Skip to content

I
ir-tp-net
Commit 21896135 authored by Clement Vachet's avatar Clement Vachet
Browse files
Options

Update configuration filenames

parent fc5c34cb
Pipeline #2712 failed with stages
      Hide whitespace changes
      Inline Side-by-side
      Showing with 9 additions and 89 deletions
      AI_Inference_CSV.py
      View file @ 21896135
      ......@@ -162,29 +162,18 @@ def main(args=None):
      padding_mode = padding_mode,
      )
      len_grid_sampler = len(grid_sampler)
      #print('length grid_sampler', len(grid_sampler))
      patch_loader = torch.utils.data.DataLoader(grid_sampler, batch_size=bs)
      aggregator = tio.inference.GridAggregator(grid_sampler, overlap_mode = 'average')
      with torch.no_grad():
      for patch_idx, patches_batch in enumerate(patch_loader):
      # print('\n\t\t patch_idx: ', patch_idx)
      #print('\t\t Preparing data...')
      inputs = patches_batch['Combined'][tio.DATA]
      # print('\t\t inputs shape: ', inputs.shape)
      input1_tiles, input2_tiles_real, GroundTruth_real = dataset.prepare_data_withfiltering(inputs, nb_image_layers, nb_corr_layers, tile_size, adjacent_tiles_dim)
      #print('\t\t Preparing data - done -')
      input1_tiles = input1_tiles.to(device)
      input2_tiles_real = input2_tiles_real.to(device)
      #GroundTruth_real = GroundTruth_real.to(self.device)
      # Reducing last dimension to compute loss
      #GroundTruth_real = torch.squeeze(GroundTruth_real, dim=2)
      # print('\t\t input1_tiles shape: ', input1_tiles.shape)
      # print('\t\t input2_tiles_real shape:', input2_tiles_real.shape)
      if mc_dropout:
      # Perform multiple inference (mc_passes)
      ......@@ -193,47 +182,37 @@ def main(args=None):
      outputs = model(input1_tiles, input2_tiles_real)
      outputs_all[i] = torch.squeeze(outputs)
      # Compute mean, median, std, CV (coefficient of variation), SE (standard error)
      # Compute mean, median, std, CV (coefficient of variation)
      outputs_mean = torch.mean(outputs_all,0)
      outputs_median = torch.median(outputs_all,0)[0]
      outputs_std = torch.std(outputs_all,0)
      outputs_cv = torch.div(outputs_std, torch.abs(outputs_mean))
      # outputs_se = torch.div(outputs_std, math.sqrt(mc_passes))
      outputs_combined = torch.stack((outputs_mean, outputs_median, outputs_cv), dim=1)
      else:
      outputs_combined = model(input1_tiles, input2_tiles_real)
      # print('\t\t outputs_combined shape: ', outputs_combined.shape)
      # print('outputs_combined device', outputs_combined.device)
      # Reshape outputs
      outputs_combined_reshape = torch.reshape(outputs_combined,[outputs_combined.shape[0],outputs_combined.shape[1],1,1,1])
      print('\t\t outputs_combined_reshape shape: ', outputs_combined_reshape.shape)
      input_location = patches_batch[tio.LOCATION]
      # print('\t\t input_location shape: ', input_location.shape)
      # print('\t\t input_location: ', input_location)
      # Reshape input_location to prediction_location, to fit output image size (78,62,1)
      pred_location = dataset.prediction_patch_location(input_location, tile_size, adjacent_tiles_dim)
      # print('\t\t pred_location shape: ', pred_location.shape)
      # print('\t\t pred_location: ', pred_location)
      # Add batch with location to TorchIO aggregator
      aggregator.add_batch(outputs_combined_reshape, pred_location)
      # output_tensor shape [3, 1170, 930, 122]
      output_tensor_combined = aggregator.get_output_tensor()
      # print('output_tensor_combined type: ', output_tensor_combined.dtype)
      # print('output_tensor_combined device', output_tensor_combined.device)
      # print('output_tensor_combined shape: ', output_tensor_combined.shape)
      # Extract real information of interest [3,78,62]
      output_tensor_combined_real = output_tensor_combined[:,:NbTiles_H,:NbTiles_W,0]
      # print('output_tensor_combined_real shape: ', output_tensor_combined_real.shape)
      output_combined_np = output_tensor_combined_real.numpy().squeeze()
      # print('output_combined_np type', output_combined_np.dtype)
      # print('output_combined_np shape', output_combined_np.shape)
      if mc_dropout:
      output_mean_np = output_combined_np[0,...]
      ......@@ -243,26 +222,18 @@ def main(args=None):
      imageio_output_mean = np.moveaxis(output_mean_np, 0,1)
      imageio_output_median = np.moveaxis(output_median_np, 0,1)
      imageio_output_cv = np.moveaxis(output_cv_np, 0,1)
      # print('imageio_output_mean shape', imageio_output_mean.shape)
      # print('imageio_output_median shape', imageio_output_median.shape)
      # print('imageio_output_cv shape', imageio_output_cv.shape)
      else:
      output_np = output_combined_np
      imageio_output = np.moveaxis(output_np, 0,1)
      # print('imageio_output shape', imageio_output.shape)
      time_elapsed2 = time.time() - since2
      time_inference_list.append(time_elapsed2)
      if mc_dropout:
      # print('Writing output mean image via imageio...')
      imageio.imwrite(PredictionFile_mean, imageio_output_mean)
      # print('Writing output median image via imageio...')
      imageio.imwrite(PredictionFile_median, imageio_output_median)
      # print('Writing output CV image via imageio...')
      imageio.imwrite(PredictionFile_cv, imageio_output_cv)
      else:
      # print('Writing output image via imageio...')
      imageio.imwrite(PredictionFile, imageio_output)
      print('\t\t Inference in {:.2f}s---'.format(time_elapsed2))
      ......
      AI_Inference_Direct.py
      View file @ 21896135
      ......@@ -92,19 +92,11 @@ def main(args=None):
      NbImageLayers = InputFile_Shape[3]
      NbCorrLayers = NbImageLayers -4
      InputDepth = NbCorrLayers
      print('InputFile_Shape: ', InputFile_Shape)
      print('NbTiles_H: ', NbTiles_H)
      print('NbTiles_W: ', NbTiles_W)
      print('NbImageLayers: ', NbImageLayers)
      print('InputDepth: ', InputDepth)
      # GridSampler
      print('\nGenerating Grid Sampler...')
      patch_size, patch_overlap, padding_mode = dataset.initialize_gridsampler_variables(NbImageLayers, TileSize, AdjacentTilesDim, padding_mode=None)
      print('patch_size: ',patch_size)
      print('patch_overlap: ',patch_overlap)
      print('padding_mode: ',padding_mode)
      grid_sampler = tio.data.GridSampler(
      ......@@ -114,8 +106,7 @@ def main(args=None):
      padding_mode = padding_mode,
      )
      len_grid_sampler = len(grid_sampler)
      print('length grid_sampler', len(grid_sampler))
      patch_loader = torch.utils.data.DataLoader(grid_sampler, batch_size=bs)
      aggregator = tio.data.GridAggregator(grid_sampler, overlap_mode = 'average')
      ......@@ -134,23 +125,13 @@ def main(args=None):
      #model = nn.Identity().eval()
      with torch.no_grad():
      for patch_idx, patches_batch in enumerate(patch_loader):
      print('\t patch_idx: ', patch_idx)
      #print('\t\t Preparing data...')
      inputs = patches_batch['Combined'][tio.DATA]
      print('\t\t inputs shape: ', inputs.shape)
      input1_tiles, input2_tiles_real, GroundTruth_real = dataset.prepare_data_withfiltering(inputs, NbImageLayers, NbCorrLayers, TileSize, AdjacentTilesDim)
      #print('\t\t Preparing data - done -')
      input1_tiles = input1_tiles.to(device)
      input2_tiles_real = input2_tiles_real.to(device)
      #GroundTruth_real = GroundTruth_real.to(device)
      # Reducing last dimension to compute loss
      #GroundTruth_real = torch.squeeze(GroundTruth_real, dim=2)
      print('\t\t input1_tiles shape: ', input1_tiles.shape)
      print('\t\t input2_tiles_real shape:', input2_tiles_real.shape)
      if mc_dropout:
      # Perform multiple inference (mc_passes)
      outputs_all = torch.empty(size=(mc_passes, input1_tiles.shape[0])).to(device)
      ......@@ -158,62 +139,34 @@ def main(args=None):
      outputs = model(input1_tiles, input2_tiles_real)
      outputs_all[i] = torch.squeeze(outputs)
      # Compute mean, std, CV (coefficient of variation), SE (standard error)
      # Compute mean, std, CV (coefficient of variation)
      outputs_mean = torch.mean(outputs_all,0)
      outputs_median = torch.median(outputs_all,0)[0]
      outputs_std = torch.std(outputs_all,0)
      outputs_cv = torch.div(outputs_std, torch.abs(outputs_mean))
      # outputs_se = torch.div(outputs_std, math.sqrt(mc_passes))
      outputs_combined = torch.stack((outputs_mean, outputs_median, outputs_cv), dim=1)
      print('\t\t outputs shape: ',outputs.shape)
      print('\t\t outputs device', outputs.device)
      print('\t\t outputs_all shape: ', outputs_all.shape)
      print('\t\t outputs_all device', outputs_all.device)
      print('\t\t outputs_mean shape: ', outputs_mean.shape)
      print('\t\t outputs_median shape: ', outputs_median.shape)
      print('\t\t outputs_median type: ', outputs_median.type())
      print('\t\t outputs_combined shape: ', outputs_combined.shape)
      print('\t\t outputs_mean[:20]',outputs_mean[:20])
      print('\t\t outputs_median[:20]',outputs_median[:20])
      print('\t\t outputs_std[:20]',outputs_std[:20])
      print('\t\t outputs_cv[:20]',outputs_cv[:20])
      else:
      outputs_combined = model(input1_tiles, input2_tiles_real)
      print('\t\t outputs_combined device', outputs_combined.device)
      print('\t\t outputs_combined shape: ', outputs_combined.shape)
      # Reshape outputs to match location dimensions
      outputs_combined_reshape = torch.reshape(outputs_combined,[outputs_combined.shape[0],outputs_combined.shape[1],1,1,1])
      print('\t\t outputs_combined_reshape shape: ', outputs_combined_reshape.shape)
      input_location = patches_batch[tio.LOCATION]
      print('\t\t input_location shape: ', input_location.shape)
      print('\t\t input_location type: ', input_location.dtype)
      print('\t\t input_location[:20]: ', input_location[:20])
      # Reshape input_location to prediction_location, to fit output image size (78,62,1)
      pred_location = dataset.prediction_patch_location(input_location, TileSize, AdjacentTilesDim)
      print('\t\t pred_location shape: ', pred_location.shape)
      print('\t\t pred_location[:20]: ', pred_location[:20])
      # Add batch with location to TorchIO aggregator
      aggregator.add_batch(outputs_combined_reshape, pred_location)
      # output_tensor shape [3, 1170, 930, 124]
      output_tensor_combined = aggregator.get_output_tensor()
      print('output_tensor_combined type: ', output_tensor_combined.dtype)
      print('output_tensor_combined shape: ', output_tensor_combined.shape)
      # Extract real information of interest [3, 78,62]
      output_tensor_combined_real = output_tensor_combined[:,:NbTiles_H,:NbTiles_W,0]
      print('output_tensor_combined_real shape: ', output_tensor_combined_real.shape)
      output_combined_np = output_tensor_combined_real.numpy().squeeze()
      print('output_combined_np type', output_combined_np.dtype)
      print('output_combined_np shape', output_combined_np.shape)
      if mc_dropout:
      output_mean_np = output_combined_np[0,...]
      ......@@ -223,14 +176,10 @@ def main(args=None):
      imageio_output_mean = np.moveaxis(output_mean_np, 0,1)
      imageio_output_median = np.moveaxis(output_median_np, 0,1)
      imageio_output_cv = np.moveaxis(output_cv_np, 0,1)
      print('imageio_output_mean shape', imageio_output_mean.shape)
      print('imageio_output_median shape', imageio_output_median.shape)
      print('imageio_output_cv shape', imageio_output_cv.shape)
      else:
      output_np = output_combined_np
      imageio_output = np.moveaxis(output_np, 0,1)
      print('imageio_output shape', imageio_output.shape)
      time_elapsed2 = time.time() - since2
      ......@@ -246,7 +195,7 @@ def main(args=None):
      print('Writing output image via imageio...')
      imageio.imwrite(OutputFile, imageio_output)
      time_elapsed3 = time.time() - since2
      time_elapsed1 = time.time() - since1
      print('--- Inference in {:.2f}s---'.format(time_elapsed2))
      ......
      README.md
      View file @ 21896135
      ......@@ -29,12 +29,12 @@ Update your CSV files to point to your training and validation datasets.
      Command line:
      ```
      python3 AI_Training.py --config ./Config_Files/AI_Training_Config.yaml
      python3 AI_Training.py --config ./Config_Files/AI_Training_Config_Tiles3x3.yaml
      ```
      ### 3.2 DNN inference using configuration file
      Command line:
      ```
      python3 AI_Inference_CSV.py --config ./Config_Files/AI_Inference_Config.yaml --verbose
      python3 AI_Inference_CSV.py --config ./Config_Files/AI_Inference_Config_Tiles3x3.yaml --verbose
      ```
      Markdown is supported
      0% or
      You are about to add 0 people to the discussion. Proceed with caution.
      Finish editing this message first!
      Please register or to comment