Consistent case for Python, Fortran, C, C++, CMake

README.md

@@ -71,7 +71,7 @@ _For a similar approach to calling TensorFlow models from Fortran please see [Fo
 
 To install the library requires the following to be installed on the system:
 
-* cmake >= 3.1
+* CMake >= 3.1
 * [libtorch](https://pytorch.org/cppdocs/installing.html)<sup>*</sup> or [PyTorch](https://pytorch.org/)
 * Fortran, C++ (must fully support C++17), and C compilers
 
@@ -133,12 +133,12 @@ To build and install the library:
 
     <sup>1</sup> _On Windows this may need to be the full path to the compiler if CMake cannot locate it by default._  
 
-    <sup>2</sup> _The path to the Torch installation needs to allow cmake to locate the relevant Torch cmake files.  
+    <sup>2</sup> _The path to the Torch installation needs to allow CMake to locate the relevant Torch CMake files.  
           If Torch has been [installed as libtorch](https://pytorch.org/cppdocs/installing.html)
           then this should be the absolute path to the unzipped libtorch distribution.
-          If Torch has been installed as PyTorch in a python [venv (virtual environment)](https://docs.python.org/3/library/venv.html),
+          If Torch has been installed as PyTorch in a Python [venv (virtual environment)](https://docs.python.org/3/library/venv.html),
           e.g. with `pip install torch`, then this should be `</path/to/venv/>lib/python<3.xx>/site-packages/torch/`.  
-		  You can find the location of your torch install by importing torch from your python environment (`import torch`) and running `print(torch.__file__)`_
+		  You can find the location of your torch install by importing torch from your Python environment (`import torch`) and running `print(torch.__file__)`_
 	  
 4. Make and install the library to the desired location with either:
 	```
@@ -150,7 +150,7 @@ To build and install the library:
     cmake --install .
     ```
 
-    Note: If you are using cmake<3.15 then you will need to build and install separately
+    Note: If you are using CMake < 3.15 then you will need to build and install separately
     using the make system specific commands.
     For example, if using `make` on UNIX this would be:
     ```

examples/1_SimpleNet/README.md

@@ -9,7 +9,7 @@ covered in later examples.
 
 ## Description
 
-A python file `simplenet.py` is provided that defines a very simple PyTorch 'net' that takes an input
+A Python file `simplenet.py` is provided that defines a very simple PyTorch 'net' that takes an input
 vector of length 5 and applies a single `Linear` layer to multiply it by 2.
 
 A modified version of the `pt2ts.py` tool saves this simple net to TorchScript.
@@ -21,15 +21,15 @@ TorchScript model in inference mode.
 
 To run this example requires:
 
-- cmake
-- fortran compiler
+- CMake
+- Fortran compiler
 - FTorch (installed as described in main package)
-- python3
+- Python 3
 
 ## Running
 
 To run this example install FTorch as described in the main documentation.
-Then from this directory create a virtual environment and install the necessary python
+Then from this directory create a virtual environment and install the necessary Python
 modules:
 ```
 python3 -m venv venv
@@ -63,12 +63,12 @@ This reads the model in from the TorchScript file and runs it with an input tens
 tensor([[0, 2, 4, 6, 8]])
 ```
 
-At this point we no longer require python, so can deactivate the virtual environment:
+At this point we no longer require Python, so can deactivate the virtual environment:
 ```
 deactivate
 ```
 
-To call the saved SimpleNet model from fortran we need to compile the `simplenet_infer`
+To call the saved SimpleNet model from Fortran we need to compile the `simplenet_infer`
 files.
 This can be done using the included `CMakeLists.txt` as follows:
 ```
@@ -89,7 +89,7 @@ This runs the model with the array `[0.0, 1.0, 2.0, 3.0, 4.0]` should produce th
    0.00000000       2.00000000       4.00000000       6.00000000       8.00000000
 ```
 
-Alternatively we can use `make`, instead of cmake, with the included Makefile.
+Alternatively we can use `make`, instead of CMake, with the included Makefile.
 However, to do this you will need to modify `Makefile` to link to and include your
 installation of FTorch as described in the main documentation. Also check that the compiler is the same as the one you built the Library with.
 ```

examples/2_ResNet18/README.md

@@ -6,7 +6,7 @@ As the input to this model is four-dimensional (batch size, colour, x, y), care
 
 ## Description
 
-A python file is provided that downloads the pretrained
+A Python file is provided that downloads the pretrained
 [ResNet-18](https://pytorch.org/vision/main/models/generated/torchvision.models.resnet18.html)
 model from [TorchVision](https://pytorch.org/vision/stable/index.html).
 
@@ -19,15 +19,15 @@ TorchScript ResNet-18 model in inference mode.
 
 To run this example requires:
 
-- cmake
-- fortran compiler
+- CMake
+- Fortran compiler
 - FTorch (installed as described in main package)
-- python3
+- Python 3
 
 ## Running
 
 To run this example install FTorch as described in the main documentation.
-Then from this directory create a virtual environment an install the neccessary python
+Then from this directory create a virtual environment an install the neccessary Python
 modules:
 ```
 python3 -m venv venv
@@ -59,12 +59,12 @@ To save the pretrained ResNet-18 model to TorchScript run the modified version o
 python3 pt2ts.py
 ```
 
-At this point we no longer require python, so can deactivate the virtual environment:
+At this point we no longer require Python, so can deactivate the virtual environment:
 ```
 deactivate
 ```
 
-To call the saved ResNet-18 model from fortran we need to compile the `resnet_infer`
+To call the saved ResNet-18 model from Fortran we need to compile the `resnet_infer`
 files.
 This can be done using the included `CMakeLists.txt` as follows:
 ```
@@ -89,7 +89,7 @@ This should produce the same top result:
 ```
 
 
-Alternatively we can use `make`, instead of cmake, with the included Makefile.
+Alternatively we can use `make`, instead of CMake, with the included Makefile.
 However, to do this you will need to modify `Makefile` to link to and include your
 installation of FTorch as described in the main documentation. Also check that the compiler is the same as the one you built the Library with.  
 You will also likely need to add the location of the `.so` files to your `LD_LIBRARY_PATH`:

examples/3_MultiGPU/README.md

@@ -6,7 +6,7 @@ multiple GPU devices.
 
 ## Description
 
-The same python file `simplenet.py` is used from the earlier example. Recall that it
+The same Python file `simplenet.py` is used from the earlier example. Recall that it
 defines a very simple PyTorch network that takes an input of length 5 and applies a
 single `Linear` layer to multiply it by 2.
 
@@ -19,16 +19,16 @@ TorchScript model in inference mode.
 
 To run this example requires:
 
-- cmake
+- CMake
 - An MPI installation.
 - mpif90
 - FTorch (installed as described in main package)
-- python3
+- Python 3
 
 ## Running
 
 To run this example install FTorch as described in the main documentation. Then from
-this directory create a virtual environment and install the necessary python modules:
+this directory create a virtual environment and install the necessary Python modules:
 ```
 python3 -m venv venv
 source venv/bin/activate
@@ -70,7 +70,7 @@ entry. The result should be (some permutation of):
 3: tensor([[ 6., 8., 10., 12., 14.]])
 ```
 
-At this point we no longer require python, so can deactivate the virtual environment:
+At this point we no longer require Python, so can deactivate the virtual environment:
 ```
 deactivate
 ```
@@ -105,7 +105,7 @@ output on rank2: [  4.0,  6.0,  8.0, 10.0, 12.0]
 output on rank3: [  6.0,  8.0, 10.0, 12.0, 14.0]
 ```
 
-Alternatively, we can use `make`, instead of cmake, copying the Makefile over from the
+Alternatively, we can use `make`, instead of CMake, copying the Makefile over from the
 first example:
 ```
 cp ../1_SimpleNet/Makefile .

examples/n_c_and_cpp/README.md

@@ -1,12 +1,12 @@
-# Example n - ResNet-18 for c and c++
+# Example n - ResNet-18 for C and C++
 
 **Note: This Example is not currently functional and is still being developed.**
 
-This example provides a demonstration of how to use the library to interface to c and c++.
+This example provides a demonstration of how to use the library to interface to C and C++.
 
 ## Description
 
-A python file is provided that downloads the pretrained
+A Python file is provided that downloads the pretrained
 [ResNet-18](https://pytorch.org/vision/main/models/generated/torchvision.models.resnet18.html)
 model from [TorchVision](https://pytorch.org/vision/stable/index.html).
 
@@ -19,16 +19,16 @@ TorchScript ResNet-18 model in inference mode.
 
 To run this example requires:
 
-- cmake
-- c compiler
-- c++ compiler
+- CMake
+- C compiler
+- C++ compiler
 - FTorch (installed as described in main package)
-- python3
+- Python 3
 
 ## Running
 
 To run this example install FTorch as described in the main documentation.
-Then from this directory create a virtual environment an install the neccessary python
+Then from this directory create a virtual environment an install the neccessary Python
 modules:
 ```
 python3 -m venv venv
@@ -48,12 +48,12 @@ To save the pretrained ResNet-18 model to TorchScript run the modified version o
 python3 pt2ts.py
 ```
 
-At this point we no longer require python, so can deactivate the virtual environment:
+At this point we no longer require Python, so can deactivate the virtual environment:
 ```
 deactivate
 ```
 
-To call the saved ResNet-18 model from fortran we need to compile the `resnet_infer`
+To call the saved ResNet-18 model from Fortran we need to compile the `resnet_infer`
 files.
 This can be done using the included `CMakeLists.txt` as follows:
 ```
@@ -86,7 +86,7 @@ language you wish
 * ctorch.h   - Header file for C++ wrapper
 * ftorch.f90 - Wrapper onto PyTorch for Fortran
 
-* CMakeLists.txt - Provides the cmake build system configuration
+* CMakeLists.txt - Provides the CMake build system configuration
 
 
 
@@ -96,7 +96,7 @@ language you wish
     make
 
 This will build the separate examples. Next, from this directory, you need
-to run the python code to generate the saved model:
+to run the Python code to generate the saved model:
 
     python3 ../pt2ts.py
 

pages/cmake.md

@@ -61,7 +61,7 @@ The following CMake flags are available and can be passed as arguments through `
 > <sup>2</sup> _The path to the Torch installation needs to allow CMake to locate the relevant Torch CMake files.  
 >       If Torch has been [installed as libtorch](https://pytorch.org/cppdocs/installing.html)
 >       then this should be the absolute path to the unzipped libtorch distribution.
->       If Torch has been installed as PyTorch in a python [venv (virtual environment)](https://docs.python.org/3/library/venv.html),
+>       If Torch has been installed as PyTorch in a Python [venv (virtual environment)](https://docs.python.org/3/library/venv.html),
 >       e.g. with `pip install torch`, then this should be `</path/to/venv/>lib/python<3.xx>/site-packages/torch/`._
 > 
 > <sup>3</sup> _This is often overridden by PyTorch. When installing with pip, the `index-url` flag can be used to ensure a CPU or GPU only version is installed, e.g.

pages/examples.md

@@ -22,7 +22,7 @@ This can be done from within your code using the
 [`jit.script`](https://pytorch.org/docs/stable/generated/torch.jit.script.html#torch.jit.script)
 or
 [`jit.trace`](https://pytorch.org/docs/stable/generated/torch.jit.trace.html#torch.jit.trace)
-functionalities from within python.
+functionalities from within Python.
 
 If you are not familiar with these we provide a tool
 [`pt2ts.py`](https://github.com/Cambridge-ICCS/FTorch/blob/main/utils/pt2ts.py)

pages/troubleshooting.md

@@ -28,7 +28,7 @@ From CMD this can be done with:
 "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
 ```
 
-Finally you will need to add `-G "NMake Makefiles"` to the cmake command in the
+Finally you will need to add `-G "NMake Makefiles"` to the `cmake` command in the
 [regular install instructions](doc/page/cmake.html).\
 So the basic command to build from CMD becomes:
 ```
@@ -49,7 +49,7 @@ cmake --install .
 
 It may be tempting to build on Windows using MinGW.
 However, [libtorch does not currently support MinGW](https://github.com/pytorch/pytorch/issues/15099).
-Instead please build using Visual Studio and the intel fortran compiler (ifort) as
+Instead please build using Visual Studio and the intel Fortran compiler (ifort) as
 detailed in the project README.
 
 ## Apple Silicon

src/README.md

@@ -23,4 +23,4 @@ To build, follow the instructions in the [main repository README](/README.md).
 * ctorch.cpp - Wrapper onto PyTorch for C++
 * ctorch.h   - Header file for C++ wrapper
 * ftorch.f90 - Wrapper onto PyTorch for Fortran
-* CMakeLists.txt - Provides the cmake build system configuration
+* CMakeLists.txt - Provides the CMake build system configuration