fixes assertion conditions

Signed-off-by: Wenqi Li <wenqil@nvidia.com>

monai/inferers/utils.py

@@ -82,7 +82,7 @@ def sliding_window_inference(
 
     """
     num_spatial_dims = len(inputs.shape) - 2
-    if 0 > overlap:
+    if overlap < 0 or overlap >= 1:
         raise AssertionError("overlap must be >= 0 and < 1.")
 
     # determine image spatial size and batch size

monai/networks/blocks/dynunet_block.py

@@ -277,9 +277,8 @@ def get_padding(
     kernel_size_np = np.atleast_1d(kernel_size)
     stride_np = np.atleast_1d(stride)
     padding_np = (kernel_size_np - stride_np + 1) / 2
-    error_msg = "padding value should not be negative, please change the kernel size and/or stride."
     if np.min(padding_np) < 0:
-        raise AssertionError(error_msg)
+        raise AssertionError("padding value should not be negative, please change the kernel size and/or stride.")
     padding = tuple(int(p) for p in padding_np)
 
     return padding if len(padding) > 1 else padding[0]
@@ -295,9 +294,8 @@ def get_output_padding(
     padding_np = np.atleast_1d(padding)
 
     out_padding_np = 2 * padding_np + stride_np - kernel_size_np
-    error_msg = "out_padding value should not be negative, please change the kernel size and/or stride."
     if np.min(out_padding_np) < 0:
-        raise AssertionError(error_msg)
+        raise AssertionError("out_padding value should not be negative, please change the kernel size and/or stride.")
     out_padding = tuple(int(p) for p in out_padding_np)
 
     return out_padding if len(out_padding) > 1 else out_padding[0]

monai/networks/nets/dynunet.py

@@ -176,9 +176,8 @@ def check_kernel_stride(self):
     def check_deep_supr_num(self):
         deep_supr_num, strides = self.deep_supr_num, self.strides
         num_up_layers = len(strides) - 1
-        error_msg = "deep_supr_num should be less than the number of up sample layers."
-        if 1 > deep_supr_num:
-            raise AssertionError(error_msg)
+        if deep_supr_num < 1 or deep_supr_num >= num_up_layers:
+            raise AssertionError("deep_supr_num should be less than the number of up sample layers.")
 
     def forward(self, x):
         out = self.skip_layers(x)

monai/transforms/intensity/array.py

@@ -471,9 +471,9 @@ class ScaleIntensityRangePercentiles(Transform):
     def __init__(
         self, lower: float, upper: float, b_min: float, b_max: float, clip: bool = False, relative: bool = False
     ) -> None:
-        if 0.0 > lower:
+        if lower < 0.0 or lower > 100.0:
             raise AssertionError("Percentiles must be in the range [0, 100]")
-        if 0.0 > upper:
+        if upper < 0.0 or upper > 100.0:
             raise AssertionError("Percentiles must be in the range [0, 100]")
         self.lower = lower
         self.upper = upper

monai/utils/aliases.py

@@ -60,7 +60,7 @@ def resolve_name(name):
     with alias_lock:
         obj = GlobalAliases.get(name, None)
 
-    if not (name not in GlobalAliases or obj is not None):
+    if name in GlobalAliases and obj is None:
         raise AssertionError
 
     # attempt to resolve a qualified name

setup.py

@@ -38,7 +38,7 @@
     BUILD_CUDA = (torch.cuda.is_available() and (CUDA_HOME is not None)) or FORCE_CUDA
 
     _pt_version = pkg_resources.parse_version(torch.__version__).release  # type: ignore[attr-defined]
-    if not (_pt_version is not None and len(_pt_version) >= 3):
+    if _pt_version is None or len(_pt_version) < 3:
         raise AssertionError("unknown torch version")
     TORCH_VERSION = int(_pt_version[0]) * 10000 + int(_pt_version[1]) * 100 + int(_pt_version[2])
 except (ImportError, TypeError, AssertionError, AttributeError) as e: