Add documentation on how to convert RCAN model (#6259)

* Add documentation on how to convert RCAN model

* Apply review feedback

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_PyTorch.md

@@ -21,6 +21,8 @@ Here is the list of models that are tested and guaranteed to be supported. Howev
   steps 2 and 3 of [regular instructions](#typical-pytorch).
 * YOLACT topology can be converted using steps described in [Convert PyTorch\* YOLACT to the IR](pytorch_specific/Convert_YOLACT.md)
   instruction which is used instead of steps 2 and 3 of [regular instructions](#typical-pytorch).
+* [RCAN](https://github.com/yulunzhang/RCAN) topology can be converted using the steps described in [Convert PyTorch\* RCAN to the IR](pytorch_specific/Convert_RCAN.md)
+instruction which is used instead of steps 2 and 3 of [regular instructions](#typical-pytorch).
 
 ## Typical steps to convert PyTorch\* model <a name="typical-pytorch"></a>
 

docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_RCAN.md

@@ -0,0 +1,31 @@
+# Convert PyTorch* RCAN to the Intermediate Representation {#openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_RCAN}
+
+[RCAN](https://github.com/yulunzhang/RCAN): Image Super-Resolution Using Very Deep Residual Channel Attention Networks
+
+## Download and Convert the Model to ONNX*
+
+To download the pre-trained model or train the model yourself, refer to the
+[instruction](https://github.com/yulunzhang/RCAN/blob/master/README.md) in the RCAN model repository. Firstly,
+convert the model to ONNX\* format. Create and run the script with the following content in the root
+directory of the model repository:
+```python
+from argparse import Namespace
+
+import torch
+
+from RCAN_TestCode.code.model.rcan import RCAN
+
+config = Namespace(n_feats=64, n_resblocks=4, n_resgroups=2, reduction=16, scale=[2], data_train='DIV2K', res_scale=1,
+                   n_colors=3, rgb_range=255)
+net = RCAN(config)
+net.eval()
+dummy_input = torch.randn(1, 3, 360, 640)
+torch.onnx.export(net, dummy_input, 'RCAN.onnx')
+```
+The script generates the ONNX\* model file RCAN.onnx. You can find more information about model parameters (`n_resblocks`, `n_resgroups`, and others) in the model repository and use different values of them. The model conversion was tested with the repository hash commit `3339ebc59519c3bb2b5719b87dd36515ec7f3ba7`.
+
+## Convert ONNX* RCAN Model to IR
+
+```sh
+./mo.py --input_model RCAN.onnx
+```

docs/doxygen/ie_docs.xml

@@ -60,6 +60,7 @@ limitations under the License.
                             <tab type="user" title="Convert PyTorch* RNN-T Model " url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_RNNT"/>
                             <tab type="user" title="Convert PyTorch* YOLACT Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_YOLACT"/>
                             <tab type="user" title="Convert PyTorch* F3Net Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_F3Net"/>
+                            <tab type="user" title="Convert PyTorch* RCAN Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_RCAN">
                             </tab>
                         </tab>
                         <tab type="user" title="Model Optimizations Techniques" url="@ref openvino_docs_MO_DG_prepare_model_Model_Optimization_Techniques"/>