def compute_metrics(net, valLoader, upscaling, multiscale):
net.eval()
psnr_low = 0
psnr_super = 0
ssim_low = 0
ssim_super = 0
cpu = torch.device("cpu")
cuda = torch.device("cuda:0")
with torch.no_grad():
for i, batch in enumerate(valLoader):
highres, lowres = batch
lowres = lowres.to(cuda)
highres = highres.to(cuda)
if (multiscale):
superres = net(lowres, upscaling)
else:
superres = net(lowres)
lowres = lowres.to(cpu)
lowres = ToImage()(lowres.view(lowres.size()[1:]))
lowres = lowres.resize(
(lowres.size[0] * upscaling, lowres.size[1] * upscaling),
Image.BICUBIC)
lowres = ToTensor()(lowres)
lowres = lowres.view([1] + list(lowres.size()))
lowres = lowres.to(cuda)
psnr_super += compute_psnr(superres, highres)
psnr_low += compute_psnr(lowres, highres)
ssim_super += compute_msssim(superres, highres)
print("|")
ssim_low += compute_msssim(lowres, highres)
return psnr_low / len(valLoader), psnr_super / len(valLoader), \
ssim_low / len(valLoader), ssim_super / len(valLoader)
def normalized_dicom_pixels(ds):
signed = ds.PixelRepresentation == 1
slope = float(ds.RescaleSlope)
intercept = float(ds.RescaleIntercept)
x = ds.pixel_array
if ds.BitsStored == 12 and not signed and int(intercept) > -100:
# see: https://www.kaggle.com/jhoward/cleaning-the-data-for-rapid-prototyping-fastai
x += 1000
px_mode = 4096
x[x >= px_mode] = x[x >= px_mode] - px_mode
intercept -= 1000
x = np.frombuffer(x, dtype='int16' if signed else 'uint16')
x = np.array(x, dtype='float32')
x = x * slope + intercept
x = torch.Tensor(x)
if x.numel() != 512 * 512:
#dim = torch.sqrt(torch.Tensor([x.numel()]))
#if dim.floor() != dim.ceil():
# raise ValueError('Non-square number of input elements '
# f'got {x.numel()} (dcm header reports {ds.Rows}x{ds.Columns})')
#dim = dim.int().item()
if ds.Columns * ds.Rows != x.numel():
raise ValueError(
f'dimensions {ds.Rows}x{ds.Columns} does not match numel {x.numel()}'
)
x = x.view(1, ds.Columns, ds.Rows)
x = ToPILImage()(x)
x = Resize((512, 512))(x)
x = ToTensor()(x)
#print(f'Successfully resized from {ds.Rows}x{ds.Columns}')
x = x.view(1, 512, 512)
return x
