forked from openvinotoolkit/openvino
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Extend MO for operation GatherND (openvinotoolkit#2540)
* Extend MO for operation GatherND * Update documentation * Rename GatherNd.py to gathernd.py Signed-off-by: Roman Kazantsev <[email protected]>
- Loading branch information
Showing
8 changed files
with
433 additions
and
56 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,32 @@ | ||
| """ | ||
| Copyright (C) 2020 Intel Corporation | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| """ | ||
|
|
||
| from extensions.ops.gathernd import GatherND | ||
| from mo.front.extractor import FrontExtractorOp | ||
| from mo.front.onnx.extractors.utils import onnx_attr | ||
|
|
||
|
|
||
| class GatherNDFrontExtractor(FrontExtractorOp): | ||
| op = 'GatherND' | ||
| enabled = True | ||
|
|
||
| @classmethod | ||
| def extract(cls, node): | ||
| attrs = { | ||
| 'batch_dims': onnx_attr(node, 'batch_dims', 'i', default=0) | ||
| } | ||
| GatherND.update_node_stat(node, attrs) | ||
| return cls.enabled |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,30 @@ | ||
| """ | ||
| Copyright (C) 2020 Intel Corporation | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| """ | ||
| from extensions.ops.gathernd import GatherND | ||
| from mo.front.extractor import FrontExtractorOp | ||
|
|
||
|
|
||
| class GatherNDFrontExtractor(FrontExtractorOp): | ||
| op = 'GatherNd' | ||
| enabled = True | ||
|
|
||
| @classmethod | ||
| def extract(cls, node): | ||
| attrs = { | ||
| 'batch_dims': 0, | ||
| } | ||
| GatherND.update_node_stat(node, attrs) | ||
| return cls.enabled |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file was deleted.
Oops, something went wrong.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,102 @@ | ||
| """ | ||
| Copyright (C) 2018-2020 Intel Corporation | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| """ | ||
|
|
||
| import numpy as np | ||
|
|
||
| from mo.front.common.partial_infer.utils import int64_array | ||
| from mo.graph.graph import Node, Graph | ||
| from mo.ops.op import Op | ||
|
|
||
|
|
||
| class GatherND(Op): | ||
| op = 'GatherND' | ||
|
|
||
| def __init__(self, graph: Graph, attrs: dict): | ||
| mandatory_props = { | ||
| 'type': self.op, | ||
| 'op': self.op, | ||
| 'version': 'opset5', | ||
| 'infer': self.infer, | ||
| 'in_ports_count': 2, | ||
| 'out_ports_count': 1, | ||
| 'batch_dims': 0 | ||
| } | ||
| super().__init__(graph, mandatory_props, attrs) | ||
|
|
||
| def backend_attrs(self): | ||
| return ['batch_dims'] | ||
|
|
||
| @staticmethod | ||
| def infer(node: Node): | ||
| node_name = node.soft_get('name', node.id) | ||
| connected_in_ports = [port for port in node.in_ports().values() if not port.disconnected()] | ||
| assert len(connected_in_ports) == 2, \ | ||
| "Incorrect number of inputs for {} node".format(node_name) | ||
|
|
||
| data_shape = node.in_port(0).data.get_shape() | ||
| data_value = node.in_port(0).data.get_value() | ||
| indices_shape = node.in_port(1).data.get_shape() | ||
| indices_value = node.in_port(1).data.get_value() | ||
|
|
||
| assert node.has_valid('batch_dims'), "Node {} must contain `batch_dims` attribute".format(node_name) | ||
| batch_dims = node.batch_dims | ||
|
|
||
| # check that a number of batch dimensions is less than both ranks of data and indices tensors | ||
| assert batch_dims < len(data_shape), "Number of batch dimensions must be less than a rank of data" | ||
| assert batch_dims < len(indices_shape), "Number of batch dimensions must be less than a rank of indices" | ||
|
|
||
| # check that batch dimensions of data and indices are the same | ||
| for batch_dim in range(batch_dims): | ||
| assert data_shape[batch_dim] == indices_shape[batch_dim], \ | ||
| "The dimension {} for data and indices tensors must be the same".format(batch_dim) | ||
|
|
||
| # check ranks of input tensors | ||
| assert len(data_shape) > 0, "Data must not be a scalar" | ||
| assert len(indices_shape) > 0, "Indices must not be a scalar" | ||
| assert (batch_dims + indices_shape[-1]) <= len(data_shape), \ | ||
| "Length of a tuple with indices must not exceed a rank of data tensor excluding batch dimensions" | ||
|
|
||
| # compute output shape | ||
| number_batches = [np.prod(data_shape[:batch_dims]).tolist()] if batch_dims > 0 else list() | ||
| slice_shape = list(data_shape[(batch_dims + indices_shape[-1]):]) | ||
| output_shape = number_batches + list(indices_shape[batch_dims:-1]) + slice_shape | ||
| node.out_port(0).data.set_shape(int64_array(output_shape)) | ||
|
|
||
| # compute output value if all input values are defined | ||
| if data_value is not None and indices_value is not None: | ||
| output_value = np.zeros(output_shape, dtype=data_value.dtype) | ||
| if batch_dims == 0: | ||
| output_indices_range = int64_array(indices_shape[:-1]) | ||
| for output_index in np.ndindex(tuple(output_indices_range)): | ||
| indices_tuple = indices_value[output_index] | ||
| output_value[output_index] = data_value[tuple(indices_tuple.T)] | ||
| else: | ||
| batch_dims_range = int64_array(indices_shape[:batch_dims]) | ||
| for batch_indices in np.ndindex(tuple(batch_dims_range)): | ||
| # compute batch index in output tensor | ||
| batch_ind = 0 | ||
| num_elements = 1 | ||
| for ind in reversed(range(len(batch_dims_range))): | ||
| batch_ind += batch_indices[ind] * num_elements | ||
| num_elements *= batch_dims_range[ind] | ||
| output_indices_range = int64_array(indices_shape[batch_dims:-1]) | ||
| for output_index in np.ndindex(tuple(output_indices_range)): | ||
| tmp_ind = batch_indices + output_index | ||
| indices_tuple = tuple(indices_value[tmp_ind].T) | ||
| full_input_ind = batch_indices + indices_tuple | ||
| full_output_ind = tuple(np.array([batch_ind]).T) + output_index | ||
| output_value[full_output_ind] = data_value[full_input_ind] | ||
| node.out_port(0).data.set_value(output_value) |
Oops, something went wrong.