[DOC] ShapeInference.md update. slyalin comments (openvinotoolkit#3355)…

… (openvinotoolkit#4104)

* [DOC] ShapeInference.md update. slyalin comments

* Apply suggestions from code review

Co-authored-by: Alina Alborova <[email protected]>

* Apply suggestions from code review

Co-authored-by: Alina Alborova <[email protected]>

* Update docs/IE_DG/ShapeInference.md

Co-authored-by: Alina Alborova <[email protected]>

Co-authored-by: Alina Alborova <[email protected]>

docs/IE_DG/ShapeInference.md

@@ -1,6 +1,36 @@
 Using Shape Inference {#openvino_docs_IE_DG_ShapeInference}
 ==========================================
 
+OpenVINO™ provides the following methods for runtime model reshaping:
+
+* **Set a new input shape** with the `InferenceEngine::CNNNetwork::reshape` method.<br>
+   The `InferenceEngine::CNNNetwork::reshape` method updates input shapes and propagates them down to the outputs of the model through all intermediate layers. 
+
+> **NOTES**:
+> - Starting with the 2021.1 release, the Model Optimizer converts topologies keeping shape-calculating sub-graphs by default, which enables correct shape propagation during reshaping in most cases.
+> - Older versions of IRs are not guaranteed to reshape successfully. Please regenerate them with the Model Optimizer of the latest version of OpenVINO™.<br>
+> - If an ONNX model does not have a fully defined input shape and the model was imported with the ONNX importer, reshape the model before loading it to the plugin.
+
+* **Set a new batch dimension value** with the `InferenceEngine::CNNNetwork::setBatchSize` method.<br>     
+   The meaning of a model batch may vary depending on the model design.
+   This method does not deduce batch placement for inputs from the model architecture.
+   It assumes that the batch is placed at the zero index in the shape for all inputs and uses the `InferenceEngine::CNNNetwork::reshape` method to propagate updated shapes through the model.
+
+   The method transforms the model before a new shape propagation to relax a hard-coded batch dimension in the model, if any.
+
+   Use `InferenceEngine::CNNNetwork::reshape` instead of `InferenceEngine::CNNNetwork::setBatchSize` to set new input shapes for the model in case the model has:
+   * Multiple inputs with different zero-index dimension meanings
+   * Input without a batch dimension
+   * 0D, 1D, or 3D shape
+
+   The `InferenceEngine::CNNNetwork::setBatchSize` method is a high-level API method that wraps the `InferenceEngine::CNNNetwork::reshape` method call and works for trivial models from the batch placement standpoint.
+   Use `InferenceEngine::CNNNetwork::reshape` for other models.
+
+   Using the `InferenceEngine::CNNNetwork::setBatchSize` method for models with a non-zero index batch placement or for models with inputs that do not have a batch dimension may lead to undefined behaviour.
+
+You can change input shapes multiple times using the `InferenceEngine::CNNNetwork::reshape` and `InferenceEngine::CNNNetwork::setBatchSize` methods in any order.
+If a model has a hard-coded batch dimension, use `InferenceEngine::CNNNetwork::setBatchSize` first to change the batch, then call `InferenceEngine::CNNNetwork::reshape` to update other dimensions, if needed.
+
 Inference Engine takes three kinds of a model description as an input, which are converted into an `InferenceEngine::CNNNetwork` object:
 1. [Intermediate Representation (IR)](../MO_DG/IR_and_opsets.md) through `InferenceEngine::Core::ReadNetwork`
 2. [ONNX model](../IE_DG/OnnxImporterTutorial.md) through `InferenceEngine::Core::ReadNetwork`
@@ -23,33 +53,7 @@ for (const auto & parameter : parameters) {
 
 To feed input data of a shape that is different from the model input shape, reshape the model first.
 
-OpenVINO™ provides the following methods for runtime model reshaping:
-
-* **Set a new input shape** with the `InferenceEngine::CNNNetwork::reshape` method.<br>
-   The `InferenceEngine::CNNNetwork::reshape` method updates input shapes and propagates them down to the outputs of the model through all intermediate layers. 
-   You can reshape a model multiple times like in this application scheme:
-   ```
-   ReadNetwork -> reshape(input_1_shape) -> LoadNetwork -> infer(input_1)
-              \
-               -> reshape(input_2_shape) -> LoadNetwork -> infer(input_2)
-   ```
-   > **NOTES**:
-   > - Starting with the 2021.1 release, the Model Optimizer converts topologies keeping shape-calculating sub-graphs by default, which enables correct shape propagation during reshaping.
-   > - Older versions of IRs are not guaranteed to reshape successfully. Please regenerate them with the Model Optimizer of the latest version of OpenVINO™.<br>
-   > - If an ONNX model does not have a fully defined input shape and the model was imported with the ONNX importer, reshape the model before loading it to the plugin.
-* **Set a new batch dimension value** with the `InferenceEngine::CNNNetwork::setBatchSize` method.<br>     
-   The meaning of a model batch may vary depending on the model design.
-   The `InferenceEngine::CNNNetwork::setBatchSize` method deduces the index of a batch dimension based only on the input rank. 
-   This method does not work for models with a non-zero index batch placement or models with inputs without a batch dimension. 
-   The batch-setting algorithm does not involve the shape inference mechanism.
-   Batch of input and output shapes for all layers is set to a new batch value without layer validation.
-   It may cause both positive and negative side effects.
-   Due to the limitations described above, the current method is not recommended to use.
-   If you need to set a new batch size for the model, use the `CNNNetwork::reshape` method instead.
-
-Do not use runtime reshaping methods simultaneously, especially do not call the `CNNNetwork::reshape` method after you use `InferenceEngine::CNNNetwork::setBatchSize`.
-The `InferenceEngine::CNNNetwork::setBatchSize` method causes irreversible conversion of the internal model representation into the legacy model representation.
-The method does not use nGraph for shape inference which leads to reduced reshape opportunities and may affect the performance of the model.
+Once the input shape of `InferenceEngine::CNNNetwork` is set, call the `InferenceEngine::Core::LoadNetwork` method to get an `InferenceEngine::ExecutableNetwork` object for inference with updated shapes.
 
 There are other approaches to reshape the model during the stage of <a href="_docs_MO_DG_prepare_model_convert_model_Converting_Model_General.html#when_to_specify_input_shapes">IR generation</a> or [nGraph::Function creation](../nGraph_DG/build_function.md).
 
@@ -62,8 +66,8 @@ Shape collision during shape propagation may be a sign that a new shape does not
 Changing the model input shape may result in intermediate operations shape collision.
 
 Examples of such operations:
-- <a href="_docs_MO_DG_prepare_model_convert_model_IR_V10_opset1.html#Reshape">`Reshape` operation</a> with a hard-coded output shape value
-- <a href="_docs_MO_DG_prepare_model_convert_model_IR_V10_opset1.html#MatMul">`MatMul` operation</a> with the `Const` second input cannot be resized by spatial dimensions due to operation semantics
+- [`Reshape` operation](../ops/shape/Reshape_1.md) with a hard-coded output shape value
+- [`MatMul` operation](../ops/matrix/MatMul_1.md) with the `Const` second input cannot be resized by spatial dimensions due to operation semantics
 
 Model structure and logic should not change significantly after model reshaping.
 - The Global Pooling operation is commonly used to reduce output feature map of classification models output.