From 54f39294de312b26c2ab816d158fb80fdb6e313e Mon Sep 17 00:00:00 2001 From: Jan Iwaszkiewicz Date: Fri, 25 Feb 2022 11:02:04 +0100 Subject: [PATCH] [PYTHON] Fix style in python doc strings (#10606) * Fix style in python doc strings * New line quotes --- .../python/src/openvino/runtime/ie_api.py | 13 +- .../python/src/openvino/runtime/opset1/ops.py | 212 +++++++++--------- .../python/src/openvino/runtime/opset2/ops.py | 28 +-- .../python/src/openvino/runtime/opset3/ops.py | 38 ++-- .../python/src/openvino/runtime/opset4/ops.py | 26 +-- .../python/src/openvino/runtime/opset5/ops.py | 18 +- .../python/src/openvino/runtime/opset6/ops.py | 10 +- .../python/src/openvino/runtime/opset7/ops.py | 12 +- .../python/src/openvino/runtime/opset8/ops.py | 34 +-- .../src/pyopenvino/core/async_infer_queue.cpp | 6 +- .../src/pyopenvino/core/compiled_model.cpp | 34 +-- .../python/src/pyopenvino/core/core.cpp | 29 ++- .../src/pyopenvino/core/infer_request.cpp | 30 +-- .../core/offline_transformations.cpp | 2 +- .../python/src/pyopenvino/core/tensor.cpp | 8 +- .../python/src/pyopenvino/graph/model.cpp | 2 +- .../src/pyopenvino/graph/node_output.hpp | 2 +- .../python/src/pyopenvino/graph/util.cpp | 2 +- 18 files changed, 254 insertions(+), 252 deletions(-) diff --git a/src/bindings/python/src/openvino/runtime/ie_api.py b/src/bindings/python/src/openvino/runtime/ie_api.py index 67bb84ef68a..b43d335463d 100644 --- a/src/bindings/python/src/openvino/runtime/ie_api.py +++ b/src/bindings/python/src/openvino/runtime/ie_api.py @@ -132,7 +132,7 @@ class InferRequest(InferRequestBase): :param inputs: Data to be set on input tensors. :type inputs: Union[Dict[keys, values], List[values]], optional - :param userdata: Any data that will be passed inside callback call. + :param userdata: Any data that will be passed inside the callback. :type userdata: Any """ super().start_async( @@ -164,8 +164,8 @@ class CompiledModel(CompiledModelBase): Blocks all methods of CompiledModel while request is running. Method creates new temporary InferRequest and run inference on it. - It is advised to use dedicated InferRequest class for performance, - optimizing workflows and creating advanced pipelines. + It is advised to use a dedicated InferRequest class for performance, + optimizing workflows, and creating advanced pipelines. The allowed types of keys in the `inputs` dictionary are: @@ -188,7 +188,10 @@ class CompiledModel(CompiledModelBase): ) def __call__(self, inputs: Union[dict, list] = None) -> dict: - """Callable infer wrapper for CompiledModel. Look at `infer_new_request` for reference.""" + """Callable infer wrapper for CompiledModel. + + Take a look at `infer_new_request` for reference. + """ return self.infer_new_request(inputs) @@ -245,7 +248,7 @@ class Core(CoreBase): """Core class represents OpenVINO runtime Core entity. User applications can create several Core class instances, but in this - case the underlying plugins are created multiple times and not shared + case, the underlying plugins are created multiple times and not shared between several Core instances. The recommended way is to have a single Core instance per application. """ diff --git a/src/bindings/python/src/openvino/runtime/opset1/ops.py b/src/bindings/python/src/openvino/runtime/opset1/ops.py index 7507acb2a3f..c05fe5159a2 100644 --- a/src/bindings/python/src/openvino/runtime/opset1/ops.py +++ b/src/bindings/python/src/openvino/runtime/opset1/ops.py @@ -43,7 +43,7 @@ def absolute(node: NodeInput, name: Optional[str] = None) -> Node: :param node: One of: input node, array or scalar. :param name: Optional new name for output node. - returns New node with Abs operation applied on it. + :return: New node with Abs operation applied on it. """ return _get_node_factory_opset1().create("Abs", [node]) @@ -54,7 +54,7 @@ def acos(node: NodeInput, name: Optional[str] = None) -> Node: :param node: One of: input node, array or scalar. :param name: Optional new name for output node. - returns New node with arccos operation applied on it. + :return: New node with arccos operation applied on it. """ return _get_node_factory_opset1().create("Acos", [node]) @@ -78,7 +78,7 @@ def asin(node: NodeInput, name: Optional[str] = None) -> Node: :param node: One of: input node, array or scalar. :param name: Optional new name for output node. - returns New node with arcsin operation applied on it. + :return: New node with arcsin operation applied on it. """ return _get_node_factory_opset1().create("Asin", [node]) @@ -89,7 +89,7 @@ def atan(node: NodeInput, name: Optional[str] = None) -> Node: :param node: One of: input node, array or scalar. :param name: Optional new name for output node. - returns New node with arctan operation applied on it. + :return: New node with arctan operation applied on it. """ return _get_node_factory_opset1().create("Atan", [node]) @@ -120,7 +120,7 @@ def avg_pool( [None, 'same_upper', 'same_lower', 'valid'] :param name: Optional name for the new output node. - returns New node with AvgPool operation applied on its data. + :return: New node with AvgPool operation applied on its data. """ if auto_pad is None: auto_pad = "explicit" @@ -159,7 +159,7 @@ def batch_norm_inference( :param epsilon: The number to be added to the variance to avoid division by zero when normalizing a value. :param name: The optional name of the output node. - returns The new node which performs BatchNormInference. + :return: The new node which performs BatchNormInference. """ inputs = as_nodes(gamma, beta, data, mean, variance) return _get_node_factory_opset1().create("BatchNormInference", inputs, {"epsilon": epsilon}) @@ -190,7 +190,7 @@ def binary_convolution( :param pad_value: Floating-point value used to fill pad area. :param auto_pad: The type of padding. Range of values: explicit, same_upper, same_lower, valid. :param name: The optional new name for output node. - returns New node performing binary convolution operation. + :return: New node performing binary convolution operation. """ return _get_node_factory_opset1().create( "BinaryConvolution", @@ -224,7 +224,7 @@ def broadcast( :param mode: The type of broadcasting that specifies mapping of input tensor axes to output shape axes. Range of values: NUMPY, EXPLICIT. :param name: Optional new name for output node. - returns New node with broadcast shape. + :return: New node with broadcast shape. """ inputs = as_nodes(data, target_shape) if mode.upper() == "EXPLICIT": @@ -247,7 +247,7 @@ def ctc_greedy_decoder( :param sequence_mask: The tensor with sequence masks for each sequence in the batch. :param merge_repeated: The flag for merging repeated labels during the CTC calculation. :param name: Optional name for output node. - returns The new node performing an CTCGreedyDecoder operation on input tensor. + :return: The new node performing an CTCGreedyDecoder operation on input tensor. """ node_inputs = as_nodes(data, sequence_mask) return _get_node_factory_opset1().create( @@ -261,7 +261,7 @@ def ceiling(node: NodeInput, name: Optional[str] = None) -> Node: :param node: The node providing data to ceiling operation. :param name: Optional name for output node. - returns The node performing element-wise ceiling. + :return: The node performing element-wise ceiling. """ return _get_node_factory_opset1().create("Ceiling", [node]) @@ -276,7 +276,7 @@ def clamp( :param min_value: The lower bound of the range. Scalar value. :param max_value: The upper bound of the range. Scalar value. :param name: Optional output node name. - returns The new node performing a clamp operation on its input data element-wise. + :return: The new node performing a clamp operation on its input data element-wise. Performs a clipping operation on an input value between a pair of boundary values. @@ -306,7 +306,7 @@ def concat(nodes: List[NodeInput], axis: int, name: Optional[str] = None) -> Nod :param nodes: The nodes we want concatenate into single new node. :param axis: The axis along which we want to concatenate input nodes. :param name: The optional new name for output node. - returns Return new node that is a concatenation of input nodes. + :return: Return new node that is a concatenation of input nodes. """ return _get_node_factory_opset1().create("Concat", as_nodes(*nodes), {"axis": axis}) @@ -322,7 +322,7 @@ def constant( :param value: One of: array of values or scalar to initialize node with. :param dtype: The data type of provided data. :param name: Optional name for output node. - returns The Constant node initialized with provided data. + :return: The Constant node initialized with provided data. """ return make_constant_node(value, dtype) @@ -336,7 +336,7 @@ def convert( :param data: Node which produces the input tensor. :param destination_type: Provides the target type for the conversion. :param name: Optional name for the output node. - returns New node performing the conversion operation. + :return: New node performing the conversion operation. """ if not isinstance(destination_type, str): destination_type = get_element_type_str(destination_type) @@ -352,7 +352,7 @@ def convert_like(data: NodeInput, like: NodeInput, name: Optional[str] = None) - :param data: Node which produces the input tensor :param like: Node which provides the target type information for the conversion :param name: Optional name for the output node. - returns New node performing the conversion operation. + :return: New node performing the conversion operation. """ return _get_node_factory_opset1().create("ConvertLike", [data, like]) @@ -378,7 +378,7 @@ def convolution( :param dilations: The data batch dilation strides. :param auto_pad: The type of padding. Range of values: explicit, same_upper, same_lower, valid. :param name: The optional new name for output node. - returns New node performing batched convolution operation. + :return: New node performing batched convolution operation. """ return _get_node_factory_opset1().create( "Convolution", @@ -419,7 +419,7 @@ def convolution_backprop_data( in the filter. :param name: The node name. - returns The node object representing ConvolutionBackpropData operation. + :return: The node object representing ConvolutionBackpropData operation. """ spatial_dim_count = len(strides) if pads_begin is None: @@ -456,7 +456,7 @@ def cos(node: NodeInput, name: Optional[str] = None) -> Node: :param node: One of: input node, array or scalar. :param name: Optional new name for output node. - returns New node with cos operation applied on it. + :return: New node with cos operation applied on it. """ return _get_node_factory_opset1().create("Cos", [node]) @@ -467,7 +467,7 @@ def cosh(node: NodeInput, name: Optional[str] = None) -> Node: :param node: One of: input node, array or scalar. :param name: Optional new name for output node. - returns New node with cosh operation applied on it. + :return: New node with cosh operation applied on it. """ return _get_node_factory_opset1().create("Cosh", [node]) @@ -499,7 +499,7 @@ def deformable_convolution( :param deformable_group: The number of groups which deformable values and output should be split into along the channel axis. :param name: The optional new name for output node. - returns New node performing deformable convolution operation. + :return: New node performing deformable convolution operation. """ return _get_node_factory_opset1().create( "DeformableConvolution", @@ -548,7 +548,7 @@ def deformable_psroi_pooling( :param part_size: The number of parts the output tensor spatial dimensions are divided into. :param offsets: Optional node. 4D input blob with transformation values (offsets). :param name: The optional new name for output node. - returns New node performing DeformablePSROIPooling operation. + :return: New node performing DeformablePSROIPooling operation. """ node_inputs = as_nodes(feature_maps, coords) if offsets is not None: @@ -592,7 +592,7 @@ def depth_to_space(node: Node, mode: str, block_size: int = 1, name: str = None) :param block_size: The size of the spatial block of values describing how the tensor's data is to be rearranged. :param name: Optional output node name. - returns The new node performing an DepthToSpace operation on its input tensor. + :return: The new node performing an DepthToSpace operation on its input tensor. """ return _get_node_factory_opset1().create( "DepthToSpace", [node], {"mode": mode, "block_size": block_size}, @@ -618,7 +618,7 @@ def detection_output( :param aux_class_preds: The 2D input tensor with additional class predictions information. :param aux_box_preds: The 2D input tensor with additional box predictions information. :param name: Optional name for the output node. - returns Node representing DetectionOutput operation. + :return: Node representing DetectionOutput operation. Available attributes are: @@ -774,7 +774,7 @@ def divide( :param right_node: The node providing divisor data. :param auto_broadcast: Specifies rules used for auto-broadcasting of input tensors. :param name: Optional name for output node. - returns The node performing element-wise division. + :return: The node performing element-wise division. """ return _get_node_factory_opset1().create( "Divide", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()} @@ -793,7 +793,7 @@ def elu(data: NodeInput, alpha: NumericType, name: Optional[str] = None) -> Node :param data: Input tensor. One of: input node, array or scalar. :param alpha: Scalar multiplier for negative values. :param name: Optional output node name. - returns The new node performing an ELU operation on its input data element-wise. + :return: The new node performing an ELU operation on its input data element-wise. """ return _get_node_factory_opset1().create("Elu", [as_node(data)], {"alpha": alpha}) @@ -812,7 +812,7 @@ def equal( :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. :param name: The optional name for output new node. - returns The node performing element-wise equality check. + :return: The node performing element-wise equality check. """ return _get_node_factory_opset1().create( "Equal", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()} @@ -825,7 +825,7 @@ def erf(node: NodeInput, name: Optional[str] = None) -> Node: :param node: The node providing data for operation. :param name: The optional name for new output node. - returns The new node performing element-wise Erf operation. + :return: The new node performing element-wise Erf operation. """ return _get_node_factory_opset1().create("Erf", [node]) @@ -836,7 +836,7 @@ def exp(node: NodeInput, name: Optional[str] = None) -> Node: :param node: The node providing data for operation. :param name: The optional name for new output node. - returns The new node performing natural exponential operation. + :return: The new node performing natural exponential operation. """ return _get_node_factory_opset1().create("Exp", [node]) @@ -862,7 +862,7 @@ def fake_quantize( :param levels: The number of quantization levels. Integer value. :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. - returns New node with quantized value. + :return: New node with quantized value. Input floating point values are quantized into a discrete set of floating point values. @@ -895,7 +895,7 @@ def floor(node: NodeInput, name: Optional[str] = None) -> Node: :param node: The input node providing data. :param name: The optional name for new output node. - returns The node performing element-wise floor operation. + :return: The node performing element-wise floor operation. """ return _get_node_factory_opset1().create("Floor", [node]) @@ -913,7 +913,7 @@ def floor_mod( :param right_node: The second input node for FloorMod operation. :param auto_broadcast: Specifies rules used for auto-broadcasting of input tensors. :param name: Optional name for output node. - returns The node performing element-wise FloorMod operation. + :return: The node performing element-wise FloorMod operation. """ return _get_node_factory_opset1().create( "FloorMod", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()} @@ -930,7 +930,7 @@ def gather( :param indices: Tensor with indexes to gather. :param axis: The dimension index to gather data from. :param name: Optional name for output node. - returns The new node performing a Gather operation on the data input tensor. + :return: The new node performing a Gather operation on the data input tensor. """ node_inputs = as_nodes(data, indices, axis) return _get_node_factory_opset1().create("Gather", node_inputs) @@ -951,7 +951,7 @@ def gather_tree( :param max_seq_len: The tensor with maximum lengths for each sequence in the batch. :param end_token: The scalar tensor with value of the end marker in a sequence. :param name: Optional name for output node. - returns The new node performing a GatherTree operation. + :return: The new node performing a GatherTree operation. The GatherTree node generates the complete beams from the indices per each step and the parent beam indices. @@ -988,7 +988,7 @@ def greater( :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. :param name: The optional new name for output node. - returns The node performing element-wise check whether left_node is greater than right_node. + :return: The node performing element-wise check whether left_node is greater than right_node. """ return _get_node_factory_opset1().create( "Greater", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()} @@ -1009,7 +1009,7 @@ def greater_equal( :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. :param name: The optional new name for output node. - returns The node performing element-wise check whether left_node is greater than or equal + :return: The node performing element-wise check whether left_node is greater than or equal right_node. """ return _get_node_factory_opset1().create( @@ -1027,7 +1027,7 @@ def grn(data: Node, bias: float, name: Optional[str] = None) -> Node: :param data: The node with data tensor. :param bias: The bias added to the variance. Scalar value. :param name: Optional output node name. - returns The new node performing a GRN operation on tensor's channels. + :return: The new node performing a GRN operation on tensor's channels. """ return _get_node_factory_opset1().create("GRN", [data], {"bias": bias}) @@ -1062,7 +1062,7 @@ def group_convolution( Ceil(num_dims/2) at the end VALID: No padding :param name: Optional output node name. - returns The new node performing a Group Convolution operation on tensor from input node. + :return: The new node performing a Group Convolution operation on tensor from input node. """ return _get_node_factory_opset1().create( "GroupConvolution", @@ -1113,7 +1113,7 @@ def group_convolution_backprop_data( :param output_padding: The additional amount of paddings added per each spatial axis in the output tensor. :param name: Optional output node name. - returns The new node performing a Group Convolution operation on tensor from input node. + :return: The new node performing a Group Convolution operation on tensor from input node. """ spatial_dim_count = len(strides) if dilations is None: @@ -1150,7 +1150,7 @@ def hard_sigmoid(data: Node, alpha: NodeInput, beta: NodeInput, name: Optional[s :param alpha: A node producing the alpha parameter. :param beta: A node producing the beta parameter :param name: Optional output node name. - returns The new node performing a Hard Sigmoid element-wise on input tensor. + :return: The new node performing a Hard Sigmoid element-wise on input tensor. Hard Sigmoid uses the following logic: @@ -1171,7 +1171,7 @@ def interpolate( :param output_shape: 1D tensor describing output shape for spatial axes. :param attrs: The dictionary containing key, value pairs for attributes. :param name: Optional name for the output node. - returns Node representing interpolation operation. + :return: Node representing interpolation operation. Available attributes are: @@ -1251,7 +1251,7 @@ def less( :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. :param name: The optional new name for output node. - returns The node performing element-wise check whether left_node is less than the right_node. + :return: The node performing element-wise check whether left_node is less than the right_node. """ return _get_node_factory_opset1().create( "Less", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()} @@ -1272,7 +1272,7 @@ def less_equal( :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. :param name: The optional new name for output node. - returns The node performing element-wise check whether left_node is less than or equal the + :return: The node performing element-wise check whether left_node is less than or equal the right_node. """ return _get_node_factory_opset1().create( @@ -1286,7 +1286,7 @@ def log(node: NodeInput, name: Optional[str] = None) -> Node: :param node: The input node providing data for operation. :param name: The optional new name for output node. - returns The new node performing log operation element-wise. + :return: The new node performing log operation element-wise. """ return _get_node_factory_opset1().create("Log", [node]) @@ -1305,7 +1305,7 @@ def logical_and( :param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes to output shape axes. Range of values: numpy, explicit. :param name: The optional new name for output node. - returns The node performing logical and operation on input nodes corresponding elements. + :return: The node performing logical and operation on input nodes corresponding elements. """ return _get_node_factory_opset1().create( "LogicalAnd", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()} @@ -1318,7 +1318,7 @@ def logical_not(node: NodeInput, name: Optional[str] = None) -> Node: :param node: The input node providing data. :param name: The optional new name for output node. - returns The node performing element-wise logical NOT operation with given tensor. + :return: The node performing element-wise logical NOT operation with given tensor. """ return _get_node_factory_opset1().create("LogicalNot", [node]) @@ -1337,7 +1337,7 @@ def logical_or( :param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes to output shape axes. Range of values: numpy, explicit. :param name: The optional new name for output node. - returns The node performing logical or operation on input nodes corresponding elements. + :return: The node performing logical or operation on input nodes corresponding elements. """ return _get_node_factory_opset1().create( "LogicalOr", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()} @@ -1358,7 +1358,7 @@ def logical_xor( :param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes to output shape axes. Range of values: numpy, explicit. :param name: The optional new name for output node. - returns The node performing logical or operation on input nodes corresponding elements. + :return: The node performing logical or operation on input nodes corresponding elements. """ return _get_node_factory_opset1().create( "LogicalXor", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()} @@ -1383,7 +1383,7 @@ def lrn( :param bias: An offset (usually positive) to avoid dividing by 0. :param size: Width of the 1-D normalization window. :param name: An optional name of the output node. - returns The new node which performs LRN. + :return: The new node which performs LRN. """ attributes = {"alpha": alpha, "beta": beta, "bias": bias, "size": size} return _get_node_factory_opset1().create("LRN", as_nodes(data, axes), attributes) @@ -1419,7 +1419,7 @@ def lstm_cell( :param clip: Specifies bound values [-C, C] for tensor clipping performed before activations. :param name: An optional name of the output node. - returns The new node represents LSTMCell. Node outputs count: 2. + :return: The new node represents LSTMCell. Node outputs count: 2. """ if activations is None: activations = ["sigmoid", "tanh", "tanh"] @@ -1493,7 +1493,7 @@ def lstm_sequence( :param clip: Specifies bound values [-C, C] for tensor clipping performed before activations. :param name: An optional name of the output node. - returns The new node represents LSTMSequence. Node outputs count: 3. + :return: The new node represents LSTMSequence. Node outputs count: 3. """ if activations is None: activations = ["sigmoid", "tanh", "tanh"] @@ -1546,7 +1546,7 @@ def matmul( :param data_b: right-hand side matrix :param transpose_a: should the first matrix be transposed before operation :param transpose_b: should the second matrix be transposed - returns MatMul operation node + :return: MatMul operation node """ return _get_node_factory_opset1().create( "MatMul", as_nodes(data_a, data_b), {"transpose_a": transpose_a, "transpose_b": transpose_b} @@ -1578,7 +1578,7 @@ def max_pool( [None, 'same_upper', 'same_lower', 'valid'] :param name: The optional name for the created output node. - returns The new node performing max pooling operation. + :return: The new node performing max pooling operation. """ if auto_pad is None: auto_pad = "explicit" @@ -1635,7 +1635,7 @@ def mod( :param right_node: The second input node for mod operation. :param auto_broadcast: Specifies rules used for auto-broadcasting of input tensors. :param name: Optional name for output node. - returns The node performing element-wise Mod operation. + :return: The node performing element-wise Mod operation. """ return _get_node_factory_opset1().create( "Mod", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()} @@ -1683,7 +1683,7 @@ def non_max_suppression( :param box_encoding: Format of boxes data encoding. Range of values: corner or cente. :param sort_result_descending: Flag that specifies whenever it is necessary to sort selected boxes across batches or not. - returns The new node which performs NonMaxSuppression + :return: The new node which performs NonMaxSuppression """ if max_output_boxes_per_class is None: max_output_boxes_per_class = make_constant_node(0, np.int64) @@ -1711,7 +1711,7 @@ def normalize_l2( :param axes: Node indicating axes along which L2 reduction is calculated :param eps: The epsilon added to L2 norm :param eps_mode: how eps is combined with L2 value (`add` or `max`) - returns New node which performs the L2 normalization. + :return: New node which performs the L2 normalization. """ return _get_node_factory_opset1().create( "NormalizeL2", as_nodes(data, axes), {"eps": eps, "mode": eps_mode} @@ -1732,7 +1732,7 @@ def not_equal( :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. :param name: The optional name for output new node. - returns The node performing element-wise inequality check. + :return: The node performing element-wise inequality check. """ return _get_node_factory_opset1().create( "NotEqual", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()} @@ -1759,7 +1759,7 @@ def one_hot( by indices in input take. :param name: The optional name for new output node. - returns New node performing one-hot operation. + :return: New node performing one-hot operation. """ return _get_node_factory_opset1().create( "OneHot", as_nodes(indices, depth, on_value, off_value), {"axis": axis} @@ -1783,7 +1783,7 @@ def pad( :param pads_end: number of padding elements to be added after the last element. :param pad_mode: "constant", "edge", "reflect" or "symmetric" :param arg_pad_value: value used for padding if pad_mode is "constant" - returns Pad operation node. + :return: Pad operation node. """ input_nodes = as_nodes(arg, pads_begin, pads_end) if arg_pad_value: @@ -1818,7 +1818,7 @@ def power( :param name: The optional name for the new output node. :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. - returns The new node performing element-wise exponentiation operation on input nodes. + :return: The new node performing element-wise exponentiation operation on input nodes. """ return _get_node_factory_opset1().create( "Power", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()} @@ -1832,7 +1832,7 @@ def prelu(data: NodeInput, slope: NodeInput, name: Optional[str] = None) -> Node :param data: The node with data tensor. :param slope: The node with the multipliers for negative values. :param name: Optional output node name. - returns The new node performing a PRelu operation on tensor's channels. + :return: The new node performing a PRelu operation on tensor's channels. PRelu uses the following logic: @@ -1858,7 +1858,7 @@ def prior_box_clustered( specifies shape of the image for which boxes are generated. :param attrs: The dictionary containing key, value pairs for attributes. :param name: Optional name for the output node. - returns Node representing PriorBoxClustered operation. + :return: Node representing PriorBoxClustered operation. Available attributes are: @@ -1942,7 +1942,7 @@ def prior_box( :param image_shape: Shape of image to which prior boxes are scaled. :param attrs: The dictionary containing key, value pairs for attributes. :param name: Optional name for the output node. - returns Node representing prior box operation. + :return: Node representing prior box operation. Available attributes are: @@ -2062,7 +2062,7 @@ def proposal( :param image_shape: The 1D input tensor with 3 or 4 elements describing image shape. :param attrs: The dictionary containing key, value pairs for attributes. :param name: Optional name for the output node. - returns Node representing Proposal operation. + :return: Node representing Proposal operation. * base_size The size of the anchor to which scale and ratio attributes are applied. Range of values: a positive unsigned integer number @@ -2196,15 +2196,15 @@ def psroi_pooling( ) -> Node: """Return a node which produces a PSROIPooling operation. - :param input: Input feature map {N, C, ...} - :param coords: Coordinates of bounding boxes - :param output_dim: Output channel number - :param group_size: Number of groups to encode position-sensitive scores - :param spatial_scale: Ratio of input feature map over input image size - :param spatial_bins_x: Numbers of bins to divide the input feature maps over - :param spatial_bins_y: Numbers of bins to divide the input feature maps over - :param mode: Mode of pooling - "avg" or "bilinear" - returns PSROIPooling node + :param input: Input feature map `{N, C, ...}`. + :param coords: Coordinates of bounding boxes. + :param output_dim: Output channel number. + :param group_size: Number of groups to encode position-sensitive scores. + :param spatial_scale: Ratio of input feature map over input image size. + :param spatial_bins_x: Numbers of bins to divide the input feature maps over. + :param spatial_bins_y: Numbers of bins to divide the input feature maps over. + :param mode: Mode of pooling - "avg" or "bilinear". + :return: PSROIPooling node """ mode = mode.lower() return _get_node_factory_opset1().create( @@ -2225,11 +2225,11 @@ def psroi_pooling( def range(start: Node, stop: NodeInput, step: NodeInput, name: Optional[str] = None) -> Node: """Return a node which produces the Range operation. - :param start: The start value of the generated range - :param stop: The stop value of the generated range - :param step: The step value for the generated range + :param start: The start value of the generated range. + :param stop: The stop value of the generated range. + :param step: The step value for the generated range. :param name: Optional name for output node. - returns Range node + :return: Range node """ return _get_node_factory_opset1().create("Range", as_nodes(start, stop, step)) @@ -2240,7 +2240,7 @@ def relu(node: NodeInput, name: Optional[str] = None) -> Node: :param node: One of: input node, array or scalar. :param name: The optional output node name. - returns The new node performing relu operation on its input element-wise. + :return: The new node performing relu operation on its input element-wise. """ return _get_node_factory_opset1().create("Relu", [node]) @@ -2253,9 +2253,9 @@ def reduce_logical_and( :param node: The tensor we want to reduce. :param reduction_axes: The axes to eliminate through AND operation. - :param keep_dims: If set to True it holds axes that are used for reduction + :param keep_dims: If set to True it holds axes that are used for reduction. :param name: Optional name for output node. - returns The new node performing reduction operation. + :return: The new node performing reduction operation. """ return _get_node_factory_opset1().create( "ReduceLogicalAnd", as_nodes(node, reduction_axes), {"keep_dims": keep_dims} @@ -2270,9 +2270,9 @@ def reduce_logical_or( :param node: The tensor we want to reduce. :param reduction_axes: The axes to eliminate through OR operation. - :param keep_dims: If set to True it holds axes that are used for reduction + :param keep_dims: If set to True it holds axes that are used for reduction. :param name: Optional name for output node. - returns The new node performing reduction operation. + :return: The new node performing reduction operation. """ return _get_node_factory_opset1().create( "ReduceLogicalOr", as_nodes(node, reduction_axes), {"keep_dims": keep_dims} @@ -2287,7 +2287,7 @@ def reduce_max( :param node: The tensor we want to max-reduce. :param reduction_axes: The axes to eliminate through max operation. - :param keep_dims: If set to True it holds axes that are used for reduction + :param keep_dims: If set to True it holds axes that are used for reduction. :param name: Optional name for output node. """ return _get_node_factory_opset1().create( @@ -2303,9 +2303,9 @@ def reduce_mean( :param node: The tensor we want to mean-reduce. :param reduction_axes: The axes to eliminate through mean operation. - :param keep_dims: If set to True it holds axes that are used for reduction + :param keep_dims: If set to True it holds axes that are used for reduction. :param name: Optional name for output node. - returns The new node performing mean-reduction operation. + :return: The new node performing mean-reduction operation. """ return _get_node_factory_opset1().create( "ReduceMean", as_nodes(node, reduction_axes), {"keep_dims": keep_dims} @@ -2338,7 +2338,7 @@ def reduce_prod( :param reduction_axes: The axes to eliminate through product operation. :param keep_dims: If set to True it holds axes that are used for reduction :param name: Optional name for output node. - returns The new node performing product-reduction operation. + :return: The new node performing product-reduction operation. """ return _get_node_factory_opset1().create( "ReduceProd", as_nodes(node, reduction_axes), {"keep_dims": keep_dims} @@ -2355,7 +2355,7 @@ def reduce_sum( :param reduction_axes: The axes to eliminate through summation. :param keep_dims: If set to True it holds axes that are used for reduction :param name: The optional new name for output node. - returns The new node performing summation along `reduction_axes` element-wise. + :return: The new node performing summation along `reduction_axes` element-wise. """ return _get_node_factory_opset1().create( "ReduceSum", as_nodes(node, reduction_axes), {"keep_dims": keep_dims} @@ -2387,7 +2387,7 @@ def region_yolo( :param end_axis: Axis to end softmax on :param anchors: A flattened list of pairs `[width, height]` that describes prior box sizes :param name: Optional name for output node. - returns RegionYolo node + :return: RegionYolo node """ if anchors is None: anchors = [] @@ -2434,7 +2434,7 @@ def result(data: NodeInput, name: Optional[str] = None) -> Node: """Return a node which represents an output of a graph (Model). :param data: The tensor containing the input data - returns Result node + :return: Result node """ return _get_node_factory_opset1().create("Result", [data]) @@ -2453,7 +2453,7 @@ def reverse_sequence( :param seq_lengths: 1D tensor of integers with sequence lengths in the input tensor. :param batch_axis: index of the batch dimension. :param seq_axis: index of the sequence dimension. - returns ReverseSequence node + :return: ReverseSequence node """ return _get_node_factory_opset1().create( "ReverseSequence", @@ -2479,7 +2479,7 @@ def select( item value is `False`. :param auto_broadcast: Mode specifies rules used for auto-broadcasting of input tensors. :param name: The optional new name for output node. - returns The new node with values selected according to provided arguments. + :return: The new node with values selected according to provided arguments. """ inputs = as_nodes(cond, then_node, else_node) return _get_node_factory_opset1().create( @@ -2499,7 +2499,7 @@ def selu( :param alpha: Alpha coefficient of SELU operation :param lambda_value: Lambda coefficient of SELU operation :param name: The optional output node name. - returns The new node performing relu operation on its input element-wise. + :return: The new node performing relu operation on its input element-wise. """ return _get_node_factory_opset1().create("Selu", as_nodes(data, alpha, lambda_value)) @@ -2509,7 +2509,7 @@ def shape_of(data: NodeInput, name: Optional[str] = None) -> Node: """Return a node which produces a tensor containing the shape of its input data. :param data: The tensor containing the input data. - returns ShapeOf node + :return: ShapeOf node """ return _get_node_factory_opset1().create("ShapeOf", [as_node(data)]) @@ -2519,7 +2519,7 @@ def sigmoid(data: NodeInput, name: Optional[str] = None) -> Node: """Return a node which applies the sigmoid function element-wise. :param data: The tensor containing the input data - returns Sigmoid node + :return: Sigmoid node """ return _get_node_factory_opset1().create("Sigmoid", [data]) @@ -2530,7 +2530,7 @@ def sign(node: NodeInput, name: Optional[str] = None) -> Node: :param node: One of: input node, array or scalar. :param name: The optional new name for output node. - returns The node with mapped elements of the input tensor to -1 (if it is negative), + :return: The node with mapped elements of the input tensor to -1 (if it is negative), 0 (if it is zero), or 1 (if it is positive). """ return _get_node_factory_opset1().create("Sign", [node]) @@ -2542,7 +2542,7 @@ def sin(node: NodeInput, name: Optional[str] = None) -> Node: :param node: One of: input node, array or scalar. :param name: Optional new name for output node. - returns New node with sin operation applied on it. + :return: New node with sin operation applied on it. """ return _get_node_factory_opset1().create("Sin", [node]) @@ -2553,7 +2553,7 @@ def sinh(node: NodeInput, name: Optional[str] = None) -> Node: :param node: One of: input node, array or scalar. :param name: Optional new name for output node. - returns New node with sin operation applied on it. + :return: New node with sin operation applied on it. """ return _get_node_factory_opset1().create("Sinh", [node]) @@ -2564,7 +2564,7 @@ def softmax(data: NodeInput, axis: int, name: Optional[str] = None) -> Node: :param data: The tensor providing input data. :param axis: An axis along which Softmax should be calculated - returns The new node with softmax operation applied on each element. + :return: The new node with softmax operation applied on each element. """ return _get_node_factory_opset1().create("Softmax", [as_node(data)], {"axis": axis}) @@ -2574,7 +2574,7 @@ def space_to_depth(data: Node, mode: str, block_size: int = 1, name: str = None) """Perform SpaceToDepth operation on the input tensor. SpaceToDepth rearranges blocks of spatial data into depth. - The operator returns a copy of the input tensor where values from the height + The operator :return: a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension. :param data: The node with data tensor. @@ -2585,7 +2585,7 @@ def space_to_depth(data: Node, mode: str, block_size: int = 1, name: str = None) :param block_size: The size of the block of values to be moved. Scalar value. :param name: Optional output node name. - returns The new node performing a SpaceToDepth operation on input tensor. + :return: The new node performing a SpaceToDepth operation on input tensor. """ return _get_node_factory_opset1().create( "SpaceToDepth", [data], {"mode": mode, "block_size": block_size}, @@ -2599,7 +2599,7 @@ def split(data: NodeInput, axis: NodeInput, num_splits: int, name: Optional[str] :param data: The input tensor to be split :param axis: Axis along which the input data will be split :param num_splits: Number of the output tensors that should be produced - returns Split node + :return: Split node """ return _get_node_factory_opset1().create( "Split", @@ -2614,7 +2614,7 @@ def sqrt(node: NodeInput, name: Optional[str] = None) -> Node: :param node: One of: input node, array or scalar. :param name: Optional new name for output node. - returns The new node with sqrt operation applied element-wise. + :return: The new node with sqrt operation applied element-wise. """ return _get_node_factory_opset1().create("Sqrt", [node]) @@ -2632,7 +2632,7 @@ def squared_difference( :param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes to output shape axes. Range of values: numpy, explicit. :param name: Optional new name for output node. - returns The new node performing a squared difference between two tensors. + :return: The new node performing a squared difference between two tensors. """ return _get_node_factory_opset1().create( "SquaredDifference", [x1, x2], {"auto_broadcast": auto_broadcast.upper()} @@ -2647,7 +2647,7 @@ def squeeze(data: NodeInput, axes: NodeInput, name: Optional[str] = None) -> Nod :param axes: List of non-negative integers, indicate the dimensions to squeeze. One of: input node or array. :param name: Optional new name for output node. - returns The new node performing a squeeze operation on input tensor. + :return: The new node performing a squeeze operation on input tensor. Remove single-dimensional entries from the shape of a tensor. Takes a parameter `axes` with a list of axes to squeeze. @@ -2690,7 +2690,7 @@ def strided_slice( :param new_axis_mask: A mask indicating dimensions where '1' should be inserted :param shrink_axis_mask: A mask indicating which dimensions should be deleted :param ellipsis_mask: Indicates positions where missing dimensions should be inserted - returns StridedSlice node + :return: StridedSlice node """ if new_axis_mask is None: new_axis_mask = [] @@ -2725,7 +2725,7 @@ def subtract( :param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes to output shape axes. Range of values: numpy, explicit. :param name: The optional name for output node. - returns The new output node performing subtraction operation on both tensors element-wise. + :return: The new output node performing subtraction operation on both tensors element-wise. """ return _get_node_factory_opset1().create( "Subtract", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()} @@ -2738,7 +2738,7 @@ def tan(node: NodeInput, name: Optional[str] = None) -> Node: :param node: One of: input node, array or scalar. :param name: Optional new name for output node. - returns New node with tan operation applied on it. + :return: New node with tan operation applied on it. """ return _get_node_factory_opset1().create("Tan", [node]) diff --git a/src/bindings/python/src/openvino/runtime/opset2/ops.py b/src/bindings/python/src/openvino/runtime/opset2/ops.py index 9d863962c8d..c833e3a31ea 100644 --- a/src/bindings/python/src/openvino/runtime/opset2/ops.py +++ b/src/bindings/python/src/openvino/runtime/opset2/ops.py @@ -54,7 +54,7 @@ def batch_to_space( :param crops_begin: Specifies the amount to crop from the beginning along each axis of `data`. :param crops_end: Specifies the amount to crop from the end along each axis of `data`. :param name: Optional output node name. - returns The new node performing a BatchToSpace operation. + :return: The new node performing a BatchToSpace operation. """ return _get_node_factory_opset2().create( "BatchToSpace", as_nodes(data, block_shape, crops_begin, crops_end) @@ -73,7 +73,7 @@ def gelu(node: NodeInput, name: Optional[str] = None) -> Node: :param node: Input tensor. One of: input node, array or scalar. :param name: Optional output node name. - returns The new node performing a GELU operation on its input data element-wise. + :return: The new node performing a GELU operation on its input data element-wise. """ return _get_node_factory_opset2().create("Gelu", [node]) @@ -96,9 +96,9 @@ def mvn( :param across_channels: Denotes if mean values are shared across channels. :param normalize_variance: Denotes whether to perform variance normalization. :param eps: The number added to the variance to avoid division by zero - when normalizing the value. Scalar value. + when normalizing the value. Scalar value. :param name: Optional output node name. - returns The new node performing a MVN operation on input tensor. + :return: The new node performing a MVN operation on input tensor. """ return _get_node_factory_opset2().create( "MVN", @@ -111,10 +111,10 @@ def mvn( def reorg_yolo(input: Node, stride: List[int], name: Optional[str] = None) -> Node: """Return a node which produces the ReorgYolo operation. - :param input: Input data - :param stride: Stride to reorganize input by + :param input: Input data. + :param stride: Stride to reorganize input by. :param name: Optional name for output node. - returns ReorgYolo node + :return: ReorgYolo node. """ return _get_node_factory_opset2().create("ReorgYolo", [input], {"stride": stride}) @@ -130,12 +130,12 @@ def roi_pooling( ) -> Node: """Return a node which produces an ROIPooling operation. - :param input: Input feature map {N, C, ...} - :param coords: Coordinates of bounding boxes - :param output_size: Height/Width of ROI output features (shape) - :param spatial_scale: Ratio of input feature map over input image size (float) - :param method: Method of pooling - string: "max" or "bilinear" - returns ROIPooling node + :param input: Input feature map `{N, C, ...}`. + :param coords: Coordinates of bounding boxes. + :param output_size: Height/Width of ROI output features (shape). + :param spatial_scale: Ratio of input feature map over input image size (float). + :param method: Method of pooling - string: "max" or "bilinear". + :return: ROIPooling node. """ method = method.lower() return _get_node_factory_opset2().create( @@ -164,7 +164,7 @@ def space_to_batch( :param pads_begin: Specifies the padding for the beginning along each axis of `data`. :param pads_end: Specifies the padding for the ending along each axis of `data`. :param name: Optional output node name. - returns The new node performing a SpaceToBatch operation. + :return: The new node performing a SpaceToBatch operation. """ return _get_node_factory_opset2().create( "SpaceToBatch", as_nodes(data, block_shape, pads_begin, pads_end) diff --git a/src/bindings/python/src/openvino/runtime/opset3/ops.py b/src/bindings/python/src/openvino/runtime/opset3/ops.py index 6afefdf20fb..47f745228ae 100644 --- a/src/bindings/python/src/openvino/runtime/opset3/ops.py +++ b/src/bindings/python/src/openvino/runtime/opset3/ops.py @@ -44,7 +44,7 @@ def assign(new_value: NodeInput, variable_id: str, name: Optional[str] = None) - :param new_value: Node producing a value to be assigned to a variable. :param variable_id: Id of a variable to be updated. :param name: Optional name for output node. - returns Assign node + :return: Assign node """ return _get_node_factory_opset3().create( "Assign", @@ -70,7 +70,7 @@ def broadcast( :param broadcast_spec: The type of broadcasting that specifies mapping of input tensor axes to output shape axes. Range of values: NUMPY, EXPLICIT, BIDIRECTIONAL. :param name: Optional new name for output node. - returns New node with broadcast shape. + :return: New node with broadcast shape. """ inputs = as_nodes(data, target_shape) if broadcast_spec.upper() == "EXPLICIT": @@ -96,7 +96,7 @@ def bucketize( :param with_right_bound: indicates whether bucket includes the right or left edge of interval. default true = includes right edge :param name: Optional name for output node. - returns Bucketize node + :return: Bucketize node """ return _get_node_factory_opset3().create( "Bucketize", @@ -119,7 +119,7 @@ def cum_sum( :param axis: zero dimension tensor specifying axis position along which sum will be performed. :param exclusive: if set to true, the top element is not included :param reverse: if set to true, will perform the sums in reverse direction - returns New node performing the operation + :return: New node performing the operation """ return _get_node_factory_opset3().create( "CumSum", as_nodes(arg, axis), {"exclusive": exclusive, "reverse": reverse} @@ -143,7 +143,7 @@ def embedding_bag_offsets_sum( :param per_sample_weights: Tensor with weights for each sample. :param default_index: Scalar containing default index in embedding table to fill empty bags. :param name: Optional name for output node. - returns The new node which performs EmbeddingBagOffsetsSum + :return: The new node which performs EmbeddingBagOffsetsSum """ inputs = [emb_table, as_node(indices), as_node(offsets)] if per_sample_weights is not None: @@ -171,7 +171,7 @@ def embedding_bag_packed_sum( :param indices: Tensor with indices. :param per_sample_weights: Weights to be multiplied with embedding table. :param name: Optional name for output node. - returns EmbeddingBagPackedSum node + :return: EmbeddingBagPackedSum node """ inputs = [as_node(emb_table), as_node(indices)] if per_sample_weights is not None: @@ -202,7 +202,7 @@ def embedding_segments_sum( :param default_index: Scalar containing default index in embedding table to fill empty bags. :param per_sample_weights: Weights to be multiplied with embedding table. :param name: Optional name for output node. - returns EmbeddingSegmentsSum node + :return: EmbeddingSegmentsSum node """ inputs = [as_node(emb_table), as_node(indices), as_node(segment_ids)] if per_sample_weights is not None: @@ -235,7 +235,7 @@ def extract_image_patches( :param rates: Element seleciton rate for creating a patch. :param auto_pad: Padding type. :param name: Optional name for output node. - returns ExtractImagePatches node + :return: ExtractImagePatches node """ return _get_node_factory_opset3().create( "ExtractImagePatches", @@ -288,7 +288,7 @@ def gru_cell( :param linear_before_reset: Flag denotes if the layer behaves according to the modification of GRUCell described in the formula in the ONNX documentation. :param name: Optional output node name. - returns The new node performing a GRUCell operation on tensor from input node. + :return: The new node performing a GRUCell operation on tensor from input node. """ if activations is None: activations = ["sigmoid", "tanh"] @@ -333,7 +333,7 @@ def non_max_suppression( :param sort_result_descending: Flag that specifies whenever it is necessary to sort selected boxes across batches or not. :param output_type: Output element type. - returns The new node which performs NonMaxSuppression + :return: The new node which performs NonMaxSuppression """ if max_output_boxes_per_class is None: max_output_boxes_per_class = make_constant_node(0, np.int64) @@ -359,7 +359,7 @@ def non_zero(data: NodeInput, output_type: str = "i64", name: Optional[str] = No :param data: Input data. :param output_type: Output tensor type. - returns The new node which performs NonZero + :return: The new node which performs NonZero """ return _get_node_factory_opset3().create( "NonZero", @@ -375,7 +375,7 @@ def read_value(init_value: NodeInput, variable_id: str, name: Optional[str] = No :param init_value: Node producing a value to be returned instead of an unassigned variable. :param variable_id: Id of a variable to be read. :param name: Optional name for output node. - returns ReadValue node + :return: ReadValue node """ return _get_node_factory_opset3().create( "ReadValue", @@ -422,7 +422,7 @@ def rnn_cell( :param clip: The value defining clipping range [-clip, clip] on input of activation functions. :param name: Optional output node name. - returns The new node performing a RNNCell operation on tensor from input node. + :return: The new node performing a RNNCell operation on tensor from input node. """ if activations is None: activations = ["tanh"] @@ -467,7 +467,7 @@ def roi_align( :param spatial_scale: Multiplicative spatial scale factor to translate ROI coordinates. :param mode: Method to perform pooling to produce output feature map elements. - returns The new node which performs ROIAlign + :return: The new node which performs ROIAlign """ inputs = as_nodes(data, rois, batch_indices) attributes = { @@ -494,7 +494,7 @@ def scatter_elements_update( :param indices: The tensor with indexes which will be updated. :param updates: The tensor with update values. :param axis: The axis for scatter. - returns ScatterElementsUpdate node + :return: ScatterElementsUpdate node ScatterElementsUpdate creates a copy of the first input tensor with updated elements specified with second and third input tensors. @@ -523,7 +523,7 @@ def scatter_update( :param indices: The tensor with indexes which will be updated. :param updates: The tensor with update values. :param axis: The axis at which elements will be updated. - returns ScatterUpdate node + :return: ScatterUpdate node """ return _get_node_factory_opset3().create( "ScatterUpdate", @@ -537,7 +537,7 @@ def shape_of(data: NodeInput, output_type: str = "i64", name: Optional[str] = No :param data: The tensor containing the input data. :param output_type: Output element type. - returns ShapeOf node + :return: ShapeOf node """ return _get_node_factory_opset3().create( "ShapeOf", @@ -557,7 +557,7 @@ def shuffle_channels(data: Node, axis: int, group: int, name: Optional[str] = No :param group: The channel dimension specified by the axis parameter should be split into this number of groups. :param name: Optional output node name. - returns The new node performing a permutation on data in the channel dimension + :return: The new node performing a permutation on data in the channel dimension of the input tensor. The operation is the equivalent with the following transformation of the input tensor @@ -617,7 +617,7 @@ def topk( :param mode: Compute TopK largest ('max') or smallest ('min') :param sort: Order of output elements (sort by: 'none', 'index' or 'value') :param index_element_type: Type of output tensor with indices. - returns The new node which performs TopK (both indices and values) + :return: The new node which performs TopK (both indices and values) """ return _get_node_factory_opset3().create( "TopK", diff --git a/src/bindings/python/src/openvino/runtime/opset4/ops.py b/src/bindings/python/src/openvino/runtime/opset4/ops.py index 8ee3ffef925..3c16762e549 100644 --- a/src/bindings/python/src/openvino/runtime/opset4/ops.py +++ b/src/bindings/python/src/openvino/runtime/opset4/ops.py @@ -59,7 +59,7 @@ def ctc_loss( :param preprocess_collapse_repeated: Flag for preprocessing labels before loss calculation. :param ctc_merge_repeated: Flag for merging repeated characters in a potential alignment. :param unique: Flag to find unique elements in a target. - returns The new node which performs CTCLoss + :return: The new node which performs CTCLoss """ if blank_index is not None: inputs = as_nodes(logits, logit_length, labels, label_length, blank_index) @@ -99,7 +99,7 @@ def non_max_suppression( :param sort_result_descending: Flag that specifies whenever it is necessary to sort selected boxes across batches or not. :param output_type: Output element type. - returns The new node which performs NonMaxSuppression + :return: The new node which performs NonMaxSuppression """ if max_output_boxes_per_class is None: max_output_boxes_per_class = make_constant_node(0, np.int64) @@ -123,7 +123,7 @@ def softplus(data: NodeInput, name: Optional[str] = None) -> Node: """Apply SoftPlus operation on each element of input tensor. :param data: The tensor providing input data. - returns The new node with SoftPlus operation applied on each element. + :return: The new node with SoftPlus operation applied on each element. """ return _get_node_factory_opset4().create("SoftPlus", as_nodes(data), {}) @@ -133,7 +133,7 @@ def mish(data: NodeInput, name: Optional[str] = None,) -> Node: """Return a node which performs Mish. :param data: Tensor with input data floating point type. - returns The new node which performs Mish + :return: The new node which performs Mish """ return _get_node_factory_opset4().create("Mish", as_nodes(data), {}) @@ -143,7 +143,7 @@ def hswish(data: NodeInput, name: Optional[str] = None,) -> Node: """Return a node which performs HSwish (hard version of Swish). :param data: Tensor with input data floating point type. - returns The new node which performs HSwish + :return: The new node which performs HSwish """ return _get_node_factory_opset4().create("HSwish", as_nodes(data), {}) @@ -157,7 +157,7 @@ def swish( """Return a node which performing Swish activation function Swish(x, beta=1.0) = x * sigmoid(x * beta)). :param data: Tensor with input data floating point type. - returns The new node which performs Swish + :return: The new node which performs Swish """ if beta is None: beta = make_constant_node(1.0, np.float32) @@ -170,7 +170,7 @@ def acosh(node: NodeInput, name: Optional[str] = None) -> Node: :param node: One of: input node, array or scalar. :param name: Optional new name for output node. - returns New node with arccosh operation applied on it. + :return: New node with arccosh operation applied on it. """ return _get_node_factory_opset4().create("Acosh", [node]) @@ -181,7 +181,7 @@ def asinh(node: NodeInput, name: Optional[str] = None) -> Node: :param node: One of: input node, array or scalar. :param name: Optional new name for output node. - returns New node with arcsinh operation applied on it. + :return: New node with arcsinh operation applied on it. """ return _get_node_factory_opset4().create("Asinh", [node]) @@ -192,7 +192,7 @@ def atanh(node: NodeInput, name: Optional[str] = None) -> Node: :param node: One of: input node, array or scalar. :param name: Optional new name for output node. - returns New node with arctanh operation applied on it. + :return: New node with arctanh operation applied on it. """ return _get_node_factory_opset4().create("Atanh", [node]) @@ -292,7 +292,7 @@ def proposal( } Optional attributes which are absent from dictionary will be set with corresponding default. - returns Node representing Proposal operation. + :return: Node representing Proposal operation. """ requirements = [ ("base_size", True, np.unsignedinteger, is_positive_value), @@ -328,7 +328,7 @@ def reduce_l1( :param reduction_axes: The axes to eliminate through mean operation. :param keep_dims: If set to True it holds axes that are used for reduction :param name: Optional name for output node. - returns The new node performing mean-reduction operation. + :return: The new node performing mean-reduction operation. """ return _get_node_factory_opset4().create( "ReduceL1", as_nodes(node, reduction_axes), {"keep_dims": keep_dims} @@ -345,7 +345,7 @@ def reduce_l2( :param reduction_axes: The axes to eliminate through mean operation. :param keep_dims: If set to True it holds axes that are used for reduction :param name: Optional name for output node. - returns The new node performing mean-reduction operation. + :return: The new node performing mean-reduction operation. """ return _get_node_factory_opset4().create( "ReduceL2", as_nodes(node, reduction_axes), {"keep_dims": keep_dims} @@ -382,7 +382,7 @@ def lstm_cell( :param clip: Specifies bound values [-C, C] for tensor clipping performed before activations. :param name: An optional name of the output node. - returns The new node represents LSTMCell. Node outputs count: 2. + :return: The new node represents LSTMCell. Node outputs count: 2. """ if activations is None: activations = ["sigmoid", "tanh", "tanh"] diff --git a/src/bindings/python/src/openvino/runtime/opset5/ops.py b/src/bindings/python/src/openvino/runtime/opset5/ops.py index b024210f4a8..f02200cf3ae 100644 --- a/src/bindings/python/src/openvino/runtime/opset5/ops.py +++ b/src/bindings/python/src/openvino/runtime/opset5/ops.py @@ -57,7 +57,7 @@ def batch_norm_inference( :param epsilon: The number to be added to the variance to avoid division by zero when normalizing a value. :param name: The optional name of the output node. - @return: The new node which performs BatchNormInference. + :return: The new node which performs BatchNormInference. """ inputs = as_nodes(data, gamma, beta, mean, variance) return _get_node_factory_opset5().create("BatchNormInference", inputs, {"epsilon": epsilon}) @@ -75,7 +75,7 @@ def gather_nd( :param data: N-D tensor with data for gathering :param indices: K-D tensor of tuples with indices by which data is gathered :param batch_dims: Scalar value of batch dimensions - @return: The new node which performs GatherND + :return: The new node which performs GatherND """ inputs = as_nodes(data, indices) @@ -92,7 +92,7 @@ def log_softmax(data: NodeInput, axis: int, name: Optional[str] = None) -> Node: :param data: The tensor providing input data. :param axis: An axis along which LogSoftmax should be calculated - @return: The new node with LogSoftmax operation applied on each element. + :return: The new node with LogSoftmax operation applied on each element. """ return _get_node_factory_opset5().create("LogSoftmax", [as_node(data)], {"axis": axis}) @@ -123,7 +123,7 @@ def non_max_suppression( :param sort_result_descending: Flag that specifies whenever it is necessary to sort selected boxes across batches or not. :param output_type: Output element type. - @return: The new node which performs NonMaxSuppression + :return: The new node which performs NonMaxSuppression """ if max_output_boxes_per_class is None: max_output_boxes_per_class = make_constant_node(0, np.int64) @@ -158,7 +158,7 @@ def round(data: NodeInput, mode: str = "half_to_even", name: Optional[str] = Non integer or rounding in such a way that the result heads away from zero if `mode` attribute is 'half_away_from_zero`. :param name: An optional name of the output node. - @return: The new node with Round operation applied on each element. + :return: The new node with Round operation applied on each element. """ return _get_node_factory_opset5().create("Round", as_nodes(data), {"mode": mode.upper()}) @@ -205,7 +205,7 @@ def lstm_sequence( :param clip: Specifies bound values [-C, C] for tensor clipping performed before activations. :param name: An optional name of the output node. - @return: The new node represents LSTMSequence. Node outputs count: 3. + :return: The new node represents LSTMSequence. Node outputs count: 3. """ if activations is None: activations = ["sigmoid", "tanh", "tanh"] @@ -231,7 +231,7 @@ def hsigmoid(data: NodeInput, name: Optional[str] = None,) -> Node: """Return a node which performs HSigmoid. :param data: Tensor with input data floating point type. - @return: The new node which performs HSigmoid + :return: The new node which performs HSigmoid """ return _get_node_factory_opset5().create("HSigmoid", as_nodes(data), {}) @@ -277,7 +277,7 @@ def gru_sequence( of GRU described in the formula in the ONNX documentation. :param name: An optional name of the output node. - @return: The new node represents GRUSequence. Node outputs count: 2. + :return: The new node represents GRUSequence. Node outputs count: 2. """ if activations is None: activations = ["sigmoid", "tanh"] @@ -337,7 +337,7 @@ def rnn_sequence( :param clip: Specifies bound values [-C, C] for tensor clipping performed before activations. :param name: An optional name of the output node. - @return: The new node represents RNNSequence. Node outputs count: 2. + :return: The new node represents RNNSequence. Node outputs count: 2. """ if activations is None: activations = ["tanh"] diff --git a/src/bindings/python/src/openvino/runtime/opset6/ops.py b/src/bindings/python/src/openvino/runtime/opset6/ops.py index 08d62ea1b3b..45b295a216e 100644 --- a/src/bindings/python/src/openvino/runtime/opset6/ops.py +++ b/src/bindings/python/src/openvino/runtime/opset6/ops.py @@ -53,7 +53,7 @@ def ctc_greedy_decoder_seq_len( :param sequence_length: Input 1D tensor with sequence length. Shape: [batch_size] :param blank_index: Scalar or 1D tensor with specifies the class index to use for the blank class. Optional parameter. Default value is num_classes-1. - @return: The new node which performs CTCGreedyDecoderSeqLen. + :return: The new node which performs CTCGreedyDecoderSeqLen. """ if blank_index is not None: inputs = as_nodes(data, sequence_length, blank_index) @@ -81,7 +81,7 @@ def gather_elements( :param data: N-D tensor with data for gathering :param indices: N-D tensor with indices by which data is gathered :param axis: axis along which elements are gathered - @return: The new node which performs GatherElements + :return: The new node which performs GatherElements """ inputs = as_nodes(data, indices) @@ -110,7 +110,7 @@ def mvn( when normalizing the value. Scalar value. :param eps_mode: how eps is applied (`inside_sqrt` or `outside_sqrt`) :param name: Optional output node name. - returns The new node performing a MVN operation on input tensor. + :return: The new node performing a MVN operation on input tensor. """ inputs = as_nodes(data, axes) @@ -130,7 +130,7 @@ def assign(new_value: NodeInput, variable_id: str, name: Optional[str] = None) - :param new_value: Node producing a value to be assigned to a variable. :param variable_id: Id of a variable to be updated. :param name: Optional name for output node. - returns Assign node + :return: Assign node """ return _get_node_factory_opset6().create( "Assign", @@ -146,7 +146,7 @@ def read_value(init_value: NodeInput, variable_id: str, name: Optional[str] = No :param init_value: Node producing a value to be returned instead of an unassigned variable. :param variable_id: Id of a variable to be read. :param name: Optional name for output node. - returns ReadValue node + :return: ReadValue node """ return _get_node_factory_opset6().create( "ReadValue", diff --git a/src/bindings/python/src/openvino/runtime/opset7/ops.py b/src/bindings/python/src/openvino/runtime/opset7/ops.py index b07772fb572..f5ee112beab 100644 --- a/src/bindings/python/src/openvino/runtime/opset7/ops.py +++ b/src/bindings/python/src/openvino/runtime/opset7/ops.py @@ -46,7 +46,7 @@ def einsum( :param inputs: The list of input nodes :param equation: Einsum equation - @return: The new node performing Einsum operation on the inputs + :return: The new node performing Einsum operation on the inputs """ attributes = { "equation": equation @@ -66,7 +66,7 @@ def gelu( :param data: The node with data tensor. :param approximation_mode: defines which approximation to use ('tanh' or 'erf') :param name: Optional output node name. - returns The new node performing a Gelu activation with the input tensor. + :return: The new node performing a Gelu activation with the input tensor. """ inputs = as_nodes(data) @@ -88,7 +88,7 @@ def roll( :param data: The node with data tensor. :param shift: The node with the tensor with numbers of places by which elements are shifted. :param axes: The node with the tensor with axes along which elements are shifted. - returns The new node performing a Roll operation on the input tensor. + :return: The new node performing a Roll operation on the input tensor. """ inputs = as_nodes(data, shift, axes) @@ -108,7 +108,7 @@ def gather( :param indices: N-D tensor with indices by which data is gathered :param axis: axis along which elements are gathered :param batch_dims: number of batch dimensions - @return: The new node which performs Gather + :return: The new node which performs Gather """ inputs = as_nodes(data, indices, axis) attributes = { @@ -127,7 +127,7 @@ def dft( :param data: Tensor with transformed data. :param axes: Tensor with axes to transform. :param signal_size: Tensor specifying signal size with respect to axes from the input 'axes'. - @return: The new node which performs DFT operation on the input data tensor. + :return: The new node which performs DFT operation on the input data tensor. """ if signal_size is None: inputs = as_nodes(data, axes) @@ -148,7 +148,7 @@ def idft( :param data: Tensor with transformed data. :param axes: Tensor with axes to transform. :param signal_size: Tensor specifying signal size with respect to axes from the input 'axes'. - @return: The new node which performs IDFT operation on the input data tensor. + :return: The new node which performs IDFT operation on the input data tensor. """ if signal_size is None: inputs = as_nodes(data, axes) diff --git a/src/bindings/python/src/openvino/runtime/opset8/ops.py b/src/bindings/python/src/openvino/runtime/opset8/ops.py index 0a809f00bf5..0aa381592fb 100644 --- a/src/bindings/python/src/openvino/runtime/opset8/ops.py +++ b/src/bindings/python/src/openvino/runtime/opset8/ops.py @@ -62,7 +62,7 @@ def deformable_convolution( :param bilinear_interpolation_pad: The flag that determines the mode of bilinear interpolation execution. :param name: The optional new name for output node. - returns New node performing deformable convolution operation. + :return: New node performing deformable convolution operation. """ if mask is None: inputs = as_nodes(data, offsets, filters) @@ -94,7 +94,7 @@ def adaptive_avg_pool( :param data: The list of input nodes :param output_shape: the shape of spatial dimentions after operation - @return: The new node performing AdaptiveAvgPool operation on the data + :return: The new node performing AdaptiveAvgPool operation on the data """ inputs = as_nodes(data, output_shape) return _get_node_factory_opset8().create("AdaptiveAvgPool", inputs) @@ -111,7 +111,7 @@ def adaptive_max_pool( :param data: The list of input nodes :param output_shape: the shape of spatial dimentions after operation :param index_element_type: Type of indices output. - @return: The new node performing AdaptiveMaxPool operation on the data + :return: The new node performing AdaptiveMaxPool operation on the data """ inputs = as_nodes(data, output_shape) @@ -158,7 +158,7 @@ def multiclass_nms( :param background_class: Specifies the background class id, -1 meaning to keep all classes :param nms_eta: Specifies eta parameter for adpative NMS, in close range [0, 1.0] :param normalized: Specifies whether boxes are normalized or not - @return: The new node which performs MuticlassNms + :return: The new node which performs MuticlassNms """ inputs = as_nodes(boxes, scores) @@ -218,7 +218,7 @@ def matrix_nms( :param post_threshold: Specifies threshold to filter out boxes with low confidence score after decaying :param normalized: Specifies whether boxes are normalized or not - @return: The new node which performs MatrixNms + :return: The new node which performs MatrixNms """ inputs = as_nodes(boxes, scores) @@ -253,7 +253,7 @@ def gather( indicate reverse indexing from the end :param axis: axis along which elements are gathered :param batch_dims: number of batch dimensions - @return: The new node which performs Gather + :return: The new node which performs Gather """ inputs = as_nodes(data, indices, axis) attributes = { @@ -296,7 +296,7 @@ def max_pool( starting at the provided axis. Defaults to 0. :param name: The optional name for the created output node. - returns The new node performing max pooling operation. + :return: The new node performing max pooling operation. """ if auto_pad is None: auto_pad = "explicit" @@ -335,7 +335,7 @@ def random_uniform( 'i64', 'i32', 'f64', 'f32', 'f16', 'bf16'. :param global_seed: Specifies global seed value. Required to be a positive integer or 0. :param op_seed: Specifies operational seed value. Required to be a positive integer or 0. - returns The new node which performs generation of random values from uniform distribution. + :return: The new node which performs generation of random values from uniform distribution. """ inputs = as_nodes(output_shape, min_val, max_val) @@ -370,7 +370,7 @@ def slice( :param step: The node providing step values. :param axes: The optional node providing axes to slice, default [0, 1, ..., len(start)-1]. :param name: The optional name for the created output node. - returns The new node performing Slice operation. + :return: The new node performing Slice operation. """ if axes is None: inputs = as_nodes(data, start, stop, step) @@ -392,7 +392,7 @@ def gather_nd( :param data: N-D tensor with data for gathering :param indices: K-D tensor of tuples with indices by which data is gathered :param batch_dims: Scalar value of batch dimensions - @return: The new node which performs GatherND + :return: The new node which performs GatherND """ inputs = as_nodes(data, indices) @@ -413,7 +413,7 @@ def prior_box( :param image_shape: Shape of image to which prior boxes are scaled. :param attrs: The dictionary containing key, value pairs for attributes. :param name: Optional name for the output node. - returns Node representing prior box operation. + :return: Node representing prior box operation. Available attributes are: * min_size The minimum box size (in pixels). Range of values: positive floating point numbers @@ -524,7 +524,7 @@ def i420_to_bgr( :param arg_u: The node providing U plane data. Required for separate planes. :param arg_v: The node providing V plane data. Required for separate planes. :param name: The optional name for the created output node. - returns The new node performing I420toBGR operation. + :return: The new node performing I420toBGR operation. """ if arg_u is None and arg_v is None: inputs = as_nodes(arg) @@ -551,7 +551,7 @@ def i420_to_rgb( :param arg_u: The node providing U plane data. Required for separate planes. :param arg_v: The node providing V plane data. Required for separate planes. :param name: The optional name for the created output node. - returns The new node performing I420toRGB operation. + :return: The new node performing I420toRGB operation. """ if arg_u is None and arg_v is None: inputs = as_nodes(arg) @@ -576,7 +576,7 @@ def nv12_to_bgr( :param arg: The node providing single or Y plane data. :param arg_uv: The node providing UV plane data. Required for separate planes. :param name: The optional name for the created output node. - returns The new node performing NV12toBGR operation. + :return: The new node performing NV12toBGR operation. """ if arg_uv is None: inputs = as_nodes(arg) @@ -597,7 +597,7 @@ def nv12_to_rgb( :param arg: The node providing single or Y plane data. :param arg_uv: The node providing UV plane data. Required for separate planes. :param name: The optional name for the created output node. - returns The new node performing NV12toRGB operation. + :return: The new node performing NV12toRGB operation. """ if arg_uv is None: inputs = as_nodes(arg) @@ -626,7 +626,7 @@ def detection_output( :param aux_class_preds: The 2D input tensor with additional class predictions information. :param aux_box_preds: The 2D input tensor with additional box predictions information. :param name: Optional name for the output node. - returns Node representing DetectionOutput operation. + :return: Node representing DetectionOutput operation. Available attributes are: * background_label_id The background label id. Range of values: integer value @@ -751,6 +751,6 @@ def softmax(data: NodeInput, axis: int, name: Optional[str] = None) -> Node: :param data: The tensor providing input data. :param axis: An axis along which Softmax should be calculated. Can be positive or negative. :param name: Optional name for the node. - returns The new node with softmax operation applied on each element. + :return: The new node with softmax operation applied on each element. """ return _get_node_factory_opset8().create("Softmax", [as_node(data)], {"axis": axis}) diff --git a/src/bindings/python/src/pyopenvino/core/async_infer_queue.cpp b/src/bindings/python/src/pyopenvino/core/async_infer_queue.cpp index 6d69aa81c6e..c6438228ea1 100644 --- a/src/bindings/python/src/pyopenvino/core/async_infer_queue.cpp +++ b/src/bindings/python/src/pyopenvino/core/async_infer_queue.cpp @@ -202,7 +202,7 @@ void regclass_AsyncInferQueue(py::module m) { py::arg("inputs"), py::arg("userdata"), R"( - Run asynchronous inference using next available InferRequest. + Run asynchronous inference using the next available InferRequest. This function releases the GIL, so another Python thread can work while this function runs in the background. @@ -262,8 +262,8 @@ void regclass_AsyncInferQueue(py::module m) { }, R"( Sets unified callback on all InferRequests from queue's pool. - Signature of such function should have two arguments, where - first one is InferRequest object and second one is userdata + The signature of such function should have two arguments, where + the first one is InferRequest object and the second one is userdata connected to InferRequest from the AsyncInferQueue's pool. .. code-block:: python diff --git a/src/bindings/python/src/pyopenvino/core/compiled_model.cpp b/src/bindings/python/src/pyopenvino/core/compiled_model.cpp index 884bcf9b0bf..768768a1872 100644 --- a/src/bindings/python/src/pyopenvino/core/compiled_model.cpp +++ b/src/bindings/python/src/pyopenvino/core/compiled_model.cpp @@ -53,11 +53,11 @@ void regclass_CompiledModel(py::module m) { py::arg("inputs"), R"( Infers specified input(s) in synchronous mode. - Blocks all methods of CompiledModel while request is running. + Blocks all methods of CompiledModel while the request is running. Method creates new temporary InferRequest and run inference on it. - It is advised to use dedicated InferRequest class for performance, - optimizing workflows and creating advanced pipelines. + It is advised to use a dedicated InferRequest class for performance, + optimizing workflows, and creating advanced pipelines. :param inputs: Data to set on input tensors. :type inputs: Dict[Union[int, str, openvino.runtime.ConstOutput], openvino.runtime.Tensor] @@ -108,10 +108,10 @@ void regclass_CompiledModel(py::module m) { R"( Exports the compiled model to bytes/output stream. - Advanced version of `export_model`. It utilizes, streams from standard + Advanced version of `export_model`. It utilizes, streams from the standard Python library `io`. - Function performs flushing of the stream, writes to it and then rewinds + Function performs flushing of the stream, writes to it, and then rewinds the stream to the beginning (using seek(0)). :param model_stream: A stream object to which the model will be serialized. @@ -168,12 +168,12 @@ void regclass_CompiledModel(py::module m) { R"( Gets runtime model information from a device. - This object (returned model) represents the internal device specific model - which is optimized for particular accelerator. It contains device specific nodes, - runtime information and can be used only to understand how the source model - is optimized and which kernels, element types and layouts are selected. + This object (returned model) represents the internal device-specific model + which is optimized for the particular accelerator. It contains device-specific nodes, + runtime information, and can be used only to understand how the source model + is optimized and which kernels, element types, and layouts are selected. - :return: Model containing Executable Graph information. + :return: Model, containing Executable Graph information. :rtype: openvino.runtime.Model )"); @@ -201,7 +201,7 @@ void regclass_CompiledModel(py::module m) { py::arg("index"), R"( Gets input of a compiled model identified by an index. - If an input with given index is not found, this method throws an exception. + If the input with given index is not found, this method throws an exception. :param index: An input index. :type index: int @@ -214,9 +214,9 @@ void regclass_CompiledModel(py::module m) { py::arg("tensor_name"), R"( Gets input of a compiled model identified by a tensor_name. - If an input with given tensor name is not found, this method throws an exception. + If the input with given tensor name is not found, this method throws an exception. - :param tensor_name: An input tensor's name. + :param tensor_name: An input tensor name. :type tensor_name: str :return: A compiled model input. :rtype: openvino.runtime.ConstOutput @@ -235,7 +235,7 @@ void regclass_CompiledModel(py::module m) { (ov::Output(ov::CompiledModel::*)() const) & ov::CompiledModel::output, R"( Gets a single output of a compiled model. - If a model has more than one output, this method throws an exception. + If the model has more than one output, this method throws an exception. :return: A compiled model output. :rtype: openvino.runtime.ConstOutput @@ -246,7 +246,7 @@ void regclass_CompiledModel(py::module m) { py::arg("index"), R"( Gets output of a compiled model identified by an index. - If an output with given index is not found, this method throws an exception. + If the output with given index is not found, this method throws an exception. :param index: An output index. :type index: int @@ -259,9 +259,9 @@ void regclass_CompiledModel(py::module m) { py::arg("tensor_name"), R"( Gets output of a compiled model identified by a tensor_name. - If an output with given tensor name is not found, this method throws an exception. + If the output with given tensor name is not found, this method throws an exception. - :param tensor_name: An output tensor's name. + :param tensor_name: An output tensor name. :type tensor_name: str :return: A compiled model output. :rtype: openvino.runtime.ConstOutput diff --git a/src/bindings/python/src/pyopenvino/core/core.cpp b/src/bindings/python/src/pyopenvino/core/core.cpp index 20b32618bdf..7163bbe129b 100644 --- a/src/bindings/python/src/pyopenvino/core/core.cpp +++ b/src/bindings/python/src/pyopenvino/core/core.cpp @@ -27,7 +27,7 @@ void regclass_Core(py::module m) { py::class_> cls(m, "Core"); cls.doc() = "openvino.runtime.Core class represents OpenVINO runtime Core entity. User applications can create several " - "Core class instances, but in this case the underlying plugins are created multiple times and not shared " + "Core class instances, but in this case, the underlying plugins are created multiple times and not shared " "between several Core instances. The recommended way is to have a single Core instance per application."; cls.def(py::init(), py::arg("xml_config_file") = ""); @@ -82,12 +82,12 @@ void regclass_Core(py::module m) { py::arg("config") = py::dict(), R"( Creates a compiled model from a source model object. - Users can create as many compiled models as they need and use them simultaneously + Users can create as many compiled models as they need, and use them simultaneously (up to the limitation of the hardware resources). :param model: Model acquired from read_model function. :type model: openvino.runtime.Model - :param device_name: Name of the device to load the model to. + :param device_name: Name of the device which will load the model. :type device_name: str :param properties: Optional dict of pairs: (property name, property value) relevant only for this load operation. :type properties: dict @@ -106,7 +106,7 @@ void regclass_Core(py::module m) { py::arg("config") = py::dict(), R"( Creates and loads a compiled model from a source model to the default OpenVINO device - selected by AUTO plugin. Users can create as many compiled models as they need and use + selected by AUTO plugin. Users can create as many compiled models as they need, and use them simultaneously (up to the limitation of the hardware resources). :param model: Model acquired from read_model function. @@ -216,8 +216,8 @@ void regclass_Core(py::module m) { :param model: A path to a model in IR / ONNX / PDPD format. :type model: str :param weights: A path to a data file For IR format (*.bin): if path is empty, - will try to read bin file with the same name as xml and if bin - file with the same name was not found, will load IR without weights. + it tries to read a bin file with the same name as xml and if the bin + file with the same name was not found, loads IR without weights. For ONNX format (*.onnx): weights parameter is not used. For PDPD format (*.pdmodel) weights parameter is not used. :type weights: str @@ -255,9 +255,8 @@ void regclass_Core(py::module m) { :param model: A string with model in IR / ONNX / PDPD format. :type model: str :param weights: A path to a data file For IR format (*.bin): if path is empty, - will try to read bin file with the same name as xml and if bin - file with the same name was not found, will load IR without weights. - For ONNX format (*.onnx): weights parameter is not used. + it tries to read a bin file with the same name as xml and if the bin + file with the same name was not found, loads IR without weights. For ONNX format (*.onnx): weights parameter is not used. For PDPD format (*.pdmodel) weights parameter is not used. :type weights: str :return: A model. @@ -280,10 +279,10 @@ void regclass_Core(py::module m) { R"( Imports a compiled model from a previously exported one. - :param model_stream: Input stream containing a model previously exported using export_model method. + :param model_stream: Input stream, containing a model previously exported, using export_model method. :type model_stream: bytes - :param device_name: Name of device to import compiled model for. - Note, if device_name device was not used to compile the original mode, an exception is thrown. + :param device_name: Name of device to which compiled model is imported. + Note: if device_name is not used to compile the original model, an exception is thrown. :type device_name: str :param properties: Optional map of pairs: (property name, property value) relevant only for this load operation. :type properties: dict, optional @@ -332,10 +331,10 @@ void regclass_Core(py::module m) { Python library `io`. - :param model_stream: Input stream containing a model previously exported using export_model method. + :param model_stream: Input stream, containing a model previously exported, using export_model method. :type model_stream: io.BytesIO - :param device_name: Name of device to import compiled model for. - Note, if device_name device was not used to compile the original mode, an exception is thrown. + :param device_name: Name of device to which compiled model is imported. + Note: if device_name is not used to compile the original model, an exception is thrown. :type device_name: str :param properties: Optional map of pairs: (property name, property value) relevant only for this load operation. :type properties: dict, optional diff --git a/src/bindings/python/src/pyopenvino/core/infer_request.cpp b/src/bindings/python/src/pyopenvino/core/infer_request.cpp index c9aac19a9e1..8d70c7bd23c 100644 --- a/src/bindings/python/src/pyopenvino/core/infer_request.cpp +++ b/src/bindings/python/src/pyopenvino/core/infer_request.cpp @@ -51,8 +51,8 @@ void regclass_InferRequest(py::module m) { py::arg("tensors"), R"( Sets batch of tensors for input data to infer by tensor name. - Model input shall have batch dimension and number of tensors shall - match with batch size. Current version supports set tensors to model inputs only. + Model input needs to have batch dimension and the number of tensors needs to be + matched with batch size. Current version supports set tensors to model inputs only. In case if `tensor_name` is associated with output (or any other non-input node), an exception will be thrown. @@ -60,7 +60,7 @@ void regclass_InferRequest(py::module m) { :type tensor_name: str :param tensors: Input tensors for batched infer request. The type of each tensor must match the model input element type and shape (except batch dimension). - Total size of tensors shall match with input's size. + Total size of tensors needs to match with input's size. :type tensors: List[openvino.runtime.Tensor] )"); @@ -73,8 +73,8 @@ void regclass_InferRequest(py::module m) { py::arg("tensors"), R"( Sets batch of tensors for input data to infer by tensor name. - Model input shall have batch dimension and number of tensors shall - match with batch size. Current version supports set tensors to model inputs only. + Model input needs to have batch dimension and the number of tensors needs to be + matched with batch size. Current version supports set tensors to model inputs only. In case if `port` is associated with output (or any other non-input node), an exception will be thrown. @@ -83,7 +83,7 @@ void regclass_InferRequest(py::module m) { :type port: openvino.runtime.ConstOutput :param tensors: Input tensors for batched infer request. The type of each tensor must match the model input element type and shape (except batch dimension). - Total size of tensors shall match with input's size. + Total size of tensors needs to match with input's size. :type tensors: List[openvino.runtime.Tensor] :rtype: None )"); @@ -130,12 +130,12 @@ void regclass_InferRequest(py::module m) { py::arg("tensors"), R"( Sets batch of tensors for single input data. - Model input shall have batch dimension and number of `tensors` - shall match with batch size. + Model input needs to have batch dimension and the number of `tensors` + needs to match with batch size. :param tensors: Input tensors for batched infer request. The type of each tensor must match the model input element type and shape (except batch dimension). - Total size of tensors shall match with input's size. + Total size of tensors needs to match with input's size. :type tensors: List[openvino.runtime.Tensor] )"); @@ -148,14 +148,14 @@ void regclass_InferRequest(py::module m) { py::arg("tensors"), R"( Sets batch of tensors for single input data to infer by index. - Model input shall have batch dimension and number of `tensors` - shall match with batch size. + Model input needs to have batch dimension and the number of `tensors` + needs to match with batch size. :param idx: Index of input tensor. :type idx: int :param tensors: Input tensors for batched infer request. The type of each tensor must match the model input element type and shape (except batch dimension). - Total size of tensors shall match with input's size. + Total size of tensors needs to match with input's size. )"); cls.def( @@ -513,8 +513,8 @@ void regclass_InferRequest(py::module m) { return self._request.get_profiling_info(); }, R"( - Queries performance measures per layer to get feedback of what - is the most time consuming operation, not all plugins provide + Queries performance is measured per layer to get feedback on what + is the most time-consuming operation, not all plugins provide meaningful data. :return: List of profiling information for operations in model. @@ -616,7 +616,7 @@ void regclass_InferRequest(py::module m) { return self._request.get_profiling_info(); }, R"( - Performance measures per layer to get feedback of what is the most time consuming operation. + Performance is measured per layer to get feedback on the most time-consuming operation. Not all plugins provide meaningful data! :return: Inference time. diff --git a/src/bindings/python/src/pyopenvino/core/offline_transformations.cpp b/src/bindings/python/src/pyopenvino/core/offline_transformations.cpp index 319f1ad2705..3495033cf26 100644 --- a/src/bindings/python/src/pyopenvino/core/offline_transformations.cpp +++ b/src/bindings/python/src/pyopenvino/core/offline_transformations.cpp @@ -146,7 +146,7 @@ void regmodule_offline_transformations(py::module m) { py::arg("weights_path"), py::arg("version") = "UNSPECIFIED", R"( - Serialize given model into IR. The generated .xml and .bin files will be save + Serialize given model into IR. The generated .xml and .bin files will be saved into provided paths. :param model: model which will be converted to IR representation diff --git a/src/bindings/python/src/pyopenvino/core/tensor.cpp b/src/bindings/python/src/pyopenvino/core/tensor.cpp index bedf9b2186d..7d3d77098f4 100644 --- a/src/bindings/python/src/pyopenvino/core/tensor.cpp +++ b/src/bindings/python/src/pyopenvino/core/tensor.cpp @@ -26,10 +26,10 @@ void regclass_Tensor(py::module m) { :param array: Array to create tensor from. :type array: numpy.array - :param shared_memory: If `True` this Tensor memory is being shared with a host, + :param shared_memory: If `True`, this Tensor memory is being shared with a host, that means the responsibility of keeping host memory is on the side of a user. Any action performed on the host - memory will be reflected on this Tensor's memory! + memory is reflected on this Tensor's memory! If `False`, data is being copied to this Tensor. Requires data to be C_CONTIGUOUS if `True`. :type shared_memory: bool @@ -43,8 +43,8 @@ void regclass_Tensor(py::module m) { R"( Another Tensor's special constructor. - It take an array or slice of it and shape that will be - selected starting from the first element of given array/slice. + It takes an array or slice of it, and shape that will be + selected, starting from the first element of the given array/slice. Please use it only in advanced cases if necessary! :param array: Underlaying methods will retrieve pointer on first element diff --git a/src/bindings/python/src/pyopenvino/graph/model.cpp b/src/bindings/python/src/pyopenvino/graph/model.cpp index d4a17bd0686..b2e3af2811a 100644 --- a/src/bindings/python/src/pyopenvino/graph/model.cpp +++ b/src/bindings/python/src/pyopenvino/graph/model.cpp @@ -631,7 +631,7 @@ void regclass_graph_Model(py::module m) { Return -1 if parameter not matched. - :param parameter: Parameter which index is to be found. + :param parameter: Parameter, which index is to be found. :type parameter: op.Parameter :return: Index for parameter :rtype: int diff --git a/src/bindings/python/src/pyopenvino/graph/node_output.hpp b/src/bindings/python/src/pyopenvino/graph/node_output.hpp index 30305555040..b891a828aa1 100644 --- a/src/bindings/python/src/pyopenvino/graph/node_output.hpp +++ b/src/bindings/python/src/pyopenvino/graph/node_output.hpp @@ -101,7 +101,7 @@ void regclass_graph_Output(py::module m, std::string typestring) output.def("get_target_inputs", &ov::Output::get_target_inputs, R"( - A set containing handles for all inputs targeted by the output + A set containing handles for all inputs, targeted by the output, referenced by this output handle. :return: Set of Inputs. diff --git a/src/bindings/python/src/pyopenvino/graph/util.cpp b/src/bindings/python/src/pyopenvino/graph/util.cpp index 5bfdf7f6a16..497cbe52fde 100644 --- a/src/bindings/python/src/pyopenvino/graph/util.cpp +++ b/src/bindings/python/src/pyopenvino/graph/util.cpp @@ -27,7 +27,7 @@ void regmodule_graph_util(py::module m) { :param index: Output node. :type index: openvino.runtime.Output :return: If it succeeded to calculate both bounds and - they are the same returns Constant operation + they are the same, returns Constant operation from the resulting bound, otherwise Null. :rtype: openvino.runtime.op.Constant or openvino.runtime.Node )");