def forward(self, in0, in1, retPerLayer=None):
assert(in0.size()[0]==1) # currently only supports batchSize 1
if(self.colorspace=='RGB'):
value = util.dssim(1.*util.tensor2im(in0.data), 1.*util.tensor2im(in1.data), range=255.).astype('float')
elif(self.colorspace=='Lab'):
value = util.dssim(util.tensor2np(util.tensor2tensorlab(in0.data,to_norm=False)),
util.tensor2np(util.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float')
ret_var = Variable( torch.Tensor((value,) ) )
if(self.use_gpu):
ret_var = ret_var.cuda()
return ret_var
def get_current_visuals(self):
zoom_factor = 256 / self.var_ref.data.size()[2]
ref_img = util.tensor2im(self.var_ref.data)
p0_img = util.tensor2im(self.var_p0.data)
p1_img = util.tensor2im(self.var_p1.data)
ref_img_vis = zoom(ref_img, [zoom_factor, zoom_factor, 1], order=0)
p0_img_vis = zoom(p0_img, [zoom_factor, zoom_factor, 1], order=0)
p1_img_vis = zoom(p1_img, [zoom_factor, zoom_factor, 1], order=0)
return OrderedDict([('ref', ref_img_vis), ('p0', p0_img_vis),
('p1', p1_img_vis)])
def forward(self, in0, in1, retPerLayer=None):
assert in0.size()[0] == 1 # currently only supports batchSize 1
if self.colorspace == "RGB":
value = util.dssim(
1.0 * util.tensor2im(in0.data),
1.0 * util.tensor2im(in1.data),
range=255.0,
).astype("float")
elif self.colorspace == "Lab":
value = util.dssim(
util.tensor2np(util.tensor2tensorlab(in0.data, to_norm=False)),
util.tensor2np(util.tensor2tensorlab(in1.data, to_norm=False)),
range=100.0,
).astype("float")
ret_var = Variable(torch.Tensor((value, )))
if self.use_gpu:
ret_var = ret_var.cuda()
return ret_var
ref = lpips.im2tensor(lpips.load_image(opt.ref_path))
pred = Variable(lpips.im2tensor(lpips.load_image(opt.pred_path)),
requires_grad=True)
if (opt.use_gpu):
with torch.no_grad():
ref = ref.cuda()
pred = pred.cuda()
optimizer = torch.optim.Adam([
pred,
], lr=1e-3, betas=(0.9, 0.999))
plt.ion()
fig = plt.figure(1)
ax = fig.add_subplot(131)
ax.imshow(lpips.tensor2im(ref))
ax.set_title('target')
ax = fig.add_subplot(133)
ax.imshow(lpips.tensor2im(pred.data))
ax.set_title('initialization')
for i in range(1000):
dist = loss_fn.forward(pred, ref)
optimizer.zero_grad()
dist.backward()
optimizer.step()
pred.data = torch.clamp(pred.data, -1, 1)
if i % 10 == 0:
print('iter %d, dist %.3g' % (i, dist.view(-1).data.cpu().numpy()[0]))
pred.data = torch.clamp(pred.data, -1, 1)
