Merge branch 'master' into master

GANDLF/cli/generate_metrics.py

@@ -253,18 +253,20 @@ def __percentile_clip(input_tensor, reference_tensor=None, p_min=0.5, p_max=99.5
  ] = structural_similarity_index(gt_image_infill, output_infill, mask).item()
 
  # ncc metrics
- overall_stats_dict[current_subject_id]["ncc_mean"] = ncc_mean(
- gt_image_infill, output_infill
- )
- overall_stats_dict[current_subject_id]["ncc_std"] = ncc_std(
- gt_image_infill, output_infill
- )
- overall_stats_dict[current_subject_id]["ncc_max"] = ncc_max(
- gt_image_infill, output_infill
- )
- overall_stats_dict[current_subject_id]["ncc_min"] = ncc_min(
- gt_image_infill, output_infill
- )
+ compute_ncc = parameters.get("compute_ncc", True)
+ if compute_ncc:
+ overall_stats_dict[current_subject_id]["ncc_mean"] = ncc_mean(
+ gt_image_infill, output_infill
+ )
+ overall_stats_dict[current_subject_id]["ncc_std"] = ncc_std(
+ gt_image_infill, output_infill
+ )
+ overall_stats_dict[current_subject_id]["ncc_max"] = ncc_max(
+ gt_image_infill, output_infill
+ )
+ overall_stats_dict[current_subject_id]["ncc_min"] = ncc_min(
+ gt_image_infill, output_infill
+ )
 
  # only voxels that are to be inferred (-> flat array)
  # these are required for mse, psnr, etc.

GANDLF/logger.py

@@ -34,8 +34,7 @@ def write_header(self, mode="train"):
  if os.stat(self.filename).st_size == 0:
  mode_lower = mode.lower()
  row = "epoch_no," + mode_lower + "_loss,"
- for metric in self.metrics:
- row += mode_lower + "_" + metric + ","
+ row += ",".join([mode_lower + "_" + metric for metric in self.metrics]) + ","
  row = row[:-1]
  row += "\n"
  self.csv.write(row)

GANDLF/utils/write_parse.py

@@ -22,8 +22,7 @@ def writeTrainingCSV(
  channelsID_list = channelsID.split(",") # split into list
 
  outputToWrite = "SubjectID,"
- for i, n in enumerate(channelsID_list):
- outputToWrite += "Channel_" + str(i) + ","
+ outputToWrite += ",".join(["Channel_" + str(i) for i, n in enumerate(channelsID_list)]) + ","
  if labelID is not None:
  outputToWrite += "Label"
  outputToWrite += "\n"

testing/test_full.py

@@ -218,12 +218,15 @@ def sanitize_outputDir():
  Path(outputDir).mkdir(parents=True, exist_ok=True)
 
 
-def get_temp_config_path():
+def write_temp_config_path(parameters_to_write):
  print("02_2: Creating path for temporary config file")
  temp_config_path = os.path.join(outputDir, "config_temp.yaml")
  # if found in previous run, discard.
  if os.path.exists(temp_config_path):
  os.remove(temp_config_path)
+ if parameters_to_write is not None:
+ with open(temp_config_path, "w") as file:
+ yaml.dump(parameters_to_write, file)
  return temp_config_path
 
 
@@ -482,9 +485,7 @@ def test_train_regression_brainage_rad_2d(device):
  reset=True,
  )
 
- file_config_temp = get_temp_config_path()
- with open(file_config_temp, "w") as file:
- yaml.dump(parameters_temp, file)
+ # file_config_temp = write_temp_config_path(parameters_temp)
  model_path = os.path.join(outputDir, "brain_age_best.pth.tar")
  config_path = os.path.join(outputDir, "parameters.pkl")
  optimization_result = post_training_model_optimization(model_path, config_path)
@@ -755,10 +756,7 @@ def test_train_inference_optimize_classification_rad_3d(device):
  reset=True,
  )
 
- file_config_temp = get_temp_config_path()
- parameters_temp["model"]["onnx_export"] = True
- with open(file_config_temp, "w") as file:
- yaml.dump(parameters_temp, file)
+ # file_config_temp = write_temp_config_path(parameters_temp)
  model_path = os.path.join(outputDir, all_models_regression[0] + "_best.pth.tar")
  config_path = os.path.join(outputDir, "parameters.pkl")
  optimization_result = post_training_model_optimization(model_path, config_path)
@@ -964,10 +962,7 @@ def test_train_scheduler_classification_rad_2d(device):
  parameters["nested_training"]["validation"] = -5
  sanitize_outputDir()
  ## ensure parameters are parsed every single time
- file_config_temp = get_temp_config_path()
-
- with open(file_config_temp, "w") as file:
- yaml.dump(parameters, file)
+ file_config_temp = write_temp_config_path(parameters)
 
  parameters = parseConfig(file_config_temp, version_check_flag=False)
  TrainingManager(
@@ -1090,9 +1085,7 @@ def test_train_normtype_segmentation_rad_3d(device):
  # these should raise exceptions
  for norm_type in ["none", None]:
  parameters["model"]["norm_type"] = norm_type
- file_config_temp = get_temp_config_path()
- with open(file_config_temp, "w") as file:
- yaml.dump(parameters, file)
+ file_config_temp = write_temp_config_path(parameters)
  with pytest.raises(Exception) as exc_info:
  parameters = parseConfig(file_config_temp, version_check_flag=False)
 
@@ -1151,10 +1144,7 @@ def test_train_metrics_segmentation_rad_2d(device):
  parameters["model"]["architecture"] = "resunet"
  parameters["model"]["onnx_export"] = False
  parameters["model"]["print_summary"] = False
- file_config_temp = get_temp_config_path()
-
- with open(file_config_temp, "w") as file:
- yaml.dump(parameters, file)
+ file_config_temp = write_temp_config_path(parameters)
 
  parameters = parseConfig(file_config_temp, version_check_flag=False)
  training_data, parameters["headers"] = parseTrainingCSV(
@@ -1220,9 +1210,7 @@ def get_parameters_after_alteration(loss_type: str) -> dict:
  testingDir + "/config_segmentation.yaml", version_check_flag=False
  )
  parameters["loss_function"] = loss_type
- file_config_temp = get_temp_config_path()
- with open(file_config_temp, "w") as file:
- yaml.dump(parameters, file)
+ file_config_temp = write_temp_config_path(parameters)
  # read and parse csv
  parameters = parseConfig(file_config_temp, version_check_flag=True)
  parameters["nested_training"]["testing"] = -5
@@ -1274,15 +1262,13 @@ def get_parameters_after_alteration(loss_type: str) -> dict:
 
 def test_generic_config_read():
  print("24: Starting testing reading configuration")
- file_config_temp = get_temp_config_path()
  parameters = parseConfig(
  os.path.join(baseConfigDir, "config_all_options.yaml"),
  version_check_flag=False,
  )
  parameters["data_preprocessing"]["resize_image"] = [128, 128]
 
- with open(file_config_temp, "w") as file:
- yaml.dump(parameters, file)
+ file_config_temp = write_temp_config_path(parameters)
 
  # read and parse csv
  parameters = parseConfig(file_config_temp, version_check_flag=True)
@@ -1361,7 +1347,6 @@ def test_generic_cli_function_preprocess():
  print("25: Starting testing cli function preprocess")
  file_config = os.path.join(testingDir, "config_segmentation.yaml")
  sanitize_outputDir()
- file_config_temp = get_temp_config_path()
  file_data = os.path.join(inputDir, "train_2d_rad_segmentation.csv")
 
  input_data_df, _ = parseTrainingCSV(file_data, train=False)
@@ -1391,9 +1376,7 @@ def test_generic_cli_function_preprocess():
  parameters["data_preprocessing"]["to_canonical"] = None
  parameters["data_preprocessing"]["rgba_to_rgb"] = None
 
- # store this separately for preprocess testing
- with open(file_config_temp, "w") as outfile:
- yaml.dump(parameters, outfile, default_flow_style=False)
+ file_config_temp = write_temp_config_path(parameters)
 
  preprocess_and_save(temp_csv, file_config_temp, outputDir)
  training_data, parameters["headers"] = parseTrainingCSV(
@@ -1459,7 +1442,6 @@ def test_generic_cli_function_mainrun(device):
  parameters = parseConfig(
  testingDir + "/config_segmentation.yaml", version_check_flag=False
  )
- file_config_temp = get_temp_config_path()
 
  parameters["modality"] = "rad"
  parameters["patch_size"] = patch_size["2D"]
@@ -1475,8 +1457,7 @@ def test_generic_cli_function_mainrun(device):
  ]
  parameters["model"]["architecture"] = "unet"
 
- with open(file_config_temp, "w") as file:
- yaml.dump(parameters, file)
+ file_config_temp = write_temp_config_path(parameters)
 
  file_data = os.path.join(inputDir, "train_2d_rad_segmentation.csv")
 
@@ -2142,7 +2123,6 @@ def test_train_inference_segmentation_histology_2d(device):
  Path(output_dir_patches).mkdir(parents=True, exist_ok=True)
  output_dir_patches_output = os.path.join(output_dir_patches, "histo_patches_output")
  Path(output_dir_patches_output).mkdir(parents=True, exist_ok=True)
- file_config_temp = get_temp_config_path()
 
  parameters_patch = {}
  # extracting minimal number of patches to ensure that the test does not take too long
@@ -2151,8 +2131,7 @@ def test_train_inference_segmentation_histology_2d(device):
  # define patches to be extracted in terms of microns
  parameters_patch["patch_size"] = ["1000m", "1000m"]
 
- with open(file_config_temp, "w") as file:
- yaml.dump(parameters_patch, file)
+ file_config_temp = write_temp_config_path(parameters_patch)
 
  patch_extraction(
  inputDir + "/train_2d_histo_segmentation.csv",
@@ -2218,7 +2197,6 @@ def test_train_inference_classification_histology_large_2d(device):
  Path(output_dir_patches).mkdir(parents=True, exist_ok=True)
  output_dir_patches_output = os.path.join(output_dir_patches, "histo_patches_output")
  Path(output_dir_patches_output).mkdir(parents=True, exist_ok=True)
- file_config_temp = get_temp_config_path()
 
  for sub in ["1", "2"]:
  file_to_check = os.path.join(
@@ -2233,8 +2211,7 @@ def test_train_inference_classification_histology_large_2d(device):
  parameters_patch["patch_size"] = [128, 128]
  parameters_patch["value_map"] = {0: 0, 255: 255}
 
- with open(file_config_temp, "w") as file:
- yaml.dump(parameters_patch, file)
+ file_config_temp = write_temp_config_path(parameters_patch)
 
  patch_extraction(
  inputDir + "/train_2d_histo_classification.csv",
@@ -2315,9 +2292,7 @@ def resize_for_ci(filename, scale):
  )
  parameters["modality"] = "histo"
  parameters["patch_size"] = parameters_patch["patch_size"][0]
- file_config_temp = get_temp_config_path()
- with open(file_config_temp, "w") as file:
- yaml.dump(parameters, file)
+ file_config_temp = write_temp_config_path(parameters)
  parameters = parseConfig(file_config_temp, version_check_flag=False)
  parameters["model"]["dimension"] = 2
  # read and parse csv
@@ -2394,16 +2369,14 @@ def test_train_inference_classification_histology_2d(device):
  shutil.rmtree(output_dir_patches)
  Path(output_dir_patches).mkdir(parents=True, exist_ok=True)
  output_dir_patches_output = os.path.join(output_dir_patches, "histo_patches_output")
- file_config_temp = get_temp_config_path()
 
  parameters_patch = {}
  # extracting minimal number of patches to ensure that the test does not take too long
  parameters_patch["patch_size"] = [128, 128]
 
  for num_patches in [-1, 3]:
  parameters_patch["num_patches"] = num_patches
- with open(file_config_temp, "w") as file:
- yaml.dump(parameters_patch, file)
+ file_config_temp = write_temp_config_path(parameters_patch)
 
  if os.path.exists(output_dir_patches_output):
  shutil.rmtree(output_dir_patches_output)
@@ -2426,9 +2399,7 @@ def test_train_inference_classification_histology_2d(device):
  )
  parameters["modality"] = "histo"
  parameters["patch_size"] = 128
- file_config_temp = get_temp_config_path()
- with open(file_config_temp, "w") as file:
- yaml.dump(parameters, file)
+ file_config_temp = write_temp_config_path(parameters)
  parameters = parseConfig(file_config_temp, version_check_flag=False)
  parameters["model"]["dimension"] = 2
  # read and parse csv
@@ -2846,9 +2817,7 @@ def test_generic_cli_function_configgenerator():
  "key_1": {"key_2": "value"}
  }
 
- file_config_temp = get_temp_config_path()
- with open(file_config_temp, "w") as file:
- yaml.dump(generator_config, file)
+ file_config_temp = write_temp_config_path(generator_config)
 
  # test for failure
  with pytest.raises(Exception) as exc_info:
@@ -2892,7 +2861,7 @@ def test_generic_cli_function_recoverconfig():
  resume=False,
  reset=True,
  )
- output_config_path = get_temp_config_path()
+ output_config_path = write_temp_config_path(None)
  assert recover_config(
  outputDir, output_config_path
  ), "recover_config returned false"
@@ -2960,6 +2929,7 @@ def test_generic_deploy_docker():
  if entrypoint_script:
  msg += " with custom entrypoint script"
  assert result, msg
+
  sanitize_outputDir()
 
  print("passed")
@@ -2970,7 +2940,6 @@ def test_collision_subjectid_test_segmentation_rad_2d(device):
  parameters = parseConfig(
  testingDir + "/config_segmentation.yaml", version_check_flag=False
  )
- file_config_temp = get_temp_config_path()
 
  parameters["modality"] = "rad"
  parameters["patch_size"] = patch_size["2D"]
@@ -2987,8 +2956,7 @@ def test_collision_subjectid_test_segmentation_rad_2d(device):
  parameters["model"]["architecture"] = "unet"
  outputDir = os.path.join(testingDir, "data_output")
 
- with open(file_config_temp, "w") as file:
- yaml.dump(parameters, file)
+ file_config_temp = write_temp_config_path(parameters)
 
  # test the case where outputDir is explicitly provided to InferenceManager
  train_data_path = inputDir + "/train_2d_rad_segmentation.csv"
@@ -3092,9 +3060,7 @@ def test_generic_cli_function_metrics_cli_rad_nd():
 
  output_file = os.path.join(outputDir, "output.yaml")
 
- temp_config = get_temp_config_path()
- with open(temp_config, "w") as file:
- yaml.dump(parameters, file)
+ temp_config = write_temp_config_path(parameters)
 
  # run the metrics calculation
  generate_metrics_dict(temp_infer_csv, temp_config, output_file)
@@ -3121,3 +3087,7 @@ def test_generic_deploy_metrics_docker():
  sanitize_outputDir()
 
  print("passed")
+
+ sanitize_outputDir()
+
+ print("passed")