fix typo (#4482)

tools/caffe/caffe.proto

@@ -1115,7 +1115,7 @@ message PriorBoxParameter {
   // Variance for adjusting the prior bboxes.
   repeated float variance = 6;
   // By default, we calculate img_height, img_width, step_x, step_y based on
-  // bottom[0] (feat) and bottom[1] (img). Unless these values are explicitely
+  // bottom[0] (feat) and bottom[1] (img). Unless these values are explicitly
   // provided.
   // Explicitly provide the img_size.
   optional uint32 img_size = 7;

tools/mlir/tf_types.cc

@@ -293,7 +293,7 @@ bool BroadcastCompatible(TypeRange lhs, TypeRange rhs)
 // The two types are considered cast compatible if they have dynamically equal
 // shapes and element type. For element types that do not have subtypes, they
 // must be equal. However for TensorFlow types such as Resource and Variant,
-// that also have subtypes, we recursively check for subtype compatibilty for
+// that also have subtypes, we recursively check for subtype compatibility for
 // Resource types and assume all variant types are cast compatible. If either
 // one of `a` or `b` have empty subtypes, they are considered cast compatible.
 //

tools/pnnx/README.md

@@ -188,7 +188,7 @@ weight binaries can be listed or modified with any archive application eg. 7zip
 # PNNX operator
 PNNX always preserve operators from what PyTorch python api provides.
 
-Here is the netron visualization comparision among ONNX, TorchScript and PNNX with the original PyTorch python code shown.
+Here is the netron visualization comparison among ONNX, TorchScript and PNNX with the original PyTorch python code shown.
 
 ```python
 import torch
@@ -212,7 +212,7 @@ class Model(nn.Module):
 # PNNX expression operator
 PNNX trys to preserve expression from what PyTorch python code writes.
 
-Here is the netron visualization comparision among ONNX, TorchScript and PNNX with the original PyTorch python code shown.
+Here is the netron visualization comparison among ONNX, TorchScript and PNNX with the original PyTorch python code shown.
 
 ```python
 import torch
@@ -228,7 +228,7 @@ def foo(x, y):
 # PNNX torch function operator
 PNNX trys to preserve torch functions and Tensor member functions as one operator from what PyTorch python api provides.
 
-Here is the netron visualization comparision among ONNX, TorchScript and PNNX with the original PyTorch python code shown.
+Here is the netron visualization comparison among ONNX, TorchScript and PNNX with the original PyTorch python code shown.
 
 ```python
 import torch
@@ -270,7 +270,7 @@ inline module = utils.activations.SiLU
 pnnx yolov5s.pt inputshape=[1,3,640,640] moduleop=models.common.Focus,models.yolo.Detect
 ```
 
-Here is the netron visualization comparision among ONNX, TorchScript and PNNX with the original PyTorch python code shown.
+Here is the netron visualization comparison among ONNX, TorchScript and PNNX with the original PyTorch python code shown.
 
 ```python
 import torch