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 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)
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('latent_real_A', latent_real_A),
('latent_show', latent_show), ('real_B', real_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)])