Move append items to list definition (#4417)

monai/apps/detection/utils/anchor_utils.py

@@ -234,7 +234,8 @@ def grid_anchors(self, grid_sizes: List[List[int]], strides: List[List[Tensor]])
         """
         anchors = []
         cell_anchors = self.cell_anchors
-        assert cell_anchors is not None
+        if cell_anchors is None:
+            raise AssertionError
 
         if not (len(grid_sizes) == len(strides) == len(cell_anchors)):
             raise ValueError(

monai/networks/blocks/feature_pyramid_network.py

@@ -197,7 +197,8 @@ def __init__(
                 nn.init.constant_(m.bias, 0.0)  # type: ignore
 
         if extra_blocks is not None:
-            assert isinstance(extra_blocks, ExtraFPNBlock)
+            if not isinstance(extra_blocks, ExtraFPNBlock):
+                raise AssertionError
         self.extra_blocks = extra_blocks
 
     def get_result_from_inner_blocks(self, x: Tensor, idx: int) -> Tensor:

tests/test_anchor_box.py

@@ -20,23 +20,21 @@
 
 _, has_torchvision = optional_import("torchvision")
 
-TEST_CASES_2D = []
-TEST_CASES_2D.append(
+TEST_CASES_2D = [
     [
         {"sizes": ((10, 12, 14, 16), (20, 24, 28, 32)), "aspect_ratios": ((1.0, 0.5, 2.0), (1.0, 0.5, 2.0))},
         (5, 3, 128, 128),
         ((5, 7, 64, 32), (5, 7, 32, 16)),
     ]
-)
+]
 
-TEST_CASES_SHAPE_3D = []
-TEST_CASES_SHAPE_3D.append(
+TEST_CASES_SHAPE_3D = [
     [
         {"feature_map_scales": (1, 2), "base_anchor_shapes": ((4, 3, 6), (8, 2, 4))},
         (5, 3, 128, 128, 128),
         ((5, 7, 64, 32, 32), (5, 7, 32, 16, 16)),
     ]
-)
+]
 
 
 @SkipIfBeforePyTorchVersion((1, 11))

tests/test_fpn_block.py

@@ -23,26 +23,22 @@
 
 _, has_torchvision = optional_import("torchvision")
 
-TEST_CASES = []
-TEST_CASES.append(
+TEST_CASES = [
     [
         {"spatial_dims": 3, "in_channels_list": [32, 64], "out_channels": 6},
         ((7, 32, 16, 32, 64), (7, 64, 8, 16, 32)),
         ((7, 6, 16, 32, 64), (7, 6, 8, 16, 32)),
-    ]
-)
-TEST_CASES.append(
+    ],
     [
         {"spatial_dims": 2, "in_channels_list": [32, 64], "out_channels": 6},
         ((7, 32, 16, 32), (7, 64, 8, 16)),
         ((7, 6, 16, 32), (7, 6, 8, 16)),
-    ]
-)
+    ],
+]
 
-TEST_CASES2 = []
-TEST_CASES2.append(
+TEST_CASES2 = [
     [{"spatial_dims": 3, "returned_layers": [1]}, (7, 3, 32, 64, 32), ((7, 256, 16, 32, 16), (7, 256, 8, 16, 8))]
-)
+]
 
 
 class TestFPNBlock(unittest.TestCase):

tests/test_generate_pos_neg_label_crop_centers.py

@@ -18,8 +18,7 @@
 from monai.utils.misc import set_determinism
 from tests.utils import TEST_NDARRAYS, assert_allclose
 
-TESTS = []
-TESTS.append(
+TESTS = [
     [
         {
             "spatial_size": [2, 2, 2],
@@ -33,7 +32,7 @@
         2,
         3,
     ]
-)
+]
 
 
 class TestGeneratePosNegLabelCropCenters(unittest.TestCase):

tests/test_hardnegsampler.py

@@ -17,17 +17,16 @@
 from monai.apps.detection.utils.hard_negative_sampler import HardNegativeSampler
 from tests.utils import assert_allclose
 
-TEST_CASE = []
-TEST_CASE.append([[], [], [], [torch.tensor([]), torch.tensor([])], [torch.tensor([]), torch.tensor([])]])
-TEST_CASE.append(
+TEST_CASE = [
+    [[], [], [], [torch.tensor([]), torch.tensor([])], [torch.tensor([]), torch.tensor([])]],
     [
         [0, 1],
         [1, 0, 2, 3],
         [0.1, 0.9, 0.4, 0.3, 0.3, 0.5],
         [torch.tensor([0, 1]), torch.tensor([1, 0, 1, 1])],
         [torch.tensor([1, 0]), torch.tensor([0, 1, 0, 0])],
-    ]
-)
+    ],
+]
 
 select_sample_size_per_image = 6
 positive_fraction = 0.5

tests/test_optim_novograd.py

@@ -32,9 +32,10 @@ def build_test_cases(data):
         {"params": [bias], "lr": 1e-2, "amsgrad": True, "grad_averaging": True, "weight_decay": 0.1},
     ]
 
-    test_cases = []
-    test_cases.append([test_case_same_param, default_params, weight, bias, input])
-    test_cases.append([test_case_diff_param, default_params, weight, bias, input])
+    test_cases = [
+        [test_case_same_param, default_params, weight, bias, input],
+        [test_case_diff_param, default_params, weight, bias, input],
+    ]
     return test_cases
 
 

tests/test_rand_crop_by_pos_neg_label.py

@@ -18,8 +18,7 @@
 from monai.transforms import RandCropByPosNegLabel
 from tests.utils import TEST_NDARRAYS
 
-TESTS = []
-TESTS.append(
+TESTS = [
     [
         {
             "label": np.random.randint(0, 2, size=[3, 3, 3, 3]),
@@ -32,9 +31,7 @@
         },
         {"img": np.random.randint(0, 2, size=[3, 3, 3, 3])},
         (3, 2, 2, 3),
-    ]
-)
-TESTS.append(
+    ],
     [
         {
             "label": np.random.randint(0, 2, size=[3, 3, 3, 3]),
@@ -47,9 +44,7 @@
         },
         {"img": np.random.randint(0, 2, size=[3, 3, 3, 3])},
         (3, 2, 2, 2),
-    ]
-)
-TESTS.append(
+    ],
     [
         {
             "label": None,
@@ -66,9 +61,7 @@
             "image": np.random.randint(0, 2, size=[3, 3, 3, 3]),
         },
         (3, 2, 2, 2),
-    ]
-)
-TESTS.append(
+    ],
     [
         {
             "label": np.random.randint(0, 2, size=[3, 3, 3, 3]),
@@ -81,9 +74,7 @@
         },
         {"img": np.random.randint(0, 2, size=[3, 3, 3, 3])},
         (3, 3, 3, 2),
-    ]
-)
-TESTS.append(
+    ],
     [
         {
             "label": np.random.randint(0, 2, size=[3, 3, 3, 3]),
@@ -96,8 +87,8 @@
         },
         {"img": np.random.randint(0, 2, size=[3, 3, 3, 3])},
         (3, 3, 3, 3),
-    ]
-)
+    ],
+]
 
 
 class TestRandCropByPosNegLabel(unittest.TestCase):

tests/test_to_tensor.py

@@ -21,13 +21,11 @@
 
 im = [[1, 2], [3, 4]]
 
-TESTS = []
-TESTS.append((im, (2, 2)))
+TESTS = [(im, (2, 2))]
 for p in TEST_NDARRAYS:
     TESTS.append((p(im), (2, 2)))
 
-TESTS_SINGLE = []
-TESTS_SINGLE.append([5])
+TESTS_SINGLE = [[5]]
 for p in TEST_NDARRAYS:
     TESTS_SINGLE.append([p(5)])
 

tests/test_upsample_block.py

@@ -76,8 +76,8 @@
         test_case = [
             {"dimensions": 3, "in_channels": 3, "out_channels": 5, "mode": t, "scale_factor": s, "align_corners": True},
             (16, 3, 4, 5, 6),
+            expected_shape,
         ]
-        test_case.append(expected_shape)
         TEST_CASES_EQ.append(test_case)