deprecate mean_file option in Caffe (#8707)

* deprecate mean_file option

* add deprecation warning even if error happens

* removed Suppress for now deprecated options

* review fixes

* added dot

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md

@@ -63,10 +63,10 @@ Caffe*-specific parameters:
   -k K                  Path to CustomLayersMapping.xml to register custom
                         layers
   --mean_file MEAN_FILE, -mf MEAN_FILE
-                        Mean image to be used for the input. Should be a
+                        [DEPRECATED] Mean image to be used for the input. Should be a
                         binaryproto file
   --mean_file_offsets MEAN_FILE_OFFSETS, -mo MEAN_FILE_OFFSETS
-                        Mean image offsets to be used for the input
+                        [DEPRECATED] Mean image offsets to be used for the input
                         binaryproto file. When the mean image is bigger than
                         the expected input, it is cropped. By default, centers
                         of the input image and the mean image are the same and

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Paddle.md

@@ -42,7 +42,7 @@ To convert a Paddle\* model:
 Parameters to convert your model:
 
 * [Framework-agnostic parameters](Converting_Model_General.md): These parameters are used to convert a model trained with any supported framework.
-> **NOTE:** `--scale`, `--scale_values`, `--mean_values`, `--mean_file` are not supported in the current version of mo_paddle.
+> **NOTE:** `--scale`, `--scale_values`, `--mean_values` are not supported in the current version of mo_paddle.
 
 ### Example of Converting a Paddle* Model
 Below is the example command to convert yolo v3 Paddle\* network to OpenVINO IR network with Model Optimizer.

docs/MO_DG/prepare_model/convert_model/Converting_Model.md

@@ -6,7 +6,7 @@ mo --input_model INPUT_MODEL --output_dir <OUTPUT_MODEL_DIR>
 ```
 You need to have have write permissions for an output directory.
 
-> **NOTE**: Some models require using additional arguments to specify conversion parameters, such as `--input_shape`, `--scale`, `--scale_values`, `--mean_values`, `--mean_file`. To learn about when you need to use these parameters, refer to [Converting a Model Using General Conversion Parameters](Converting_Model_General.md).
+> **NOTE**: Some models require using additional arguments to specify conversion parameters, such as `--input_shape`, `--scale`, `--scale_values`, `--mean_values`. To learn about when you need to use these parameters, refer to [Converting a Model Using General Conversion Parameters](Converting_Model_General.md).
 
 To adjust the conversion process, you may use general parameters defined in the [Converting a Model Using General Conversion Parameters](Converting_Model_General.md) and 
 Framework-specific parameters for:

docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md

@@ -151,7 +151,7 @@ Usually neural network models are trained with the normalized input data. This m
  
 In the first case, the Model Optimizer generates the IR with required pre-processing layers and Inference Engine samples may be used to infer the model. 
  
-In the second case, information about mean/scale values should be provided to the Model Optimizer to embed it to the generated IR. Model Optimizer provides a number of command line parameters to specify them: `--scale`, `--scale_values`, `--mean_values`, `--mean_file`. 
+In the second case, information about mean/scale values should be provided to the Model Optimizer to embed it to the generated IR. Model Optimizer provides a number of command line parameters to specify them: `--scale`, `--scale_values`, `--mean_values`. 
 
 If both mean and scale values are specified, the mean is subtracted first and then scale is applied. Input values are *divided* by the scale value(s).