From 42a5fffc16c460584ff0a452a79279a2df3b724b Mon Sep 17 00:00:00 2001 From: Nikolay Tyukaev Date: Wed, 16 Dec 2020 14:53:09 +0300 Subject: [PATCH] fix comments ngraph api 2021.2 (#3520) * fix comments ngraph api * remove whitespace * fixes Co-authored-by: Nikolay Tyukaev --- docs/doxygen/ngraph_py_api.xml | 5 +- ngraph/python/src/ngraph/__init__.py | 2 +- ngraph/python/src/ngraph/exceptions.py | 8 +- ngraph/python/src/ngraph/helpers.py | 4 +- ngraph/python/src/ngraph/impl/op/__init__.py | 2 +- ngraph/python/src/ngraph/opset1/ops.py | 368 +++++++++--------- ngraph/python/src/ngraph/opset2/ops.py | 17 +- ngraph/python/src/ngraph/opset3/ops.py | 76 ++-- ngraph/python/src/ngraph/opset4/ops.py | 33 +- ngraph/python/src/ngraph/opset_utils.py | 2 +- ngraph/python/src/ngraph/utils/__init__.py | 2 +- .../python/src/ngraph/utils/broadcasting.py | 2 +- ngraph/python/src/ngraph/utils/decorators.py | 6 +- .../src/ngraph/utils/input_validation.py | 14 +- .../python/src/ngraph/utils/node_factory.py | 16 +- ngraph/python/src/ngraph/utils/reduction.py | 2 +- .../src/ngraph/utils/tensor_iterator_types.py | 32 +- ngraph/python/src/ngraph/utils/types.py | 18 +- 18 files changed, 300 insertions(+), 309 deletions(-) diff --git a/docs/doxygen/ngraph_py_api.xml b/docs/doxygen/ngraph_py_api.xml index a7e3cd03dadb2e..ab67a10f73e117 100644 --- a/docs/doxygen/ngraph_py_api.xml +++ b/docs/doxygen/ngraph_py_api.xml @@ -19,10 +19,7 @@ - - - - + diff --git a/ngraph/python/src/ngraph/__init__.py b/ngraph/python/src/ngraph/__init__.py index bb1247d9e7e895..172190ba7d3c10 100644 --- a/ngraph/python/src/ngraph/__init__.py +++ b/ngraph/python/src/ngraph/__init__.py @@ -13,7 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ****************************************************************************** -"""! ngraph module namespace, exposing factory functions for all ops and other classes.""" +"""ngraph module namespace, exposing factory functions for all ops and other classes.""" # noqa: F401 from pkg_resources import get_distribution, DistributionNotFound diff --git a/ngraph/python/src/ngraph/exceptions.py b/ngraph/python/src/ngraph/exceptions.py index 43348e3b2c545b..4bfceb26926b02 100644 --- a/ngraph/python/src/ngraph/exceptions.py +++ b/ngraph/python/src/ngraph/exceptions.py @@ -13,16 +13,16 @@ # See the License for the specific language governing permissions and # limitations under the License. # ****************************************************************************** -"""! ngraph exceptions hierarchy. All exceptions are descendants of NgraphError.""" +"""ngraph exceptions hierarchy. All exceptions are descendants of NgraphError.""" class NgraphError(Exception): - """! Base class for Ngraph exceptions.""" + """Base class for Ngraph exceptions.""" class UserInputError(NgraphError): - """! User provided unexpected input.""" + """User provided unexpected input.""" class NgraphTypeError(NgraphError, TypeError): - """! Type mismatch error.""" + """Type mismatch error.""" diff --git a/ngraph/python/src/ngraph/helpers.py b/ngraph/python/src/ngraph/helpers.py index ed5b2db967491b..b10f458e13b0d7 100644 --- a/ngraph/python/src/ngraph/helpers.py +++ b/ngraph/python/src/ngraph/helpers.py @@ -13,14 +13,14 @@ # See the License for the specific language governing permissions and # limitations under the License. # ****************************************************************************** -"""! nGraph helper functions.""" +"""nGraph helper functions.""" from ngraph.impl import Function from openvino.inference_engine import IENetwork def function_from_cnn(cnn_network: IENetwork) -> Function: - """! Get nGraph function from Inference Engine CNN network.""" + """Get nGraph function from Inference Engine CNN network.""" capsule = cnn_network._get_function_capsule() ng_function = Function.from_capsule(capsule) return ng_function diff --git a/ngraph/python/src/ngraph/impl/op/__init__.py b/ngraph/python/src/ngraph/impl/op/__init__.py index 3654f9b9450c5a..a1cb59fc7343ac 100644 --- a/ngraph/python/src/ngraph/impl/op/__init__.py +++ b/ngraph/python/src/ngraph/impl/op/__init__.py @@ -24,7 +24,7 @@ from _pyngraph.op import Constant -""" Retrieve Constant inner data. +"""Retrieve Constant inner data. Internally uses PyBind11 Numpy's buffer protocol. diff --git a/ngraph/python/src/ngraph/opset1/ops.py b/ngraph/python/src/ngraph/opset1/ops.py index 5af81cfac4b973..9ccc5b1f979fce 100644 --- a/ngraph/python/src/ngraph/opset1/ops.py +++ b/ngraph/python/src/ngraph/opset1/ops.py @@ -14,7 +14,7 @@ # limitations under the License. # ****************************************************************************** -"""! Factory functions for all ngraph ops.""" +"""Factory functions for all ngraph ops.""" from typing import Callable, Iterable, List, Optional, Set, Union import numpy as np @@ -60,7 +60,7 @@ @unary_op def absolute(node: NodeInput, name: Optional[str] = None) -> Node: - """! Return node which applies f(x) = abs(x) to the input node element-wise. + """Return node which applies f(x) = abs(x) to the input node element-wise. @param node: One of: input node, array or scalar. @param name: Optional new name for output node. @@ -71,7 +71,7 @@ def absolute(node: NodeInput, name: Optional[str] = None) -> Node: @unary_op def acos(node: NodeInput, name: Optional[str] = None) -> Node: - """! Apply inverse cosine function on the input node element-wise. + """Apply inverse cosine function on the input node element-wise. @param node: One of: input node, array or scalar. @param name: Optional new name for output node. @@ -87,7 +87,7 @@ def add( auto_broadcast: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Return node which applies f(x) = A+B to the input nodes element-wise.""" + """Return node which applies f(x) = A+B to the input nodes element-wise.""" return _get_node_factory_opset1().create( "Add", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()} ) @@ -95,7 +95,7 @@ def add( @unary_op def asin(node: NodeInput, name: Optional[str] = None) -> Node: - """! Apply inverse sine function on the input node element-wise. + """Apply inverse sine function on the input node element-wise. @param node: One of: input node, array or scalar. @param name: Optional new name for output node. @@ -106,7 +106,7 @@ def asin(node: NodeInput, name: Optional[str] = None) -> Node: @unary_op def atan(node: NodeInput, name: Optional[str] = None) -> Node: - """! Apply inverse tangent function on the input node element-wise. + """Apply inverse tangent function on the input node element-wise. @param node: One of: input node, array or scalar. @param name: Optional new name for output node. @@ -127,7 +127,7 @@ def avg_pool( auto_pad: Optional[str] = None, name: Optional[str] = None, ) -> Node: - """! Return average pooling node. + """Return average pooling node. @param data_batch: The input node providing data. @param strides: The window movement strides. @@ -170,7 +170,7 @@ def batch_norm_inference( epsilon: float, name: Optional[str] = None, ) -> Node: - """! Perform layer normalizes a input tensor by mean and variance with appling scale and offset. + """Perform layer normalizes a input tensor by mean and variance with appling scale and offset. @param data: The input tensor with data for normalization. @param gamma: The scalar scaling for normalized value. @@ -199,7 +199,7 @@ def binary_convolution( auto_pad: str = "EXPLICIT", name: Optional[str] = None, ) -> Node: - """! Create node performing convolution with binary weights, binary input and integer output. + """Create node performing convolution with binary weights, binary input and integer output. @param data: The node providing data batch tensor. @param filter: The node providing filters tensor. @@ -236,7 +236,7 @@ def broadcast( mode: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Create a node which broadcasts the input node's values along specified axes to a desired shape. + """Create a node which broadcasts the input node's values along specified axes to a desired shape. @param data: The node with input tensor data. @param target_shape: The node with a new shape we want to broadcast tensor to. @@ -262,7 +262,7 @@ def ctc_greedy_decoder( merge_repeated: bool = True, name: Optional[str] = None, ) -> Node: - """! Perform greedy decoding on the logits given in input (best path). + """Perform greedy decoding on the logits given in input (best path). @param data: Logits on which greedy decoding is performed. @param sequence_mask: The tensor with sequence masks for each sequence in the batch. @@ -278,7 +278,7 @@ def ctc_greedy_decoder( @unary_op def ceiling(node: NodeInput, name: Optional[str] = None) -> Node: - """! Return node which applies ceiling to the input node element-wise. + """Return node which applies ceiling to the input node element-wise. @param node: The node providing data to ceiling operation. @param name: Optional name for output node. @@ -291,7 +291,13 @@ def ceiling(node: NodeInput, name: Optional[str] = None) -> Node: def clamp( data: NodeInput, min_value: ScalarData, max_value: ScalarData, name: Optional[str] = None ) -> Node: - """! Perform clamp element-wise on data from input node. + """Perform clamp element-wise on data from input node. + + @param data: Input tensor. One of: input node, array or scalar. + @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. + @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. @@ -302,18 +308,12 @@ def clamp( Clamp uses the following logic: - ~~~~~~~~~~~~~~~~~~~~~~~~{.py} + @code{.py} if data < min_value: data=min_value elif data > max_value: data=max_value - ~~~~~~~~~~~~~~~~~~~~~~~~ - - @param data: Input tensor. One of: input node, array or scalar. - @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. - @return The new node performing a clamp operation on its input data element-wise. + @endcode """ return _get_node_factory_opset1().create( "Clamp", [as_node(data)], {"min": min_value, "max": max_value} @@ -322,7 +322,7 @@ def clamp( @nameable_op def concat(nodes: List[NodeInput], axis: int, name: Optional[str] = None) -> Node: - """! Concatenate input nodes into single new node along specified axis. + """Concatenate input nodes into single new node along specified axis. @param nodes: The nodes we want concatenate into single new node. @param axis: The axis along which we want to concatenate input nodes. @@ -334,7 +334,7 @@ def concat(nodes: List[NodeInput], axis: int, name: Optional[str] = None) -> Nod @nameable_op def constant(value: NumericData, dtype: NumericType = None, name: Optional[str] = None) -> Constant: - """! Create a Constant node from provided value. + """Create a Constant node from provided value. @param value: One of: array of values or scalar to initialize node with. @param dtype: The data type of provided data. @@ -348,7 +348,7 @@ def constant(value: NumericData, dtype: NumericType = None, name: Optional[str] def convert( data: NodeInput, destination_type: Union[str, NumericType], name: Optional[str] = None ) -> Node: - """! Return node which casts input node values to specified type. + """Return node which casts input node values to specified type. @param data: Node which produces the input tensor. @param destination_type: Provides the target type for the conversion. @@ -364,7 +364,7 @@ def convert( @binary_op def convert_like(data: NodeInput, like: NodeInput, name: Optional[str] = None) -> Node: - """! Return node which casts data node values to the type of another node. + """Return node which casts data node values to the type of another node. @param data: Node which produces the input tensor @param like: Node which provides the target type information for the conversion @@ -385,7 +385,7 @@ def convolution( auto_pad: str = "EXPLICIT", name: Optional[str] = None, ) -> Node: - """! Return node performing batched convolution operation. + """Return node performing batched convolution operation. @param data: The node providing data batch tensor. @param filter: The node providing filters tensor. @@ -423,7 +423,7 @@ def convolution_backprop_data( output_padding: Optional[List[int]] = None, name: Optional[str] = None, ) -> Node: - """! Create node performing a batched-convolution backprop data operation. + """Create node performing a batched-convolution backprop data operation. @param data: The node producing data from forward-prop @param filters: The node producing the filters from forward-prop. @@ -469,7 +469,7 @@ def convolution_backprop_data( @unary_op def cos(node: NodeInput, name: Optional[str] = None) -> Node: - """! Apply cosine function on the input node element-wise. + """Apply cosine function on the input node element-wise. @param node: One of: input node, array or scalar. @param name: Optional new name for output node. @@ -480,7 +480,7 @@ def cos(node: NodeInput, name: Optional[str] = None) -> Node: @unary_op def cosh(node: NodeInput, name: Optional[str] = None) -> Node: - """! Apply hyperbolic cosine function on the input node element-wise. + """Apply hyperbolic cosine function on the input node element-wise. @param node: One of: input node, array or scalar. @param name: Optional new name for output node. @@ -503,7 +503,7 @@ def deformable_convolution( deformable_group: int = 1, name: Optional[str] = None, ) -> Node: - """! Create node performing deformable convolution. + """Create node performing deformable convolution. @param data: The node providing data batch tensor. @param filter: The node providing filters tensor. @@ -548,7 +548,7 @@ def deformable_psroi_pooling( offsets: Optional[NodeInput] = None, name: Optional[str] = None, ) -> Node: - """! Return node performing DeformablePSROIPooling operation. + """Return node performing DeformablePSROIPooling operation. DeformablePSROIPooling computes position-sensitive pooling on regions of interest specified by input. @@ -589,7 +589,7 @@ def deformable_psroi_pooling( @nameable_op def depth_to_space(node: Node, mode: str, block_size: int = 1, name: str = None) -> Node: - """! Rearranges input tensor from depth into blocks of spatial data. + """Rearranges input tensor from depth into blocks of spatial data. Values from the height and width dimensions are moved to the depth dimension. @@ -626,7 +626,7 @@ def detection_output( aux_box_preds: NodeInput = None, name: Optional[str] = None, ) -> Node: - """! Generate the detection output using information on location and confidence predictions. + """Generate the detection output using information on location and confidence predictions. @param box_logits: The 2D input tensor with box logits. @param class_preds: The 2D input tensor with class predictions. @@ -635,6 +635,7 @@ 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. + @return Node representing DetectionOutput operation. Available attributes are: @@ -726,7 +727,7 @@ def detection_output( Required: no Example of attribute dictionary: - ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.py} + @code{.py} # just required ones attrs = { 'num_classes': 85, @@ -743,11 +744,9 @@ def detection_output( 'input_height': [32], 'input_width': [32], } - ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + @endcode Optional attributes which are absent from dictionary will be set with corresponding default. - - @return Node representing DetectionOutput operation. """ requirements = [ ("num_classes", True, np.integer, is_positive_value), @@ -786,7 +785,7 @@ def divide( auto_broadcast: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Return node which applies f(x) = A/B to the input nodes element-wise. + """Return node which applies f(x) = A/B to the input nodes element-wise. @param left_node: The node providing dividend data. @param right_node: The node providing divisor data. @@ -801,7 +800,7 @@ def divide( @nameable_op def elu(data: NodeInput, alpha: NumericType, name: Optional[str] = None) -> Node: - """! Perform Exponential Linear Unit operation element-wise on data from input node. + """Perform Exponential Linear Unit operation element-wise on data from input node. Computes exponential linear: alpha * (exp(data) - 1) if < 0, data otherwise. @@ -823,7 +822,7 @@ def equal( auto_broadcast: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Return node which checks if input nodes are equal element-wise. + """Return node which checks if input nodes are equal element-wise. @param left_node: The first input node for equal operation. @param right_node: The second input node for equal operation. @@ -839,7 +838,7 @@ def equal( @unary_op def erf(node: NodeInput, name: Optional[str] = None) -> Node: - """! Return node which calculates Gauss error function element-wise with given tensor. + """Return node which calculates Gauss error function element-wise with given tensor. @param node: The node providing data for operation. @param name: The optional name for new output node. @@ -850,7 +849,7 @@ def erf(node: NodeInput, name: Optional[str] = None) -> Node: @unary_op def exp(node: NodeInput, name: Optional[str] = None) -> Node: - """! Return node which applies exponential function to the input node element-wise. + """Return node which applies exponential function to the input node element-wise. @param node: The node providing data for operation. @param name: The optional name for new output node. @@ -870,18 +869,28 @@ def fake_quantize( auto_broadcast: str = "NUMPY", name: Optional[str] = None, ) -> Node: - r"""! Perform an element-wise linear quantization on input data. + r"""Perform an element-wise linear quantization on input data. + + @param data: The node with data tensor. + @param input_low: The node with the minimum for input values. + @param input_high: The node with the maximum for input values. + @param output_low: The node with the minimum quantized value. + @param output_high: The node with the maximum quantized value. + @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. + @return New node with quantized value. Input floating point values are quantized into a discrete set of floating point values. - ~~~~~~~~~~~~~{.py} + @code{.py} if x <= input_low: output = output_low if x > input_high: output = output_high else: output = fake_quantize(output) - ~~~~~~~~~~~~~ + @endcode Fake quantize uses the following logic: @@ -889,16 +898,6 @@ def fake_quantize( \dfrac{round( \dfrac{data - input\_low}{(input\_high - input\_low)\cdot (levels-1)})} {(levels-1)\cdot (output\_high - output\_low)} + output\_low \f] - - @param data: The node with data tensor. - @param input_low: The node with the minimum for input values. - @param input_high: The node with the maximum for input values. - @param output_low: The node with the minimum quantized value. - @param output_high: The node with the maximum quantized value. - @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. - @return New node with quantized value. """ return _get_node_factory_opset1().create( "FakeQuantize", @@ -909,7 +908,7 @@ def fake_quantize( @unary_op def floor(node: NodeInput, name: Optional[str] = None) -> Node: - """! Return node which applies floor to the input node element-wise. + """Return node which applies floor to the input node element-wise. @param node: The input node providing data. @param name: The optional name for new output node. @@ -925,7 +924,7 @@ def floor_mod( auto_broadcast: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Return node performing element-wise FloorMod (division reminder) with two given tensors. + """Return node performing element-wise FloorMod (division reminder) with two given tensors. @param left_node: The first input node for FloorMod operation. @param right_node: The second input node for FloorMod operation. @@ -942,7 +941,7 @@ def floor_mod( def gather( data: NodeInput, indices: NodeInput, axis: NodeInput, name: Optional[str] = None ) -> Node: - """! Return Gather node which takes slices from axis of data according to indices. + """Return Gather node which takes slices from axis of data according to indices. @param data: The tensor from which slices are gathered. @param indices: Tensor with indexes to gather. @@ -962,13 +961,20 @@ def gather_tree( end_token: NodeInput, name: Optional[str] = None, ) -> Node: - """! Perform GatherTree operation. + """Perform GatherTree operation. + + @param step_ids: The tensor with indices from per each step. + @param parent_idx: The tensor with with parent beam indices. + @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. + @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. GatherTree uses the following logic: - ~~~~~~~~~~~~~{.py} + @code{.py} for batch in range(BATCH_SIZE): for beam in range(BEAM_WIDTH): max_sequence_in_beam = min(MAX_TIME, max_seq_len[batch]) @@ -979,15 +985,7 @@ def gather_tree( final_idx[level, batch, beam] = step_idx[level, batch, parent] parent = parent_idx[level, batch, parent] - ~~~~~~~~~~~~~ - - - @param step_ids: The tensor with indices from per each step. - @param parent_idx: The tensor with with parent beam indices. - @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. - @return The new node performing a GatherTree operation. + @endcode """ node_inputs = as_nodes(step_ids, parent_idx, max_seq_len, end_token) return _get_node_factory_opset1().create("GatherTree", node_inputs) @@ -1000,7 +998,7 @@ def greater( auto_broadcast: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Return node which checks if left input node is greater than the right node element-wise. + """Return node which checks if left input node is greater than the right node element-wise. @param left_node: The first input node providing data. @param right_node: The second input node providing data. @@ -1021,7 +1019,7 @@ def greater_equal( auto_broadcast: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Return node which checks if left node is greater or equal to the right node element-wise. + """Return node which checks if left node is greater or equal to the right node element-wise. @param left_node: The first input node providing data. @param right_node: The second input node providing data. @@ -1037,7 +1035,7 @@ def greater_equal( def grn(data: Node, bias: float, name: Optional[str] = None) -> Node: - r"""! Perform Global Response Normalization with L2 norm (across channels only). + r"""Perform Global Response Normalization with L2 norm (across channels only). Computes GRN operation on channels for input tensor: @@ -1062,7 +1060,7 @@ def group_convolution( auto_pad: str = "EXPLICIT", name: Optional[str] = None, ) -> Node: - """! Perform Group Convolution operation on data from input node. + """Perform Group Convolution operation on data from input node. @param data: The node producing input data. @param filters: The node producing filters data. @@ -1109,7 +1107,7 @@ def group_convolution_backprop_data( output_padding: Optional[List[int]] = None, name: Optional[str] = None, ) -> Node: - """! Perform Group Convolution operation on data from input node. + """Perform Group Convolution operation on data from input node. @param data: The node producing input data. @param filters: The node producing filter data. @@ -1163,19 +1161,19 @@ def group_convolution_backprop_data( @nameable_op def hard_sigmoid(data: Node, alpha: NodeInput, beta: NodeInput, name: Optional[str] = None) -> Node: - """! Perform Hard Sigmoid operation element-wise on data from input node. - - Hard Sigmoid uses the following logic: - - ~~~~~~~~~~~~~{.py} - y = max(0, min(1, alpha * data + beta)) - ~~~~~~~~~~~~~ + """Perform Hard Sigmoid operation element-wise on data from input node. @param data: The node with data tensor. @param alpha: A node producing the alpha parameter. @param beta: A node producing the beta parameter @param name: Optional output node name. @return The new node performing a Hard Sigmoid element-wise on input tensor. + + Hard Sigmoid uses the following logic: + + @code{.py} + y = max(0, min(1, alpha * data + beta)) + @endcode """ return _get_node_factory_opset1().create("HardSigmoid", [data, as_node(alpha), as_node(beta)]) @@ -1184,12 +1182,13 @@ def hard_sigmoid(data: Node, alpha: NodeInput, beta: NodeInput, name: Optional[s def interpolate( image: Node, output_shape: NodeInput, attrs: dict, name: Optional[str] = None ) -> Node: - """! Perform interpolation of independent slices in input tensor. + """Perform interpolation of independent slices in input tensor. @param image: The node providing input tensor with data for interpolation. @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. + @return Node representing interpolation operation. Available attributes are: @@ -1224,7 +1223,7 @@ def interpolate( Required: no Example of attribute dictionary: - ~~~~~~~~~~~~~ + @code{.py} # just required ones attrs = { 'axes': [2, 3], @@ -1237,10 +1236,8 @@ def interpolate( 'antialias': True, 'pads_begin': [2, 2, 2], } - ~~~~~~~~~~~~~ + @endcode Optional attributes which are absent from dictionary will be set with corresponding default. - - @return Node representing interpolation operation. """ requirements = [ ("axes", True, np.integer, is_non_negative_value), @@ -1263,7 +1260,7 @@ def less( auto_broadcast: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Return node which checks if left input node is less than the right node element-wise. + """Return node which checks if left input node is less than the right node element-wise. @param left_node: The first input node providing data. @param right_node: The second input node providing data. @@ -1284,7 +1281,7 @@ def less_equal( auto_broadcast: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Return node which checks if left input node is less or equal the right node element-wise. + """Return node which checks if left input node is less or equal the right node element-wise. @param left_node: The first input node providing data. @param right_node: The second input node providing data. @@ -1301,7 +1298,7 @@ def less_equal( @unary_op def log(node: NodeInput, name: Optional[str] = None) -> Node: - """! Return node which applies natural logarithm to the input node element-wise. + """Return node which applies natural logarithm to the input node element-wise. @param node: The input node providing data for operation. @param name: The optional new name for output node. @@ -1317,7 +1314,7 @@ def logical_and( auto_broadcast: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Return node which perform logical and operation on input nodes element-wise. + """Return node which perform logical and operation on input nodes element-wise. @param left_node: The first input node providing data. @param right_node: The second input node providing data. @@ -1333,7 +1330,7 @@ def logical_and( @unary_op def logical_not(node: NodeInput, name: Optional[str] = None) -> Node: - """! Return node which applies element-wise logical negation to the input node. + """Return node which applies element-wise logical negation to the input node. @param node: The input node providing data. @param name: The optional new name for output node. @@ -1349,7 +1346,7 @@ def logical_or( auto_broadcast: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Return node which performs logical OR operation on input nodes element-wise. + """Return node which performs logical OR operation on input nodes element-wise. @param left_node: The first input node providing data. @param right_node: The second input node providing data. @@ -1370,7 +1367,7 @@ def logical_xor( auto_broadcast: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Return node which performs logical XOR operation on input nodes element-wise. + """Return node which performs logical XOR operation on input nodes element-wise. @param left_node: The first input node providing data. @param right_node: The second input node providing data. @@ -1394,7 +1391,7 @@ def lrn( size: int = 5, name: Optional[str] = None, ) -> Node: - """! Return a node which performs element-wise Local Response Normalization (LRN) operation. + """Return a node which performs element-wise Local Response Normalization (LRN) operation. @param data: Input data. @param alpha: A scale factor (usually positive). @@ -1423,7 +1420,7 @@ def lstm_cell( clip: float = 0.0, name: Optional[str] = None, ) -> Node: - """! Return a node which performs LSTMCell operation. + """Return a node which performs LSTMCell operation. @param X: The input tensor with shape: [batch_size, input_size]. @param initial_hidden_state: The hidden state tensor with shape: [batch_size, hidden_size]. @@ -1489,7 +1486,7 @@ def lstm_sequence( clip: float = 0.0, name: Optional[str] = None, ) -> Node: - """! Return a node which performs LSTMSequence operation. + """Return a node which performs LSTMSequence operation. @param X: The input tensor. Shape: [batch_size, seq_length, input_size]. @param initial_hidden_state: The hidden state tensor. @@ -1559,7 +1556,7 @@ def matmul( transpose_b: bool, name: Optional[str] = None, ) -> Node: - """! Return the Matrix Multiplication operation. + """Return the Matrix Multiplication operation. @param data_a: left-hand side matrix @param data_b: right-hand side matrix @@ -1584,7 +1581,7 @@ def max_pool( auto_pad: Optional[str] = None, name: Optional[str] = None, ) -> Node: - """! Perform max pooling operation with given parameters on provided data. + """Perform max pooling operation with given parameters on provided data. @param data: The node providing input data. @param strides: The distance (in pixels) to slide the filter on the feature map @@ -1623,7 +1620,7 @@ def maximum( auto_broadcast: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Return node which applies the maximum operation to input nodes elementwise.""" + """Return node which applies the maximum operation to input nodes elementwise.""" return _get_node_factory_opset1().create( "Maximum", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()} ) @@ -1636,7 +1633,7 @@ def minimum( auto_broadcast: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Return node which applies the minimum operation to input nodes elementwise.""" + """Return node which applies the minimum operation to input nodes elementwise.""" return _get_node_factory_opset1().create( "Minimum", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()} ) @@ -1649,7 +1646,7 @@ def mod( auto_broadcast: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Return node performing element-wise division reminder with two given tensors. + """Return node performing element-wise division reminder with two given tensors. @param left_node: The first input node for mod operation. @param right_node: The second input node for mod operation. @@ -1669,7 +1666,7 @@ def multiply( auto_broadcast: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Return node which applies f(x) = A*B to the input nodes elementwise.""" + """Return node which applies f(x) = A*B to the input nodes elementwise.""" return _get_node_factory_opset1().create( "Multiply", [left_node, right_node], {"auto_broadcast": auto_broadcast.upper()} ) @@ -1677,7 +1674,7 @@ def multiply( @unary_op def negative(node: NodeInput, name: Optional[str] = None) -> Node: - """! Return node which applies f(x) = -x to the input node elementwise.""" + """Return node which applies f(x) = -x to the input node elementwise.""" return _get_node_factory_opset1().create("Negative", [node]) @@ -1692,7 +1689,7 @@ def non_max_suppression( sort_result_descending: bool = True, name: Optional[str] = None, ) -> Node: - """! Return a node which performs NonMaxSuppression. + """Return a node which performs NonMaxSuppression. @param boxes: Tensor with box coordinates. @param scores: Tensor with box scores. @@ -1725,7 +1722,7 @@ def non_max_suppression( def normalize_l2( data: NodeInput, axes: NodeInput, eps: float, eps_mode: str, name: Optional[str] = None ) -> Node: - """! Construct an NormalizeL2 operation. + """Construct an NormalizeL2 operation. @param data: Node producing the input tensor @param axes: Node indicating axes along which L2 reduction is calculated @@ -1745,7 +1742,7 @@ def not_equal( auto_broadcast: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Return node which checks if input nodes are unequal element-wise. + """Return node which checks if input nodes are unequal element-wise. @param left_node: The first input node for not-equal operation. @param right_node: The second input node for not-equal operation. @@ -1768,7 +1765,7 @@ def one_hot( axis: int, name: Optional[str] = None, ) -> Node: - """! Create node performing one-hot encoding on input data. + """Create node performing one-hot encoding on input data. @param indices: Input tensor of rank N with indices of any supported integer data type. @param depth: Scalar of any supported integer type that specifies number of classes and @@ -1795,7 +1792,7 @@ def pad( arg_pad_value: Optional[NodeInput] = None, name: Optional[str] = None, ) -> Node: - """! Return a generic padding operation. + """Return a generic padding operation. @param arg: The node producing input tensor to be padded. @param pads_begin: number of padding elements to be added before position 0 @@ -1817,7 +1814,7 @@ def pad( def parameter( shape: TensorShape, dtype: NumericType = np.float32, name: Optional[str] = None ) -> Parameter: - """! Return an ngraph Parameter object.""" + """Return an ngraph Parameter object.""" element_type = get_element_type(dtype) return Parameter(element_type, PartialShape(shape)) @@ -1829,7 +1826,7 @@ def power( auto_broadcast: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Return node which perform element-wise exponentiation operation. + """Return node which perform element-wise exponentiation operation. @param left_node: The node providing the base of operation. @param right_node: The node providing the exponent of operation. @@ -1845,21 +1842,21 @@ def power( @nameable_op def prelu(data: NodeInput, slope: NodeInput, name: Optional[str] = None) -> Node: - """! Perform Parametrized Relu operation element-wise on data from input node. + """Perform Parametrized Relu operation element-wise on data from input node. + + @param data: The node with data tensor. + @param slope: The node with the multipliers for negative values. + @param name: Optional output node name. + @return The new node performing a PRelu operation on tensor's channels. PRelu uses the following logic: - ~~~~~~~~~~~~~{.py} + @code{.py} if data < 0: data = data * slope elif data >= 0: data = data - ~~~~~~~~~~~~~ - - @param data: The node with data tensor. - @param slope: The node with the multipliers for negative values. - @param name: Optional output node name. - @return The new node performing a PRelu operation on tensor's channels. + @endcode """ return _get_node_factory_opset1().create("PRelu", as_nodes(data, slope)) @@ -1868,7 +1865,7 @@ def prelu(data: NodeInput, slope: NodeInput, name: Optional[str] = None) -> Node def prior_box_clustered( output_size: Node, image_size: NodeInput, attrs: dict, name: Optional[str] = None ) -> Node: - """! Generate prior boxes of specified sizes normalized to the input image size. + """Generate prior boxes of specified sizes normalized to the input image size. @param output_size: 1D tensor with two integer elements [height, width]. Specifies the spatial size of generated grid with boxes. @@ -1876,6 +1873,7 @@ 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. + @return Node representing PriorBoxClustered operation. Available attributes are: @@ -1916,7 +1914,7 @@ def prior_box_clustered( Required: no Example of attribute dictionary: - ~~~~~~~~~~~~~{.py} + @code{.py} # just required ones attrs = { 'offset': 85, @@ -1927,11 +1925,9 @@ def prior_box_clustered( 'clip': False, 'step_widths': [1.5, 2.0, 2.5] } - ~~~~~~~~~~~~~ + @endcode Optional attributes which are absent from dictionary will be set with corresponding default. - - @return Node representing PriorBoxClustered operation. """ requirements = [ ("widths", False, np.floating, is_positive_value), @@ -1954,12 +1950,13 @@ def prior_box_clustered( def prior_box( layer_shape: Node, image_shape: NodeInput, attrs: dict, name: Optional[str] = None ) -> Node: - """! Generate prior boxes of specified sizes and aspect ratios across all dimensions. + """Generate prior boxes of specified sizes and aspect ratios across all dimensions. @param layer_shape: Shape of layer for which prior boxes are computed. @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. + @return Node representing prior box operation. Available attributes are: @@ -2027,7 +2024,7 @@ def prior_box( Required: no Example of attribute dictionary: - ~~~~~~~~~~~~~{.py} + @code{.py} # just required ones attrs = { 'offset': 85, @@ -2039,11 +2036,9 @@ def prior_box( 'clip': True, 'fixed_size': [32, 64, 128] } - ~~~~~~~~~~~~~ + @endcode Optional attributes which are absent from dictionary will be set with corresponding default. - - @return Node representing prior box operation. """ requirements = [ ("offset", True, np.floating, is_non_negative_value), @@ -2073,13 +2068,14 @@ def proposal( attrs: dict, name: Optional[str] = None, ) -> Node: - """! Filter bounding boxes and outputs only those with the highest prediction confidence. + """Filter bounding boxes and outputs only those with the highest prediction confidence. @param class_probs: 4D input floating point tensor with class prediction scores. @param bbox_deltas: 4D input floating point tensor with box logits. @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. + @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 @@ -2159,23 +2155,21 @@ def proposal( Example of attribute dictionary: - ~~~~~~~~~~~~~{.py} - # just required ones - attrs = { - 'base_size': 85, - 'pre_nms_topn': 10, - 'post_nms_topn': 20, - 'nms_thresh': 0.34, - 'feat_stride': 16, - 'min_size': 32, - 'ratio': [0.1, 1.5, 2.0, 2.5], - 'scale': [2, 3, 3, 4], - } - ~~~~~~~~~~~~~ + @code{.py} + # just required ones + attrs = { + 'base_size': 85, + 'pre_nms_topn': 10, + 'post_nms_topn': 20, + 'nms_thresh': 0.34, + 'feat_stride': 16, + 'min_size': 32, + 'ratio': [0.1, 1.5, 2.0, 2.5], + 'scale': [2, 3, 3, 4], + } + @endcode Optional attributes which are absent from dictionary will be set with corresponding default. - - @return Node representing Proposal operation. """ requirements = [ ("base_size", True, np.unsignedinteger, is_positive_value), @@ -2213,7 +2207,7 @@ def psroi_pooling( mode: str, name: Optional[str] = None, ) -> Node: - """! Return a node which produces a PSROIPooling operation. + """Return a node which produces a PSROIPooling operation. @param input: Input feature map {N, C, ...} @param coords: Coordinates of bounding boxes @@ -2242,7 +2236,7 @@ def psroi_pooling( @nameable_op def range(start: Node, stop: NodeInput, step: NodeInput, name: Optional[str] = None) -> Node: - """! Return a node which produces the Range operation. + """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 @@ -2255,7 +2249,7 @@ def range(start: Node, stop: NodeInput, step: NodeInput, name: Optional[str] = N @unary_op def relu(node: NodeInput, name: Optional[str] = None) -> Node: - """! Perform rectified linear unit operation on input node element-wise. + """Perform rectified linear unit operation on input node element-wise. @param node: One of: input node, array or scalar. @param name: The optional output node name. @@ -2268,7 +2262,7 @@ def relu(node: NodeInput, name: Optional[str] = None) -> Node: def reduce_logical_and( node: NodeInput, reduction_axes: NodeInput, keep_dims: bool = False, name: Optional[str] = None ) -> Node: - """! Logical AND reduction operation on input tensor, eliminating the specified reduction axes. + """Logical AND reduction operation on input tensor, eliminating the specified reduction axes. @param node: The tensor we want to reduce. @param reduction_axes: The axes to eliminate through AND operation. @@ -2285,7 +2279,7 @@ def reduce_logical_and( def reduce_logical_or( node: NodeInput, reduction_axes: NodeInput, keep_dims: bool = False, name: Optional[str] = None ) -> Node: - """! Logical OR reduction operation on input tensor, eliminating the specified reduction axes. + """Logical OR reduction operation on input tensor, eliminating the specified reduction axes. @param node: The tensor we want to reduce. @param reduction_axes: The axes to eliminate through OR operation. @@ -2302,7 +2296,7 @@ def reduce_logical_or( def reduce_max( node: NodeInput, reduction_axes: NodeInput, keep_dims: bool = False, name: Optional[str] = None ) -> Node: - """! Max-reduction operation on input tensor, eliminating the specified reduction axes. + """Max-reduction operation on input tensor, eliminating the specified reduction axes. @param node: The tensor we want to max-reduce. @param reduction_axes: The axes to eliminate through max operation. @@ -2318,7 +2312,7 @@ def reduce_max( def reduce_mean( node: NodeInput, reduction_axes: NodeInput, keep_dims: bool = False, name: Optional[str] = None ) -> Node: - """! Mean-reduction operation on input tensor, eliminating the specified reduction axes. + """Mean-reduction operation on input tensor, eliminating the specified reduction axes. @param node: The tensor we want to mean-reduce. @param reduction_axes: The axes to eliminate through mean operation. @@ -2335,7 +2329,7 @@ def reduce_mean( def reduce_min( node: NodeInput, reduction_axes: NodeInput, keep_dims: bool = False, name: Optional[str] = None ) -> Node: - """! Min-reduction operation on input tensor, eliminating the specified reduction axes. + """Min-reduction operation on input tensor, eliminating the specified reduction axes. @param node: The tensor we want to min-reduce. @param reduction_axes: The axes to eliminate through min operation. @@ -2351,7 +2345,7 @@ def reduce_min( def reduce_prod( node: NodeInput, reduction_axes: NodeInput, keep_dims: bool = False, name: Optional[str] = None ) -> Node: - """! Product-reduction operation on input tensor, eliminating the specified reduction axes. + """Product-reduction operation on input tensor, eliminating the specified reduction axes. @param node: The tensor we want to product-reduce. @param reduction_axes: The axes to eliminate through product operation. @@ -2368,7 +2362,7 @@ def reduce_prod( def reduce_sum( node: NodeInput, reduction_axes: NodeInput, keep_dims: bool = False, name: Optional[str] = None ) -> Node: - """! Perform element-wise sums of the input tensor, eliminating the specified reduction axes. + """Perform element-wise sums of the input tensor, eliminating the specified reduction axes. @param node: The node providing data for operation. @param reduction_axes: The axes to eliminate through summation. @@ -2394,7 +2388,7 @@ def region_yolo( anchors: List[float] = None, name: Optional[str] = None, ) -> Node: - """! Return a node which produces the RegionYolo operation. + """Return a node which produces the RegionYolo operation. @param input: Input data @param coords: Number of coordinates for each region @@ -2431,7 +2425,7 @@ def region_yolo( def reshape( node: NodeInput, output_shape: NodeInput, special_zero: bool, name: Optional[str] = None ) -> Node: - """! Return reshaped node according to provided parameters. + """Return reshaped node according to provided parameters. @param node: The tensor we want to reshape. @param output_shape: The node with a new shape for input tensor. @@ -2450,7 +2444,7 @@ def reshape( @unary_op def result(data: NodeInput, name: Optional[str] = None) -> Node: - """! Return a node which represents an output of a graph (Function). + """Return a node which represents an output of a graph (Function). @param data: The tensor containing the input data @return Result node @@ -2466,7 +2460,7 @@ def reverse_sequence( seq_axis: NumericData, name: Optional[str] = None, ) -> Node: - """! Return a node which produces a ReverseSequence operation. + """Return a node which produces a ReverseSequence operation. @param input: tensor with input data to reverse @param seq_lengths: 1D tensor of integers with sequence lengths in the input tensor. @@ -2489,7 +2483,7 @@ def select( auto_broadcast: str = "numpy", name: Optional[str] = None, ) -> Node: - """! Perform an element-wise selection operation on input tensors. + """Perform an element-wise selection operation on input tensors. @param cond: Tensor with selection mask of type `boolean`. @param then_node: Tensor providing data to be selected if respective `cond` @@ -2512,7 +2506,7 @@ def select( def selu( data: NodeInput, alpha: NodeInput, lambda_value: NodeInput, name: Optional[str] = None ) -> Node: - """! Perform a Scaled Exponential Linear Unit (SELU) operation on input node element-wise. + """Perform a Scaled Exponential Linear Unit (SELU) operation on input node element-wise. @param data: input node, array or scalar. @param alpha: Alpha coefficient of SELU operation @@ -2525,7 +2519,7 @@ def selu( @nameable_op def shape_of(data: NodeInput, name: Optional[str] = None) -> Node: - """! Return a node which produces a tensor containing the shape of its input data. + """Return a node which produces a tensor containing the shape of its input data. @param data: The tensor containing the input data. @return ShapeOf node @@ -2535,7 +2529,7 @@ def shape_of(data: NodeInput, name: Optional[str] = None) -> Node: @unary_op def sigmoid(data: NodeInput, name: Optional[str] = None) -> Node: - """! Return a node which applies the sigmoid function element-wise. + """Return a node which applies the sigmoid function element-wise. @param data: The tensor containing the input data @return Sigmoid node @@ -2545,7 +2539,7 @@ def sigmoid(data: NodeInput, name: Optional[str] = None) -> Node: @unary_op def sign(node: NodeInput, name: Optional[str] = None) -> Node: - """! Perform element-wise sign operation. + """Perform element-wise sign operation. @param node: One of: input node, array or scalar. @param name: The optional new name for output node. @@ -2557,7 +2551,7 @@ def sign(node: NodeInput, name: Optional[str] = None) -> Node: @unary_op def sin(node: NodeInput, name: Optional[str] = None) -> Node: - """! Apply sine function on the input node element-wise. + """Apply sine function on the input node element-wise. @param node: One of: input node, array or scalar. @param name: Optional new name for output node. @@ -2568,7 +2562,7 @@ def sin(node: NodeInput, name: Optional[str] = None) -> Node: @unary_op def sinh(node: NodeInput, name: Optional[str] = None) -> Node: - """! Apply hyperbolic sine function on the input node element-wise. + """Apply hyperbolic sine function on the input node element-wise. @param node: One of: input node, array or scalar. @param name: Optional new name for output node. @@ -2579,7 +2573,7 @@ def sinh(node: NodeInput, name: Optional[str] = None) -> Node: @nameable_op def softmax(data: NodeInput, axis: int, name: Optional[str] = None) -> Node: - """! Apply softmax operation on each element of input tensor. + """Apply softmax operation on each element of input tensor. @param data: The tensor providing input data. @param axis: An axis along which Softmax should be calculated @@ -2590,7 +2584,7 @@ def softmax(data: NodeInput, axis: int, name: Optional[str] = None) -> Node: @nameable_op def space_to_depth(data: Node, mode: str, block_size: int = 1, name: str = None) -> Node: - """! Perform SpaceToDepth operation on the input tensor. + """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 @@ -2613,7 +2607,7 @@ def space_to_depth(data: Node, mode: str, block_size: int = 1, name: str = None) @nameable_op def split(data: NodeInput, axis: NodeInput, num_splits: int, name: Optional[str] = None) -> Node: - """! Return a node which splits the input tensor into same-length slices. + """Return a node which splits the input tensor into same-length slices. @param data: The input tensor to be split @param axis: Axis along which the input data will be split @@ -2629,7 +2623,7 @@ def split(data: NodeInput, axis: NodeInput, num_splits: int, name: Optional[str] @unary_op def sqrt(node: NodeInput, name: Optional[str] = None) -> Node: - """! Return node which applies square root to the input node element-wise. + """Return node which applies square root to the input node element-wise. @param node: One of: input node, array or scalar. @param name: Optional new name for output node. @@ -2642,7 +2636,7 @@ def sqrt(node: NodeInput, name: Optional[str] = None) -> Node: def squared_difference( x1: NodeInput, x2: NodeInput, auto_broadcast: str = "NUMPY", name: Optional[str] = None ) -> Node: - r"""! Perform an element-wise squared difference between two tensors. + r"""Perform an element-wise squared difference between two tensors. \f[ y[i] = (x_1[i] - x_2[i])^2 \f] @@ -2660,7 +2654,13 @@ def squared_difference( @nameable_op def squeeze(data: NodeInput, axes: NodeInput, name: Optional[str] = None) -> Node: - """! Perform squeeze operation on input tensor. + """Perform squeeze operation on input tensor. + + @param data: The node with data tensor. + @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. + @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. @@ -2673,12 +2673,6 @@ def squeeze(data: NodeInput, axes: NodeInput, name: Optional[str] = None) -> Nod Inputs: tensor with shape [1, 2, 1, 3, 1, 1], axes=[2, 4] Result: tensor with shape [1, 2, 3, 1] - - @param data: The node with data tensor. - @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. - @return The new node performing a squeeze operation on input tensor. """ return _get_node_factory_opset1().create("Squeeze", as_nodes(data, axes)) @@ -2696,7 +2690,7 @@ def strided_slice( ellipsis_mask: Optional[List[int]] = None, name: Optional[str] = None, ) -> Node: - """! Return a node which dynamically repeats(replicates) the input data tensor. + """Return a node which dynamically repeats(replicates) the input data tensor. @param data: The tensor to be sliced @param begin: 1D tensor with begin indexes for input blob slicing @@ -2737,7 +2731,7 @@ def subtract( auto_broadcast: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Return node which applies f(x) = A-B to the input nodes element-wise. + """Return node which applies f(x) = A-B to the input nodes element-wise. @param left_node: The node providing data for left hand side of operator. @param right_node: The node providing data for right hand side of operator. @@ -2753,7 +2747,7 @@ def subtract( @unary_op def tan(node: NodeInput, name: Optional[str] = None) -> Node: - """! Apply tangent function on the input node element-wise. + """Apply tangent function on the input node element-wise. @param node: One of: input node, array or scalar. @param name: Optional new name for output node. @@ -2764,7 +2758,7 @@ def tan(node: NodeInput, name: Optional[str] = None) -> Node: @unary_op def tanh(node: NodeInput, name: Optional[str] = None) -> Node: - """! Return node which applies hyperbolic tangent to the input node element-wise. + """Return node which applies hyperbolic tangent to the input node element-wise. @param node: One of: input node, array or scalar. @param name: Optional new name for output node. @@ -2784,7 +2778,7 @@ def tensor_iterator( concat_output_desc: List[TensorIteratorConcatOutputDesc], name: Optional[str] = None, ) -> Node: - """! Perform recurrent execution of the network described in the body, iterating through the data. + """Perform recurrent execution of the network described in the body, iterating through the data. @param inputs: The provided to TensorIterator operator. @param graph_body: The graph representing the body we execute. @@ -2818,7 +2812,7 @@ def tensor_iterator( @nameable_op def tile(data: NodeInput, repeats: NodeInput, name: Optional[str] = None) -> Node: - """! Return a node which dynamically repeats(replicates) the input data tensor. + """Return a node which dynamically repeats(replicates) the input data tensor. @param data: The input tensor to be tiled @param repeats: Per-dimension replication factors @@ -2836,7 +2830,7 @@ def topk( sort: str, name: Optional[str] = None, ) -> Node: - """! Return a node which performs TopK. + """Return a node which performs TopK. @param data: Input data. @param k: K. @@ -2854,7 +2848,7 @@ def topk( @nameable_op def transpose(data: NodeInput, input_order: NodeInput, name: Optional[str] = None) -> Node: - """! Return a node which transposes the data in the input tensor. + """Return a node which transposes the data in the input tensor. @param data: The input tensor to be transposed @param input_order: Permutation of axes to be applied to the input tensor @@ -2864,7 +2858,7 @@ def transpose(data: NodeInput, input_order: NodeInput, name: Optional[str] = Non def unsqueeze(data: NodeInput, axes: NodeInput, name: Optional[str] = None) -> Node: - """! Perform unsqueeze operation on input tensor. + """Perform unsqueeze operation on input tensor. Insert single-dimensional entries to the shape of a tensor. Takes one required argument axes, a list of dimensions that will be inserted. @@ -2885,7 +2879,7 @@ def unsqueeze(data: NodeInput, axes: NodeInput, name: Optional[str] = None) -> N def variadic_split( data: NodeInput, axis: NodeInput, split_lengths: NodeInput, name: Optional[str] = None ) -> Node: - """! Return a node which splits the input tensor into variadic length slices. + """Return a node which splits the input tensor into variadic length slices. @param data: The input tensor to be split @param axis: Axis along which the input data will be split diff --git a/ngraph/python/src/ngraph/opset2/ops.py b/ngraph/python/src/ngraph/opset2/ops.py index ec49c9113b98be..2f542e6aad9338 100644 --- a/ngraph/python/src/ngraph/opset2/ops.py +++ b/ngraph/python/src/ngraph/opset2/ops.py @@ -14,7 +14,7 @@ # limitations under the License. # ****************************************************************************** -"""! Factory functions for all ngraph ops.""" +"""Factory functions for all ngraph ops.""" from typing import Callable, Iterable, List, Optional, Set, Union import numpy as np @@ -66,7 +66,7 @@ def batch_to_space( crops_end: NodeInput, name: Optional[str] = None, ) -> Node: - """! Perform BatchToSpace operation on the input tensor. + """Perform BatchToSpace operation on the input tensor. BatchToSpace permutes data from the batch dimension of the data tensor into spatial dimensions. @@ -84,14 +84,13 @@ def batch_to_space( @unary_op def gelu(node: NodeInput, name: Optional[str] = None) -> Node: - r"""! Perform Gaussian Error Linear Unit operation element-wise on data from input node. + r"""Perform Gaussian Error Linear Unit operation element-wise on data from input node. Computes GELU function: \f[ f(x) = 0.5\cdot x\cdot(1 + erf( \dfrac{x}{\sqrt{2}}) \f] - For more information refer to: - `Gaussian Error Linear Unit (GELU) `_ + For more information refer to [Gaussian Error Linear Unit (GELU)](https://arxiv.org/pdf/1606.08415.pdf>) @param node: Input tensor. One of: input node, array or scalar. @param name: Optional output node name. @@ -108,7 +107,7 @@ def mvn( eps: float = 1e-9, name: str = None, ) -> Node: - r"""! Perform Mean Variance Normalization operation on data from input node. + r"""Perform Mean Variance Normalization operation on data from input node. Computes MVN on the input tensor `data` (called `X`) using formula: @@ -131,7 +130,7 @@ def mvn( @nameable_op def reorg_yolo(input: Node, stride: List[int], name: Optional[str] = None) -> Node: - """! Return a node which produces the ReorgYolo operation. + """Return a node which produces the ReorgYolo operation. @param input: Input data @param stride: Stride to reorganize input by @@ -150,7 +149,7 @@ def roi_pooling( method: str, name: Optional[str] = None, ) -> Node: - """! Return a node which produces an ROIPooling operation. + """Return a node which produces an ROIPooling operation. @param input: Input feature map {N, C, ...} @param coords: Coordinates of bounding boxes @@ -175,7 +174,7 @@ def space_to_batch( pads_end: NodeInput, name: Optional[str] = None, ) -> Node: - """! Perform SpaceToBatch operation on the input tensor. + """Perform SpaceToBatch operation on the input tensor. SpaceToBatch permutes data tensor blocks of spatial data into batch dimension. The operator returns a copy of the input tensor where values from spatial blocks dimensions diff --git a/ngraph/python/src/ngraph/opset3/ops.py b/ngraph/python/src/ngraph/opset3/ops.py index 119bd6670a294e..615441cc54fb34 100644 --- a/ngraph/python/src/ngraph/opset3/ops.py +++ b/ngraph/python/src/ngraph/opset3/ops.py @@ -14,7 +14,7 @@ # limitations under the License. # ****************************************************************************** -"""! Factory functions for all ngraph ops.""" +"""Factory functions for all ngraph ops.""" from typing import Callable, Iterable, List, Optional, Set, Union import numpy as np @@ -60,7 +60,7 @@ @nameable_op def assign(new_value: NodeInput, variable_id: str, name: Optional[str] = None) -> Node: - """! Return a node which produces the Assign operation. + """Return a node which produces the Assign operation. @param new_value: Node producing a value to be assigned to a variable. @param variable_id: Id of a variable to be updated. @@ -82,7 +82,7 @@ def broadcast( broadcast_spec: str = "NUMPY", name: Optional[str] = None, ) -> Node: - """! Create a node which broadcasts the input node's values along specified axes to a desired shape. + """Create a node which broadcasts the input node's values along specified axes to a desired shape. @param data: The node with input tensor data. @param target_shape: The node with a new shape we want to broadcast tensor to. @@ -109,7 +109,7 @@ def bucketize( with_right_bound: bool = True, name: Optional[str] = None, ) -> Node: - """! Return a node which produces the Bucketize operation. + """Return a node which produces the Bucketize operation. @param data: Input data to bucketize @param buckets: 1-D of sorted unique boundaries for buckets @@ -134,7 +134,7 @@ def cum_sum( reverse: bool = False, name: Optional[str] = None, ) -> Node: - """! Construct a cumulative summation operation. + """Construct a cumulative summation operation. @param arg: The tensor to be summed. @param axis: zero dimension tensor specifying axis position along which sum will be performed. @@ -156,7 +156,7 @@ def embedding_bag_offsets_sum( per_sample_weights: Optional[NodeInput] = None, name: Optional[str] = None, ) -> Node: - """! Return a node which performs sums of bags of embeddings without the intermediate embeddings. + """Return a node which performs sums of bags of embeddings without the intermediate embeddings. @param emb_table: Tensor containing the embedding lookup table. @param indices: Tensor with indices. @@ -183,7 +183,7 @@ def embedding_bag_packed_sum( per_sample_weights: Optional[NodeInput] = None, name: Optional[str] = None, ) -> Node: - """! Return an EmbeddingBagPackedSum node. + """Return an EmbeddingBagPackedSum node. EmbeddingSegmentsSum constructs an output tensor by replacing every index in a given input tensor with a row (from the weights matrix) at that index @@ -211,7 +211,7 @@ def embedding_segments_sum( per_sample_weights: Optional[NodeInput] = None, name: Optional[str] = None, ) -> Node: - """! Return an EmbeddingSegmentsSum node. + """Return an EmbeddingSegmentsSum node. EmbeddingSegmentsSum constructs an output tensor by replacing every index in a given input tensor with a row (from the weights matrix) at that index @@ -248,7 +248,7 @@ def extract_image_patches( auto_pad: str, name: Optional[str] = None, ) -> Node: - """! Return a node which produces the ExtractImagePatches operation. + """Return a node which produces the ExtractImagePatches operation. @param image: 4-D Input data to extract image patches. @param sizes: Patch size in the format of [size_rows, size_cols]. @@ -280,7 +280,7 @@ def gru_cell( linear_before_reset: bool = False, name: Optional[str] = None, ) -> Node: - """! Perform GRUCell operation on the tensor from input node. + """Perform GRUCell operation on the tensor from input node. GRUCell represents a single GRU Cell that computes the output using the formula described in the paper: https://arxiv.org/abs/1406.1078 @@ -342,7 +342,7 @@ def non_max_suppression( output_type: str = "i64", name: Optional[str] = None, ) -> Node: - """! Return a node which performs NonMaxSuppression. + """Return a node which performs NonMaxSuppression. @param boxes: Tensor with box coordinates. @param scores: Tensor with box scores. @@ -375,7 +375,7 @@ def non_max_suppression( @nameable_op def non_zero(data: NodeInput, output_type: str = "i64", name: Optional[str] = None,) -> Node: - """! Return the indices of the elements that are non-zero. + """Return the indices of the elements that are non-zero. @param data: Input data. @param output_type: Output tensor type. @@ -391,7 +391,7 @@ def non_zero(data: NodeInput, output_type: str = "i64", name: Optional[str] = No @nameable_op def read_value(init_value: NodeInput, variable_id: str, name: Optional[str] = None) -> Node: - """! Return a node which produces the Assign operation. + """Return a node which produces the Assign operation. @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. @@ -419,7 +419,7 @@ def rnn_cell( clip: float = 0.0, name: Optional[str] = None, ) -> Node: - """! Perform RNNCell operation on tensor from input node. + """Perform RNNCell operation on tensor from input node. It follows notation and equations defined as in ONNX standard: https://github.com/onnx/onnx/blob/master/docs/Operators.md#RNN @@ -475,7 +475,7 @@ def roi_align( mode: str, name: Optional[str] = None, ) -> Node: - """! Return a node which performs ROIAlign. + """Return a node which performs ROIAlign. @param data: Input data. @param rois: RoIs (Regions of Interest) to pool over. @@ -509,23 +509,23 @@ def scatter_elements_update( axis: NodeInput, name: Optional[str] = None, ) -> Node: - """! Return a node which produces a ScatterElementsUpdate operation. + """Return a node which produces a ScatterElementsUpdate operation. + + @param data: The input tensor to be updated. + @param indices: The tensor with indexes which will be updated. + @param updates: The tensor with update values. + @param axis: The axis for scatter. + @return ScatterElementsUpdate node ScatterElementsUpdate creates a copy of the first input tensor with updated elements specified with second and third input tensors. - For each entry in `updates`, the target index in `data` is obtained by combining the corresponding entry in `indices` with the index of the entry itself: the index-value for dimension equal to `axis` is obtained from the value of the corresponding entry in `indices` and the index-value for dimension not equal to `axis` is obtained from the index of the entry itself. - @param data: The input tensor to be updated. - @param indices: The tensor with indexes which will be updated. - @param updates: The tensor with update values. - @param axis: The axis for scatter. - @return ScatterElementsUpdate node """ return _get_node_factory_opset3().create( "ScatterElementsUpdate", as_nodes(data, indices, updates, axis) @@ -536,7 +536,7 @@ def scatter_elements_update( def scatter_update( data: Node, indices: NodeInput, updates: NodeInput, axis: NodeInput, name: Optional[str] = None ) -> Node: - """! Return a node which produces a ScatterUpdate operation. + """Return a node which produces a ScatterUpdate operation. ScatterUpdate sets new values to slices from data addressed by indices. @@ -554,7 +554,7 @@ def scatter_update( @nameable_op def shape_of(data: NodeInput, output_type: str = "i64", name: Optional[str] = None) -> Node: - """! Return a node which produces a tensor containing the shape of its input data. + """Return a node which produces a tensor containing the shape of its input data. @param data: The tensor containing the input data. @param output_type: Output element type. @@ -569,7 +569,17 @@ def shape_of(data: NodeInput, output_type: str = "i64", name: Optional[str] = No @nameable_op def shuffle_channels(data: Node, axis: int, groups: int, name: Optional[str] = None) -> Node: - """! Perform permutation on data in the channel dimension of the input tensor. + """Perform permutation on data in the channel dimension of the input tensor. + + @param data: The node with input tensor. + @param axis: Channel dimension index in the data tensor. + A negative value means that the index should be calculated + from the back of the input data shape. + @param group: The channel dimension specified by the axis parameter + should be split into this number of groups. + @param name: Optional output node name. + @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 `data` of shape [N, C, H, W]: @@ -582,7 +592,7 @@ def shuffle_channels(data: Node, axis: int, groups: int, name: Optional[str] = N For example: - ~~~~~~~~~~~~~{.py} + @code{.py} Inputs: tensor of shape [1, 6, 2, 2] data = [[[[ 0., 1.], [ 2., 3.]], @@ -603,17 +613,7 @@ def shuffle_channels(data: Node, axis: int, groups: int, name: Optional[str] = N [[ 4., 5.], [ 6., 7.]], [[12., 13.], [14., 15.]], [[20., 21.], [22., 23.]]]] - ~~~~~~~~~~~~~ - - @param data: The node with input tensor. - @param axis: Channel dimension index in the data tensor. - A negative value means that the index should be calculated - from the back of the input data shape. - @param group: The channel dimension specified by the axis parameter - should be split into this number of groups. - @param name: Optional output node name. - @return The new node performing a permutation on data in the channel dimension - of the input tensor. + @endcode """ return _get_node_factory_opset3().create( "ShuffleChannels", [as_node(data)], {"axis": axis, "groups": groups} @@ -630,7 +630,7 @@ def topk( index_element_type: str = "i32", name: Optional[str] = None, ) -> Node: - """! Return a node which performs TopK. + """Return a node which performs TopK. @param data: Input data. @param k: K. diff --git a/ngraph/python/src/ngraph/opset4/ops.py b/ngraph/python/src/ngraph/opset4/ops.py index 6e3fc1b7e2fa62..5e6ca19c3a85fb 100644 --- a/ngraph/python/src/ngraph/opset4/ops.py +++ b/ngraph/python/src/ngraph/opset4/ops.py @@ -14,7 +14,7 @@ # limitations under the License. # ****************************************************************************** -"""! Factory functions for all ngraph ops.""" +"""Factory functions for all ngraph ops.""" from typing import Callable, Iterable, List, Optional, Set, Union import numpy as np @@ -70,7 +70,7 @@ def ctc_loss( unique: bool = False, name: Optional[str] = None, ) -> Node: - """! Return a node which performs CTCLoss. + """Return a node which performs CTCLoss. @param logits: 3-D tensor of logits. @param logit_length: 1-D tensor of lengths for each object from a batch. @@ -108,7 +108,7 @@ def non_max_suppression( output_type: str = "i64", name: Optional[str] = None, ) -> Node: - """! Return a node which performs NonMaxSuppression. + """Return a node which performs NonMaxSuppression. @param boxes: Tensor with box coordinates. @param scores: Tensor with box scores. @@ -141,7 +141,7 @@ def non_max_suppression( @nameable_op def softplus(data: NodeInput, name: Optional[str] = None) -> Node: - """! Apply SoftPlus operation on each element of input tensor. + """Apply SoftPlus operation on each element of input tensor. @param data: The tensor providing input data. @return The new node with SoftPlus operation applied on each element. @@ -151,7 +151,7 @@ def softplus(data: NodeInput, name: Optional[str] = None) -> Node: @nameable_op def mish(data: NodeInput, name: Optional[str] = None,) -> Node: - """! Return a node which performs Mish. + """Return a node which performs Mish. @param data: Tensor with input data floating point type. @return The new node which performs Mish @@ -161,7 +161,7 @@ def mish(data: NodeInput, name: Optional[str] = None,) -> Node: @nameable_op def hswish(data: NodeInput, name: Optional[str] = None,) -> Node: - """! Return a node which performs HSwish (hard version of Swish). + """Return a node which performs HSwish (hard version of Swish). @param data: Tensor with input data floating point type. @return The new node which performs HSwish @@ -175,7 +175,7 @@ def swish( beta: Optional[NodeInput] = None, name: Optional[str] = None, ) -> Node: - """! Return a node which performing Swish activation function Swish(x, beta=1.0) = x * sigmoid(x * beta)). + """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. @return The new node which performs Swish @@ -187,7 +187,7 @@ def swish( @nameable_op def acosh(node: NodeInput, name: Optional[str] = None) -> Node: - """! Apply hyperbolic inverse cosine function on the input node element-wise. + """Apply hyperbolic inverse cosine function on the input node element-wise. @param node: One of: input node, array or scalar. @param name: Optional new name for output node. @@ -198,7 +198,7 @@ def acosh(node: NodeInput, name: Optional[str] = None) -> Node: @nameable_op def asinh(node: NodeInput, name: Optional[str] = None) -> Node: - """! Apply hyperbolic inverse sinus function on the input node element-wise. + """Apply hyperbolic inverse sinus function on the input node element-wise. @param node: One of: input node, array or scalar. @param name: Optional new name for output node. @@ -209,7 +209,7 @@ def asinh(node: NodeInput, name: Optional[str] = None) -> Node: @nameable_op def atanh(node: NodeInput, name: Optional[str] = None) -> Node: - """! Apply hyperbolic inverse tangent function on the input node element-wise. + """Apply hyperbolic inverse tangent function on the input node element-wise. @param node: One of: input node, array or scalar. @param name: Optional new name for output node. @@ -226,7 +226,7 @@ def proposal( attrs: dict, name: Optional[str] = None, ) -> Node: - """! Filter bounding boxes and outputs only those with the highest prediction confidence. + """Filter bounding boxes and outputs only those with the highest prediction confidence. @param class_probs: 4D input floating point tensor with class prediction scores. @param bbox_deltas: 4D input floating point tensor with corrected predictions of bounding boxes @@ -295,8 +295,9 @@ def proposal( Object Detection API models Default value: "" (empty string) Required: no + Example of attribute dictionary: - ~~~~~~~~~~~~~~~~~~~~~~~~{.py} + @code{.py} # just required ones attrs = { 'base_size': 85, @@ -308,7 +309,7 @@ def proposal( 'ratio': [0.1, 1.5, 2.0, 2.5], 'scale': [2, 3, 3, 4], } - ~~~~~~~~~~~~~~~~~~~~~~~~ + @endcode Optional attributes which are absent from dictionary will be set with corresponding default. @return Node representing Proposal operation. """ @@ -340,7 +341,7 @@ def proposal( def reduce_l1( node: NodeInput, reduction_axes: NodeInput, keep_dims: bool = False, name: Optional[str] = None ) -> Node: - """! L1-reduction operation on input tensor, eliminating the specified reduction axes. + """L1-reduction operation on input tensor, eliminating the specified reduction axes. @param node: The tensor we want to mean-reduce. @param reduction_axes: The axes to eliminate through mean operation. @@ -357,7 +358,7 @@ def reduce_l1( def reduce_l2( node: NodeInput, reduction_axes: NodeInput, keep_dims: bool = False, name: Optional[str] = None ) -> Node: - """! L2-reduction operation on input tensor, eliminating the specified reduction axes. + """L2-reduction operation on input tensor, eliminating the specified reduction axes. @param node: The tensor we want to mean-reduce. @param reduction_axes: The axes to eliminate through mean operation. @@ -385,7 +386,7 @@ def lstm_cell( clip: float = 0.0, name: Optional[str] = None, ) -> Node: - """! Return a node which performs LSTMCell operation. + """Return a node which performs LSTMCell operation. @param X: The input tensor with shape: [batch_size, input_size]. @param initial_hidden_state: The hidden state tensor with shape: [batch_size, hidden_size]. diff --git a/ngraph/python/src/ngraph/opset_utils.py b/ngraph/python/src/ngraph/opset_utils.py index 49b0d29c4dfd00..f487c72b63e993 100644 --- a/ngraph/python/src/ngraph/opset_utils.py +++ b/ngraph/python/src/ngraph/opset_utils.py @@ -27,7 +27,7 @@ def _get_node_factory(opset_version: Optional[str] = None) -> NodeFactory: - """! Return NodeFactory configured to create operators from specified opset version.""" + """Return NodeFactory configured to create operators from specified opset version.""" if opset_version: return NodeFactory(opset_version) else: diff --git a/ngraph/python/src/ngraph/utils/__init__.py b/ngraph/python/src/ngraph/utils/__init__.py index 65f6dfac3f6ac4..1f257d1d90c921 100644 --- a/ngraph/python/src/ngraph/utils/__init__.py +++ b/ngraph/python/src/ngraph/utils/__init__.py @@ -13,4 +13,4 @@ # See the License for the specific language governing permissions and # limitations under the License. # ****************************************************************************** -"""! Generic utilities. Factor related functions out to separate files.""" +"""Generic utilities. Factor related functions out to separate files.""" diff --git a/ngraph/python/src/ngraph/utils/broadcasting.py b/ngraph/python/src/ngraph/utils/broadcasting.py index 8f52c8c6d1c845..1508e9b0fb605a 100644 --- a/ngraph/python/src/ngraph/utils/broadcasting.py +++ b/ngraph/python/src/ngraph/utils/broadcasting.py @@ -26,7 +26,7 @@ def get_broadcast_axes( output_shape: TensorShape, input_shape: TensorShape, axis: int = None ) -> AxisSet: - """! Generate a list of broadcast axes for ngraph++ broadcast. + """Generate a list of broadcast axes for ngraph++ broadcast. Informally, a broadcast "adds" axes to the input tensor, replicating elements from the input tensor as needed to fill the new dimensions. diff --git a/ngraph/python/src/ngraph/utils/decorators.py b/ngraph/python/src/ngraph/utils/decorators.py index cb59961394dd84..7dc3ad86c69416 100644 --- a/ngraph/python/src/ngraph/utils/decorators.py +++ b/ngraph/python/src/ngraph/utils/decorators.py @@ -27,7 +27,7 @@ def _set_node_friendly_name(node: Node, **kwargs: Any) -> Node: def nameable_op(node_factory_function: Callable) -> Callable: - """! Set the name to the ngraph operator returned by the wrapped function.""" + """Set the name to the ngraph operator returned by the wrapped function.""" @wraps(node_factory_function) def wrapper(*args: Any, **kwargs: Any) -> Node: @@ -39,7 +39,7 @@ def wrapper(*args: Any, **kwargs: Any) -> Node: def unary_op(node_factory_function: Callable) -> Callable: - """! Convert the first input value to a Constant Node if a numeric value is detected.""" + """Convert the first input value to a Constant Node if a numeric value is detected.""" @wraps(node_factory_function) def wrapper(input_value: NodeInput, *args: Any, **kwargs: Any) -> Node: @@ -52,7 +52,7 @@ def wrapper(input_value: NodeInput, *args: Any, **kwargs: Any) -> Node: def binary_op(node_factory_function: Callable) -> Callable: - """! Convert the first two input values to Constant Nodes if numeric values are detected.""" + """Convert the first two input values to Constant Nodes if numeric values are detected.""" @wraps(node_factory_function) def wrapper(left: NodeInput, right: NodeInput, *args: Any, **kwargs: Any) -> Node: diff --git a/ngraph/python/src/ngraph/utils/input_validation.py b/ngraph/python/src/ngraph/utils/input_validation.py index 5bb34d59fd4dd0..b6c3d790c250d2 100644 --- a/ngraph/python/src/ngraph/utils/input_validation.py +++ b/ngraph/python/src/ngraph/utils/input_validation.py @@ -14,7 +14,7 @@ # limitations under the License. # ****************************************************************************** -"""! Helper functions for validating user input.""" +"""Helper functions for validating user input.""" import logging from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type @@ -27,7 +27,7 @@ def assert_list_of_ints(value_list: Iterable[int], message: str) -> None: - """! Verify that the provided value is an iterable of integers.""" + """Verify that the provided value is an iterable of integers.""" try: for value in value_list: if not isinstance(value, int): @@ -39,7 +39,7 @@ def assert_list_of_ints(value_list: Iterable[int], message: str) -> None: def _check_value(op_name, attr_key, value, val_type, cond=None): # type: (str, str, Any, Type, Optional[Callable[[Any], bool]]) -> bool - """! Check whether provided value satisfies specified criteria. + """Check whether provided value satisfies specified criteria. @param op_name: The operator name which attributes are checked. @param attr_key: The attribute name. @@ -67,7 +67,7 @@ def _check_value(op_name, attr_key, value, val_type, cond=None): def check_valid_attribute(op_name, attr_dict, attr_key, val_type, cond=None, required=False): # type: (str, dict, str, Type, Optional[Callable[[Any], bool]], Optional[bool]) -> bool - """! Check whether specified attribute satisfies given criteria. + """Check whether specified attribute satisfies given criteria. @param op_name: The operator name which attributes are checked. @param attr_dict: Dictionary containing key-value attributes to check. @@ -110,7 +110,7 @@ def check_valid_attributes( requirements, # type: List[Tuple[str, bool, Type, Optional[Callable]]] ): # type: (...) -> bool - """! Perform attributes validation according to specified type, value criteria. + """Perform attributes validation according to specified type, value criteria. @param op_name: The operator name which attributes are checked. @param attributes: The dictionary with user provided attributes to check. @@ -130,7 +130,7 @@ def check_valid_attributes( def is_positive_value(x): # type: (Any) -> bool - """! Determine whether the specified x is positive value. + """Determine whether the specified x is positive value. @param x: The value to check. @@ -140,7 +140,7 @@ def is_positive_value(x): # type: (Any) -> bool def is_non_negative_value(x): # type: (Any) -> bool - """! Determine whether the specified x is non-negative value. + """Determine whether the specified x is non-negative value. @param x: The value to check. diff --git a/ngraph/python/src/ngraph/utils/node_factory.py b/ngraph/python/src/ngraph/utils/node_factory.py index 550e887b962a96..77241b81984307 100644 --- a/ngraph/python/src/ngraph/utils/node_factory.py +++ b/ngraph/python/src/ngraph/utils/node_factory.py @@ -9,10 +9,10 @@ class NodeFactory(object): - """! Factory front-end to create node objects.""" + """Factory front-end to create node objects.""" def __init__(self, opset_version: str = DEFAULT_OPSET) -> None: - """! Create the NodeFactory object. + """Create the NodeFactory object. @param opset_version: The opset version the factory will use to produce ops from. """ @@ -21,7 +21,7 @@ def __init__(self, opset_version: str = DEFAULT_OPSET) -> None: def create( self, op_type_name: str, arguments: List[Node], attributes: Optional[Dict[str, Any]] = None ) -> Node: - """! Create node object from provided description. + """Create node object from provided description. The user does not have to provide all node's attributes, but only required ones. @@ -65,7 +65,7 @@ def create( @staticmethod def _normalize_attr_name(attr_name: str, prefix: str) -> str: - """! Normalize attribute name. + """Normalize attribute name. @param attr_name: The attribute name. @param prefix: The prefix to attach to attribute name. @@ -79,7 +79,7 @@ def _normalize_attr_name(attr_name: str, prefix: str) -> str: @classmethod def _normalize_attr_name_getter(cls, attr_name: str) -> str: - """! Normalize atr name to be suitable for getter function name. + """Normalize atr name to be suitable for getter function name. @param attr_name: The attribute name to normalize @@ -89,7 +89,7 @@ def _normalize_attr_name_getter(cls, attr_name: str) -> str: @classmethod def _normalize_attr_name_setter(cls, attr_name: str) -> str: - """! Normalize attribute name to be suitable for setter function name. + """Normalize attribute name to be suitable for setter function name. @param attr_name: The attribute name to normalize @@ -99,7 +99,7 @@ def _normalize_attr_name_setter(cls, attr_name: str) -> str: @staticmethod def _get_node_attr_value(node: Node, attr_name: str) -> Any: - """! Get provided node attribute value. + """Get provided node attribute value. @param node: The node we retrieve attribute value from. @param attr_name: The attribute name. @@ -113,7 +113,7 @@ def _get_node_attr_value(node: Node, attr_name: str) -> Any: @staticmethod def _set_node_attr_value(node: Node, attr_name: str, value: Any) -> None: - """! Set the node attribute value. + """Set the node attribute value. @param node: The node we change attribute value for. @param attr_name: The attribute name. diff --git a/ngraph/python/src/ngraph/utils/reduction.py b/ngraph/python/src/ngraph/utils/reduction.py index 97197da063e910..310be6593dadac 100644 --- a/ngraph/python/src/ngraph/utils/reduction.py +++ b/ngraph/python/src/ngraph/utils/reduction.py @@ -20,7 +20,7 @@ def get_reduction_axes(node: Node, reduction_axes: Optional[Iterable[int]]) -> Iterable[int]: - """! Get reduction axes if it is None and convert it to set if its type is different. + """Get reduction axes if it is None and convert it to set if its type is different. If reduction_axes is None we default to reduce all axes. diff --git a/ngraph/python/src/ngraph/utils/tensor_iterator_types.py b/ngraph/python/src/ngraph/utils/tensor_iterator_types.py index 51b5a8507571b9..f4e1e15bdc3cce 100644 --- a/ngraph/python/src/ngraph/utils/tensor_iterator_types.py +++ b/ngraph/python/src/ngraph/utils/tensor_iterator_types.py @@ -13,7 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ****************************************************************************** -"""! Helper classes for aggregating TensorIterator input/output desciptor attributes.""" +"""Helper classes for aggregating TensorIterator input/output desciptor attributes.""" from typing import List @@ -22,14 +22,14 @@ class GraphBody(object): - """! Class containing graph parameters and results.""" + """Class containing graph parameters and results.""" def __init__(self, parameters: List[Parameter], results: List[Node],) -> None: self.parameters = parameters self.results = results def serialize(self) -> dict: - """! Serialize GraphBody as a dictionary.""" + """Serialize GraphBody as a dictionary.""" return { "parameters": self.parameters, "results": self.results, @@ -37,14 +37,14 @@ def serialize(self) -> dict: class TensorIteratorInputDesc(object): - """! Represents a generic input descriptor for TensorIterator operator.""" + """Represents a generic input descriptor for TensorIterator operator.""" def __init__(self, input_idx: int, body_parameter_idx: int,) -> None: self.input_idx = input_idx self.body_parameter_idx = body_parameter_idx def serialize(self) -> dict: - """! Serialize TensorIteratorInputDesc as a dictionary.""" + """Serialize TensorIteratorInputDesc as a dictionary.""" return { "input_idx": self.input_idx, "body_parameter_idx": self.body_parameter_idx, @@ -52,7 +52,7 @@ def serialize(self) -> dict: class TensorIteratorSliceInputDesc(TensorIteratorInputDesc): - """! Represents a TensorIterator graph body input formed from slices of TensorIterator input.""" + """Represents a TensorIterator graph body input formed from slices of TensorIterator input.""" def __init__( self, @@ -72,7 +72,7 @@ def __init__( self.axis = axis def serialize(self) -> dict: - """! Serialize TensorIteratorSliceInputDesc as a dictionary.""" + """Serialize TensorIteratorSliceInputDesc as a dictionary.""" output = super().serialize() output["start"] = self.start output["stride"] = self.stride @@ -83,7 +83,7 @@ def serialize(self) -> dict: class TensorIteratorMergedInputDesc(TensorIteratorInputDesc): - """! Represents a TensorIterator graph body input with initial value in the first iteration. + """Represents a TensorIterator graph body input with initial value in the first iteration. Later on, this input value is computed inside graph body. """ @@ -93,28 +93,28 @@ def __init__(self, input_idx: int, body_parameter_idx: int, body_value_idx: int, self.body_value_idx = body_value_idx def serialize(self) -> dict: - """! Serialize TensorIteratorMergedInputDesc as a dictionary.""" + """Serialize TensorIteratorMergedInputDesc as a dictionary.""" output = super().serialize() output["body_value_idx"] = self.body_value_idx return output class TensorIteratorInvariantInputDesc(TensorIteratorInputDesc): - """! Represents a TensorIterator graph body input that has invariant value during iteration.""" + """Represents a TensorIterator graph body input that has invariant value during iteration.""" def __init__(self, input_idx: int, body_parameter_idx: int,) -> None: super().__init__(input_idx, body_parameter_idx) class TensorIteratorOutputDesc(object): - """! Represents a generic output descriptor for TensorIterator operator.""" + """Represents a generic output descriptor for TensorIterator operator.""" def __init__(self, body_value_idx: int, output_idx: int,) -> None: self.body_value_idx = body_value_idx self.output_idx = output_idx def serialize(self) -> dict: - """! Serialize TensorIteratorOutputDesc as a dictionary.""" + """Serialize TensorIteratorOutputDesc as a dictionary.""" return { "body_value_idx": self.body_value_idx, "output_idx": self.output_idx, @@ -122,21 +122,21 @@ def serialize(self) -> dict: class TensorIteratorBodyOutputDesc(TensorIteratorOutputDesc): - """! Represents an output from a specific iteration.""" + """Represents an output from a specific iteration.""" def __init__(self, body_value_idx: int, output_idx: int, iteration: int,) -> None: super().__init__(body_value_idx, output_idx) self.iteration = iteration def serialize(self) -> dict: - """! Serialize TensorIteratorBodyOutputDesc as a dictionary.""" + """Serialize TensorIteratorBodyOutputDesc as a dictionary.""" output = super().serialize() output["iteration"] = self.iteration return output class TensorIteratorConcatOutputDesc(TensorIteratorOutputDesc): - """! Represents an output produced by concatenation of output from each iteration.""" + """Represents an output produced by concatenation of output from each iteration.""" def __init__( self, @@ -156,7 +156,7 @@ def __init__( self.axis = axis def serialize(self) -> dict: - """! Serialize TensorIteratorConcatOutputDesc as a dictionary.""" + """Serialize TensorIteratorConcatOutputDesc as a dictionary.""" output = super().serialize() output["start"] = self.start output["stride"] = self.stride diff --git a/ngraph/python/src/ngraph/utils/types.py b/ngraph/python/src/ngraph/utils/types.py index 265de69f6360d6..185503fa61a29d 100644 --- a/ngraph/python/src/ngraph/utils/types.py +++ b/ngraph/python/src/ngraph/utils/types.py @@ -13,7 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ****************************************************************************** -"""! Functions related to converting between Python and numpy types and ngraph types.""" +"""Functions related to converting between Python and numpy types and ngraph types.""" import logging from typing import List, Union @@ -66,7 +66,7 @@ def get_element_type(data_type: NumericType) -> NgraphType: - """! Return an ngraph element type for a Python type or numpy.dtype.""" + """Return an ngraph element type for a Python type or numpy.dtype.""" if data_type is int: log.warning("Converting int type of undefined bitwidth to 32-bit ngraph integer.") return NgraphType.i32 @@ -85,7 +85,7 @@ def get_element_type(data_type: NumericType) -> NgraphType: def get_element_type_str(data_type: NumericType) -> str: - """! Return an ngraph element type string representation for a Python type or numpy dtype.""" + """Return an ngraph element type string representation for a Python type or numpy dtype.""" if data_type is int: log.warning("Converting int type of undefined bitwidth to 32-bit ngraph integer.") return "i32" @@ -105,7 +105,7 @@ def get_element_type_str(data_type: NumericType) -> str: def get_dtype(ngraph_type: NgraphType) -> np.dtype: - """! Return a numpy.dtype for an ngraph element type.""" + """Return a numpy.dtype for an ngraph element type.""" np_type = next( (np_type for (ng_type, np_type) in ngraph_to_numpy_types_map if ng_type == ngraph_type), None, @@ -118,14 +118,14 @@ def get_dtype(ngraph_type: NgraphType) -> np.dtype: def get_ndarray(data: NumericData) -> np.ndarray: - """! Wrap data into a numpy ndarray.""" + """Wrap data into a numpy ndarray.""" if type(data) == np.ndarray: return data return np.array(data) def get_shape(data: NumericData) -> TensorShape: - """! Return a shape of NumericData.""" + """Return a shape of NumericData.""" if type(data) == np.ndarray: return data.shape # type: ignore elif type(data) == list: @@ -134,7 +134,7 @@ def get_shape(data: NumericData) -> TensorShape: def make_constant_node(value: NumericData, dtype: NumericType = None) -> Constant: - """! Return an ngraph Constant node with the specified value.""" + """Return an ngraph Constant node with the specified value.""" ndarray = get_ndarray(value) if dtype: element_type = get_element_type(dtype) @@ -145,12 +145,12 @@ def make_constant_node(value: NumericData, dtype: NumericType = None) -> Constan def as_node(input_value: NodeInput) -> Node: - """! Return input values as nodes. Scalars will be converted to Constant nodes.""" + """Return input values as nodes. Scalars will be converted to Constant nodes.""" if issubclass(type(input_value), Node): return input_value return make_constant_node(input_value) def as_nodes(*input_values: NodeInput) -> List[Node]: - """! Return input values as nodes. Scalars will be converted to Constant nodes.""" + """Return input values as nodes. Scalars will be converted to Constant nodes.""" return [as_node(input_value) for input_value in input_values]