1889 - fixes typos

docs/source/networks.rst

@@ -99,7 +99,7 @@ Blocks
 .. autoclass:: SEResNetBottleneck
     :members:
 
-`Squeeze-and-Excitation ResneXt Bottleneck`
+`Squeeze-and-Excitation ResNeXt Bottleneck`
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. autoclass:: SEResNeXtBottleneck
     :members:

monai/apps/pathology/datasets.py

@@ -283,7 +283,7 @@ def _load_a_patch(self, index):
         """
         Load sample given the index
 
-        Since index is sequential and the patches are comming in an stream from different images,
+        Since index is sequential and the patches are coming in an stream from different images,
         this method, first, finds the whole slide image and the patch that should be extracted,
         then it loads the patch and provide it with its image name and the corresponding mask location.
         """

monai/csrc/filtering/bilateral/bilateralfilter_cuda_phl.cu

@@ -95,7 +95,7 @@ void BilateralFilterPHLCuda(
   cudaMalloc(&data, desc.batchCount * desc.channelStride * desc.channelCount * sizeof(scalar_t));
   cudaMalloc(&features, desc.batchCount * desc.channelStride * featureChannelCount * sizeof(scalar_t));
 
-  // Prparing constant memory
+  // Preparing constant memory
   cudaMemcpyToSymbol(cBatchStride, &desc.batchStride, sizeof(int));
   cudaMemcpyToSymbol(cChannelStride, &desc.channelStride, sizeof(int));
   cudaMemcpyToSymbol(cSpatialStrides, desc.strides, sizeof(int) * desc.dimensions);

monai/csrc/filtering/permutohedral/hash_table.cuh

@@ -15,7 +15,7 @@ limitations under the License.
 
 //#define USE_ADDITIVE_HASH
 
-// turn this on if you want to get slighly less memory consumption and slightly longer run times.
+// turn this on if you want to get slightly less memory consumption and slightly longer run times.
 //#define LINEAR_D_MEMORY
 
 #define USE_CUSTOM_MODULO

monai/networks/blocks/crf.py

@@ -20,7 +20,7 @@
 class CRF(torch.nn.Module):
     """
     Conditional Random Field: Combines message passing with a class
-    compatability convolution into an iterative process designed
+    compatibility convolution into an iterative process designed
     to successively minimise the energy of the class labeling.
 
     In this implementation, the message passing step is a weighted
@@ -40,7 +40,7 @@ def __init__(
         bilateral_color_sigma: float = 0.5,
         gaussian_spatial_sigma: float = 5.0,
         update_factor: float = 3.0,
-        compatability_kernel_range: int = 1,
+        compatibility_kernel_range: int = 1,
         iterations: int = 5,
     ):
         """
@@ -51,7 +51,7 @@ def __init__(
             bilateral_color_sigma: standard deviation in color space for the bilateral term.
             gaussian_spatial_sigma: standard deviation in spatial coordinates for the gaussian term.
             update_factor: determines the magnitude of each update.
-            compatability_kernel_range: the range of the kernel used in the compatability convolution.
+            compatibility_kernel_range: the range of the kernel used in the compatibility convolution.
             iterations: the number of iterations.
         """
         super(CRF, self).__init__()
@@ -61,14 +61,14 @@ def __init__(
         self.bilateral_color_sigma = bilateral_color_sigma
         self.gaussian_spatial_sigma = gaussian_spatial_sigma
         self.update_factor = update_factor
-        self.compatability_kernel_range = compatability_kernel_range
+        self.compatibility_kernel_range = compatibility_kernel_range
         self.iterations = iterations
 
     def forward(self, input_tensor: torch.Tensor, reference_tensor: torch.Tensor):
         """
         Args:
             input_tensor: tensor containing initial class logits.
-            referenece_tensor: the reference tensor used to guide the message passing.
+            reference_tensor: the reference tensor used to guide the message passing.
 
         Returns:
             output (torch.Tensor): output tensor.
@@ -77,7 +77,7 @@ def forward(self, input_tensor: torch.Tensor, reference_tensor: torch.Tensor):
         # useful values
         spatial_dim = input_tensor.dim() - 2
         class_count = input_tensor.size(1)
-        padding = self.compatability_kernel_range
+        padding = self.compatibility_kernel_range
 
         # constructing spatial feature tensor
         spatial_features = _create_coordinate_tensor(reference_tensor)
@@ -88,18 +88,18 @@ def forward(self, input_tensor: torch.Tensor, reference_tensor: torch.Tensor):
         )
         gaussian_features = spatial_features / self.gaussian_spatial_sigma
 
-        # compatability matrix (potts model (1 - diag) for now)
-        compatability_matrix = _potts_model_weights(class_count).to(device=input_tensor.device)
+        # compatibility matrix (potts model (1 - diag) for now)
+        compatibility_matrix = _potts_model_weights(class_count).to(device=input_tensor.device)
 
         # expanding matrix to kernel
-        compatability_kernel = _expand_matrix_to_kernel(
-            compatability_matrix, spatial_dim, self.compatability_kernel_range
+        compatibility_kernel = _expand_matrix_to_kernel(
+            compatibility_matrix, spatial_dim, self.compatibility_kernel_range
         )
 
         # choosing convolution function
         conv = [conv1d, conv2d, conv3d][spatial_dim - 1]
 
-        # seting up output tensor
+        # setting up output tensor
         output_tensor = softmax(input_tensor, dim=1)
 
         # mean field loop
@@ -114,7 +114,7 @@ def forward(self, input_tensor: torch.Tensor, reference_tensor: torch.Tensor):
 
             # compatibility convolution
             combined_output = pad(combined_output, 2 * spatial_dim * [padding], mode="replicate")
-            compatibility_update = conv(combined_output, compatability_kernel)
+            compatibility_update = conv(combined_output, compatibility_kernel)
 
             # update and normalize
             output_tensor = softmax(input_tensor - self.update_factor * compatibility_update, dim=1)

monai/networks/blocks/regunet_block.py

@@ -227,7 +227,7 @@ def __init__(
             spatial_dims: number of spatial dimensions
             extract_levels: spatial levels to extract feature from, 0 refers to the input scale
             num_channels: number of channels at each scale level,
-                List or Tuple of lenth equals to `depth` of the RegNet
+                List or Tuple of length equals to `depth` of the RegNet
             out_channels: number of output channels
             kernel_initializer: kernel initializer
             activation: kernel activation function

monai/networks/layers/filtering.py

@@ -32,7 +32,7 @@ class BilateralFilter(torch.autograd.Function):
         input: input tensor.
 
         spatial sigma: the standard deviation of the spatial blur. Higher values can
-            hurt performace when not using the approximate method (see fast approx).
+            hurt performance when not using the approximate method (see fast approx).
 
         color sigma: the standard deviation of the color blur. Lower values preserve
             edges better whilst higher values tend to a simple gaussian spatial blur.
@@ -95,7 +95,7 @@ def forward(ctx, input, features, sigmas=None):
 
     @staticmethod
     def backward(ctx, grad_output):
-        raise NotImplementedError("PHLFilter does not currently support backpropergation")
+        raise NotImplementedError("PHLFilter does not currently support Backpropagation")
         # scaled_features, = ctx.saved_variables
         # grad_input = _C.phl_filter(grad_output, scaled_features)
         # return grad_input

monai/transforms/compose.py

@@ -19,7 +19,7 @@
 
 from monai.transforms.inverse import InvertibleTransform
 
-# For backwards compatiblity (so this still works: from monai.transforms.compose import MapTransform)
+# For backwards compatibility (so this still works: from monai.transforms.compose import MapTransform)
 from monai.transforms.transform import (  # noqa: F401
     MapTransform,
     Randomizable,

tests/test_crf_cpu.py

@@ -30,7 +30,7 @@
             0.5,  # bilateral_color_sigma
             5.0,  # gaussian_spatial_sigma
             1.0,  # update_factor
-            1,  # compatability_kernel_range
+            1,  # compatibility_kernel_range
             5,  # iterations
         ],
         # Input
@@ -92,7 +92,7 @@
             0.5,  # bilateral_color_sigma
             5.0,  # gaussian_spatial_sigma
             1.0,  # update_factor
-            1,  # compatability_kernel_range
+            1,  # compatibility_kernel_range
             5,  # iterations
         ],
         # Input
@@ -189,7 +189,7 @@
             0.1,  # bilateral_color_sigma
             5.0,  # gaussian_spatial_sigma
             1.0,  # update_factor
-            1,  # compatability_kernel_range
+            1,  # compatibility_kernel_range
             2,  # iterations
         ],
         # Input

tests/test_crf_cuda.py

@@ -30,7 +30,7 @@
             0.5,  # bilateral_color_sigma
             5.0,  # gaussian_spatial_sigma
             1.0,  # update_factor
-            1,  # compatability_kernel_range
+            1,  # compatibility_kernel_range
             5,  # iterations
         ],
         # Input
@@ -92,7 +92,7 @@
             0.5,  # bilateral_color_sigma
             5.0,  # gaussian_spatial_sigma
             1.0,  # update_factor
-            1,  # compatability_kernel_range
+            1,  # compatibility_kernel_range
             5,  # iterations
         ],
         # Input
@@ -189,7 +189,7 @@
             0.1,  # bilateral_color_sigma
             5.0,  # gaussian_spatial_sigma
             1.0,  # update_factor
-            1,  # compatability_kernel_range
+            1,  # compatibility_kernel_range
             2,  # iterations
         ],
         # Input