refactor: Replace print statements with logger in run.py and network.py

- Replaced print statements with logger.info() in ReconstructionRunner class
- Added logging for input and output tensor shapes in NHRepNet forward method
- Improved logging consistency and added docstring for network forward method

code/conversion/run.py

@@ -242,12 +242,12 @@ class ReconstructionRunner:
 
 
             if epoch % self.conf.get_int('train.status_frequency') == 0:
-                print('Train Epoch: [{}/{} ({:.0f}%)]\tTrain Loss: {:.6f}\t Manifold loss: {:.6f}'
+                logger.info('Train Epoch: [{}/{} ({:.0f}%)]\tTrain Loss: {:.6f}\t Manifold loss: {:.6f}'
                     '\tManifold patch loss: {:.6f}\t grad loss h: {:.6f}\t normals loss all: {:.6f}\t normals loss h: {:.6f}\t Manifold consistency loss: {:.6f}\tCorrection loss: {:.6f}\t Offsurface loss: {:.6f}'.format(
                     epoch, self.nepochs, 100. * epoch / self.nepochs,
                     loss.item(), mnfld_loss.item(), mnfld_loss_patch.item(), grad_loss_h.item(), normals_loss.item(), normals_loss_h.item(), mnfld_consistency_loss.item(), correction_loss.item(), offsurface_loss.item()))
                 if args.feature_sample:
-                    print('feature mnfld loss: {} patch loss: {} cons loss: {}'.format(feature_mnfld_loss.item(), feature_loss_patch.item(), feature_loss_cons.item()))
+                    logger.info('feature mnfld loss: {} patch loss: {} cons loss: {}'.format(feature_mnfld_loss.item(), feature_loss_patch.item(), feature_loss_cons.item()))
 
         self.tracing()
 
@@ -278,7 +278,7 @@ class ReconstructionRunner:
         example = torch.rand(224,3).to(device)
         traced_script_module = torch.jit.trace(network, example)
         traced_script_module.save(save_prefix + self.foldername + "_model_h.pt")
-        print('converting to pt finished')
+        logger.info('converting to pt finished')
 
     def plot_shapes(self, epoch, path=None, with_cuts=False, file_suffix="all"):
         # plot network validation shapes

code/model/network.py

@@ -3,6 +3,7 @@ import torch.nn as nn
 import torch
 from torch.autograd import grad
 
+from utils.logger import logger
 #borrowed from siren paper
 class Sine(nn.Module):
     def __init(self):
@@ -143,10 +144,18 @@ class NHRepNet(nn.Module):
 
 
     def forward(self, input):
+        '''
+        :param input: 2D array of floats, each row represents a point in 3D space
+            e.g. input = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+        :return: 2D array of floats, each row represents a point in 3D space
+            e.g. output = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+        :note: The input tensor should have a shape of (N, 3), where N is the number of points. usually N = 100000
+               The output tensor will have a shape of (N, 5), where each row corresponds to the processed output for the respective input point.
+        '''
+        # logger.info(f"input shape: {input.shape}")
         x = input
         for layer in range(0, self.sdf_layers - 1):
             lin = getattr(self, "sdf_" + str(layer))
-
             if layer in self.skip_in:
                 x = torch.cat([x, input], -1) / np.sqrt(2)
 
@@ -160,8 +169,13 @@ class NHRepNet(nn.Module):
         # h = self.nested_cvx_output_soft_blend(output_value, self.csg_tree, self.flag_convex)
         
         if self.flag_output == 0:
+            # logger.info(f"h shape: {h.shape}")
+            # logger.info(f"output_value shape: {output_value.shape}")
             return torch.cat((h, output_value), 1) # return all
         elif self.flag_output == 1:
+            # logger.info(f"h shape: {h.shape}")
             return h #return h
         else:
+            # logger.info(f"output_value shape: {output_value.shape}")
+            # logger.info(f"flag_output: {self.flag_output}")
             return output_value[:, self.flag_output - 2] #return f_i