[ MO ] Warnings fix (#5105)

* warnings fix

* remove redundant backslash

* add whitespace in the error message

model-optimizer/extensions/back/InterpolateReshape.py

@@ -15,7 +15,7 @@ from mo.ops.shape import Shape
 
 
 class InterpolateConcat(BackReplacementPattern):
-    """
+    r"""
     Replaces hard-coded 1-port input of Interpolate with reshape-able sub-graph using the following Concat inputs
 
     BEFORE:
@@ -85,7 +85,7 @@ class InterpolateConcat(BackReplacementPattern):
 
 
 class InterpolateReshapeWA(BackReplacementPattern):
-    """
+    r"""
     Replaces hard-coded 1-port input of Interpolate with reshape-able sub-graph.
     WARNING: Could cause troubles if model has hard-coded Interpolate intentionally -- rare situation
     BEFORE:

model-optimizer/extensions/back/MatMulNormalizer.py

@@ -70,7 +70,7 @@ class MatMulConstTransposesExtraction(BackReplacementPattern):
 
 
 class PullTransposeThroughFQUp(BackReplacementPattern):
-    """
+    r"""
         BEFORE                                      AFTER
                                                         T  T T  T  T
          \ \ | / /                                       \ \ | / /
@@ -135,7 +135,7 @@ class PullTransposeThroughFQUp(BackReplacementPattern):
 
 
 class SmartReshape_HC_Reshape_MatMul(BackReplacementPattern):
-    """
+    r"""
     Relaxes hard-coded input of Reshape in such sub-graphs:
 
     input_1     Constant

model-optimizer/extensions/back/ReverseInputChannels.py

@@ -97,7 +97,7 @@ class ReverseChannelsPropagationDown(BackReplacementPattern):
 
     @staticmethod
     def pass_rc_through_conv(node, reverse_channels):
-        """
+        r"""
         For non grouped convolution:
         BEFORE                          AFTER
 
@@ -167,7 +167,7 @@ class ReverseChannelsPropagationDown(BackReplacementPattern):
 
     @staticmethod
     def pass_rc_through_eltwise(node, reverse_channels):
-        """
+        r"""
         BEFORE                              AFTER
 
           previous_op                                       previous_op'
@@ -268,7 +268,7 @@ class ReverseChannelsPropagationUp(BackReplacementPattern):
 
     @staticmethod
     def lift_up_through_eltwise(node: Node, reverse_channels: Node):
-        """
+        r"""
         BEFORE                      AFTER
 
                                     previous_op              previous_op'

model-optimizer/extensions/back/compress_quantized_weights.py

@@ -14,7 +14,7 @@ from mo.ops.const import Const
 
 
 class CompressQuantizeWeights(BackReplacementPattern):
-    """
+    r"""
     Compress weights transformation goal is to pre-quantize data to minimize runtime calculations with constant data.
     To achieve this goal we perform FakeQuantize decomposition to separate quantization from dequantization in it.
 

model-optimizer/extensions/front/DropoutWithRandomUniformReplacer.py

@@ -12,7 +12,7 @@ from mo.ops.broadcast import Broadcast
 
 
 class DropoutWithRandomUniformReplacer(FrontReplacementSubgraph):
-    """
+    r"""
     This transformation replaces possible Dropout block (in inference mode) with RandomUniform
     to Broadcast of half-ones in a sub-graph.
     WARNING: the transformation can be triggered for other block with RandomUniform by mistake,

model-optimizer/extensions/front/broadcast_with_range.py

@@ -13,7 +13,7 @@ from mo.ops.unsqueeze import Unsqueeze
 
 
 class ExpandRangeConstant(FrontReplacementSubgraph):
-    """
+    r"""
     Searches for Constant operations filled with range values starting from 0 and replaces it with Range operation
     Faced in ONNX BERT -- replacing it makes model reshape-able by sequence length
 

model-optimizer/extensions/front/interpolate_reshape.py

@@ -13,7 +13,7 @@ from mo.ops.shape import Shape
 
 
 class InterpolateWithConcat(FrontReplacementPattern):
-    """
+    r"""
     Replaces hard-coded 1-port input of Interpolate with reshape-able sub-graph using the following Concat inputs
 
     BEFORE:

model-optimizer/extensions/front/kaldi/apply_counts.py

@@ -13,7 +13,7 @@ from mo.utils.utils import refer_to_faq_msg
 
 
 def apply_biases_to_last_layer(graph, counts):
-    """
+    r"""
     When user provides counts file, it is a file that contains log-apriory probabilities,
     technically it should be subtracted from the bias of the last layer unless it is a SoftMax.
 

model-optimizer/extensions/front/kaldi/memory_offset_adjustment.py

@@ -64,7 +64,7 @@ def align_frame_time(graph: Graph, node: Node, frame_time_max):
 
 
 class MemoryOffsetAdjustment(FrontReplacementSubgraph):
-    """
+    r"""
     Pass used to fix wrong results in the following situation:
                               input
                               |   \

model-optimizer/extensions/front/kaldi/tdnn_component_replacer.py

@@ -11,7 +11,7 @@ from mo.ops.memoryoffset import MemoryOffset
 
 
 class TdnnComponentReplacer(FrontReplacementPattern):
-    '''
+    r"""
     Expand TdnnComponent into MemoryOffsets, Concat and FullyConected nodes
 
     BEFORE:
@@ -31,7 +31,7 @@ class TdnnComponentReplacer(FrontReplacementPattern):
                              |
                         FullyConnected
                              |
-    '''
+    """
     enabled = True
     run_not_recursively = True
 

model-optimizer/extensions/front/tf/NonConstBeginStridedSliceReplacement.py

@@ -13,7 +13,7 @@ from mo.ops.unsqueeze import Unsqueeze
 
 
 class NonConstBeginStridedSliceReplacement(FrontReplacementSubgraph):
-    """
+    r"""
     The transformation handles StridedSlice operation with dynamic begin and end values
     when slicing performs along just one dimension with a dynamic index.
     For example, StridedSlice with begin=(0,idx,0), end=(0,idx+1,0),

model-optimizer/extensions/front/tf/UnpackPackReverseInputChannels.py

@@ -12,7 +12,7 @@ from mo.graph.graph import Graph
 
 
 class UnpackPackReverseInputChannels(FrontReplacementSubgraph):
-    """
+    r"""
     Unpack - Pack nodes sequence from TensorFlow connected like it shown below is a way to ReverseChannels
 
            /  0 - 2  \

model-optimizer/extensions/front/tf/fifo_replacer.py

@@ -35,7 +35,7 @@ class FIFOQueue(FrontReplacementSubgraph):
 
     @staticmethod
     def replace_sub_graph(graph: Graph, match: dict, **kwargs):
-        """
+        r"""
         Usually graph looks like:
 
           main_graph

model-optimizer/extensions/front/tf/floor_div_decomposition.py

@@ -8,7 +8,7 @@ from mo.graph.graph import Graph, Node, rename_node
 
 
 class FloorDivDecomposition(FrontReplacementPattern):
-    """
+    r"""
     BEFORE:                     AFTER:
     input_0     input_1         input_0     input_1
         \       /                   \       /

model-optimizer/extensions/front/tf/identityN_to_identity.py

@@ -7,7 +7,7 @@ from mo.graph.graph import Graph, Node
 
 
 class IdentityN_to_Identity(FrontReplacementPattern):
-    """
+    r"""
     Replaces IdentityN op with several Identity ops.
 
     Example:

model-optimizer/extensions/middle/BlockLSTMtoLSTMSequence.py

@@ -90,7 +90,7 @@ class BlockLSTMtoLSTMSequence(MiddleReplacementPattern):
     @staticmethod
     def replace_pattern(graph: Graph, match: dict):
         time_len = match['concatenated_hidden_states'].shape[0]
-        """
+        r"""
         Working with concatenated_cell_states_data part first, because IE TensorIterator primitive doesn't have
         concatenated cell states output and if we can not collapse it, then we does not support this type of BlockLSTM
 

model-optimizer/extensions/middle/MarkSubgraphsWithCorrectLayout.py

@@ -165,7 +165,7 @@ class MarkSubGraphsWithCorrectLayout(MiddleReplacementPattern):
 
     @staticmethod
     def walk_up_from_in_ports_to_out_ports(in_ports: Set[Port], out_ports: Set[Port], port_condition=None):
-        """"
+        r""""
         Returns all intermediate ports and nodes of such a sub-graph:
 
             out_ports

model-optimizer/extensions/middle/RemoveUselessConcatSplit.py

@@ -6,7 +6,7 @@ from mo.middle.replacement import MiddleReplacementPattern
 
 
 class RemoveUselessConcatSplitPattern(MiddleReplacementPattern):
-    """
+    r"""
     Remove useless construction with concat and split like follows:
          /    /   |    \     \
        br1  br2   ..  br(n-1)br(n)

model-optimizer/extensions/middle/RemoveUselessCrops.py

@@ -6,7 +6,7 @@ from mo.middle.replacement import MiddleReplacementPattern
 
 
 class RemoveUselessCropsPattern(MiddleReplacementPattern):
-    """
+    r"""
     Remove useless construction with crops and concat like follows:
                 in_node
          /    /   |    \     \

model-optimizer/extensions/middle/ReplaceSpliceNodePattern.py

@@ -18,7 +18,7 @@ from mo.ops.result import Result
 
 
 class ReplaceSpliceNodePattern(MiddleReplacementPattern):
-    """
+    r"""
        This pass decomposes Splice layer to the sequence Slice Concat and Memory layers
        For example:
            Let's suppose we have next graph:

model-optimizer/extensions/middle/StridedSliceNormalizer.py

@@ -17,7 +17,7 @@ from mo.utils.error import Error
 
 
 class StridedSliceNormalizer(MiddleReplacementPattern):
-    """
+    r"""
     StridedSlice is not normal if it cannot be permuted by ApplyPermutations. This normalizer
     inserts blank colons ':' in slice expression so that it can be correctly permuted
     from NHWC to NCHW layout. It changes masks and inserts blank begin, end and strides values.

model-optimizer/extensions/middle/TensorIteratorInput.py

@@ -245,7 +245,7 @@ class BackEdgeSimpleInputMatcher(MiddleReplacementPattern):
 
 
 class SmartMatcherInputSlicingWithGather(MiddleReplacementPattern):
-    """
+    r"""
     The transformation matches a sub-graph where input tensor is consequently sliced along some axis
     for each time step (or index) inside TensorFlow 1.x while_loop operation.
     In the original graph StridedSlice with non-constant begin and end attributes performs this slicing.

model-optimizer/mo/front/tf/graph_utils.py

@@ -143,7 +143,7 @@ def add_convolution_to_swap_xy_coordinates(graph: Graph, input_node: Node, coord
 
 
 def add_fake_background_loc(graph: Graph, input_node: Node):
-    """
+    r"""
     DetectionOutput layer expects that box coordinates contains coordinates of boxes for the "background" class also,
     but in the TensorFlow\* Object Detection API the tensor contains information about real object classes only.
     The function copies a slice of the output data of the node 'input_node' and then concats it to the beginning of the

model-optimizer/mo/graph/connection.py

@@ -203,8 +203,8 @@ class Connection:
             return {}, None
 
         if self.destinations and len(self.destinations) > 1:
-            raise Error("set_destination applicable only for connections that has exactly one destination or \
-                         when there is no destinations")
+            raise Error("set_destination applicable only for connections that has exactly one destination or "
+                        "when there is no destinations")
 
         if port.type == 'out':
             raise Error("Wrong port type in set_destination method. Should be 'in' but given 'out'")