def check_transformation():
args = [
'--dataroot', '/data/mri/data/pdd_sliced', '--fineSize', '128',
'--input_nc', '1', '--input_channels', '0', '--data_suffix', 'npy',
'--T', '1'
]
opt = TestOptions().parse(args)
opt.same_hemisphere = True
#opt.nThreads = 1 # test code only supports nThreads = 1
#opt.batchSize = 1 # test code only supports batchSize = 1
#opt.serial_batches = True # no shuffle
dataset = SliceDataset(opt)
for i, d in enumerate(dataset):
#print(d['A'].shape, d['B'].shape, d['A_original'].shape, d['B_original'].shape)
print('B:', d['B'].min(), d['B'].max(), 'B_original:',
d['B_original'].min(), d['B_original'].max())
#np_B = util.tensor2im(d['B'], undo_norm=False)
#np_B_original = util.tensor2im(d['B_original'], undo_norm=False)
np_B = util.tensor2np(d['B'])
np_B_original = util.tensor2np(d['B_original'])
print('np_B:', np_B.min(), np_B.max(), 'np_B_original:',
np_B_original.min(), np_B_original.max())
util.save_image(np_B, 'plots/%d_B_t.png' % i)
util.save_image(np_B_original, 'plots/%d_B_real.png' % i)
print('angular error')
print(np.equal(np_B, np_B_original).sum(), np.equal(np_B, np_B).sum())
np_B = np_B * 2 - 1
np_B_original = np_B_original * 2 - 1
angular_errors(np_B, np_B)
print('-----------------')
angular_errors(np_B, np_B_original)
if i == 0:
break
def forward(self, in0, in1):
if (self.colorspace == 'RGB'):
(N, C, X, Y) = in0.size()
value = torch.mean(torch.mean(torch.mean((in0 - in1)**2,
dim=1).view(N, 1, X, Y),
dim=2).view(N, 1, 1, Y),
dim=3).view(N)
return value
elif (self.colorspace == 'Gray'):
(N, C, X, Y) = in0.size()
in0 = util.tensor2tensorGrayscale(in0)
in1 = util.tensor2tensorGrayscale(in1)
value = torch.mean(torch.mean(torch.mean((in0 - in1)**2,
dim=1).view(N, 1, X, Y),
dim=2).view(N, 1, 1, Y),
dim=3).view(N)
return value
elif (self.colorspace == 'Lab'):
assert (in0.size()[0] == 1) # currently only supports batchSize 1
value = util.l2(
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 test(self):
super().test() # forward() method is called by this
real_B = util.tensor2np(self.real_B)
fake_B = util.tensor2np(self.fake_B.detach())
psnr_B = self.PSNR(real_B, fake_B, 2.)
ssim_B = self.SSIM(real_B, fake_B, multichannel=len(real_B.shape)==3, data_range=2)
return {'PSNR': psnr_B,'SSIM': ssim_B}
def forward(self, in0, in1):
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 forward(self, in0, in1):
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 forward(self, in0, in1):
assert(in0.size()[0]==1) # currently only supports batchSize 1
if(self.colorspace=='RGB'):
(N,C,X,Y) = in0.size()
value = torch.mean(torch.mean(torch.mean((in0-in1)**2,dim=1).view(N,1,X,Y),dim=2).view(N,1,1,Y),dim=3).view(N)
return value
elif(self.colorspace=='Lab'):
value = util.l2(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_numpy(self, idx):
#print(self.real_A.data[0].min(), self.real_A.data[0].max(), self.fake_B.data[0].min(), self.fake_B.data[0].max(), self.real_B.data[0].min(), self.real_B.data[0].max())
real_A = util.tensor2np(self.real_A.data, idx)
fake_B = util.tensor2np(self.fake_B.data, idx)
real_B = util.tensor2np(self.real_B.data, idx)
return OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('real_B', real_B)])
def pose2img(pose, colors, H=512, W=512):
pose_numpy = tensor2np(pose)
pix_numpy = pose2pix(pose_numpy, H)
image_numpy = joints2image(pix_numpy, colors, H, W)
image_numpy = cv2.flip(image_numpy, 0)
return image_numpy
