Cleanup/remove some unnecessary control-flow statement around uncondi…

…tional control-flow.
drivendataorg · Feb 27, 2018 · 67b8406 · 67b8406
1 parent 8489058
commit 67b8406
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Showing 7 changed files with 26 additions and 27 deletions.
diff --git a/prediction/src/algorithms/classify/src/gtr123_model.py b/prediction/src/algorithms/classify/src/gtr123_model.py
@@ -46,12 +46,11 @@ def __init__(self, n_in, n_out, stride=1):
         self.conv2 = nn.Conv3d(n_out, n_out, kernel_size=3, padding=1)
         self.bn2 = nn.BatchNorm3d(n_out)
 
+        self.shortcut = None
         if stride != 1 or n_out != n_in:
             self.shortcut = nn.Sequential(
                 nn.Conv3d(n_in, n_out, kernel_size=1, stride=stride),
                 nn.BatchNorm3d(n_out))
-        else:
-            self.shortcut = None
 
     def forward(self, x):
         residual = x

diff --git a/prediction/src/algorithms/evaluation/metrics.py b/prediction/src/algorithms/evaluation/metrics.py
@@ -23,5 +23,5 @@ def logloss(true_label, predicted, eps=1e-15):
 
     if true_label == 1:
         return -np.log(p)
-    else:
-        return -np.log(1 - p)
+
+    return -np.log(1 - p)
diff --git a/prediction/src/algorithms/identify/src/gtr123_model.py b/prediction/src/algorithms/identify/src/gtr123_model.py
@@ -49,12 +49,11 @@ def __init__(self, n_in, n_out, stride=1):
         self.conv2 = nn.Conv3d(n_out, n_out, kernel_size=3, padding=1)
         self.bn2 = nn.BatchNorm3d(n_out)
 
+        self.shortcut = None
         if stride != 1 or n_out != n_in:
             self.shortcut = nn.Sequential(
                 nn.Conv3d(n_in, n_out, kernel_size=1, stride=stride),
                 nn.BatchNorm3d(n_out))
-        else:
-            self.shortcut = None
 
     def forward(self, x):
         """

diff --git a/prediction/src/algorithms/segment/src/models/unet_3d_model.py b/prediction/src/algorithms/segment/src/models/unet_3d_model.py
@@ -111,18 +111,18 @@ def compute_level_output_shape(filters, depth, pool_size, image_shape):
 
 def get_upconv(depth, nb_filters, pool_size, image_shape, kernel_size=(2, 2, 2), strides=(2, 2, 2),
                deconvolution=False):
-    if deconvolution:
-        try:
-            from keras_contrib.layers import Deconvolution3D
-        except ImportError:
-            raise ImportError("Install keras_contrib in order to use deconvolution. Otherwise set deconvolution=False.")
-
-        return Deconvolution3D(filters=nb_filters, kernel_size=kernel_size,
-                               output_shape=compute_level_output_shape(filters=nb_filters, depth=depth,
-                                                                       pool_size=pool_size, image_shape=image_shape),
-                               strides=strides, input_shape=compute_level_output_shape(filters=nb_filters,
-                                                                                       depth=depth + 1,
-                                                                                       pool_size=pool_size,
-                                                                                       image_shape=image_shape))
-    else:
+    if not deconvolution:
         return UpSampling3D(size=pool_size)
+
+    try:
+        from keras_contrib.layers import Deconvolution3D
+    except ImportError:
+        raise ImportError("Install keras_contrib in order to use deconvolution. Otherwise set deconvolution=False.")
+
+    return Deconvolution3D(filters=nb_filters, kernel_size=kernel_size,
+                           output_shape=compute_level_output_shape(filters=nb_filters, depth=depth,
+                                                                   pool_size=pool_size, image_shape=image_shape),
+                           strides=strides, input_shape=compute_level_output_shape(filters=nb_filters,
+                                                                                   depth=depth + 1,
+                                                                                   pool_size=pool_size,
+                                                                                   image_shape=image_shape))
diff --git a/prediction/src/preprocess/load_ct.py b/prediction/src/preprocess/load_ct.py
@@ -198,5 +198,5 @@ def __init__(self, meta):
 
         elif isinstance(self.meta, MetaData):
             self.non_copy_constructor(meta)
-        else:
-            raise ValueError('The meta should be either list[dicom.dataset.FileDataset] or SimpleITK.SimpleITK.Image')
+
+        raise ValueError('The meta should be either list[dicom.dataset.FileDataset] or SimpleITK.SimpleITK.Image')
diff --git a/prediction/src/tests/__init__.py b/prediction/src/tests/__init__.py
@@ -23,5 +23,5 @@ def get_timeout():
 
     if run_slow_tests:
         return 0
-    else:
-        return int(os.environ.get('TESTS_TIMEOUT', DEFAULT_TIMEOUT))
+
+    return int(os.environ.get('TESTS_TIMEOUT', DEFAULT_TIMEOUT))
diff --git a/prediction/src/tests/test_grt123_preprocess.py b/prediction/src/tests/test_grt123_preprocess.py
@@ -94,7 +94,8 @@ def resample(imgs, spacing, new_spacing, order=2):
             imgs = zoom(imgs, resize_factor, mode='nearest', order=order)
 
         return imgs, true_spacing
-    elif len(imgs.shape) == 4:
+
+    if len(imgs.shape) == 4:
         n = imgs.shape[-1]
         newimg = []
 
@@ -106,8 +107,8 @@ def resample(imgs, spacing, new_spacing, order=2):
         newimg = np.transpose(np.array(newimg), [1, 2, 3, 0])
 
         return newimg, true_spacing
-    else:
-        raise ValueError('wrong shape')
+
+    raise ValueError('wrong shape')
 
 
 def test_lum_trans(metaimage_path):