* Fix TensorRT detection bug

1. Add new search path for TensorRT at tensorrt.cmake
2. Add better debug message
3. Fix the bug of detection of TensorRT version

In NVIDIA official docker image, TensorRT headers are located at
`/usr/include/x86_64-linux-gnu` and TensorRT libraries are located
at `/usr/lib/x86_64-linux-gnu`, so using `-DTENSORRT_ROOT` will
fail to detect TensorRT.

There is no debug/warning message to tell developer that TensorRT
is failed to be detected.

In later version of TensorRT (e.g. v6), `NV_TENSORRT_MAJOR` is
defined at `NvInferVersion.h` instead of `NvInfer.h`, so add
compatibility fix.

* Fix TensorRT variables in CMake

1. Replace `${TENSORRT_ROOT}/include` with `${TENSORRT_INCLUDE_DIR}`
2. Replace `${TENSORRT_ROOT}/lib` with `${TENSORRT_LIBRARY}`

Manually type path may locate incorrect path of TensorRT. Use the
paths detected by system instead.

* Fix TensorRT library path

1. Add new variable - `${TENSORRT_LIBRARY_DIR}`
2. Fix TensorRT library path

inference_lib.cmake and setup.py.in need the path of TensorRT library
instead of the file of TensorRT library, so add new variable to fix it.

* Add more general search rule for TensoRT

Let system detect architecture instead of manually assign it, so
replace `x86_64-linux-gnu` with `${CMAKE_LIBRARY_ARCHITECTURE}`.

* Add more general search rule for TensorRT

Remove duplicate search rules for TensorRT libraries. Use
`${TENSORRT_LIBRARY_DIR}` to get full path of libnvinfer.so

test=release/1.6

* add tensorrt support for windows

2. refine trt code
test=develop

test=develop