Refactor unnecessary else / elif when if block has a return statement

monai/apps/datasets.py

@@ -291,10 +291,9 @@ def get_properties(self, keys: Optional[Union[Sequence[str], str]] = None):
         """
         if keys is None:
             return self._properties
-        elif self._properties is not None:
+        if self._properties is not None:
             return {key: self._properties[key] for key in ensure_tuple(keys)}
-        else:
-            return {}
+        return {}
 
     def _generate_data_list(self, dataset_dir: str) -> List[Dict]:
         section = "training" if self.section in ["training", "validation"] else "test"

monai/csrc/resample/pushpull_cpu.cpp

@@ -97,19 +97,25 @@ MONAI_NAMESPACE_DEVICE { // cpu
         bool do_sgrad)
         : dim(dim),
           bound0(bound.size() > 0 ? bound[0] : BoundType::Replicate),
-          bound1(bound.size() > 1 ? bound[1] : bound.size() > 0 ? bound[0] : BoundType::Replicate),
+          bound1(
+              bound.size() > 1       ? bound[1]
+                  : bound.size() > 0 ? bound[0]
+                                     : BoundType::Replicate),
           bound2(
-              bound.size() > 2 ? bound[2]
-                               : bound.size() > 1 ? bound[1] : bound.size() > 0 ? bound[0] : BoundType::Replicate),
+              bound.size() > 2       ? bound[2]
+                  : bound.size() > 1 ? bound[1]
+                  : bound.size() > 0 ? bound[0]
+                                     : BoundType::Replicate),
           interpolation0(interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
           interpolation1(
-              interpolation.size() > 1 ? interpolation[1]
-                                       : interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
+              interpolation.size() > 1       ? interpolation[1]
+                  : interpolation.size() > 0 ? interpolation[0]
+                                             : InterpolationType::Linear),
           interpolation2(
-              interpolation.size() > 2
-                  ? interpolation[2]
+              interpolation.size() > 2       ? interpolation[2]
                   : interpolation.size() > 1 ? interpolation[1]
-                                             : interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
+                  : interpolation.size() > 0 ? interpolation[0]
+                                             : InterpolationType::Linear),
           extrapolate(extrapolate),
           do_pull(do_pull),
           do_push(do_push),
@@ -136,13 +142,14 @@ MONAI_NAMESPACE_DEVICE { // cpu
           bound2(bound),
           interpolation0(interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
           interpolation1(
-              interpolation.size() > 1 ? interpolation[1]
-                                       : interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
+              interpolation.size() > 1       ? interpolation[1]
+                  : interpolation.size() > 0 ? interpolation[0]
+                                             : InterpolationType::Linear),
           interpolation2(
-              interpolation.size() > 2
-                  ? interpolation[2]
+              interpolation.size() > 2       ? interpolation[2]
                   : interpolation.size() > 1 ? interpolation[1]
-                                             : interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
+                  : interpolation.size() > 0 ? interpolation[0]
+                                             : InterpolationType::Linear),
           extrapolate(extrapolate),
           do_pull(do_pull),
           do_push(do_push),
@@ -165,10 +172,15 @@ MONAI_NAMESPACE_DEVICE { // cpu
         bool do_sgrad)
         : dim(dim),
           bound0(bound.size() > 0 ? bound[0] : BoundType::Replicate),
-          bound1(bound.size() > 1 ? bound[1] : bound.size() > 0 ? bound[0] : BoundType::Replicate),
+          bound1(
+              bound.size() > 1       ? bound[1]
+                  : bound.size() > 0 ? bound[0]
+                                     : BoundType::Replicate),
           bound2(
-              bound.size() > 2 ? bound[2]
-                               : bound.size() > 1 ? bound[1] : bound.size() > 0 ? bound[0] : BoundType::Replicate),
+              bound.size() > 2       ? bound[2]
+                  : bound.size() > 1 ? bound[1]
+                  : bound.size() > 0 ? bound[0]
+                                     : BoundType::Replicate),
           interpolation0(interpolation),
           interpolation1(interpolation),
           interpolation2(interpolation),

monai/csrc/resample/pushpull_cuda.cu

@@ -94,19 +94,25 @@ MONAI_NAMESPACE_DEVICE { // cuda
         bool do_sgrad)
         : dim(dim),
           bound0(bound.size() > 0 ? bound[0] : BoundType::Replicate),
-          bound1(bound.size() > 1 ? bound[1] : bound.size() > 0 ? bound[0] : BoundType::Replicate),
+          bound1(
+              bound.size() > 1       ? bound[1]
+                  : bound.size() > 0 ? bound[0]
+                                     : BoundType::Replicate),
           bound2(
-              bound.size() > 2 ? bound[2]
-                               : bound.size() > 1 ? bound[1] : bound.size() > 0 ? bound[0] : BoundType::Replicate),
+              bound.size() > 2       ? bound[2]
+                  : bound.size() > 1 ? bound[1]
+                  : bound.size() > 0 ? bound[0]
+                                     : BoundType::Replicate),
           interpolation0(interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
           interpolation1(
-              interpolation.size() > 1 ? interpolation[1]
-                                       : interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
+              interpolation.size() > 1       ? interpolation[1]
+                  : interpolation.size() > 0 ? interpolation[0]
+                                             : InterpolationType::Linear),
           interpolation2(
-              interpolation.size() > 2
-                  ? interpolation[2]
+              interpolation.size() > 2       ? interpolation[2]
                   : interpolation.size() > 1 ? interpolation[1]
-                                             : interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
+                  : interpolation.size() > 0 ? interpolation[0]
+                                             : InterpolationType::Linear),
           extrapolate(extrapolate),
           do_pull(do_pull),
           do_push(do_push),
@@ -133,13 +139,14 @@ MONAI_NAMESPACE_DEVICE { // cuda
           bound2(bound),
           interpolation0(interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
           interpolation1(
-              interpolation.size() > 1 ? interpolation[1]
-                                       : interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
+              interpolation.size() > 1       ? interpolation[1]
+                  : interpolation.size() > 0 ? interpolation[0]
+                                             : InterpolationType::Linear),
           interpolation2(
-              interpolation.size() > 2
-                  ? interpolation[2]
+              interpolation.size() > 2       ? interpolation[2]
                   : interpolation.size() > 1 ? interpolation[1]
-                                             : interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
+                  : interpolation.size() > 0 ? interpolation[0]
+                                             : InterpolationType::Linear),
           extrapolate(extrapolate),
           do_pull(do_pull),
           do_push(do_push),
@@ -162,10 +169,15 @@ MONAI_NAMESPACE_DEVICE { // cuda
         bool do_sgrad)
         : dim(dim),
           bound0(bound.size() > 0 ? bound[0] : BoundType::Replicate),
-          bound1(bound.size() > 1 ? bound[1] : bound.size() > 0 ? bound[0] : BoundType::Replicate),
+          bound1(
+              bound.size() > 1       ? bound[1]
+                  : bound.size() > 0 ? bound[0]
+                                     : BoundType::Replicate),
           bound2(
-              bound.size() > 2 ? bound[2]
-                               : bound.size() > 1 ? bound[1] : bound.size() > 0 ? bound[0] : BoundType::Replicate),
+              bound.size() > 2       ? bound[2]
+                  : bound.size() > 1 ? bound[1]
+                  : bound.size() > 0 ? bound[0]
+                                     : BoundType::Replicate),
           interpolation0(interpolation),
           interpolation1(interpolation),
           interpolation2(interpolation),

monai/data/dataset.py

@@ -699,8 +699,7 @@ def _try_shutdown(self):
                 self._round = 0
                 self._replace_done = False
                 return True
-            else:
-                return False
+            return False
 
     def shutdown(self):
         """

monai/data/decathlon_datalist.py

@@ -39,13 +39,12 @@ def _compute_path(base_dir, element):
     """
     if isinstance(element, str):
         return os.path.normpath(os.path.join(base_dir, element))
-    elif isinstance(element, list):
+    if isinstance(element, list):
         for e in element:
             if not isinstance(e, str):
                 raise TypeError(f"Every file path in element must be a str but got {type(element).__name__}.")
         return [os.path.normpath(os.path.join(base_dir, e)) for e in element]
-    else:
-        raise TypeError(f"element must be one of (str, list) but is {type(element).__name__}.")
+    raise TypeError(f"element must be one of (str, list) but is {type(element).__name__}.")
 
 
 def _append_paths(base_dir: str, is_segmentation: bool, items: List[Dict]) -> List[Dict]:

monai/engines/utils.py

@@ -95,10 +95,9 @@ def default_prepare_batch(
             batchdata[CommonKeys.IMAGE].to(device=device, non_blocking=non_blocking),
             batchdata[CommonKeys.LABEL].to(device=device, non_blocking=non_blocking),
         )
-    elif GanKeys.REALS in batchdata:
+    if GanKeys.REALS in batchdata:
         return batchdata[GanKeys.REALS].to(device=device, non_blocking=non_blocking)
-    else:
-        return batchdata[CommonKeys.IMAGE].to(device=device, non_blocking=non_blocking), None
+    return batchdata[CommonKeys.IMAGE].to(device=device, non_blocking=non_blocking), None
 
 
 def default_make_latent(

monai/handlers/confusion_matrix.py

@@ -122,6 +122,5 @@ def compute(self):
                     "ConfusionMatrix metric must have at least one example before it can be computed."
                 )
             return self._sum / self._num_examples
-        else:
-            confusion_matrix = torch.tensor([self._total_tp, self._total_fp, self._total_tn, self._total_fn])
-            return compute_confusion_matrix_metric(self.metric_name, confusion_matrix)
+        confusion_matrix = torch.tensor([self._total_tp, self._total_fp, self._total_tn, self._total_fn])
+        return compute_confusion_matrix_metric(self.metric_name, confusion_matrix)

monai/metrics/confusion_matrix.py

@@ -103,16 +103,15 @@ def __call__(self, y_pred: torch.Tensor, y: torch.Tensor):
                 confusion_matrix = compute_confusion_matrix_metric(self.metric_name, confusion_matrix)
                 f, not_nans = do_metric_reduction(confusion_matrix, self.reduction)
                 return f, not_nans
-            else:
-                if len(self.metric_name) < 1:
-                    raise ValueError("the sequence should at least has on metric name.")
-                results = []
-                for metric_name in self.metric_name:
-                    sub_confusion_matrix = compute_confusion_matrix_metric(metric_name, confusion_matrix)
-                    f, not_nans = do_metric_reduction(sub_confusion_matrix, self.reduction)
-                    results.append(f)
-                    results.append(not_nans)
-                return results
+            if len(self.metric_name) < 1:
+                raise ValueError("the sequence should at least has on metric name.")
+            results = []
+            for metric_name in self.metric_name:
+                sub_confusion_matrix = compute_confusion_matrix_metric(metric_name, confusion_matrix)
+                f, not_nans = do_metric_reduction(sub_confusion_matrix, self.reduction)
+                results.append(f)
+                results.append(not_nans)
+            return results
         else:
             return confusion_matrix
 
@@ -264,8 +263,7 @@ def compute_confusion_matrix_metric(metric_name: str, confusion_matrix: torch.Te
 
     if isinstance(denominator, torch.Tensor):
         return torch.where(denominator != 0, numerator / denominator, nan_tensor)
-    else:
-        return numerator / denominator
+    return numerator / denominator
 
 
 def check_confusion_matrix_metric_name(metric_name: str):
@@ -284,37 +282,36 @@ def check_confusion_matrix_metric_name(metric_name: str):
     metric_name = metric_name.lower()
     if metric_name in ["sensitivity", "recall", "hit_rate", "true_positive_rate", "tpr"]:
         return "tpr"
-    elif metric_name in ["specificity", "selectivity", "true_negative_rate", "tnr"]:
+    if metric_name in ["specificity", "selectivity", "true_negative_rate", "tnr"]:
         return "tnr"
-    elif metric_name in ["precision", "positive_predictive_value", "ppv"]:
+    if metric_name in ["precision", "positive_predictive_value", "ppv"]:
         return "ppv"
-    elif metric_name in ["negative_predictive_value", "npv"]:
+    if metric_name in ["negative_predictive_value", "npv"]:
         return "npv"
-    elif metric_name in ["miss_rate", "false_negative_rate", "fnr"]:
+    if metric_name in ["miss_rate", "false_negative_rate", "fnr"]:
         return "fnr"
-    elif metric_name in ["fall_out", "false_positive_rate", "fpr"]:
+    if metric_name in ["fall_out", "false_positive_rate", "fpr"]:
         return "fpr"
-    elif metric_name in ["false_discovery_rate", "fdr"]:
+    if metric_name in ["false_discovery_rate", "fdr"]:
         return "fdr"
-    elif metric_name in ["false_omission_rate", "for"]:
+    if metric_name in ["false_omission_rate", "for"]:
         return "for"
-    elif metric_name in ["prevalence_threshold", "pt"]:
+    if metric_name in ["prevalence_threshold", "pt"]:
         return "pt"
-    elif metric_name in ["threat_score", "critical_success_index", "ts", "csi"]:
+    if metric_name in ["threat_score", "critical_success_index", "ts", "csi"]:
         return "ts"
-    elif metric_name in ["accuracy", "acc"]:
+    if metric_name in ["accuracy", "acc"]:
         return "acc"
-    elif metric_name in ["balanced_accuracy", "ba"]:
+    if metric_name in ["balanced_accuracy", "ba"]:
         return "ba"
-    elif metric_name in ["f1_score", "f1"]:
+    if metric_name in ["f1_score", "f1"]:
         return "f1"
-    elif metric_name in ["matthews_correlation_coefficient", "mcc"]:
+    if metric_name in ["matthews_correlation_coefficient", "mcc"]:
         return "mcc"
-    elif metric_name in ["fowlkes_mallows_index", "fm"]:
+    if metric_name in ["fowlkes_mallows_index", "fm"]:
         return "fm"
-    elif metric_name in ["informedness", "bookmaker_informedness", "bm"]:
+    if metric_name in ["informedness", "bookmaker_informedness", "bm"]:
         return "bm"
-    elif metric_name in ["markedness", "deltap", "mk"]:
+    if metric_name in ["markedness", "deltap", "mk"]:
         return "mk"
-    else:
-        raise NotImplementedError("the metric is not implemented.")
+    raise NotImplementedError("the metric is not implemented.")

monai/metrics/hausdorff_distance.py

@@ -166,7 +166,6 @@ def compute_percent_hausdorff_distance(
     if not percentile:
         return surface_distance.max()
 
-    elif 0 <= percentile <= 100:
+    if 0 <= percentile <= 100:
         return np.percentile(surface_distance, percentile)
-    else:
-        raise ValueError(f"percentile should be a value between 0 and 100, get {percentile}.")
+    raise ValueError(f"percentile should be a value between 0 and 100, get {percentile}.")

monai/metrics/rocauc.py

@@ -114,33 +114,30 @@ def compute_roc_auc(
         if softmax:
             warnings.warn("y_pred has only one channel, softmax=True ignored.")
         return _calculate(y, y_pred)
-    else:
-        n_classes = y_pred.shape[1]
-        if to_onehot_y:
-            y = one_hot(y, n_classes)
-        if softmax and other_act is not None:
-            raise ValueError("Incompatible values: softmax=True and other_act is not None.")
-        if softmax:
-            y_pred = y_pred.float().softmax(dim=1)
-        if other_act is not None:
-            if not callable(other_act):
-                raise TypeError(f"other_act must be None or callable but is {type(other_act).__name__}.")
-            y_pred = other_act(y_pred)
-
-        assert y.shape == y_pred.shape, "data shapes of y_pred and y do not match."
-
-        average = Average(average)
-        if average == Average.MICRO:
-            return _calculate(y.flatten(), y_pred.flatten())
-        y, y_pred = y.transpose(0, 1), y_pred.transpose(0, 1)
-        auc_values = [_calculate(y_, y_pred_) for y_, y_pred_ in zip(y, y_pred)]
-        if average == Average.NONE:
-            return auc_values
-        if average == Average.MACRO:
-            return np.mean(auc_values)
-        if average == Average.WEIGHTED:
-            weights = [sum(y_) for y_ in y]
-            return np.average(auc_values, weights=weights)
-        raise ValueError(
-            f'Unsupported average: {average}, available options are ["macro", "weighted", "micro", "none"].'
-        )
+    n_classes = y_pred.shape[1]
+    if to_onehot_y:
+        y = one_hot(y, n_classes)
+    if softmax and other_act is not None:
+        raise ValueError("Incompatible values: softmax=True and other_act is not None.")
+    if softmax:
+        y_pred = y_pred.float().softmax(dim=1)
+    if other_act is not None:
+        if not callable(other_act):
+            raise TypeError(f"other_act must be None or callable but is {type(other_act).__name__}.")
+        y_pred = other_act(y_pred)
+
+    assert y.shape == y_pred.shape, "data shapes of y_pred and y do not match."
+
+    average = Average(average)
+    if average == Average.MICRO:
+        return _calculate(y.flatten(), y_pred.flatten())
+    y, y_pred = y.transpose(0, 1), y_pred.transpose(0, 1)
+    auc_values = [_calculate(y_, y_pred_) for y_, y_pred_ in zip(y, y_pred)]
+    if average == Average.NONE:
+        return auc_values
+    if average == Average.MACRO:
+        return np.mean(auc_values)
+    if average == Average.WEIGHTED:
+        weights = [sum(y_) for y_ in y]
+        return np.average(auc_values, weights=weights)
+    raise ValueError(f'Unsupported average: {average}, available options are ["macro", "weighted", "micro", "none"].')

monai/networks/layers/factories.py

@@ -178,14 +178,13 @@ def split_args(args):
 
     if isinstance(args, str):
         return args, {}
-    else:
-        name_obj, name_args = args
+    name_obj, name_args = args
 
-        if not isinstance(name_obj, (str, Callable)) or not isinstance(name_args, dict):
-            msg = "Layer specifiers must be single strings or pairs of the form (name/object-types, argument dict)"
-            raise TypeError(msg)
+    if not isinstance(name_obj, (str, Callable)) or not isinstance(name_args, dict):
+        msg = "Layer specifiers must be single strings or pairs of the form (name/object-types, argument dict)"
+        raise TypeError(msg)
 
-        return name_obj, name_args
+    return name_obj, name_args
 
 
 # Define factories for these layer types