4302e2c120
renamed logits -> bbox_deltas
updated ngraph unittests for Proposal
removed validate_and_infer_types Proposal-4
removed validate_and_infer_types Proposal-4
changed validate_and_infer_types in parent class of Proposal
removed get_output_size
successfully inferred Proposal on SSH and Faster-RCNN
added unittests for Proposal-4
added unittests for Proposal-4
added unittests for Proposal-4
returned back default namespace for Proposal
reduced number of outputs in v0::Proposal
correct conversion of Proposal-4 -> propodal_ie with 2 outputs
removed creator for proposal v0
removed converter for proposal v0
added Proposal-4 to MO
removed `for_deformable` attribute
added Proposal-4 to MO and nGraph Python API
removed typo in Proposal-4 specification
style corrections
style corrections and removed some redundant code
rename proposal Python api test
removed 'attrs' context from visitor
returned back AttrVisitor to check if passes OpenVINO ONNX pipeline
Should pass OpenVINO ONNX pipeline (returned back AttrVisitor just to check)
python api for Proposal-4 works ok
(style correction) python api for Proposal-4 works ok
parametrized proposal_ie some other corrections
removed 'attrs.' context from nGraph Python API tests for Proposal
minor corrections in replacer proposal->proposal_ie
corrected Python API OpenVINO-ONNX tests should pass
Improved workaround for AttributeVisitor for Proposal
Add additional check of im_info tensor shape to Proposal node in MKLDNNPlugin
😠 removed 4 extra spaces from test_dyn_attributes.py to match The Style
added new nGraph RTTI declarations, removed throwing exception in transformation
added new nGraph RTTI declarations, removed throwing exception in transformation, corrected exception in MKLDNNplugin
corrected im_info size checking in Proposal node of MKLDNNPlugin
78 lines
3.7 KiB
Python
78 lines
3.7 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import logging as log
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import numpy as np
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from extensions.back.ReshapeMutation import ReshapeMutation
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from extensions.back.StridedSliceMasksNormalizer import StridedSliceMasksNormalizer
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from mo.back.replacement import BackReplacementPattern
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from mo.front.common.partial_infer.utils import int64_array
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from mo.front.tf.graph_utils import create_op_with_const_inputs, create_op_node_with_second_input
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from mo.graph.graph import Graph
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from mo.ops.reshape import Reshape
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from mo.ops.strided_slice import StridedSlice
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class ProposalMutation(BackReplacementPattern):
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enabled = True
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force_clean_up = True
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def run_before(self):
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return [ReshapeMutation, StridedSliceMasksNormalizer]
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@staticmethod
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def pattern():
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return dict(
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nodes=[('proposal', {'type': 'Proposal'})],
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edges=[],
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)
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@staticmethod
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def replace_pattern(graph: Graph, match: dict):
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node = match['proposal']
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assert len(node.in_ports()) == 3, "Proposal op must have exactly 3 input ports"
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im_info_shape = node.in_port(2).data.get_shape()
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assert im_info_shape is not None
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if np.array_equal(im_info_shape, [1, 6]):
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log.error('The model contains Proposal layer "{}" with input of shape [1, 6]. Inference Engine '
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'implementation of the Proposal layer uses only 4 first values (indices 0, 1, 2 and 3). '
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'Elements with indices 4 and 5 will be ignored.'.format(node.soft_get('name', node.id)),
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extra={'is_warning': True})
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cropped_im_info = create_op_with_const_inputs(graph, StridedSlice, {1: np.array([0, 0], dtype=np.int32),
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2: np.array([1, 3], dtype=np.int32),
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3: np.array([1, 1], dtype=np.int32)},
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{'name': 'cropped_im_info',
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'begin_mask': int64_array([1, 1]),
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'end_mask': int64_array([1, 1]),
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'new_axis_mask': int64_array([0]),
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'shrink_axis_mask': int64_array([0]),
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'ellipsis_mask': int64_array([0]),
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'override_output_shape': True,
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})
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node.in_port(2).get_connection().insert_node(cropped_im_info)
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# update the im_info_shape so the next 'if' statement become true
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im_info_shape = int64_array([1, 3])
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if np.array_equal(im_info_shape, [1, 3]) or np.array_equal(im_info_shape, [1, 4]):
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reshape = create_op_node_with_second_input(graph, Reshape, [im_info_shape[1]], {'name': 'im_info/Reshape'})
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node.in_port(2).get_connection().set_destination(reshape.in_port(0))
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reshape.out_port(0).connect(node.in_port(2))
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