Put resume in trainer

clinicadl/commandline/pipelines/train/from_json/cli.py

@@ -1,14 +1,12 @@
 from logging import getLogger
-from pathlib import Path
 
 import click
 
 from clinicadl.commandline import arguments
 from clinicadl.commandline.modules_options import (
     cross_validation,
-    reproducibility,
 )
-from clinicadl.train.tasks_utils import create_training_config
+from clinicadl.trainer.trainer import Trainer
 
 
 @click.command(name="from_json", no_args_is_help=True)
@@ -24,23 +22,11 @@ def cli(**kwargs):
 
     OUTPUT_MAPS_DIRECTORY is the path to the MAPS folder where outputs and results will be saved.
     """
-    from clinicadl.trainer.trainer import Trainer
-    from clinicadl.utils.maps_manager.maps_manager_utils import read_json
 
     logger = getLogger("clinicadl")
     logger.info(f"Reading JSON file at path {kwargs['config_file']}...")
-    config_dict = read_json(kwargs["config_file"])
-    # temporary
-    config_dict["tsv_directory"] = config_dict["tsv_path"]
-    if ("track_exp" in config_dict) and (config_dict["track_exp"] == ""):
-        config_dict["track_exp"] = None
-    config_dict["maps_dir"] = kwargs["output_maps_directory"]
-    config_dict["preprocessing_json"] = config_dict["preprocessing_dict"][
-        "extract_json"
-    ]
-    ###
-    config = create_training_config(config_dict["network_task"])(
-        output_maps_directory=kwargs["output_maps_directory"], **config_dict
+
+    trainer = Trainer.from_json(
+        config_file=kwargs["config_file"], maps_path=kwargs["output_maps_directory"]
     )
-    trainer = Trainer(config)
     trainer.train(split_list=kwargs["split"], overwrite=True)

clinicadl/commandline/pipelines/train/resume/cli.py

@@ -4,6 +4,7 @@
 from clinicadl.commandline.modules_options import (
     cross_validation,
 )
+from clinicadl.trainer import Trainer
 
 
 @click.command(name="resume", no_args_is_help=True)
@@ -14,6 +15,5 @@ def cli(input_maps_directory, split):
 
     INPUT_MAPS_DIRECTORY is the path to the MAPS folder where training job has started.
     """
-    from clinicadl.train.resume import automatic_resume
-
-    automatic_resume(input_maps_directory, user_split_list=split)
+    trainer = Trainer.from_maps(input_maps_directory)
+    trainer.resume(split)

clinicadl/config/config/cross_validation.py

@@ -34,5 +34,5 @@ def validator_split(cls, v):
     def adapt_cross_val_with_maps_manager_info(self, maps_manager: MapsManager):
         # TEMPORARY
         if not self.split:
-            self.split = maps_manager._find_splits()
+            self.split = maps_manager.find_splits()
         logger.debug(f"List of splits {self.split}")

clinicadl/predict/predict_manager.py

@@ -795,7 +795,7 @@ def _check_data_group(
                     raise MAPSError("Cannot overwrite train or validation data group.")
                 else:
                     # if not split_list:
-                    #     split_list = self.maps_manager._find_splits()
+                    #     split_list = self.maps_manager.find_splits()
                     assert self._config.split
                     for split in self._config.split:
                         selection_metrics = self.maps_manager._find_selection_metrics(

clinicadl/trainer/trainer.py

@@ -14,19 +14,20 @@
 from torch.utils.data import DataLoader
 from torch.utils.data.distributed import DistributedSampler
 
+from clinicadl.transforms.transforms import get_transforms
 from clinicadl.caps_dataset.data_utils import return_dataset
 from clinicadl.utils.early_stopping import EarlyStopping
 from clinicadl.utils.exceptions import MAPSError
 from clinicadl.utils.maps_manager.ddp import DDP, cluster
 from clinicadl.utils.maps_manager.logwriter import LogWriter
+from clinicadl.utils.maps_manager.maps_manager_utils import read_json
 from clinicadl.utils.metric_module import RetainBest
 from clinicadl.utils.seed import pl_worker_init_function, seed_everything
-from clinicadl.transforms.transforms import get_transforms
 from clinicadl.utils.maps_manager import MapsManager
 from clinicadl.utils.seed import get_seed
-
 from clinicadl.utils.enum import Task
-from .trainer_utils import create_parameters_dict
+from .trainer_utils import create_parameters_dict, patch_to_read_json
+from clinicadl.train.tasks_utils import create_training_config
 
 if TYPE_CHECKING:
     from clinicadl.callbacks.callbacks import Callback
@@ -42,33 +43,128 @@ class Trainer:
     def __init__(
         self,
         config: TrainConfig,
-        maps_manager: Optional[MapsManager] = None,
     ) -> None:
         """
         Parameters
         ----------
-        config : BaseTaskConfig
+        config : TrainConfig
         """
         self.config = config
-        if maps_manager:
-            self.maps_manager = maps_manager
-        else:
-            self.maps_manager = self._init_maps_manager(config)
+        self.maps_manager = self._init_maps_manager(config)
         self._check_args()
 
     def _init_maps_manager(self, config) -> MapsManager:
         # temporary: to match CLI data. TODO : change CLI data
 
         parameters, maps_path = create_parameters_dict(config)
-        return MapsManager(
-            maps_path, parameters, verbose=None
-        )  # TODO : precise which parameters in config are useful
+        if maps_path.is_dir():
+            return MapsManager(
+                maps_path, verbose=None
+            )  # TODO : precise which parameters in config are useful
+        else:
+            return MapsManager(
+                maps_path, parameters, verbose=None
+            )  # TODO : precise which parameters in config are useful
+
+    @classmethod
+    def from_json(cls, config_file: str | Path, maps_path: str | Path) -> Trainer:
+        """
+        Creates a Trainer from a json configuration file.
+
+        Parameters
+        ----------
+        config_file : str | Path
+            The parameters, stored in a json files.
+        maps_path : str | Path
+            The folder where the results of a futur training will be stored.
+
+        Returns
+        -------
+        Trainer
+            The Trainer object, instantiated with parameters found in config_file.
+
+        Raises
+        ------
+        FileNotFoundError
+            If config_file doesn't exist.
+        """
+        config_file = Path(config_file)
+
+        if not (config_file).is_file():
+            raise FileNotFoundError(f"No file found at {str(config_file)}.")
+        config_dict = patch_to_read_json(read_json(config_file))  # TODO : remove patch
+        config_dict["maps_dir"] = maps_path
+        config_object = create_training_config(config_dict["network_task"])(
+            **config_dict
+        )
+        return cls(config_object)
+
+    @classmethod
+    def from_maps(cls, maps_path: str | Path) -> Trainer:
+        """
+        Creates a Trainer from a json configuration file.
+
+        Parameters
+        ----------
+        maps_path : str | Path
+            The path of the MAPS folder.
+
+        Returns
+        -------
+        Trainer
+            The Trainer object, instantiated with parameters found in maps_path.
+
+        Raises
+        ------
+        MAPSError
+            If maps_path folder doesn't exist or there is no maps.json file in it.
+        """
+        maps_path = Path(maps_path)
+
+        if not (maps_path / "maps.json").is_file():
+            raise MAPSError(
+                f"MAPS was not found at {str(maps_path)}."
+                f"To initiate a new MAPS please give a train_dict."
+            )
+        return cls.from_json(maps_path / "maps.json", maps_path)
+
+    def resume(self, splits: List[int]) -> None:
+        """
+        Resume a prematurely stopped training.
+
+        Parameters
+        ----------
+        splits : List[int]
+            The splits that must be resumed.
+        """
+        stopped_splits = set(self.maps_manager.find_stopped_splits())
+        finished_splits = set(self.maps_manager.find_finished_splits())
+        # TODO : check these two lines. Why do we need a split_manager?
+        split_manager = self.maps_manager._init_split_manager(split_list=splits)
+        split_iterator = split_manager.split_iterator()
+        ###
+        absent_splits = set(split_iterator) - stopped_splits - finished_splits
+
+        logger.info(
+            f"Finished splits {finished_splits}\n"
+            f"Stopped splits {stopped_splits}\n"
+            f"Absent splits {absent_splits}"
+        )
+
+        if len(stopped_splits) == 0 and len(absent_splits) == 0:
+            raise ValueError(
+                "Training has been completed on all the splits you passed."
+            )
+        if len(stopped_splits) > 0:
+            self._resume(list(stopped_splits))
+        if len(absent_splits) > 0:
+            self.train(list(absent_splits), overwrite=True)
 
     def _check_args(self):
         self.config.reproducibility.seed = get_seed(self.config.reproducibility.seed)
         # if (len(self.config.data.label_code) == 0):
         #     self.config.data.label_code = self.maps_manager.label_code
-        # TODO : deal with label_code and replace self.maps_manager.label_code
+        # TODO: deal with label_code and replace self.maps_manager.label_code
 
     def train(
         self,
@@ -120,7 +216,7 @@ def train(
         else:
             self._train_single(split_list, resume=False)
 
-    def resume(
+    def _resume(
         self,
         split_list: Optional[List[int]] = None,
     ) -> None:

clinicadl/trainer/trainer_utils.py

@@ -65,3 +65,19 @@ def create_parameters_dict(config):
         del parameters["mask_path"]
 
     return parameters, maps_path
+
+
+def patch_to_read_json(config_dict):
+    config_dict["tsv_directory"] = config_dict["tsv_path"]
+    if ("track_exp" in config_dict) and (config_dict["track_exp"] == ""):
+        config_dict["track_exp"] = None
+    config_dict["preprocessing_json"] = config_dict["preprocessing_dict"][
+        "extract_json"
+    ]
+    if "label_code" not in config_dict or config_dict["label_code"] is None:
+        config_dict["label_code"] = {}
+    if "preprocessing_json" not in config_dict:
+        config_dict["preprocessing_json"] = config_dict["preprocessing_dict"][
+            "extract_json"
+        ]
+    return config_dict

clinicadl/utils/maps_manager/maps_manager.py

@@ -332,13 +332,43 @@ def _compute_output_tensors(
                 torch.save(output, tensor_path / output_filename)
                 logger.debug(f"File saved at {[input_filename, output_filename]}")
 
-    def _find_splits(self):
-        """Find which splits were trained in the MAPS."""
-        return [
+    def find_splits(self) -> List[int]:
+        """Find which splits that were trained in the MAPS."""
+        splits = [
             int(split.name.split("-")[1])
             for split in list(self.maps_path.iterdir())
             if split.name.startswith(f"{self.split_name}-")
         ]
+        return splits
+
+    def find_stopped_splits(self) -> List[int]:
+        """Find which splits for which training was not completed."""
+        existing_split_list = self.find_splits()
+        stopped_splits = [
+            split
+            for split in existing_split_list
+            if (self.maps_path / f"{self.split_name}-{split}" / "tmp")
+            in list((self.maps_path / f"{self.split_name}-{split}").iterdir())
+        ]
+        return stopped_splits
+
+    def find_finished_splits(self) -> List[int]:
+        """Find which splits for which training was completed."""
+        finished_splits = list()
+        existing_split_list = self.find_splits()
+        stopped_splits = self.find_stopped_splits()
+        for split in existing_split_list:
+            if split not in stopped_splits:
+                performance_dir_list = [
+                    performance_dir
+                    for performance_dir in list(
+                        (self.maps_path / f"{self.split_name}-{split}").iterdir()
+                    )
+                    if "best-" in performance_dir.name
+                ]
+                if len(performance_dir_list) > 0:
+                    finished_splits.append(split)
+        return finished_splits
 
     def _ensemble_prediction(
         self,