Evgeny Lazarev
970b1301b5
* Removed back phase transformations related to IRv7 * Fixed setting value for the input port using the 'set_value' method * Removed front and middle phase transformations related to IRv7 * Cleanup the rest of the Model Optimizer transformations from IRv7 specific transformations * Final cleanup of the deprecated IR v7 related code * Removed 'blobs_as_input' usage in the Model Optimizer. * Removed function '_fuse_add' from the Model Optimizer since it is not used anymore. * Removed 'keep_in_IR' node attribute for FakeQuantize ops in the MO * Disabled failing gpu_engine.user_context test
50 lines
1.9 KiB
Python
50 lines
1.9 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 numpy as np
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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.graph.graph import Graph
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from mo.ops.const import Const
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class GroupedConvWeightsNormalize(BackReplacementPattern):
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"""
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This pass is a workaround for nGraph GroupedConvolution operation
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It requires that weights layout will be next: G*O*I,1,H,W
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"""
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enabled = True
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force_clean_up = True
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def pattern(self):
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return dict(
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nodes=[
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('conv', {'type': 'Convolution', 'group': lambda x: x != 1}),
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('weights', {'type': 'Const', 'kind': 'op'}),
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('weights_data', {'kind': 'data'}),
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],
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edges=[('weights', 'weights_data'), ('weights_data', 'conv')]
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)
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def replace_pattern(self, graph: Graph, match: dict):
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conv = match['conv']
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weights = match['weights']
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input_shape = conv.in_port(0).data.get_shape()
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new_weights_shape = int64_array([(weights.value.shape[0] * weights.value.shape[1]) / (input_shape[1] / conv.group), input_shape[1] / conv.group, *weights.value.shape[2:]])
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new_weights = Const(graph, {'value': np.reshape(weights.value, new_weights_shape)}).create_node()
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weights.out_port(0).get_connection().set_source(new_weights.out_port(0))
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new_weights.infer(new_weights)
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