Eugeny Volosenkov
6f512142b6
* add class ConcatFrontExtractor for onnx * add class ConcatFrontExtractor for onnx * Delete concat from mo/front/onnx/extractor.py * Add identity, reshape extractors classes for onnx * import FrontExtractorOp * Added BatchNormalizationExtractor * Added extra line * Fix import modules * fix caffe bn.py and bn_test.py * Fix BatchNormInference * Modify convert_batch_norm * Modify convert_batch_norm * Modify bn_test.py * Fix old comments BN->batchNormInference
113 lines
5.4 KiB
Python
113 lines
5.4 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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from extensions.front.div import Div
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from extensions.front.sub import Sub
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from extensions.middle.AddFakeQuantizeFuse import AddFakeQuantizeFuse
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from extensions.middle.EltwiseInputReshape import normalize_eltwise_inputs
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from extensions.middle.MulFakeQuantizeFuse import MulFakeQuantizeFuse
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from extensions.middle.RemoveRedundantReshapes import RemoveRedundantReshapes
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from extensions.middle.pass_separator import PostMiddleStart
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from extensions.middle.quantize_fuses import MarkNodesToFuseUpToFakeQuantize, FakeQuantizeFuse
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from mo.graph.graph import Graph
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from mo.middle.passes.conv import fuse_pad
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from mo.middle.passes.fusing.decomposition import convert_scale_shift_to_mul_add, convert_batch_norm
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from mo.middle.passes.fusing.fuse_grouped_conv import grouped_convolutions_fusing
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from mo.middle.passes.fusing.fuse_linear_ops import fuse_linear_ops
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from mo.middle.passes.fusing.fuse_linear_seq import fuse_mul_add_sequence
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from mo.middle.passes.fusing.mark_unfused_nodes import mark_unfused_nodes
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from mo.middle.passes.fusing.resnet_optimization import stride_optimization
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from mo.middle.pattern_match import for_graph_and_each_sub_graph_recursively
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from mo.middle.replacement import MiddleReplacementPattern
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class Fusing(MiddleReplacementPattern):
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enabled = True
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replacement_id = "fusing"
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force_clean_up = True
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run_not_recursively = True
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def run_after(self):
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from extensions.middle.pass_separator import MiddleFinish
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return [MiddleFinish]
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def run_before(self):
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# the Fusing transformation adds Reshape layers in some cases which could be removed by the
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# RemoveRedundantReshapes transformation
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return [PostMiddleStart, RemoveRedundantReshapes]
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def find_and_replace_pattern(self, graph: Graph):
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fw = graph.graph['fw']
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argv = graph.graph['cmd_params']
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layout = graph.graph['layout']
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for_graph_and_each_sub_graph_recursively(graph, fuse_pad)
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for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
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# Mark nodes with attr 'can_be_fused': False to disable fusing for specified nodes
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for_graph_and_each_sub_graph_recursively(graph, lambda graph: mark_unfused_nodes(graph, argv.finegrain_fusing))
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# Converting FusedBatchNorm layer to Mul->Add->Mul->Add sequence
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# IE doesn't support batchNormInference with 4 inputs, so we have to split it to two ScaleShift
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for_graph_and_each_sub_graph_recursively(graph, convert_batch_norm)
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if fw == 'caffe':
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# Converting ScaleShift layer to Mul->Add
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for_graph_and_each_sub_graph_recursively(graph, convert_scale_shift_to_mul_add)
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for_graph_and_each_sub_graph_recursively(graph, Div().find_and_replace_pattern)
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for_graph_and_each_sub_graph_recursively(graph, Sub().find_and_replace_pattern)
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for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
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if not argv.disable_fusing:
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if fw != 'caffe':
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# Converting ScaleShift layer to Mul->Add
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for_graph_and_each_sub_graph_recursively(graph, convert_scale_shift_to_mul_add)
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for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
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# Fusing the sequences of Mul/Add operations
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for_graph_and_each_sub_graph_recursively(graph, fuse_mul_add_sequence)
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for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
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normalize_eltwise_inputs(graph)
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for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
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# Fusing linear operation to Convolution
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for_graph_and_each_sub_graph_recursively(graph, fuse_linear_ops)
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for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
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if not argv.disable_gfusing:
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for_graph_and_each_sub_graph_recursively(graph, grouped_convolutions_fusing)
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for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
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if not argv.disable_fusing:
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for_graph_and_each_sub_graph_recursively(graph, fuse_linear_ops)
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for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
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normalize_eltwise_inputs(graph)
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for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
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MarkNodesToFuseUpToFakeQuantize().find_and_replace_pattern(graph)
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FakeQuantizeFuse().find_and_replace_pattern(graph)
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AddFakeQuantizeFuse().find_and_replace_pattern(graph)
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MulFakeQuantizeFuse().find_and_replace_pattern(graph)
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for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
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for_graph_and_each_sub_graph_recursively(graph, fuse_pad)
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for_graph_and_each_sub_graph_recursively(graph, lambda G: G.clean_up())
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if layout != 'NHWC' and not argv.disable_resnet_optimization:
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stride_optimization(graph)
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