Refactor code with new pre commit configuration (#207)

* Remove CLang and build options from runtests.sh

* Remove CLang and build options from runtests.sh

* Run ./runtests.sh to format code, fix flake8 errors

* Add changes to the ipynb files

* Comment black and isort from pre-commit

Signed-off-by: Walter Hugo Lopez Pinaya <[email protected]>

Author: Warvito

.clang-format

@@ -63,12 +63,12 @@ repos:
       - id: pycln
         args: [--config=pyproject.toml]
 
-  - repo: https://github.com/psf/black
-    rev: 22.3.0
-    hooks:
-      - id: black
-
-  - repo: https://github.com/PyCQA/isort
-    rev: 5.9.3
-    hooks:
-      - id: isort
+#  - repo: https://github.com/psf/black
+#    rev: 22.3.0
+#    hooks:
+#      - id: black
+#
+#  - repo: https://github.com/PyCQA/isort
+#    rev: 5.9.3
+#    hooks:
+#      - id: isort

.pre-commit-config.yaml

@@ -89,7 +89,7 @@ Before submitting a pull request, we recommend that all linting and unit tests
 should pass, by running the following command locally:
 
 ```bash
-./runtests.sh -u --net
+./runtests.sh -f -u --net
 ```
 or (for new features that would not break existing functionality):
 

CONTRIBUTING.md

@@ -11,7 +11,7 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
+from typing import TYPE_CHECKING, Any, Callable, Iterable, Sequence
 
 import torch
 from monai.config import IgniteInfo
@@ -88,34 +88,34 @@ class AdversarialTrainer(Trainer):
 
     def __init__(
         self,
-        device: Union[torch.device, str],
+        device: torch.device | str,
         max_epochs: int,
-        train_data_loader: Union[Iterable, DataLoader],
+        train_data_loader: Iterable | DataLoader,
         g_network: torch.nn.Module,
         g_optimizer: Optimizer,
         g_loss_function: Callable,
         recon_loss_function: Callable,
         d_network: torch.nn.Module,
         d_optimizer: Optimizer,
         d_loss_function: Callable,
-        epoch_length: Optional[int] = None,
+        epoch_length: int | None = None,
         non_blocking: bool = False,
-        prepare_batch: Union[Callable[[Engine, Any], Any], None] = default_prepare_batch,
-        iteration_update: Optional[Callable] = None,
-        g_inferer: Optional[Inferer] = None,
-        d_inferer: Optional[Inferer] = None,
-        postprocessing: Optional[Transform] = None,
-        key_train_metric: Optional[Dict[str, Metric]] = None,
-        additional_metrics: Optional[Dict[str, Metric]] = None,
+        prepare_batch: Callable[[Engine, Any], Any] | None = default_prepare_batch,
+        iteration_update: Callable | None = None,
+        g_inferer: Inferer | None = None,
+        d_inferer: Inferer | None = None,
+        postprocessing: Transform | None = None,
+        key_train_metric: dict[str, Metric] | None = None,
+        additional_metrics: dict[str, Metric] | None = None,
         metric_cmp_fn: Callable = default_metric_cmp_fn,
-        train_handlers: Optional[Sequence] = None,
+        train_handlers: Sequence | None = None,
         amp: bool = False,
-        event_names: Union[List[Union[str, EventEnum]], None] = None,
-        event_to_attr: Union[dict, None] = None,
+        event_names: list[str | EventEnum] | None = None,
+        event_to_attr: dict | None = None,
         decollate: bool = True,
         optim_set_to_none: bool = False,
-        to_kwargs: Union[dict, None] = None,
-        amp_kwargs: Union[dict, None] = None,
+        to_kwargs: dict | None = None,
+        amp_kwargs: dict | None = None,
     ):
         super().__init__(
             device=device,
@@ -183,8 +183,8 @@ def _complete_state_dict_user_keys(self) -> None:
             self._state_dict_user_keys.append("recon_loss_function")
 
     def _iteration(
-        self, engine: AdversarialTrainer, batchdata: Dict[str, torch.Tensor]
-    ) -> Dict[str, Union[torch.Tensor, int, float, bool]]:
+        self, engine: AdversarialTrainer, batchdata: dict[str, torch.Tensor]
+    ) -> dict[str, torch.Tensor | int | float | bool]:
         """
         Callback function for the Adversarial Training processing logic of 1 iteration in Ignite Engine.
         Return below items in a dictionary:
@@ -219,8 +219,8 @@ def _iteration(
 
         if len(batch) == 2:
             inputs, targets = batch
-            args: Tuple = ()
-            kwargs: Dict = {}
+            args: tuple = ()
+            kwargs: dict = {}
         else:
             inputs, targets, args, kwargs = batch
 

generative/engines/trainer.py

@@ -141,7 +141,7 @@ def get_likelihood(
             progress_bar = iter(scheduler.timesteps)
         intermediates = []
         noise = torch.randn_like(inputs).to(inputs.device)
-        total_kl = torch.zeros((inputs.shape[0])).to(inputs.device)
+        total_kl = torch.zeros(inputs.shape[0]).to(inputs.device)
         for t in progress_bar:
             timesteps = torch.full(inputs.shape[:1], t, device=inputs.device).long()
             noisy_image = self.scheduler.add_noise(original_samples=inputs, noise=noise, timesteps=timesteps)
@@ -228,8 +228,8 @@ def _get_decoder_log_likelihood(
         inputs: torch.Tensor,
         means: torch.Tensor,
         log_scales: torch.Tensor,
-        original_input_range: Optional[Tuple] = [0, 255],
-        scaled_input_range: Optional[Tuple] = [0, 1],
+        original_input_range: Optional[Tuple] = (0, 255),
+        scaled_input_range: Optional[Tuple] = (0, 1),
     ) -> torch.Tensor:
         """
         Compute the log-likelihood of a Gaussian distribution discretizing to a
@@ -304,11 +304,7 @@ def __call__(
             latent = autoencoder_model.encode_stage_2_inputs(inputs) * self.scale_factor
 
         prediction = super().__call__(
-            inputs=latent,
-            diffusion_model=diffusion_model,
-            noise=noise,
-            timesteps=timesteps,
-            condition=condition,
+            inputs=latent, diffusion_model=diffusion_model, noise=noise, timesteps=timesteps, condition=condition
         )
 
         return prediction

generative/inferers/inferer.py

@@ -128,7 +128,7 @@ def forward(
         if type(input) is not list:
             input = [input]
         target_ = []
-        for disc_ind, disc_out in enumerate(input):
+        for _, disc_out in enumerate(input):
             if self.criterion != AdversarialCriterions.HINGE.value:
                 target_.append(self.get_target_tensor(disc_out, target_is_real))
             else:

generative/losses/adversarial_loss.py

@@ -38,11 +38,7 @@ class PerceptualLoss(nn.Module):
     """
 
     def __init__(
-        self,
-        spatial_dims: int,
-        network_type: str = "alex",
-        is_fake_3d: bool = True,
-        fake_3d_ratio: float = 0.5,
+        self, spatial_dims: int, network_type: str = "alex", is_fake_3d: bool = True, fake_3d_ratio: float = 0.5
     ):
         super().__init__()
 
@@ -58,11 +54,7 @@ def __init__(
         elif "radimagenet_" in network_type:
             self.perceptual_function = RadImageNetPerceptualSimilarity(net=network_type, verbose=False)
         else:
-            self.perceptual_function = LPIPS(
-                pretrained=True,
-                net=network_type,
-                verbose=False,
-            )
+            self.perceptual_function = LPIPS(pretrained=True, net=network_type, verbose=False)
         self.is_fake_3d = is_fake_3d
         self.fake_3d_ratio = fake_3d_ratio
 
@@ -90,26 +82,12 @@ def batchify_axis(x: torch.Tensor, fake_3d_perm: Tuple) -> torch.Tensor:
         preserved_axes.remove(spatial_axis)
 
         channel_axis = 1
-        input_slices = batchify_axis(
-            x=input,
-            fake_3d_perm=(
-                spatial_axis,
-                channel_axis,
-            )
-            + tuple(preserved_axes),
-        )
+        input_slices = batchify_axis(x=input, fake_3d_perm=(spatial_axis, channel_axis) + tuple(preserved_axes))
         indices = torch.randperm(input_slices.shape[0])[: int(input_slices.shape[0] * self.fake_3d_ratio)].to(
             input_slices.device
         )
         input_slices = torch.index_select(input_slices, dim=0, index=indices)
-        target_slices = batchify_axis(
-            x=target,
-            fake_3d_perm=(
-                spatial_axis,
-                channel_axis,
-            )
-            + tuple(preserved_axes),
-        )
+        target_slices = batchify_axis(x=target, fake_3d_perm=(spatial_axis, channel_axis) + tuple(preserved_axes))
         target_slices = torch.index_select(target_slices, dim=0, index=indices)
 
         axis_loss = torch.mean(self.perceptual_function(input_slices, target_slices))
@@ -150,11 +128,7 @@ class MedicalNetPerceptualSimilarity(nn.Module):
         verbose: if false, mute messages from torch Hub load function.
     """
 
-    def __init__(
-        self,
-        net: str = "medicalnet_resnet10_23datasets",
-        verbose: bool = False,
-    ) -> None:
+    def __init__(self, net: str = "medicalnet_resnet10_23datasets", verbose: bool = False) -> None:
         super().__init__()
         torch.hub._validate_not_a_forked_repo = lambda a, b, c: True
         self.model = torch.hub.load("Warvito/MedicalNet-models", model=net, verbose=verbose)
@@ -216,11 +190,7 @@ class RadImageNetPerceptualSimilarity(nn.Module):
         verbose: if false, mute messages from torch Hub load function.
     """
 
-    def __init__(
-        self,
-        net: str = "radimagenet_resnet50",
-        verbose: bool = False,
-    ) -> None:
+    def __init__(self, net: str = "radimagenet_resnet50", verbose: bool = False) -> None:
         super().__init__()
         self.model = torch.hub.load("Warvito/radimagenet-models", model=net, verbose=verbose)
         self.eval()

generative/losses/perceptual.py

@@ -80,12 +80,7 @@ def _get_fft_amplitude(self, images: torch.Tensor) -> torch.Tensor:
         Returns:
             fourier transformation amplitude
         """
-        img_fft = fftn(
-            images,
-            s=self.fft_signal_size,
-            dim=self.fft_dim,
-            norm=self.fft_norm,
-        )
+        img_fft = fftn(images, s=self.fft_signal_size, dim=self.fft_dim, norm=self.fft_norm)
 
         amplitude = torch.sqrt(torch.real(img_fft) ** 2 + torch.imag(img_fft) ** 2)
 

generative/losses/spectral_loss.py

@@ -60,7 +60,7 @@ def get_fid_score(y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
     y_pred = y_pred.float()
 
     if y.ndimension() > 2:
-        raise ValueError(f"Inputs should have (number images, number of features) shape.")
+        raise ValueError("Inputs should have (number images, number of features) shape.")
 
     mu_y_pred = torch.mean(y_pred, dim=0)
     sigma_y_pred = _cov(y_pred, rowvar=False)
@@ -114,9 +114,9 @@ def _sqrtm_newton_schulz(matrix: torch.Tensor, num_iters: int = 100) -> tuple[to
     error = torch.empty(1, device=matrix.device, dtype=matrix.dtype)
 
     for _ in range(num_iters):
-        T = 0.5 * (3.0 * i_matrix - z_matrix.mm(y_matrix))
-        y_matrix = y_matrix.mm(T)
-        z_matrix = T.mm(z_matrix)
+        t = 0.5 * (3.0 * i_matrix - z_matrix.mm(y_matrix))
+        y_matrix = y_matrix.mm(t)
+        z_matrix = t.mm(z_matrix)
 
         s_matrix = y_matrix * torch.sqrt(norm_of_matrix)
 

generative/metrics/fid.py

@@ -69,7 +69,8 @@ def _compute_metric(self, y: torch.Tensor, y_pred: torch.Tensor) -> torch.Tensor
 
         if y_pred.shape != y.shape:
             raise ValueError(
-                f"y_pred and y shapes dont match after being processed by their transforms, received y_pred: {y_pred.shape} and y: {y.shape}"
+                "y_pred and y shapes dont match after being processed "
+                f"by their transforms, received y_pred: {y_pred.shape} and y: {y.shape}"
             )
 
         for d in range(len(y.shape) - 1, 1, -1):