Add physical coords into dataset

src/datasets.py

@@ -7,6 +7,7 @@ class singlePhotonDataset(Dataset):
         self.sampleFileList = sampleList
         self.sampleRatio = sampleRatio
         self.datasetName = datasetName
+        self._init_coords()
 
         all_samples = []
         all_labels = []
@@ -43,21 +44,34 @@ class singlePhotonDataset(Dataset):
             print(f'Adding Gaussian noise with sigma = {noiseKeV} keV to samples in {self.datasetName} dataset')
             noise = np.random.normal(loc=0.0, scale=noiseKeV, size=self.samples.shape)
             self.samples = self.samples + noise
-        self.labels = np.concatenate(all_labels, axis=0)
+        self.labels = torch.tensor(np.concatenate(all_labels, axis=0))
         self.referencePoint = np.concatenate(all_ref_pts, axis=0) if all_ref_pts else None
         
         if self.samples.shape[1] == 5: ### if sample size is 5x5, remove border pixels to make it 3x3
             self.samples = self.samples[:, 1:-1, 1:-1]  ### remove border pixels
-            self.labels = self.labels - np.array([1, 1, 0, 0])  ### adjust labels accordingly
+            self.labels = self.labels - torch.tensor([1, 1, 0, 0])  ### adjust labels accordingly
+        self.samples = torch.tensor(self.samples).unsqueeze(1).float()
+        x_grids = self.x_grid.expand(self.samples.size(0), 1, -1, -1)
+        y_grids = self.y_grid.expand(self.samples.size(0), 1, -1, -1)
+        self.samples = torch.cat([self.samples, x_grids, y_grids], dim=1)  ### concatenate coordinate channels
+        self.labels -= torch.tensor([self.samples.shape[1]/2., self.samples.shape[1]/2., 0, 0])  ### adjust labels to be centered at (0,0)
+        self.labels[:, 2] /= 650. ### normalize z coordinate (depth) to [0, 1]
         ### total number of samples
         self.length = int(self.samples.shape[0] * self.sampleRatio)
         print(f"[{self.datasetName} dataset] \t Total number of samples: {self.length}")
 
+    def _init_coords(self):
+        # Create a coordinate grid for 3x3 input
+        x = torch.linspace(-0.5, 0.5, 3)
+        y = torch.linspace(-0.5, 0.5, 3)    
+        x_grid, y_grid = torch.meshgrid(x, y, indexing='ij')  # (3,3), (3,3)
+        self.x_grid = x_grid.unsqueeze(0)  # (1, 3, 3)
+        self.y_grid = y_grid.unsqueeze(0)  # (1, 3, 3)
+
     def __getitem__(self, index):
         sample = self.samples[index]
-        sample = np.expand_dims(sample, axis=0)
         label = self.labels[index]
-        return torch.tensor(sample, dtype=torch.float32), torch.tensor(label, dtype=torch.float32)
+        return sample, label
 
     def __len__(self):
         return self.length