Update for new versions of black and pylint

direct/nn/cirim/cirim.py

@@ -161,9 +161,7 @@ def __init__(
             bias=bias,
         )
         self.hh = nn.Parameter(
-            nn.init.normal_(
-                torch.empty(1, hidden_channels, 1, 1), std=1.0 / (hidden_channels * (1 + kernel_size**2))
-            )
+            nn.init.normal_(torch.empty(1, hidden_channels, 1, 1), std=1.0 / (hidden_channels * (1 + kernel_size**2)))
         )
 
         self.reset_parameters()

direct/nn/didn/didn.py

@@ -41,9 +41,7 @@ def __init__(
             Padding size. Default: 0.
         """
         super().__init__()
-        self.conv = nn.Conv2d(
-            in_channels, out_channels * upscale_factor**2, kernel_size=kernel_size, padding=padding
-        )
+        self.conv = nn.Conv2d(in_channels, out_channels * upscale_factor**2, kernel_size=kernel_size, padding=padding)
         self.pixelshuffle = nn.PixelShuffle(upscale_factor)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:

direct/nn/get_nn_model_config.py

@@ -50,11 +50,11 @@ def _get_model_config(
             {
                 "hidden_channels": kwargs.get("conv_hidden_channels", 64),
                 "n_convs": kwargs.get("conv_n_convs", 15),
-                "activation": nn.PReLU()
-                if kwargs.get("conv_activation", "prelu") == ActivationType.prelu
-                else nn.ReLU()
-                if kwargs.get("conv_activation", "relu") == ActivationType.relu
-                else nn.LeakyReLU(),
+                "activation": (
+                    nn.PReLU()
+                    if kwargs.get("conv_activation", "prelu") == ActivationType.prelu
+                    else nn.ReLU() if kwargs.get("conv_activation", "relu") == ActivationType.relu else nn.LeakyReLU()
+                ),
                 "batchnorm": kwargs.get("conv_batchnorm", False),
             }
         )

direct/nn/mri_models.py

@@ -586,10 +586,14 @@ def reconstruct_volumes(  # type: ignore
                 )
                 # Maybe not needed.
                 del data
-                yield (curr_volume, curr_target, reduce_list_of_dicts(loss_dict_list), filename) if add_target else (
-                    curr_volume,
-                    reduce_list_of_dicts(loss_dict_list),
-                    filename,
+                yield (
+                    (curr_volume, curr_target, reduce_list_of_dicts(loss_dict_list), filename)
+                    if add_target
+                    else (
+                        curr_volume,
+                        reduce_list_of_dicts(loss_dict_list),
+                        filename,
+                    )
                 )
 
     @torch.no_grad()

direct/nn/unet/unet_engine.py

@@ -38,9 +38,9 @@ def __init__(
     def forward_function(self, data: Dict[str, Any]) -> Tuple[torch.Tensor, None]:
         output_image = self.model(
             masked_kspace=data["masked_kspace"],
-            sensitivity_map=data["sensitivity_map"]
-            if self.cfg.model.image_initialization == "sense"  # type: ignore
-            else None,
+            sensitivity_map=(
+                data["sensitivity_map"] if self.cfg.model.image_initialization == "sense" else None  # type: ignore
+            ),
         )
         output_image = T.modulus(output_image)
 

tests/tests_data/test_mri_transforms.py

@@ -1,9 +1,9 @@
-# coding=utf-8
 # Copyright (c) DIRECT Contributors
 
 """Tests for the direct.data.mri_transforms module."""
 
 import functools
+import warnings
 
 import numpy as np
 import pytest
@@ -372,7 +372,7 @@ def test_EstimateSensitivityMap(shape, type_of_map, gaussian_sigma, espirit_iter
     else:
         transform = EstimateSensitivityMap(**args)
         if shape[0] == 1 or sense_map_in_sample:
-            with pytest.warns(None):
+            with warnings.catch_warnings(record=True):
                 sample = transform(sample)
         else:
             sample = transform(sample)