A.forward(self.real_A, self.real_A_gray)
# self.rec_A = self.netG_B.forward(self.fake_B)
real_A = util.tensor2im(self.real_A.data)
fake_B = util.tensor2im(self.fake_B.data)
A_gray = util.atten2im(self.real_A_gray.data)
# rec_A = util.tensor2im(self.rec_A.data)
# if self.opt.skip == 1:
# latent_real_A = util.tensor2im(self.latent_real_A.data)
# latent_show = util.latent2im(self.latent_real_A.data)
# max_image = util.max2im(self.fake_B.data, self.latent_real_A.data)
# return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
# ('latent_show', latent_show), ('max_image', max_image), ('A_gray', A_gray)])
# else:
# return OrderedDict([('real_A', real_A), ('fake_B', fake_B)])
# return OrderedDict([('fake_B', fake_B)])
return OrderedDict([('real_A', real_A), ('fake_B', fake_B)])
def get_current_visuals(self):
real_A = util.tensor2im(self.real_A.data)
fake_B = util.tensor2im(self.fake_B.data)
real_B = util.tensor2im(self.real_B.data)
if self.opt.skip > 0:
latent_real_A = util.tensor2im(self.latent_real_A.data)
latent_show = util.latent2im(self.latent_real_A.data)
if self.opt.patchD:
fake_patch = util.tensor2im(self.fake_patch.data)
real_patch = util.tensor2im(self.real_patch.data)
if self.opt.patch_vgg:
input_patch = util.tensor2im(self.input_patch.data)
if not self.opt.self_attention:
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
('latent_show', latent_show), ('real_B', real_B), ('real_patch', real_patch),
('fake_patch', fake_patch), ('input_patch', input_patch)])
else:
self_attention = util.atten2im(self.real_A_gray.data)
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
('latent_show', latent_show), ('real_B', real_B), ('real_patch', real_patch),
('fake_patch', fake_patch), ('input_patch', input_patch), ('self_attention', self_attention)])
else:
if not self.opt.self_attention:
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
('latent_show', latent_show), ('real_B', real_B), ('real_patch', real_patch),
('fake_patch', fake_patch)])
else:
self_attention = util.atten2im(self.real_A_gray.data)
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
('latent_show', latent_show), ('real_B', real_B), ('real_patch', real_patch),
('fake_patch', fake_patch), ('self_attention', self_attention)])
else:
if not self.opt.self_attention:
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
('latent_show', latent_show), ('real_B', real_B)])
else:
self_attention = util.atten2im(self.real_A_gray.data)
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('real_B', real_B),
('latent_real_A', latent_real_A), ('latent_show', latent_show),
('self_attention', self_attention)])
else:
if not self.opt.self_attention:
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('real_B', real_B)])
else:
self_attention = util.atten2im(self.real_A_gray.data)
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('real_B', real_B),
('self_attention', self_attention)])
def predict(self):
self.real_A = Variable(self.input_A)
self.real_A_gray = Variable(self.input_A_gray)
self.real_A_U = Variable(self.input_A_U)
self.real_A_V = Variable(self.input_A_V)
self.Thr = random.uniform(0.01, 0.05) #0~1
if self.opt.noise > 0:
self.noise = Variable(
torch.cuda.FloatTensor(self.real_A.size()).normal_(
mean=0, std=self.opt.noise / 255.))
self.real_A = self.real_A + self.noise
if self.opt.input_linear:
self.real_A = (self.real_A - torch.min(self.real_A)) / (
torch.max(self.real_A) - torch.min(self.real_A))
# print(np.transpose(self.real_A.data[0].cpu().float().numpy(),(1,2,0))[:2][:2][:])
if self.opt.skip == 1:
self.fake_B, self.latent_real_A, gray_out = self.netG_A.forward(
self.real_A, self.real_A_gray, self.real_A_U, self.real_A_V,
self.Thr)
else:
self.fake_B = self.netG_A.forward(self.real_A, self.real_A_gray,
self.real_A_constraint)
# self.rec_A = self.netG_B.forward(self.fake_B)
real_A = util.tensor2im(self.real_A.data)
fake_B = util.tensor2im(self.fake_B.data)
A_gray = util.atten2im(gray_out.data)
latent_V, latent_H, latent_S = util.latent_dff_2im(
self.latent_real_A.data)
# rec_A = util.tensor2im(self.rec_A.data)
# if self.opt.skip == 1:
# latent_real_A = util.tensor2im(self.latent_real_A.data)
# latent_show = util.latent2im(self.latent_real_A.data)
# max_image = util.max2im(self.fake_B.data, self.latent_real_A.data)
# return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
# ('latent_show', latent_show), ('max_image', max_image), ('A_gray', A_gray)])
# else:
# return OrderedDict([('real_A', real_A), ('fake_B', fake_B)])
# return OrderedDict([('fake_B', fake_B)])
return OrderedDict([('real_A', real_A), ('fake_B', fake_B),
('latent_V', latent_V), ('latent_H', latent_H),
('latent_S', latent_S)])
def predict(self):
self.real_A = Variable(self.input_A, volatile=True)
self.real_A_gray = Variable(self.input_A_gray, volatile=True)
if self.opt.noise > 0:
self.noise = Variable(torch.cuda.FloatTensor(self.real_A.size()).normal_(mean=0, std=self.opt.noise/255.))
self.real_A = self.real_A + self.noise
if self.opt.input_linear:
self.real_A = (self.real_A - torch.min(self.real_A))/(torch.max(self.real_A) - torch.min(self.real_A))
if self.opt.skip == 1:
self.fake_B, self.latent_real_A = self.netG_A.forward(self.real_A, self.real_A_gray)
else:
self.fake_B = self.netG_A.forward(self.real_A, self.real_A_gray)
real_A = util.tensor2im(self.real_A.data)
fake_B = util.tensor2im(self.fake_B.data)
A_gray = util.atten2im(self.real_A_gray.data)
return OrderedDict([('real_A', real_A), ('fake_B', fake_B)])
