Some cleaning (#629)

* remove check_gpu from cmdline utils and adapt qc to config

clinicadl/cmdline.py

@@ -10,9 +10,9 @@
 from clinicadl.commandline.pipelines.prepare_data.prepare_data_from_bids_cli import (
     cli as prepare_data_from_bids_cli,
 )
+from clinicadl.commandline.pipelines.quality_check.cli import cli as qc_cli
 from clinicadl.commandline.pipelines.train.cli import cli as train_cli
 from clinicadl.hugging_face.hugging_face_cli import cli as hf_cli
-from clinicadl.quality_check.qc_cli import cli as qc_cli
 from clinicadl.random_search.random_search_cli import cli as random_search_cli
 from clinicadl.tsvtools.cli import cli as tsvtools_cli
 from clinicadl.utils.logger import setup_logging

clinicadl/commandline/modules_options/computational.py

@@ -19,8 +19,8 @@
     "this flag is already set to FSDP to that the zero flag is never actually removed.",
 )
 gpu = click.option(
-    "--gpu/--no-gpu",
-    default=get_default("gpu", ComputationalConfig),
+    "--no-gpu",
+    is_flag=True,
     help="Use GPU by default. Please specify `--no-gpu` to force using CPU.",
     show_default=True,
 )

clinicadl/quality_check/pet_linear/cli.py → clinicadl/commandline/pipelines/quality_check/pet_linear/cli.py

@@ -47,7 +47,9 @@ def cli(
     """
     from clinicadl.caps_dataset.caps_dataset_config import CapsDatasetConfig
 
-    from .quality_check import quality_check as pet_linear_qc
+    from .....quality_check.pet_linear.quality_check import (
+        quality_check as pet_linear_qc,
+    )
 
     config = CapsDatasetConfig.from_preprocessing_and_extraction_method(
         caps_directory=caps_directory,

clinicadl/quality_check/t1_linear/cli.py → clinicadl/commandline/pipelines/quality_check/t1_linear/cli.py

@@ -2,8 +2,11 @@
 
 import click
 
+from clinicadl.caps_dataset.caps_dataset_config import CapsDatasetConfig
 from clinicadl.commandline import arguments
 from clinicadl.commandline.modules_options import computational, data, dataloader
+from clinicadl.config.config.computational import ComputationalConfig
+from clinicadl.utils.enum import ExtractionMethod, Preprocessing
 
 
 @click.command(name="t1-linear", no_args_is_help=True)
@@ -31,7 +34,6 @@
 @click.option(
     "--use_tensor",
     type=bool,
-    default=False,
     is_flag=True,
     help="Flag allowing the pipeline to run on the extracted tensors and not on the nifti images",
 )
@@ -42,7 +44,7 @@ def cli(
     threshold,
     batch_size,
     n_proc,
-    gpu,
+    no_gpu,
     amp,
     network,
     use_tensor,
@@ -54,23 +56,28 @@ def cli(
 
     OUTPUT_TSV is the path to the tsv file where results will be saved.
     """
-    from clinicadl.utils.cmdline_utils import check_gpu
-
-    if gpu:
-        check_gpu()
+    from clinicadl.quality_check.t1_linear.quality_check import (
+        quality_check as linear_qc,
+    )
 
-    from .quality_check import quality_check as linear_qc
+    computational_config = ComputationalConfig(amp=amp, gpu=not no_gpu)
+    config = CapsDatasetConfig.from_preprocessing_and_extraction_method(
+        caps_directory=caps_directory,
+        extraction=ExtractionMethod.IMAGE,
+        preprocessing_type=Preprocessing.T1_LINEAR,
+        preprocessing=Preprocessing.T1_LINEAR,
+        use_uncropped_image=use_uncropped_image,
+        data_tsv=participants_tsv,
+        n_proc=n_proc,
+        batch_size=batch_size,
+        use_tensor=use_tensor,
+    )
 
     linear_qc(
-        caps_directory,
         output_path=results_tsv,
-        tsv_path=participants_tsv,
         threshold=threshold,
-        batch_size=batch_size,
-        n_proc=n_proc,
-        gpu=gpu,
-        amp=amp,
         network=network,
         use_tensor=use_tensor,
-        use_uncropped_image=use_uncropped_image,
+        config=config,
+        computational_config=computational_config,
     )

clinicadl/quality_check/t1_volume/cli.py → clinicadl/commandline/pipelines/quality_check/t1_volume/cli.py

@@ -23,7 +23,7 @@ def cli(
 
     GROUP_LABEL is the group associated to the gray matter DARTEL template in CAPS_DIRECTORY.
     """
-    from .quality_check import quality_check as volume_qc
+    from .....quality_check.t1_volume.quality_check import quality_check as volume_qc
 
     volume_qc(
         caps_directory,

clinicadl/config/config/computational.py

@@ -3,7 +3,6 @@
 from pydantic import BaseModel, ConfigDict, model_validator
 from typing_extensions import Self
 
-from clinicadl.utils.cmdline_utils import check_gpu
 from clinicadl.utils.exceptions import ClinicaDLArgumentError
 
 logger = getLogger("clinicadl.computational_config")
@@ -19,9 +18,14 @@ class ComputationalConfig(BaseModel):
     model_config = ConfigDict(validate_assignment=True)
 
     @model_validator(mode="after")
-    def validator_gpu(self) -> Self:
+    def check_gpu(self) -> Self:
         if self.gpu:
-            check_gpu()
+            import torch
+
+            if not torch.cuda.is_available():
+                raise ClinicaDLArgumentError(
+                    "No GPU is available. To run on CPU, please set gpu to false or add the --no-gpu flag if you use the commandline."
+                )
         elif self.amp:
             raise ClinicaDLArgumentError(
                 "AMP is designed to work with modern GPUs. Please add the --gpu flag."

clinicadl/quality_check/t1_linear/quality_check.py

@@ -4,16 +4,17 @@
 
 from logging import getLogger
 from pathlib import Path
+from typing import Optional
 
 import pandas as pd
 import torch
 from torch.cuda.amp import autocast
 from torch.utils.data import DataLoader
 
 from clinicadl.caps_dataset.caps_dataset_config import CapsDatasetConfig
+from clinicadl.config.config.computational import ComputationalConfig
 from clinicadl.generate.generate_utils import load_and_check_tsv
 from clinicadl.utils.clinica_utils import RemoteFileStructure, fetch_file
-from clinicadl.utils.enum import ExtractionMethod, Preprocessing
 from clinicadl.utils.exceptions import ClinicaDLArgumentError
 
 from .models import resnet_darq_qc_18 as darq_r18
@@ -25,17 +26,12 @@
 
 
 def quality_check(
-    caps_dir: Path,
+    config: CapsDatasetConfig,
     output_path: Path,
-    tsv_path: Path = None,
     threshold: float = 0.5,
-    batch_size: int = 1,
-    n_proc: int = 0,
-    gpu: bool = True,
-    amp: bool = False,
     network: str = "darq",
-    use_tensor: bool = False,
-    use_uncropped_image: bool = True,
+    use_tensor: bool = True,
+    computational_config: Optional[ComputationalConfig] = None,
 ):
     """
     Performs t1-linear quality-check
@@ -64,16 +60,9 @@ def quality_check(
         To use uncropped images instead of the cropped ones.
 
     """
-
+    if computational_config is None:
+        computational_config = ComputationalConfig()
     logger = getLogger("clinicadl.quality_check")
-    config = CapsDatasetConfig.from_preprocessing_and_extraction_method(
-        caps_directory=caps_dir,
-        extraction=ExtractionMethod.IMAGE,
-        preprocessing_type=Preprocessing.T1_LINEAR,
-        preprocessing=Preprocessing.T1_LINEAR,
-        use_uncropped_image=use_uncropped_image,
-        data_tsv=tsv_path,
-    )
 
     if output_path.suffix != ".tsv":
         raise ValueError("please enter a tsv path")
@@ -124,10 +113,10 @@ def quality_check(
     logger.debug("Loading quality check model.")
     model.load_state_dict(torch.load(model_file))
     model.eval()
-    if gpu:
+    if computational_config.gpu:
         logger.debug("Working on GPU.")
         model = model.cuda()
-    elif amp:
+    elif computational_config.amp:
         raise ClinicaDLArgumentError(
             "AMP is designed to work with modern GPUs. Please add the --gpu flag."
         )
@@ -139,12 +128,15 @@ def quality_check(
         # Load DataFrame
         logger.debug("Loading data to check.")
         config.data.data_df = load_and_check_tsv(
-            tsv_path, caps_dict, output_path.resolve().parent
+            config.data.data_tsv, caps_dict, output_path.resolve().parent
         )
 
         dataset = QCDataset(config, use_extracted_tensors=use_tensor)
         dataloader = DataLoader(
-            dataset, num_workers=n_proc, batch_size=batch_size, pin_memory=True
+            dataset,
+            num_workers=config.dataloader.n_proc,
+            batch_size=config.dataloader.batch_size,
+            pin_memory=True,
         )
 
         columns = ["participant_id", "session_id", "pass_probability", "pass"]
@@ -159,9 +151,9 @@ def quality_check(
         for data in dataloader:
             logger.debug(f"Processing subject {data['participant_id']}.")
             inputs = data["image"]
-            if gpu:
+            if computational_config.gpu:
                 inputs = inputs.cuda()
-            with autocast(enabled=amp):
+            with autocast(enabled=computational_config.amp):
                 outputs = softmax(model(inputs))
             # We cast back to 32bits. It should be a no-op as softmax is not eligible
             # to fp16 and autocast is forbidden on CPU (output would be bf16 otherwise).

clinicadl/utils/maps_manager/maps_manager.py

@@ -15,7 +15,6 @@
 )
 from clinicadl.caps_dataset.extraction.utils import path_encoder
 from clinicadl.transforms.config import TransformsConfig
-from clinicadl.utils.cmdline_utils import check_gpu
 from clinicadl.utils.exceptions import (
     ClinicaDLArgumentError,
     ClinicaDLConfigurationError,
@@ -423,12 +422,6 @@ def _check_args(self, parameters):
                     f"No value was given for {arg}."
                 )
         self.parameters = add_default_values(parameters)
-        if self.parameters["gpu"]:
-            check_gpu()
-        elif self.parameters["amp"]:
-            raise ClinicaDLArgumentError(
-                "AMP is designed to work with modern GPUs. Please add the --gpu flag."
-            )
 
         transfo_config = TransformsConfig(
             normalize=self.normalize,