def _apply_mask(self, output_dir): try: transforms.Compose([ LoadNifti(), ApplyMask(os.path.join(output_dir, "labels.nii.gz")) ])(os.path.join(output_dir, "brainmask.nii.gz")) except Exception as e: self.LOGGER.warning(e)
def apply_mask(file, mask): transform_ = transforms.Compose([ApplyMask(mask)]) return transform_(file)
generated_mrbrains = transform( "/mnt/md0/Research/DualUNet/Reconstructed_Normalized_MRBrainS_Image_80.nii.gz" ) generated_abide = transform( "/mnt/md0/Research/DualUNet/Reconstructed_Normalized_ABIDE_Image_80.nii.gz" ) segmentation_iseg = transform( "/mnt/md0/Research/DualUNet/Reconstructed_Segmented_iSEG_Image_80.nii.gz" ) segmentation_mrbrains = transform( "/mnt/md0/Research/DualUNet/Reconstructed_Segmented_MRBrainS_Image_80.nii.gz" ) segmentation_abide = transform( "/mnt/md0/Research/DualUNet/Reconstructed_Segmented_ABIDE_Image_80.nii.gz" ) generated_iseg = torch.tensor(ApplyMask(segmentation_iseg)(generated_iseg)) generated_mrbrains = torch.tensor( ApplyMask(segmentation_mrbrains)(generated_mrbrains)) generated_abide = torch.tensor( ApplyMask(segmentation_abide)(generated_abide)) train_samples = torch.cat([iseg_inputs, mrbrains_inputs, abide_inputs], dim=0).view(90, c * d * h * w) generated_samples = torch.cat( [generated_iseg, generated_mrbrains, generated_abide], dim=0).view(3, c * d * h * w) p_inputs = compute_probs(train_samples) p_generated = compute_probs(generated_samples) js_divergence_inputs = compute_js_divergence(p_inputs).item()