fix black error

Signed-off-by: Yiheng Wang <[email protected]>
Project-MONAI · Jan 27, 2024 · 249eaa6 · 249eaa6
1 parent 87b0ffe
commit 249eaa6
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Showing 6 changed files with 12 additions and 19 deletions.
diff --git a/ci/unit_tests/test_spleen_deepedit_annotation.py b/ci/unit_tests/test_spleen_deepedit_annotation.py
@@ -124,9 +124,9 @@ def test_infer_config(self, override):
     @parameterized.expand([TEST_CASE_2])
     def test_infer_click_config(self, override):
         override["dataset_dir"] = self.dataset_dir
-        override[
-            "dataset#data"
-        ] = "$[{'image': i, 'background': [], 'spleen': [[6, 6, 6], [8, 8, 8]]} for i in @datalist]"
+        override["dataset#data"] = (
+            "$[{'image': i, 'background': [], 'spleen': [[6, 6, 6], [8, 8, 8]]} for i in @datalist]"
+        )
         bundle_root = override["bundle_root"]
         sys.path = [bundle_root] + sys.path
 

diff --git a/models/renalStructures_UNEST_segmentation/configs/metadata.json b/models/renalStructures_UNEST_segmentation/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.2.3",
+    "version": "0.2.4",
     "changelog": {
+        "0.2.4": "fix black 24.1 format error",
         "0.2.3": "update AddChanneld with EnsureChannelFirstd and remove meta_dict",
         "0.2.2": "add name tag",
         "0.2.1": "fix license Copyright error",

diff --git a/models/renalStructures_UNEST_segmentation/scripts/networks/nest/utils.py b/models/renalStructures_UNEST_segmentation/scripts/networks/nest/utils.py
@@ -37,9 +37,7 @@ def drop_block_2d(
     total_size = w * h
     clipped_block_size = min(block_size, min(w, h))
     # seed_drop_rate, the gamma parameter
-    gamma = (
-        gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((w - block_size + 1) * (h - block_size + 1))
-    )
+    gamma = gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((w - block_size + 1) * (h - block_size + 1))
 
     # Forces the block to be inside the feature map.
     w_i, h_i = torch.meshgrid(torch.arange(w).to(x.device), torch.arange(h).to(x.device))
@@ -89,9 +87,7 @@ def drop_block_fast_2d(
     b, c, h, w = x.shape
     total_size = w * h
     clipped_block_size = min(block_size, min(w, h))
-    gamma = (
-        gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((w - block_size + 1) * (h - block_size + 1))
-    )
+    gamma = gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((w - block_size + 1) * (h - block_size + 1))
 
     block_mask = torch.empty_like(x).bernoulli_(gamma)
     block_mask = F.max_pool2d(

diff --git a/models/valve_landmarks/configs/metadata.json b/models/valve_landmarks/configs/metadata.json
@@ -1,8 +1,7 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220729.json",
-    "version": "0.5.1",
+    "version": "0.5.0",
     "changelog": {
-        "0.5.1": "test",
         "0.5.0": "Fix transform usage",
         "0.4.3": "README.md fix",
         "0.4.2": "add name tag",

diff --git a/models/wholeBrainSeg_Large_UNEST_segmentation/configs/metadata.json b/models/wholeBrainSeg_Large_UNEST_segmentation/configs/metadata.json
@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.2.3",
+    "version": "0.2.4",
     "changelog": {
+        "0.2.4": "fix black 24.1 format error",
         "0.2.3": "fix PYTHONPATH in readme.md",
         "0.2.2": "add name tag",
         "0.2.1": "fix license Copyright error",

diff --git a/models/wholeBrainSeg_Large_UNEST_segmentation/scripts/networks/nest/utils.py b/models/wholeBrainSeg_Large_UNEST_segmentation/scripts/networks/nest/utils.py
@@ -37,9 +37,7 @@ def drop_block_2d(
     total_size = w * h
     clipped_block_size = min(block_size, min(w, h))
     # seed_drop_rate, the gamma parameter
-    gamma = (
-        gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((w - block_size + 1) * (h - block_size + 1))
-    )
+    gamma = gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((w - block_size + 1) * (h - block_size + 1))
 
     # Forces the block to be inside the feature map.
     w_i, h_i = torch.meshgrid(torch.arange(w).to(x.device), torch.arange(h).to(x.device))
@@ -89,9 +87,7 @@ def drop_block_fast_2d(
     b, c, h, w = x.shape
     total_size = w * h
     clipped_block_size = min(block_size, min(w, h))
-    gamma = (
-        gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((w - block_size + 1) * (h - block_size + 1))
-    )
+    gamma = gamma_scale * drop_prob * total_size / clipped_block_size**2 / ((w - block_size + 1) * (h - block_size + 1))
 
     block_mask = torch.empty_like(x).bernoulli_(gamma)
     block_mask = F.max_pool2d(